Omics meet networks - using systems approaches to infer regulatory networks in plants

Brassinosteroid gene regulatory networks at cellular resolution in the Arabidopsis root

Brassinosteroids are plant steroid hormones that regulate diverse processes, such as cell division and cell elongation, through gene regulatory networks that vary in space and time. By using time series single-cell RNA sequencing to profile brassinosteroid-responsive gene expression specific to different cell types and developmental stages of the Arabidopsis root, we identified the elongating cortex as a site where brassinosteroids trigger a shift from proliferation to elongation associated with increased expression of cell wall-related genes. Our analysis revealed HOMEOBOX FROM ARABIDOPSIS THALIANA 7 (HAT7) and GT-2-LIKE 1 (GTL1) as brassinosteroid-responsive transcription factors that regulate cortex cell elongation. These results establish the cortex as a site of brassinosteroid-mediated growth and unveil a brassinosteroid signaling network regulating the transition from proliferation to elongation, which illuminates aspects of spatiotemporal hormone responses.

Advances in Metabolomics-Driven Diagnostic Breeding and Crop Improvement

Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.

Transcriptome Meta-Analysis Associated Targeting Hub Genes and Pathways of Drought and Salt Stress Responses in Cotton (Gossypium hirsutum): A Network Biology Approach

Abiotic stress tolerance is an intricate feature controlled through several genes and networks in the plant system. In abiotic stress, salt, and drought are well known to limit cotton productivity. Transcriptomics meta-analysis has arisen as a robust method to unravel the stress-responsive molecular network in crops. In order to understand drought and salt stress tolerance mechanisms, a meta-analysis of transcriptome studies is crucial. To confront these issues, here, we have given details of genes and networks associated with significant differential expression in response to salt and drought stress. The key regulatory hub genes of drought and salt stress conditions have notable associations with functional drought and salt stress-responsive (DSSR) genes. In the network study, nodulation signaling pathways 2 (NSP2), Dehydration-responsive element1 D (DRE1D), ethylene response factor (ERF61), cycling DOF factor 1 (CDF1), and tubby like protein 3 (TLP3) genes in drought and tubby like protein 1 (TLP1), thaumatin-like proteins (TLP), ethylene-responsive transcription factor ERF109 (EF109), ETS-Related transcription Factor (ELF4), and Arabidopsis thaliana homeodomain leucine-zipper gene (ATHB7) genes in salt showed the significant putative functions and pathways related to providing tolerance against drought and salt stress conditions along with the significant expression values. These outcomes provide potential candidate genes for further in-depth functional studies in cotton, which could be useful for the selection of an improved genotype of Gossypium hirsutum against drought and salt stress conditions.

OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security

The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Transcriptome and proteome analysis of walnut (Juglans regia L.) fruit in response to infection by Colletotrichum gloeosporioides

BACKGROUND

Walnut anthracnose induced by Colletotrichum gloeosporioides is a disastrous disease affecting walnut production. The resistance of walnut fruit to C. gloeosporioides is a highly complicated and genetically programmed process. However, the underlying mechanisms have not yet been elucidated.

RESULTS

To understand the molecular mechanism underlying the defense of walnut to C. gloeosporioides, we used RNA sequencing and label-free quantitation technologies to generate transcriptomic and proteomic profiles of tissues at various lifestyle transitions of C. gloeosporioides, including 0 hpi, pathological tissues at 24 hpi, 48 hpi, and 72 hpi, and distal uninoculated tissues at 120 hpi, in anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts, which were defined through scanning electron microscopy. A total of 21,798 differentially expressed genes (DEGs) and 1929 differentially expressed proteins (DEPs) were identified in F26 vs. F423 at five time points, and the numbers of DEGs and DEPs were significantly higher in the early infection stage. Using pairwise comparisons and weighted gene co-expression network analysis of the transcriptome, we identified two modules significantly related to disease resistance and nine hub genes in the transcription expression gene networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs and DEPs revealed that many genes were mainly related to immune response, plant hormone signal transduction, and secondary metabolites, and many DEPs were involved in carbon metabolism and photosynthesis. Correlation analysis between the transcriptome data and proteome data also showed that the consistency of the differential expression of the mRNA and corresponding proteins was relatively higher in the early stage of infection.

CONCLUSIONS

Collectively, these results help elucidate the molecular response of walnut fruit to C. gloeosporioides and provide a basis for the genetic improvement of walnut disease resistance.

Integrated Metabolomics and Transcriptome Analysis of Flavonoid Biosynthesis in Safflower (Carthamus tinctorius L.) With Different Colors

