Peer Review #2 of "Genome-wide identification and expression analysis of new cytokinin metabolic genes in bread wheat (Triticum aestivum L.) (v0.2)"
Bibliographic record
Abstract
Cytokinins (CKs) are involved in determining the final grain yield in wheat.Multiple gene families are responsible for the controlled production of CKs in plants, including isopentenyl transferases for de novo synthesis, zeatin O-glucosyltransferases for reversible inactivation, β-glucosidases for reactivation, and CK oxidases/dehydrogenases for permanent degradation.Identifying and characterizing the genes of these families is an important step in furthering our understanding of CK metabolism.Using bioinformatics tools, we identified four new TaIPT, four new TaZOG, and 25 new TaGLU genes in common wheat.All of the genes harbored the characteristic conserved domains of their respective gene families.We renamed TaCKX genes on the basis of their true orthologs in rice and maize to remove inconsistencies in the nomenclature.Phylogenetic analysis revealed the early divergence of monocots from dicots, and the gene duplication event after speciation was obvious.Abscisic acid-, auxin-, salicylic acid-, sulfur-, drought-and light-responsive cis-regulatory elements were common to most of the genes under investigation.Expression profiling of CK metabolic gene families was carried out at the seedlings stage in AA genome donor of common wheat.Exogenous application of phytohormones (6benzylaminopurine, salicylic acid, indole-3-acetic acid, gibberellic acid, and abscisic acid) for 3 h significantly upregulated the transcript levels of all four gene families, suggesting that plants tend to maintain CK stability.A 6-benzylaminopurine-specific maximum foldchange was observed for TuCKX1 and TuCKX3 in root and shoot tissues, respectively; however, the highest expression level was observed in the TuGLU gene family, indicating that the reactivation of the dormant CK isoform is the quickest way to counter external stress.The identification of new CK metabolic genes provides the foundation for their indepth functional characterization and for elucidating their association with grain yield.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.252 | 0.179 |
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".