The influence of light on the availability of photoreactive and photostable iron(<scp>III</scp>)–ligand complexes to diatoms
Bibliographic record
Abstract
Abstract Uptake of iron (Fe) by eukaryotic phytoplankton is suggested to proceed by a reductive pathway in which Fe(III) bound to inorganic and organic ligands is reduced prior to Fe internalization. Photochemical reduction of the Fe–ligand complexes can increase Fe bioavailability, but the role of light in regulating physiological processes related to Fe acquisition is not well known. Here, we report how model Fe–ligand complexes differing in photolability affected growth, cellular Fe reduction, and Fe uptake rates of two marine diatoms as a function of irradiance. Growth rates of Thalassiosira oceanica and Phaeodactylum tricornutum in media amended with Fe complexed with ethylenediaminetetraacetic acid (Fe‐EDTA) and desferrioxamine B (FeDFB) increased linearly with light between 50 and 400 μmol photons m−2 s−1 under Fe‐limiting conditions. Steady‐state Fe uptake rates (ρSS) were also light dependent, increasing proportionally with growth irradiance for both Fe–ligand complexes. Photolysis of the Fe‐EDTA complex could explain the increase in ρSS in Fe‐EDTA‐amended medium because it increased [Fe(III)’], but it could not account for the faster rate of Fe uptake from FeDFB at high light because FeDFB is photostable. Short‐term Fe uptake rates measured in the absence of photochemically mediated Fe(III) reduction showed that cells preconditioned to high growth irradiance took up Fe faster than cells grown at low light. Thus, growth at high light induced a physiological response that increased Fe uptake. High growth irradiance also increased the rate of cellular Fe(III)’ reduction in the dark, similar to its stimulatory effect on short‐term Fe uptake. These results identify a previously unrecognized Fe–light interaction that could be important in enhancing Fe availability to diatoms. We suggest that the light effect could be the result of a light‐dependent regulation of cellular Fe(III) reduction.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".