Rational Design of Novel Fluorescent Enzyme Biosensors for Direct Detection of Strigolactones
Bibliographic record
Abstract
Strigolactones are plant hormones and rhizosphere signaling molecules with key roles in plant development, mycorrhizal fungal symbioses, and plant parasitism. Currently, sensitive, specific, and high-throughput methods of detecting strigolactones are limited. Here, we developed genetically encoded fluorescent strigolactone biosensors based on the strigolactone receptors DAD2 from Petunia hybrida, and HTL7 from Striga hermonthica. The biosensors were constructed via domain insertion of circularly permuted GFP. The biosensors exhibited loss of cpGFP fluorescence in vitro upon treatment with the strigolactones 5-deoxystrigol and orobanchol, or the strigolactone analogue rac -GR24, and the ShHTL7 biosensor also responded to a specific antagonist. To overcome biosensor sensitivity to changes in expression level and protein degradation, an additional strigolactone-insensitive fluorophore, LSSmOrange, was included as an internal normalization control. Other plant hormones and karrikins resulted in no fluorescence change, demonstrating that the biosensors report on compounds that specifically bind the SL receptors. The DAD2 biosensor likewise responded to strigolactones in an in vivo protoplast system, and retained strigolactone hydrolysis activity. These biosensors have applications in high-throughput screening for agrochemical compounds, and may also have utility in understanding strigolactone mediated signaling in plants.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".