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Toll-like receptors in the pathogenesis of neuroinflammation
Abstract
Toll-like receptors (TLRs) are discovered as crucial pattern recognition receptors (PRRs) involved in the recognition of pathogen-associated molecular patterns (PAMPs). Later studies showed their involvement in the recognition of various damage/danger-associated molecular patterns (DAMPs) generated by host itself. Thus, TLRs are capable of recognizing wide-array of patterns/molecules derived from pathogens and host as well and initiating a proinflammatory immune response through the activation of NF-κB and other transcription factors causing synthesis of proinflammatory molecules. The process of neuroinflammation is seen under both sterile and infectious inflammatory diseases of the central nervous system (CNS) and may lead to the development of neurodegeneration. The present article is designed to highlight the importance of TLRs in the pathogenesis of neuroinflammation under diverse conditions. TLRs are expressed by various immune cells present in CNS along with neurons. However out of thirteen TLRs described in mammals, some are present and active in these cells, while some are absent and are described in detail in main text. The role of various immune cells present in the brain and their role in the pathogenesis of neuroinflammation depending on the type of TLR expressed is described. Thereafter the role of TLRs in bacterial meningitis, viral encephalitis, stroke, Alzheimer's disease (AD), Parkinson's disease (PD), and autoimmune disease including multiple sclerosis (MS) is described. The article is designed for both neuroscientists needing information regarding TLRs in neuroinflammation and TLR biologists or immunologists interested in neuroinflammation.
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“Several of them, especially TLR2, 4, and 9, have vital contributions to the development of neurodegenerative diseases.276”
From Role of neuroinflammation in neurodegeneration development (2023) · cites this paper as [276]
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“TLR activation leads to receptor dimerization and the recruitment of adapter proteins, such as Toll/interleukin 1 receptor (TIR) domain-containing adapter interferon-β (TRIF) and myeloid differentiation primary-response protein 88 (MyD88)276”
From Role of neuroinflammation in neurodegeneration development (2023) · cites this paper as [276]
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Metadata
- Authors
- Kumar, V.
- Affiliations
- The University of Queensland (AU)
- Corresponding author
- Kumar, V.
- ORCID
- Kumar, V.
- Venue
- Journal of Neuroimmunology
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 332
- Pages
- 16-30
- ISSN
- 0165-5728 (ISSN-L)
- OpenAlex
- W2924166680
- Cited by
- 331
- References
- 255
- Citations by year
- 2026: 25 · 2025: 41 · 2024: 50 · 2023: 55 · 2022: 61 · 2021: 47 · 2020: 46 · 2019: 6
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neuroimmunology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Life Sciences › Immunology and Microbiology › Immunology › Immune Response and Inflammation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Inflammasome and immune disorders
- Keywords
- Toll-like receptors, neuroinflammation, pattern recognition receptors, damage-associated molecular patterns, NF-κB signaling, inflammatory cytokines, neurodegeneration, bacterial meningitis, viral encephalitis, stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis
- MeSH terms
- Alarmins, Pathogen-Associated Molecular Pattern Molecules, Animals, Astrocytes, Brain, Brain Diseases, Cerebrovascular Disorders, Encephalitis, Humans, Meningitis, Neurons, Oligodendroglia, Neuroimmunomodulation, Signal Transduction, Microglia, Neurodegenerative Diseases, Receptors, Pattern Recognition, Toll-Like Receptors, Myeloid Differentiation Factor 88
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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