Development of a versatile TaqMan™ real-time quantitative PCR (RT-qPCR) compliant anchor sequence to quantify bacterial gene transcripts from RNA samples containing carryover genomic DNA
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: In bacterial systems, the sequence congruence of genomic DNA (gDNA) and cDNA obtained following reverse transcription of RNA, makes gDNA an automatic target for qPCR primers. This could lead to aberrant gene expression quantification. This is why a rigorous treatment of bacterial RNA with DNase I is usually required to remove any traces of carryover gDNA. As bacterial RNA is known to be extremely labile, any procedure that affects RNA yield, such as DNase I treatment, can be logically assumed to also influence detection and quantification of gene transcripts, leading to either an underestimation or no detection at all. To address such problems, we have developed a novel and versatile TaqMan RT-qPCR compliant anchor sequence (MYT4) for quantifying bacterial gene transcripts without the need for DNase I treatment. RESULTS: A non-genomic anchor sequence, henceforth referred to as MYT4 was designed using a synthetic DNA sequence called myIC, previously shown to share no significant homology to any known accession in the GenBank database. The sequence characteristic of MYT4 was kept within the design parameters required for the TaqMan RT-qPCR platform. The specificity and robustness of the novel MYT4 sequence was validated on RNA extracted from the bacterium Pseudomonas sp. LBUM300, grown under liquid culture and spiked soil conditions. Two transcripts, namely hcnC and phlD, were quantified from these two experimental systems. Using the MYT4 anchor, no RT-qPCR signal was detected from non-DNase I treated RNA, while strong signals were obtained using conventional reverse primers and RT-qPCR, indicating the presence of carryover gDNA in the RNA, extracted from either liquid culture or soil. Serial treatment of the RNA samples with DNase I (required to achieve absolute gDNA elimination) resulted in 50-70% loss of RNA which, when submitted to conventional RT-qPCR, significantly altered the transcript numbers detected when compared to the MYT4-based approach. CONCLUSIONS: Implementation of the versatile approach described in this study, which can be "retrofitted" to any existing TaqMan RT-qPCR system, should contribute to reducing the time and lowering the costs required to perform adequate bacterial RNA purification for downstream quantification of gene transcripts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it