Coxsackievirus A7 and Enterovirus A71 Significantly Reduce SARS-CoV-2 Infection in Cell and Animal Models.
Victor A SvyatchenkoStanislav S LegostaevRoman Y LutkovskiyElena V ProtopopovaEugenia P PonomarevaVladimir V OmigovOleg Svyatoslavovich TaranovVladimir A TernovoiAlexander P AgafonovValery B LoktevPublished in: Viruses (2024)
In this study, we investigated the features of co-infection with SARS-CoV-2 and the enterovirus vaccine strain LEV8 of coxsackievirus A7 or enterovirus A71 for Vero E6 cells and Syrian hamsters. The investigation of co-infection with SARS-CoV-2 and LEV-8 or EV-A71 in the cell model showed that a competitive inhibitory effect for these viruses was especially significant against SARS-CoV-2. Pre-infection with enteroviruses in the animals caused more than a 100-fold decrease in the levels of SARS-CoV-2 virus replication in the respiratory tract and more rapid clearance of infectious SARS-CoV-2 from the lower respiratory tract. Co-infection with SARS-CoV-2 and LEV-8 or EV-A71 also reduced the severity of clinical manifestations of the SARS-CoV-2 infection in the animals. Additionally, the histological data illustrated that co-infection with strain LEV8 of coxsackievirus A7 decreased the level of pathological changes induced by SARS-CoV-2 in the lungs. Research into the chemokine/cytokine profile demonstrated that the studied enteroviruses efficiently triggered this part of the antiviral immune response, which is associated with the significant inhibition of SARS-CoV-2 infection. These results demonstrate that there is significant viral interference between the studied strain LEV-8 of coxsackievirus A7 or enterovirus A71 and SARS-CoV-2 in vitro and in vivo.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- respiratory tract
- immune response
- single cell
- induced apoptosis
- cell therapy
- cell death
- machine learning
- oxidative stress
- mass spectrometry
- cell cycle arrest
- toll like receptor
- big data
- single molecule
- deep learning
- inflammatory response
- endoplasmic reticulum stress
- sensitive detection
- data analysis
- atomic force microscopy