scholarly journals Differential Signaling Mediated by ApoE2, ApoE3, and ApoE4 in Human Neurons Parallels Alzheimer’s Disease Risk

2018 ◽  
Author(s):  
Yu-Wen Alvin Huang ◽  
Bo Zhou ◽  
Amber M. Nabet ◽  
Marius Wernig ◽  
Thomas C. Südhof
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Signaling Pathways ◽  
Synapse Formation ◽  
Disease Risk ◽  
Rank Order ◽  
Neuronal Signaling ◽  
Differential Signaling ◽  
Human Neurons ◽  
Apoe Receptor

AbstractApolipoprotein E (ApoE) mediates clearance of circulating lipoproteins from blood by binding to ApoE receptors. Humans express three genetic variants, ApoE2, ApoE3, and ApoE4, that exhibit distinct ApoE receptor binding properties. In brain, ApoE is abundantly produced by activated astrocytes and microglia, and three variants differentially affect Alzheimer’s disease (AD), such that ApoE2 protects against, and ApoE4 predisposes to the disease. A role for ApoE4 in driving microglial dysregulation and impeding Aβ clearance in AD is well documented, but the direct effects of three variants on neurons are poorly understood. Extending previous studies, we here demonstrate that ApoE variants differentially activate multiple neuronal signaling pathways and regulate synaptogenesis. Specifically, using human neurons cultured in the absence of glia to exclude indirect glial mechanisms, we show that ApoE broadly stimulates signal transduction cascades which among others enhance synapse formation with an ApoE4>ApoE3>ApoE2 potency rank order, paralleling the relative risk for AD conferred by these variants. Unlike the previously described induction of APP transcription, however, ApoE-induced synaptogenesis involves CREB rather than cFos activation. We thus propose that in brain, ApoE acts as a glia-secreted paracrine signal and activates neuronal signaling pathways in what may represent a protective response, with the differential potency of ApoE variants causing distinct levels of chronic signaling that may contribute to AD pathogenesis.

scholarly journals Differential Signaling Mediated by ApoE2, ApoE3, and ApoE4 in Human Neurons Parallels Alzheimer's Disease Risk

2019 ◽  
Vol 39 (37) ◽  
pp. 7408-7427 ◽  
Author(s):  
Yu-Wen Alvin Huang ◽  
Bo Zhou ◽  
Amber M. Nabet ◽  
Marius Wernig ◽  
Thomas C. Südhof
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Differential Signaling ◽  
Human Neurons

The Role of Lipids and Membranes in the Pathogenesis of Alzheimer's Disease: A Comprehensive View

2018 ◽  
Vol 15 (13) ◽  
pp. 1191-1212 ◽  
Author(s):  
Botond Penke ◽  
Gábor Paragi ◽  
János Gera ◽  
Róbert Berkecz ◽  
Zsolt Kovács ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Signaling Pathways ◽  
Membrane Lipids ◽  
Specific Protein ◽  
Dietary Factors ◽  
Lipid Signaling ◽  
Special Role ◽  
Aβ Peptides

Lipids participate in Amyloid Precursor Protein (APP) trafficking and processing - important factors in the initiation of Alzheimer’s disease (AD) pathogenesis and influence the formation of neurotoxic β-amyloid (Aβ) peptides. An important risk factor, the presence of ApoE4 protein in AD brain cells binds the lipids to AD. In addition, lipid signaling pathways have a crucial role in the cellular homeostasis and depend on specific protein-lipid interactions. The current review focuses on pathological alterations of membrane lipids (cholesterol, glycerophospholipids, sphingolipids) and lipid metabolism in AD and provides insight in the current understanding of biological membranes, their lipid structures and functions, as well as their role as potential therapeutic targets. Novel methods for studying the membrane structure and lipid composition will be reviewed in a broad sense whereas the use of lipid biomarkers for early diagnosis of AD will be shortly summarized. Interactions of Aβ peptides with the cell membrane and different subcellular organelles are reviewed. Next, the details of the most important lipid signaling pathways, including the role of the plasma membrane as stress sensor and its therapeutic applications are given. 4-hydroxy-2-nonenal may play a special role in the initiation of the pathogenesis of AD and thus the “calpain-cathepsin hypothesis” of AD is highlighted. Finally, the most important lipid dietary factors and their possible use and efficacy in the prevention of AD are discussed.

scholarly journals MAPT H1 Haplotype is Associated with Late-Onset Alzheimer’s Disease Risk in APOE ɛ4 Noncarriers: Results from the Dementia Genetics Spanish Consortium

2015 ◽  
Vol 49 (2) ◽  
pp. 343-352 ◽  
Author(s):  
Pau Pastor ◽  
Fermín Moreno ◽  
Jordi Clarimón ◽  
Agustín Ruiz ◽  
Onofre Combarros ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Late Onset

scholarly journals A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaker El-Sappagh ◽  
Jose M. Alonso ◽  
S. M. Riazul Islam ◽  
Ahmad M. Sultan ◽  
Kyung Sup Kwak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Disease Risk ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Large Set ◽  
Detection Model ◽  
Multi Class Classification ◽  
Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

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