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ARS Home » Pacific West Area » Albany, California » Western Regional Research Center » Produce Safety and Microbiology Research » Research » Publications at this Location » Publication #336152

Research Project: Ecology and Detection of Human Pathogens in the Produce Production Continuum

Location: Produce Safety and Microbiology Research

Title: Decoding the ecological function of accessory genome

item Carter, Michelle

Submitted to: Trends in Microbiology
Publication Type: Review Article
Publication Acceptance Date: 11/21/2016
Publication Date: 12/2/2016
Citation: Carter, M.Q. 2016. Decoding the ecological function of accessory genome. Trends in Microbiology. 25(1):6-8. doi:org/10.1016/j.tim.2016.11.012.

Interpretive Summary: Shiga toxin-producing Escherichia coli (STEC) includes a group of diverse E. coli strains that cause foodborne disease. STEC naturally resides in ruminants, primarily cattle, and can be spread into the environment by fecal shedding. Transmission of STEC to humans occurs mainly through food items and contact with STEC-positive animals, contaminated water, or soil. The STEC serotype O157:H7 has been considered the most frequent cause of STEC-associated outbreaks. A recent article by Lupolova et al. published in PNAS applied a machine-learning approach and predicted that only a subpopulation of bovine isolates have potential to cause disease in humans. This finding has very important implications for development of effective and targeted control strategies since this subset of bovine isolates can be traced genetically. Considering that such STEC subpopulation may also exist in other ecological niches, identifying the overlapping functions between the human hosts and other natural reservoirs of STEC and the corresponding selection forces would provide valuable information on the factors promoting the emergence of hyper-virulence STEC strains.

Technical Abstract: Shiga toxin-producing Escherichia coli O157:H7 primarily resides in cattle asymptomatically, and can be transmitted to humans through food. A study by Lupolova et al applied a machine-learning approach to complex pan-genome information and predicted that only a small subset of bovine isolates have the potential to cause diseases in humans.