Antimicrobial resistance, diarrheagenic and avian pathogenic virulence genes in Escherichia coli from poultry feed and the ingredients

Brito, Benito Guimarães de; Kobayashi, Renata Katsuko Takayama; Koga, Vanessa Lumi; Sarmiento, Juan Josue Puno; Nakazato, Gerson; Lopes, Luis Felipe Dias; Balsan, Laércio André Gassen; Grassotti, T. T.; Brito, Kelly Cristina Tagliari de

doi:10.1590/1678-4162-10355

Cited by 6 publications

(22 citation statements)

References 25 publications

(22 reference statements)

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“…Regarding the E. coli isolated from water and poultry feed, these strains were not identified as MDR microorganisms or ESBL producers. This finding characterizes the samples as not critical points for the spread of antimicrobial resistance in broiler production ( Miles et al, 2006 ; Rossato et al, 2019 ; Gazal et al, 2021 ).…”

Section: Discussionmentioning

confidence: 88%

High-Frequency Detection of fosA3 and blaCTX–M–55 Genes in Escherichia coli From Longitudinal Monitoring in Broiler Chicken Farms

et al. 2022

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Considering the worrying emergence of multidrug resistance, including in animal husbandry and especially in food-producing animals, the need to detect antimicrobial resistance strains in poultry environments is relevant, mainly considering a One Health approach. Thus, this study aimed to conduct longitudinal monitoring of antimicrobial resistance in broiler chicken farms, with an emphasis on evaluating the frequency of resistance to fosfomycin and β-lactams. Escherichia coli was isolated from broiler chicken farms (cloacal swabs, meconium, poultry feed, water, poultry litter, and Alphitobius diaperinus) in northern Paraná from 2019 to 2020 during three periods: the first period (1st days of life), the second period (20th to 25th days of life), and third period (40th to 42nd days of life). Antibiogram tests and the detection of phenotypic extended-spectrum β-lactamase (ESBL) were performed, and they were confirmed by seaching for genes from the blaCTX–M group. The other resistance genes searched were mcr-1 and fosA3. Some ESBL blaCTX–M–1 group strains were selected for ESBL identification by sequencing and enterobacterial repetitive intergenic consensus-polymerase chain reaction analysis. To determine the transferability of the blaCTX–M–1– and fosA3-carrying plasmids, strains were subjected to conjugation experiments. A total of 507 E. coli were analyzed: 360 from cloacal swabs, 24 from meconium samples, 3 from poultry feed samples, 18 from water samples, 69 from poultry litter samples, and 33 from A. diaperinus samples. Among the strain isolate, 80% (406/507) were multidrug-resistant (MDR), and 51% (260/507) were ESBL-positive, with the blaCTX–M–1 group being the most frequent. For the fosA3 gene, 68% (344/507) of the strains isolated were positive, deserves to be highlighted E. coli isolated from day-old chickens (OR 6.34, CI 2.34–17.17), when compared with strains isolated from other origins (poultry litter, A. diaperinus, water, and poultry feed). This work alerts us to the high frequency of the fosA3 gene correlated with the CTX-M-1 group (OR 3.57, CI 95% 2.7–4.72, p < 0.05), especially the blaCTX–M–55 gene, in broiler chickens. This profile was observed mainly in day-old chicken, with a high percentage of E. coli that were MDR. The findings emphasize the importance of conducting longitudinal monitoring to detect the primary risk points during poultry production.

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Section: Discussionmentioning

confidence: 88%

High-Frequency Detection of fosA3 and blaCTX–M–55 Genes in Escherichia coli From Longitudinal Monitoring in Broiler Chicken Farms

et al. 2022

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“…Strains of antimicrobial-resistant E. coli were isolated from the feed, water, and litter of chickens in India [36]. Da Costa et al and Rossato et al also reported that feed is a source of antimicrobial-resistant E. coli [37,38]. For the group receiving NP, the MIC values noted are lower compared to those noted in the control group, and an increase in the MIC by 2-fold for amoxicillin and tylosin and 4-fold for colistin was observed.…”

Section: Discussionmentioning

confidence: 99%

In Vitro and In Vivo Comparison of Changes in Antibiotics Susceptibility of E. coli and Chicken’s Intestinal Flora after Exposure to Amoxicillin or Thymol

