A new set of reference genes for RT-qPCR assays in the yeast <i>Dekkera bruxellensis</i>
Bibliographic record
Abstract
The yeast Dekkera bruxellensis has been recently regarded as an important microorganism for bioethanol production owing to its ability to convert glucose, sucrose, and cellobiose to ethanol. The aim of this work was to validate a new set of reference genes for gene expression analysis by quantitative real-time PCR in D. bruxellensis and compare the influence of the method of choice for quantification of mRNA levels with the reliability of our data. Three candidate reference genes, DbEFA1, DbEFB1, and DbYNA1, were used in a quantitative analysis of 4 genes of interest, DbYNR1, DbTPS1, DbADH7, and DbUBA4, based on an approach for calculating the normalization factors by means of the geNorm applet. Each reference gene was also individually used for a 2(-ΔΔC(q)) (comparative C(q) method) calculation of the relative expression of genes of interest. Our results showed that the 3 reference genes provided enough stability and were complementary to the normalization factors method in different culture conditions. This work was able to confirm the usefulness of a previously reported reference gene, EFA1/TEF1, and increased the set of possible reference genes in D. bruxellensis to 4. Moreover, this can improve the reliability of the analysis of the regulation of gene expression in the industrial yeast D. bruxellensis.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".