RNA-sequencing reveals previously unannotated protein- and microRNA-coding genes expressed in aleurone cells of rice seeds

A vacuolar transporter plays important roles in zinc and cadmium accumulation in rice grain

Rice grain is a poor dietary source of zinc (Zn) but the primary source of cadmium (Cd) for humans; however, the molecular mechanisms for their accumulation in rice grain remain incompletely understood. This study functionally characterized a tonoplast-localized transporter, OsMTP1. OsMTP1 was preferentially expressed in the roots, aleurone layer, and embryo of seeds. OsMTP1 knockout decreased Zn concentration in the root cell sap, roots, aleurone layer and embryo, and subsequently increased Zn concentration in shoots and polished rice (endosperm) without yield penalty. OsMTP1 haplotype analysis revealed elite alleles associated with increased Zn level in polished rice, mostly because of the decreased OsMTP1 transcripts. OsMTP1 expression in yeast enhanced Zn tolerance but did not affect that of Cd. While OsMTP1 knockout resulted in decreased uptake, translocation and accumulation of Cd in plant and rice grain, which could be attributed to the indirect effects of altered Zn accumulation. Our results suggest that rice OsMTP1 primarily functions as a tonoplast-localized transporter for sequestrating Zn into vacuole. OsMTP1 knockout elevated Zn concentration but prevented Cd deposition in polished rice without yield penalty. Thus, OsMTP1 is a candidate gene for enhancing Zn level and reducing Cd level in rice grains.

The unique epigenetic features of Pack-MULEs and their impact on chromosomal base composition and expression spectrum

Acquisition and rearrangement of host genes by transposable elements (TEs) is an important mechanism to increase gene diversity as exemplified by the ∼3000 Pack-Mutator-like TEs in the rice genome which have acquired gene sequences (Pack-MULEs), yet remain enigmatic. To identify signatures of functioning Pack-MULEs and Pack-MULE evolution, we generated transcriptome, translatome, and epigenome datasets and compared Pack-MULEs to genes and other TE families. Approximately 40% of Pack-MULEs were transcribed with 9% having translation evidence, clearly distinguishing them from other TEs. Pack-MULEs exhibited a unique expression profile associated with specificity in reproductive tissues that may be associated with seed traits. Expressed Pack-MULEs resemble regular protein-coding genes as exhibited by a low level of DNA methylation, association with active histone marks and DNase I hypersensitive sites, and an absence of repressive histone marks, suggesting that a substantial fraction of Pack-MULEs are potentially functional in vivo. Interestingly, the expression capacity of Pack-MULEs is independent of the local genomic environment, and the insertion and expression of Pack-MULEs may have altered the local chromosomal expression pattern as well as counteracted the impact of recombination on chromosomal base composition, which has profound consequences on the evolution of chromosome structure.

RicetissueTFDB: A Genome-Wide Identification of Tissue-Specific Transcription Factors in Rice

Transcription factors (TFs) regulate plant gene expression in different tissues. To investigate TF genes in rice ( L.), a genome-wide TF identification was conducted with the japonica rice genome. This study identified 3078 putative TFs in 59 families. The TF number of the top 10 TF families accounted for 58% of the 3078 rice TFs. The three largest TF families were the myeloblastosis (MYB) superfamily, basic helix-loop-helix (bHLH), and far-red-impaired response (FAR1), which contained 413, 228, and 210 TF members, respectively. The expression profiles of the 3078 TF genes were surveyed with the RNA sequencing (RNA-seq) data of 13 rice tissue types. Based on these expression profiles, we validated 1087 TFs expressed in 13 rice tissue types, which accounted for 35.32% of the 3078 putative TFs. We further analyzed the tissue-specific TFs in rice. In total, 28, 14, 14, 10, 9, 5, 5, 4, 3, 3, 2, 11, and 1 tissue-specific TF sequences were identified in the dry seed, pistil, spikelet, aleurone, anther, ovules, embryo 25 d after pollination (DAP), seed 5 DAP, root, leaf, seed 10 DAP, shoot, and endosperm 25 DAP, respectively. Moreover, we constructed RicetissueTFDB (), a comprehensive and public rice TF database that integrates tissue expression characters, genomic location, and Gene Ontology (GO) terms for each TF. The RicetissueTFDB database will facilitate the identification of target TFs and the functional studies about rice TFs.

Transcriptome profiling of drought responsive noncoding RNAs and their target genes in rice

BACKGROUND

Plant transcriptome profiling has provided a tool for understanding the mechanisms by which plants respond to stress conditions. Analysis of genome-wide transcriptome will provides a useful dataset of drought responsive noncoding RNAs and their candidate target genes that may be involved in drought stress responses.

RESULTS

Here RNA-seq analyses of leaves from drought stressed rice plants was performed, producing differential expression profiles of noncoding RNAs. We found that the transcript levels of 66 miRNAs changed significantly in response to drought conditions and that they were negatively correlated with putative target genes during the treatments. The negative correlations were further validated by qRT-PCR using total RNAs from both drought-treated leaves and various tissues at different developmental stages. The drought responsive miRNA/target pairs were confirmed by the presence of decay intermediates generated by miRNA-guided cleavages in Parallel Analysis of RNA Ends (PARE) libraries. We observed that the precursor miR171f produced two different mature miRNAs, miR171f-5p and miR171f-3p with 4 candidate target genes, the former of which was responsive to drought conditions. We found that the expression levels of the miR171f precursor negatively correlated with those of one candidate target gene, but not with the others, suggesting that miR171f-5p was drought-responsive, with Os03g0828701-00 being a likely target. Pre-miRNA expression profiling indicated that miR171f is involved in the progression of rice root development and growth, as well as the response to drought stress. Ninety-eight lncRNAs were also identified, together with their corresponding antisense transcripts, some of which were responsive to drought conditions.

