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. 2017 Feb 1;10(1):56.
doi: 10.1186/s13071-017-1990-2.

Transcriptomic Responses of Water Buffalo Liver to Infection With the Digenetic Fluke Fasciola Gigantica

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Free PMC article

Transcriptomic Responses of Water Buffalo Liver to Infection With the Digenetic Fluke Fasciola Gigantica

Fu-Kai Zhang et al. Parasit Vectors. .
Free PMC article

Abstract

Background: Fasciola gigantica, the tropical liver fluke, infects buffaloes in Asian and African countries and causes significant economic losses and poses public health threat in these countries. However, little is known of the transcriptional response of buffaloes to infection with F. gigantica. The objective of the present study was to perform the first transcriptomic analysis of buffalo liver infected by F. gigantica. Understanding the mechanisms that underpin F. gigantica infection in buffaloes will contribute to our ability to control this parasite.

Methods: We challenged buffaloes with 500 viable F. gigantica metacercariae and collected liver samples through a time course at 3, 42 and 70 days post-infection (dpi). Then, we performed gene expression analysis on liver samples using RNA sequencing (RNA-Seq) Illumina technology and confirmed the RNA-Seq data by quantitative RT-PCR analysis.

Results: Totals of 496, 880 and 441 differentially expressed transcripts were identified in the infected livers at 3, 42 and 70 dpi, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that transcriptional changes in the liver of infected buffaloes evolve over the course of infection. The predominant response of buffaloes to infection was mediated by certain pathways, such as MHC antigen processing and presentation, Toll-like receptor 4 (TLR4), transforming growth factor beta (TGF-β), and the cytochrome P450. Hepatic drug metabolizing enzymes and bile secretion were also affected.

Conclusions: Fasciola gigantica can induce statistically significant and biologically plausible differences in the hepatic gene expression of infected buffaloes. These findings provide new insights into the response of buffaloes to F. gigantica over the course of infection, which may be useful in determining pathways that can modulate host-parasite interaction and thus potentially important for clearance of the parasite.

Keywords: Fasciola gigantica; Immunomodulation; RNA-sequencing; Transcriptome; Water buffalo.

Figures

Fig. 1
Fig. 1
Volcano map of the differentially expressed genes between infected and control buffaloes. Significantly differentially expressed genes are shown as red (up) or green (down) dots. No significant difference between the expressions of genes is indicated by blue dots. Ordinate represents the magnitude of gene expression changes. The x-axis represents the value of log2(fold change) and the y-axis shows the value of -log10(pval). a, b and c represent differentially expressed genes at 3, 42 and 70 days post-infection, respectively
Fig. 2
Fig. 2
Verification of the gene expression by qRT-PCR. Eleven genes were selected randomly for validation of the RNA-seq data. Data of RNA-seq verified by qRT-PCR at 3 (a), 42 (b) and 70 (c) days post-infection
Fig. 3
Fig. 3
Venn diagram showing the overlap of the differentially expressed genes between Fasciola gigantica-infected liver sample groups at 3 (a), 42 (b) and 70 (c) days post-infection. Transcripts that are common to multiple time points are shown by the overlap
Fig. 4
Fig. 4
Differentially expressed GO terms. Differentially expressed genes (DEGs) were classified into three main categories: molecular function, cellular component and biological process. The identified functions and the corresponding numbers of DEGs for each GO category are shown. a Top 30 DE molecular function, cellular component and biological process in A2T (infected) vs A2C (control) at 3 dpi. b Top 30 DE molecular function and biological process in A5T (infected) vs A5C (control) at 42 dpi. c Top 30 DE molecular function, cellular component and biological process in A6T (infected) vs A6C (control) at 70 dpi
Fig. 5
Fig. 5
Statistics of KEGG pathway enrichment. The x-axis shows the enrichment factor; the y-axis corresponds to KEGG Pathway. The color of the dot represents q value and size of the dot represents the number of DEGs mapped to the reference pathways. a, b and c represent the top 20 statistics of KEGG pathway enrichment for DEGs observed at 3, 42 and 70 dpi, respectively
Fig. 6
Fig. 6
Heatmap of the differentially expressed (DE) transcription factors. a, b and c are differentially expressed transcription factors at 3, 42 and 70 dpi, respectively. The red (up) and green (down) dots represent the significantly differential expressed transcripts; the black represents the transcripts whose expression levels did not reach statistical significance

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