Chloroplast-mitochondria synergy modulates responses to iron limitation in two <i>Thalassiosira</i> diatom species
Bibliographic record
Abstract
Abstract Iron is naturally present at low levels in modern oceans, but it remains essential for marine life. Ocean-dwelling organisms such as oceanic phytoplankton must therefore adapt to the available levels. Among phytoplankton, diatoms are a highly diverse and successful taxon that includes the Thalassiosira genus. As a group, diatoms contribute around 20% of global primary photosynthetic productivity, they have also developed specific resources allowing them to thrive in low-iron regions. However, the major biological factors underlying their success in these ocean environments remain unknown. Here, we compared two Thalassiosira species: T. oceanica from iron-poor open-ocean; and T. pseudonana , from iron-rich coastal waters. Since iron is essential for both photosynthesis and respiration, we examined the specificities of the bioenergetic machineries in these organisms using a combination of photo-physiological, proteomics, and FIB-SEM methods. We particularly focused on chloroplast-mitochondrial coupling, a mechanism deployed by diatoms to ensure optimal transfer of photosynthetic products to promote cell growth. This study of this mechanism in the context of iron limitation reveals that the two diatoms differentially remodel chloroplast compartments in response to iron limitation. Their tolerance to these conditions is also linked to distinct constitutive mitochondrial architectures.
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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".