Browsing by Subject "Neurodegenerative disease"

Now showing 1 - 6 of 6
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    Antiphospholipid autoantibodies as blood biomarkers for detection of early stage Alzheimer's disease
    (Taylor & Francis, 2015-08) McIntyre, John A.; Ramsey, Curtis J.; Gitter, Bruce D.; Saykin, Andrew J.; Wagenknecht, Dawn R.; Hyslop, Paul A.; Department of Radiology and Imaging Sciences, IU School of Medicine
    A robust blood biomarker is urgently needed to facilitate early prognosis for those at risk for Alzheimer's disease (AD). Redox reactive autoantibodies (R-RAAs) represent a novel family of antibodies detectable only after exposure of cerebrospinal fluid (CSF), serum, plasma or immunoglobulin fractions to oxidizing agents. We have previously reported that R-RAA antiphospholipid antibodies (aPLs) are significantly decreased in the CSF and serum of AD patients compared to healthy controls (HCs). These studies were extended to measure R-RAA aPL in serum samples obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI). Serum samples from the ADNI-1 diagnostic groups from participants with mild cognitive impairment (MCI), AD and HCs were blinded for diagnosis and analyzed for R-RAA aPL by ELISA. Demographics, cognitive data at baseline and yearly follow-up were subsequently provided by ADNI after posting assay data. As observed in CSF, R-RAA aPL in sera from the AD diagnostic group were significantly reduced compared to HC. However, the sera from the MCI population contained significantly elevated R-RAA aPL activity relative to AD patient and/or HC sera. The data presented in this study indicate that R-RAA aPL show promise as a blood biomarker for detection of early AD, and warrant replication in a larger sample. Longitudinal testing of an individual for increases in R-RAA aPL over a previously established baseline may serve as a useful early sero-epidemiologic blood biomarker for individuals at risk for developing dementia of the Alzheimer's type.
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    Crystal structure of a conformational antibody that binds tau oligomers and inhibits pathological seeding by extracts from donors with Alzheimer's disease
    (American Society for Biochemistry and Molecular Biology, 2020-07-31) Abskharon, Romany; Seidler, Paul M.; Sawaya, Michael R.; Cascio, Duilio; Yang, Tianxiao P.; Philipp, Stephan; Williams, Christopher Kazu; Newell, Kathy L.; Ghetti, Bernardino; DeTure, Michael A.; Dickson, Dennis W.; Vinters, Harry V.; Felgner, Philip L.; Nakajima, Rie; Glabe, Charles G.; Eisenberg, David S.; Pathology and Laboratory Medicine, School of Medicine
    Soluble oligomers of aggregated tau accompany the accumulation of insoluble amyloid fibrils, a histological hallmark of Alzheimer disease (AD) and two dozen related neurodegenerative diseases. Both oligomers and fibrils seed the spread of Tau pathology, and by virtue of their low molecular weight and relative solubility, oligomers may be particularly pernicious seeds. Here, we report the formation of in vitro tau oligomers formed by an ionic liquid (IL15). Using IL15-induced recombinant tau oligomers and a dot blot assay, we discovered a mAb (M204) that binds oligomeric tau, but not tau monomers or fibrils. M204 and an engineered single-chain variable fragment (scFv) inhibited seeding by IL15-induced tau oligomers and pathological extracts from donors with AD and chronic traumatic encephalopathy. This finding suggests that M204-scFv targets pathological structures that are formed by tau in neurodegenerative diseases. We found that M204-scFv itself partitions into oligomeric forms that inhibit seeding differently, and crystal structures of the M204-scFv monomer, dimer, and trimer revealed conformational differences that explain differences among these forms in binding and inhibition. The efficiency of M204-scFv antibodies to inhibit the seeding by brain tissue extracts from different donors with tauopathies varied among individuals, indicating the possible existence of distinct amyloid polymorphs. We propose that by binding to oligomers, which are hypothesized to be the earliest seeding-competent species, M204-scFv may have potential as an early-stage diagnostic for AD and tauopathies, and also could guide the development of promising therapeutic antibodies.
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    Developments in understanding early onset Alzheimer’s disease
    (Wiley, 2023) Griffin, Percy; Apostolova, Liana; Dickerson, Bradford C.; Rabinovici, Gil; Salloway, Stephen; Raghuram, Srilath; Brandt, Katie; Hall, Stephen; Masdeu, Joseph; Carrillo, Maria C.; Hammers, Dustin; Neurology, School of Medicine
    On September 25 and 26, 2021, the Alzheimer's Association hosted the first meeting focused on people with early-onset Alzheimer's disease (EOAD)-sometimes referred to as younger onset Alzheimer's disease (AD). Though a diagnosis of AD can be devastating at any age, those with a younger onset-defined as symptoms developing prior to 65 years of age-face unique challenges. EOAD occurs when people are in the prime of their lives, often with multiple responsibilities including careers, community activities, and raising children and caring for older family members. These challenges warrant special consideration and study, yet people with EOAD are often excluded from AD research because of their atypical age of onset. To help fill this gap, we designed and launched the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) to enroll and follow 500 people with EOAD from > 15 sites in the United States, which the National Institute on Aging funded in 2018. The September 2021 meeting was designed to inform people with EOAD and their family members and caregivers about the latest research on the biology of EOAD, treatments in the pipeline, practical considerations about legal and financial arrangements for families, and the support networks available to them. More than 217 registrants attended.
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    Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease
    (Bentham Science, 2019) Li, Jin; Chen, Feng; Zhang, Qiushi; Meng, Xianglian; Yao, Xiaohui; Risacher, Shannon L.; Yan, Jingwen; Saykin, Andrew J.; Liang, Hong; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Background: The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. Objective: The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. Methods: First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. Results: We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. Conclusion: The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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    Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality
    (Public Library of Science, 2024) Rajwa, Bartek; Naved, Md Mobasshir Arshed; Adibuzzaman, Mohammad; Grama, Ananth Y.; Khan, Babar A.; Dundar, M. Murat; Rochet, Jean-Christophe; Computer Science, Luddy School of Informatics, Computing, and Engineering
    As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.
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    MN-166 (ibudilast) in amyotrophic lateral sclerosis in a Phase IIb/III study: COMBAT-ALS study design
    (Taylor & Francis, 2021) Oskarsson, Björn; Maragakis, Nicholas; Bedlack, Richard S.; Goyal, Namita; Meyer, Jenny A.; Genge, Angela; Bodkin, Cynthia; Maiser, Samuel; Staff, Nathan; Zinman, Lorne; Olney, Nicholas; Turnbull, John; Brooks, Benjamin Rix; Klonowski, Emelia; Makhay, Malath; Yasui, Seiichi; Matsuda, Kazuko; Neurology, School of Medicine
    Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with motor neuron loss as a defining feature. Despite significant effort, therapeutic breakthroughs have been modest. MN-166 (ibudilast) has demonstrated neuroprotective action by various mechanisms: inhibition of proinflammatory cytokines and macrophage migration inhibitory factor, phosphodiesterase inhibition, and attenuation of glial cell activation in models of ALS. Early-phase studies suggest that MN-166 may improve survival outcomes and slow disease progression in patients with ALS. This article describes the rationale and design of COMBAT-ALS, an ongoing randomized, double-blind, placebo-controlled, multicenter Phase IIb/III study in ALS. This study is designed to evaluate the pharmacokinetics, safety and tolerability and assess the efficacy of MN-166 on function, muscle strength, quality of life and survival in ALS.