Nipah Virus Matrix Protein Influences Fusogenicity and Is Essential for Particle Infectivity and Stability
Bibliographic record
Abstract
UNLABELLED: Nipah virus (NiV) causes fatal encephalitic infections in humans. To characterize the role of the matrix (M) protein in the viral life cycle, we generated a reverse genetics system based on NiV strain Malaysia. Using an enhanced green fluorescent protein (eGFP)-expressing M protein-deleted NiV, we observed a slightly increased cell-cell fusion, slow replication kinetics, and significantly reduced peak titers compared to the parental virus. While increased amounts of viral proteins were found in the supernatant of cells infected with M-deleted NiV, the infectivity-to-particle ratio was more than 100-fold reduced, and the particles were less thermostable and of more irregular morphology. Taken together, our data demonstrate that the M protein is not absolutely required for the production of cell-free NiV but is necessary for proper assembly and release of stable infectious NiV particles. IMPORTANCE: Henipaviruses cause a severe disease with high mortality in human patients. Therefore, these viruses can be studied only in biosafety level 4 (BSL-4) laboratories, making it more challenging to characterize their life cycle. Here we investigated the role of the Nipah virus matrix protein in virus-mediated cell-cell fusion and in the formation and release of newly produced particles. We found that even though low levels of infectious viruses are produced in the absence of the matrix protein, it is required for the release of highly infectious and stable particles. Fusogenicity of matrixless viruses was slightly enhanced, further demonstrating the critical role of this protein in different steps of Nipah virus spread.
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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) | 0.001 | 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".