Differences between two green algae in biological availability of iron bound to strong chelators
Bibliographic record
Abstract
N,N′-di(2-hydroxybenzoyl)-ethylenediamine-N,N′-diacetic acid (HBED) is a very strong Fe3+ chelator. Strategy I vascular plants, which use a reductive system for iron acquisition, similar to many green algae, are able to access iron from HBED (R.L. Chaney. 1988. J. Plant Nutr. 11: 1033–1050). However, iron-limited cells of the Strategy I green alga Chlamydomonas reinhardtii Dangeard were unable to access iron present as Fe3+–HBED. In contrast, Fe3+ chelated with hydroxyethylethylenediaminetriacetic acid (HEDTA; a weaker chelator) was rapidly taken up by iron-limited Chlamydomonas cells. Chlamydomonas ferric reduction rates with Fe3+–HBED were approximately 15% of the rate observed with Fe3+–HEDTA, suggesting that low reduction rates with Fe3+–HBED might be one factor in the low rate of iron acquisition. By contrast, iron-limited cells of the Strategy I green alga Chlorella kessleri Fott et Nováková were able to rapidly assimilate Fe3+ chelated by HBED, although ferric reduction rates with Fe3+–HBED were approximately 38% the rate of activity with Fe3+–HEDTA. Similar differential iron uptake rates for the two algal species were obtained using the strong Fe3+ chelator (and siderophore analogue) desferrioxamine B mesylate and the cyanobacterial siderophore schizokinen. These results suggest that there are differences among Strategy I green algae in their abilities to acquire Fe3+ from various ferric chelates: not all Strategy I algae can equally access tightly complexed Fe3+. Chlamydomonas appears to be the first documented Strategy I organism that is unable to acquire iron from Fe3+–HBED. These results also suggest that green algal iron acquisition from siderophores is species dependent. Finally, we suggest that iron acquisition from Fe3+–HBED might serve as an assay for an organisms’ ability to access tightly complexed iron.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".