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The human gut is colonized by a vast bacterial community that is currently rather well characterized at the species level. Yet, each of these species harbor a tremendous amount of individual genetic variations. Our understanding of the human gut microbiome, its dynamics, composition and impact on host health requires a deeper characterization of its bacteria. The amount of publicly available shotgun sequencing data as well as development of computational tools allowed to reach strain-level resolution in metagenomic analysis. In this thesis, I present systematic approaches to study the strain-level variation using complementary phylogenetic and pangenomic methods aiming to address fundamental questions about microbiome transmission in early life as well as impact of functions encoded by microbiome strains on host health. Across two different cohorts, I used a recently-developed strain-tracking method to assess the impact of delivery conditions on the initial seeding of the infant gut microbiome. While mode of delivery (vaginal or C-section) had a great impact on the amount of mother strains transmitted to the infant, place of delivery (home or hospital) and breastfeeding duration also had an impact on the ongoing development, strain replacement or persistence over the first year of life. In comparison, the father appeared as a stable source of strains independent of the delivery mode. This initial mother seeding, despite being reduced in C-section delivery, can be compensated by Fecal Microbiota Transfer, demonstrating the need of fecal microbiota exposure in seeding during vaginal delivery. In addition, strain dynamics was shown partially explained by differences in the carbohydrates degrading capacities, especially the ability to feed on Human Milk Oligosaccharides. These differences in metabolism between strains were also observed by their respective empirical growth rate that was seen associated with transmission and persistence in the infant gut. To further systematically assess the differences of metabolic capacities between strains and the impact on hosts, I developed a new method to identify gene groups (PanPhlAn Genomic Islands, PGIs) co-present across conspecific strains in metagenomic samples. By applying this method on a large collection of over 10,000 samples, I was able to build a set of 5,315 PGIs. Deeper characterization of these PGIs revealed horizontal gene transfer across species, high variation in carbohydrate metabolism capacities and association with the host lifestyle and health status. Together, these analyses demonstrated the complementary aspects of strain variation andstressed out the need to encompass both strain phylogeny and gene content to fully understand the microbiome at the strain-level.

The human gut microbiome is home to many hundreds of different microbes which play a crucial role in human physiology. For most of them, little is known about how their genetic diversity translates into functional traits and how they interact with their host, which is to some extent due to the lack of isolate genomes. Cultivation-free metagenomic approaches yield extensive amounts of bacterial genetic data, and recently developed algorithms allow strain-level resolution and reconstruction of bacterial genomes from metagenomes, yet bacterial within-species diversity and transmission dynamics after fecal microbiota transplantation remain largely unexplored over cohorts and using these technological advances. To investigate bacterial within-species diversity I first undertook large-scale exploratory studies to characterize the population-level genomic makeup of the two key human gut microbes Eubacterium rectale and Akkermansia muciniphila , leveraging many hundreds of bacterial draft genomes reconstructed from short-read shotgun metagenomics datasets from all around the planet. For E. rectale , I extended previous observations about clustering of subspecies with geography, which suggested isolation by distance and the putative ancestral loss of four distinct motility operons, rendering a subspecies specifically found in Europe immotile. For A. muciniphila, I found that there are several closely related but undescribed Akkermansia spp. in the human gut that are all likely human-specific but are differentially associated with host body mass index, showcasing metabolic differences and distinct co-abundance patterns with putative cognate phages . For both species, I discovered distinct subspecies-level genetic variation in structural polysaccharide synthesis operons. Next, utilizing a complementary strain-resolved approach to track strains between individuals, I undertook a fecal microbiota transplantation (FMT) meta-analysis integrating 24 distinct clinical metagenomic datasets. I found that patients with an infectious disease or those who underwent antibiotic treatment displayed increased donor strain uptake and that some bacterial clades engraft more consistently than others. Furthermore, I developed a machine-learning framework that allows optimizing microbial parameters - such as bacterial richness - in the recipient after FMT based on donor microbiome features, representing first steps towards making a rational donor choice. Taken together, in my work I extended the strain-level understanding of human gut commensals and showcased that genomes from metagenomes can be suitable to conduct large-scale bacterial population genetics studies on other understudied human gut commensals. I further confirmed that strain-resolved metagenomics allows tracking of strains and thus inference of strain engraftment characteristics in an FMT meta-analysis, revealing important differences in engraftment over cohorts and species and paving the way towards better designed FMTs. I believe that my work is an important contribution to the field of microbiome research, showcasing the power of shotgun metagenomics, modern algorithms and large-scale data analysis to reveal previously unattainable insights about the human gut microbiome.

