Integrated time-series transcriptomic and metabolomic analyses reveal different inflammatory and adaptive immune responses contributing to host resistance to PRRSV

Abstract

Porcine reproductive and respiratory syndrome virus (PRRSV) is a highly contagious disease that affects the global pig industry. To understand mechanisms of susceptibility/resistance to PRRSV, this study profiled the time-serial white blood cells transcriptomic and serum metabolomic responses to PRRSV in piglets from a crossbred population of PRRSV-resistant Tongcheng pigs and PRRSV-susceptible Large White pigs. Gene set enrichment analysis (GSEA) illustrated that PRRSV infection up-regulated the expression levels of marker genes of dendritic cells, monocytes and neutrophils and inflammatory response, but down-regulated T cells, B cells and NK cells markers. CIBERSORT analysis confirmed the higher T cells proportion in resistant pigs during PRRSV infection. Resistant pigs showed a significantly higher level of T cell activation and lower expression levels of monocyte surface signatures post infection than susceptible pigs, corresponding to more severe suppression of T cell immunity and inflammatory response in susceptible pigs. Differentially expressed genes between resistant/susceptible pigs during the course of infection were significantly enriched in oxidative stress, innate immunity and humoral immunity, cell cycle, biotic stimulated cellular response, wounding response and behavior related pathways. Fourteen of these genes were distributed in 5 different QTL regions associated with PRRSV-related traits. Chemokine CXCL10 levels post PRRSV infection were differentially expressed between resistant pigs and susceptible pigs and can be a promising marker for susceptibility/resistance to PRRSV. Furthermore, the metabolomics dataset indicated differences in amino acid pathways and lipid metabolism between pre-infection/post-infection and resistant/susceptible pigs. The majority of metabolites levels were also down-regulated after PRRSV infection and were significantly positively correlated to the expression levels of marker genes in adaptive immune response. The integration of transcriptome and metabolome revealed concerted molecular events triggered by the infection, notably involving inflammatory response, adaptive immunity and G protein-coupled receptor downstream signaling. This study has increased our knowledge of the immune response differences induced by PRRSV infection and susceptibility differences at the transcriptomic and metabolomic levels, providing the basis for the PRRSV resistance mechanism and effective PRRS control.

Keywords: PRRSV; Tongcheng pigs; adaptive immunity; disease resistance; inflammatory response; metabolome; transcriptome.

Figure 1

Study Overview. (A) Schematic representation of the experimental design in terms of sample types, target tissues, and sampling time points (0 dpi, 4 dpi, 7 dpi, 11 dpi) after PRRSV infection. (B) Serum viral loads and peripheral blood lymphocyte percentages in susceptible and resistant pigs.

Figure 2

(A) Number of genes differentially expressed (|log2(FoldChange)| > 1 and adjusted p-value < 0.05) relative to day 0 in resistant (left) and susceptible (right) pigs on days 4, 7 and 11 post-infection. (B) Venn diagram comparing identified DEGs in resistant pigs. (C) Venn diagram comparing identified DEGs in susceptible pigs.

Figure 3

(A) BTMs significantly enriched (FDR < 0.05 & NES >1) in all (left), resistant (center) and susceptible (right) pigs post infection by GSEA; Blue to red scale indicates negative or positive associations based on normalized enrichment scores (NES). (B) Temporal expression patterns of modules with significant differences between resistant (red) and susceptible (blue) pigs: (a) M7.1: T cell activation(II); (b) S4: Monocyte surface signatures; (c) M7.2: enriched in NK cells(I). (C) Temporal expression patterns of genes with significant differences between resistant (red) and susceptible (blue) pigs. Significance levels are shown as *, p-value < 0.05; **, p-value < 0.01; ****, p-value < 0.0001. (D) The proportion of CD4 T cells between resistant (red) and susceptible (blue) pigs.

Figure 4

(A) Temporal expression patterns of clustered DEGs in resistant (top) and susceptible (bottom) pigs post infection. (B) Gene ontology enrichment analyses of DEGs within clusters. (C) The expression values of significant DEGs related to immune response between resistant (red) and susceptible (blue) pigs; (a) CXCL10, (b) MTDH. Significance level is shown by *, p-value < 0.05.

Figure 5

(A) PLS-DA of metabolite-intensity data in resistant and susceptible pigs on days 0 and 7 post-infection; (B) Number of metabolites differentially expressed (|log2(FoldChange)| > log2(1.5) & p-value < 0.05) in resistant and susceptible pigs on days 0 and 7 post-infection; (C) KEGG enrichment analysis of DE-metabolites within five groups; (D) The classification of significant DE-metabolites within five comparisons.

Figure 6

Heatmap of DE metabolites by one way ANOVA. Blue to red scale indicates negative or positive associations based on metabolites expression levels. Each column represents a sample, and each row represents a DE metabolite.

Figure 7

(A) Correlation between transcriptomic BTM clusters and metabolomic clusters; The magenta to green scale color indicates a positive to negative Pearson’s correlation coefficient, and coefficient values and the corresponding p-values were labeled on the boxes. (B(a)) Correlation between B cell surface signature (S2) module and 2,5-dihydroxybenzoate. (B(b)) Correlation between T cell activation (II) (M7.1) module and L-tryptophan. (C) Correlation between DE metabolites in cluster 1, 4 and marker genes in T cells, B cells, DCs, neutrophils and monocytes related modules; the horizontal axis represents DEGs, and the vertical axis represents DE metabolites. Top 2 significant metabolites were marked by "*".

Figure 8

(A) Correlation between transcriptomic clusters of DEGs between resistant/susceptible pigs and metabolomic clusters; The magenta to green scale color indicates a positive to negative Pearson’s coefficient value, and coefficient values and the corresponding p-values are labeled on the boxes. (B) Correlation between bufotenin and transcriptomics cluster 6. (C) Correlation between DE metabolites in metabolomics cluster 2 and 5 and DEGs in transcriptomics cluster 2; the horizontal axis represents DE metabolites, and the vertical axis represents DEGs. Candidate combination of the metabolite and gene was marked by "*". (D) Correlation between creatinine and CRISP3. meta1~meta15 respectively represent metabolites in cluster 2 and 5: Bufotenin, (+)-7-Isojasmonic acid, 12-Hydroxydodecanoic acid, Myristic acid, Hexadecanedioate, L-Alanine, Uracil, Creatinine, 4,5-Dihydroorotic acid, 2-Dehydro-3-deoxy-L-rhamnonate, 4-Acetamidobutanoic acid,3-Dehydroshikimate, gamma-Glutamylcysteine, 3-Hydroxyphenylacetic acid, L-Tyrosine.

Figure 9

Integrated transcriptomic and metabolomic response network to PRRSV infection. Each node represents a significant correlation (|r| > 0.3 & p-value < 0.05), box represents metabolite, circle represents gene, the shared genes between immune system and GPCR downstream signaling pathways colored in yellow.