Huntingtin polyglutamine expansions misdirect axonal transport by perturbing motor and adaptor recruitment
Bibliographic record
Abstract
Summary Huntington’s disease (HD) is caused by polyglutamine (polyQ) expansions in huntingtin (HTT). Polyglutamine repeat lengths >35Q lead to neurodegeneration and longer repeats correspond to earlier symptom onset. HTT scaffolds kinesin-1 and dynein to a variety of vesicles and organelles directly and through adaptors. To characterize the effects of HTT polyQ expansions on axonal transport, we tracked BDNF vesicles, mitochondria, and lysosomes in neurons induced from an isogenic set of human stem cell lines with repeat lengths of 30, 45, 65, and 81Q. Mild and intermediate pathogenic polyQ expansions caused increased BDNF motility, while HTT-81Q misdirected BDNF towards the distal tip. In comparison, mitochondria and lysosome transport showed mild defects with polyQHTT. We next examined the effect of polyQHTT in combination with neuroinflammatory stress. Under stress, BDNF cargoes in HTT-30Q neurons were more processive. Stress in HTT-81Q resulted in a stark decrease in the number of BDNF cargoes. However, the few remaining BDNF cargoes displayed more frequent long-range motility in both directions. Under neuroinflammatory stress, lysosomes were more abundant in HTT-81Q neurons, and motile lysosomes moved less processively and had an anterograde bias while lysosomes in HTT-30Q where not strongly affected. To examine how HTT-polyQ expansions altered the motors and adaptors on vesicular cargoes, we isolated BDNF cargoes from neurons and quantified the proteins associated with them. BDNF-endosomes isolated from HTT-81Q neurons associated with 2.5 kinesin-1 and 3.9 HAP1 molecules on average, compared to 1.0 kinesin-1 and 1.0 HAP1 molecule for HTT-30Q neurons. Together, these results show that polyQ expansions in HTT cause aberrant motor and adaptor recruitment to cargoes, resulting in dysregulated transport and responses to neuroinflammatory stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".