Upregulated NF-κB pathway proteins may underlie <i>APOE44</i> associated astrocyte phenotypes in sporadic Alzheimer’s disease
Bibliographic record
Abstract
Abstract The Apolipoprotein-E4 allele (APOE) is the strongest genetic risk factor for sporadic Alzheimer’s disease but its role in disease pathogenesis is incompletely understood. The APOE gene encodes Apolipoprotein E (ApoE). Astrocytes are the main source of ApoE in the central nervous system (CNS) and are essential for homeostasis in health and disease. In response to CNS insult, a coordinated multicellular inflammatory response is triggered causing reactive astrogliosis with changes in astrocytic gene expression, cellular structure and function. Human embryonic stem-cells with the ‘neutral’ APOE33 genotype were edited using CRISPR Cas-9 gene-editing to create isogenic APOE lines with an APOE44 genotype. Quiescent astrocytes were differentiated then stimulated with TNF-α, IL1α and C1q inducing an astrogliotic A1 phenotype. Several potentially pathological APOE44 -related phenotypes were identified in both quiescent cells and reactive A1 astrocytes including significantly decreased phagocytosis, impaired glutamate and a defective immunomodulatory response. In quiescent APOE44 astrocytes there was significantly decreased secretion of IL6, IL8 and several oxylipins. In A1 astrocytes there was a pro-inflammatory phenotype in APOE44 astrocytes with increases in GRO, ENA78, IL6 and IL8, a decrease in IL10 as well as significant differences in oxylipin expression. As TNF-α induced signaling in astrocytes is driven by Nuclear factor kappa B (NF-κB) proteins of this pathway were measured. Significantly higher levels of the p50, p65 and IκBα sub-units were found in both quiescent and A1 APOE44 astrocytes. This suggests that perturbation of NF-κB signaling may contribute to the damaging APOE44 cell phenotypes observed providing a new direction for targeted disease therapeutics.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".