Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks

Ancient eukaryotic protein interactions illuminate modern genetic traits and disorders

All eukaryotes share a common ancestor from roughly 1.5 - 1.8 billion years ago, a single-celled, swimming microbe known as LECA, the Last Eukaryotic Common Ancestor. Nearly half of the genes in modern eukaryotes were present in LECA, and many current genetic diseases and traits stem from these ancient molecular systems. To better understand these systems, we compared genes across modern organisms and identified a core set of 10,092 shared protein-coding gene families likely present in LECA, a quarter of which are uncharacterized. We then integrated >26,000 mass spectrometry proteomics analyses from 31 species to infer how these proteins interact in higher-order complexes. The resulting interactome describes the biochemical organization of LECA, revealing both known and new assemblies. We analyzed these ancient protein interactions to find new human gene-disease relationships for bone density and congenital birth defects, demonstrating the value of ancestral protein interactions for guiding functional genetics today.

Combined Proteomic and Metabolomic Analyses Reveal the Comprehensive Regulation of Stropharia rugosoannulata Mycelia Exposed to Cadmium Stress

The potential of Stropharia rugosoannulata as a microbial remediation material for cadmium (Cd)-contaminated soil lies in its capacity to absorb and accumulate Cd in its mycelia. This study utilized the TMT and LC-MS techniques to conduct integrated proteomic and metabolomic analyses with the aim of investigating the mycelial response mechanisms of S. rugosoannulata under low- and high-Cd stresses. The results revealed that mycelia employed a proactive defense mechanism to maintain their physiological functions, leading to reduced sensitivity to low-Cd stress. The ability of mycelia to withstand high levels of Cd stress was influenced primarily by the comprehensive regulation of six metabolic pathways, which led to a harmonious balance between nitrogen and carbohydrate metabolism and to reductions in oxidative stress and growth inhibition caused by Cd. The results provide valuable insights into the molecular mechanisms involved in the response of S. rugosoannulata mycelia to Cd stress.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

From Classical to Modern Computational Approaches to Identify Key Genetic Regulatory Components in Plant Biology

The selection of plant genotypes with improved productivity and tolerance to environmental constraints has always been a major concern in plant breeding. Classical approaches based on the generation of variability and selection of better phenotypes from large variant collections have improved their efficacy and processivity due to the implementation of molecular biology techniques, particularly genomics, Next Generation Sequencing and other omics such as proteomics and metabolomics. In this regard, the identification of interesting variants before they develop the phenotype trait of interest with molecular markers has advanced the breeding process of new varieties. Moreover, the correlation of phenotype or biochemical traits with gene expression or protein abundance has boosted the identification of potential new regulators of the traits of interest, using a relatively low number of variants. These important breakthrough technologies, built on top of classical approaches, will be improved in the future by including the spatial variable, allowing the identification of gene(s) involved in key processes at the tissue and cell levels.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Regulatory Modules of Metabolites and Protein Phosphorylation in Arabidopsis Genotypes With Altered Sucrose Allocation

Multi-omics data sets are increasingly being used for the interpretation of cellular processes in response to environmental cues. Especially, the posttranslational modification of proteins by phosphorylation is an important regulatory process affecting protein activity and/or localization, which, in turn, can have effects on metabolic processes and metabolite levels. Despite this importance, relationships between protein phosphorylation status and metabolite abundance remain largely underexplored. Here, we used a phosphoproteomics-metabolomics data set collected at the end of day and night in shoots and roots of Arabidopsis to propose regulatory relationships between protein phosphorylation and accumulation or allocation of metabolites. For this purpose, we introduced a novel, robust co-expression measure suited to the structure of our data sets, and we used this measure to construct metabolite-phosphopeptide networks. These networks were compared between wild type and plants with perturbations in key processes of sugar metabolism, namely, sucrose export (sweet11/12 mutant) and starch synthesis (pgm mutant). The phosphopeptide-metabolite network turned out to be highly sensitive to perturbations in sugar metabolism. Specifically, KING1, the regulatory subunit of SnRK1, was identified as a primary candidate connecting protein phosphorylation status with metabolism. We additionally identified strong changes in the fatty acid network of the sweet11/12 mutant, potentially resulting from a combination of fatty acid signaling and metabolic overflow reactions in response to high internal sucrose concentrations. Our results further suggest novel protein-metabolite relationships as candidates for future targeted research.

