Translating 'big data': better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry.
Loïc DeblaisDipak KathayatYosra A HelmyGary ClossGireesh RajashekaraPublished in: Animal health research reviews (2020)
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.
Keyphrases
- big data
- antimicrobial resistance
- single cell
- artificial intelligence
- machine learning
- escherichia coli
- gram negative
- healthcare
- public health
- mass spectrometry
- health information
- endothelial cells
- gene expression
- social media
- deep learning
- emergency department
- human health
- risk factors
- risk assessment
- sensitive detection
- case control
- biofilm formation
- microbial community
- adverse drug
- induced pluripotent stem cells
- listeria monocytogenes
- staphylococcus aureus
- climate change