Full-Length Transcriptome Sequencing and Comparative Transcriptomic Analyses Provide Comprehensive Insight Into Molecular Mechanisms of Cellulose and Lignin Biosynthesis in Cunninghamia lanceolata
Bibliographic record
Abstract
Cunninghamia lanceolata is an essential timber species that provide 20%–30% raw materials for China’s timber industry. Although a few transcriptomes have been published in C. lanceolata, full-length mRNA transcripts and regulatory mechanisms behind the cellulose and lignin biosynthesis have not been thoroughly investigated. Here, PacBio Iso-seq and RNA-seq analyses were adapted to identify the full-length and differentially expressed transcripts along a developmental gradient from apex to base of C. lanceolata shoots. A total of 48,846 high-quality full-length transcripts were obtained, of which 88.0% are completed transcriptome based on benchmarking universal single-copy orthologs (BUSCO) assessment. Along stem developmental gradient, 18,714 differentially expressed genes (DEGs) were detected. Further, 28 and 125 DEGs were identified as enzyme-coding genes of cellulose and lignin biosynthesis, respectively. Moreover, 57 transcription factors (TFs), including MYB and NAC, were identified to be involved in the regulatory network of cellulose and lignin biosynthesis through weighted gene co-expression network analysis (WGCNA). These TFs are composed of a comparable regulatory network of secondary cell wall formation in angiosperms, revealing a similar mechanism may exist in gymnosperms. Further, through qRT-PCR, we also investigated eight specific TFs involved in compression wood formation. Our findings provide a comprehensive and valuable source for molecular genetics breeding of C. lanceolata and will be beneficial for molecular-assisted selection.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".