Nottingham University experts have created a groundbreaking software that combines DNA sequencing with machine learning to help them determine where and how much antibiotic resistant bacteria is being transmitted between humans and animals.
The study is published in PLOS Computational biology, was led at the University by Dr Tania Dottorini, School of Veterinary Medicine and Science.
Anthropogenic environments (spaces created by humans), such as areas of intensive livestock farming, are seen as ideal breeding grounds for antimicrobial-resistant bacteria and antimicrobial resistant genes, which are capable of infecting humans and carrying resistance to drugs used in human medicine. This can have major implications on how certain infections and illnesses can be treated.
China has an extensive intensive livestock farming industry. China’s poultry sector is the second largest source of meat in China and the largest user of antibiotics in food production.
The new study was conducted by a team consisting of experts who examined a large-scale Chinese commercial poultry farm. They collected 154 samples from workers, animals, and their environments. They isolated a specific bacteria from the samples. Escherichia coli (E. coli). These bacteria can live in the gut quite normally, but can be pathogenic and have resistance genes that can cause severe stomach cramps and diarrhea.
Researchers used a computational approach that combines machine learning, whole-genome sequencing, gene sharing networks, and mobile genetic elements to characterise the various types of pathogens found on the farm. They discovered that both pathogenic and nonpathogenic bacteria contained antimicrobial genes.
Machine learning was used to reveal a whole network of genes associated in antimicrobial resistance. It was shared among animals, farm workers, as well as the environment. Notably, the network included genes known for causing antibiotic resistance as well unknown genes associated with antibiotic resistance.
Dr. Dottorini said that while it is difficult to pinpoint the origin of the bacteria, it can be stated that it was found and shared by humans and animals. This is concerning, as we know that there has been sharing. People can become resistant to drugs in two ways: directly from contact with animals or indirectly from eating contaminated meat. This could be a problem in poultry farming as it is the most commonly used meat in the world.
“The computational tools that have been developed will allow us to analyse large, complex data from many sources and identify hotspots of certain bacteria. They are fast, precise, and can be applied to large environments — such as multiple farms at once.
“There are many antimicrobial-resistant genes that we already know about. But how do we go beyond them and uncover new targets to create new drugs?
“Our machine learning approach opens up new opportunities for the development of quick, affordable, and effective computational methods that can provide new insight into the epidemiology antimicrobial resistance in livestock agriculture.”
The research was carried out in collaboration by Professor Junshi Chen, Professor Fengqin Lu, and Professor Zixin peng from China National Center for Food Safety Risk Assessment.