AtMetExpress Development: A Phytochemical Atlas of Arabidopsis Development
Bibliographic record
Abstract
Plants possess many metabolic genes for the production of a wide variety of phytochemicals in a tissue-specific manner. However, the metabolic systems behind the diversity and tissue-dependent regulation still remain unknown due to incomplete characterization of phytochemicals produced in a single plant species. Thus, having a metabolome dataset in addition to the genome and transcriptome information resources would enrich our knowledge of plant secondary metabolism. Here we analyzed phytochemical accumulation during development of the model plant Arabidopsis (Arabidopsis thaliana) using liquid chromatography-mass spectrometry in samples covering many growth stages and organs. We also obtained tandem mass spectrometry spectral tags of many metabolites as a resource for elucidation of metabolite structure. These are part of the AtMetExpress metabolite accumulation atlas. Based on the dataset, we detected 1,589 metabolite signals from which the structures of 167 metabolites were elucidated. The integrated analyses with transcriptome data demonstrated that Arabidopsis produces various phytochemicals in a highly tissue-specific manner, which often accompanies the expression of key biosynthesis-related genes. We also found that a set of biosynthesis-related genes is coordinately expressed among the tissues. These data suggested that the simple mode of regulation, transcript to metabolite, is an origin of the dynamics and diversity of plant secondary metabolism.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".