Selection of housekeeping genes for gene expression studies in larvae from flatfish using real-time PCR
Bibliographic record
Abstract
BACKGROUND: Flatfish metamorphosis involves major physiological and morphological changes. Due to its importance in aquaculture and as a model for developmental studies, some gene expression studies have focused on the understanding of this process using quantitative real-time PCR (qRT-PCR) technique. Therefore, adequate reference genes for accurate normalization are required. RESULTS: The stability of 12 potential reference genes was examined during larval development in Senegalese sole (Solea senegalensis) and Atlantic halibut (Hippoglossus hippoglossus) to determine the most suitable genes for qRT-PCR analysis. Transcription levels of genes encoding beta-Actin (ACTB), glyceraldehyde-3P-dehydrogenase (GAPDH), annexin A2 (ANXA2), glutathione S-transferase (GST), ornithine decarboxylase (ODC), hypoxanthine phosphoribosyltransferase (HPRT1), ubiquitin (UBQ), elongation factor 1 alpha (eEF1A1), 18S ribosomal RNA, and the ribosomal proteins S4 (RPS4) and L13a (RPL13a) were quantitated. Two paralogous genes for ACTB were analyzed in each of both flatfish species. In addition, two paralogous genes for GAPDH were studied in Senegalese sole. RPL13a represented non-orthologous genes between both flatfish species. GeNorm and NormFinder analyses for expression stability revealed RPS4, UBQ and eEF1A1 as the most stable genes in Senegalese sole, Atlantic halibut and in a combined analysis. In all cases, paralogous genes exhibited differences in expression stability. CONCLUSION: This work suggests RPS4, UBQ, and eEF1A1 genes as useful reference genes for accurate normalization in qRT-PCR studies in Senegalese sole and Atlantic halibut larvae. The congruent results between both species in spite of the drastic differences in larval development suggest that selected housekeeping genes (HKGs) could be useful in other flatfish species. However, the finding of paralogous gene copies differentially expressed during development in some HKGs underscores the necessity to identify orthologous genes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".