Gene Immunotherapy Against Amyloid Pathology in a Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease is characterized by progressive accumulation of amyloid-beta peptides (Aβ) in the brain and severe cognitive decline. Several anti-Aβ immunotherapies failed to efficiently slow disease progression and improve cognition in clinical trials. The blood-brain barrier (BBB) limits the bioavailability of intravenously administered antibodies to the brain, potentially contributing to reduce efficacy. I hypothesized that engineering neurons to continuously produce anti-Aβ antibodies in the brain will increase therapeutic efficacy. To evaluate this hypothesis, I developed a gene immunotherapy approach; delivering adeno-associated virus 6 (AAV6) that encoded for a recombinant anti-Aβ single-chain variable fragment (rSol), AAV6-rSol, into the brain of TgCRND8 mice, a preclinical model of amyloidosis. rSol was derived from Solanezumab, a human antibody against soluble Aβ. I tested my hypothesis by completing three objectives. Firstly, I designed, constructed, and purified rSol, and then I confirmed its high affinity for soluble Aβ. Secondly, I engineered rSol under the control of synapsin promoter into an AAV6 and demonstrated that the expression of rSol was neuronal-specific in vitro and in vivo. In TgCRND8 mice, I found that rSol had prolonged neuronal expression for over 14 months, efficiently preventing Aβ deposition into Aβ plaques, reducing associated dystrophic neurites and decreasing the activation of astrocytes and microglia at 6-month post-intracranial delivery in neonates. Thirdly, I delivered AAV6-rSol into the brain of adult TgCRND8 mice non-invasively and evaluated the impact of this treatment on mice behaviour. Focused ultrasound (FUS), guided by magnetic resonance imaging (MRIgFUS) was used to temporarily increase the permeability of the BBB, allowing AAV6-rSol to reach the brain. No amelioration was detected in nest building activity and locomotion of TgCRND8 mice. Low gene delivery in these experiments likely contributed to the lack of robust effect on behaviour. I suggest that MRIgFUS-mediated gene delivery can be optimized using higher AAV dosage or more efficient serotypes such as AAV9, guided by the methodological protocol outlined in this thesis. In conclusion, I developed a neuronal gene immunotherapy with therapeutic efficacy against Aβ deposition. Combined with promising non-invasive gene delivery to the brain, gene immunotherapy could lead to clinical benefits in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".