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Researchers at the University of Colorado Boulder have developed a novel machine learning method to predict the environmental pH preferences of bacteria by analyzing their genomic data. This advancement has significant implications for ecological restoration, agriculture, and probiotics development, as it can greatly streamline the traditionally arduous process of culturing bacteria. The study analyzed over 250,000 bacterial types from nearly 1,500 soil, lake, and stream samples. Understanding the pH preferences of various bacteria can help in the effective restoration of ecosystems and enhance agricultural practices by providing insights into which bacterial communities might support the growth of specific crops in different environments.
Lead author Josep Ramoneda highlighted the method’s potential for predicting how microbes may adapt to environmental changes, such as salinity shifts from rising sea levels. The research confirms that genomic data alone can infer pH preferences, leading to quicker culturing of previously difficult-to-grow bacteria, significantly benefiting microbial ecologists and agricultural scientists. The team also intends to investigate temperature preferences in bacteria, furthering their understanding of how warming could affect soil microbial communities. The study was published in Science Advances on April 28, 2023, and received support from various scientific funding organizations.
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