Multidisciplinary Investigation of the Gut-Brain Ecosystem in a Model of Alzheimer's Disease

The search for an understanding of the causal elements that lead to neurodegenerative diseases has motivated researchers for decades. Today, Alzheimer's disease is the most prevalent form of dementia and affects approximately fifty million people worldwide, with only 5% of patients carrying causal genetic mutations. In recent years, studies of animal models as well as observations in humans have brought the gut microbiome to the forefront of AD research, as a significant contributor to the development of Alzheimer's disease. In fact, the gut microbiome is now understood to be a modulator of health and disease in numerous areas of physiology, including energy metabolism, immunity, endocrinology, and most relevant to this thesis, brain health and cognition. The gut microbiome is a complex environment composed of symbiotic bacteria, fungi and viruses (the microbiota) that produce a wide range of signalling molecules that are interpreted both by members of the microbiota and by the host (the microbiome). The host-gut interface is an ecosystem that consists of bi-directional signals and pressures applied by both constituents in order to maintain symbiotic homeostasis. Significant deleterious effects on health occur when homeostasis is lost, which may take place due to a variety of factors. These include host genetic factors and environmental factors, such as diet. Modulation of the gut microbiome has become a potentially viable therapeutic avenue for Alzheimer's disease, though it currently remains at the early stage of discovery. In this thesis, I characterize the evolution of Alzheimer's disease in the 5xFAD mouse model, with a focus on the gut microbiome and on cognitive decline and brain biochemistry. Gut microbiota modulaiton was implemented using germ-free and low-complexity bacterial cocktails, in which I outline the effects microbiota modulation on hallmarks of AD and provide insights into the metabolite profile of the brain altered by the microbiota and AD. The work in this thesis was equally motivated by the development of diagnostic tools using microscopy techniques. Biochemical assessments of disease status and progression provide information pertaining to "what" is interacting and "how much" this affects disease. With microscopy, we may gain the additional knowledge of "where" physiological constituents are and how they interact with the surrounding environment. I describe a novel multi-modal microscopy pipeline that enables the observation of signals of interest in large volumes and at high resolution with a capacity to cross-reference regions of interest in both modalities. The pipeline, known as gutOPT, consists of a sample preparation workflow compatible with mesoscale optical projection tomography (OPT) of fluorescently-labelled targets and subsequent imaging of pre-selected regions of interest in high-resolution modalities, such as confocal microscopy.

Associations between usual diet and gut microbiota composition: results from the Milieu Interieur cross-sectional study

Jacques Fellay

Background: Diet is widely recognized as one of the main modifiable drivers of gut microbiota variability, and its influence on microbiota composition is an active area of investigation. Objective: The present work aimed to explore the associations between usual diet and gut microbiota composition in a large sample of healthy French adults. Methods: Gut microbiota composition was established through sequencing of the 16S rRNA gene in stool samples from 862 healthy French adults of the Milieu Interieur study. Usual dietary consumptions were determined through the administration of a food-frequency questionnaire. The associations between dietary variables and alpha- and beta-diversity indexes and relative taxa abundances were tested using Spearman correlations, permutational ANOVAs, and multivariate analyses with linear models, respectively. Results: Foods generally considered as healthy (raw fruits, fish) were positively associated with alpha-diversity, whereas food items for which a limited consumption is generally recommended (fried products, sodas or sugary drinks, fatty sweet products, processed meats, ready-cooked meals, and desserts) were negatively associated with alpha-diversity. Fruits, fried products, ready-cooked meals, and cheese contributed to shifts within microbiota composition (beta-diversity). Our results also highlighted a number of associations between various food group intakes and abundances of specific phyla, genera, and species. For instance, the consumption of cheese was negatively associated with Akkermansia muciniphila abundance. Conclusions: This large-scale population-based study supports that the usual consumption of certain food items is associated with several gut microbial features, and extends the mechanistic arguments linking Western diet to an altered microbiota composition. These results provide new insights into the understanding of complex diet-gut microbiota relations, and their implications for host health deserve further investigation because altered microbiota diversity was consistently linked to increased risk of several health outcomes. This trial was registered at clinicaltrials.gov as NCT01699893.

