Short-term response to iron resupply in an iron-limited open ocean diatom reveals rapid decay of iron-responsive transcripts
Bibliographic record
Abstract
In large areas of the ocean, iron concentrations are insufficient to promote phytoplankton growth. Numerous studies have been conducted to characterize the effect of iron on algae and how algae cope with fluctuating iron concentrations. Fertilization experiments in low-iron areas resulted primarily in diatom-dominated algal blooms, leading to laboratory studies on diatoms comparing low- and high-iron conditions. Here, we focus on the short-term temporal response following iron addition to an iron-starved open ocean diatom, Thalassiosira oceanica. We employed the NanoString platform and analyzed a high-resolution time series on 54 transcripts encoding proteins involved in photosynthesis, N-linked glycosylation, iron transport, as well as transcription factors. Nine transcripts were iron-responsive, with an immediate response to the addition of iron. The fastest response observed was the decrease in transcript levels of proteins involved in iron uptake, followed by an increase in transcript levels of iron-containing enzymes and a simultaneous decrease in the transcript levels of their iron-free replacement enzymes. The transcription inhibitor actinomycin D was used to understand the underlying mechanisms of the decrease of the iron-responsive transcripts and to determine their half-lives. Here, Mn-superoxide dismutase (Mn-SOD), plastocyanin (PETE), ferredoxin (PETF) and cellular repressor of EA1-stimulated genes (CREGx2) revealed longer than average half-lives. Four iron-responsive transcripts showed statistically significant differences in their decay rates between the iron-recovery samples and the actD treatment. These differences suggest regulatory mechanisms influencing gene transcription and mRNA stability. Overall, our study contributes towards a detailed understanding of diatom cell biology in the context of iron fertilization response and provides important observations to assess oceanic diatom responses following sudden changes in iron concentrations.
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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 | 0.001 |
| 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".