Safflower is widely used in dying and in traditional medicine, and C-glucosylquinochalcones are the main metabolic species in the red color of safflower. Various safflower cultivars have flowers with different colors. However, the metabolic and transcriptional differences among safflower cultivars with different-colored flowers and the genes participating in C-glucosylquinochalcone biosynthesis are largely unknown. To provide insights on this issue, we performed integrated metabolomics and transcriptome analyses on the flavonoid biosynthesis of flowers of different colors in safflower (white-W, yellow-Y, light red-LR, and deep red-DR). The metabolic analysis showed that flavonoid metabolites showed great differences among the different colors of safflower. More flavonoid metabolic species were detected in Y and W, while C-glucosylquinochalcones were not detected in W. The content of C-glucosylquinochalcones increased with increasing color. Transcriptional analysis showed that most of the annotated flavonoid biosynthesis genes were significantly increased in W. The expression of genes related to flavonoid biosynthesis decreased with increasing color. We analyzed the candidate genes associated with C-glucosylquinochalcones, and an integration of the metabolic and transcriptional analyses indicated that the differential expression of the chalcone synthase (CHS) gene is one of the main reasons for the difference in flavonoid species and content among the different colors of safflower. Combined with the expression pattern analysis, these results indicated that HH_035319, HH_032689, and HH_018025 are likely involved in C-glucosylquinochalcones biosynthesis. In addition, we found that their expression showed greatly increased after the methyl jasmonate (MeJA) treatment. Therefore, HH_035319, HH_032689, and HH_018025 might participate in C-glucosylquinochalcone biosynthesis, which ultimately leads to the red color in safflower.

Identification of a New Giant Emrbryo Allele, and Integrated Transcriptomics and Metabolomics Analysis of Giant Embryo Development in Rice

Rice embryos are rich in high-quality protein, lipid, vitamins and minerals, representing the most important nutritional part of brown rice. However, the molecular mechanism of rice embryo development is poorly understood. In this study, two rice cultivars with contrasting embryo size (the giant embryo cultivar Dapeimi and the normal embryo cultivar 187R) were used to explore excellent genes controlling embryo size, and the developed near-isogenic lines (NILs) (NIL-D, which has the giant embryo phenotype, and its matching line, NIL-X) were used to explore transcript and metabolic properties in the earlier maturation stage of giant embryo development under natural conditions. The map-based cloning results demonstrated that Dapeimi is a novel allelic mutant of the rice GIANT EMBRYO (GE) gene, and the functional mutation site is a single cytosine deletion in the exon1. A total of 285 differentially accumulated metabolites (DAMs) and 677 differentially expressed genes (DEGs) were identified between NIL-D and NIL-X. The analysis of DAMs indicated that plants lacking GE mainly promoted energy metabolism, amino acid metabolism, and lipid metabolism pathways in the rice embryo. Pearson correlation coefficient showed that 300 pairs of gene-metabolites were highly correlated. Among them, OsZS_02G0528500 and OsZS_12G0013700 were considered to be key genes regulating L-Aspartic acid and L-Tryptophan content during rice giant embryo development, which are promising to be good candidate genes to improve rice nutrition. By analyzing rice embryo development through a combination of strategies, this research contributes to a greater understanding of the molecular mechanism of rice embryo development, and provides a theoretical foundation for breeding high-nutrition varieties.

Classification and Computational Analysis of Arabidopsis thaliana Sperm Cell-Specific F-Box Protein Gene 3p.AtFBP113

In plants, F-box proteins (FBPs) constitute one of the largest superfamilies of regulatory proteins. Most F-box proteins are shown to be an integral part of SCF complexes, which carry out the degradation of proteins and regulate diverse important biological processes. Anthers and pollen development have a huge importance in crop breeding. Despite the vast diversity of FBPs in Arabidopsis male reproductive organs, their role in anther and pollen development is not much explored. Moreover, a standard nomenclature for naming FBPs is also lacking. Here, we propose a standard nomenclature for naming the FBPs of Arabidopsis thaliana uniformly and carry out a systematic analysis of sperm cell-specific FBP gene, i.e., 3p.AtFBP113 due to its reported high and preferential expression, for detailed functional annotation. The results revealed that 3p.AtFBP113 is located on the small arm of chromosome and encodes 397 amino acid long soluble, stable, and hydrophilic protein with the possibility of localization in various cellular compartments. The presence of the C-terminal F-box associated domain (FBA) with immunoglobulin-like fold anticipated its role in protein binding. Gene ontology based functional annotation and tissue-specific gene co-expression analysis further strengthened its role in protein binding and ubiquitination. Moreover, various potential post/co-translational modifications were anticipated and the predicted tertiary structure also showed the presence of characteristic domains and fold. Thus, the outcomes of the study will be useful in developing a better understating of the function of 3p.AtFBP113 during the process of pollen development, which will be helpful for targeting the gene for manipulation of male fertility that has immense importance in hybrid breeding.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Metabolome and transcriptome analyses reveal chlorophyll and anthocyanin metabolism pathway associated with cucumber fruit skin color

BACKGROUND

Fruit skin color play important role in commercial value of cucumber, which is mainly determined by the content and composition of chlorophyll and anthocyanins. Therefore, understanding the related genes and metabolomics involved in composition of fruit skin color is essential for cucumber quality and commodity value.

RESULTS

The results showed that chlorophyll a, chlorophyll b and carotenoid content in fruit skin were higher in Lv (dark green skin) than Bai (light green skin) on fruit skin. Cytological observation showed more chloroplast existed in fruit skin cells of Lv. A total of 162 significantly different metabolites were found between the fruit skin of the two genotypes by metabolome analysis, including 40 flavones, 9 flavanones, 8 flavonols, 6 anthocyanins, and other compounds. Crucial anthocyanins and flavonols for fruit skin color, were detected significantly decreased in fruit skin of Bai compared with Lv. By RNA-seq assay, 4516 differentially expressed genes (DEGs) were identified between two cultivars. Further analyses suggested that low expression level of chlorophyll biosynthetic genes, such as chlM, por and NOL caused less chlorophylls or chloroplast in fruit skin of Bai. Meanwhile, a predicted regulatory network of anthocyanin biosynthesis was established to illustrate involving many DEGs, especially 4CL, CHS and UFGT.

