Pectin Modification in Seed Coat Mucilage by <i>In Vivo</i> Expression of Rhamnogalacturonan-I- and Homogalacturonan-Degrading Enzymes
Bibliographic record
Abstract
The cell wall is essential for plant survival. Determining the relationship between cell wall structure and function using mutant analysis or overexpressing cell wall-modifying enzymes has been challenging due to the complexity of the cell wall and the appearance of secondary, compensatory effects when individual polymers are modified. In addition, viability of the plants can be severely impacted by wall modification. A useful model system for studying structure-function relationships among extracellular matrix components is the seed coat epidermal cells of Arabidopsis thaliana. These cells synthesize relatively simple, easily accessible, pectin-rich mucilage that is not essential for plant viability. In this study, we expressed enzymes predicted to modify polysaccharide components of mucilage in the apoplast of seed coat epidermal cells and explored their impacts on mucilage. The seed coat epidermal-specific promoter TESTA ABUNDANT2 (TBA2) was used to drive expression of these enzymes to avoid adverse effects in other parts of the plant. Mature transgenic seeds expressing Rhamnogalacturonate lyase A (RglA) or Rhamnogalacturonate lyase B (RglB) that degrade the pectin rhamnogalacturonan-I (RG-I), a major component of mucilage, had greatly reduced mucilage capsules surrounding the seeds and concomitant decreases in the monosaccharides that comprise the RG-I backbone. Degradation of the minor mucilage component homogalacturonan (HG) using the HG-degrading enzymes Pectin lyase A (PLA) or ARABIDOPSIS DEHISCENCE ZONE POLYGALACTURONASE2 (ADPG2) resulted in developing seed coat epidermal cells with disrupted cell-cell adhesion and signs of early cell death. These results demonstrate the feasibility of manipulating the seed coat epidermal cell extracellular matrix using a targeted genetic engineering approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".