Metabolic acclimation to excess light intensity in <i><scp>C</scp>hlamydomonas reinhardtii</i>
Bibliographic record
Abstract
There are several well-described acclimation responses to excess light in green algae but the effect on metabolism has not been thoroughly investigated. This study examines the metabolic changes during photoacclimation to high-light (HL) stress in Chlamydomonas reinhardtii using nuclear magnetic resonance and mass spectrometry. Using principal component analysis, a clear metabolic response to HL intensity was observed on global metabolite pools, with major changes in the levels of amino acids and related nitrogen metabolites. Amino acid pools increased during short-term photoacclimation, but were especially prominent in HL-acclimated cultures. Unexpectedly, we observed an increase in mitochondrial metabolism through downstream photorespiratory pathways. The expression of two genes encoding key enzymes in the photorespiratory pathway, glycolate dehydrogenase and malate synthase, were highly responsive to the HL stress. We propose that this pathway contributes to metabolite pools involved in nitrogen assimilation and may play a direct role in photoacclimation. Our results suggest that primary and secondary metabolism is highly pliable and plays a critical role in coping with the energetic imbalance during HL exposure and a necessary adjustment to support an increased growth rate that is an effective energy sink for the excess reducing power generated during HL 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 | 0.001 |
| 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".