A highly sensitive strand-specific multiplex RT-qPCR assay for quantitation of Zika virus replication
Bibliographic record
Abstract
Abstract Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) is widely used to quantify viral RNA genomes for diagnostics and research, yet conventional RT-qPCR protocols are unable to accurately distinguish between the different viral RNA species that exist during infection. Here we show that false-priming and self-priming occur during reverse transcription with several published Zika virus (ZIKV) primer sets. We developed a RT-qPCR assay using tagged primers and thermostable reverse transcriptase, which greatly reduced the occurrence of nonspecific cDNA products. Furthermore, we optimized the assay for use in multiplex qPCR which allows for simultaneous quantitative detection of positive-strand and negative-strand ZIKV RNA along with an internal control from both human and mosquito cells. Importantly, this assay is sensitive enough to study early stages of virus infection in vitro . Strikingly, using this assay, we detected ZIKV negative-strand RNA as early as 3 h post-infection in mammalian cell culture, at a time point prior to the onset of positive-strand RNA synthesis. Overall, the strand-specific RT-qPCR assay developed herein is a valuable tool to quantify ZIKV RNA and to study viral replication dynamics during infection. The application of these findings has the potential to increase accuracy of RNA detection methods for a variety of viral pathogens. Highlights Self-primed cDNA is amplified by widely-used ZIKV qPCR primer sets Use of tagged primers and thermostable RT increases strand-specificity for RT-qPCR Multiplexed qPCR allows for simultaneous quantitation of (+) and (-) strand viral RNAs, and an internal control Strand-specific RT-qPCR can detect fewer than one copy of viral RNA per cell in human and mosquito cells
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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 | 0.001 |
| 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".