Combine machine learning with DNA sequencing and what do you get? The ability to find where, and to what extent, antibiotic-resistant bacteria are being transmitted between humans, animals, and the environment.
In a recent study by researchers at the U.K.’s University of Nottingham, 154 samples from animals, carcasses, laborers, and their households and environments were gathered from a commercial poultry farm in China. Using machine learning, whole genome sequencing, gene sharing networks, and mobile genetic components, the researchers were able to isolate E. coli bacteria from the samples and classify distinct pathogens found at the farm.
The team uncovered a complete network of genes that correlated with antimicrobial resistance. That network was shared between animals, farm workers, and the surrounding environment.
The researchers were led by led by Dr Tania Dottorini from the School of Veterinary Medicine and Science at the University and the Future Food Beacon leadership team. Dr. Dottorini commented, “We cannot say at this stage where the bacteria originated from, we can only say we found it and it has been shared between animals and humans. As we already know there has been sharing, this is worrying, because people can acquire resistances to drugs from two different ways – from direct contact with an animal, or indirectly by eating contaminated meat. This could be a particular problem in poultry farming, as it is the most widely used meat in the world.”
Dr. Dottorini also noted that the combination of machine learning and DNA sequencing “will enable us to analyze large complex data from different sources, at the same time as identifying where hotspots for certain bacteria may be.”
The ultimate goal? The faster development of targeted new drugs, particularly for antimicrobial-resistant genes.