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  • Mathematical Model Reveals Why Some Bacteria Cause Disease at Low Levels
    A new mathematical model developed at the University of California, Davis, might explain why some bacteria can cause disease, such as foodborne illness, even in small doses while others are harmless.

    The model suggests that pathogens — organisms that cause disease — with small genomes don't need to evolve to cause disease compared with their commensal, nonpathogenic counterparts with large genomes.

    The model, published in the journal eLife, is based on the idea that the evolution of virulence, or the severity of a pathogenic infection, is a type of bet hedging — a strategy used to reduce risk and maximize fitness in unpredictable environments.

    When pathogens encounter hosts with immunity to infection, they either reduce their virulence to avoid the immune response (a costly gamble) or maintain high virulence even at the risk of being killed (a low-cost gamble).

    The researchers' mathematical model shows that pathogens with smaller genomes can "tolerate" less virulence reduction as the pathogen population grows, thereby evolving higher overall levels of virulence. The reason is that the mutation rate for a small genome is faster, allowing the pathogen population to adapt more quickly.

    The model also shows that pathogens with small genomes can maintain higher virulence than those with large genomes under a wide range of conditions, meaning they are more likely to cause disease, even in small doses.

    "Our model suggests that bacterial pathogens with small genomes tend to be more virulent not so much because of some special mechanism that enables them to cause more harm, but rather as a result of their faster evolutionary rate," said lead author Ryan Giordano, a UC Davis postdoctoral researcher in evolution and ecology.

    The researchers caution that although the model is a useful tool for studying the evolution of virulence in bacteria, it requires more biological data and experimental evidence to be validated.

    "A better fundamental understanding of this phenomenon could help us design new and improved antibacterial treatments and help predict which bacteria are more likely to cause disease," said Jonathan Eisen, senior author of the study, UC Davis professor of evolution and ecology, and a member of the One Health Institute.

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