Superoxide dismutases shape manganese stoichiometry in Southern Ocean diatoms
Bibliographic record
Abstract
Abstract Elemental stoichiometry of biomass is a focal point that connects different biogeochemical cycles. Yet, the mechanistic underpinnings of elemental stoichiometry are poorly quantified in many cases. We combined targeted and untargeted metaproteomics, Bayesian statistical modelling, and geochemical measurements to quantify the contribution of specific proteins to metal stoichiometry in natural populations of Southern Ocean diatoms. Our analyses indicate that a substantial amount of non-photosynthetic manganese (Mn) in diatoms in an Antarctic polynya can be attributed to superoxide dismutases (∼0.7 µmol Mn: mol Carbon; ∼20% of the total cellular Mn quota). We then used cultures and proteomic profiling of the key polar diatom Fragilariopsis cylindrus to identify environmental controls on superoxide dismutases, and discovered that iron concentration has little influence on the abundance of two Mn superoxide dismutases, while Mn limitation induces the depletion of these Mn superoxide dismutases and an accompanying increase of nickel superoxide dismutase. Overall, we combined metaproteomic approaches to quantify proteomic composition and connected these measurements to metal-to-carbon ratios and their responses to metal availability. Because metal quotas are key parameters in some biogeochemical models, our approach provides a direct mechanism for informing ecosystem-scale models with molecular measurements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".