Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION: Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression. METHODS: We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD. RESULTS: We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions. DISCUSSION: Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

Common Transcriptional Signatures in Brain Tissue from Patients with HIV-Associated Neurocognitive Disorders, Alzheimer’s Disease, and Multiple Sclerosis

HIV-Associated Neurocognitive Disorders (HAND) is a common manifestation of HIV infection that afflicts about 50 % of HIV-positive individuals. As people with access to antiretroviral treatments live longer, HAND can be found in increasing segments of populations at risk for other chronic, neurodegenerative conditions such as Alzheimer’s disease (AD) and Multiple Sclerosis (MS). If brain diseases of diverse etiologies utilize similar biological pathways in the brain, they may coexist in a patient and possibly exacerbate neuropathogenesis and morbidity. To test this proposition, we conducted comparative meta-analysis of selected publicly available microarray datasets from brain tissues of patients with HAND, AD, and MS. In pair-wise and three-way analyses, we found a large number of dysregulated genes and biological processes common to either HAND and AD or HAND and MS, or to all three diseases. The common characteristic of all three diseases was up-regulation of broadly ranging immune responses in the brain. In addition, HAND and AD share down-modulation of processes involved, among others, in synaptic transmission and cell-cell signaling while HAND and MS share defective processes of neurogenesis and calcium/calmodulin-dependent protein kinase activity. Our approach could provide insight into the identification of common disease mechanisms and better intervention strategies for complex neurocognitive disorders. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11481-012-9409-5) contains supplementary material, which is available to authorized users

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

VASCULAR COGNITIVE IMPAIRMENT AND DEMENTIA: THE IMPORTANCE OF MIXED PATHOLOGIES FROM MOUSE MODELS TO HUMANS

Age-related neurologic disease is a significant and growing burden on our society. Although the largest share of research effort has typically been devoted to the common neurodegenerative illnesses (such as Alzheimer’s disease, or AD), the reality is that nearly all cases of neurodegenerative disease possess elements of mixed pathology. Vascular contributions to cognitive impairment and dementia (VCID) is a complex form of dementia, combining aspects of vascular disease and other forms of dementia, such as Alzheimer’s disease. This pathology is heterogeneous and can include cerebral amyloid angiopathy (CAA), hemorrhages, white matter infarcts, and changes to the neurovascular unit. Given the heterogeneous nature of VCID, we hypothesized that we could further elucidate mechanisms that drive dementia in VCID by examining pathology in mouse models and use this data to guide the study of human autopsy cases. Using a mouse model of VCID, we identified NHE1, a sodium hydrogen exchanger that was upregulated in these mice, as a possible candidate for a factor involved in cerebrovascular disease in humans. We saw a significant age effect of NHE1 in cases with Down syndrome (DS), leading us to further examine cerebrovascular pathology in individuals with DS. People with DS are at a high risk of developing cognitive impairment and dementia after the age of 50. In fact, virtually all adults with DS develop the neuropathology for an AD (beta-amyloid (Aß) senile plaques and tau neurofibrillary tangles) diagnosis by the age of 40 due to a triplication of chromosome 21. We found that these individuals develop CAA and microhemorrhages as a function of age, and that these rates are as severe as sporadic AD, despite an age difference of ~30 years. We also found that individuals with DS have different microglial morphologies than controls or individuals with AD. This data indicates that people with DS develop significant cerebrovascular and AD pathology, indicative of VCID. Overall, we found that mixed pathologies, specifically VCID, is an important contributor to the development of dementia and should be studied further to better understand how this pathology drives cognitive impairment. Further, it is clear that mouse models map imperfectly onto complex human diseases, and that significant work remains to be done towards achieving an adequate model of VCID

Transcriptional signatures of synaptic vesicle genes define myotonic dystrophy type I neurodegeneration

Aim: To delineate the neurogenetic profiles of brain degeneration patterns in myotonic dystrophy type I (DM1). Methods: In two cohorts of DM1 patients, brain maps of volume loss (VL) and neuropsychological deficits (NDs) were intersected to large-scale transcriptome maps provided by the Allen Human Brain Atlas (AHBA). For validation, neuropathological and RNA analyses were performed in a small series of DM1 brain samples. Results: Twofold: (1) From a list of preselected hypothesis-driven genes, confirmatory analyses found that three genes play a major role in brain degeneration: dystrophin (DMD), alpha-synuclein (SNCA) and the microtubule-associated protein tau (MAPT). Neuropathological analyses confirmed a highly heterogeneous Tau-pathology in DM1, different to the one in Alzheimer's disease. (2) Exploratory analyses revealed gene clusters enriched for key biological processes in the central nervous system, such as synaptic vesicle recycling, localization, endocytosis and exocytosis, and the serotonin and dopamine neurotransmitter pathways. RNA analyses confirmed synaptic vesicle dysfunction. Conclusions: The combination of large-scale transcriptome interactions with brain imaging and cognitive function sheds light on the neurobiological mechanisms of brain degeneration in DM1 that might help define future therapeutic strategies and research into this condition

Imaging genetics : Methodological approaches to overcoming high dimensional barriers

Imaging genetics is still a quite novel area of research which attempts to discover how genetic factors affect brain structures and functions. In this thesis, using a various methodological approaches I showed how it can contribute to our understanding of the complex genetic architecture of the human brain