Transcriptome analysis reveals plant response to colchicine treatment during on chromosome doubling
Bibliographic record
Abstract
Colchicine was commonly used to artificially double chromosomes while the transcriptome changes in colchicine treated plants has rarely been characterized. To understand the molecular mechanism of colchicine on chromosome doubling, we characterized transcriptome data of diploid orchardgrass root after colchicine treatment. Our results showed that 3381 of differentially expressed genes (DEGs) were mainly affected by water stress, 1258 DEGs that were expressed significantly in sample DacR5tr but not in DacR5ck were considered to be mainly affected by colchicine and combination of water and colchicine. These DEGs mainly regulated by colchicine were enriched to gene ontology (GO) accessions of cation binding, catalytic activity, membrane and transporter activity, and enriched to Kyoto Encyclopedia of Genes and Genome (KEGG) pathways of phenylpropanoid biosynthesis, phenylalanine metabolism, plant hormone signal transduction and starch and sucrose metabolism. Genes related to microtubule, spindle, chromosomal kinetochore, vesicle, cellulose and processes of cytoplasm movement, chromatid segregation, membrane and cell wall development were inhibited by colchicine. Our results revealed that colchicine restrained the microtubules and inhibited gene expression of cytokinesis, which might slow down the cell activity, delay the cell into anaerobic respiration, resulting in apoptosis at late stage, and relieving of waterlogging.
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) | 0.001 | 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".