Comparative Analyses of the Transcriptome and Proteome of Comte de Paris and Smooth Cayenne to Improve the Understanding of Ethephon-Induced Floral Transition in Pineapple
Bibliographic record
Abstract
BACKGROUND/AIMS: Ethylene is usually used to induce floral transition in pineapple. However, its successful induction in plants categorized as Cayenne is difficult or completely ineffective, and information concerned is limited. The present study was undertaken to investigate the molecular mechanisms underlying this obstacle. METHODS: Transcriptome and proteome comparative analyses were performed to explore the important regulation and pathway variations after ethephon induction in the induction-easy 'Comte de Paris' (CP) and induction-hard 'Smooth Cayenne' (SC) cultivars via RNA-seq (RNA-sequencing) and iTRAQ (isobaric tags for relative and absolute quantification). RESULTS: CP and SC exhibited basic differences at the transcriptomic and proteomic levels before ethephon treatment, including the expression of genes and proteins related to ethylene signal transduction. After ethephon induction, the expression of genes and proteins involved in plant ethylene signal transduction and carbohydrate metabolism responded more strongly in CP than in SC. The expression of the floral meristem identity (FMI) genes AG, TFL and FT exhibited greater changes in CP, and more transcription factors responded in SC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that many differentially expressed genes (DEGs) in CP were annotated to terms and pathways involved in photoperiodism and shared components involved in carbohydrate metabolism and plant hormone signal transduction. CONCLUSION: These findings contribute to the understanding of the molecular mechanism underlying the variation between CP and SC in response to ethephon-mediated floral induction.
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".