Analysis of the Proteome of the Marine Diatom <i>Phaeodactylum tricornutum</i> Exposed to Aluminum Providing Insights into Aluminum Toxicity Mechanisms
Bibliographic record
Abstract
Trace aluminum (Al) concentrations can be toxic to marine phytoplankton, the basis of the marine food web, but the fundamental Al toxicity and detoxification mechanisms at the molecular levels are poorly understood. Using an array of proteomic, transcriptomic, and biochemical techniques, we describe in detail the cellular response of the model marine diatom Phaeodactylum tricornutum to a short-term sublethal Al stress (4 h of exposure to 200 μM total initial Al). A total of 2204 proteins were identified and quantified by isobaric tags for relative and absolute quantification (iTRAQ) in response to the Al stress. Among them, 87 and 78 proteins performing various cell functions were up- and down-regulated after Al treatment, respectively. We found that photosynthesis was a key Al toxicity target. The Al-induced decrease in electron transport rates in thylakoid membranes lead to an increase in reactive oxygen species (ROS) production, which cause increased lipid peroxidation. Several ROS-detoxifying proteins were induced to help decrease Al-induced oxidative stress. In parallel, glycolysis and pentose phosphate pathway were up-regulated in order to produce cell energy (NADPH, ATP) and carbon skeleton for cell growth, partially circumventing the Al-induced toxicity effects on photosynthesis. These cellular responses to Al stress were coordinated by the activation of various signal transduction pathways. The identification of Al-responsive proteins in the model marine phytoplankton P. tricornutum provides new insights on Al stress responses as well as a good start for further exploring Al detoxification mechanisms.
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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".