Archaic connectivity between the sulfated heparan sulfate and the herpesviruses - An evolutionary potential for cross-species interactions

Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions

A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.

Prunella vulgaris polysaccharide inhibits herpes simplex virus infection by blocking TLR-mediated NF-κB activation

BACKGROUND

Prunella vulgaris polysaccharide extracted by hot water and 30% ethanol precipitation (PVE30) was reported to possess potent antiviral effects against herpes simplex virus (HSV) infection. However, its anti-HSV mechanism has not yet been fully elucidated.

PURPOSE

This study aimed to investigate the potential mechanisms of PVE30 against HSV infection.

METHODS

Antiviral activity was evaluated by a plaque reduction assay, and the EC50 value was calculated. Immunofluorescence staining and heparin bead pull-down assays confirmed the interactions between PVE30 and viral glycoproteins. Real-time PCR was conducted to determine the mRNA levels of viral genes, including UL54, UL29, UL27, UL44, and US6, and the proinflammatory cytokines IL-6 and TNF-α. The protein expression of viral proteins (ICP27, ICP8, gB, gC, and gD), the activity of the TLR-NF-κB signalling pathway, and necroptotic-associated proteins were evaluated by Western blotting. The proportion of necroptotic cells was determined by flow cytometric analysis.

RESULTS

The P. vulgaris polysaccharide PVE30 was shown to compete with heparan sulfate for interaction with HSV surface glycoprotein B and gC, thus strongly inhibiting HSV attachment to cells. In addition, PVE30 downregulated the expression of IE genes, which subsequently downregulated the expression of E and L viral gene products, and thus effectively restricted the yield of progeny virus. Further investigation confirmed that PVE30 inhibited TLR2 and TLR3 signalling, leading to the effective suppression of NF-κB activation and IL-6 and TNF-α expression levels, and blocked HSV-1-induced necroptosis by reducing HSV-1-induced phosphorylation of MLKL.

CONCLUSION

Our results demonstrate that the P. vulgaris polysaccharide PVE30 is a potent anti-HSV agent that blocks TLR-mediated NF-κB activation.