Neurofibrillary tangles in Alzheimer's disease and progressive supranuclear palsy: antigenic similarities and differences

1987 ◽  
Vol 74 (1) ◽  
pp. 39-46 ◽  
Author(s):  
C. Bancher ◽  
H. Lassmann ◽  
H. Budka ◽  
I. Grundke-Iqbal ◽  
K. Iqbal ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Progressive Supranuclear Palsy ◽  
Neurofibrillary Tangles ◽  
Similarities And Differences

Liquid crystalline ordering of paired helical filaments in neurofibrillary tangles of Alzheimer's disease

1986 ◽  
Vol 44 ◽  
pp. 348-349
Author(s):  
D.F. Clapin ◽  
V.J.A. Montpetit
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurofibrillary Tangles ◽  
Liquid Crystalline ◽  
Amyloid Fibrils ◽  
Geometric Analysis ◽  
Paired Helical Filaments ◽  
Microscopic Level ◽  
Light Microscopic Level ◽  
Regular Spacing

Alzheimer's disease is characterized by the accumulation of abnormal filamentous proteins. The most important of these are amyloid fibrils and paired helical filaments (PHF). PHF are located intraneuronally forming bundles called neurofibrillary tangles. The designation of these structures as "tangles" is appropriate at the light microscopic level. However, localized domains within individual tangles appear to demonstrate a regular spacing which may indicate a liquid crystalline phase. The purpose of this paper is to present a statistical geometric analysis of PHF packing.

scholarly journals NLRP3 Inflammasome: A Starring Role in Amyloid-β- and Tau-Driven Pathological Events in Alzheimer’s Disease

2021 ◽  
pp. 1-22
Author(s):  
Mariana Van Zeller ◽  
Diogo M. Dias ◽  
Ana M. Sebastião ◽  
Cláudia A. Valente
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Glial Cells ◽  
Neurofibrillary Tangles ◽  
Nlrp3 Inflammasome ◽  
Neuronal Death ◽  
Inflammatory Responses ◽  
Senile Plaques ◽  
Amyloid Β ◽  
Wide Range

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease commonly diagnosed among the elderly population. AD is characterized by the loss of synaptic connections, neuronal death, and progressive cognitive impairment, attributed to the extracellular accumulation of senile plaques, composed by insoluble aggregates of amyloid-β (Aβ) peptides, and to the intraneuronal formation of neurofibrillary tangles shaped by hyperphosphorylated filaments of the microtubule-associated protein tau. However, evidence showed that chronic inflammatory responses, with long-lasting exacerbated release of proinflammatory cytokines by reactive glial cells, contribute to the pathophysiology of the disease. NLRP3 inflammasome (NLRP3), a cytosolic multiprotein complex sensor of a wide range of stimuli, was implicated in multiple neurological diseases, including AD. Herein, we review the most recent findings regarding the involvement of NLRP3 in the pathogenesis of AD. We address the mechanisms of NLRP3 priming and activation in glial cells by Aβ species and the potential role of neurofibrillary tangles and extracellular vesicles in disease progression. Neuronal death by NLRP3-mediated pyroptosis, driven by the interneuronal tau propagation, is also discussed. We present considerable evidence to claim that NLRP3 inhibition, is undoubtfully a potential therapeutic strategy for AD.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Overexpression of four-repeat tau mRNA isoforms in progressive supranuclear palsy but not in Alzheimer's disease

1999 ◽  
Vol 46 (3) ◽  
pp. 325-332 ◽  
Author(s):  
Christopher B. Chambers ◽  
John M. Lee ◽  
Juan C. Troncoso ◽  
Stephen Reich ◽  
Nancy A. Muma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Progressive Supranuclear Palsy ◽  
Mrna Isoforms

scholarly journals Transcriptome analyses reveal tau isoform-driven changes in transposable element and gene expression

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0251611
Author(s):  
Jennifer Grundman ◽  
Brian Spencer ◽  
Floyd Sarsoza ◽  
Robert A. Rissman
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transposable Elements ◽  
Transposable Element ◽  
Progressive Supranuclear Palsy ◽  
Rna Sequencing ◽  
Amyloid Beta ◽  
Specific Level ◽  
Tau Isoforms ◽  
Tau Isoform

Alternative splicing of the gene MAPT produces several isoforms of tau protein. Overexpression of these isoforms is characteristic of tauopathies, which are currently untreatable neurodegenerative diseases. Though non-canonical functions of tau have drawn interest, the role of tau isoforms in these diseases has not been fully examined and may reveal new details of tau-driven pathology. In particular, tau has been shown to promote activation of transposable elements—highly regulated nucleotide sequences that replicate throughout the genome and can promote immunologic responses and cellular stress. This study examined tau isoforms’ roles in promoting cell damage and dysregulation of genes and transposable elements at a family-specific and locus-specific level. We performed immunofluorescence, Western blot and cytotoxicity assays, along with paired-end RNA sequencing on differentiated SH-SY5Y cells infected with lentiviral constructs of tau isoforms and treated with amyloid-beta oligomers. Our transcriptomic findings were validated using publicly available RNA-sequencing data from Alzheimer’s disease, progressive supranuclear palsy and control human samples from the Accelerating Medicine’s Partnership for AD (AMP-AD). Significance for biochemical assays was determined using Wilcoxon ranked-sum tests and false discovery rate. Transcriptome analysis was conducted through DESeq2 and the TEToolkit suite available from the Hammell lab at Cold Spring Harbor Laboratory. Our analyses show overexpression of different tau isoforms and their interactions with amyloid-beta in SH-SY5Y cells result in isoform-specific changes in the transcriptome, with locus-specific transposable element dysregulation patterns paralleling those seen in patients with Alzheimer’s disease and progressive supranuclear palsy. Locus-level transposable element expression showed increased dysregulation of L1 and Alu sites, which have been shown to drive pathology in other neurological diseases. We also demonstrated differences in rates of cell death in SH-SY5Y cells depending on tau isoform overexpression. These results demonstrate the importance of examining tau isoforms’ role in neurodegeneration and of further examining transposable element dysregulation in tauopathies and its role in activating the innate immune system.

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