There has been a rapid increase in the number of overweight and obese individuals worldwide in the past 50 years. It has been assumed that an increased caloric intake and a more sedentary lifestyle are the main causes of this rise. However, recent evidence has shown that the microbes that live in the human gastrointestinal tract may play a role in the regulation of weight and obesity development. These microbes, termed the gut microbiota, are commensal and symbiotic microbes that are densely populated throughout an individual's gastrointestinal tract. This paper presents the relevant research and possible mechanisms of how these microbes, mainly bacteria, are thought to play a role in weight regulation and obesity.

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Agarose and porphyran are related galactans that are only found within red marine algae. As such, marine microorganisms have adapted to using these polysaccharides as carbon sources through the acquisition of unique Carbohydrate Active enZymes (CAZymes). A recent metagenome study of the microbiomes from a Japanese human population identified putative CAZymes in several bacterial species, including Bacteroides plebeius that have significant amino acid sequence similarity with those from marine bacteria. Analysis of one potential CAZyme from B. plebeius (BpGH50) is described here. While displaying up to 30% sequence identity with β-agarases, BpGH50 has no detectable agarase activity. Its crystal structure reveals that the topology of the active site is much different than previously characterized agarases, while containing the same core catalytic machinery. It is unclear whether the enzyme has endo- or exo- activity; the large binding ‘groove’ is typical of an endo-acting enzyme, while a loop at one end of the groove may provide a terminal pocket for the substrate, which is suggestive of exo-activity. Furthermore, the enzyme contains a basic pocket that may dock a sulphated substrate, like porphyran. While no quantifiable porphyran activity was observed, properties of the putative active site suggest that this unusual enzyme may be specific on an unusual substrate, such as a porphyran-agarose hybrid. / Graduate

The focus of this thesis is a comparative study of approaches in discovery of carbohydrate-active enzymes (CAZymes). CAZymes synthesise, bind to, and degrade all the multitude of carbohydrates found in nature. As such, when aiming for sustainable methods to degrade plant biomass for the generation of biofuels, for which there is a strong drive in society, CAZymes are a natural source of environmentally friendly molecular tools. In nature, microorganisms are the principal degraders of carbohydrates. Not only do they degrade plant matter in forests and aquatic habitats, but also break down the majority of carbohydrates ingested by animals. These symbiotic microorganisms, known as the microbiota, reside in animal digestive tracts in immense quantities, where one of the key nutrient sources is complex carbohydrates. Thus, microorganisms are a plentiful source of CAZymes, and strategies in the discovery of new enzymes from bacterial sources have been the basis for the work presented here, combined with biochemical characterisation of several enzymes. Novel enzymatic activities for the glycoside hydrolase family 31 have been described as a result of the initial projects of the thesis. These later evolved into projects studying bacterial multi-gene systems for the partial or complete degradation of the heterogeneous plant polysaccharide xyloglucan. These systems contain, in addition to various hydrolytic CAZymes, necessary binding-, transport-, and regulatory proteins. The results presented here show, in detail, how very complex carbohydrates can efficiently be degraded by bacterial enzymes of industrial relevance. / <p>QC 20130826</p>

abstract: The Human Gut Microbiome (GM) modulates a variety of structural, metabolic, and protective functions to benefit the host. A few recent studies also support the role of the gut microbiome in the regulation of bone health. The relationship between GM and bone health was analyzed based on the data collected from a group of twenty-three adolescent boys and girls who participated in a controlled feeding study, during which two different doses (0 g/d fiber and 12 g/d fiber) of Soluble Corn Fiber (SCF) were added to their diet. This analysis was performed by predicting measures of Bone Mineral Density (BMD) and Bone Mineral Content (BMC) which are indicators of bone strength, using the GM sequence of proportions of 178 microbes collected from 23 subjects, by building a machine learning regression model. The model developed was evaluated by calculating performance metrics such as Root Mean Squared Error, Pearson’s correlation coefficient, and Spearman’s rank correlation coefficient, using cross-validation. A noticeable correlation was observed between the GM and bone health, and it was observed that the overall prediction correlation was higher with SCF intervention (r ~ 0.51). The genera of microbes that played an important role in this relationship were identified. Eubacterium (g), Bacteroides (g), Megamonas (g), Acetivibrio (g), Faecalibacterium (g), and Paraprevotella (g) were some of the microbes that showed an increase in proportion with SCF intervention. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020