Identification and Characterization of Arabidopsis Seed Coat Mucilage Proteins
Bibliographic record
Abstract
Plant cell wall proteins are important regulators of cell wall architecture and function. However, because cell wall proteins are difficult to extract and analyze, they are generally poorly understood. Here, we describe the identification and characterization of proteins integral to the Arabidopsis (Arabidopsis thaliana) seed coat mucilage, a specialized layer of the extracellular matrix composed of plant cell wall carbohydrates that is used as a model for cell wall research. The proteins identified in mucilage include those previously identified by genetic analysis, and several mucilage proteins are reduced in mucilage-deficient mutant seeds, suggesting that these proteins are genuinely associated with the mucilage. Arabidopsis mucilage has both nonadherent and adherent layers. Both layers have similar protein profiles except for proteins involved in lipid metabolism, which are present exclusively in the adherent mucilage. The most abundant mucilage proteins include a family of proteins named TESTA ABUNDANT1 (TBA1) to TBA3; a less abundant fourth homolog was named TBA-LIKE (TBAL). TBA and TBAL transcripts and promoter activities were detected in developing seed coats, and their expression requires seed coat differentiation regulators. TBA proteins are secreted to the mucilage pocket during differentiation. Although reverse genetics failed to identify a function for TBAs/TBAL, the TBA promoters are highly expressed and cell type specific and so should be very useful tools for targeting proteins to the seed coat epidermis. Altogether, these results highlight the mucilage proteome as a model for cell walls in general, as it shares similarities with other cell wall proteomes while also containing mucilage-specific features.
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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 | 0.000 | 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 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".