Understanding cell wall signaling to open new opportunities for modifying plant cell walls
Bibliographic record
Abstract
Plant cell walls are polysaccharide-based extracellular matrices that surround all plant cells. Cell walls provide support and protection to the plant cell, while also remaining flexible enough to allow plant growth by cell expansion. Plant cell walls are important renewable resources and cell wall polysaccharides can be processed into components of food, materials, or biofuels, making plant cell wall modification a key goal of biotechnology. However, plants sense the status of their cell walls via largely unknown mechanisms, collectively called “cell wall signaling”. Here, we review the evidence that activating cell wall signaling can limit plant growth, and that this growth limitation presents a key barrier to effective cell wall modification. We next discuss the molecular mechanisms of cell wall signaling; although several receptors have been implicated in detecting cell wall changes at the cell surface, their downstream signaling partners inside the cell are less clearly defined. Finally, we discuss how changes to cell wall synthesis can affect polysaccharide solubility and secretion, making some cell wall changes more detrimental than others. Altogether, uncovering the molecular mechanism of plant cell wall signaling and the extent to which plant cell walls can be modified without triggering growth limitations may allow cell wall modification to generate improved cell wall bioproducts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".