Fe-responsive accumulation of redox proteins ferredoxin and flavodoxin in a marine cryptomonad
Bibliographic record
Abstract
Fe deficiency has been documented in diverse marine environments ranging from pelagic high-nutrient, low chlorophyll (HNLC) regions to coastal systems. Detection of specific Fe-responsive proteins in phytoplankton has provided a sensitive approach to assessing Fe deficiency in natural waters. Here, we report on the development and applicability of taxon-specific polyclonal antisera to monitor the accumulation of two such Fe-responsive proteins, flavodoxin and ferredoxin (Fd) in cryptomonad algae. Reactivity of these immunoreagents was limited primarily to cryptomonads. Anti-Fd cross-reacted with a broad spectrum of both marine and freshwater cryptophytes whereas anti-flavodoxin recognized only flavodoxin from Rhodomonas lens. Assessment of Fd and flavodoxin accumulation in R. lens grown under various levels of Fe demonstrated an apparent uncoupling of flavodoxin expression from physiological Fe deficiency in this species. It appears that flavodoxin accumulates in response to external Fe availability as supported by results of a chelator addition experiment which demonstrated rapid accumulation of flavodoxin in Fe-replete cells of R. lens without concomitant physiological deficit following addition of the fungal siderophore desferrioxamine B.
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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".