Identifying the Nuclear Localization Signal of the Arabidopsis Thaliana MADS Box Transcription Factor Flowering Locus C
Bibliographic record
Abstract
The goal of this undergraduate honours research project is to design a research strategy to identify the Nuclear Localization Signal (NLS) of the Arabidopsis thaliana MADs box transcription factor Flowering Locus C (FLC), and to begin the cloning and plant transformation of key controls required for this objective. Nuclear localization of proteins requires a specific amino acid sequence, called the Nuclear Localization Signal (NLS). The NLS of FLC has not yet been characterized, though previous work on Arabidopsis has established conserved structural-functional domains within FLC. The NLS within other MADs box proteins varies in its specific sequence, but is generally located within the MADs box domain around the N-terminal region of the polypeptide, which is a conserved region amongst MADs box proteins. I have created an FLC gene construct to be used with the GreenGate cloning system which will ultimately serve as a positive control showing nuclear localization in planta. I have also established the components necessary for the creation of a negative control line of Arabidopsis expressing GFP alone, which is not expected to nuclearly localize. Finally, I have completed a literature review to explore the existing understanding of NLS structure in general, in MADs box transcription factor proteins, and in other plant transcription factors, to propose mutations that might disrupt the NLS sequence within FLC. This work aims to fill a knowledge gap in regards to the mechanism of nuclear localization of FLC and will ultimately contribute to our understanding of nuclear localization in general. Department: Biology Faculty Mentor: Dr. Melissa Hills
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".