Selection and validation of reference genes for measuring gene expression in Piper species at different life stages using RT-qPCR analysis
Bibliographic record
Abstract
The secondary metabolism of Piper species is known to produce a myriad of natural products from various biosynthetic pathways which, represent a rich source of previously uncharacterized chemical compounds. The determination of gene expression profiles in multiple tissue/organ samples could provide valuable clues towards understanding the potential biological functions of chemical changes in these plants. Studies on gene expression by RT-qPCR require particularly careful selection of suitable reference genes as a control for normalization. Here, we provide a study for the identification of reliable reference genes in P. arboreum, P. gaudichaudianum, P. malacophyllum, and P. tuberculatum, at two different life stages: 2-month-old seedlings and adult plants. To do this, annotated sequences were recovered from transcriptome datasets of the above listed Piper spp. These sequences were subjected to expression analysis using RT-qPCR, followed by analysis using the geNorm and NormFinder algorithms. A set of five genes were identified showing stable expression: ACT7 (Actin-7), Cyclophilin (Peptidyl-prolyl cis-trans isomerase), EF1α (Elongation factor 1-alpha), RNABP (RNA-binding protein), and UBCE (Ubiquitin conjugating enzyme). The universality of these genes was then validated using two target genes, ADC (arginine decarboxylase) and SAMDC (S-adenosylmethionine decarboxylase), which are involved in the biosynthesis of polyamines. We showed that normalization genes varied according to Piper spp., and we provide a list of recommended pairs of the best combination for each species. This study provides the first set of suitable candidate genes for gene expression studies in the four Piper spp. assayed, and the findings will facilitate subsequent transcriptomic and functional gene 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".