Fragmentation of brain apolipoprotein E (ApoE) and its relevance in Alzheimer's disease
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Asiamah Ernest Amponsah
und Huixian Cui
Abstract
Alzheimer's disease (AD) is a very common cause of dementia in the elderly. It is characterized by progressive amnesia and accretions of neurofibrillary tangles (NFTs) of neurons and senile plaques in the neuropil. After aging, the inheritance of the apolipoprotein E (ApoE) epsilon 4 (ε4) allele is the greatest risk factor for late-onset AD. The ApoE protein is the translated product of the ApoE gene. This protein undergoes proteolysis, and the resulting fragments colocalize with neurofibrillary tangles and amyloid plaques, and for that matter may be involved in AD onset and/or progression. Previous studies have reported the pathogenic potential of various ApoE fragments in AD pathophysiology. However, the pathways activated by the fragments are not fully understood. In this review, ApoE fragments obtained from post-mortem brains and body fluids, cerebrospinal fluid (CSF) and plasma, are discussed. Additionally, current knowledge about the process of fragmentation is summarized. Finally, the mechanisms by which these fragments are involved in AD pathogenesis and pathophysiology are discussed.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 81801278
Funding source: Natural Science Foundation of Hebei Province
Award Identifier / Grant number: H2019206637
Funding source: China Scholarship Council
Award Identifier / Grant number: 201608130015
Funding source: Hebei University Science and technology research project
Award Identifier / Grant number: ZD2019049
Funding source: Hebei Provincial Department of Human Resources and Social Security
Award Identifier / Grant number: C20190509
Acknowledgments
This work was supported by Natural Science Foundation of China (Grant No. 81801278), Natural Science Foundation of Hebei Province (Grant No. H2019206637), China Scholarship Council (Grant No. 201608130015), Hebei University Science and technology research project (Grant No. ZD2019049), Excellent Overseas researcher Program in Hebei Provincial Department of Human Resources and Social Security (Grant No. C20190509).
Conflict of interest statement: Authors declare no competing interests.
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- GABAergic and glutamatergic effects on nigrostriatal and mesolimbic dopamine release in the rat
- Fragmentation of brain apolipoprotein E (ApoE) and its relevance in Alzheimer's disease
- Cochlear synaptopathy: new findings in animal and human research
- Neurophysiologic implications of neuronal nitric oxide synthase
- Exploring the mechanisms of action of the antidepressant effect of the ketogenic diet
- The potential anti-depressant properties of dexmedetomidine infusion: a review of mechanistic, preclinical, and clinical evidence
- Role of kinaesthetic motor imagery in mirror-induced visual illusion as intervention in post-stroke rehabilitation
- A review on the alterations in the brain of persistent postural-perceptual dizziness patients and non-pharmacological interventions for its management
- A new method to predict anomaly in brain network based on graph deep learning
Artikel in diesem Heft
- Frontmatter
- GABAergic and glutamatergic effects on nigrostriatal and mesolimbic dopamine release in the rat
- Fragmentation of brain apolipoprotein E (ApoE) and its relevance in Alzheimer's disease
- Cochlear synaptopathy: new findings in animal and human research
- Neurophysiologic implications of neuronal nitric oxide synthase
- Exploring the mechanisms of action of the antidepressant effect of the ketogenic diet
- The potential anti-depressant properties of dexmedetomidine infusion: a review of mechanistic, preclinical, and clinical evidence
- Role of kinaesthetic motor imagery in mirror-induced visual illusion as intervention in post-stroke rehabilitation
- A review on the alterations in the brain of persistent postural-perceptual dizziness patients and non-pharmacological interventions for its management
- A new method to predict anomaly in brain network based on graph deep learning