Optimizing human coronavirus OC43 growth and titration
Bibliographic record
Abstract
Coronaviruses have been at the forefront of the news for the last 2 years. Unfortunately, SARS-CoV-2, the etiologic agent for the COVID-19 pandemic, must be manipulated in biosecurity level 3 settings, which significantly limits research. Meanwhile, several less pathogenic human coronaviruses (HCoV) exist and can be studied in much more common biosafety level 2 laboratories. Among them, HCoV-OC43 is a good surrogate candidate for SARS-CoV-2 since both are phylogenetically related human Betacoronaviruses . However, one issue has been the lack of standardized means among laboratories to propagate and titer this less virulent coronavirus. The present study probes the optimal parameters to propagate HCoV-OC43. First, testing of five different cell lines (MRC-5, Huh7.5, Vero, HCT-8, HRT-18) indicated that the physiologically relevant MRC-5 human lung cell line produced among the highest viral titers. HRT-18 may however be an interesting alternative as they are quick growing cells that also led to higher viral titers and a better tropism for various HCoV-OC43 variants. We also probed the impact of serum and temperature during viral expansion and confirmed that the normal temperature of the upper respiratory track (33 °C) improves viral yields over the typical 37 °C used to grow many other viruses. Meanwhile, we did not notice any evidence that serum concentrations significantly affected the virus but interestingly noted that the virus grew quite efficiently in a serum-free media formulation. Meanwhile sonication of viral stocks somewhat improved viral titers. Four titration methods (plaque assays, TCID 50 -CPE, TCID 50 -IFA and TCID 50 –IPA) were also probed using two cell lines (VeroE6 and HRT-18). In our hands, plaque assays proved unreliable and quantification of the virus by scoring CPE positive wells was significantly less sensitive than antibody-based assays (IFA and IPA). While the latter methods were equally sensitive, we favor the TCID 50 -IPA method since simpler, faster and cheaper than the IFA protocol. Moreover, the HRT-18 cells appeared more sensitive to quantify the virus. Perhaps most importantly, these optimized protocols routinely led to high titer viral stocks in the order of 10 8 TCID 50 /ml magnitude, which should fulfill the requirements of most experimental settings.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".