Functional CTR‐type Cu(I) transporters in an oceanic diatom
Bibliographic record
Abstract
Summary Copper concentration is so low in some remote parts of the sea it limits phytoplankton growth, but may be high enough in coastal and estuarine regions to be toxic. Acclimation to variations in Cu concentration thus requires a tightly regulated Cu transport system to help maintain Cu homeostasis. In marine species, the molecular mechanisms of Cu transport are not known. We studied Cu‐responsive genes and uptake in Thalassiosira oceanica at environmentally relevant Cu concentrations varying between 0.012 and 12 900 pmol Cu′ l −1 . Copper uptake rate assessed at high Cu concentration was three‐fold faster in Cu‐limited than in Cu‐replete cells, confirming the existence of an inducible uptake pathway in this diatom. Four putative CTR‐type Cu transporters ( To CTR1, To CTR2, To CTR3a and To CTR3b) identified in the transcriptome shared conserved features with known high‐affinity Cu(I) transporters. Expression of the CTR genes was upregulated as Cu concentration declined and cells maintained maximum rates of growth. Further decreases in Cu led to decreased growth rate and increased abundance of To CTR3a/b. Both To CTR3a and 3b restored growth of a Cu transport mutant, Saccharomyces cerevisiae ctr1Δctr3Δ , in Cu‐deficient medium and increased the uptake rates of Cu(I) and Cu(II). Thus, To CTR3a/3b is a high‐affinity Cu(I) transporter that, in conjunction with the other To CTRs, may enable T. oceanica to survive in Cu‐deplete ocean environments and respond to natural variation in Cu availability.
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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) | 0.001 | 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".