Correction: Genome-Wide Investigation Using sRNA-Seq, Degradome-Seq and Transcriptome-Seq Reveals Regulatory Networks of microRNAs and Their Target Genes in Soybean during Soybean mosaic virus Infection.
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ABSTRACT:
SUBMITTER: Chen H
PROVIDER: S-EPMC5679616 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Correction: Genome-Wide Investigation Using sRNA-Seq, Degradome-Seq and Transcriptome-Seq Reveals Regulatory Networks of microRNAs and Their Target Genes in Soybean during Soybean mosaic virus Infection.
Chen Hui H Arsovski Andrej Adam AA Yu Kangfu K Wang Aiming A
PloS one 20171109 11
[This corrects the article DOI: 10.1371/journal.pone.0150582.]. ...[more]
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Project description:MicroRNAs (miRNAs) play key roles in a variety of cellular processes through regulation of their target gene expression. Accumulated experimental evidence has demonstrated that infections by viruses are associated with the altered expression profile of miRNAs and their mRNA targets in the host. However, the regulatory network of miRNA-mRNA interactions during viral infection remains largely unknown. In this study, we performed small RNA (sRNA)-seq, degradome-seq and as well as a genome-wide transcriptome analysis to profile the global gene and miRNA expression in soybean following infections by three different Soybean mosaic virus (SMV) isolates, L (G2 strain), LRB (G2 strain) and G7 (G7 strain). sRNA-seq analyses revealed a total of 253 soybean miRNAs with a two-fold or greater change in abundance compared with the mock-inoculated control. 125 transcripts were identified as the potential cleavage targets of 105 miRNAs and validated by degradome-seq analyses. Genome-wide transcriptome analysis showed that total 2679 genes are differentially expressed in response to SMV infection including 71 genes predicted as involved in defense response. Finally, complex miRNA-mRNA regulatory networks were derived using the RNAseq, small RNAseq and degradome data. This work represents a comprehensive, global approach to examining virus-host interactions. Genes responsive to SMV infection are identified as are their potential miRNA regulators. Additionally, regulatory changes of the miRNAs themselves are described and the regulatory relationships were supported with degradome data. Taken together these data provide new insights into molecular SMV-soybean interactions and offer candidate miRNAs and their targets for further elucidation of the SMV infection process.
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Project description:We profiled the global gene and miRNA expression in soybean following infections by three different Soybean mosaic virus (SMV) isolates, L (G2 strain), LRB (G2 strain) and G7 (G7 strain) by small RNA (sRNA)-seq, degradome-seq and as well as a genome-wide transcriptome analysis.
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Project description:BackgroundDrought conditions adversely affect soybean growth, resulting in severe yield losses worldwide. Increasing experimental evidence indicates miRNAs are important post-transcriptional regulators of gene expression. However, the drought-responsive molecular mechanism underlying miRNA-mRNA interactions remains largely uncharacterized in soybean. Meanwhile, the miRNA-regulated drought response pathways based on multi-omics approaches remain elusive.ResultsWe combined sRNA, transcriptome and degradome sequencing to elucidate the complex regulatory mechanism mediating soybean drought resistance. One-thousand transcripts from 384 target genes of 365 miRNAs, which were enriched in the peroxisome, were validated by degradome-seq. An integrated analysis showed 42 miRNA-target pairs exhibited inversely related expression profiles. Among these pairs, a strong induction of gma-miR398c as a major gene negatively regulates multiple peroxisome-related genes (GmCSD1a/b, GmCSD2a/b/c and GmCCS). Meanwhile, we detected that alternative splicing of GmCSD1a/b might affect soybean drought tolerance by bypassing gma-miR398c regulation. Overexpressing gma-miR398c in Arabidopsis thaliana L. resulted in decreased percentage germination, increased leaf water loss, and reduced survival under water deficiency, which displayed sensitivity to drought during seed germination and seedling growth. Furthermore, overexpressing gma-miR398c in soybean decreased GmCSD1a/b, GmCSD2a/b/c and GmCCS expression, which weakened the ability to scavenge O2.-, resulting in increased relative electrolyte leakage and stomatal opening compared with knockout miR398c and wild-type soybean under drought conditions.ConclusionThe study indicates that gma-miR398c negatively regulates soybean drought tolerance, and provides novel insights useful for breeding programs to improve drought resistance by CRISPR technology.
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Project description:BACKGROUND:High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. RESULTS:We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. CONCLUSIONS:We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.
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