Transient expression in <i>Nicotiana benthamiana</i> fluorescent marker lines provides enhanced definition of protein localization, movement and interactions <i>in planta</i>
Bibliographic record
Abstract
Here, we report on the construction of a novel series of Gateway-compatible plant transformation vectors containing genes encoding autofluorescent proteins, including Cerulean, Dendra2, DRONPA, TagRFP and Venus, for the expression of protein fusions in plant cells. To assist users in the selection of vectors, we have determined the relative in planta photostability and brightness of nine autofluorescent proteins (AFPs), and have compared the use of DRONPA and Dendra2 in photoactivation and photoconversion experiments. Additionally, we have generated transgenic Nicotiana benthamiana lines that express fluorescent protein markers targeted to nuclei, endoplasmic reticulum or actin filaments. We show that conducting bimolecular fluorescence complementation assays in plants that constitutively express cyan fluorescent protein fused to histone 2B provides enhanced data quality and content over assays conducted without the benefit of a subcellular marker. In addition to testing protein interactions, we demonstrate that our transgenic lines that express red fluorescent protein markers offer exceptional support in experiments aimed at defining nuclear or endomembrane localization. Taken together, the new combination of pSITE-BiFC and pSITEII vectors for studying intracellular protein interaction, localization and movement, in conjunction with our transgenic marker lines, constitute powerful tools for the plant biology community.
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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 | 0.001 | 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".