Gene immunotherapy regulated by astrocytic reactivity in a mouse model of amyloidosis
Bibliographic record
Abstract
BACKGROUND: Recombinant adeno-associated viruses (AAVs) capable of crossing the blood-brain barrier (e.g. AAV.PHP.eB) and encoding antibodies against amyloid beta peptides (Aβ) have potential to evaluate brain-wide gene immunotherapies in Alzheimer's disease (AD). Furthermore, leveraging astrocytic reactivity in response to Aβ pathology, the glial fibrillary acidic protein (GFAP) promoter could serve as a regulator of gene immunotherapy. HYPOTHESIS: Reactive astrocytes can regulate the expression of the recombinant anti-Aβ antibody (rSol) under the control of a GFAP promoter in the TgCRND8 (Tg) mouse model of amyloidosis. METHOD: To study GFAP expression in Tg mice, GFAP mRNA levels were quantified using qPCR in the hippocampal formation at 3, 5, and 6 months (n = 6 per group). Next, AAV.PHP.eB.GFAP.rSol-myc-tag and AAV.PHP.eB.GFAP.GFP were co-injected intravenously in Tg mice while non-Tg littermates and C57BL/6J mice served as controls. One-month post-injection, brain sections were processed for immunohistochemistry and RNAscope. RESULT: GFAP mRNA levels doubled in 6-month-old compared to 3-month-old Tg mice. Brain-wide GFP expression in astrocytes confirmed efficacy of the GFAP promoter. Notably, brain cell transduction varied across Tg mice, peaking in the C57BL/6J line. Ly6A, a protein previously shown to facilitate AAV.PHP.eB entry into the brain, may explain this variability in transduction levels. We are currently examining Ly6A expression in our transgenic mouse line to determine the Tg background that will deliver the most efficient viral transduction. CONCLUSION: These results suggest that the GFAP promoter could control the production of therapeutics, such as rSol, in response to amyloid-induced astrocytic reactivity. Long-term studies will assess whether rSol prevents Aβ pathology progression in Tg-Aβ mice.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".