Exploring plant transcriptomes using ultra high-throughput sequencing

Physiological and transcriptomic analyses of leaves from Gardenia jasminoides Ellis under waterlogging stress

Abstract Gardenia jasminoides Ellis is a Chinese herbal medicine with medicinal and economic value, but its mechanism of response to waterlogging stress remains unclear. In this study, the “double pots method” was used to simulate the waterlogging stress of Gardenia jasminoides Ellis to explore its physiological and transcriptomic response mechanism. We found no significant damage to Gardenia jasminoides Ellis membrane lipid during stress. POD played a vital antioxidant role, KEGG enrichment showed that secondary metabolites such as flavonoids might also play an antioxidant role, and PRO played a significant osmotic adjustment. Endogenous hormones regulate the Gardenia jasminoides Ellis's growth and development and play a role in signal transduction. Among them, light waterlogging stress is delayed. At the same time, there were 19631, 23693, and 15045 differentially expressed genes on the 5th, 10d, and 15d of Gardenia jasminoides Ellis under waterlogging stress. These genes were closely associated with the proteasome, endopeptidase, ribosome, MAPK signal transduction, and endogenous hormone signal transduction, plant-pathogen interaction and phenylpropanoid biosynthesis and other physiological and metabolic pathways, which regulate the turnover and transportation of protein, the reinforcement and adhesion of cell walls, the induction of stomatal closure, allergic reactions, defense reactions, leaf movements and others. It also can absorb ultraviolet rays to reduce the generation of oxygen free radicals, change the way of energy utilization and adjust the osmotic pressure of plant cells.

Metabolomics and transcriptomics strategies to reveal the mechanism of diversity of maize kernel color and quality

BACKGROUND

Maize has many kernel colors, from white to dark black. However, research on the color and nutritional quality of the different varieties is limited. The color of the maize grain is an important characteristic. Colored maize is rich in nutrients, which have received attention for their role in diet-related chronic diseases and have different degrees of anti-stress protection for animal and human health.

METHODS

A comprehensive metabolome (LC-MS/MS) and transcriptome analysis was performed in this study to compare different colored maize varieties from the perspective of multiple recombination in order to study the nutritional value of maize with different colors and the molecular mechanism of color formation.

RESULTS

Maize kernels with diverse colors contain different types of health-promoting compounds, highlighting that different maize varieties can be used as functional foods according to human needs. Among them, red-purple and purple-black maize contain more flavonoids than white and yellow kernels. Purple-black kernels have a high content of amino acids and nucleotides, while red-purple kernels significantly accumulate sugar alcohols and lipids.

CONCLUSION

Our study can provide insights for improving people's diets and provide a theoretical basis for the study of food structure for chronic diseases.

Small RNA sequencing and identification of Andrographis paniculata miRNAs with potential cross‑kingdom human gene targets

Cross-species post-transcriptional regulatory potential of plant derived small non-coding microRNAs (miRNAs) has been well documented by plenteous studies. MicroRNAs are transferred to host cells via oral ingestion wherein they play a decisive role in regulation of host genes; thus, miRNAs have evolved as the nascent bioactive molecules imparting pharmacological values to traditionally used medicinal plants. The present study aims to investigate small RNA profiling in order to uncover the potential regulatory role of miRNAs derived from Andrographis paniculata, one of the most widely used herb by tribal communities for liver disorders and document the pharmacological properties of A. paniculata miRNAs. In this study, high-throughput sequencing method was used to generate raw data, ~ 60 million sequences were generated from A. paniculata leaves. Using computational tools and bioinformatics approach, analyses of 3,480,097 clean reads resulted in identification of 3440 known and 51 putative novel miRNAs regulating 1365 and 192 human genes respectively. Remarkably, the identified plausible novel miRNAs apa-miR-5, apa-miR-1, apa-miR-26, and apa-miR-30 are projected to target significant host genes including CDK6, IKBKB, TRAF3, CHD4, MECP2, and ADIPOQ. Subsequent annotations revealed probable involvement of the target genes in various pathways for instance p38-MAPK, AKT, AMPK, NF-Kβ, ERK, WNT signalling, MYD88 dependant cascade, and pathways in cancer. Various diseases such as human papilloma virus infection, Alzheimer's, Non-alcoholic Fatty Liver, Alcoholic liver diseases, HepatoCellular Carcinoma (HCC), and numerous other cancers were predominantly found to be linked with target genes. Our findings postulate novel interpretations regarding modulation of human transcripts by A. paniculata miRNAs and exhibit the regulation of human diseases by plant-derived miRNAs. Though our study elucidates miRNAs as novel therapeutic agents, however, experimental validations for assessment of therapeutic potential of these miRNAs are still warranted.

Combination of Genomics, Transcriptomics Identifies Candidate Loci Related to Cold Tolerance in Dongxiang Wild Rice

Rice, a cold-sensitive crop, is a staple food for more than 50% of the world’s population. Low temperature severely compromises the growth of rice and challenges China’s food safety. Dongxiang wild rice (DXWR) is the most northerly common wild rice in China and has strong cold tolerance, but the genetic basis of its cold tolerance is still unclear. Here, we report quantitative trait loci (QTLs) analysis for seedling cold tolerance (SCT) using a high-density single nucleotide polymorphism linkage map in the backcross recombinant inbred lines that were derived from a cross of DXWR, and an indica cultivar, GZX49. A total of 10 putative QTLs were identified for SCT under 4 °C cold treatment, each explaining 2.0–6.8% of the phenotypic variation in this population. Furthermore, transcriptome sequencing of DXWR seedlings before and after cold treatment was performed, and 898 and 3413 differentially expressed genes (DEGs) relative to 0 h in cold-tolerant for 4 h and 12 h were identified, respectively. Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these DEGs. Using transcriptome data and genetic linkage analysis, combined with qRT-PCR, sequence comparison, and bioinformatics, LOC_Os08g04840 was putatively identified as a candidate gene for the major effect locus qSCT8. These findings provided insights into the genetic basis of SCT for the improvement of cold stress potential in rice breeding programs.

Metabolic Profiling and Transcriptome Analysis Reveal the Key Role of Flavonoids in Internode Coloration of Phyllostachys violascens cv. Viridisulcata

Bamboo, being an ornamental plant, has myriad aesthetic and economic significance. Particularly, Phyllostachys violascens cv. Viridisulcata contains an internode color phenotype in variation in green and yellow color between the sulcus and culm, respectively. This color variation is unique, but the underlying regulatory mechanism is still unknown. In this study, we used metabolomic and transcriptomic strategies to reveal the underlying mechanism of variation in internode color. A total of 81 metabolites were identified, and among those, prunin as a flavanone and rhoifolin as a flavone were discovered at a high level in the culm. We also found 424 differentially expressed genes and investigated three genes (PvGL, PvUF7GT, and PvC12RT1) that might be involved in prunin or rhoifolin biosynthesis. Their validation by qRT-PCR confirmed high transcript levels in the culm. The results revealed that PvGL, PvUF7GT, and PvC12RT1 might promote the accumulation of prunin and rhoifolin which were responsible for the variation in internode color of P. violascens. Our study also provides a glimpse into phenotypic coloration and is also a valuable resource for future studies.

