Bioinformatic and gene expression analysis of the somatostatin/cortistatin gene family in the gilthead seabream (Sparus aurata)
Bibliographic record
Abstract
Somatostatin (SST) and cortistatin (CST) are neuromodulators with distinct expression patterns and functions. While SST and CST have been extensively studied in mammalian central nervous system (CNS) and immune system, their roles in teleost fish remain poorly explored due to evolutionary emergence of multiple SST paralogous genes. This study aimed to identify SST isoforms in gilthead seabream (Sparus aurata) and assess their transcriptional levels. Phylogeny and synteny analyses reclassified the six SST genes and proteins as SST1, SST3, SST3-like, SST4, SST5, and SST6. The protein sequences showed high conservation, except for an additional region upstream of the SST3-like protein's leader region. Evolutionary differences were mainly due to specific amino acid residue changes in the mature peptide. Genetic analyses revealed constitutive expression of five genes (sst1, sst3, sst5, sst4 and sst6) in all studied organs, except for sst3 in the heart, liver, and blood. The highest expression of sst1, sst3, sst4 and sst6 genes occurred in the brain's forebrain, while sst5 was most expressed in the heart. However, sst4 exhibited very low basal expression across all analysed tissues. In vitro, λ-carrageenan and cantharidin upregulated sst6 transcription in head kidney leucocytes (HKLs), indicating a potential anti-inflammatory role similar to mammalian CST. Additionally, sst5 expression was downregulated during the innate cell-mediated cytotoxic response, suggesting a regulatory role. These findings provide insights into the SST/CST gene family in gilthead seabream, necessitating gene and protein reclassification, and underscore their significant neuroendocrine and immune system functions, relevant for teleost research.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".