CONCLUSIONS

This study uncovered significant differences between two cucumber genotypes with different fruit color using metabolome and RNA-seq analysis. We lay a foundation to understand molecular regulation mechanism on formation of cucumber skin color, by exploring valuable genes, which is helpful for cucumber breeding and improvement on fruit skin color.

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

Integrated Omics Analyses Identify Key Pathways Involved in Petiole Rigidity Formation in Sacred Lotus

Sacred lotus (Nelumbo nucifera Gaertn.) is a relic aquatic plant with two types of leaves, which have distinct rigidity of petioles. Here we assess the difference from anatomic structure to the expression of genes and proteins in two petioles types, and identify key pathways involved in petiole rigidity formation in sacred lotus. Anatomically, great variation between the petioles of floating and vertical leaves were observed. The number of collenchyma cells and thickness of xylem vessel cell wall was higher in the initial vertical leaves’ petiole (IVP) compared to the initial floating leaves’ petiole (IFP). Among quantified transcripts and proteins, 1021 and 401 transcripts presented 2-fold expression increment (named DEGs, genes differentially expressed between IFP and IVP) in IFP and IVP, 421 and 483 proteins exhibited 1.5-fold expression increment (named DEPs, proteins differentially expressed between IFP and IVP) in IFP and IVP, respectively. Gene function and pathway enrichment analysis displayed that DEGs and DEPs were significantly enriched in cell wall biosynthesis and lignin biosynthesis. In consistent with genes and proteins expressions in lignin biosynthesis, the contents of lignin monomers precursors were significantly different in IFP and IVP. These results enable us to understand lotus petioles rigidity formation better and provide valuable candidate genes information on further investigation.

Transcriptome signature of two lactation stages in Ghezel sheep identifies using RNA-Sequencing

The expression of genes and their regulation during lactation in Ghezel sheep breed remains less understood. To explore the underlying molecular mechanism of the lactation process in the mammary gland, transcriptome profiles of Iranian fat-tailed Ghezel sheep breed milk at two stages, before (BF) and after peak (AF) stages of lactation were investigated. Functional impacts of differentially expressed genes (DEGs) between BF and AF stages were surveyed using Gene Ontology (GO) and Protein-Protein Interaction (PPI) network analysis. Totally, 75 DEGs were identified between BF and AF stages of lactation. The RNA-Seq results were validated by Q-RT-PCR. Gene ontology of DEGs mainly enriched in metabolic process and oxidative phosphorylation. PPI network analysis also highlighted the contribution of peroxisome proliferator-activated receptors (PPAR) signaling, oxidative phosphorylation and metabolic pathways in the lactation process. Intriguingly, the genes involved in fat metabolism dominantly down-regulated at AF stage. Our results provide new insight into transcriptional changes and add to growing body of knowledge on the lactation process in fat-tailed sheep breeds.

Trait ontology analysis based on association mapping studies bridges the gap between crop genomics and Phenomics

BACKGROUND

Trait ontology (TO) analysis is a powerful system for functional annotation and enrichment analysis of genes. However, given the complexity of the molecular mechanisms underlying phenomes, only a few hundred gene-to-TO relationships in plants have been elucidated to date, limiting the pace of research in this "big data" era.

RESULTS

Here, we curated all the available trait associated sites (TAS) information from 79 association mapping studies of maize (Zea mays L.) and rice (Oryza sativa L.) lines with diverse genetic backgrounds and built a large-scale TAS-derived TO system for functional annotation of genes in various crops. Our TO system contains information for up to 18,042 genes (6345 in maize at the 25 k level and 11,697 in rice at the 50 k level), including gene-to-TO relationships, which covers over one fifth of the annotated gene sets for maize and rice. A comparison of Gene Ontology (GO) vs. TO analysis demonstrated that the TAS-derived TO system is an efficient alternative tool for gene functional annotation and enrichment analysis. We therefore combined information from the TO, GO, metabolic pathway, and co-expression network databases and constructed the TAS system, which is publicly available at http://tas.hzau.edu.cn . TAS provides a user-friendly interface for functional annotation of genes, enrichment analysis, genome-wide extraction of trait-associated genes, and crosschecking of different functional annotation databases.

CONCLUSIONS

TAS bridges the gap between genomic and phenomic information in crops. This easy-to-use tool will be useful for geneticists, biologists, and breeders in the agricultural community, as it facilitates the dissection of molecular mechanisms conferring agronomic traits in an easy, genome-wide manner.

Identification of drought responsive proteins and related proteomic QTLs in barley

Drought is a major abiotic stress that negatively influences crop yield. Breeding strategies for improved drought resistance require an improved knowledge of plant drought responses. We therefore applied drought to barley recombinant inbred lines and their parental genotypes shortly before tillering. A large-scale proteomic analysis of leaf and root tissue revealed proteins that respond to drought in a genotype-specific manner. Of these, Rubisco activase in chloroplast, luminal binding protein in endoplasmic reticulum, phosphoglycerate mutase, glutathione S-transferase, heat shock proteins and enzymes involved in phenylpropanoid biosynthesis showed strong genotype×environment interactions. These data were subjected to genetic linkage analysis and the identification of proteomic QTLs that have potential value in marker-assisted breeding programs.

New lncRNA annotation reveals extensive functional divergence of the transcriptome in maize

Long non-coding RNAs (lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize (Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs, non-conserved lncRNAs, and protein-coding transcripts. Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genome-wide annotation and analysis of maize lncRNAs and uncover potential functional divergence between protein-coding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.

Hierarchical Transcription Factor and Chromatin Binding Network for Wood Formation in Black Cottonwood (Populus trichocarpa)

Wood remains the world's most abundant and renewable resource for timber and pulp and is an alternative to fossil fuels. Understanding the molecular regulation of wood formation can advance the engineering of wood for more efficient material and energy productions. We integrated a black cottonwood (Populus trichocarpa) wood-forming cell system with quantitative transcriptomics and chromatin binding assays to construct a transcriptional regulatory network (TRN) directed by a key transcription factor (TF), PtrSND1-B1 (secondary wall-associated NAC-domain protein). The network consists of four layers of TF-target gene interactions with quantitative regulatory effects, describing the specificity of how the regulation is transduced through these interactions to activate cell wall genes (effector genes) for wood formation. PtrSND1-B1 directs 57 TF-DNA interactions through 17 TFs transregulating 27 effector genes. Of the 57 interactions, 55 are novel. We tested 42 of these 57 interactions in 30 genotypes of transgenic P. trichocarpa and verified that ∼90% of the tested interactions function in vivo. The TRN reveals common transregulatory targets for distinct TFs, leading to the discovery of nine TF protein complexes (dimers and trimers) implicated in regulating the biosynthesis of specific types of lignin. Our work suggests that wood formation may involve regulatory homeostasis determined by combinations of TF-DNA and TF-TF (protein-protein) regulations.

Co-expression Gene Network Analysis and Functional Module Identification in Bamboo Growth and Development

Bamboo is one of the fastest-growing non-timber forest plants. Moso bamboo (Phyllostachys edulis) is the most economically valuable bamboo in Asia, especially in China. With the release of the whole-genome sequence of moso bamboo, there are increasing demands for refined annotation of bamboo genes. Recently, large amounts of bamboo transcriptome data have become available, including data on the multiple growth stages of tissues. It is now feasible for us to construct co-expression networks to improve bamboo gene annotation and reveal the relationships between gene expression and growth traits. We integrated the genome sequence of moso bamboo and 78 transcriptome data sets to build genome-wide global and conditional co-expression networks. We overlaid the gene expression results onto the network with multiple dimensions (different development stages). Through combining the co-expression network, module classification and function enrichment tools, we identified 1,896 functional modules related to bamboo development, which covered functions such as photosynthesis, hormone biosynthesis, signal transduction, and secondary cell wall biosynthesis. Furthermore, an online database (http://bioinformatics.cau.edu.cn/bamboo) was built for searching the moso bamboo co-expression network and module enrichment analysis. Our database also includes cis-element analysis, gene set enrichment analysis, and other tools. In summary, we integrated public and in-house bamboo transcriptome data sets and carried out co-expression network analysis and functional module identification. Through data mining, we have yielded some novel insights into the regulation of growth and development. Our established online database might be convenient for the bamboo research community to identify functional genes or modules with important traits.

Combined Analysis of the Fruit Metabolome and Transcriptome Reveals Candidate Genes Involved in Flavonoid Biosynthesis in Actinidia arguta

To assess the interrelation between the change of metabolites and the change of fruit color, we performed a combined metabolome and transcriptome analysis of the flesh in two different Actinidia arguta cultivars: "HB" ("Hongbaoshixing") and "YF" ("Yongfengyihao") at two different fruit developmental stages: 70d (days after full bloom) and 100d (days after full bloom). Metabolite and transcript profiling was obtained by ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometer and high-throughput RNA sequencing, respectively. The identification and quantification results of metabolites showed that a total of 28,837 metabolites had been obtained, of which 13,715 were annotated. In comparison of HB100 vs. HB70, 41 metabolites were identified as being flavonoids, 7 of which, with significant difference, were identified as bracteatin, luteolin, dihydromyricetin, cyanidin, pelargonidin, delphinidin and (-)-epigallocatechin. Association analysis between metabolome and transcriptome revealed that there were two metabolic pathways presenting significant differences during fruit development, one of which was flavonoid biosynthesis, in which 14 structural genes were selected to conduct expression analysis, as well as 5 transcription factor genes obtained by transcriptome analysis. RT-qPCR results and cluster analysis revealed that AaF3H, AaLDOX, AaUFGT, AaMYB, AabHLH, and AaHB2 showed the best possibility of being candidate genes. A regulatory network of flavonoid biosynthesis was established to illustrate differentially expressed candidate genes involved in accumulation of metabolites with significant differences, inducing red coloring during fruit development. Such a regulatory network linking genes and flavonoids revealed a system involved in the pigmentation of all-red-fleshed and all-green-fleshed A. arguta, suggesting this conjunct analysis approach is not only useful in understanding the relationship between genotype and phenotype, but is also a powerful tool for providing more valuable information for breeding.

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Differentiated transcriptional signatures in the maize landraces of Chiapas, Mexico

Background

Landrace farmers are the keepers of crops locally adapted to the environments where they are cultivated. Patterns of diversity across the genome can provide signals of past evolution in the face of abiotic and biotic change. Understanding this rich genetic resource is imperative especially since diversity can provide agricultural security as climate continues to shift.

Results

Here we employ RNA sequencing (RNA-seq) to understand the role that conditions that vary across a landscape may have played in shaping genetic diversity in the maize landraces of Chiapas, Mexico. We collected landraces from three distinct elevational zones and planted them in a midland common garden. Early season leaf tissue was collected for RNA-seq and we performed weighted gene co-expression network analysis (WGCNA). We then used association analysis between landrace co-expression module expression values and environmental parameters of landrace origin to elucidate genes and gene networks potentially shaped by environmental factors along our study gradient. Elevation of landrace origin affected the transcriptome profiles. Two co-expression modules were highly correlated with temperature parameters of landrace origin and queries into their ‘hub’ genes suggested that temperature may have led to differentiation among landraces in hormone biosynthesis/signaling and abiotic and biotic stress responses. We identified several ‘hub’ transcription factors and kinases as candidates for the regulation of these responses.

Conclusions

These findings indicate that natural selection may influence the transcriptomes of crop landraces along an elevational gradient in a major diversity center, and provide a foundation for exploring the genetic basis of local adaptation. While we cannot rule out the role of neutral evolutionary forces in the patterns we have identified, combining whole transcriptome sequencing technologies, established bioinformatics techniques, and common garden experimentation can powerfully elucidate structure of adaptive diversity across a varied landscape. Ultimately, gaining such understanding can facilitate the conservation and strategic utilization of crop genetic diversity in a time of climate change.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4005-y) contains supplementary material, which is available to authorized users.

Systems biology and genome-wide approaches to unveil the molecular players involved in the pre-germinative metabolism: implications on seed technology traits

The pre-germinative metabolism is among the most fascinating aspects of seed biology. The early seed germination phase, or pre-germination, is characterized by rapid water uptake (imbibition), which directs a series of dynamic biochemical events. Among those are enzyme activation, DNA damage and repair, and use of reserve storage compounds, such as lipids, carbohydrates and proteins. Industrial seedling production and intensive agricultural production systems require seed stocks with high rate of synchronized germination and low dormancy. Consequently, seed dormancy, a quantitative trait related to the activation of the pre-germinative metabolism, is probably the most studied seed trait in model species and crops. Single omics, systems biology, QTLs and GWAS mapping approaches have unveiled a list of molecules and regulatory mechanisms acting at transcriptional, post-transcriptional and post-translational levels. Most of the identified candidate genes encode for regulatory proteins targeting ROS, phytohormone and primary metabolisms, corroborating the data obtained from simple molecular biology approaches. Emerging evidences show that epigenetic regulation plays a crucial role in the regulation of these mentioned processes, constituting a still unexploited strategy to modulate seed traits. The present review will provide an up-date of the current knowledge on seed pre-germinative metabolism, gathering the most relevant results from physiological, genetics, and omics studies conducted in model and crop plants. The effects exerted by the biotic and abiotic stresses and priming are also addressed. The possible implications derived from the modulation of pre-germinative metabolism will be discussed from the point of view of seed quality and technology.

Integrated omics analysis of specialized metabolism in medicinal plants

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.

Complementary Proteome and Transcriptome Profiling in Developing Grains of a Notched-Belly Rice Mutant Reveals Key Pathways Involved in Chalkiness Formation

Rice grain chalkiness is a highly complex trait involved in multiple metabolic pathways and controlled by polygenes and growth conditions. To uncover novel aspects of chalkiness formation, we performed an integrated profiling of gene activity in the developing grains of a notched-belly rice mutant. Using exhaustive tandem mass spectrometry-based shotgun proteomics and whole-genome RNA sequencing to generate a nearly complete catalog of expressed mRNAs and proteins, we reliably identified 38,476 transcripts and 3,840 proteins. Comparison between the translucent part and chalky part of the notched-belly grains resulted in only a few differently express genes (240) and differently express proteins (363), thus making it possible to focus on 'core' genes or common pathways. Several novel key pathways were identified as of relevance to chalkiness formation, in particular the shift of C and N metabolism, the down-regulation of ribosomal proteins and the resulting low abundance of storage proteins especially the 13 kDa prolamin subunit, and the suppressed photosynthetic capacity in the pericarp of the chalky part. Further, genes and proteins as transporters for carbohydrates, amino acid/peptides, proteins, lipids and inorganic ions showed an increasing expression pattern in the chalky part of the notched-belly grains. Similarly, transcripts and proteins of receptors for auxin, ABA, ethylene and brassinosteroid were also up-regulated. In summary, this joint analysis of transcript and protein profiles provides a comprehensive reference map of gene activity regarding the physiological state in the chalky endosperm.

Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in Brachypodium distachyon

We report the comprehensive identification of periodic genes and their network inference, based on a gene co-expression analysis and an Auto-Regressive eXogenous (ARX) model with a group smoothly clipped absolute deviation (SCAD) method using a time-series transcriptome dataset in a model grass, Brachypodium distachyon. To reveal the diurnal changes in the transcriptome in B. distachyon, we performed RNA-seq analysis of its leaves sampled through a diurnal cycle of over 48 h at 4 h intervals using three biological replications, and identified 3,621 periodic genes through our wavelet analysis. The expression data are feasible to infer network sparsity based on ARX models. We found that genes involved in biological processes such as transcriptional regulation, protein degradation, and post-transcriptional modification and photosynthesis are significantly enriched in the periodic genes, suggesting that these processes might be regulated by circadian rhythm in B. distachyon. On the basis of the time-series expression patterns of the periodic genes, we constructed a chronological gene co-expression network and identified putative transcription factors encoding genes that might be involved in the time-specific regulatory transcriptional network. Moreover, we inferred a transcriptional network composed of the periodic genes in B. distachyon, aiming to identify genes associated with other genes through variable selection by grouping time points for each gene. Based on the ARX model with the group SCAD regularization using our time-series expression datasets of the periodic genes, we constructed gene networks and found that the networks represent typical scale-free structure. Our findings demonstrate that the diurnal changes in the transcriptome in B. distachyon leaves have a sparse network structure, demonstrating the spatiotemporal gene regulatory network over the cyclic phase transitions in B. distachyon diurnal growth.

Co-expression network analysis of duplicate genes in maize (Zea mays L.) reveals no subgenome bias

Background

Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized.

Results

To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks – maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize.

Conclusions

Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types, duplication ages and co-expression consequences.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3194-0) contains supplementary material, which is available to authorized users.

50 years of Arabidopsis research: highlights and future directions

922 I. 922 II. 922 III. 925 IV. 925 V. 926 VI. 927 VII. 928 VIII. 929 IX. 930 X. 931 XI. 932 XII. 933 XIII. Natural variation and genome-wide association studies 934 XIV. 934 XV. 935 XVI. 936 XVII. 937 937 References 937 SUMMARY: The year 2014 marked the 25(th) International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. We present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlighting some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.

Moving toward a comprehensive map of central plant metabolism

Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

Cracking the Kranz enigma with systems biology

Leaves with Kranz anatomy exhibit a highly characteristic arrangement of closely spaced veins surrounded by concentric wreaths of bundle sheath and mesophyll cells. This anatomical framework is vital for effective C4 photosynthesis in nearly all known land plant lineages and has evolved independently on over 60 occasions. Over the last 3 years, technological advances, particularly in high-throughput DNA sequencing, have allowed the development of Kranz anatomy to be interrogated at unprecedented depth. This review highlights the recent advances in our understanding that have been facilitated by systems biology approaches, and proposes a testable model for the regulation of Kranz development.

Discovering functional modules across diverse maize transcriptomes using COB, the Co-expression Browser

Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.

Modeling transcriptional networks regulating secondary growth and wood formation in forest trees

The complex interactions among the genes that underlie a biological process can be modeled and presented as a transcriptional network, in which genes (nodes) and their interactions (edges) are shown in a graphical form similar to a wiring diagram. A large number of genes have been identified that are expressed during the radial woody growth of tree stems (secondary growth), but a comprehensive understanding of how these genes interact to influence woody growth is currently lacking. Modeling transcriptional networks has recently been made tractable by next-generation sequencing-based technologies that can comprehensively catalog gene expression and transcription factor-binding genome-wide, but has not yet been extensively applied to undomesticated tree species or woody growth. Here we discuss basic features of transcriptional networks, approaches for modeling biological networks, and examples of biological network models developed for forest trees to date. We discuss how transcriptional network research is being developed in the model forest tree genus, Populus, and how this research area can be further developed and applied. Transcriptional network models for forest tree secondary growth and wood formation could ultimately provide new predictive models to accelerate hypothesis-driven research and develop new breeding applications.

Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis

Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

Adventitious roots and lateral roots: similarities and differences

In addition to its role in water and nutrient uptake, the root system is fundamentally important because it anchors a plant to its substrate. Although a wide variety of root systems exist across different species, all plants have a primary root (derived from an embryonic radicle) and different types of lateral roots. Adventitious roots, by comparison, display the same functions as lateral roots but develop from aerial tissues. In addition, they not only develop as an adaptive response to various stresses, such as wounding or flooding, but also are a key limiting component of vegetative propagation. Lateral and adventitious roots share key elements of the genetic and hormonal regulatory networks but are subject to different regulatory mechanisms. In this review, we discuss the developmental processes that give rise to lateral and adventitious roots and highlight knowledge acquired over the past few years about the mechanisms that regulate adventitious root formation.

Gene networks controlling Arabidopsis thaliana flower development

The formation of flowers is one of the main models for studying the regulatory mechanisms that underlie plant development and evolution. Over the past three decades, extensive genetic and molecular analyses have led to the identification of a large number of key floral regulators and to detailed insights into how they control flower morphogenesis. In recent years, genome-wide approaches have been applied to obtaining a global view of the gene regulatory networks underlying flower formation. Furthermore, mathematical models have been developed that can simulate certain aspects of this process and drive further experimentation. Here, we review some of the main findings made in the field of Arabidopsis thaliana flower development, with an emphasis on recent advances. In particular, we discuss the activities of the floral organ identity factors, which are pivotal for the specification of the different types of floral organs, and explore the experimental avenues that may elucidate the molecular mechanisms and gene expression programs through which these master regulators of flower development act.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Network component analysis provides quantitative insights on an Arabidopsis transcription factor-gene regulatory network

Background

Gene regulatory networks (GRNs) are models of molecule-gene interactions instrumental in the coordination of gene expression. Transcription factor (TF)-GRNs are an important subset of GRNs that characterize gene expression as the effect of TFs acting on their target genes. Although such networks can qualitatively summarize TF-gene interactions, it is highly desirable to quantitatively determine the strengths of the interactions in a TF-GRN as well as the magnitudes of TF activities. To our knowledge, such analysis is rare in plant biology. A computational methodology developed for this purpose is network component analysis (NCA), which has been used for studying large-scale microbial TF-GRNs to obtain nontrivial, mechanistic insights. In this work, we employed NCA to quantitatively analyze a plant TF-GRN important in floral development using available regulatory information from AGRIS, by processing previously reported gene expression data from four shoot apical meristem cell types.

