Modulation of Energy Metabolism Is Important for Low-Oxygen Stress Adaptation in Brassicaceae Species
Bibliographic record
Abstract
Low-oxygen stress, mainly caused by soil flooding, is a serious abiotic stress affecting crop productivity worldwide. To understand the mechanisms of low-oxygen stress responses and adaptation of plants, we characterized and compared low-oxygen responses in six species with different accessions of the Brassicaceae family. Based on the growth and survival responses to submergence or low-oxygen condition, these accessions could be divided into three groups: (i) Highly tolerant species (Rorippa islandica and Arabis stelleri); (ii) moderately tolerant species (Arabidopsis thaliana [esk-1, Ler, Ws and Col-0 ecotype]); and (iii) intolerant species (Thlaspi arvense, Thellungiella salsuginea [Shandong and Yukon ecotype], and Thellungiella parvula). Gene expression profiling using Operon Arabidopsis microarray was carried out with RNA from roots of A. thaliana (Col-0), A. stelleri, R. islandica, and T. salsuginea (Shandong) treated with low-oxygen stress (0.1% O2/99.9% N2) for 0, 1, 3, 8, 24, and 72 h. We performed a comparative analysis of the gene expression profiles using the gene set enrichment analysis (GSEA) method. Our comparative analysis suggested that under low-oxygen stress each species distinctively reconfigures the energy metabolic pathways including sucrose–starch metabolism, glycolysis, fermentation and nitrogen metabolism, tricarboxylic acid flow, and fatty acid degradation via beta oxidation and glyoxylate cycle. In A. thaliana, a moderately tolerant species, the dynamical reconfiguration of energy metabolisms occurred in the early time points of low-oxygen treatment, but the energy reconfiguration in the late time points was not as dynamic as in the early time points. Highly tolerant A. stelleri appeared to have high photosynthesis capacity that could produce more O2 and in turn additional ATP energy to cope with energy depletion caused by low-oxygen stress. R. islandica seemed to retain some ATP energy produced by anaerobic energy metabolism during a prolonged period of low-oxygen conditions. Intolerant T. salsuginea did not show significant changes in the expression of genes involved in anaerobic energy metabolisms. These results indicate that plants developed different energy metabolisms to cope with the energy crisis caused by low-oxygen stress.
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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".