Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.4
4.5
Journals
Microorganisms
Special Issues
Antimicrobial Resistance in the Food Chain
share announcement

Antimicrobial Resistance in the Food Chain

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Food Microbiology".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 8192

Special Issue Editors

Dr. Elena Alexandra Alexa

E-Mail Website
Guest Editor
Teagasc Food Research Centre, Ashtown, Ireland
Interests: food safety, antimicrobial resistance, microbial biofilms; foodborne pathogens; next generation sequencing
Dr. José F. Cobo-Díaz

E-Mail Website
Guest Editor
Department of Food Hygiene and Technology and Institute of Food Science and Technology, Universidad de León, León, Spain
Interests: next generation sequencing; antimicrobial resistance; food safety; bioinformatics; foodborne pathogens
Special Issues, Collections and Topics in MDPI journals
Dr. Avelino Álvarez-Ordoñez

E-Mail Website
Guest Editor
Department of Food Hygiene and Technology and Institute of Food Science and Technology, University of León, León, Spain
Interests: food safety; foodborne pathogens; bacterial physiology; bacterial stress responses; ecology and biocontrol of microbial biofilms; antimicrobial resistance; food microbiome; novel technologies of food preservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The spread of antimicrobial resistance (AMR) through the food chain is a serious health concern as it can contribute to the global burden of human and animal infections. Different niches within the food chain can act as potential hotspots for AMR development and the persistence of resistant pathogens. Although the high prevalence of AMR in the food chain has been mainly attributed to the overuse or misuse of antibiotics to treat animal infections, other antimicrobials such as disinfectants are of concern due to a decreased susceptibility shown by targeted microorganisms.

Conventional techniques for the detection and identification of resistant microorganisms have been considered the primary method for providing insights into the spread of AMR within the food chain. Nevertheless, more powerful tools such as next-generation sequencing allow for a higher resolution when targeting resistance determinants. In addition, they also provide an overview regarding microbiome composition, opening new avenues for assessing the risks linked to AMR occurrence and spread.

Authors are invited to submit original research articles, research reviews, and short communications covering aspects related to the spread of AMR through the food chain and providing insights into at least one of the following topics:

  • Dissemination of foodborne pathogens carrying AMR, as determined by culture-dependent and/or culture-independent approaches;
  • Occurrence and/or prevalence of determinants of resistance to antimicrobials and/or their spread via horizontal gene transfer or through co-resistance or cross-resistance events;
  • Sources and routes of transmission of bacteria carrying resistances against antibiotics/biocides and/or of their associated resistance determinants;
  • Novel approaches and/or interventions to minimize the AMR burden in the food chain;
  • Control of AMR by developing novel and effective antimicrobial strategies.

Dr. Elena Alexandra Alexa
Dr. José F. Cobo-Díaz
Dr. Avelino Álvarez-Ordoñez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microorganisms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • food chain
  • antimicrobial resistance
  • foodborne pathogens
  • transmission
  • occurrence
  • next generation sequencing
  • processing
  • novel technologies
  • antimicrobial strategies
  • genomics
  • metagenomics

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 7678 KiB  
Article
Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility
by Moses B. Ayoola, Athish Ram Das, B. Santhana Krishnan, David R. Smith, Bindu Nanduri and Mahalingam Ramkumar
Microorganisms 2024, 12(1), 134; https://doi.org/10.3390/microorganisms12010134 - 10 Jan 2024
Cited by 1 | Viewed by 1064
Abstract
Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC [...] Read more.
Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the “Genome Feature Extractor Pipeline” that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens. Full article
(This article belongs to the Special Issue Antimicrobial Resistance in the Food Chain)
►▼ Show Figures

Figure 1

Review

Jump to: Research

31 pages, 1791 KiB  
Review
Intervention Strategies to Control Campylobacter at Different Stages of the Food Chain
by Khaled Taha-Abdelaziz, Mankerat Singh, Shayan Sharif, Shreeya Sharma, Raveendra R. Kulkarni, Mohammadali Alizadeh, Alexander Yitbarek and Yosra A. Helmy
Microorganisms 2023, 11(1), 113; https://doi.org/10.3390/microorganisms11010113 - 01 Jan 2023
Cited by 19 | Viewed by 6159
Abstract
Campylobacter is one of the most common bacterial pathogens of food safety concern. Campylobacter jejuni infects chickens by 2–3 weeks of age and colonized chickens carry a high C. jejuni load in their gut without developing clinical disease. Contamination of meat products by [...] Read more.
Campylobacter is one of the most common bacterial pathogens of food safety concern. Campylobacter jejuni infects chickens by 2–3 weeks of age and colonized chickens carry a high C. jejuni load in their gut without developing clinical disease. Contamination of meat products by gut contents is difficult to prevent because of the high numbers of C. jejuni in the gut, and the large percentage of birds infected. Therefore, effective intervention strategies to limit human infections of C. jejuni should prioritize the control of pathogen transmission along the food supply chain. To this end, there have been ongoing efforts to develop innovative ways to control foodborne pathogens in poultry to meet the growing customers’ demand for poultry meat that is free of foodborne pathogens. In this review, we discuss various approaches that are being undertaken to reduce Campylobacter load in live chickens (pre-harvest) and in carcasses (post-harvest). We also provide some insights into optimization of these approaches, which could potentially help improve the pre- and post-harvest practices for better control of Campylobacter. Full article
(This article belongs to the Special Issue Antimicrobial Resistance in the Food Chain)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop