Imaging Genetics and Biomarker Variations of Clinically Diagnosed Alzheimer's Disease

Indiana University-Purdue University Indianapolis (IUPUI)Neuroimaging biomarkers play a crucial role in our understanding of Alzheimer’s disease. Beyond providing a fast and accurate in vivo picture of the neuronal structure and biochemistry, these biomarkers make up a research framework, defined in a 2018 as the A(amyloid)/T(tau)/N(neurodegeneration) framework after three of the hallmarks of Alzheimer’s disease. I first used imaging measures of amyloid, tau and neurodegeneration to study clinically diagnosed Alzheimer’s disease. After dividing subjects into early (onset younger than 65) and late-onset (onset of 65 and older) amyloid-positive (AD) and amyloid-negative (nonAD) groups, I saw radically differing topographical distribution of tau and neurodegeneration. AD subjects with an early disease onset had a much more severe amyloid, tau and neurodegeneration than lateonset AD. In the nonAD group, neurodegeneration was found only in early-onset FDG PET data and in a nonAlzheimer’s-like MRI and FDG pattern for late-onset. The late-onset nonAD resembled that of limbic-predominant age-related TDP-43 encephalopathy. I next utilized an imaging genetics approach to associate genome-wide significant Alzheimer’s risk variants to structural (MRI), metabolic (FDG PET) and tau (tau PET) imaging biomarkers. Linear regression was used to select variants for each of the models and included a pooled sample, cognitively normal, mild cognitive impairment and dementia groups in order to fully capture the cognitive spectrum from normal cognition to the most severely impaired. Model selected variants were replicated using voxelwise regression in an exploratory analysis of spatial associations for each modality. For each imaging type, I replicated some associations to the biomarkers previously seen, as well as identified several novel associations. Several variants identified with crucial Alzheimer’s biomarkers may be potential future targets for drug interventions

Big data and Parkinson’s: Exploration, analyses, data challenges and visualization

In healthcare, a tremendous amount of clinical, laboratory tests, imaging, prescription and medication data are collected. Big data analytics on these data aim at early detection of disease which will help in developing preventive measures and in improving patient care. Parkinson disease is the second-most common neurodegenerative disorder in the United States. To find a cure for Parkinson\u27s disease biological, clinical and behavioral data of different cohorts are collected, managed and propagated through Parkinson’s Progression Markers Initiative (PPMI). Applying big data technology to this data will lead to the identification of the potential biomarkers of Parkinson’s disease. Data collected in human clinical studies is imbalanced, heterogeneous, incongruent and sparse. This study focuses on the ways to overcome the challenges offered by PPMI data which is wide and incongruent. This work leverages the initial discoveries made through descriptive studies of various attributes. The exploration of data led to identifying the significant attributes. This research project focuses on data munging or data wrangling, creating the structural metadata, curating the data, imputing the missing values, using the emerging big data analysis methods of dimensionality reduction, supervised machine learning on the reduced dimensions dataset, and finally an interactive visualization. The simple interactive visualization platform will abstract the domain expertise from the sophisticated mathematics and will enable a democratization of the exploration process. Visualization build on D3.Js is interactive and will enable manual exploration of traits that correlate with the disease severity

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

2019 Abstract Booklet

Complete Schedule of Events for the 21st Annual Undergraduate Research Symposium at Minnesota State University, Mankato

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Abstract Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.http://deepblue.lib.umich.edu/bitstream/2027.42/134522/1/13742_2016_Article_117.pd

Genes and Gene Networks Related to Age-associated Learning Impairments

The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI. Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research

MAPping out distribution routes for kinesin couriers

In the crowded environment of eukaryotic cells, diffusion is an inefficient distribution mechanism for cellular components. Long-distance active transport is required and is performed by molecular motors including kinesins. Furthermore, in highly polarized, compartmentalized and plastic cells such as neurons, regulatory mechanisms are required to ensure appropriate spatio-temporal delivery of neuronal components. The kinesin machinery has diversified into a large number of kinesin motor proteins as well as adaptor proteins that are associated with subsets of cargo. However, many mechanisms contribute to the correct delivery of these cargos to their target domains. One mechanism is through motor recognition of subdomain-specific microtubule (MT) tracks, sign-posted by different tubulin isoforms, tubulin post-translational modifications (PTMs), tubulin GTPase activity and MT associated proteins (MAPs). With neurons as a model system, a critical review of these regulatory mechanisms is presented here, with particular focus on the emerging contribution of compartmentalised MAPs. Overall, we conclude that – especially for axonal cargo – alterations to the MT track can influence transport, although in vivo, it is likely that multiple track-based effects act synergistically to ensure accurate cargo distribution

