F. prausnitzii

Algorithms that can analyze the microbiome after diet induced changes

An increasing body of evidence supports that the gut microbiome composition can alter host metabolism, and eventually result in disease.  To date most of these studies have pointed to associations between the microbiota and host metabolism, but little has been able to demonstrate causal relationships between the two.  To address this, researchers from Sweden developed a specialized computational platform called CASINO (Community and System-level Interactive Optimization) to quantify the release and consumption of metabolites from gut microbiota, and pairing this data to dietary intake characteristics and patterns.  CASINO in a multidimensional platform, but ties both species richness/diversity to dietary intake in the gut microbiome.  The algorithms were optimized to distinguish bacteria that consumed carbohydrates/metabolites, and those that produce metabolites instead. 

In an in vitro validation test, the CASINO simulation was able to predict net production of metabolites produced by each community and was even able to distinguish between the syntheses of more essential amino acids as compared to non-essential amino acids.  In the past, researchers have been able to link two to three species to metabolic consumption rates.  Using CASINO, researchers in this study were able to write algorithms that could analyze at least five species.  The analysis quantified the contribution of individual bacteria to the overall microbiome, as it was shown that B. thetaiotaomicron, E. rectale, and F. prausnitzii dominated metabolism. 

CASINO was also used in a clinical experiment.  Data was examined from an experiment in which 45 overweight and obese individuals were given a restricted low-calorie diet for 6 weeks.  The simulation was able to characterize species diversity and composition.  After characterizing the species, CASINO was also used to simulate the effect of diet on the gut microbiome composition at baseline and after diet intervention in the test subjects.  CASINO algorithms were able to predict a decrease in carbohydrate consumption and increase in amino acid consumption (i.e. protein) by analysis of microbiota metabolites of the five select gut bacterial species. 

The CASINO algorithms examined most metabolic functions and allowed several species to be included in a simulation.  Furthermore, the authors propose that the program is scalable to include more than five species.  CASINO could pave the way toward development of quantification methods that could serve as a predictive interaction tool, especially in light of the importance of biomarkers in predicting disease onset.  We discussed biomarkers last week in our blog regarding renal disease, and these types of tools could provide exceptional value for clinical diagnostics.

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