Identification and analysis of differentially expressed genes in differentiating xylem of Chinese fir (<i>Cunninghamia lanceolata</i>) by suppression subtractive hybridization
Bibliographic record
Abstract
Wood is an important raw material for global industries with rapidly increasing demand. To isolate the genes differentially expressed during xylogenesis of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), we used a novel system. Forward and reverse subtracted cDNA libraries were constructed using the suppression subtractive hybridization method; for the forward library we used cDNA from the mutant Dugansha as the tester and cDNA from the wild-type clone Jurong 0 as the driver, and for the reverse library we used Jurong 0 cDNA as the tester and Dugansha cDNA as the driver. Transcriptional profiling was performed using a macroarray with 4 digoxigenin-labeled probes. We obtained 618 and 409 clones from the forward and the reverse subtracted library, respectively. A total of 405 unique expressed sequence tags (ESTs) were obtained. Forty percent of the ESTs exhibited homologies with proteins of known function and fell into 4 major classes: metabolism, cell wall biogenesis and remodeling, signal transduction, and stress. Real-time PCR was performed to confirm the results. The expression levels of 11 selected ESTs were consistent with both macroarray and real-time PCR results. The systematic analysis of genes involved in wood formation in Chinese fir provides valuable insights into the molecular mechanisms involved in xylem differentiation and is an important resource for forest research that can be directed toward understanding the genetic control of wood formation and future endeavors to modify wood and fiber properties for industrial use.
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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".