No description available.

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

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.

Metagenomics is a cultivation-independent approach for obtaining the genomic composition of microbial communities. Microbial communities are ubiquitous in nature. Microbes which are associated with the human body play important roles in human health and disease. These roles span from protecting us against infections from other bacteria, to being the causes of these diseases. A deeper understanding of these communities and how they function inside our bodies allows for advancements in treatments and preventions for these diseases. Recent developments in metagenomics have been driven by the emergence of Next-Generation Sequencing technologies and Third-Generation Sequencing technologies that have enabled cost-effective DNA sequencing and the generation of large volumes of genomic data. These technologies have allowed for the introduction of hybrid DNA assembly techniques to recover the genomes of the constituent microbes. While Next-Generation Sequencing technologies use paired-end sequencing reads from DNA fragments into short reads and have a relatively lower error rate, Third-Generation Sequencing technologies use much longer DNA fragments to generate longer reads, bringing contigs together for larger scaffolds with a higher error rate. Hybrid assemblers leverage both short and long read sequencing technologies and can be a critical step in the advancements of metagenomics, combining these technologies to allow for longer assemblies of DNA with lower error rates. We evaluate the strengths and weaknesses of the hybrid assembly framework using several state-of-the-art assemblers and simulated human microbiome datasets. Our work provides insights into metagenomic assembly and genome recovery, an important step towards a deeper understanding of the microbial communities that influence our well-being.

Despite the association of the human gut microbiome and various diseases, a systematic definition of what constitutes a healthy human gut microbiome has not been established. This is crucial for microbiome research as it provides a basis for evaluating whether a given microbiome sample may deviate from the homeostasis state and is thus prone to the development of chronic diseases. This work aims to propose one such definition by using species/strain-resolved Genome-scale (GEM) models of metabolism. More specifically, we have constructed sample-specific GEMs from 30 healthy subjects using the taxonomic profiling of fecal metagenomic samples. We then computationally simulated these GEMs under a relevant diet (a supplemented typical Western diet) to determine which microbes in each sample contribute to the production of 17 key metabolites curated from literature and reported to be produced and secreted by the gut microbiota of healthy subjects. Beyond this pilot study, we plan to expand our analyses by creating samples-specific GEMs for a large-scale database of all publicly available metagenomic data from healthy subjects (~2,500 samples so far). We will additionally identify a core set of microbial species/strains that are necessary to perform all essential functions of a healthy microbiome. Taken together, this project offers a new paradigm to establish a healthy baseline microbiome definition by identifying generalized and personalized microbial blueprints that could serve as viable markers of health.

No description available.

The human gut microbiome is a complex ecosystem with hundreds of species interacting with each other and the host. One function of the microbiome is to break down undigested nutrients into smaller nutrients, sometimes available for uptake by the host. The digestion of such macromolecules can involve several species where one feeds on another’s byproducts, forming a large cross-feeding network. The method of digital evolution can be of great aid in studying such complex ecosystems by creating models of the studied system. In this study, the digital evolution software Avida was used to study the effects of perturbations in the system on byproduct cross-feeding chain length. Intense perturbations were found to shorten the chain lengths in general whereas weaker perturbations had either a small or no effect. When perturbations ceased, most byproduct chains displayed recovery to lengths similar to the preperturbation lengths. This indicates that byproduct chain lengths may be kept short by common ecological mechanisms alone, explaining why very long chains are rarely observed while still theoretically possible.

No description available.

