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Register a new userReduction of Alzheimers disease beta-amyloid pathology in the absence of gut microbiota
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Alzheimers disease is the most common form of dementia in the western world, however there is no cure available for this devastating neurodegenerative disorder. Despite clinical and experimental evidence implicating the intestinal microbiota in a number of brain disorders, its impact on Alzheimers disease is not known. We generated a germ-free mouse model of Alzheimers disease and discovered a drastic reduction of cerebral Ab amyloid pathology when compared to control Alzheimers disease animals with intestinal microbiota. Sequencing bacterial 16S rRNA from fecal samples revealed a remarkable shift in the gut microbiota of conventionally-raised Alzheimers disease mice as compared to healthy, wild-type mice. Colonization of germ-free Alzheimer mice with harvested microbiota from conventionally-raised Alzheimer mice dramatically increased cerebral Ab pathology. In contrast, colonization with microbiota from control wild-type mice was ineffective in increasing cerebral Ab levels. Our results indicate a microbial involvement in the development of Alzheimers disease pathology, and suggest that microbiota may contribute to the development of neurodegenerative diseases.
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A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ~570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently-produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.
In order to find effective treatments for Alzheimers disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be used to perform early diagnosis, as well as predict the subjects disease stages and future evolution. However, these models have not yet been applied to rare neurodegenerative diseases, are not suitable to understand the complex dynamics of biomarkers, work only on large multimodal datasets, and their predictive performance has not been objectively validated. In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimers disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing visual deficits. My first contribution is a study on the progression of Posterior Cortical Atrophy, using models already developed: the Event-based Model (EBM) and the Differential Equation Model (DEM). My second contribution is the development of DIVE, a novel spatio-temporal model of disease progression that estimates fine-grained spatial patterns of pathology, potentially enabling us to understand complex disease mechanisms relating to pathology propagation along brain networks. My third contribution is the development of Disease Knowledge Transfer (DKT), a novel disease progression model that estimates the multimodal progression of rare neurodegenerative diseases from limited, unimodal datasets, by transferring information from larger, multimodal datasets of typical neurodegenerative diseases. My fourth contribution is the development of novel extensions for the EBM and the DEM, and the development of novel measures for performance evaluation of such models. My last contribution is the organization of the TADPOLE challenge, a competition which aims to identify algorithms and features that best predict the evolution of AD.
Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimers Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed learning-to-rank approach PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend PLTR to better separate the most effective cognitive assessments and the less effective ones. Results: Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions: The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.
Alterations in the human gut bacteriome can be associated with human health issues, such as type-2 diabetes and cardiovascular disease. Both external and internal factors can drive changes in the composition and in the interactions of the human gut bacteriome, impacting negatively on the host cells. In this paper, we focus on the human gut bacteriome metabolism and we propose a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human gut bacteriome network system. Our results show that the regulation of molecular inputs can strongly affect bacterial population growth and create an unbalanced network, as shown by the shift in the node weights based on the molecular signals that are produced. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human gut bacteriome network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviors of the human gut bacteriome depending on the molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial growth and exchange of metabolites.
Currently, many studies of Alzheimers disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.
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