Polyglutamine toxicity assays highlight the advantages of mScarlet for imaging in Saccharomyces cerevisiae
Bibliographic record
Abstract
Development of fluorescent proteins (FPs) enabled researchers to visualize protein localization and trafficking in living cells and organisms. The extended palette of available FPs allows simultaneous detection of multiple fluorescent fusion proteins. Importantly, FPs are originally derived from different organisms from jelly fish to corals and each FP displays its own biophysical properties. Among these properties, the tendency of FPs to oligomerize inherently affects the behavior of its fusion partner. Here we employed the budding yeast Saccharomyces cerevisiae to determine the impact of the latest generation of red FPs on their binding partner. We used a yeast assay based on the aggregation and toxicity of misfolded polyQ expansion proteins linked to Huntington’s disease. Since polyQ aggregation and toxicity are highly dependent on the sequences flanking the polyQ region, polyQ expansions provide an ideal tool to assess the impact of FPs on their fusion partners. We found that unlike what is observed for green FP variants, yemRFP and yFusionRed-tagged polyQ expansions show reduced toxicity. However, polyQ expansions tagged with the bright synthetically engineered ymScarlet displayed severe polyQ toxicity. Our data indicate that ymScarlet might have significant advantages over the previous generation of red FPs for use in fluorescent fusions in yeast.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".