Fig 1.
The analysis flow chart of small-RNA sequencing information.
Fig 2.
(A) Principal component analysis (PCA) showing the distribution and clustering of the individual sample groups. Each spot represents a single array. EV71/CA16-infected samples were distinct from the Con sample. (B) Venn diagram representing Known differentially expressed miRNAs.(C) Time- and strain-specific regulation of differential miRNAs during EV71 and CA16 infections. The columns correspond to expression patterns of differentially expressed miRNAs during the EV71 and CA16 infections relative to Con samples at 6 hpi and 12 hpi. Significance was determined using a fold-change threshold of at least 2 and a P value cutoff of 0.05. The intensity of the miRNA expression is indicated in green (lower level of expression) and red (higher level of expression). Dendrograms between samples and between miRNAs are depicted, where the closest branches of the tree represents samples/miRNAs with the most similar expression pattern.
Table 1.
Overview of small-RNA sequencing information and subsequent data analysis.
Table 2.
Significantly differentially expressed miRNAs during the course of EV71 and CA16 infection.
Fig 3.
qRT-PCR validation of differentially expressed miRNAs and partial predicted target genes.
(A) Eight significantly differentially expressed miRNAs were chosen to perform qRT-PCR validation. Data from qRT-PCR analysis are shown as mean ± SEM (N = 3). N, Times of the experiment was performed. Expression changes are in the same direction as determined by the miRNAs sequencing. (B) Six predicted target genes were detected. The data were calculated as mean ± SEM from triplicates with normalization by β-actin.
Fig 4.
Trend analysis of differentially expressed miRNAs in response to EV71 and CA16 infection over time.
The miRNAs that showed opposite expression patterns during the progression of EV71 and CA16 infection are shown in boxes.
Table 3.
Oppositely expressed miRNAs during the course of EV71 and CA16 infection.
Fig 5.
Gene ontology enrichment terms and KEGG pathway analysis of the predicted targets of differentially expressed miRNAs.
The DAVID web-based tool was used to analyze (A) biological process, (B) molecular function and (C) cellular component. GO functional enrichment annotations exhibit in pie charts of the left panel. Bars indicate the number of GOs annotated as unique GO terms and are presented in the right panel. (D) There are 85 pathways in total, as depicted in the pie chart.
Fig 6.
Complexity of the miRNA-regulation networks after infection with EV71 and CA16.
miRNAs are depicted as a red colored node at (A), (B) and (C). (A) miRNA-targets network. The targets are displayed by light blue color rounded rectangles. (B) miRNA-GOs network. GO term nodes are depicted as green color rounded rectangles. Edges in the network represent inhibitory effect of miRNAs on GOs. (C) miRNA-pathways network. Purple rectangle-shaped nodes denote pathways. Edges indicate a negative correlation between miRNA and pathways. (D) Co-expression network were constructed by adhesion-related targets. Red nodes are target genes and yellow nodes are co-expression genes. Genes with bigger size are more centralized in the network and have a stronger capacity of modulating adjacent genes. Different color lines mean the different interactions between these genes. (E) Hierarchical GO categories of genes involved in adhesions. A P value cutoff of 0.05 was used to determine significantly enriched GOs. The color gradient of the yellow nodes, from light to dark, displays the P value, from high to low (the lower the P value, the greater the GO significance level). White nodes are no significant.
Table 4.
Biological adhesion process related to miRNAs and their target genes.