Researchers have figured out a way to predict bacteria’s environmental pH preferences from a quick look at their genomes, using machine learning.
Researchers have figured out a way to predict bacteria’s environmental pH preferences from a quick look at their genomes, using machine learning. Led by experts at CU Boulder, the new approach promises to help guide ecological restoration efforts, agriculture, and even the development of health-related probiotics.
“We know that in any environment, there’s a ton of bacteria with important ecological functions, but their environmental preferences often remain unknown,” said Noah Fierer, a fellow of the Cooperative Institute for Research in Environmental Sciences, CIRES, and professor of ecology and evolutionary biology at CU Boulder. “The idea is to use this technique to figure out the basics of their natural history.”
Understanding whether certain bacteria are most likely to thrive in acidic, neutral, or basic environments is just a first step, said lead author Josep Ramoneda, a CIRES visiting scholar. “You could use this approach to anticipate how microbes will adapt to almost any environmental change,” he said. Say, for example, sea-level rise is bringing more saline water into a coastal wetland. “We can anticipate how microbes will respond to these environmental changes,” Ramoneda said.
Read more at: University of Colorado at Boulder
University of Colorado Boulder Ph.D. student Corinne Walsh works with soil samples containing microbes associated with wheat plants. A new machine-learning approach may help microbial ecologists like Walsh figure out the environmental preferences of bacteria from a quick look at their genomes, making some lab work more efficient and agricultural science more successful. (Photo Credit: Cooperative Institute for Research in Environmental Sciences (2020))
