Research study reports intriguing findings made through innovative artificial intelligence analysis about yeasts -- small fungi that are key contributors to biotechnology, food production, and human health.
These findings on simple yeast organisms not only challenge widely accepted ideas about yeast evolution, but also provides access to an incredibly rich yeast analysis dataset that could have major implications for future evolutionary biology and bioinformatics research for years to come.
This is the flagship study of the Y1000+ Project, a massive inter-institutional yeast genome sequencing and phenotyping endeavor that LaBella joined as a postdoctoral researcher at Vanderbilt University. LaBella and her co-authors -- through an artificial intelligence-assisted, machine-learning analysis of the Y1000+ Project's dataset comprising 1,154 strains of the ancient, single-cell yeast Saccaromycotina -- attempted to answer an important question. That is: Why do some yeasts eat only a few types of carbon for energy while others can eat more than a dozen?
LaBella and her colleagues found ample evidence supporting the idea that there are identifiable, intrinsic genetic differences in yeast specialists versus generalists, specifically that generalists tend to have a larger total number of genes than specialists. For example, they found that generalists are more likely to be able to synthesize carnitine, a molecule that is involved in energy production and often sold as an exercise supplement.
While the findings of this specific experiment and the innovative machine-learning mechanisms used in its analysis could have major implications for bioinformatics, ecology, metabolics and evolutionary biology, the publishing of this study means that the Y1000+ Project's massive compendium of yeast data is now available for scholars worldwide to use as a starting point to amplify their own yeast research.Dana A. Opulente, Abigail Leavitt LaBella, Marie-Claire Harrison, John F.
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