Identification of MicroRNAs and Their Target Genes Associated with Ovarian Development in Black Tiger Shrimp (Penaeus monodon) Using High-Throughput Sequencing
Bibliographic record
Abstract
Abstract Plenty of evidence showing that microRNAs (miRNAs) post-transcriptionally regulate gene expression and are involved in a wide range of biological processes. However, the roles of miRNAs in ovarian development process remain largely unknown in shrimp. In the present study, high-throughput sequencing of small RNAs was performed to find specific miRNAs that are involved in ovarian development process in Penaeus monodon. Two small RNA libraries were constructed from undeveloped (UNDEV group) and developed (DEV group) ovarian tissues in P. monodon. In total, 43 differentially expressed miRNAs were identified between the two groups (P ≤ 0.05, |log2 ratio| ≥1), and their expression profiles were validated by qRT-PCR. In order to further clarify the functional roles of these differentially expressed miRNAs during ovarian development process, target gene prediction was performed. In total, 4,102 target genes of 43 miRNAs were predicted, then clustered by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; only four specific pathways related to ovarian development were obtained (P < 0.05). Dual-luciferase reporter assays and integrated expression analysis were also conducted to further clarify the interaction between the miRNAs and their target mRNAs. This study provides important information about the function of miRNAs involved in ovarian developmental stages in P. monodon.
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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".