AtmiRNET: a web-based resource for reconstructing regulatory networks of Arabidopsis microRNAs

Abstract

Compared with animal microRNAs (miRNAs), our limited knowledge of how miRNAs involve in significant biological processes in plants is still unclear. AtmiRNET is a novel resource geared toward plant scientists for reconstructing regulatory networks of Arabidopsis miRNAs. By means of highlighted miRNA studies in target recognition, functional enrichment of target genes, promoter identification and detection of cis- and trans-elements, AtmiRNET allows users to explore mechanisms of transcriptional regulation and miRNA functions in Arabidopsis thaliana, which are rarely investigated so far. High-throughput next-generation sequencing datasets from transcriptional start sites (TSSs)-relevant experiments as well as five core promoter elements were collected to establish the support vector machine-based prediction model for Arabidopsis miRNA TSSs. Then, high-confidence transcription factors participate in transcriptional regulation of Arabidopsis miRNAs are provided based on statistical approach. Furthermore, both experimentally verified and putative miRNA-target interactions, whose validity was supported by the correlations between the expression levels of miRNAs and their targets, are elucidated for functional enrichment analysis. The inferred regulatory networks give users an intuitive insight into the pivotal roles of Arabidopsis miRNAs through the crosstalk between miRNA transcriptional regulation (upstream) and miRNA-mediate (downstream) gene circuits. The valuable information that is visually oriented in AtmiRNET recruits the scant understanding of plant miRNAs and will be useful (e.g. ABA-miR167c-auxin signaling pathway) for further research. Database URL: http://AtmiRNET.itps.ncku.edu.tw/

Figure 1.

The concept of AtmiRNET. AtmiRNET summarizes the means of up-to-date miRNA research focusing on target recognition, functional enrichment of targets, promoter identification, detection of cis- and trans- elements to infer regulatory networks of Arabidopsis miRNAs, which are effective to augment the understanding of miRNA functions and transcriptional regulation.

Figure 2.

TSS tags, histone modification of H3K9ac and H3K4me3, and core promoter motifs (Y patch, CA, Inr, GA and REG) are enriched around experimentally verified TSSs, whereas nucleosome H3 are declined. The vertical and horizontal axis represent the accumulative number of each feature (X 1000 ) and genomic location (from −5 to +5 kb relative to experimentally verified TSSs, window size = 200 bp) representatively.

Figure 3.

The workflow of TSS prediction model for Arabidopsis.

Figure 4.

Identification of ath-miR5630b TSS. Distribution of TSS-relevant evidence (including 4 high-throughput sequencing datasets and 5 core promoter motifs) is displayed in the 10 kb upstream of the ath-miR5630b precursor. The orange line and the pink rectangles represent the initiation of expressed sequence tags (ESTs) and conservation blocks, respectively. On the other hand, the red line denotes the representative TSS for ath-miR5630b (Rank 1), and the blue lines denote other TSS candidates of ath-miR5630b.

Figure 5.

The demonstration of search result for detecting cis- and trans- elements of ath-miR167c.

Figure 6.

Reconstruction of miR167c regulatory network.

Figure 7.

An inferred regulatory relation revolves around ath-miR167c. ABA-induced ABI5 and Myb-15 regulate miR167c, and miR167c targets ARF6 and ARF8.