Proteomic comparison of «Arabidopsis thaliana» under high and low nitrogen fertilization
Bibliographic record
Abstract
Nitrogen (N) and the levels of N in plants play a vital role in the physiology, regulating their development and metabolism. We grew Arabidopsis thaliana under agronomic conditions at low (6 mg N/L) and high (106 mg N/L) N fertilizer regimes, maintaining a constant NO3-N to NH4-N ratio (3:1). Using a shotgun mass spectrometry proteomics approach, multi-dimensional protein identification technology (MudPIT), we characterized a total of 2134 reproducibly identified proteins shared between the two N treatments. By statistical analysis in both treatments we found 37 differentially expressed proteins that satisfied both the AC test and the FDR q-value specified cutoffs, where 18 proteins were down regulated and 19 proteins were up regulated under low and high N treatments. We also found 35 differentially expressed proteins that are statistically important but did not satisfy the q- test. These differentially expressed proteins appear to have roles in glycolysis, metabolic, developmental, and signaling processes, or protein binding, transport and nucleic acid binding. The proteins associated with glycolysis indicate glutamine metabolism is of major importance in the plant N economy since it provides N to young developing tissues. Our study indicates that under varying N level treatments, proteins responsible for glutamate synthase (GOGAT), glutamine synthase (GS), and dehydrogenase activity (DH) that serve as enzymes to catalyze a link between carbohydrate and amino acid metabolism are up regulated. Thus, this study has enabled us to apply comparative shotgun proteomics to characterize A. thaliana at the proteomic level and will provide the tools necessary to provide an improved understanding of how and what up-regulates and down regulates different proteins under varying environmental conditions.
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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 | 0.001 | 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".