The role of sub-genomic RNA in discordant results from RT-PCR tests for COVID-19
Bibliographic record
Abstract
Abstract Reverse transcription-PCR (RT-PCR) is the standard method of diagnosing COVID-19. An inconclusive test result occurs when one RT-PCR target is positive for SARS-CoV-2 and one RT-PCR target is negative within the same sample. An inconclusive result generally requires retesting. One reason why a sample may yield an inconclusive result is that one target is at a higher concentration than another target. It was hypothesized that concentration differences across targets may be due to the transcription of sub-genomic RNA, as this would result in an increase in the concentration of gene targets near the 3’ end of the SARS-CoV-2 genome. A panel of six digital droplet (dd)PCR assays was designed to quantitate the ORF1, E-gene, and N-gene of SARS-CoV-2. This panel was used to quantify viral cultures of SARS-CoV-2 that were harvested during the eclipse phase and at peak infectivity in such a way as to maximize gene-to-gene copy ratios. Eleven clinical nasopharyngeal swabs were also tested with this panel. In culture, infected cells showed higher N-gene/ORF1 copy ratios than culture supernatants. Both the highest specific infectivity (copies/pfu) and the highest differences between gene targets were observed at 6 hours post-infection (eclipse phase) in infected cells. The same trends in the relative abundance of copies across different targets observed in infected cells was observed in clinical samples, though trends were more pronounced in infected cells. This study showed that a greater copy number of N-gene relative to E-gene and ORF1 transcripts could potentially explain inconclusive results for some RT-PCR tests on low viral load samples. The use of N-gene RT-PCR target(s) as opposed to ORF1 targets for routine testing is supported by this data. Author Summary This paper provides insight into a drawback of the standard method of testing for COVID-19 (RT-PCR). The results presented here propose an explanation for why inconclusive results sometimes occur with this method. These results can aid microbiologists in the interpretation of inconclusive test results. These results can also aid in decisions about which COVID-19 test a laboratory should use, as there are a plethora of options available. This is important because this standard testing method will remain a critical tool – globally – for managing the COVID-19 pandemic and any future viral pandemics and epidemics. Thus, it is important to investigate every facet of the testing method. The findings presented here are applicable to any virus which makes sub-genomic transcripts as part of its life cycle. Trends observed in viral cultures are presented alongside the same trends observed in clinical samples. Unlike similar papers in the field, this paper did not strive to develop a new methodology or tool.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".