Reporter‐based forward genetic screen to identify bundle sheath anatomy mutants in <i>A. thaliana</i>
Bibliographic record
Abstract
Summary The evolution of C4 photosynthesis proceeded stepwise with each small step increasing the fitness of the plant. An important pre‐condition for the introduction of a functional C4 cycle is the photosynthetic activation of the C3 bundle sheath by increasing its volume and organelle number. Therefore, to engineer C4 photosynthesis into existing C3 crops, information about genes that control the bundle sheath cell size and organelle content is needed. However, very little information is known about the genes that could be manipulated to create a more C4–like bundle sheath. To this end, an ethylmethanesulfonate (EMS)‐based forward genetic screen was established in the Brassicaceae C3 species Arabidopsis thaliana. To ensure a high‐throughput primary screen, the bundle sheath cells of A. thaliana were labeled using a luciferase (LUC68) or by a chloroplast‐targeted green fluorescent protein (sGFP) reporter using a bundle sheath specific promoter. The signal strengths of the reporter genes were used as a proxy to search for mutants with altered bundle sheath anatomy. Here, we show that our genetic screen predominantly identified mutants that were primarily affected in the architecture of the vascular bundle, and led to an increase in bundle sheath volume. By using a mapping‐by‐sequencing approach the genomic segments that contained mutated candidate genes were identified.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".