Treatment with Tau fibrils impact Huntington's disease-related phenotypes in cell and mouse models
Bibliographic record
Abstract
There is now compelling evidence for the presence of pathological forms of Tau in tissues of both patients and animal models of Huntington's disease (HD). While the root cause of this illness is a mutation within the huntingtin gene, a number of studies now suggest that HD could also be considered a secondary tauopathy. However, the contributory role of Tau in the pathogenesis and pathophysiology of this condition, as well as its implications in cellular toxicity and consequent behavioral impairments are largely unknown. We therefore performed intracerebral stereotaxic injections of recombinant human Tau monomers and fibrils into the knock-in zQ175 mouse model of HD. Tau fibrils induced cognitive and anxiety-like phenotypes predominantly in zQ175 mice and increased the number and size of insoluble mutant huntingtin (mHTT) aggregates in the brains of treated animals. To better understand the putative mechanisms through which Tau could initiate and/or contribute to pathology, we incubated StHdh striatal cells, an in vitro model of HD, with the different Tau forms and evaluated the effects on cell functionality and heat shock proteins Hsp70 and Hsp90. Calcium imaging experiments showed functional impairments of HD StHdh cells following treatment with Tau fibrils, as well as significant changes to the levels of both heat shock proteins which were found trapped within mHTT aggregates. The accumulation of Hsp70 and 90 within aggregates was also present in mouse tissue which suggests that alteration of molecular chaperone-dependent protein quality control may influence aggregation, implicating proteostasis in the mHTT-Tau interplay.
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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) | 0.002 | 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".