Transcriptomic insights into pseudorabies virus suppressed cell death pathways in neuroblastoma cells

Abstract

Pseudorabies virus (PRV) exhibits a complex interplay of host-pathogen interactions, primarily by modulating host cell death pathways to optimize its replication and spread in Neuro-2a cells. Using high-throughput RNA sequencing, we identified 2,382 upregulated differentially expressed genes (DEGs) and 3,998 downregulated DEGs, indicating a intricate interaction between viral pathogenesis and host cellular responses. This research offers valuable insights into the molecular processes involved in PRV infection, highlighting the substantial inhibition of crucial cell death pathways in Neuro-2a cells, including necroptosis, pyroptosis, autophagy, ferroptosis, and cuproptosis. Cells infected with PRV exhibit decreased expression of genes critical in these pathways, potentially as a mechanism to avoid host immune reactions and ensure cell survival to support ongoing viral replication. This extensive inhibition of apoptosis and metabolic alterations highlights the sophisticated tactics utilized by PRV, enhancing our comprehension of herpesvirus biology and the feasibility of creating specific antiviral treatments. This research contributes to our understanding of how viruses manipulate host cell death and presents potential opportunities for therapeutic interventions to disrupt the virus's lifecycle.

Keywords: Neuro-2a cells; cell death; inflammatory; pseudorabies virus; transcriptomics.

Figure 1

Changes in differential gene expression in Neuro-2a at 48 h.p.i. after PRV infection. (A) Volcano plots showing fold changes and adjusted p-values for genes differentially expressed between unstimulated (mock) and PRV-stimulated Neuro-2a. (B) The upregulated/downregulated and the total number of DEGs (≥2 FC, p < 0.05) between unstimulated (mock) and PRV-stimulated PAMs in the transcriptomic data at 6, 12, and 24 h, respectively. (C) The principal component analysis (PCA). (D) GO analysis of the genes with expression changes for the integrated data of the three time points. (E) KEGG analysis of the genes with expression changes at 48 h.p.i. and the integrated data.

Figure 2

DEGs in Neuro-2a after in vitro PRV-specific stimulation reveal concomitant necroptosis, pyroptosis, and autophagy responses. (A–C) Heatmap analysis of PRV DEGs for necroptosis, pyroptosis, and autophagy post-PRV infection.

Figure 3

DEGs in Neuro-2a after in vitro PRV-specific stimulation reveal concomitant disulfidptosis, ferroptosis, and cuproptosis responses. (A–C) Heatmap analysis of PRV DEGs for disulfidptosis, ferroptosis, and cuproptosis post-PRV infection.

Figure 4

Effect of PRV infection on the transcription of anti-viral factors, cytokines, chemokines, and inflammatory associated genes in PRV infected PAMs. (A) Heatmap analysis of PRV DEGs for inflammation post-PRV infection. (B) Heatmap analysis of PRV DEGs for cytokines and chemokines post-PRV infection.

Figure 5

Molecular markers associated with PRV immunopathology based on machine learning methods. (A) Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to reduce the dimension of the grouping characteristics. (B) PPI network of the molecular markers including PDIA4, COPA, POLR2C, DZIP3, TM9SF4, and EXT2. (C–H) The molecular markers including PDIA4, COPA, POLR2C, DZIP3, TM9SF4, and EXT2 were significantly downregulated in PRV-infected cells than in mock-infected cells. A significance threshold of *p<0.05 was used to determine statistical significance, with more stringent levels indicated by **p<0.01 and ***p<0.001, signifying very significant and highly significant outcomes, respectively.