Biochemical and proteomic response of the freshwater green alga Pseudochlorella pringsheimii to iron and salinity stressors
Bibliographic record
Abstract
BACKGROUND: Pseudochlorella pringsheimii (Ppr) is a green unicellular alga rich with chlorophyll, carotenoids, and antioxidants. As a widespread organism, Ppr must face, and adapt to, many environmental stresses and these are becoming more frequent and more extreme under the conditions of climate change. We therefore focused on salinity induced by NaCl and iron (Fe) variation stresses, which are commonly encountered by algae in their natural environment. RESULTS: The relatively low stress levels improved the biomass, growth rate, and biochemical components of Ppr. In addition, the radical-scavenging activity, reducing power, and chelating activity were stimulated by lower iron concentrations and all NaCl concentrations. We believe that the alga has adapted to the stressors by increasing certain biomolecules such as carotenoids, phenolics, proteins, and carbohydrates. These act as antioxidants and osmoregulators to protect cell membranes and other cellular components from the harmful effects of ions. We have used SDS-PAGE and 2D-PAGE in combination with tandem mass spectrometry to identify responsive proteins in the proteomes of stressed vs. non-stressed Ppr. The results of 2D-PAGE analysis showed a total of 67 differentially expressed proteins, and SDS-PAGE identified 559 peptides corresponding to 77 proteins. Of these, 15, 8, and 17 peptides were uniquely identified only under the control, iron, and salinity treatments, respectively. The peptides were classified into 12 functional categories: energy metabolism (the most notable proteins), carbohydrate metabolism, regulation, photosynthesis, protein synthesis, stress proteins, oxido-reductase proteins, transfer proteins, ribonucleic-associated proteins, hypothetical proteins, and unknown proteins. The number of identified peptides was higher under salinity stress compared to iron stress. CONCLUSIONS: A proposed mechanism for the adaptation of Ppr to stress is discussed based on the collected data. This data could serve as reference material for algal proteomics and the mechanisms involved in mediating stress tolerance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".