Hriouech

Akhmouch

Tanghort

et al. 2020

Veterinary Medicine International

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This study aims at verifying, in vitro, the extent to which the use of amoxicillin or thymol induces the selection of resistant bacteria and at evaluating in vivo their effects on the development of antimicrobial resistance in the intestinal flora of poultry. E. coli strain was subcultured on agar plates containing increasing concentrations of either amoxicillin or thymol. Thereafter, minimal inhibitory concentrations (MICs) of thymol, amoxicillin, and two other antibiotics, tylosin and colistin, were determined using the microdilution method. Groups of chicks were subjected to a 2-week regime of either amoxicillin or thymol added to their drinking water. During the treatment with either thymol or amoxicillin, the total aerobic mesophilic flora (TAMF) was counted on thymol-gradient plates or amoxicillin-gradient plates and the MICs of antibiotics and thymol for E. coli isolates were determined. The in vitro test showed that for E. coli, which had been serially subcultured on increasing concentrations of amoxicillin, a 32-fold increase in MIC values for amoxicillin and a 4-fold increase for colistin and tylosin were noted. However, the MIC of thymol for this strain remained constant. For the E. coli, which had been serially subcultured on increasing concentrations of thymol, no change in the MIC values for antibiotics and thymol was observed. The in vivo test confirmed the in vitro one. It demonstrated that exposure to amoxicillin induced a selection of antimicrobial resistance in TAMF and intestinal E. coli, whereas exposure to thymol did not. The results showed that the group receiving thymol had a lower consumption index compared to the other groups. This study demonstrates the feasibility of this natural product as an alternative solution to the current use of antibiotics in poultry farming.

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“…Use order statistics to score rank vectors: In most cases, it is to find genes that rank higher in many preference lists, thereby ignoring a small number of studies that do not provide information. Therefore, we can assume that all the normalized ranks that provide information come from a distribution [52]. For any normalized rank vector M, let…”

Section: B Deep Learning Model Analysismentioning

confidence: 99%

Screening of Pathogenic Genes for Colorectal Cancer and Deep Learning in the Diagnosis of Colorectal Cancer

Zhang

Xing

2020

IEEE Access

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Based on complex networks and machine learning methods, this paper studies the mining of colorectal cancer treatment genes, and innovatively combines a variety of feature extraction and comparative analysis methods, from gene network features, gene attribute features, network and attribute integration The three aspects of characteristics comprehensively excavate the genetic characteristics, and demonstrate the feasibility of the study through comparative analysis from different perspectives. Constructing a colorectal cancer gene network, analyzing the changes in the network structure during the development of colorectal cancer, and mining the network characteristics of genes are the first issues to be studied in this paper. The analysis of the network structure compares the changes in the network structure of the driver genes in the Normal network and the Tumor network and the edge mechanism of the driver genes, and the distribution of the eigenvalues of the driver genes and non-driver genes in the Tumor network. During the development of colorectal cancer, the network structure of the gene has changed significantly, and the prediction results based on the network structure show better prediction results than the non-network structure. These findings are feasible for the research direction of this paper. The argument is carried out, and the relevant analysis results are also given in the article. However, the research method of the thesis is based on network research, so comparing structural features with non-structural features only shows that structural features have a good classification ability, and cannot directly explain that modeling using gene networks is better than not using gene networks. Finally, based on the random forest, the optimized classification is improved to reveal the important factors affecting the diagnosis of colorectal cancer, and then to identify the true potential colorectal cancer driver genes, providing guidance for the clinical research of colorectal cancer and driving gene mining. INDEX TERMS colorectal cancer; gene screening; diagnosis; deep learning.

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Antimicrobial resistance, diarrheagenic and avian pathogenic virulence genes in Escherichia coli from poultry feed and the ingredients

Cited by 6 publications

References 25 publications

High-Frequency Detection of fosA3 and blaCTX–M–55 Genes in Escherichia coli From Longitudinal Monitoring in Broiler Chicken Farms

High-Frequency Detection of fosA3 and blaCTX–M–55 Genes in Escherichia coli From Longitudinal Monitoring in Broiler Chicken Farms

In Vitro and In Vivo Comparison of Changes in Antibiotics Susceptibility of E. coli and Chicken’s Intestinal Flora after Exposure to Amoxicillin or Thymol

Screening of Pathogenic Genes for Colorectal Cancer and Deep Learning in the Diagnosis of Colorectal Cancer

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