Characterization of the Polysialylation Status in Ovaries of the Salmonid Fish Coregonus maraena and the Percid Fish Sander lucioperca
Bibliographic record
Abstract
In vertebrates, the carbohydrate polymer polysialic acid (polySia) is especially well known for its essential role during neuronal development, regulating the migration and proliferation of neural precursor cells, for instance. Nevertheless, sialic acid polymers seem to be regulatory elements in other physiological systems, such as the reproductive tract. Interestingly, trout fish eggs have polySia, but we know little of its cellular distribution and role during oogenesis. Therefore, we localized α2,8-linked N-acetylneuraminic acid polymers in the ovaries of Coregonus maraena by immunohistochemistry and found that prevalent clusters of oogonia showed polySia signals on their surfaces. Remarkably, the genome of this salmonid fish contains two st8sia2 genes and one st8sia4 gene, that is, three polysialyltransferases. The expression analysis revealed that for st8sia2-r2, 60 times more mRNA was present than st8sia2-r1 and st8sia4. To compare polysialylation status regarding various polySiaT configurations, we performed a comparable analysis in Sander lucioperca. The genome of this perciform fish contains only one st8sia2 and no st8sia4 gene. Here, too, clusters of oogonia showed polysialylated cell surfaces, and we detected high mRNA values for st8sia2. These results suggest that in teleosts, polySia is involved in the cellular processes of oogonia during oogenesis.
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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".