TLR4 mutation reduces microglial activation, increases Aβ deposits and exacerbates cognitive deficits in a mouse model of Alzheimer's disease
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".