Results

The NCA model satisfactorily accounted for gene expression measurements in a TF-GRN of seven TFs (LFY, AG, SEPALLATA3 [SEP3], AP2, AGL15, HY5 and AP3/PI) and 55 genes. NCA found strong interactions between certain TF-gene pairs including LFY → MYB17, AG → CRC, AP2 → RD20, AGL15 → RAV2 and HY5 → HLH1, and the direction of the interaction (activation or repression) for some AGL15 targets for which this information was not previously available. The activity trends of four TFs - LFY, AG, HY5 and AP3/PI as deduced by NCA correlated well with the changes in expression levels of the genes encoding these TFs across all four cell types; such a correlation was not observed for SEP3, AP2 and AGL15.

Conclusions

For the first time, we have reported the use of NCA to quantitatively analyze a plant TF-GRN important in floral development for obtaining nontrivial information about connectivity strengths between TFs and their target genes as well as TF activity. However, since NCA relies on documented connectivity information about the underlying TF-GRN, it is currently limited in its application to larger plant networks because of the lack of documented connectivities. In the future, the identification of interactions between plant TFs and their target genes on a genome scale would allow the use of NCA to provide quantitative regulatory information about plant TF-GRNs, leading to improved insights on cellular regulatory programs.

SND1 transcription factor-directed quantitative functional hierarchical genetic regulatory network in wood formation in Populus trichocarpa

Wood is an essential renewable raw material for industrial products and energy. However, knowledge of the genetic regulation of wood formation is limited. We developed a genome-wide high-throughput system for the discovery and validation of specific transcription factor (TF)-directed hierarchical gene regulatory networks (hGRNs) in wood formation. This system depends on a new robust procedure for isolation and transfection of Populus trichocarpa stem differentiating xylem protoplasts. We overexpressed Secondary Wall-Associated NAC Domain 1s (Ptr-SND1-B1), a TF gene affecting wood formation, in these protoplasts and identified differentially expressed genes by RNA sequencing. Direct Ptr-SND1-B1-DNA interactions were then inferred by integration of time-course RNA sequencing data and top-down Graphical Gaussian Modeling-based algorithms. These Ptr-SND1-B1-DNA interactions were verified to function in differentiating xylem by anti-PtrSND1-B1 antibody-based chromatin immunoprecipitation (97% accuracy) and in stable transgenic P. trichocarpa (90% accuracy). In this way, we established a Ptr-SND1-B1-directed quantitative hGRN involving 76 direct targets, including eight TF and 61 enzyme-coding genes previously unidentified as targets. The network can be extended to the third layer from the second-layer TFs by computation or by overexpression of a second-layer TF to identify a new group of direct targets (third layer). This approach would allow the sequential establishment, one two-layered hGRN at a time, of all layers involved in a more comprehensive hGRN. Our approach may be particularly useful to study hGRNs in complex processes in plant species resistant to stable genetic transformation and where mutants are unavailable.

Network analysis for gene discovery in plant-specialized metabolism

Recent omics technologies provide information on multiple components of biological networks. Web-based data mining tools are continuously being developed. Because genes involved in specialized (secondary) metabolism are often co-ordinately regulated at the transcriptional level, a number of gene discovery studies have been successfully conducted using network analysis, especially by integrating gene co-expression network analysis and metabolomic investigation. In addition, next-generation sequencing technologies are currently utilized in functional genomics investigations of Arabidopsis and non-model plant species including medicinal plants. Systems-based approaches are expected to gain importance in medicinal plant research. This review discussed network analysis in Arabidopsis and gene discovery in plant-specialized metabolism in non-model plants.

Comparative analyses of stress-responsive genes in Arabidopsis thaliana: insight from genomic data mining, functional enrichment, pathway analysis and phenomics