Omics Path to Increasing Productivity in Less-Studied Crops Under Changing Climate-Lentil a Case Study

Conventional breeding techniques for crop improvement have reached their full potential, and hence, alternative routes are required to ensure a sustained genetic gain in lentils. Although high-throughput omics technologies have been effectively employed in major crops, less-studied crops such as lentils have primarily relied on conventional breeding. Application of genomics and transcriptomics in lentils has resulted in linkage maps and identification of QTLs and candidate genes related to agronomically relevant traits and biotic and abiotic stress tolerance. Next-generation sequencing (NGS) complemented with high-throughput phenotyping (HTP) technologies is shown to provide new opportunities to identify genomic regions and marker-trait associations to increase lentil breeding efficiency. Recent introduction of image-based phenotyping has facilitated to discern lentil responses undergoing biotic and abiotic stresses. In lentil, proteomics has been performed using conventional methods such as 2-D gel electrophoresis, leading to the identification of seed-specific proteome. Metabolomic studies have led to identifying key metabolites that help differentiate genotypic responses to drought and salinity stresses. Independent analysis of differentially expressed genes from publicly available transcriptomic studies in lentils identified 329 common transcripts between heat and biotic stresses. Similarly, 19 metabolites were common across legumes, while 31 were common in genotypes exposed to drought and salinity stress. These common but differentially expressed genes/proteins/metabolites provide the starting point for developing high-yielding multi-stress-tolerant lentils. Finally, the review summarizes the current findings from omic studies in lentils and provides directions for integrating these findings into a systems approach to increase lentil productivity and enhance resilience to biotic and abiotic stresses under changing climate.

Global Comparative Label-Free Yeast Proteome Analysis by LC-MS/MS After High-pH Reversed-Phase Peptide Fractionation Using Solid-Phase Extraction Cartridges

Discovery-driven comparative proteomics employing the bottom-up strategy with label-free quantification on high-resolution mass analyzers like an Orbitrap in a hybrid instrument has the capacity to reveal unique biological processes in the context of plant metabolic engineering. However, proteins are very heterogeneous in nature with a wide range of expression levels, and overall coverage may be suboptimal regarding both the number of protein identifications and sequence coverage of the identified proteins using conventional data-dependent acquisitions without sample fractionation before online nanoflow liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MS/MS). In this chapter, we detail a simple and robust method employing high-pH reversed-phase (HRP) peptide fractionation using solid-phase extraction cartridges for label-free proteomic analyses. Albeit HRP fractionation separates peptides according to their hydrophobicity like the subsequent nanoflow gradient reversed-phased LC relying on low pH mobile phase, the two methods are orthogonal. Presented here as a protocol with yeast (Saccharomyces cerevisiae) as a frequently used model organism and hydrogen peroxide to exert cellular stress and survey its impact compared to unstressed control as an example, the described workflow can be adapted to a wide range of proteome samples for applications to plant metabolic engineering research.

Application of jasmonic acid at the stage of visible brown necrotic spots in Magnaporthe oryzae infection as a novel and environment-friendly control strategy for rice blast disease

Rice blast disease is one of the most common rice diseases worldwide. It is essential to improve disease resistance through environment-friendly methods, while maintaining yield and quality parameters. In this study, jasmonic acid (JA), a plant hormone with anti-fungal activity, was obtained, at both low (100 μmol/L) and high (400 μmol/L) concentrations in rice leaves, before, during, and after infection, respectively. JA could inhibit germination and appressorium formation of rice blast spores in a dose-dependent manner. A total of 400-μmol/L JA treatment significantly enhanced cell viability and endogenous JA level in rice leaves. Furthermore, rice leaves inoculated with Magnaporthe oryzae and sprayed with JA 72 h post-inoculation showed the maximum symptom relief and the highest endogenous JA production among all treatment approaches. The expressions of defense-related genes, OsPR10a and OsAOS2, were highly up-regulated in response to JA, whereas OsEDS1 was down-regulated. Hence, we revealed that exogenous JA could activate JA signaling to effectively control the symptoms of rice blast.

Characterization of ASR gene and its role in drought tolerance in chickpea (Cicer arietinum L.)

