Quantitative Evaluation of Protein Heterogeneity within Herpes Simplex Virus 1 Particles
Bibliographic record
Abstract
ABSTRACT Several virulence genes have been identified thus far in the herpes simplex virus 1 genome. It is also generally accepted that protein heterogeneity among virions further impacts viral fitness. However, linking this variability directly with infectivity has been challenging at the individual viral particle level. To address this issue, we resorted to flow cytometry (flow virometry), a powerful approach we recently employed to analyze individual viral particles, to identify which tegument proteins vary and directly address if such variability is biologically relevant. We found that the stoichiometry of the U L 37, ICP0, and VP11/12 tegument proteins in virions is more stable than the VP16 and VP22 tegument proteins, which varied significantly among viral particles. Most interestingly, viruses sorted for their high VP16 or VP22 content yielded modest but reproducible increases in infectivity compared to their corresponding counterparts containing low VP16 or VP22 content. These findings were corroborated for VP16 in short interfering RNA experiments but proved intriguingly more complex for VP22. An analysis by quantitative Western blotting revealed substantial alterations of virion composition upon manipulation of individual tegument proteins and suggests that VP22 protein levels acted indirectly on viral fitness. These findings reaffirm the interdependence of the virion components and corroborate that viral fitness is influenced not only by the genome of viruses but also by the stoichiometry of proteins within each virion. IMPORTANCE The ability of viruses to spread in animals has been mapped to several viral genes, but other factors are clearly involved, including virion heterogeneity. To directly probe whether the latter influences viral fitness, we analyzed the protein content of individual herpes simplex virus 1 particles using an innovative flow cytometry approach. The data confirm that some viral proteins are incorporated in more controlled amounts, while others vary substantially. Interestingly, this correlates with the VP16 trans -activating viral protein and indirectly with VP22, a second virion component whose modulation profoundly alters virion composition. This reaffirms that not only the presence but also the amount of specific tegument proteins is an important determinant of viral fitness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".