Back

A dominant role of cell death in limiting Chandipura virus propagation at cell-saturating high multiplicity of infection

Basak, S.; Bhawna, ; Basu, S.; Mian, S. Y.; Arora, S.; Ratra, Y.; Ganguly, K.; Bais, S. S.; Banerjee, M.; Singh, A.

2025-09-09 systems biology
10.1101/2025.09.04.673995 bioRxiv
Show abstract

Viruses transit from a low to high multiplicity of infection (MOI) regime in infected tissues. Type-1 interferons (IFNs) enforce a cellular state refractory to virus multiplication, while the death of infected cells eliminates viral replicative niche. Here, we investigated how these two innate antiviral mechanisms cooperate at various MOIs upon cell infection by Chandipura virus (CHPV), a cytopathic RNA virus implicated in several outbreaks of acute encephalitis in India. We found that as expected, a gradual increase in the input MOI from 0.02 to 2 led to a proportionate surge in the viral yield. Surprisingly, a further rise to MOI 20 caused a reduction in the progeny titer. Our mathematical modeling together with ex vivo infection studies involving mutant cells suggested that cell death - more so than virus-induced type-1 IFNs - restricted CHPV propagation at cell-saturating high MOIs, leading to a net fall in the yield at MOI 20. We argue that the distinct involvement of innate immune pathways at varied MOIs imparts robust cellular defense against cytopathic viruses. Significance sentenceCell death - more so than type-1 interferons - limits Chandipura virus propagation at cell-saturating high multiplicity of infection. Highlights# Type-1 IFNs and cell death cooperate at varied MOI in limiting CHPV multiplication. # At sub-saturating low MOI, type-1 IFNs play a dominant role in controlling CHPV propagation. # At cell-saturating high MOI, cell death determines the progeny yield.

Published in mBio (predicted rank #6) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.