Translating 'big data': better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry.
Campylobacter
Escherichia coli
Salmonella
omics
therapeutic targets
Journal
Animal health research reviews
ISSN: 1475-2654
Titre abrégé: Anim Health Res Rev
Pays: England
ID NLM: 101083072
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
pubmed:
8
1
2020
medline:
16
1
2021
entrez:
8
1
2020
Statut:
ppublish
Résumé
Recent technological advances has led to the generation, storage, and sharing of colossal sets of information ('big data'), and the expansion of 'omics' in science. To date, genomics/metagenomics, transcriptomics, proteomics, and metabolomics are arguably the most ground breaking approaches in food and public safety. Here we review some of the recent studies of foodborne pathogens (Campylobacter spp., Salmonella spp., and Escherichia coli) in poultry using big data. Genomic/metagenomic approaches have reveal the importance of the gut microbiota in health and disease. They have also been used to identify, monitor, and understand the epidemiology of antibiotic-resistance mechanisms and provide concrete evidence about the role of poultry in human infections. Transcriptomics studies have increased our understanding of the pathophysiology and immunopathology of foodborne pathogens in poultry and have led to the identification of host-resistance mechanisms. Proteomic/metabolomic approaches have aided in identifying biomarkers and the rapid detection of low levels of foodborne pathogens. Overall, 'omics' approaches complement each other and may provide, at least in part, a solution to our current food-safety issues by facilitating the development of new rapid diagnostics, therapeutic drugs, and vaccines to control foodborne pathogens in poultry. However, at this time most 'omics' approaches still remain underutilized due to their high cost and the high level of technical skills required.
Identifiants
pubmed: 31907101
doi: 10.1017/S1466252319000124
pii: S1466252319000124
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM