The immense therapeutic and economic values of medicinal plants have attracted increasing attention from the worldwide researchers. It has been recognized that production of the authentic and high-quality herbal drugs became the prerequisite for maintaining the healthy development of the traditional medicine industry. To this end, intensive research efforts have been devoted to the basic studies, in order to pave a way for standardized authentication of the plant materials, and bioengineering of the metabolic pathways in the medicinal plants. In this paper, the recent advances of omics studies on the medicinal plants were summarized from several aspects, including phenomics and taxonomics, genomics, transcriptomics, proteomics and metabolomics. We proposed a multi-omics data-based workflow for medicinal plant research. It was emphasized that integration of the omics data was important for plant authentication and mechanistic studies on plant metabolism. Additionally, the computational tools for proper storage, efficient processing and high-throughput analyses of the omics data have been introduced into the workflow. According to the workflow, authentication of the medicinal plant materials should not only be performed at the phenomics level but also be implemented by genomic and metabolomic marker-based examination. On the other hand, functional genomics studies, transcriptional regulatory networks and protein-protein interactions will contribute greatly for deciphering the secondary metabolic pathways. Finally, we hope that our work could inspire further efforts on the bioinformatics-assisted, integrated omics studies on the medicinal plants.

Most of the microRNAs (miRNAs) play their regulatory roles through posttranscriptional target decay or translational inhibition. For both plants and animals, these regulatory events were previously considered to take place in cytoplasm, as mature miRNAs were observed to be exported to the cytoplasm for Argonaute (AGO) loading and subsequent target binding. Recently, this notion was challenged by increasing pieces of evidence in the animal cells that uncovered the nuclear importation and action of the AGO-associated miRNAs. The nuclear-localized regulatory mode was also reported for the plant miRNAs. However, evidence is still lacking to show the universality and conservation of the miRNA-mediated regulation in the plant nuclei. Here, we introduced a bioinformatics workflow for genome-wide investigation of miRNA-guided, cleavage-based regulation of the nascent nuclear transcripts. Facilitated by the tool package PmiRNTSA (Plant microRNA-mediated nascent transcript slicing analyzer), plant biologists could perform a comprehensive search for the miRNA slicing sites located within the introns or the exon-intron/intron-exon junctions of the target transcripts, which are supported by degradome sequencing data. The results enable the researchers to examine the co-transcriptional regulatory model of the miRNAs for a specific plant species. Moreover, a case study was performed to search for the slicing sites located within the exon-intron/intron-exon junctions in two model plants. A case study was performed to show the feasibility and reliability of our workflow. Together, we hope that this work could inspire much more innovative research efforts to expand the current understanding of the miRNA action modes in plants.

Most plant microRNAs (miRNAs) perform their repressive regulation through target cleavages. The resulting slicing sites on the target transcripts could be mapped by sequencing of the 3'-cleavage remnants, called degradome sequencing. The high sequence complementarity between miRNAs and their targets has greatly facilitated the development of the target prediction tools for plant miRNAs. The prediction results were then subjected to degradome sequencing data-based validation, through which numerous miRNA-target interactions have been extracted. However, some drawbacks are unavoidable when using this forward approach. Essentially, a known list of plant miRNAs should be obtained in advance of target prediction and validation. This becomes an obstacle to discover novel miRNAs and their targets. Here, after reviewing the current available algorithms for reverse identification of miRNA-target pairs in plants, a case study was performed by using a newly established framework with adjustable parameters. In this workflow, integration of degradome and ARGONAUTE 1-enriched small RNA sequencing data was recommended to do a relatively comprehensive and reliable search. Besides, several computational algorithms such as BLAST, target plots and RNA secondary structure prediction were used. The results demonstrated the prevalent utility of the reversed approach for uncovering miRNA-target interactions in plants.

Current achievements in plant microRNA (miRNA) research area are inspiring. Molecular cloning and functional elucidation have greatly advanced our understanding of this small RNA species. As one of the ultimate goals, many research efforts devoted to draw a comprehensive view of miRNA-mediated gene regulatory networks in plants. Numerous bioinformatics tools competent for network analysis have been available. However, the most important point for network construction is to obtain reliable analytical results based on sufficient experimental data. Here, we introduced a general workflow to retrieve and analyze the desired data sets that serve as the cornerstones for network construction. For the upstream analyses of miRNA genes, the sequence feature of miRNA promoters should be characterized. And, regulatory relationships between transcription factors (TFs) and miRNA genes need to be investigated. For the downstream part, we emphasized that the high-throughput degradome sequencing data were especially useful for genuine miRNA-target pair identification. Functional characterization of the miRNA targets is essential to provide deep biological insights into certain miRNA-mediated pathways. For miRNAs themselves, studies on their organ- or tissue-specific expression patterns and the mechanism of self-regulation were discussed. Besides, exhaustive literature mining is required to further support or improve the established networks. It is desired that the introduced framework for miRNA-mediated network construction is timely and useful and could inspire more research efforts in the miRNA research area.

BACKGROUND: Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa. RESULTS: To better understand the interactions of proteins in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization. CONCLUSIONS: PRIN is the first well annotated protein interaction database for the important model plant Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology. PRIN is available online at http://bis.zju.edu.cn/prin/.

High-throughput sequencing (HTS) has opened up a new era for small RNA (sRNA) exploration. Using HTS data for a global survey of sRNAs in 26 angiosperms, elevated GC contents were detected in the monocots, whereas the 5(')-terminal compositions were quite uniform among the angiosperms. Chromosome-wide distribution patterns of sRNAs were investigated by using scrolling-window analysis. We performed de novo natural antisense transcript (NAT) prediction, and found that the overlapping regions of trans-NATs, but not cis-NATs, were hotspots for sRNA generation. One cis-NAT generates phased natural antisense short interfering RNAs (nat-siRNAs) specifically from flowers in Arabidopsis, while one in rice produces phased nat-siRNAs from grains, suggesting their organ-specific regulatory roles.