Biotic and abiotic stresses adversely affect agriculture by reducing crop growth and productivity worldwide. To investigate the abiotic stress-responsive genes in Arabidopsis thaliana, we compiled a dataset of stress signals and differentially upregulated genes (>= 2.5 fold change) from Stress-responsive transcription Factors DataBase (STIFDB) with additional set of stress signals and genes curated from PubMed and Gene Expression Omnibus. A dataset of 3091 genes differentially upregulated due to 14 different stress signals (abscisic acid, aluminum, cold, cold-drought-salt, dehydration, drought, heat, iron, light, NaCl, osmotic stress, oxidative stress, UV-B and wounding) were curated and used for the analysis. Details about stress-responsive enriched genes and their association with stress signals can be obtained from STIFDB2 database . The gene-stress-signal data were analyzed using an enrichment-based meta-analysis framework consisting of two different ontologies (Gene Ontology and Plant Ontology), biological pathway and functional domain annotations. We found several shared and distinct biological processes, cellular components and molecular functions associated with stress-responsive genes. Pathway analysis revealed that stress-responsive genes perturbed the pathways under the "Metabolic pathways" category. We also found several shared and stress-signal specific protein domains, suggesting functional mechanisms regulating stress-response. Phenomic characteristics of abiotic stress-responsive genes were ascertained for several stresses and found to be shared by multiple stresses in both anatomy and temporal categories of Plant Ontology. We found several constitutive stress-responsive genes that are differentially upregulated due to perturbation of different stress signals, for example a gene (AT1G68440) involved in phenylpropanoid metabolism and polyamine catabolism as responsive to seven different stress signals. We also performed structure-function prediction of five genes associated responsive to multiple abiotic stress signals. We envisage that results from our analysis that provide insight into functional repertoire, metabolic pathways and phenomic characteristics common and specifically associated with stress signals would help to understand abiotic stress regulome in Arabidopsis thaliana and may also help to develop an improved plant variety using molecular breeding and genetic engineering techniques that are rapidly stress-responsive and tolerant.

A developmental transcriptional network for maize defines coexpression modules

Here, we present a genome-wide overview of transcriptional circuits in the agriculturally significant crop species maize (Zea mays). We examined transcript abundance data at 50 developmental stages, from embryogenesis to senescence, for 34,876 gene models and classified genes into 24 robust coexpression modules. Modules were strongly associated with tissue types and related biological processes. Sixteen of the 24 modules (67%) have preferential transcript abundance within specific tissues. One-third of modules had an absence of gene expression in specific tissues. Genes within a number of modules also correlated with the developmental age of tissues. Coexpression of genes is likely due to transcriptional control. For a number of modules, key genes involved in transcriptional control have expression profiles that mimic the expression profiles of module genes, although the expression of transcriptional control genes is not unusually representative of module gene expression. Known regulatory motifs are enriched in several modules. Finally, of the 13 network modules with more than 200 genes, three contain genes that are notably clustered (P < 0.05) within the genome. This work, based on a carefully selected set of major tissues representing diverse stages of maize development, demonstrates the remarkable power of transcript-level coexpression networks to identify underlying biological processes and their molecular components.

Designed transcriptional regulators for trait development

Development is largely controlled by proteins that regulate gene expression at the level of transcription. These regulatory proteins, the genes that control them, and the genes that they control, are organized in a hierarchical structure of complex interactions. Altering the expression of genes encoding regulatory proteins controlling critical nodes in this hierarchy has potential for dramatic phenotypic modification. Constitutive over-expression of genes encoding regulatory proteins in transgenic plants has resulted in agronomically interesting phenotypes along with developmental abnormalities. For trait development, the magnitude and timing of expression of genes encoding key regulatory proteins will need to be precisely controlled and targeted to specific cells and tissues at certain developmental timepoints. Such control is made possible by designed transcriptional regulators which are fusions of engineered DNA binding proteins and activator or repressor domains. Expression of genes encoding such designed transcriptional regulators enable the selective modulation of endogenous gene expression. Genes encoding proteins controlling regulatory networks are prime targets for up- or down-regulation via such designed transcriptional regulators.

STIFDB2: an updated version of plant stress-responsive transcription factor database with additional stress signals, stress-responsive transcription factor binding sites and stress-responsive genes in Arabidopsis and rice

Understanding the principles of abiotic and biotic stress responses, tolerance and adaptation remains important in plant physiology research to develop better varieties of crop plants. Better understanding of plant stress response mechanisms and application of knowledge derived from integrated experimental and bioinformatics approaches are gaining importance. Earlier, we showed that compiling a database of stress-responsive transcription factors and their corresponding target binding sites in the form of Hidden Markov models at promoter, untranslated and upstream regions of stress-up-regulated genes from expression analysis can help in elucidating various aspects of the stress response in Arabidopsis. In addition to the extensive content in the first version, STIFDB2 is now updated with 15 stress signals, 31 transcription factors and 5,984 stress-responsive genes from three species (Arabidopsis thaliana, Oryza sativa subsp. japonica and Oryza sativa subsp. indica). We have employed an integrated biocuration and genomic data mining approach to characterize the data set of transcription factors and consensus binding sites from literature mining and stress-responsive genes from the Gene Expression Omnibus. STIFDB2 currently has 38,798 associations of stress signals, stress-responsive genes and transcription factor binding sites predicted using the Stress-responsive Transcription Factor (STIF) algorithm, along with various functional annotation data. As a unique plant stress regulatory genomics data platform, STIFDB2 can be utilized for targeted as well as high-throughput experimental and computational studies to unravel principles of the stress regulome in dicots and gramineae. STIFDB2 is available from the URL: http://caps.ncbs.res.in/stifdb2.

Personalized medicine: Has it started yet? A reconstruction of the early history

Within the last few years the field personalized medicine entered the stage. Accompanied with great hopes and expectations it is believed that this field may have the potential to revolutionize medical and clinical care by utilizing genomics information about the individual patients themselves. In this paper, we reconstruct the early footprints of personalized medicine as reflected by information retrieved from PubMed and Google Scholar. That means we are providing a data-driven perspective of this field to estimate its current status and potential problems.