Integrative RNA-Seq analysis of Capsicum annuum L.-Phytophthora capsici L. pathosystem reveals molecular cross-talk and activation of host defence response

Chili pepper (Capsicum annuum L.) is economically one of the most important spice. But, it's productivity is highly affected by the pathogen, Phytophthora capsici L. Our current understanding of the molecular mechanisms associated with the defence response in C. annuum-P. capsici pathosystem is limited. The current study used RNA-seq technology to dissect the genes associated with defence response against P. capsici infection in two contrasting landraces, i.e. GojamMecha_9086 (Resistant) and Dabat_80045 (Susceptible) exposed to P. capsici infection. The transcriptomes from four leaf samples (RC, RI, SC and SI) of chili pepper resulted in a total of 118,879 assembled transcripts along with 52,384 pooled unigenes. The enrichment analysis of the transcripts indicated 23 different KEGG pathways under five main categories. Out of 774 and 484 differentially expressed genes (DEGs) of two landraces (under study), respectively, 57 and 29 DEGs were observed as associated with defence responses against P. capsici infection in RC vs. RI and SC vs. SI leaf samples, respectively. qRT-PCR analysis of six randomly selected genes validated the results of Illumina NextSeq500 sequencing. A total of 58 transcription factor families (bHLH most abundant) and 2095 protein families (Protein kinase most abundant) were observed across all the samples with maximum hits in RI and SI samples. Expression analysis revealed differential regulation of genes associated with defence and signalling response with shared coordination of molecular function, cellular component and biological processing. The results presented here would enhance our present understanding of the defence response in chili pepper against P. capsici infection, which the molecular breeders could utilize to develop resistant chili genotypes.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-01122-y.

Transcriptional regulation and stress-defensive key genes induced by γ-aminobutyric acid in association with tolerance to water stress in creeping bentgrass

γ-Aminobutyric acid (GABA) acts as an important regulator involved in the mediation of cell signal transduction and stress tolerance in plants. However, the function of GABA in transcriptional regulation is not fully understood in plants under water stress. The creeping bentgrass (Agrostis stolonifera) was pretreated with or without GABA (0.5 mM) for 24 hours before being exposed to 5 days of water stress. Physiological analysis showed that GABA-treated plants maintained significantly higher endogenous GABA content, leaf relative water content, net photosynthetic rate, and lower osmotic potential than untreated plants under water stress. The GABA application also significantly alleviated stress-induced increases in superoxide anion (O2.-) content, hydrogen peroxide (H2O2) content, and electrolyte leakage through enhancing total antioxidant capacity, superoxide dismutase (SOD) activity, and peroxidase (POD) activity in response to water stress. The transcriptomic analysis demonstrated that the GABA-induced changes in differentially expressed genes (DEGs) involved in carbohydrates, amino acids, and secondary metabolism helped to maintain better osmotic adjustment, energy supply, and metabolic homeostasis when creeping bentgrass suffers from water stress. The GABA triggered Ca2+-dependent protein kinase (CDPK) signaling and improved transcript levels of DREB1/2 and WRKY1/24/41 that could be associated with the upregulation of stress-related functional genes such as POD, DHNs, and HSP70 largely contributing to improved tolerance to water stress in relation to the antioxidant, prevention of cell dehydration, and protein protection in leaves.

Genome-Wide Characterization of HSP90 Gene Family in Cucumber and Their Potential Roles in Response to Abiotic and Biotic Stresses

Heat shock protein 90 (HSP90) possesses critical functions in plant developmental control and defense reactions. The HSP90 gene family has been studied in various plant species. However, the HSP90 gene family in cucumber has not been characterized in detail. In this study, a total of six HSP90 genes were identified from the cucumber genome, which were distributed to five chromosomes. Phylogenetic analysis divided the cucumber HSP90 genes into two groups. The structural characteristics of cucumber HSP90 members in the same group were similar but varied among different groups. Synteny analysis showed that only one cucumber HSP90 gene, Csa1G569290, was conservative, which was not collinear with any HSP90 gene in Arabidopsis and rice. The other five cucumber HSP90 genes were collinear with five Arabidopsis HSP90 genes and six rice HSP90 genes. Only one pair of paralogous genes in the cucumber HSP90 gene family, namely one pair of tandem duplication genes (Csa1G569270/Csa1G569290), was detected. The promoter analysis showed that the promoters of cucumber HSP90 genes contained hormone, stress, and development-related cis-elements. Tissue-specific expression analysis revealed that only one cucumber HSP90 gene Csa3G183950 was highly expressed in tendril but low or not expressed in other tissues, while the other five HSP90 genes were expressed in all tissues. Furthermore, the expression levels of cucumber HSP90 genes were differentially induced by temperature and photoperiod, gibberellin (GA), downy mildew, and powdery mildew stimuli. Two cucumber HSP90 genes, Csa1G569270 and Csa1G569290, were both differentially expressed in response to abiotic and biotic stresses, which means that these two HSP90 genes play important roles in the process of cucumber growth and development. These findings improve our understanding of cucumber HSP90 family genes and provide preliminary information for further studies of cucumber HSP90 gene functions in plant growth and development.

High-throughput sequencing techniques to flax genetics and breeding

Flax (Linum usitatissimum L.) is an important oil and fiber crop. Using modern methods for flax breeding allows accelerating the introduction of some desired genes into the genotypes of future varieties. Today, an important condition for their creation is the development of research, that is based on next-generation sequencing (NGS). This review summarizes the results obtained using NGS in flax research. To date, a linkage map with a high marker density has been obtained for L. usitatissimum, which is already being used for a more efficient search for quantitative traits loci. Comparative studies of transcriptomes and miRNomes of flax under stress and in control conditions elucidated molecular-genetic mechanisms of abiotic and biotic stress responses. The very accurate model for genomic selection of flax resistant to pasmo was constructed. Based on NGS-sequencing also some details of the genus Linum evolution were clarified. The knowledge systematized in the review can be useful for researchers working in flax breeding and whereas fundamental interest for understanding the phylogenetic relationships within the genus Linum, the ontogenesis, and the mechanisms of the response of flax plants to various stress factors.

