Nitrate regulation of Fe reduction and transport by Fe‐limited Thalassiosira oceanica
Bibliographic record
Abstract
Under Fe‐limiting conditions, nitrate (NO3−)‐grown marine diatoms have higher intracellular Fe requirements, but divide as fast or faster than ammonium (NH4+)‐grown cells by maintaining faster steady‐state Fe uptake rates. Here we report that Thalassiosira oceanica, clone 1003, possesses an Fe reductase that reduces Fe(III) bound to a variety of organic ligands, including the siderophore desferrioxamine B (DFB), a high affinity, Fe(III)‐specific ligand. Reduction is mediated extracellularly and is induced by Fe deficiency. Cellular rates of Fe(III) reduction are significantly faster in NO3− ‐ than in NH4+ ‐grown cultures suggesting a link with N metabolism. At subsaturating Fe concentrations, short‐ and long‐term Fe uptake rates are also significantly faster in NO3− ‐ than in NH4+ ‐grown cells. The results suggest that when Fe is limiting, faster rates of reduction of organically bound Fe(III) by phyto‐plankton promote faster rates of Fe transport and growth. The implications of these findings could be significant for understanding phytoplankton Fe nutrition in oceanic waters where organic complexation dominates the speciation of Fe. We hypothesize that the reductive Fe transport pathway may enable phytoplankton to directly utilize Fe bound to strong organic ligands in the sea.
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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".