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TLR4 Cross-Talk With NLRP3 Inflammasome and Complement Signaling Pathways in Alzheimer's Disease
Open accessAbstract
Amyloid plaques, mainly composed of abnormally aggregated amyloid β-protein (Aβ) in the brain parenchyma, and neurofibrillary tangles (NFTs), consisting of hyperphosphorylated tau protein aggregates in neurons, are two pathological hallmarks of Alzheimer's disease (AD). Aβ fibrils and tau aggregates in the brain are closely associated with neuroinflammation and synapse loss, characterized by activated microglia and dystrophic neurites. Genome-wide genetic association studies revealed important roles of innate immune cells in the pathogenesis of late-onset AD by recognizing a dozen genetic risk loci that modulate innate immune activities. Furthermore, microglia, brain resident innate immune cells, have been increasingly recognized to play key, opposing roles in AD pathogenesis by either eliminating toxic Aβ aggregates and enhancing neuronal plasticity or producing proinflammatory cytokines, reactive oxygen species, and synaptotoxicity. Aggregated Aβ binds to toll-like receptor 4 (TLR4) and activates microglia, resulting in increased phagocytosis and cytokine production. Complement components are associated with amyloid plaques and NFTs. Aggregated Aβ can activate complement, leading to synapse pruning and loss by microglial phagocytosis. Systemic inflammation can activate microglial TLR4, NLRP3 inflammasome, and complement in the brain, leading to neuroinflammation, Aβ accumulation, synapse loss and neurodegeneration. The host immune response has been shown to function through complex crosstalk between the TLR, complement and inflammasome signaling pathways. Accordingly, targeting the molecular mechanisms underlying the TLR-complement-NLRP3 inflammasome signaling pathways can be a preventive and therapeutic approach for AD.
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“Among the mentioned PRRs, TLRs play crucial role in the inflammatory responses to pathogenic infection and have been a target for many studies exploring phagocytosis course [201,202,203].”
From MicroRNAs as Regulators of Phagocytosis (2022) · cites this paper as [201]
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Metadata
- Authors
- Yang, J., Wise, L., & Fukuchi, K.-I.
- Affiliations
- Peoria campus of the University of Illinois System (US)
- Corresponding author
- Fukuchi, Ken-ichiro
- ORCID
- Yang, Junling
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2020-04-23
- Volume
- 11
- Pages
- 724-724
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC7190872
- OpenAlex
- W3019520705
- Cited by
- 387
- References
- 210
- Citations by year
- 2026: 64 · 2025: 73 · 2024: 72 · 2023: 73 · 2022: 67 · 2021: 30 · 2020: 7
- FWCI
- 4.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health
- National Institute on Aging (R21AG064811, R21AG056862, R21AG054937, R21AG062179, R21AG053719)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Inflammasome and immune disorders
- Keywords
- Alzheimer's disease, TLR4, NLRP3 inflammasome, complement system, innate immunity, microglia, amyloid-beta, neurofibrillary tangles, neuroinflammation, synaptic loss, systemic inflammation, neurodegeneration, phagocytosis, synaptic pruning
- MeSH terms
- NLR Family, Pyrin Domain-Containing 3 Protein, Alzheimer Disease, Animals, Complement System Proteins, Humans, Signal Transduction, Amyloid beta-Peptides, Toll-Like Receptor 4, Mice, Inflammasomes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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