Potential of fecal microbiota to predict detoxification and reactivation of dietary carcinogens

Metagenome analysis has proven to be a useful tool to identify markers associated with CRC, as previous studies have identified microbial species and genes which were associated with CRC. These predictions were then successfully validated against external metagenomic cohorts (Thomas et al., 2019; Wirbel et al., 2019; Zeller et al., 2014). Particularly, one study inferred elevated production of secondary bile acids in CRC microbiota, suggesting an association between CRC and fat- and meat-rich diet, which in turn might me correlated with high intake of HCAs and lower potential of the gut microbiota to detoxify or higher potential to reactivate them.However, metagenome analysis only identifies the genetic potential and not the functional activity. Having identified the functional activity of two selected enzymatic activities, the dataset generated with the human cohort recruited gives the opportunity to link genetic potential and functional activity for two connected enzymatic activities, which will help to be able to predict activity in vivo.The specific objectives are1. Generate metagenomes of 20 fecal samples tested for GDH and B-GUS activity2. Generate a gene catalogue to report abundance of gdh and b-gus3. Assemble metagenomes from assembled genomes to identify gdh and b-gus contributing taxa4. Apply machine learning to use the dataset generated with the human cohort recruited (metagenome and activity data) to generate a model able to predict key activities from additional cohort data, as described in Wirbel et al. (2020).