Editorial: Advances in metabolism and chemodiversity - focus - plant enzymes
Bibliographic record
Abstract
Advances in metabolism and chemodiversity -focus -plant enzymes Plant enzymes are indispensable for plant metabolism and are critical determinants of the extensive chemodiversity observed in plants.These enzymes serve as primary catalysts in biosynthetic pathways, enabling the biosynthesis of a diverse range of secondary metabolites, such as alkaloids, terpenes, and phenolics.These metabolites are essential for the distinctive sensory qualities of plants, e.g., flavors, scents, and colors, and they also possess significant biological activities with promising applications in agriculture, medicine, and industry.Recent scientific investigations have been devoted to unraveling the intricate biochemistry of enzymes in photosynthetic organisms, elucidating their catalytic mechanisms, regulatory processes, and evolutionary trajectories.For instance, recent researches have elucidated dynamic diversifying mechanisms underlying the synthesis of phenolic acid in Salvia miltiorrhiza (Zhou et al., 2021), alkaloids in Catharanthus roseus (Eng et al., 2022), triterpenes in Eriobotrya japonica (Su et al., 2021), and xanthones in Hypericum species (Singh et al., 2020;Singh et al., 2021).These studies have yielded valuable insights into the biosynthetic pathways of pivotal compounds in non-model plants.By augmenting the understanding of plant enzyme biochemistry, scientists can harness their immense potential for advancing sustainable agriculture, facilitating drug discovery, and fostering the development of plant-based products, thereby endrosing positive impacts on human health and the environment.This Research Topic includes six original research articles, with a special focus on the function of plant enzymes.Zhang et al. reported that Prunus mume cinnamyl alcohol dehydrogenase 1 and 2 (PmCAD1 and 2) are the major contributors to the cinnamyl alcohol biosynthesis and emission, which was identified after analysing the endogenous volatile compounds and the transcriptomes gleaned from six P. mume cultivars (Figure 1A).Another research on floral fragrant components in Rosa rugosa revealed 156 differential volatile organic compounds, from two metabolic pathways: the monoterpenoid biosynthetic pathway and the amino acid (phenylalanine, tyrosine, and Frontiers in Plant Science frontiersin.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".