Immunotherapy Targeting Pathological Tau Conformers in a Tangle Mouse Model Reduces Brain Pathology with Associated Functional Improvements
Bibliographic record
Abstract
Immunotherapies for various neurodegenerative diseases have recently emerged as a promising approach for clearing pathological protein conformers in these disorders. This type of treatment has not been assessed in models that develop neuronal tau aggregates as observed in frontotemporal dementia and Alzheimer's disease. Here, we present that active immunization with a phosphorylated tau epitope, in P301L tangle model mice, reduces aggregated tau in the brain and slows progression of the tangle-related behavioral phenotype. Females had more tau pathology than males but were also more receptive to the immunotherapy. The tau antibodies generated in these animals recognized pathological tau on brain sections. Performance on behavioral assays that require extensive motor coordination correlated with tau pathology in corresponding brain areas, and antibody levels against the immunogen correlated inversely with tau pathology. Interestingly, age-dependent autoantibodies that recognized recombinant tau protein but not the immunogen were detected in the P301L mice. To confirm that anti-tau antibodies could enter the brain and bind to pathological tau, FITC-tagged antibodies purified from a P301L mouse, with a high antibody titer against the immunogen, were injected into the carotid artery of P301L mice. These antibodies were subsequently detected within the brain and colocalized with PHF1 and MC1 antibodies that recognize pathological tau. Currently, no treatment is available for clearing tau aggregates. Our present findings may lead to a novel therapy targeting one of the major hallmarks of Alzheimer's disease and frontotemporal dementia.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".