Comparative Shotgun Proteomic Analysis of Wastewater-Cultured Microalgae: Nitrogen Sensing and Carbon Fixation for Growth and Nutrient Removal in <i>Chlamydomonas reinhardtii</i>
Bibliographic record
Abstract
Chlamydomonas reinhardtii was batch-cultured for 12 days under continuous illumination to investigate nitrogen uptake and metabolic responses to wastewater processing. Our approach compared two conditions: (1) artificial wastewater containing nitrate and ammonia and (2) nutrient-sufficient control containing nitrate as sole form of nitrogen. Treatments did not differ in final biomass; however, comparison of group proteomes revealed significant differences. Label-free shotgun proteomic analysis identified 2358 proteins, of which 92 were significantly differentially abundant. Wastewater cells showed higher relative abundances of photosynthetic antenna proteins, enzymes related to carbon fixation, and biosynthesis of amino acids and secondary metabolites. Control cells showed higher abundances of enzymes and proteins related to nitrogen metabolism and assimilation, synthesis and utilization of starch, amino acid recycling, evidence of oxidative stress, and little lipid biosynthesis. This study of the eukaryotic microalgal proteome response to nitrogen source, availability, and switching highlights tightly controlled pathways essential to the maintenance of culture health and productivity in concert with light absorption and carbon assimilation. Enriched pathways in artificial wastewater, notably, photosynthetic carbon fixation and biosynthesis of plant hormones, and those in nitrate only control, most notably, nitrogen, amino acid, and starch metabolism, represent potential targets for genetic improvement requiring targeted elucidation.
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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) | 0.000 | 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 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".