A global reorganization of the metabolome in <i>Arabidopsis</i> during cold acclimation is revealed by metabolic fingerprinting
Bibliographic record
Abstract
Many plants, including Arabidopsis, increase their freezing tolerance in response to low, non‐freezing temperatures. This process is known as cold acclimation and involves many complex biochemical changes at the level of the metabolome. Our goal was to examine the effects of cold acclimation on the metabolome using a non‐targeted metabolic fingerprinting approach. Multivariate data analyses indicate that, in Arabidopsis, a global reprogramming of metabolism occurs as a result of cold acclimation. By measuring an entire spectrum of putative metabolites based on mass‐to‐charge (m/z) ratios, vs. an individual or group of metabolite(s), a comprehensive, unbiased assessment of metabolic processes relative to cold acclimation was determined. Whereas leaves shifted to low temperature present metabolic profiles that are constantly changing, leaves developed at low temperature demonstrate a stable complement of components. Although it appears that some metabolic networks are modulated by the environment, others require development under low‐temperature conditions for adjustment. Understanding how metabolism as a whole is regulated allows the integration of cellular, physiological and ecological attributes in a biological system, a necessity if complex traits, such as freezing tolerance, are to be modified by breeding or genetic manipulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".