Genetic neighborhoods distinguish harmful poultry bacteria from harmless strains
When it comes to identifying harmful bacteria, it helps to look at the company their genes keep. Researchers with the Arkansas Agricultural Experiment Station, the research arm of the University of Arkansas Division of Agriculture, used a machine-learning approach to study not just which genes a bacterium has, but also where those genes sit next to each other. That "genetic neighborhood" helped…
Researchers at the Arkansas Agricultural Experiment Station have developed a method to identify harmful strains of Enterococcus cecorum, a poultry pathogen, by examining the "genomic neighborhoods" where their genes are located. This approach, which looks beyond individual genes to their arrangement within larger genetic islands, helped distinguish disease-causing strains from harmless ones.
While many strains of E. cecorum are harmless, others can cause arthritis, bone infections, and lameness in poultry, leading to animal welfare issues and economic losses. Using machine learning, the researchers analyzed the genomic locations of 145 E. cecorum strains, finding that disease-causing strains were more likely to contain specific genetic islands enriched with antibiotic resistance genes and other mobile genetic elements.
The novel method, which treats bacterial genomes like maps rather than shopping lists, suggests that the organization of genes may provide valuable insights into how bacterial pathogens evolve and emerge. The researchers stress that this approach is currently intended as a research tool, requiring further validation before routine use in poultry monitoring or disease detection.
However, the study highlights the potential of computational biology and machine learning to complement traditional microbiology and reveal hidden patterns within large genomic datasets, potentially benefiting surveillance and the understanding of pathogen evolution across various bacteria.
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