Toll-Like Receptor 2 Acts as a Natural Innate Immune Receptor to Clear Amyloid β<sub>1–42</sub>and Delay the Cognitive Decline in a Mouse Model of Alzheimer's Disease
Bibliographic record
Abstract
Microglia are the immune cells of the brain, they are activated in the brain of Alzheimer's disease (AD) patients and mouse models of AD, and they express the innate immune receptor toll-like receptor 2 (TLR2). The present study investigated role of this receptor in the progression of AD-like pathologies. Here we show that amyloid beta (A beta) stimulates TLR2 expression in a small proportion of microglia. We then generated triple transgenic mice that are deficient in TLR2 from mice that harbor a mutant human presenelin 1 and a chimeric mouse/human amyloid precursor protein (APP) genes. TLR2 deficiency accelerated spatial and contextual memory impairments, which correlated with increased levels of A beta(1-42) and transforming growth factor beta1 in the brain. NMDA receptors 1 and 2A expression levels were also lower in the hippocampus of APP-TLR2(-/-) mice. Gene therapy in cells of the bone marrow using lentivirus constructs expressing TLR2 rescued the cognitive impairment of APP-TLR2(-/-) mice. Indeed, lenti-green fluorescent protein/TLR2 treatment had beneficial effects by restoring the memory consolidation process disrupted by TLR2 deficiency in APP mice. These data suggest that TLR2 acts as an endogenous receptor for the clearance of toxic A beta by bone-marrow-derived immune cells. The cognitive decline is markedly accelerated in a context of TLR2 deficiency. Upregulating this innate immune receptor may then be considered as a potential new powerful therapeutic approach for AD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".