Identifying sexually dimorphic circulating microRNAs in gonochoristic and hermaphroditic marine fish species
Bibliographic record
Abstract
Abstract In many fish species, males and females are hard to distinguish at the juvenile stage and size differences appear during maturation, often favoring females. Molecular tools for sexing live fish would benefit aquaculture, fisheries, and conservation research. Here we aimed to explores to what extent circulating microRNAs (miRNAs) can decipher phenotypic males from phenotypic females in gonochoristic fish species (the European seabass Dicentrarchus labrax , the Turbot Scophthalmus maximus , the Red drum Sciaenops ocellatus , and the Blue Runner Caranx crysos ) as well as sexual stages in a protandrous hermaphrodite species (the Gilthead seabream Sparus aurata ). We collected and extracted total RNA from 258 plasma samples, of which 96 samples with satisfactory RNA quality were sequenced using small RNA-seq. Circulating miRNAs detected in the plasma allowed to easily discriminate species, but some miRNAs were significantly correlated to one sex, independently of the species. In immature fishes, 3 miRNAs: miR-21a-3p, miR-18a-3p, and miR-29-1a-5p were overexpressed in females compared to males, while in mature fish, miR-21a-3p exhibited an opposite pattern. In the Gilthead seabream, we detected that both the miR-21a-3p and the miR-125b-2/3-5p were likely involved in the sexual transition from male to female. A complementary analysis on the 3′UTR sequences of all fish species allowed to predict potential mRNA targets of those two miRNAs, some of them being particularly relevant regarding sexual development ( i . e . wnt4, esrrb, esrrga and hsd17b1). The identification of miRNAs like miR-21a-3p and miR-125b-2/3-5p as potential sex markers could offer a new, poorly-invasive method to monitor sex and developmental stages in fishes.
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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".