Elemental allocation to molecular drivers of biogeochemistry in the Southern Ocean
Bibliographic record
Abstract
Abstract Metabolic processes underpinning ocean biogeochemistry are powered by molecular machines, proteins, that require various elements to function. Yet, the allocation of elements to these proteins, and subsequent implications for biogeochemical processes, remain poorly characterized. Here we integrate elemental measurements with metaproteomics to quantitatively examine elemental use in Southern Ocean microbial proteins and metabolic processes. We demonstrate that iron availability influences elemental allocation, including decreased iron allocation to photosynthesis and compensatory incorporation of non-iron metals into metalloproteins under iron scarcity. Manganese was primarily allocated to photosynthesis in iron-replete conditions, and reallocated to other metabolic roles under low iron. Photosystem I:II protein mass ratios impacted both iron and manganese allocation, and appeared to be driven by iron availability. Approximately half of biogenic copper was found in plastocyanin, likely substituting for iron-containing cytochromes in photosynthesis. Moreover, biogenic nitrogen to phosphorus ratios were decoupled from ribosomal abundance, contrary to prevailing assumptions about ribosomal influence on stoichiometric regulation in the ocean. Instead, our results suggest that community composition and intracellular storage are important regulators of N:P in the Southern Ocean. Together, our findings identify key molecular mechanisms that modulate elemental demand and limitation, and provide a foundation for quantitatively connecting molecular measurements with biogeochemical models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".