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.

Complex microbiomes are part of the food we eat: they are naturally present on the raw material, they merge along the food system, or they can be intentionally inoculated. Whether their presence is desired, such as in case of fermentation or probiotic supplementation, or undesired, in case of pathogenic or spoilage microbes, depends on who they are and what they are doing and therefore several studies investigated the microbiota of specific foods. However, the diversity of food microbiomes remains largely unexplored and similar studies present inconsistencies in methods and results. The study of the food microbiome is relevant also in light of the human microbiome and its multifaceted connection to hosts’ health status. Diet is one of the main factors influencing the human microbiome and many studies investigated how nutrition impacts the endogenous microbial communities both in the gut and in the oral cavity. Nevertheless, they largely overlooked the possibility of direct contribution of food-origin microorganisms. The primary aim of my PhD was the comprehensive characterization of foodborne microbial communities with the ultimate goal of estimating their impact on the human microbiome. This research intended to be humble contribution to the global effort in understanding the microbial sources building these composite ecosystems inhabiting the human body. In order to explore the food microbiome diversity, I selected and collected 583 publicly available food (shotgun) metagenomes and integrated them with 1950 newly sequenced food metagenomes. Through an assembly-based pipeline, I reconstructed >10,000 metagenome-assembled genomes (MAGs) that resulted in 290 previously undescribed taxa and, hence, firstly observed in this work. I characterized the composition of microbial communities in food, proving strong specificity across food categories and types through statistical analysis and machine learning approaches. The uniformly and coherently processed curated metadata, taxonomic profiles and reconstructed genomes are publicly available in a resource called curatedFoodMetagenomicData (cFMD). To investigate the presence of food-associated bacteria among human oral and gut microbiomes, I analyzed 20,000 human metagenomes available in curatedMetagenomicData (cMD) through the same expanded pipeline used for food samples. The overlap between food and human microbiomes showed high variations according to host characteristics and the food prevalent species accounted on average for 3% of relative abundance in adult microbiomes. I recognized 43 bacterial species prevalent in both environments that were investigated at the strain level, showing close genomic similarities of strains found both in food and humans.To our knowledge this was the first attempt to investigate the global food microbiome and to estimate its involvement in human microbiome at a large-scale. Our results showedan expansion of known and yet-to-be-isolated species associated with food microbiomes, their characterization to uncover microbial diversity and provide insights on links with the human microbiome, and the release of a publicly-available resource as cFMD that will support the use of metagenomics in food microbiology and food safety, certificationand quality control applications.

Our brain, perhaps the most sophisticated and mysterious part of the human body, to some extent, determines who we are. However, it’s a vulnerable organ. When subjected to an impact, such as a traffic accident or sport, it may lead to traumatic brain injury (TBI) which can have devastating effects for those who suffer the injury. Despite lots of efforts have been put into primary injury prevention, the number of TBIs is still on an unacceptable high level in a global perspective. Brain edema is a major neurological complication of moderate and severe TBI, which consists of an abnormal accumulation of fluid within the brain parenchyma. Clinically, local and minor edema may be treated conservatively only by observation, where the treatment of choice usually follows evidence-based practice. In the first study, the gravitational force is suggested to have a significant impact on the pressure of the edema zone in the brain tissue. Thus, the objective of the study was to investigate the significance of head position on edema at the posterior part of the brain using a Finite Element (FE) model. The model revealed that water content (WC) increment at the edema zone remained nearly identical for both supine and prone positions. However, the interstitial fluid pressure (IFP) inside the edema zone decreased around 15% by having the head in a prone position compared with a supine position. The decrease of IFP inside the edema zone by changing patient position from supine to prone has the potential to alleviate the damage to axonal fibers of the central nervous system. These observations suggest that considering the patient’s head position during intensive care and at rehabilitation should be of importance to the treatment of edematous regions in TBI patients. In TBI patients with diffuse brain edema, for most severe cases with refractory intracranial hypertension, decompressive craniotomy (DC) is performed as an ultimate therapy. However, a complete consensus on its effectiveness has not been achieved due to the high levels of severe disability and persistent vegetative state found in the patients treated with DC. DC allows expansion of the swollen brain outside the skull, thereby having the potential in reducing the Intracranial Pressure (ICP). However, the treatment causes stretching of the axons and may contribute to the unfavorable outcome of the patients. The second study aimed at quantifying the stretching and WC in the brain tissue due to the neurosurgical intervention to provide more insight into the effects upon such a treatment. A nonlinear registration method was used to quantify the strain. Our analysis showed a substantial increase of the strain level in the brain tissue close to the treated side of DC compared to before the treatment. Also, the WC was related to specific gravity (SG), which in turn was related to the Hounsfield unit (HU) value in the Computerized Tomography (CT) images by a photoelectric correction according to the chemical composition of the brain tissue. The overall WC of brain tissue presented a significant increase after the treatment compared to the condition seen before the treatment. It is suggested that a quantitative model, which characterizes the stretching and WC of the brain tissue both before as well as after DC, may clarify some of the potential problems with such a treatment. Diffusion Weighted (DW) Imaging technology provides a noninvasive way to extract axonal fiber tracts in the brain. The aim of the third study, as an extension to the second study was to assess and quantify the axonal deformation (i.e. stretching and shearing)at both the pre- and post-craniotomy periods in order to provide more insight into the mechanical effects on the axonal fibers due to DC. Subarachnoid injection of artificial cerebrospinal fluid (CSF) into the CSF system is widely used in neurological practice to gain information on CSF dynamics. Mathematical models are important for a better understanding of the underlying mechanisms. Despite the critical importance of the parameters for accurate modeling, there is a substantial variation in the poroelastic constants used in the literature due to the difficulties in determining material properties of brain tissue. In the fourth study, we developed a Finite Element (FE) model including the whole brain-CSF-skull system to study the CSF dynamics during constant-rate infusion. We investigated the capacity of the current model to predict the steady state of the mean ICP. For transient analysis, rather than accurately fit the infusion curve to the experimental data, we placed more emphasis on studying the influences of each of the poroelastic parameters due to the aforementioned inconsistency in the poroelastic constants for brain tissue. It was found that the value of the specific storage term S_epsilon is the dominant factor that influences the infusion curve, and the drained Young’s modulus E was identified as the dominant parameter second to S_epsilon. Based on the simulated infusion curves from the FE model, Artificial Neural Network (ANN) was used to find an optimized parameter set that best fit the experimental curve. The infusion curves from both the FE simulations and using ANN confirmed the limitation of linear poroelasticity in modeling the transient constant-rate infusion. To summarize, the work done in this thesis is to introduce FE Modeling and imaging technologiesincluding CT, DW imaging, and image registration method as a complementarytechnique for clinical diagnosis and treatment of TBI patients. Hopefully, the result mayto some extent improve the understanding of these clinical problems and improve theirmedical treatments. / QC 20120201