Editorial: Untapped metabolic diversity in legume-characteristic pathways
Bibliographic record
Abstract
Specialized metabolism in legume species -Defense and medicinal compoundsIn this special issue, new insights are found into the specialized metabolism of legumes and the gene families that play a significant role in defence and stress response.Plant defence is strictly regulated to allocate resources when and where needed, including an arsenal of constitutively expressed first responders and bioactive derivatives (phytoalexins) deployed de novo (VanEtten et al., 1994).From the perception of stress, damage, or infection, an intricate signalling cascade is triggered, via phytohormones, rendering changes, including cell wall modifications (Lionetti et al., 2007), metabolic shifts (Simons et al., 2011;Sukumaran et al., 2018), and the biosynthesis of specialized defence compounds (Förster et al., 2022).Researchers in this special issue have probed the underlying molecular factors responding to stresses, including salt and mercury stress ( Wanget al.; Alvarez-Rivera et al.) and Phytophthora sojae infection (Khatri et al.), as well as transport mechanisms for sequestration of specialized metabolites (Islam et al.).They have also developed metabolo-transcriptomic databases to investigate medicinal plants (Lin et al.) and leveraged these pathways via plant/heterologous culture bioreactors (Istiandari et al.; Lee et al.).Deep understanding of this metabolic "design space," using the resources reported in this issue, can help us tap into the potential of plant specialized metabolism and eventually move beyond the plant as a host or its catalogue of compounds.Two papers in this issue discuss the merits of the medicinal legume Glycyrrhiza uralensis or Chinese licorice (Istiandari et al.; Wang et al.).This plant has rhizomes that can be harvested in early autumn, dried, and pounded into a saccharine powder, 50 times sweeter than sugar (Glykys, meaning sweet in Greek).This natural demulcent has a soothing effect on the digestive tract.Glycyrrhiza species are also a source of myriad legume-characteristic metabolites, notably including glycyrrhizin, a saponin molecule.Environmental stresses, including salinity, can impact the composition and content of these roots (Behdad et al., 2020).Therefore, Wang et al. used an integrated metabolo- 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".