Untargeted metabolomic analyses reveal the diversity and plasticity of the specialized metabolome in seeds of different <i>Camelina sativa</i> genotypes
Bibliographic record
Abstract
SUMMARY Despite the essential role of specialized metabolites (SMs) in the interaction of plants with the environment, studying the ability of crop seeds to produce these protective compounds has been neglected. Furthermore, seeds produce a myriad of SMs, providing an interesting model to investigate their diversity and plasticity. Camelina sativa gained a lot of interest in the past few years as a rustic oilseed crop. A characterization of seed SM landscapes in six camelina genotypes grown in the field and harvested during five growing seasons has been undertaken in this work. This allowed a comprehensive annotation of seed SMs combining analyses that cluster SMs based on their chemical structures and co‐accumulation patterns. These data showed broad effects of the environment on the stimulation of the seed SM. Among well‐annotated compounds, flavonols were identified as the metabolic class characterized by high plasticity, revealing significant variable accumulation according to the year and/or the genotype. Lastly, a deeper characterization of primary metabolites and lipids in two selected genotypes has been performed. We showed that, in addition to flavonols, alkaloids and glucosinolates displayed a higher phenotypic plasticity with respect to most of the primary metabolites, including some sugars and major storage compounds such as fatty acids, proteins and most lipid classes (e.g. diacylglycerols, triacylglycerols), but similar plasticity compared with free amino acids and carboxylic acids. This work highlighted major and unexplored effects of the environment on the seed SM, demonstrating that seeds exhibit a dynamic and plastic metabolism, with an impact on seed quality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".