Le pangénome est l’ensemble des gènes uniques retrouvé chez une espèce. Dans le cas des espèces procaryotes, notamment celles qui sont présentes dans le microbiote intestinal humain, la variation du contenu en gène est caractérisée par des événements de gain de gènes principalement par transfert horizontal de gènes (THG) et de perte de gène. Cette variation du contenu en gène peut être plus rapide que le taux de mutation et permettre aux microbes de s’adapter rapidement à des pressions sélectives. Cela justifie donc l’étude de l’évolution pangénomique des procaryotes sur des échelles de temps humaines qui sont considérées comme étant courtes du point de vue évolutif, par exemple de l’ordre de quelques années. La plupart des études sur ce sujet impliquent des espèces relativement distantes qui ont divergé depuis des millions d’années. De plus, l'équilibre des forces évolutives majeures impliquées, telles que le THG, la sélection, la dérive génétique et les mutations, n’est pas clairement défini et est au cœur d’un débat dans la littérature. Ce projet de maîtrise permet donc d’élargir le portrait évolutif des pangénomes de procaryotes en s’intéressant à l’évolution des gènes transférés horizontalement, aussi appelés gènes mobiles, sur de courtes échelles de temps. Pour ce faire, nous allons d’abord passer en revue la littérature pertinente en lien avec ce sujet, notamment les méthodes employées pour détecter les gènes mobiles et les modèles d’évolution pangénomique. Nous allons ensuite analyser l’évolution d’une collection de 37 853 gènes mobiles impliqués dans des THG récents détectés dans le microbiote intestinal d’individus provenant d’Amérique du Nord ou des îles Fidji. Pour détecter des signatures évolutives des forces en action, nous estimerons divers paramètres de génétique des populations à partir de l’alignement entre les lectures de séquençage métagénomique de 176 microbiotes fidjiens et cette collection de gènes mobiles. Nous expliquerons aussi l’outil de simulations évolutives que nous avons développé afin de valider et expliquer certaines de nos observations. Sans exclure la présence de pressions de sélection pour des gènes mobiles ayant des fonctions spécifiques, les données réelles et les simulations nous amènent à conclure que l’évolution des gènes mobiles sur de courtes échelles de temps peut être expliquée par un modèle d’évolution où les gènes mobiles ne sont pas largement adaptifs à leurs hôtes humains ou microbiens, contrairement à ce qui est parfois observé sur de longues échelles de temps évolutif. / The pangenome is the collection of unique genes found in a species. For prokaryotes, especially those present in the human gut microbiota, variation in gene content is characterized by gene gain through horizontal gene transfer (HGT) and gene loss. In human gut, gene content variations can occur at faster rates than mutation, which allow microbes to adapt rapidly to environmental changes. This justifies the study of the prokaryotes pangenome evolution on human time scales which are considered evolutionarily short, e.g. in the order of few years. Most studies about the evolution of prokaryotic pangenomes involve relatively distant species that have diverged since millions of years. In addition, the balance of major evolutionary forces involved, such as horizontal transfer, selection, genetic drift, and mutations, is not clearly defined and is debated in literature. This master's project therefore aims to broaden the evolutionary portrait of prokaryotic pangenome evolution by focusing on near-term evolution. To do this, we will first review the relevant literature related to this topic, including the methods used to detect mobile genes and the pangenome evolution models. We will then analyze the evolution of a pre-existing collection of 37 853 mobile genes involved in recent HGT events detected in the gut microbiota of individuals from North America and Fiji Islands. To detect evolutionary signatures of the forces in action, we will estimate various population genetics parameters from the alignment between metagenomic sequencing reads of 176 Fijian microbiomes and this collection of mobile genes. We will also explain the evolutionary simulation tool that we have developed in order to validate and explain some of our observations. While we don’t exclude the importance of selection for specific cellular functions for pangenome evolution, we found that the near-term evolution of mobile genes can be explained by a model in which mobile genes can spread selfishly without being largely adaptive to their human or microbial hosts, contrarily to what is often observed over longer evolutionary time scales.

The effect of gluten-free diet on β-cell residual capacity, immune function and gut microbiome in children with newly diagnosed type 1. diabetes Abstract The pathophysiology of the onset and progression of type 1 diabetes (T1D) is not fully understood. Gluten has a proinflammatory effect on the immune system and is therefore considered as one of the factors affecting the onset and progression of T1D. The aim of the thesis is to allow a complex insight into the role of the GFD on the residual β-cell capacity, T1D control, gut microbiome, gut permeability, subtypes of immune cells and the effect of gut microbiome transfer into germ-free non-obese diabetic (NOD) mice on the incidence of diabetes. On the group of 45 children with T1D (26 intervention group, 19 control group) we proved the association of the GFD with slower decrease of β-cell residual capacity (the difference in the trend of C-peptide decrease 409 pmol/l/year; p = 0,04) and lower HbA1c (by 7,8 mmol/mol; p=0,02). We also described the changes in the gut bacteria that were differentially abundant after the administration of the GFD and the changes in abundance of the regulatory and effector immune cells. We showed there was no change in the gut permeability with respect to the study group. We also proved that the transfer of human gut microbiota...