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Record W1970347605 · doi:10.1186/1742-2094-8-92

TLR4 mutation reduces microglial activation, increases Aβ deposits and exacerbates cognitive deficits in a mouse model of Alzheimer's disease

2011· article· en· W1970347605 on OpenAlexaff
Min Song, Jingji Jin, Jeong-Eun Lim, Jinghong Kou, Abhinandan Pattanayak, Jamaal Rehman, Hong‐Duck Kim, Kazuki Tahara, Robert Lalonde, Ken-ichiro Fukuchi

Bibliographic record

VenueJournal of Neuroinflammation · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversité de Montréal
FundersNational Eye InstituteNational Institute on AgingNational Institutes of HealthAlzheimer's Association
KeywordsMicrogliaTLR4Proinflammatory cytokineNeuroinflammationInnate immune systemChemokineImmunologyTREM2NeurodegenerationReceptorCognitive declineBiologyCell biologyMedicineImmune systemInflammationPathologyInternal medicineDiseaseDementia

Abstract

fetched live from OpenAlex

BACKGROUND: Amyloid plaques, a pathological hallmark of Alzheimer's disease (AD), are accompanied by activated microglia. The role of activated microglia in the pathogenesis of AD remains controversial: either clearing Aβ deposits by phagocytosis or releasing proinflammatory cytokines and cytotoxic substances. Microglia can be activated via toll-like receptors (TLRs), a class of pattern-recognition receptors in the innate immune system. We previously demonstrated that an AD mouse model homozygous for a loss-of-function mutation of TLR4 had increases in Aβ deposits and buffer-soluble Aβ in the brain as compared with a TLR4 wild-type AD mouse model at 14-16 months of age. However, it is unknown if TLR4 signaling is involved in initiation of Aβ deposition as well as activation and recruitment of microglia at the early stage of AD. Here, we investigated the role of TLR4 signaling and microglial activation in early stages using 5-month-old AD mouse models when Aβ deposits start. METHODS: Microglial activation and amyloid deposition in the brain were determined by immunohistochemistry in the AD models. Levels of cerebral soluble Aβ were determined by ELISA. mRNA levels of cytokines and chemokines in the brain and Aβ-stimulated monocytes were quantified by real-time PCR. Cognitive functions were assessed by the Morris water maze. RESULTS: While no difference was found in cerebral Aβ load between AD mouse models at 5 months with and without TLR4 mutation, microglial activation in a TLR4 mutant AD model (TLR4M Tg) was less than that in a TLR4 wild-type AD model (TLR4W Tg). At 9 months, TLR4M Tg mice had increased Aβ deposition and soluble Aβ42 in the brain, which were associated with decrements in cognitive functions and expression levels of IL-1β, CCL3, and CCL4 in the hippocampus compared to TLR4W Tg mice. TLR4 mutation diminished Aβ-induced IL-1β, CCL3, and CCL4 expression in monocytes. CONCLUSION: This is the first demonstration of TLR4-dependent activation of microglia at the early stage of β-amyloidosis. Our results indicate that TLR4 is not involved in the initiation of Aβ deposition and that, as Aβ deposits start, microglia are activated via TLR4 signaling to reduce Aβ deposits and preserve cognitive functions from Aβ-mediated neurotoxicity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.262
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations272
Published2011
Admission routes1
Has abstractyes

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