A synthetic de‐greening gene circuit provides a reporting system that is remotely detectable and has a re‐set capacity
Bibliographic record
Abstract
Plants have evolved elegant mechanisms to continuously sense and respond to their environment, suggesting that these properties can be adapted to make inexpensive and widely used biological monitors, or sentinels, for human threats. For a plant to be a sentinel, a reporting system is needed for large areas and widespread monitoring. The reporter or readout mechanism must be easily detectable, allow remote monitoring and provide a re-set capacity; all current gene reporting technologies fall short of these requirements. Chlorophyll is one of the best-recognized plant pigments with an already well-developed remote imaging technology. However, chlorophyll is very abundant, with levels regulated by both genetic and environmental factors. We designed a synthetic de-greening circuit that produced rapid chlorophyll loss on perception of a specific input. With induction of the de-greening circuit, changes were remotely detected within 2 h. Analyses of multiple de-greening circuits suggested that the de-greening circuit functioned, in part, via light-dependent damage to photosystem cores and the production of reactive oxygen species. Within 24-48 h of induction, an easily recognized white phenotype resulted. Microarray analysis showed that the synthetic de-greening initiated a process largely distinct from normal chlorophyll loss in senescence. Remarkably, synthetically de-greened white plants re-greened after removal of the inducer, providing the first easily re-settable reporter system for plants and the capacity to make re-settable biosensors. Our results showed that the de-greening circuit allowed chlorophyll to be employed as a simple but powerful reporter system useful for widespread areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".