Design and characterisation of mutant and wild-type huntingtin proteins produced from a toolkit of scalable eukaryotic expression systems
Bibliographic record
Abstract
ABSTRACT The gene mutated in Huntington’s disease (HD) patients encodes the 348 kDa huntingtin (HTT) protein. The pathogenic HD CAG-expansion mutation causes a polyglutamine (polyQ) tract at the N-terminus of the HTT protein to expand above a critical threshold of ~35 glutamine residues. The effect of HD mutations on HTT is not well understood, in part due to difficulties in carrying out biochemical, biophysical and structural studies of this large protein. To facilitate such studies, we have generated expression constructs for the scalable production of HTT in multiple eukaryotic expression systems. Our set of HTT expression clones comprises both N and C-terminally FLAG-tagged HTT constructs with polyQ lengths representative of the general population, HD patients, juvenile HD patients as well as the more extreme polyQ expansions used in some HD tissue and animal models. These reagents yield milligram quantities of pure recombinant HTT protein, including many of the previously mapped posttranslational modifications. We have characterised both apo and HTT-HAP40 complex samples produced using this HD resource, demonstrating that this toolkit can be used to generate physiologically meaningful complexes of HTT. We demonstrate how these resources can produce sufficient material for protein-intensive experiments such as small angle X-ray scattering (SAXS), providing biochemical insight into HTT protein structure. The work outlined in this manuscript and the tools generated, lay a foundation for further biochemical and structural work on the HTT protein and its functional interactions with other biomolecules.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".