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Pathogens
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Genomic Epidemiology of Foodborne Pathogens
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Genomic Epidemiology of Foodborne Pathogens

A special issue of Pathogens (ISSN 2076-0817).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 12475

Special Issue Editor

Dr. Patrick Njage

E-Mail Website
Guest Editor
Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Lyngby, Denmark
Interests: whole genome sequencing; foodborne pathogens; food safety; machine learning; risk assessment; source attribution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ability to track the spatial and temporal distribution of pathogen genomes and their genetic variations by genomic epidemiologists has revolutionarised the capability to predict and prevent infectious diseases in humans and animals, as well as the speed and resolution of their global detection and control. This has been supported by the phenomenal pace of technical advancement in microbial genomics since 1995, when the first complete genome sequence of a free-living organism, Haemophilus influenzae, was published. An outcome of this is that we can gain a more complete and deeper understanding of how pathogens cause disease, emerge, adapt to the host, and spread in human populations.

This special issue aims to provide a channel for the dissemination of current developments in genomic epidemiology to provide the best evidence of the health impact of, and the relative contribution of different genetic variants to foodborne infections. We invite the submission of manuscripts reporting research in topics such as:

- Genomics tools to track the spread of foodborne pathogens,

- Spread of foodborne pathogenic genetic variants in local and international communities,

- Sharing and dissemination of foodborne pathogen genetic information in databases,

- Programs and technology for foodborne pathogen genomic data generation, analysis, visualization and data sharing,

- Relative contribution of different foodborne pathogen sources to occurrence data from food, animals and the environment, and

- Linkage between microbial genotypes and foodborne disease phenotypes.

Dr. Patrick Njage
Guest Editor

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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. Pathogens is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • foodborne pathogen genomics
  • foodborne pathogen tracking and source attribution
  • whole genome sequencing
  • genetic variation
  • phylogenetics
  • genomic data sharing
  • genomic data visualization
  • genomic analysis pipelines 

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Published Papers (4 papers)