CONCLUSIONS

We identified rice noncoding RNAs (66 miRNAs and 98 lncRNAs), whose expression was highly regulated by drought stress conditions, and whose transcript levels negatively correlated with putative target genes.

Genome-Wide Dissection of the MicroRNA Expression Profile in Rice Embryo during Early Stages of Seed Germination

The first 24 hours after imbibition (HAI) is pivotal for rice seed germination, during which embryo cells switch from a quiescent state to a metabolically active state rapidly. MicroRNAs (miRNAs) have increasingly been shown to play important roles in rice development. Nevertheless, limited knowledge about miRNA regulation has been obtained in the early stages of rice seed germination. In this study, the small RNAs (sRNAs) from embryos of 0, 12, and 24 HAI rice seeds were sequenced to investigate the composition and expression patterns of miRNAs. The bioinformatics analysis identified 289 miRNA loci, including 59 known and 230 novel miRNAs, and 35 selected miRNAs were confirmed by stem-loop real-time RT-PCR. Expression analysis revealed that the dry and imbibed seeds have unique miRNA expression patterns compared with other tissues, particularly for the dry seeds. Using three methods, Mireap, psRNATarget and degradome analyses, 1197 potential target genes of identified miRNAs involved in various molecular functions were predicted. Among these target genes, 39 had significantly negative correlations with their corresponding miRNAs as inferred from published transcriptome data, and 6 inversely expressed miRNA-target pairs were confirmed by 5'-RACE assay. Our work provides an inventory of miRNA expression profiles and miRNA-target interactions in rice embryos, and lays a foundation for further studies of miRNA-mediated regulation in initial seed germination.

Tiling Assembly: a new tool for reference annotation-independent transcript assembly and novel gene identification by RNA-sequencing

Annotation of the rice (Oryza sativa) genome has evolved significantly since release of its draft sequence, but it is far from complete. Several published transcript assembly programmes were tested on RNA-sequencing (RNA-seq) data to determine their effectiveness in identifying novel genes to improve the rice genome annotation. Cufflinks, a popular assembly software, did not identify all transcripts suggested by the RNA-seq data. Other assembly software was CPU intensive, lacked documentation, or lacked software updates. To overcome these shortcomings, a heuristic ab initio transcript assembly algorithm, Tiling Assembly, was developed to identify genes based on short read and junction alignment. Tiling Assembly was compared with Cufflinks to evaluate its gene-finding capabilities. Additionally, a pipeline was developed to eliminate false-positive gene identification due to noise or repetitive regions in the genome. By combining Tiling Assembly and Cufflinks, 767 unannotated genes were identified in the rice genome, demonstrating that combining both programmes proved highly efficient for novel gene identification. We also demonstrated that Tiling Assembly can accurately determine transcription start sites by comparing the Tiling Assembly genes with their corresponding full-length cDNA. We applied our pipeline to additional organisms and identified numerous unannotated genes, demonstrating that Tiling Assembly is an organism-independent tool for genome annotation.

Arabidopsis uses two gluconeogenic gateways for organic acids to fuel seedling establishment

Gluconeogenesis is a fundamental metabolic process that allows organisms to make sugars from non-carbohydrate stores such as lipids and protein. In eukaryotes only one gluconeogenic route has been described from organic acid intermediates and this relies on the enzyme phosphoenolpyruvate carboxykinase (PCK). Here we show that two routes exist in Arabidopsis, and that the second uses pyruvate, orthophosphate dikinase (PPDK). Gluconeogenesis is critical to fuel the transition from seed to seedling. Arabidopsis pck1 and ppdk mutants are compromised in seed-storage reserve mobilization and seedling establishment. Radiolabelling studies show that PCK predominantly allows sugars to be made from dicarboxylic acids, which are products of lipid breakdown. However, PPDK also allows sugars to be made from pyruvate, which is a major product of protein breakdown. We propose that both routes have been evolutionarily conserved in plants because, while PCK expends less energy, PPDK is twice as efficient at recovering carbon from pyruvate.

During seed germination plants use gluconeogenesis to mobilize noncarbohydrate energy reserves. Here Eastmond et al. show that plants, unlike other eukaryotes, do not solely rely on a gluconeogenic pathway via the enzyme PCK but also use a second pathway relying on PPDK.

Integrating genomic, transcriptomic, and interactome data to improve Peptide and protein identification in shotgun proteomics

Mass spectrometry (MS)-based shotgun proteomics is an effective technology for global proteome profiling. The ultimate goal is to assign tandem MS spectra to peptides and subsequently infer proteins and their abundance. In addition to database searching and protein assembly algorithms, computational approaches have been developed to integrate genomic, transcriptomic, and interactome information to improve peptide and protein identification. Earlier efforts focus primarily on making databases more comprehensive using publicly available genomic and transcriptomic data. More recently, with the increasing affordability of the Next Generation Sequencing (NGS) technologies, personalized protein databases derived from sample-specific genomic and transcriptomic data have emerged as an attractive strategy. In addition, incorporating interactome data not only improves protein identification but also puts identified proteins into their functional context and thus facilitates data interpretation. In this paper, we survey the major integrative bioinformatics approaches that have been developed during the past decade and discuss their merits and demerits.