Transcriptomics and Proteomics Reveal the Cellulose and Pectin Metabolic Processes in the Tension Wood (Non-G-Layer) of Catalpa bungei
Bibliographic record
Abstract
Catalpa bungei is an economically important tree with high-quality wood and highly valuable to the study of wood formation. In this work, the xylem microstructure of C. bungei tension wood (TW) was observed, and we performed transcriptomics, proteomics and Raman spectroscopy of TW, opposite wood (OW) and normal wood (NW). The results showed that there was no obvious gelatinous layer (G-layer) in the TW of C. bungei and that the secondary wall deposition in the TW was reduced compared with that in the OW and NW. We found that most of the differentially expressed mRNAs and proteins were involved in carbohydrate polysaccharide synthesis. Raman spectroscopy results indicated that the cellulose and pectin content and pectin methylation in the TW were lower than those in the OW and NW, and many genes and proteins involved in the metabolic pathways of cellulose and pectin, such as galacturonosyltransferase (GAUT), polygalacturonase (PG), endoglucanase (CLE) and β-glucosidase (BGLU) genes, were significantly upregulated in TW. In addition, we found that the MYB2 transcription factor may regulate the pectin degradation genes PG1 and PG3, and ARF, ERF, SBP and MYB1 may be the key transcription factors regulating the synthesis and decomposition of cellulose. In contrast to previous studies on TW with a G-layer, our results revealed a change in metabolism in TW without a G-layer, and we inferred that the change in the pectin type, esterification and cellulose characteristics in the TW of C. bungei may contribute to high tensile stress. These results will enrich the understanding of the mechanism of TW formation.
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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) | 0.000 | 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".