Scientists have used machine learning to find new ways to identify disease in poultry farms, which will help to reduce the need for antibiotic treatment.
The school of veterinary medicine and science and future food beacon at the University ofNottingham led the study.
The use of antibiotics has increased due to the rapid increase in poultry production. This has led to a worrying increase in cases of antimicrobial resistance in animals, which could potentially be passed on to humans.
With antibiotic resistance one of the most threatening issues worldwide, effective and rapid diagnostics ofbacterial infections in chicken farming can reduce the need for antibiotics, which will reduce epidemics.
The researchers collected samples from the environment and animals on a Chinese farm. This huge amount of data has been analyzed for new diagnostic markers that will predict and detect infections in humans. Early intervention and treatment will be possible thanks to this data.
Three key findings were produced by the study. Several clinically relevant antimicrobial resistance genes (ARGs) and associated mobile genetic elements were found in both humans and chickens. There were eleven types of clinically important antibiotic resistance genes that were found between samples.
If we only used small-scale comparative analysis, we would have missed the differences between environments and hosts. The relevance of adopting a multi-scale analysis is suggested by this finding.
The team found the existence of a core chicken gut resistome when they integrated metagenomics data with culture based methods. The correlation between resistance phenotypes of individual commensal and pathogenicbacteria and the types of ARGs in the resistome was supported by these results.
The team discovered that the core resistomes are related to external factors such as temperature and humidity.
The food production industry is a major consumer of antibiotics but the risks are not fully understood. Animals and humans may be in close proximity to each other, so it's important to set out studies and improve methods. Increased adoption of machine learning technologies, as well as precision farming, cost-effective DNA Sequencing, and the opportunity to develop methods giving a better understanding and quantification of AMR risks in farming environments, offer the opportunity to develop methods.
More information: Alexandre Maciel-Guerra et al, Dissecting microbial communities and resistomes for interconnected humans, soil, and livestock, The ISME Journal (2022). DOI: 10.1038/s41396-022-01315-7 Journal information: ISME Journal
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