Novel Method Based on “En Passant” Mutagenesis Coupled with a Gaussia Luciferase Reporter Assay for Studying the Combined Effects of Human Cytomegalovirus Mutations
Bibliographic record
Abstract
Human cytomegalovirus (HCMV) resistance to antivirals is a major problem in immunocompromised patients. Drug resistance is characterized by phenotypic testing or genotypic analysis of the phosphotransferase (UL97) and DNA polymerase (UL54) genes. However, genotypic assays require further characterization of unknown mutations in the drug resistance phenotype. Here, we describe a novel method for generating single or multiple mutations anywhere in the HCMV genome and for studying their effects on drug susceptibility. This method is based on cloning of the reference AD169 strain in a bacterial artificial chromosome and the use of "en passant" mutagenesis in bacteria to introduce mutations in recombinant HCMV without scar sequences. We also used this methodology to introduce the Gaussia luciferase reporter gene into the genome of the recombinant virus. To validate our system, the well-characterized single mutants with UL97 A594V and UL54 E756K mutations as well as the undescribed A594V/E756K double mutant were generated and their drug susceptibility profiles were determined by measuring the luciferase activity in cell culture supernatants. Drug susceptibility phenotypes for the A594V (8.2-fold increase in ganciclovir 50% effective concentration [EC50]) and E756K (1.9-, 3.9-, and 3.0-fold increases in ganciclovir, foscarnet, and cidofovir EC50s, respectively) mutants were similar to those previously reported, while the double mutant exhibited 10.8-, 4.1-, and 2.0-fold increases in ganciclovir, foscarnet, and cidofovir EC50s, respectively. The combination of the Gaussia luciferase reporter-based assay with the markerless "en passant" mutagenesis methodology constitutes an efficient system for studying phenotypes with single or multiple HCMV mutations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".