Gibberellin causes wide transcriptional modifications in the early stage of grape cluster development

Yaghooti grape of Sistan is seedless, early ripening but has compact clusters. To study gibberellin effect on cluster compactness of Yaghooti grape, it has been studied transcriptomic changes in three developmental stages (cluster formation, berry formation and final size of cluster). We found out that 5409 of 22,756 genes in cluster tissue showed significant changes under gibberellin. Finally, it was showed that 2855, 2862 and 497 genes have critically important role on above developmental stages, respectively. GO enrichment analysis showed that gibberellin enhances biochemical pathways activity. Moreover, genes involved in ribosomal structure and photosynthesis rate in cluster tissue were up- and down- regulated, respectively. In addition, we observed location of metabolomic activities was transferred from nucleus to cytoplasm and from cytoplasm to cell wall and intercellular spaces during cluster development; but there is not such situation in gibberellin treated samples.

Selection of reference genes for the quantitative real-time PCR normalization of gene expression in Isatis indigotica fortune

Background

Isatis indigotica, a traditional Chinese medicine, produces a variety of active ingredients. However, little is known about the key genes and corresponding expression profiling involved in the biosynthesis pathways of these ingredients. Quantitative real-time polymerase chain reaction (qRT-PCR) is a powerful, commonly-used method for gene expression analysis, but the accuracy of the quantitative data produced depends on the appropriate selection of reference genes.

Results

In this study, the systematic analysis of the reference genes was performed for quantitative real-Time PCR normalization in I. indigotica. We selected nine candidate reference genes, including six traditional housekeeping genes (ACT, α-TUB, β-TUB, UBC, CYP, and EF1-α), and three newly stable internal control genes (MUB, TIP41, and RPL) from a transcriptome dataset of I. indigotica, and evaluated their expression stabilities in different tissues (root, stem, leaf, and petiole) and leaves exposed to three abiotic treatments (low-nitrogen, ABA, and MeJA) using geNorm, NormFinder, BestKeeper, and comprehensive RefFind algorithms. The results demonstrated that MUB and EF1-α were the two most stable reference genes for all samples. TIP41 as the optimal reference gene for low-nitrogen stress and MeJA treatment, while ACT had the highest ranking for ABA treatment and CYP was the most suitable for different tissues.

Conclusions

The results revealed that the selection and validation of appropriate reference genes for normalizing data is mandatory to acquire accurate quantification results. The necessity of specific internal control for specific conditions was also emphasized. Furthermore, this work will provide valuable information to enhance further research in gene function and molecular biology on I. indigotica and other related species.

Electronic supplementary material

The online version of this article (10.1186/s12867-019-0126-y) contains supplementary material, which is available to authorized users.

Transcriptome-based identification and validation of reference genes for plant-bacteria interaction studies using Nicotiana benthamiana

RT-qPCR is a widely used technique for the analysis of gene expression. Accurate estimation of transcript abundance relies strongly on a normalization that requires the use of reference genes that are stably expressed in the conditions analyzed. Initially, they were adopted from those used in Northern blot experiments, but an increasing number of publications highlight the need to find and validate alternative reference genes for the particular system under study. The development of high-throughput sequencing techniques has facilitated the identification of such stably expressed genes. Nicotiana benthamiana has been extensively used as a model in the plant research field. In spite of this, there is scarce information regarding suitable RT-qPCR reference genes for this species. Employing RNA-seq data previously generated from tomato plants, combined with newly generated data from N. benthamiana leaves infiltrated with Pseudomonas fluorescens, we identified and tested a set of 9 candidate reference genes. Using three different algorithms, we found that NbUbe35, NbNQO and NbErpA exhibit less variable gene expression in our pathosystem than previously used genes. Furthermore, the combined use of the first two is sufficient for robust gene expression analysis. We encourage employing these novel reference genes in future RT-qPCR experiments involving N. benthamiana and Pseudomonas spp.

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data

RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at http://tanglab.hebtu.edu.cn/tanglab/Home/DREAMSeq.

Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and non-uniform distributions for p-values under these null hypotheses. Thus, we develop a general hierarchical model for count data and a fully Bayesian analysis in which an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant-breeding scenario, both in a real RNA-seq maize dataset and in simulation studies. In the simulation studies, we show our method has well-calibrated posterior probabilities and credible intervals when the model assumed in analysis matches the model used to simulate the data. Although model misspecification can adversely affect calibration, the methodology is still able to accurately rank genes. Finally, we show that hyperparameter posteriors are extremely narrow and an empirical Bayes (eBayes) approach based on posterior means from the fully Bayesian analysis provides virtually equivalent posterior probabilities, credible intervals, and gene rankings relative to the fully Bayesian solution. This evidence of equivalence provides support for the use of eBayes procedures in RNA-seq data analysis if accurate hyperparameter estimates can be obtained.

'Omic' technologies as a helpful tool in radioecological research

This article presents a brief review of the modern 'omic' technologies, namely genomics, epigenomics, transcriptomics, proteomics, and metabolomics, as well as the examples of their possible use in radioecology. For each technology, a short description of advances, limitations, and instrumental applications is given. In addition, the review contains examples of successful use of 'omic' technologies in the assessment of biological effects of pollutants in the field conditions.

Genome-wide transcriptome profiling provides insights into panicle development of rice (Oryza sativa L.)

Panicle architecture is an important component of agronomic trait in rice, which is also a key ingredient that could influence yield and quality of rice. In the panicle growth and development process, there are a series of complicated molecular and cellular events which are regulated by many interlinking genes. In this study, to explore the potential mechanism and identify genes and pathways involved in the formation of rice panicle, we compared the transcriptional profile of rice panicles (NIL-GW8 and NIL-gw8^Amol) at three different stages of panicle development: In5 (formation of higher-order branches), In6 (differentiation of glumes) and In7 (differentiation of floral organs). A range of 40.5 to 54.1 million clean reads was aligned to 31,209 genes in our RNA-Seq analysis. In addition, we investigated transcriptomic changes between the two rice lines during different stages. A total of 726, 1121 and 2584 differentially expressed genes (DEGs) were identified at stages 1, 2 and 3, respectively. Based on an impact analysis of the DEGs, we hypothesize that MADS-box gene family, cytochrome P450 (CYP) and pentatricopeptide repeat (PPR) protein and various transcription factors may be involved in regulation of panicle development. Further, we also explored the functional properties of DEGs by gene ontology analysis, and the results showed that different numbers of DEGs genes were associated with 53 GO groups. In KEGG pathway enrichment analysis, many DEGs related to biosynthesis of secondary metabolites and plant hormone signal transduction, suggesting their important roles during panicle development. This study provides the first examination of changes in gene expression between different panicle development stages in rice. Our results of transcriptomic characterization provide important information to elucidate the complex molecular and cellular events about the panicle formation in rice or other cereal crops. Also, the findings will be helpful for the further identification of the genes related to panicle development.

Rice tolerance to suboptimal low temperatures relies on the maintenance of the photosynthetic capacity

The purpose of this research was to identify differences between two contrasting rice cultivars in their response to suboptimal low temperatures stress. A transcriptomic analysis of the seedlings was performed and results were complemented with biochemical and physiological analyses. The microarray analysis showed downregulation of many genes related with PSII and particularly with the oxygen evolving complex in the sensitive cultivar IR50. Complementary studies indicated that the PSII performance, the degree of oxygen evolving complex coupling with the PSII core and net photosynthetic rate diminished in this cultivar in response to the stress. However, the tolerant cultivar Koshihikari was able to maintain its energy equilibrium by sustaining the photosynthetic capacity. The increase of oleic acid in Koshihikari could be related with membrane remodelling of the chloroplasts and hence contribute to tolerance. Overall, these results work as a ground for future analyses that look forward to characterize possible mechanisms to tolerate this stress.

Use of RNA-seq data to identify and validate RT-qPCR reference genes for studying the tomato-Pseudomonas pathosystem

The agronomical relevant tomato-Pseudomonas syringae pv. tomato pathosystem is widely used to explore and understand the underlying mechanisms of the plant immune response. Transcript abundance estimation, mainly through reverse transcription-quantitative PCR (RT-qPCR), is a common approach employed to investigate the possible role of a candidate gene in certain biological process under study. The accuracy of this technique relies heavily on the selection of adequate reference genes. Initially, genes derived from other techniques (such as Northern blots) were used as reference genes in RT-qPCR experiments, but recent studies in different systems suggest that many of these genes are not stably expressed. The development of high throughput transcriptomic techniques, such as RNA-seq, provides an opportunity for the identification of transcriptionally stable genes that can be adopted as novel and robust reference genes. Here we take advantage of a large set of RNA-seq data originating from tomato leaves infiltrated with different immunity inducers and bacterial strains. We assessed and validated 9 genes that are much more stable than two traditional reference genes. Specifically, ARD2 and VIN3 were the most stably expressed genes and consequently we propose they be adopted for RT-qPCR experiments involving this pathosystem.

Analysis of de novo sequencing and transcriptome assembly and lignocellulolytic enzymes gene expression of Coriolopsis gallica HTC

White-rot basidiomycete Coriolopsis gallica HTC is one of the main biodegraders of poplar. In our previous study, we have shown the strong capacity of C. gallica HTC to degrade lignocellulose. In this study, equal amounts of total RNA fromC. Gallica HTC cultures grown in different conditions were pooled together. Illumina paired-end RNA sequencing was performed, and 13.2 million 90-bp paired-end reads were generated. We chose the Merged Assembly of Oases data-set for the following blast searches and gene ontology analyses. The reads were assembled de novo into 28,034 transcripts (≥ 100 bp) using combined assembly strategy MAO. The transcripts were annotated using Blast2GO. In all, 18,810 transcripts (≥100 bp) achieved BLASTX hits, of which, 7048 transcripts had GO term and 2074 had ECs. The expression level of 11 lignocellulolytic enzyme genes from the assembled C. gallica HTC transcriptome were detected by real-time quantitative polymerase chain reaction. The results showed that expression levels of these genes were affected by carbon source and nitrogen source at the level of transcription. The current abundant transcriptome data allowed the identification of many new transcripts in C. gallica HTC. Data provided here represent the most comprehensive and integrated genomic resources for cloning and identifying genes of interest from C. gallica HTC. Characterization of C. gallica HTC transcriptome provides an effective tool to understand mechanisms underlying cellular and molecular functions of C. gallica HTC.

What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment

Identifying differentially expressed (DE) genes between different conditions is one of the main goals of RNA-seq data analysis. Although a large amount of RNA-seq data were produced for two-group comparison with small sample sizes at early stage, more and more RNA-seq data are being produced in the setting of complex experimental designs such as split-plot designs and repeated measure designs. Data arising from such experiments are traditionally analyzed by mixed-effects models. Therefore an appropriate statistical approach for analyzing RNA-seq data from such designs should be generalized linear mixed models (GLMM) or similar approaches that allow for random effects. However, common practices for analyzing such data in literature either treat random effects as fixed or completely ignore the experimental design and focus on two-group comparison using partial data. In this paper, we examine the effect of ignoring the random effects when analyzing RNA-seq data. We accomplish this goal by comparing the standard GLMM model to the methods that ignore the random effects through simulation studies and real data analysis. Our studies show that, ignoring random effects in a multi-factor experiment can lead to the increase of the false positives among the top selected genes or lower power when the nominal FDR level is controlled.

Comparative transcriptomics of rice plants under cold, iron, and salt stresses

Abiotic stresses such as salinity, iron toxicity, and low temperatures are the main limiting factors of rice (Oryza sativa L.) yield. The elucidation of the genes involved in responses to these stresses is extremely important to understand the mechanisms that confer tolerance, as well as for the development of cultivars adapted to these conditions. In this study, the RNA-seq technique was used to compare the transcriptional profile of rice leaves (cv. BRS Querência) in stage V3, exposed to cold, iron, and salt stresses for 24 h. A range of 41 to 51 million reads was aligned, in which a total range of 88.47 to 89.21 % was mapped in the reference genome. For cold stress, 7905 differentially expressed genes (DEGs) were observed, 2092 for salt and 681 for iron stress; 370 of these were common to the three DEG stresses. Functional annotation by software MapMan demonstrated that cold stress usually promoted the greatest changes in the overall metabolism, and an enrichment analysis of overrepresented gene ontology (GO) terms showed that most of them are contained in plastids, ribosome, and chloroplasts. Saline stress induced a more complex interaction network of upregulated overrepresented GO terms with a relatively low number of genes compared with cold stress. Our study demonstrated a high number of differentially expressed genes under cold stress and a greater relationship between salt and iron stress levels. The physiological process most affected at the molecular level by the three stresses seems to be photosynthesis.

GpDSR7, a Novel E3 Ubiquitin Ligase Gene in Grimmia pilifera Is Involved in Tolerance to Drought Stress in Arabidopsis

The growth and development of plants under drought stress depends mainly on the expression levels of various genes and modification of proteins. To clarify the molecular mechanism of drought-tolerance of plants, suppression subtractive hybridisation cDNA libraries were screened to identify drought-stress-responsive unigenes in Grimmia pilifera, and a novel E3 ubiquitin ligase gene, GpDSR7, was identified among the 240 responsive unigenes. GpDSR7 expression was induced by various abiotic stresses, particularly by drought. GpDSR7 displayed E3 ubiquitin ligase activity in vitro and was exclusively localised on the ER membrane in Arabidopsis mesophyll protoplasts. GpDSR7-overexpressing transgenic Arabidopsis plants showed a high water content and survival ratio under drought stress. Moreover, the expression levels of some marker genes involved in drought stress were higher in the transgenic plants than in wild-type plants. These results suggest that GpDSR7, an E3 ubiquitin ligase, is involved in tolerance to drought stress at the protein modification level.

Genome-Wide Transcriptome Profiling of Mycobacterium smegmatis MC² 155 Cultivated in Minimal Media Supplemented with Cholesterol, Androstenedione or Glycerol

Mycobacterium smegmatis strain MC² 155 is an attractive model organism for the study of M. tuberculosis and other mycobacterial pathogens, as it can grow well using cholesterol as a carbon resource. However, its global transcriptomic response remains largely unrevealed. In this study, M. smegmatis MC² 155 cultivated in androstenedione, cholesterol and glycerol supplemented media were collected separately for a RNA-Sequencing study. The results showed that 6004, 6681 and 6348 genes were expressed in androstenedione, cholesterol and glycerol supplemented media, and 5891 genes were expressed in all three conditions, with 237 specially expressed in cholesterol added medium. A total of 1852 and 454 genes were significantly up-regulated by cholesterol compared with the other two supplements. Only occasional changes were observed in basic carbon and nitrogen metabolism, while almost all of the genes involved in cholesterol catabolism and mammalian cell entry (MCE) were up-regulated by cholesterol, but not by androstenedione. Eleven and 16 gene clusters were induced by cholesterol when compared with glycerol or androstenedione, respectively. This study provides a comprehensive analysis of the cholesterol responsive transcriptome of M. smegmatis. Our results indicated that cholesterol induced many more genes and increased the expression of the majority of genes involved in cholesterol degradation and MCE in M. smegmatis, while androstenedione did not have the same effect.

Analysis of the tomato leaf transcriptome during successive hemibiotrophic stages of a compatible interaction with the oomycete pathogen Phytophthora infestans

The infection of plants by hemibiotrophic pathogens involves a complex and highly regulated transition from an initial biotrophic, asymptomatic stage to a later necrotrophic state, characterized by cell death. Little is known about how this transition is regulated, and there are conflicting views regarding the significance of the plant hormones jasmonic acid (JA) and salicylic acid (SA) in the different phases of infection. To provide a broad view of the hemibiotrophic infection process from the plant perspective, we surveyed the transcriptome of tomato (Solanum lycopersicum) during a compatible interaction with the hemibiotrophic oomycete Phytophthora infestans during three infection stages: biotrophic, the transition from biotrophy to necrotrophy, and the necrotrophic phase. Nearly 10 000 genes corresponding to proteins in approximately 400 biochemical pathways showed differential transcript abundance during the three infection stages, revealing a major reorganization of plant metabolism, including major changes in source-sink relations, as well as secondary metabolites. In addition, more than 100 putative resistance genes and pattern recognition receptor genes were induced, and both JA and SA levels and associated signalling pathways showed dynamic changes during the infection time course. The biotrophic phase was characterized by the induction of many defence systems, which were either insufficient, evaded or suppressed by the pathogen.

Role of bioinformatics in establishing microRNAs as modulators of abiotic stress responses: the new revolution

microRNAs (miRs) are a class of 21-24 nucleotide long non-coding RNAs responsible for regulating the expression of associated genes mainly by cleavage or translational inhibition of the target transcripts. With this characteristic of silencing, miRs act as an important component in regulation of plant responses in various stress conditions. In recent years, with drastic change in environmental and soil conditions different type of stresses have emerged as a major challenge for plants growth and productivity. The identification and profiling of miRs has itself been a challenge for research workers given their small size and large number of many probable sequences in the genome. Application of computational approaches has expedited the process of identification of miRs and their expression profiling in different conditions. The development of High-Throughput Sequencing (HTS) techniques has facilitated to gain access to the global profiles of the miRs for understanding their mode of action in plants. Introduction of various bioinformatics databases and tools have revolutionized the study of miRs and other small RNAs. This review focuses the role of bioinformatics approaches in the identification and study of the regulatory roles of plant miRs in the adaptive response to stresses.

A bi-Poisson model for clustering gene expression profiles by RNA-seq

With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation–maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene–environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.

From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner--a multi-level bioinformatics protocol and pipeline--has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.

Transcriptome analysis of methyl jasmonate-elicited Panax ginseng adventitious roots to discover putative ginsenoside biosynthesis and transport genes

The Panax ginseng C.A. Meyer belonging to the Araliaceae has long been used as an herbal medicine. Although public databases are presently available for this family, no methyl jasmonate (MeJA) elicited transcriptomic information was previously reported on this species, with the exception of a few expressed sequence tags (ESTs) using the traditional Sanger method. Here, approximately 53 million clean reads of adventitious root transcriptome were separately filtered via Illumina HiSeq™2000 from two samples treated with MeJA (Pg-MeJA) and equal volumes of solvent, ethanol (Pg-Con). Jointly, a total of 71,095 all-unigenes from both samples were assembled and annotated, and based on sequence similarity search with known proteins, a total of 56,668 unigenes was obtained. Out of these annotated unigenes, 54,920 were assigned to the NCBI non-redundant protein (Nr) database, 35,448 to the Swiss-prot database, 43,051 to gene ontology (GO), and 19,986 to clusters of orthologous groups (COG). Searching in the Kyoto encyclopedia of genes and genomes (KEGG) pathway database indicated that 32,200 unigenes were mapped to 128 KEGG pathways. Moreover, we obtained several genes showing a wide range of expression levels. We also identified a total of 749 ginsenoside biosynthetic enzyme genes and 12 promising pleiotropic drug resistance (PDR) genes related to ginsenoside transport.

Comparative transcriptomic analysis of the response to cold acclimation in Eucalyptus dunnii

Eucalyptus dunnii is an important macrophanerophyte with high economic value. However, low temperature stress limits its productivity and distribution. To study the cold response mechanisms of E. dunnii, 5 cDNA libraries were constructed from mRNA extracted from leaves exposed to cold stress for varying lengths of time and were evaluated by RNA-Seq analysis. The assembly of the Illumina datasets was optimized using various assembly programs and parameters. The final optimized assembly generated 205,325 transcripts with an average length of 1,701 bp and N50 of 2,627 bp, representing 349.38 Mb of the E. dunnii transcriptome. Among these transcripts, 134,358 transcripts (65.4%) were annotated in the Nr database. According to the differential analysis results, most transcripts were up-regulated as the cold stress prolonging, suggesting that these transcripts may be involved in the response to cold stress. In addition, the cold-relevant GO categories, such as ‘response to stress’ and ‘translational initiation’, were the markedly enriched GO terms. The assembly of the E. dunnii gene index and the GO classification performed in this study will serve as useful genomic resources for the genetic improvement of E. dunnii and also provide insights into the molecular mechanisms of cold acclimation in E. dunnii.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

De novo assembly of red clover transcriptome based on RNA-Seq data provides insight into drought response, gene discovery and marker identification

BACKGROUND

Red clover (Trifolium pratense L.) is a versatile forage crop legume, which can tolerate a variety of soils and is suitable for silage production for winter feed and for grazing. It is one of the most important forage legumes in temperate livestock agriculture. Its beneficial attributes include ability to fix nitrogen, improve soil and provide protein rich animal feed. It is however, a short-lived perennial providing good biomass yield for two or three years. Improved persistency is thus a major breeding target. Better water-stress tolerance is one of the key factors influencing persistency, but little is known about how red clover tolerates water stress.

RESULTS

Plants from a full sib mapping family were used in a drought experiment, in which the growth rate and relative water content (RWC) identified two pools of ten plants contrasting in their tolerance to drought. Key metabolites were measured and RNA-Seq analysis was carried out on four bulked samples: the two pools sampled before and after drought. Massively parallel sequencing was used to analyse the bulked RNA samples. A de novo transcriptome reconstruction based on the RNA-Seq data was made, resulting in 45181 contigs, representing 'transcript tags'. These transcript tags were annotated with gene ontology (GO) terms. One of the most striking results from the expression analysis was that the drought sensitive plants were characterised by having approximately twice the number of differentially expressed transcript tags than the tolerant plants after drought. This difference was evident in most of the major GO terms. Before onset of drought the sensitive plants overexpressed a number of genes annotated as senescence-related. Furthermore, the concentration of three metabolites, particularly pinitol, but also proline and malate increased in leaves after drought stress.

CONCLUSIONS

This de novo assembly of a red clover transcriptome from leaf material of droughted and non-droughted plants provides a rich source for gene identification, single nucleotide polymorphisms (SNP) and short sequence repeats (SSR). Comparison of gene expression levels between pools and treatments identified candidate genes for further analysis of the genetic basis of drought tolerance in red clover.

Legume genomics: understanding biology through DNA and RNA sequencing

BACKGROUND

The legume family (Leguminosae) consists of approx. 17 000 species. A few of these species, including, but not limited to, Phaseolus vulgaris, Cicer arietinum and Cajanus cajan, are important dietary components, providing protein for approx. 300 million people worldwide. Additional species, including soybean (Glycine max) and alfalfa (Medicago sativa), are important crops utilized mainly in animal feed. In addition, legumes are important contributors to biological nitrogen, forming symbiotic relationships with rhizobia to fix atmospheric N2 and providing up to 30 % of available nitrogen for the next season of crops. The application of high-throughput genomic technologies including genome sequencing projects, genome re-sequencing (DNA-seq) and transcriptome sequencing (RNA-seq) by the legume research community has provided major insights into genome evolution, genomic architecture and domestication.

SCOPE AND CONCLUSIONS

This review presents an overview of the current state of legume genomics and explores the role that next-generation sequencing technologies play in advancing legume genomics. The adoption of next-generation sequencing and implementation of associated bioinformatic tools has allowed researchers to turn each species of interest into their own model organism. To illustrate the power of next-generation sequencing, an in-depth overview of the transcriptomes of both soybean and white lupin (Lupinus albus) is provided. The soybean transcriptome focuses on analysing seed development in two near-isogenic lines, examining the role of transporters, oil biosynthesis and nitrogen utilization. The white lupin transcriptome analysis examines how phosphate deficiency alters gene expression patterns, inducing the formation of cluster roots. Such studies illustrate the power of next-generation sequencing and bioinformatic analyses in elucidating the gene networks underlying biological processes.

RNA sequencing analysis of the gametophyte transcriptome from the liverwort, Marchantia polymorpha

The liverwort Marchantia polymorpha is a member of the most basal lineage of land plants (embryophytes) and likely retains many ancestral morphological, physiological and molecular characteristics. Despite its phylogenetic importance and the availability of previous EST studies, M. polymorpha's lack of economic importance limits accessible genomic resources for this species. We employed Illumina RNA-Seq technology to sequence the gametophyte transcriptome of M. polymorpha. cDNA libraries from 6 different male and female developmental tissues were sequenced to delineate a global view of the M. polymorpha transcriptome. Approximately 80 million short reads were obtained and assembled into a non-redundant set of 46,533 transcripts (> =  200 bp) from 46,070 loci. The average length and the N50 length of the transcripts were 757 bp and 471 bp, respectively. Sequence comparison of assembled transcripts with non-redundant proteins from embryophytes resulted in the annotation of 43% of the transcripts. The transcripts were also compared with M. polymorpha expressed sequence tags (ESTs), and approximately 69.5% of the transcripts appeared to be novel. Twenty-one percent of the transcripts were assigned GO terms to improve annotation. In addition, 6,112 simple sequence repeats (SSRs) were identified as potential molecular markers, which may be useful in studies of genetic diversity. A comparative genomics approach revealed that a substantial proportion of the genes (35.5%) expressed in M. polymorpha were conserved across phylogenetically related species, such as Selaginella and Physcomitrella, and identified 580 genes that are potentially unique to liverworts. Our study presents an extensive amount of novel sequence information for M. polymorpha. This information will serve as a valuable genomics resource for further molecular, developmental and comparative evolutionary studies, as well as for the isolation and characterization of functional genes that are involved in sex differentiation and sexual reproduction in this liverwort.

High throughput RNA sequencing of a hybrid maize and its parents shows different mechanisms responsive to nitrogen limitation

BACKGROUND

Development of crop varieties with high nitrogen use efficiency (NUE) is crucial for minimizing N loss, reducing environmental pollution and decreasing input cost. Maize is one of the most important crops cultivated worldwide and its productivity is closely linked to the amount of fertilizer used. A survey of the transcriptomes of shoot and root tissues of a maize hybrid line and its two parental inbred lines grown under sufficient and limiting N conditions by mRNA-Seq has been conducted to have a better understanding of how different maize genotypes respond to N limitation.

RESULTS

A different set of genes were found to be N-responsive in the three genotypes. Many biological processes important for N metabolism such as the cellular nitrogen compound metabolic process and the cellular amino acid metabolic process were enriched in the N-responsive gene list from the hybrid shoots but not from the parental lines' shoots. Coupled to this, sugar, carbohydrate, monosaccharide, glucose, and sorbitol transport pathways were all up-regulated in the hybrid, but not in the parents under N limitation. Expression patterns also differed between shoots and roots, such as the up-regulation of the cytokinin degradation pathway in the shoots of the hybrid and down-regulation of that pathway in the roots. The change of gene expression under N limitation in the hybrid resembled the parent with the higher NUE trait. The transcript abundances of alleles derived from each parent were estimated using polymorphic sites in mapped reads in the hybrid. While there were allele abundance differences, there was no correlation between these and the expression differences seen between the hybrid and the two parents.

CONCLUSIONS

Gene expression in two parental inbreds and the corresponding hybrid line in response to N limitation was surveyed using the mRNA-Seq technology. The data showed that the three genotypes respond very differently to N-limiting conditions, and the hybrid clearly has a unique expression pattern compared to its parents. Our results expand our current understanding of N responses and will help move us forward towards effective strategies to improve NUE and enhance crop production.

Transcriptomic analysis reveals tomato genes whose expression is induced specifically during effector-triggered immunity and identifies the Epk1 protein kinase which is required for the host response to three bacterial effector proteins

BACKGROUND

Plants have two related immune systems to defend themselves against pathogen attack. Initially,pattern-triggered immunity is activated upon recognition of microbe-associated molecular patterns by pattern recognition receptors. Pathogenic bacteria deliver effector proteins into the plant cell that interfere with this immune response and promote disease. However, some plants express resistance proteins that detect the presence of specific effectors leading to a robust defense response referred to as effector-triggered immunity. The interaction of tomato with Pseudomonas syringae pv. tomato is an established model system for understanding the molecular basis of these plant immune responses.

RESULTS

We apply high-throughput RNA sequencing to this pathosystem to identify genes whose expression changes specifically during pattern-triggered or effector-triggered immunity. We then develop reporter genes for each of these responses that will enable characterization of the host response to the large collection of P. s. pv. tomato strains that express different combinations of effectors. Virus-induced gene silencing of 30 of the effector-triggered immunity-specific genes identifies Epk1 which encodes a predicted protein kinase from a family previously unknown to be involved in immunity. Knocked-down expression of Epk1 compromises effector-triggered immunity triggered by three bacterial effectors but not by effectors from non-bacterial pathogens. Epistasis experiments indicate that Epk1 acts upstream of effector-triggered immunity-associated MAP kinase signaling.

CONCLUSIONS

Using RNA-seq technology we identify genes involved in specific immune responses. A functional genomics screen led to the discovery of Epk1, a novel predicted protein kinase required for plant defense activation upon recognition of three different bacterial effectors.

Dispersion estimation and its effect on test performance in RNA-seq data analysis: a simulation-based comparison of methods

A central goal of RNA sequencing (RNA-seq) experiments is to detect differentially expressed genes. In the ubiquitous negative binomial model for RNA-seq data, each gene is given a dispersion parameter, and correctly estimating these dispersion parameters is vital to detecting differential expression. Since the dispersions control the variances of the gene counts, underestimation may lead to false discovery, while overestimation may lower the rate of true detection. After briefly reviewing several popular dispersion estimation methods, this article describes a simulation study that compares them in terms of point estimation and the effect on the performance of tests for differential expression. The methods that maximize the test performance are the ones that use a moderate degree of dispersion shrinkage: the DSS, Tagwise wqCML, and Tagwise APL. In practical RNA-seq data analysis, we recommend using one of these moderate-shrinkage methods with the QLShrink test in QuasiSeq R package.

Model-based clustering for RNA-seq data

MOTIVATION

RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNA-seq data analysis.

RESULTS

In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectation-maximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models.

AVAILABILITY AND IMPLEMENTATION

An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.r-project.org

The transcriptomics of secondary growth and wood formation in conifers

In the last years, forestry scientists have adapted genomics and next-generation sequencing (NGS) technologies to the search for candidate genes related to the transcriptomics of secondary growth and wood formation in several tree species. Gymnosperms, in particular, the conifers, are ecologically and economically important, namely, for the production of wood and other forestry end products. Until very recently, no whole genome sequencing of a conifer genome was available. Due to the gradual improvement of the NGS technologies and inherent bioinformatics tools, two draft assemblies of the whole genomes sequence of Picea abies and Picea glauca arose in the current year. These draft genome assemblies will bring new insights about the structure, content, and evolution of the conifer genomes. Furthermore, new directions in the forestry, breeding and research of conifers will be discussed in the following. The identification of genes associated with the xylem transcriptome and the knowledge of their regulatory mechanisms will provide less time-consuming breeding cycles and a high accuracy for the selection of traits related to wood production and quality.

An optimal test with maximum average power while controlling FDR with application to RNA-seq data

The recent RNA-seq technology is an attractive method to study gene expression. One of the most important goals in RNA-seq data analysis is to detect genes differentially expressed across treatments. Although several statistical methods have been published, there are no theoretical justifications for whether these methods are optimal or how to search for the optimal test. Furthermore, most proposed tests are designed for testing whether the mean expression levels are exactly the same or not across treatments, whereas sometimes, biologists are interested in detecting genes with expression changes larger than a certain threshold. Another issue with current methods is that the false discovery rate (FDR) control is not well studied. In this manuscript, we propose a test to address all the above issues. Under model assumptions, we derive an optimal test that achieves the maximum of average power among those that control FDR at the same level. We also provide an approximated version, the approximated most average powerful (AMAP) test, for practical implementation. The proposed method allows for testing null hypotheses that are much more general than the ones most previous studies have considered, and it leads to a natural way of controlling the FDR. Through simulation studies, we show that our test has a higher power than other methods, including the widely-used edgeR, DESeq, and baySeq methods, as well as better FDR control than two other FDR control procedures commonly used in practice. For demonstration, we also apply the proposed method to a real RNA-seq dataset obtained from maize.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping.

Ectopic overexpression of SsCBF1, a CRT/DRE-binding factor from the nightshade plant Solanum lycopersicoides, confers freezing and salt tolerance in transgenic Arabidopsis

The C-repeat (CRT)/dehydration-responsive element (DRE) binding factor (CBF/DREB1) transcription factors play a key role in cold response. However, the detailed roles of many plant CBFs are far from fully understood. A CBF gene (SsCBF1) was isolated from the cold-hardy plant Solanum lycopersicoides. A subcellular localization study using GFP fusion protein indicated that SsCBF1 is localized in the nucleus. We delimited the SsCBF1 transcriptional activation domain to the C-terminal segment comprising amino acid residues 193–228 (SsCBF1^193–228). The expression of SsCBF1 could be dramatically induced by cold, drought and high salinity. Transactivation assays in tobacco leaves revealed that SsCBF1 could specifically bind to the CRT cis-elements in vivo to activate the expression of downstream reporter genes. The ectopic overexpression of SsCBF1 conferred increased freezing and high-salinity tolerance and late flowering phenotype to transgenic Arabidopsis. RNA-sequencing data exhibited that a set of cold and salt stress responsive genes were up-regulated in transgenic Arabidopsis. Our results suggest that SsCBF1 behaves as a typical CBF to contribute to plant freezing tolerance. Increased resistance to high-salinity and late flowering phenotype derived from SsCBF1 OE lines lend more credence to the hypothesis that plant CBFs participate in diverse physiological and biochemical processes related to adverse conditions.

Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine max) Embryos

Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on “guilt-by-association” relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants.

Comparative transcriptome analysis to investigate the high starch accumulation of duckweed (Landoltia punctata) under nutrient starvation

BACKGROUND

Duckweed can thrive on anthropogenic wastewater and produce tremendous biomass production. Due to its relatively high starch and low lignin percentage, duckweed is a good candidate for bioethanol fermentation. Previous studies have observed that water devoid of nutrients is good for starch accumulation, but its molecular mechanism remains unrevealed.

RESULTS

This study globally analyzed the response to nutrient starvation in order to investigate the starch accumulation in duckweed (Landoltia punctata). L. punctata was transferred from nutrient-rich solution to distilled water and sampled at different time points. Physiological measurements demonstrated that the activity of ADP-glucose pyrophosphorylase, the key enzyme of starch synthesis, as well as the starch percentage in duckweed, increased continuously under nutrient starvation. Samples collected at 0 h, 2 h and 24 h time points respectively were used for comparative gene expression analysis using RNA-Seq. A comprehensive transcriptome, comprising of 74,797 contigs, was constructed by a de novo assembly of the RNA-Seq reads. Gene expression profiling results showed that the expression of some transcripts encoding key enzymes involved in starch biosynthesis was up-regulated, while the expression of transcripts encoding enzymes involved in starch consumption were down-regulated, the expression of some photosynthesis-related transcripts were down-regulated during the first 24 h, and the expression of some transporter transcripts were up-regulated within the first 2 h. Very interestingly, most transcripts encoding key enzymes involved in flavonoid biosynthesis were highly expressed regardless of starvation, while transcripts encoding laccase, the last rate-limiting enzyme of lignifications, exhibited very low expression abundance in all three samples.

CONCLUSION

Our study provides a comprehensive expression profiling of L. punctata under nutrient starvation, which indicates that nutrient starvation down-regulated the global metabolic status, redirects metabolic flux of fixed CO2 into starch synthesis branch resulting in starch accumulation in L. punctata.

Ectopic overexpression of SlHsfA3, a heat stress transcription factor from tomato, confers increased thermotolerance and salt hypersensitivity in germination in transgenic Arabidopsis

Plant heat stress transcription factors (Hsfs) are the critical components involved in mediating responses to various environmental stressors. However, the detailed roles of many plant Hsfs are far from fully understood. In this study, an Hsf (SlHsfA3) was isolated from the cultivated tomato (Solanum lycopersicum, Sl) and functionally characterized at the genetic and developmental levels. The nucleus-localized SlHsfA3 was basally and ubiquitously expressed in different plant organs. The expression of SlHsfA3 was induced dramatically by heat stress, moderately by high salinity, and slightly by drought, but was not induced by abscisic acid (ABA). The ectopic overexpression of SlHsfA3 conferred increased thermotolerance and late flowering phenotype to transgenic Arabidopsis plants. Moreover, SlHsfA3 played a negative role in controlling seed germination under salt stress. RNA-sequencing data demonstrated that a number of heat shock proteins (Hsps) and stress-associated genes were induced in Arabidopsis plants overexpressing SlHsfA3. A gel shift experiment and transient expression assays in Nicotiana benthamiana leaves demonstrated that SlHsfA3 directly activates the expression of SlHsp26.1-P and SlHsp21.5-ER. Taken together, our results suggest that SlHsfA3 behaves as a typical Hsf to contribute to plant thermotolerance. The late flowering and seed germination phenotypes and the RNA-seq data derived from SlHsfA3 overexpression lines lend more credence to the hypothesis that plant Hsfs participate in diverse physiological and biochemical processes related to adverse conditions.

Accessing complex crop genomes with next-generation sequencing

Many important crop species have genomes originating from ancestral or recent polyploidisation events. Multiple homoeologous gene copies, chromosomal rearrangements and amplification of repetitive DNA within large and complex crop genomes can considerably complicate genome analysis and gene discovery by conventional, forward genetics approaches. On the other hand, ongoing technological advances in molecular genetics and genomics today offer unprecedented opportunities to analyse and access even more recalcitrant genomes. In this review, we describe next-generation sequencing and data analysis techniques that vastly improve our ability to dissect and mine genomes for causal genes underlying key traits and allelic variation of interest to breeders. We focus primarily on wheat and oilseed rape, two leading examples of major polyploid crop genomes whose size or complexity present different, significant challenges. In both cases, the latest DNA sequencing technologies, applied using quite different approaches, have enabled considerable progress towards unravelling the respective genomes. Our ability to discover the extent and distribution of genetic diversity in crop gene pools, and its relationship to yield and quality-related traits, is swiftly gathering momentum as DNA sequencing and the bioinformatic tools to deal with growing quantities of genomic data continue to develop. In the coming decade, genomic and transcriptomic sequencing, discovery and high-throughput screening of single nucleotide polymorphisms, presence-absence variations and other structural chromosomal variants in diverse germplasm collections will give detailed insight into the origins, domestication and available trait-relevant variation of polyploid crops, in the process facilitating novel approaches and possibilities for genomics-assisted breeding.

Application of genomic tools in plant breeding

Plant breeding has been very successful in developing improved varieties using conventional tools and methodologies. Nowadays, the availability of genomic tools and resources is leading to a new revolution of plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. Next Generation Sequencing (NGS) technologies are allowing the mass sequencing of genomes and transcriptomes, which is producing a vast array of genomic information. The analysis of NGS data by means of bioinformatics developments allows discovering new genes and regulatory sequences and their positions, and makes available large collections of molecular markers. Genome-wide expression studies provide breeders with an understanding of the molecular basis of complex traits. Genomic approaches include TILLING and EcoTILLING, which make possible to screen mutant and germplasm collections for allelic variants in target genes. Re-sequencing of genomes is very useful for the genome-wide discovery of markers amenable for high-throughput genotyping platforms, like SSRs and SNPs, or the construction of high density genetic maps. All these tools and resources facilitate studying the genetic diversity, which is important for germplasm management, enhancement and use. Also, they allow the identification of markers linked to genes and QTLs, using a diversity of techniques like bulked segregant analysis (BSA), fine genetic mapping, or association mapping. These new markers are used for marker assisted selection, including marker assisted backcross selection, ‘breeding by design’, or new strategies, like genomic selection. In conclusion, advances in genomics are providing breeders with new tools and methodologies that allow a great leap forward in plant breeding, including the ‘superdomestication’ of crops and the genetic dissection and breeding for complex traits.