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Research

12 pages, 460 KiB  
Article
A Machine Learning Model for Food Source Attribution of Listeria monocytogenes
by Collins K. Tanui, Edmund O. Benefo, Shraddha Karanth and Abani K. Pradhan
Pathogens 2022, 11(6), 691; https://doi.org/10.3390/pathogens11060691 - 16 Jun 2022
Cited by 21 | Viewed by 3934
Abstract
Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which [...] Read more.
Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources. Full article
(This article belongs to the Special Issue Genomic Epidemiology of Foodborne Pathogens)
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13 pages, 3266 KiB  
Article
Genomic Analysis Reveals That Isolation Temperature on Selective Media Introduces Genetic Variation in Campylobacter jejuni from Bovine Feces
by Sicun Fan, Derek Foster, Shaohua Zhao, Sampa Mukherjee, Yesha Shrestha, Cameron Parsons and Sophia Kathariou
Pathogens 2022, 11(6), 678; https://doi.org/10.3390/pathogens11060678 - 12 Jun 2022
Cited by 1 | Viewed by 1954
Abstract
Campylobacter jejuni is commonly isolated on selective media following incubation at 37 °C or 42 °C, but the impact of these temperatures on genome variation remains unclear. Previously, Campylobacter selective enrichments from the feces of steers before and after ceftiofur treatment were plated [...] Read more.
Campylobacter jejuni is commonly isolated on selective media following incubation at 37 °C or 42 °C, but the impact of these temperatures on genome variation remains unclear. Previously, Campylobacter selective enrichments from the feces of steers before and after ceftiofur treatment were plated on selective agar media and incubated at either 37 °C or 42 °C. Here, we analyzed the whole genome sequence of C. jejuni strains of the same multilocus sequence typing (MLST)-based sequence type (ST) and isolated from the same sample upon incubation at both temperatures. Four such strain pairs (one ST8221 and three ST8567) were analyzed using core genome and whole genome MLST (cgMLST, wgMLST). Among the 1970 wgMLST loci, 7–25 varied within each pair. In all but one of the pairs more (1.7–8.5 fold) new alleles were found at 42 °C. Most frameshift, nonsense, or start-loss mutations were also found at 42 °C. Variable loci CAMP0575, CAMP0912, and CAMP0913 in both STs may regularly respond to different temperatures. Furthermore, frameshifts in four variable loci in ST8567 occurred at multiple time points, suggesting a persistent impact of temperature. These findings suggest that the temperature of isolation may impact the sequence of several loci in C. jejuni from cattle. Full article
(This article belongs to the Special Issue Genomic Epidemiology of Foodborne Pathogens)
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15 pages, 7288 KiB  
Article
Source Attribution of Human Campylobacteriosis Using Whole-Genome Sequencing Data and Network Analysis
by Lynda Wainaina, Alessandra Merlotti, Daniel Remondini, Clementine Henri, Tine Hald and Patrick Murigu Kamau Njage
Pathogens 2022, 11(6), 645; https://doi.org/10.3390/pathogens11060645 - 3 Jun 2022
Cited by 8 | Viewed by 2932
Abstract
Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides [...] Read more.
Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides an opportunity for higher-resolution source attribution models. Important challenges, including the high dimension and complex structure of WGS data, have inspired concerted research efforts to develop new models. We propose network analysis models as an accurate, high-resolution source attribution approach for the sources of human campylobacteriosis. A weighted network analysis approach was used in this study for source attribution comparing different WGS data inputs. The compared model inputs consisted of cgMLST and wgMLST distance matrices from 717 human and 717 animal isolates from cattle, chickens, dogs, ducks, pigs and turkeys. SNP distance matrices from 720 human and 720 animal isolates were also used. The data were collected from 2015 to 2017 in Denmark, with the animal sources consisting of domestic and imports from 7 European countries. Clusters consisted of network nodes representing respective genomes and links representing distances between genomes. Based on the results, animal sources were the main driving factor for cluster formation, followed by type of species and sampling year. The coherence source clustering (CSC) values based on animal sources were 78%, 81% and 78% for cgMLST, wgMLST and SNP, respectively. The CSC values based on Campylobacter species were 78%, 79% and 69% for cgMLST, wgMLST and SNP, respectively. Including human isolates in the network resulted in 88%, 77% and 88% of the total human isolates being clustered with the different animal sources for cgMLST, wgMLST and SNP, respectively. Between 12% and 23% of human isolates were not attributed to any animal source. Most of the human genomes were attributed to chickens from Denmark, with an average attribution percentage of 52.8%, 52.2% and 51.2% for cgMLST, wgMLST and SNP distance matrices respectively, while ducks from Denmark showed the least attribution of 0% for all three distance matrices. The best-performing model was the one using wgMLST distance matrix as input data, which had a CSC value of 81%. Results from our study show that the weighted network-based approach for source attribution is reliable and can be used as an alternative method for source attribution considering the high performance of the model. The model is also robust across the different Campylobacter species, animal sources and WGS data types used as input. Full article
(This article belongs to the Special Issue Genomic Epidemiology of Foodborne Pathogens)
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11 pages, 306 KiB  
Article
Antibiotic Resistance in Non-Typhoidal Salmonella enterica Strains Isolated from Chicken Meat in Indonesia
by Minori Takaichi, Kayo Osawa, Ryohei Nomoto, Noriko Nakanishi, Masanori Kameoka, Makiko Miura, Katsumi Shigemura, Shohiro Kinoshita, Koichi Kitagawa, Atsushi Uda, Takayuki Miyara, Ni Made Mertaniasih, Usman Hadi, Dadik Raharjo, Ratna Yulistiani, Masato Fujisawa, Kuntaman Kuntaman and Toshiro Shirakawa
Pathogens 2022, 11(5), 543; https://doi.org/10.3390/pathogens11050543 - 4 May 2022
Cited by 8 | Viewed by 2712
Abstract
The increase in antibiotic resistance in non-typhoidal Salmonella enterica (NTS) has been confirmed in Indonesia by this study. We confirmed the virulence genes and antimicrobial susceptibilities of clinical NTS (n = 50) isolated from chicken meat in Indonesia and also detected antimicrobial [...] Read more.
The increase in antibiotic resistance in non-typhoidal Salmonella enterica (NTS) has been confirmed in Indonesia by this study. We confirmed the virulence genes and antimicrobial susceptibilities of clinical NTS (n = 50) isolated from chicken meat in Indonesia and also detected antimicrobial resistance genes. Of 50 strains, 30 (60%) were non-susceptible to nalidixic acid (NA) and all of them had amino acid mutations in gyrA. Among 27 tetracycline (TC) non-susceptible strains, 22 (81.5%) had tetA and/or tetB. The non-susceptibility rates to ampicillin, gentamicin or kanamycin were lower than that of NA or TC, but the prevalence of blaTEM or aadA was high. Non-susceptible strains showed a high prevalence of virulence genes compared with the susceptible strains (tcfA, p = 0.014; cdtB, p < 0.001; sfbA, p < 0.001; fimA, p = 0.002). S. Schwarzengrund was the most prevalent serotype (23 strains, 46%) and the most frequently detected as multi-antimicrobial resistant. The prevalence of virulence genes in S. Schwarzengrund was significantly higher than other serotypes in hlyE (p = 0.011) and phoP/Q (p = 0.011) in addition to the genes above. In conclusion, NTS strains isolated from Indonesian chicken had a high resistance to antibiotics and many virulence factors. In particular, S. Schwarzengrund strains were most frequently detected as multi-antimicrobial resistant and had a high prevalence of virulence genes. Full article
(This article belongs to the Special Issue Genomic Epidemiology of Foodborne Pathogens)
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