OXFORD UNIV PRESS2019

Development and implementation of ¹H magnetic resonance techniques for an improved in vivo assessment of models of cerebral metabolic disorders

Mélanie Craveiro

Nuclear magnetic resonance (NMR) is a physical phenomenon that is widely used in the biomedical field due to its non-invasive and non-destructive properties, which make it an optimal tool for the in vivo investigation of living organs such as the brain. This thesis focused on the development of 1H magnetic resonance (MR) techniques at ultra-high magnetic field strength to improve the measurement and quantification of the 1H MR signal in the rodent brain, and to accurately assess the alterations of the metabolite and water concentrations in several cerebral metabolic disorders. In rodents, 1H MR spectroscopy (MRS) allows the assessment of the concentration of up to 20 metabolites that take part in many cerebral metabolic processes such as neurotransmission, cell energy metabolism, cell growth and osmosis. 1H MRS thus provides a unique tool to non-invasively investigate the cerebral metabolism in healthy and pathological conditions in vivo. However, it is essential for reliable quantification that systematic errors and overlap of the measured metabolite signals are minimized. To that aim, the impact of a potential additional short T2 relaxation time component, which might affect the glutamine quantification at long echo times, was assessed in this thesis. The J-difference editing technique MEGA-SPECIAL was optimized to obtain the unequivocal detection of the glutamine signal at moderate echo time. As a control, the glutamine concentration obtained with this method was then compared to short echo time 1H MR spectroscopy measurements. Since the two measurements of the glutamine concentration at short and moderate echo time did not result in significant differences, this study concluded that there is a low probability of an effect of a short T2 relaxation time component on the glutamine concentration measurement. Since a reliable quantification of 1H spectra partly relies on the accurate assessment of the overlapping macromolecule contribution to the metabolites, an optimized method for the post-processing of the measured macromolecule signal was developed to ensure an accurate assessment of its contribution. This method was applied to investigate potential regional differences in the mouse brain macromolecule signals that may affect metabolite quantification when not taken into account, as well as for the assessment of macromolecule alterations in a human-glioma mouse model. No regional macro- molecule variation was found to significantly affect the metabolite quantification in the healthy mouse brain, which supports the common use of a general macromolecule spec- trum for healthy rodent brain 1H spectra quantification. However, several alterations of the macromolecule spectrum, some of which were reported for the first time, were observed in glioma tissues, and their accurate assessment was shown to be necessary for reliable metabolite quantification. Localized 1H MR spectroscopy techniques that are designed to acquire the1H MR signal from a 3D volume can also be combined with MR imaging (MRI) to acquire information on the spatial distribution of the individual metabolites, which is often of interest when heterogeneous tissues are investigated (such as in pathological conditions). This MR spectroscopic imaging (MRSI) technique is however limited in its conventional implementation by the long measurement time that is necessary to acquire the spatial information. A fast MRSI technique, spectroscopic RARE (spRARE), was therefore implemented at high magnetic field strength in this thesis in order to benefit from the advantage of high magnetic fields in addition to the rapid spatial encoding scheme offered by spRARE. Several optimizations of the metabolite signal detection were implemented in spRARE, before the efficiency of the technique was validated at 9.4 T through a comparison of the acquired in vitro metabolite signals with numerical simulations using the density matrix formalism. The in vivo quantification of the water content is also of interest as it offers perspec- tives for investigating cerebral disease progression that is associated with brain water content alterations. In addition, when the water signal is used as an internal reference for metabolite concentration quantification in MRS, such knowledge can also contribute to a more reliable quantification. A method that is based on the multi-spin-echo MRI technique was therefore developed for the absolute quantification of the water content in the rodent brain, with the aim of assessing alterations of its content in pathological conditions. It was then applied on a bile-duct-ligation rat model of chronic hepatic encephalopathy (HE) to assess the brain water content changes associated with the disease as well as its metabolic alterations. The absence of significant brain water content changes detected in the investigated HE rat model, which was supported by post-mortem brain water content measurements using gravimetry and the dry/wet weight-ratio technique, potentially opens the way for investigating cerebral metabolic processes in chronic HE in absence of edema formation. Localized 1H MRS and conventional MRSI were also applied for the investigation of rodent models of brain disorders in order to assess the metabolic profile associated with those disorders. These studies for the first time allowed the assessment of GABA as a potential early Parkinson’s disease marker, while they also provided new insights into the role of the PPAR-β brain receptor in the early neuroprotective effects following ischemia.

EPFL2013

Software Tools for Handling Magnetically Collected Ultra-thin Sections for Microscopy

Jelena Banjac

This report presents the software tools that will be available online to help users accurately segment ultra-thin sections of brain tissue in a large image to determine the section’s coordinates. In order to predict these coordinates, the goal was to use a state-of-art object instance segmentation framework called Masked Region-based Convolutional Neural Network (Masked R-CNN) on the dataset containing sections of brain tissue in the light microscopy images. The predicted coordinates of the sections will later be used for automated image acquisition in the high-resolution electron microscope. We will demonstrate that Masked R-CNN can be used to perform highly effective and efficient automatic segmentation of microscopy images containing the sections. The machine learning pipeline used will be explained in detail. In addition, we will show the results of how different parameters and settings of this pipeline were changing the performance. This semester's project was done in collaboration with the Center for Interdisciplinary Electron Microscopy (CIME) lab and Machine Learning and Optimization (MLO) lab at EPFL.