New tool can predict changes in the composition of the gut microbiome
The new computational modeling method uses snapshots of the microbial community found in an individual’s gut to identify the various types of microbes present, which are indicative of future transformation.
The combination and amount of microbes in the gut is a strong indicator and contributor to a person’s health. A better understanding of how this microbial community composition changes over time could provide key insights into health and disease. It is not yet clear, however, to what extent microbial composition at any given time influences future composition.
To investigate, the research team developed the Microbial community Temporal Variability Linear Mixed Model (MTV-LMM), which made it possible to observe temporal changes in the microbial composition of the gut. Testing revealed that MTV-LMM can predict such changes with more accuracy than existing models used for the same purpose.
Using MTV-LMM, the researchers were able to confirm that gut microbiome community composition can be accurately predicted based on earlier observations of the community in both infants and adults. Furthermore, when the model was applied to data from 39 infants, the team identified a key shift around the age of 9 months in how the gut microbiome changes over time.
“Our approach provides multiple methodological advancements, but this is still just the tip of the iceberg,” said study co-author Liat Shenhav. “Modeling the temporal behavior of the microbiome is a fundamental scientific question, with potential applications in medicine and beyond.”
The team will continue their work to further improve the prediction accuracy of the model and to explore additional applications.
The study is published in the journal PLOS Computational Biology.
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