ARIANA: Adaptive Robust and Integrative Analysis for finding Novel Associations

The effective mining of biological literature can provide a range of services such as hypothesis-generation, semantic-sensitive information retrieval, and knowledge discovery, which can be important to understand the confluence of different diseases, genes, and risk factors. Furthermore, integration of different tools at specific levels could be valuable. The main focus of the dissertation is developing and integrating tools in finding network of semantically related entities. The key contribution is the design and implementation of an Adaptive Robust and Integrative Analysis for finding Novel Associations. ARIANA is a software architecture and a web-based system for efficient and scalable knowledge discovery. It integrates semantic-sensitive analysis of text-data through ontology-mapping with database search technology to ensure the required specificity. ARIANA was prototyped using the Medical Subject Headings ontology and PubMed database and has demonstrated great success as a dynamic-data-driven system. ARIANA has five main components: (i) Data Stratification, (ii) Ontology-Mapping, (iii) Parameter Optimized Latent Semantic Analysis, (iv) Relevance Model and (v) Interface and Visualization. The other contribution is integration of ARIANA with Online Mendelian Inheritance in Man database, and Medical Subject Headings ontology to provide gene-disease associations. Empirical studies produced some exciting knowledge discovery instances. Among them was the connection between the hexamethonium and pulmonary inflammation and fibrosis. In 2001, a research study at John Hopkins used the drug hexamethonium on a healthy volunteer that ended in a tragic death due to pulmonary inflammation and fibrosis. This accident might have been prevented if the researcher knew of published case report. Since the original case report in 1955, there has not been any publications regarding that association. ARIANA extracted this knowledge even though its database contains publications from 1960 to 2012. Out of 2,545 concepts, ARIANA ranked “Scleroderma, Systemic”, “Neoplasms, Fibrous Tissue”, “Pneumonia”, “Fibroma”, and “Pulmonary Fibrosis” as the 13th, 16th, 38th, 174th and 257th ranked concept respectively. The researcher had access to such knowledge this drug would likely not have been used on healthy subjects.In today\u27s world where data and knowledge are moving away from each other, semantic-sensitive tools such as ARIANA can bridge that gap and advance dissemination of knowledge

Brain-Derived Neurotrophic Factor as a Biomarker for Aging and Dementia

The number of people with dementia due to Alzheimer’s disease (AD) is increasing worldwide. Although AD pathology begins well before clinical symptoms are apparent, identifying individuals at risk of developing AD to provide early interventions is still a challenge. Brain-derived neurotrophic factor (BDNF) is produced by neurons and glial cells and has complex interactions with AD pathology. BDNF also crosses the blood-brain barrier and can be measured in serum or plasma. In this dissertation, I investigated the biomarker potential of serum BDNF for AD using post mortem human samples, an aged rat model of cognitive impairment, and blood samples from cognitively healthy older adults. In the first part of my dissertation, I investigated the relationship between CSF, serum, and brain tissue levels of BDNF and AD-related pathology using post mortem human brain tissue samples. In these studies, I demonstrated, for the first time, that serum proBDNF levels reflect brain BDNF levels and that low brain BDNF levels are associated with increased accumulation of pTau and amyloid in the hippocampus. In the next part of my dissertation, I investigated whether serum BDNF levels were altered in an aged rat model exposed to a combination of the locus coeruleus selective neurotoxin and inflammation caused by lipopolysaccharides (LPS). I found that serum BDNF levels have an inverse relationship with inflammatory markers, and BDNF levels increase at two weeks post LPS injection, suggesting a compensatory increase. Finally, in the last part of my dissertation, I investigated whether serum BDNF levels can predict early changes in neuropsychological performance and neuroimaging measures associated with AD in cognitively healthy older adults. In this cohort of older adults, we found significant differences between women and men. Women had higher serum BDNF levels than men, the changes of neuropsychological performance over time were different in women and men, and higher baseline BDNF levels were associated with greater declines in hippocampal volume and limbic FA in men but not in women. Overall, results from this dissertation indicate that serum BDNF levels correlate with brain BDNF levels, are reduced by neurodegeneration and inflammation, and may reflect clinically-relevant changes in older adults and, therefore, should be considered for inclusion in routine blood workup in a clinical setting