Comparative Analysis of Genus Bifidobacterium: Insight into its Host Adaptation

Bifidobacteria is amongst one of the health promoting bacteria. The role of this important probiotic genera can be elucidated by understanding its genome. Comparative analysis of the whole genus of these bacteria can reveal their adaptation to a diverse host range. This study comprises of four research projects. In the first study, a reference library for genus Bifidobacterium was prepared. The core genes in each genus were selected based on a newly proposed statistical definition of core genome. Comparative analysis of Bifidobacterium with another probiotic genus Lactobacillus revealed the metabolic characteristics of genus Bifidobacterium. The second study investigated the immunomodulatory role of a B. bifidum strain TMC3115. The analysis of TMC3115 provided insights into its extracellular structures which might have their role in host interaction and immunomodulation. The study highlighted the variability among these genomes just not on species level but also on strain level in terms of host interaction. The last two studies aim to inspect the relationship between bifidobacteria and its host diet. Bifidobacteria, are both host- and niche-specific. Such adaptation of bifidobacterial species is considered relevant to the intestinal microecosystem and hosts’ oligosaccharides. Many species should have co-evolved with their hosts, but the phylogeny of Bifidobacterium is dissimilar to that of host animals. The discrepancy could be linked to the niche-specific evolution due to hosts’ dietary carbohydrates. The distribution of carbohydrate-active enzymes, in particular glycoside hydrolases (GHs) that metabolize unique oligosaccharides was examined. When bifidobacterial species were classified by their distribution of GH genes, five groups arose according to their hosts’ feeding behaviour. The distribution of GH genes was only weakly associated with the phylogeny of the host animals or with genomic features such as genome size. Thus, the hosts’ dietary pattern is the key determinant of the distribution and evolution of GH genes.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.