Two distinct pathways for iron acquisition by iron-limited cyanobacterial cells: evidence from experiments using siderophores and synthetic chelators
Bibliographic record
Abstract
Iron-limited cyanobacterial cells are generally considered to acquire extracellular iron through a siderophore-dependent system, although evidence has started to accumulate that other, as yet poorly characterized, iron acquisition systems may also play a role. Iron-limited cells of the cyanobacterium Anabaena flos-aquae (Lyng.) Brèb. are well known to produce the relatively low Fe(III) affinity dihydroxamate siderophore schizokinen. In this set of experiments we show that iron-limited A. flos-aquae cells (i) acquired iron at substantial rates in the absence of the schizokinen and (ii) acquired iron from a bacterial siderophore (the trihydroxamate molecule desferrioxamine B (DFB)), and also a synthetic chelator (N, N-bis(2-(bis(carboxymethyl)amino)ethyl)glycine (DTPA)), with substantially higher affinities for Fe(III) than schizokinen, indicating that a schizokinen-independent iron acquisition pathway was operating. We suggest that there exists a siderophore-independent iron acquisition system that is able to acquire Fe(III) from high stability Fe(III)-chelates, which are not accessible to iron-limited cells via the schizokinen-based system. As well, we present two possible models for iron acquisition by iron-limited A. flos-aquae cells. Both of these models suggest that there are two major routes for Fe(III) entry into the periplasm of iron-limited cells: (1) the well-characterized siderophore (schizokinen) dependent process and (2) a siderophore-independent process that is able to access Fe(III) sources not biologically available to the schizokinen system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".