Live cell imaging and biophotonic methods reveal two types of mutant huntingtin inclusions
Bibliographic record
Abstract
Huntington's disease (HD) is an autosomal dominant, neurodegenerative disorder that can be characterized by the presence of protein inclusions containing mutant huntingtin within a subset of neurons in the brain. Since their discovery, the relevance of inclusions to disease pathology has been controversial. We show using super-resolution fluorescence imaging and Förster resonance energy transfer (FRET) in live cells, that mutant huntingtin fragments can form two morphologically and conformationally distinct inclusion types. Using fluorescence recovery after photobleaching (FRAP), we demonstrate that the two huntingtin inclusion types have unique dynamic properties. The ability to form one or the other type of inclusion can be influenced by the phosphorylation state of serine residues at amino acid positions 13 and 16 within the huntingtin protein. We can define two types of inclusions: fibrillar, which are tightly packed, do not exchange protein with the soluble phase, and result from phospho-modification at serines 13 and 16 of the N17 domain, and globular, which are loosely packed, can readily exchange with the soluble phase, and are not phosphorylated in N17. We hypothesize that the protective effect of N17 phosphorylation or phospho-mimicry seen in animal models, at the level of protein inclusions with elevated huntingtin levels, is to induce a conformation of the huntingtin amino-terminus that causes fragments to form tightly packed inclusions that do not exit the insoluble phase, and hence exert less toxicity. The identification of these sub-types of huntingtin inclusions could allow for drug discovery to promote protective inclusions of mutant huntingtin protein in HD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".