Enzyme promiscuity approaches to predict metabolic pathways in the human gut microbiota

Abstract

Metabolism is the whole set of reactions that take place in an organism. Understanding how humans digest food and absorb nutrients is complex and challenging. The role of the different communities of microorganisms that reside in the human being, called microbiota, has gained growing interest in the last decade. In particular, the human gut microbiota has been linked to several diseases and syndromes, which has largely motivated the study of the interaction between diet, gut microbiota metabolism and health from very different angles. In the area of System Biology, the release of high-quality metabolic reconstructions of the human gut microbiota has opened new avenues to address this question. However, they are still in their infancy, and further developments are required to obtain more comprehensive metabolic models. In this doctoral thesis, the main objective is to develop computational tools to complete these metabolic reconstructions and improve the annotation of important dietary compounds degraded by the human gut microbiota. This work is divided into two parts. The first part focuses on System Biology and enzyme promiscuity with the aim of improving metabolic reconstructions of the human gut microbiota. We present AGREDA v1.1, a new metabolic reconstruction that includes predicted chemical transformations from a popular enzyme promiscuity algorithm called RetroPath RL. The new draft AGREDA v1.1 allows a better representation of phenolic compounds, a relevant class of compounds for both nutrition and health. By means of untargeted metabolomics, we validated the ability of AGREDA v1.1 to predict output microbial compounds in the human gut microbiota. Then, we introduce PROXIMAL2, a novel enzyme promiscuity algorithm that overcomes the limitations of PROXIMAL, a promising previously published algorithm, but it was unable to be applied to our problem of phenolic compound degradation in the human gut microbiota. In particular, PROXIMAL2 overcomes the dependency on KEGG database and extends the scope of application to more complex enzymatic reactions. We obtained complementary results to the ones obtained with RetroPath RL, proposing new tentative degradation pathways for phenolic compounds in the human gut microbiota. The second part of this doctoral thesis is focused on the development of tools to analyze the 16S rRNA gene-sequencing data using metabolic reconstructions of the human gut microbiota. Specifically, we present a novel Python package called q2-metnet, which contextualizes 16S rRNA gene-sequencing data into metabolic networks and provides a quantitative score for reactions and subsystems. By means of different metabolic reconstructions, users can characterize data samples and extract functional features to differentiate between clinical conditions.