The Transfer of the Ferredoxin Gene From the Chloroplast to the Nuclear Genome Is Ancient Within the Paraphyletic Genus Thalassiosira
Bibliographic record
Abstract
Ferredoxins are iron-sulfur proteins essential for a wide-range of organisms because they are electron transfer mediator involved in multiple metabolic pathways. In phytoplankton, these proteins are active in the mature chloroplasts but the petF gene, encoding for ferredoxin, has been found either in the chloroplast genome or transferred to the nuclear genome as observed in the green algae and higher plant lineage. We experimentally determined the location of the petF gene in 12 strains of Thalassiosira covering three species using DNA sequencing and qPCR assays. The results showed that petF gene is located in the nuclear genome of all confirmed T. oceanica strains (CCMP0999, 1001, 1005, 1006) tested. In contrast, all T. pseudonana (CCMP1012, 1013, 1014, 1335) and T. weissflogii (CCMP1010, 1049, 1052) strains studied retained the gene in the chloroplast genome, as generally observed for Bacillariophyceae. Our evolutionary analyses further extend the dataset on the localization of the petF gene in the Thalassiosirales. The realization that the petF gene is nuclear-encoded in the Skeletonema genus allowed us to trace the petF gene transfer back to a single event that occurred within the paraphyletic genus Thalassiosira. Phylogenetic analyses revealed the need to reassess the taxonomic assignment of the Thalassiosira strain CCMP1616, since the genes used in our study did not cluster within the T. oceanica lineage. Our results suggest that this strains’ diversification occurred prior to the ferredoxin gene transfer event. The functional transfer of petF genes provides insight into the evolutionary processes leading to chloroplast genome reduction and suggests ecological adaptation as a driving force for such chloroplast to nuclear gene transfer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".