Iron uptake proteins in algae and the role of Iron Starvation-Induced Proteins (ISIPs)
Bibliographic record
Abstract
Iron is one of the most abundant elements on Earth, and it is essential for life. Despite the abundance of iron, its chemistry leads to very limited bioavailability, which has resulted in the evolution of a wide range of proteins involved in uptake and scavenging. However, because high intracellular concentrations of iron are toxic, iron homeostasis is essential for every organism. Approximately 30–40% of the ocean’s surface is characterized by very low chlorophyll concentrations and high concentrations of nitrate and phosphate. These High Nutrient-Low Chlorophyll (HNLC) regions are also characterized by low concentrations of iron, and studies on low-iron adapted diatom strains revealed the presence of iron starvation-induced proteins (ISIP), a group of unrelated novel proteins that are strongly up-regulated under iron limitation. First functional characterizations show that some of these ISIP proteins are involved in iron uptake. Here, we compare iron-uptake/storage proteins from 15 different algal species demonstrating that some well-characterized uptake proteins such as Natural Resistance Associated Macrophage Proteins (NRAMP) or the iron transporter1 (FTR1)-like permease show high similarities throughout the 15 algal species, while others such as Zinc-Regulated Transporter (ZRT)- and Iron-Regulated Transporter (IRT)-like proteins (ZIP) have a higher diversity amongst the algae analysed here. The focus of this review is ISIP proteins, which are distinct from ZIP, NRAMP and FTR1 permeases. ISIP2 proteins are transferrin-like proteins involved in Fe3+ uptake and ISIP1 is responsible for endocytosis of siderophore-bound iron. Additionally, in-silico analysis, combined with our localization study in a marine diatom, suggests that ISIP3 acts as an iron storage protein. Overall, our analysis concludes that marine algae combine iron uptake strategies widespread in other organisms, with algal-specific ISIPs as additional proteins for the utilization of diverse iron pools, thereby securing their success in iron-poor regions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".