High throughput data analyses of the immune characteristics of Microtus fortis infected with Schistosoma japonicum - PubMed (original) (raw)

High throughput data analyses of the immune characteristics of Microtus fortis infected with Schistosoma japonicum

Yuan Hu et al. Sci Rep. 2017.

Abstract

Microtus fortis exhibits natural resistance against Schistosoma japonicum, and the parasite cannot grow and develop in M. fortis. Extensive research has been carried out, however, the associated mechanism remains unclear. In the present study, we analysed the combined data obtained from a cytokine chip assay, transcriptome, and metabolome. The cytokine profile from C57BL/6 and M. fortis mice was assessed before and after infection. Several cytokines increased during the second and third week post-infection. Some transcripts related to cytokine genes and associated proteins were also highly expressed (i.e., Hgf, C3, and Lbp). The liver metabolism of M. fortis following infection with S. japonicum was assessed. We identified 25 different metabolites between the uninfected and infected M. fortis, and 22 different metabolites between infected M. fortis and C57BL/6 mice. The metabolomic pathways of these differential metabolites were then analysed with MetPA, revealing that they were involved in histidine metabolism, valine, leucine, and isoleucine biosyntheses, and lysine degradation. Thus, the elevated expression of these metabolites and pathways may promote the phagocytic function of the neutrophils and natural killer cell activity following TLR activation. These results provide novel insight into the resistance mechanism of M. fortis against S. japonicum.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1

Figure 1

The dynamic cytokine changes exhibited by M. fortis and C57BL/6 mice following infection with S. japonicum. The cytokine levels in the sera of M . fortis were significantly higher from second to third week post-infection compared to that of C57BL/6 mice, including 1a, monokines (e.g., IL-1α, IL-1b, and GM-CSF); 1b, Th1 cytokines (e.g., IL-12, IFN-γ, and IL-2); 1c, Th2 cytokines (e.g., IL-4, IL-5, IL-10, and IL-3); 1d, Th17 cytokine (i.e., IL-17); 1e, chemokines (i.e., MCP-1); 1 f, angiogenesis factors (e.g., VEGF and TNF-α).

Figure 2

Figure 2

Regulation and interaction network model of 17 cytokines. The triangle represents cytokines. Green indicates down-regulation (Fold change <1), and red represents up-regulation (Fold change >1). The diamond denotes the KEGG signalling pathway, and the rectangle indicates biological processes. Red was significantly expressed (P value < 0.05), and green was not significantly expressed (P value > 0.05).

Figure 3

Figure 3

Regulation model of cytokines and transcripts related to cytokine genes. The triangle denotes cytokines; the circle indicates genes that have the potential to interact with cytokines and were detected by RNA-seq; the diamond denotes the KEGG pathway; and the rectangle indicates biological processes. Red represents up-regulation, green indicates down-regulation, and the node size represents the level of fold change in expression. Hgf, C3, and Lbp were substantially increased, which was related to IL-4, KC and TNFα. In contrast, some genes (e.g., CXCL1, CXCL2, and TLR5) decreased significantly.

References

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