Influence of GlycoGag on the Incorporation of Host Membrane Proteins Into the Envelope of the Moloney Murine Leukemia Virus
Bibliographic record
Abstract
Analysis of viral particle heterogeneity produced from infected cells has been limited by the inefficiency of traditional analytical methods to characterize large populations of viruses at an individual particle level. Flow virometry (FVM) is an emerging technique based on flow cytometry principles that enables a high throughput, multiparametric, and phenotypic characterization of viruses at a single particle resolution. Here, we performed FVM to analyze surface markers found on Murine Leukemia Virus (MLV) and glycosylated Gag-deficient (glycoGag) MLV. The glycoGag viral accessory protein has several roles in the MLV viral infection cycle including directing retroviral assembly and particle release at lipid rafts. Based on previous studies, we hypothesize that glycoGag modulates host protein incorporation into the viral envelope during viral assembly and budding. Here, by using FVM, we reveal that glycoGag is associated with an increased incorporation of the host-derived tetraspanins CD81 and CD63 along with the lipid raft marker and immune antigen Thy1.2 during the assembly and release of viral particles from infected NIH 3T3, EL4, and primary CD4+ T cells. Moreover, we show differences in the uptake of host proteins by viruses that are released from the two cell lines and primary T lymphocytes. Additionally, at the individual viral particle level, we observed a degree of expression heterogeneity of host-derived antigens within the viral population. Finally, certain cellular antigens can show either enrichment or exclusion from the viral envelope depending on whether glycoGag is expressed by the virus. This suggests that glycoGag is involved in a mechanism of selective host protein incorporation into the viral envelope.
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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 | 0.000 | 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 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".