Contrasting effects of copper limitation on the photosynthetic apparatus in two strains of the open ocean diatom Thalassiosira oceanica
Bibliographic record
Abstract
There is an intricate interaction between iron (Fe) and copper (Cu) physiology in diatoms. However, strategies to cope with low Cu are largely unknown. This study unveils the comprehensive restructuring of the photosynthetic apparatus in the diatom Thalassiosira oceanica (CCMP1003) in response to low Cu, at the physiological and proteomic level. The restructuring results in a shift from light harvesting for photochemistry-and ultimately for carbon fixation-to photoprotection, reducing carbon fixation and oxygen evolution. The observed decreases in the physiological parameters Fv/Fm, carbon fixation, and oxygen evolution, concomitant with increases in the antennae absorption cross section (σPSII), non-photochemical quenching (NPQ) and the conversion factor (φe:C/ηPSII) are in agreement with well documented cellular responses to low Fe. However, the underlying proteomic changes due to low Cu are very different from those elicited by low Fe. Low Cu induces a significant four-fold reduction in the Cu-containing photosynthetic electron carrier plastocyanin. The decrease in plastocyanin causes a bottleneck within the photosynthetic electron transport chain (ETC), ultimately leading to substantial stoichiometric changes. Namely, 2-fold reduction in both cytochrome b6f complex (cytb6f) and photosystem II (PSII), no change in the Fe-rich PSI and a 40- and 2-fold increase in proteins potentially involved in detoxification of reactive oxygen species (ferredoxin and ferredoxin:NADP+ reductase, respectively). Furthermore, we identify 48 light harvesting complex (LHC) proteins in the publicly available genome of T. oceanica and provide proteomic evidence for 33 of these. The change in the LHC composition within the antennae in response to low Cu underlines the shift from photochemistry to photoprotection in T. oceanica (CCMP1003). Interestingly, we also reveal very significant intra-specific strain differences. Another strain of T. oceanica (CCMP 1005) requires significantly higher Cu concentrations to sustain both its maximal and minimal growth rate compared to CCMP 1003. Under low Cu, CCMP 1005 decreases its growth rate, cell size, Chla and total protein per cell. We argue that the reduction in protein per cell is the main strategy to decrease its cellular Cu requirement, as none of the other parameters tested are affected. Differences between the two strains, as well as differences between the well documented responses to low Fe and those presented here in response to low Cu are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 | 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 teacher head, 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".