Comparative transcriptomics analysis revealing flower trichome development during flower development in two Lonicera Japonica Thunb. cultivars using RNA-seq
Bibliographic record
Abstract
Abstract Background: Trichomes comprise specialized multicellular structures that have the capacity to synthesize and secrete secondary metabolites and protect plants from biotic and abiotic stresses. However, little is known about the trichome formation mechanism during flower development in Lonicera Japonica Thunb.Results: Here, we present a genome-wide comparative transcriptome analysis between two L. japonica cultivars, toward the identification of biological processes and functional gene activities that occur during flowering stage trichome development. In this study, the density and average lengths of flower trichomes were at their highest during three green periods. Using the Illumina RNA-Seq method, we obtained 134,304 unigenes, 33,733 of which were differentially expressed. In an analysis of 40 differentially expressed unigenes (DEGs) involved in trichome development, 29 of these were transcription factors. The DEGs analysis of plant hormone signal transduction indicated that plant growth and development may be independent of GA and CTK signaling pathways, and plant stress may be independent of JA and ET signaling pathways. We successfully isolated key genes involved in the floral biosynthesis of odors, tastes, colors, and plant hormones, and proposed biosynthetic pathways for sesquiterpenoid, triterpenoid, monoterpenoid, flavonoid, and plant hormones. Furthermore, 82 DEGs were assigned to cell cycles and 2,616 were predicted as plant resistance genes (PRGs).Conclusions: This study provides a comprehensive characterization of the expression profiles of flower development during the seven developmental stages of L. japonica, thereby offering valuable insights into the molecular networks that underly flower development in L. japonica.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".