Microglial and Astrocyte priming in the APP/PS1 model of Alzheimer’s Disease: increased vulnerability to acute inflammation and cognitive deficits
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) causes devastating cognitive decline and has no disease-modifying therapies. Neuroinflammation is a significant contributor to disease progression but its precise contribution remains unclear. An emerging literature indicates that secondary inflammatory insults including acute trauma and infection alter the trajectory of chronic neurodegenerative diseases and the roles of microglia and astrocytes require elucidation. The current study, using the APP/PS1 mouse model of AD, demonstrates that microglia are primed by β-amyloid pathology to induce exaggerated IL-1β responses to acute stimulation with LPS or IL-1β. Despite disease-associated NLRP3 inflammasome activation, evidenced by ASC speck formation, APP/PS1 microglial cells show neither IL-1β induction nor NFκB p65 nuclear localisation. Upon secondary stimulation with LPS or IL-1β, NFκB-p65 nuclear localisation and exaggerated pro-IL-1 induction occur. Microglial priming was also unmasked by secondary stimulation with systemic LPS leading to significant cognitive impairment in APP/PS1 mice compared to WT LPS-treated mice. Astrocytes have also recently emerged as displaying significant phenotypic heterogeneity. Here, by-passing microglial priming, and acutely challenging mice with intra-hippocampal IL-1β we demonstrate that astrocytes proximal to Aβ-plaques are also primed to produce exaggerated CCL2, CXCL1 and CXCL10 responses. Many astrocytosis-associated genes in APP/PS1 mice share these exaggerated responses to IL-1β, while others are equally induced in both strains. Collectively the data show that the amyloid-laden brain shows multiple vulnerabilities to secondary inflammatory challenge: both microglia and astrocytes are primed to produce exaggerated secondary inflammation and systemic LPS is sufficient to cause cognitive impairments relevant to delirium, selectively in animals with prior amyloid pathology.
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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 | 0.001 | 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 | 0.001 |
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".