Chickpea has a profound nutritional and economic value in vegetarian society. Continuous decline in chickpea productivity is attributed to insufficient genetic variability and different environmental stresses. Chickpea like several other legumes is highly susceptible to terminal drought stress. Multiple genes control drought tolerance and ASR gene plays a key role in regulating different plant stresses. The present study describes the molecular characterization and functional role of Abscissic acid and stress ripening (ASR) gene from chickpea (Cicer arietinum) and the gene sequence identified was submitted to NCBI Genbank (MK937569). Molecular analysis using MUSCLE software proved that the ASR nucleotide sequences in different legumes show variations at various positions though ASR genes are conserved in chickpea with only few variations. Sequence similarity of ASR gene to chickpea putative ABA/WDS induced protein mRNA clearly indicated its potential involvement in drought tolerance. Physiological screening and qRT-PCR results demonstrated increased ASR gene expression under drought stress possibly enabled genotypes to perform better under stress. Conserved domain search, protein structure analysis, prediction and validation, network analysis using Phyre2, Swiss-PDB viewer, ProSA and STRING analysis established the role of hypothetical ASR protein NP_001351739.1 in mediating drought responses. NP_001351739.1 might have enhanced the ASR gene activity as a transcription factor regulating drought stress tolerance in chickpea. This study could be useful in identification of new ASR genes that play a major role in drought tolerance and also develop functional markers for chickpea improvement.

Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.

A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

Bioinformatics-assisted, integrated omics studies on medicinal plants

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

Enhanced Salt Tolerance of Rhizobia-inoculated Soybean Correlates with Decreased Phosphorylation of the Transcription Factor GmMYB183 and Altered Flavonoid Biosynthesis

Soybean (Glycine max (L.) Merrill) is an important component of the human diet and animal feed, but soybean production is limited by abiotic stresses especially salinity. We recently found that rhizobia inoculation enhances soybean tolerance to salt stress, but the underlying mechanisms are unaddressed. Here, we used quantitative phosphoproteomic and metabonomic approaches to identify changes in phosphoproteins and metabolites in soybean roots treated with rhizobia inoculation and salt. Results revealed differential regulation of 800 phosphopeptides, at least 32 of these phosphoproteins or their homologous were reported be involved in flavonoid synthesis or trafficking, and 27 out of 32 are transcription factors. We surveyed the functional impacts of all these 27 transcription factors by expressing their phospho-mimetic/ablative mutants in the roots of composite soybean plants and found that phosphorylation of GmMYB183 could affect the salt tolerance of the transgenic roots. Using data mining, ChIP and EMSA, we found that GmMYB183 binds to the promoter of the soybean GmCYP81E11 gene encoding for a Cytochrome P450 monooxygenase which contributes to the accumulation of ononin, a monohydroxy B-ring flavonoid that negatively regulates soybean tolerance to salinity. Phosphorylation of GmMYB183 was inhibited by rhizobia inoculation; overexpression of GmMYB183 enhanced the expression of GmCYP81E11 and rendered salt sensitivity to the transgenic roots; plants deficient in GmMYB183 function are more tolerant to salt stress as compared with wild-type soybean plants, these results correlate with the transcriptional induction of GmCYP81E11 by GmMYB183 and the subsequent accumulation of ononin. Our findings provide molecular insights into how rhizobia enhance salt tolerance of soybean plants.

Highly Resolved Systems Biology to Dissect the Etioplast-to-Chloroplast Transition in Tobacco Leaves

Upon exposure to light, plant cells quickly acquire photosynthetic competence by converting pale etioplasts into green chloroplasts. This developmental transition involves the de novo biogenesis of the thylakoid system and requires reprogramming of metabolism and gene expression. Etioplast-to-chloroplast differentiation involves massive changes in plastid ultrastructure, but how these changes are connected to specific changes in physiology, metabolism, and expression of the plastid and nuclear genomes is poorly understood. Here, we describe a new experimental system in the dicotyledonous model plant tobacco (Nicotiana tabacum) that allows us to study the leaf deetiolation process at the systems level. We have determined the accumulation kinetics of photosynthetic complexes, pigments, lipids, and soluble metabolites and recorded the dynamic changes in plastid ultrastructure and in the nuclear and plastid transcriptomes. Our data describe the greening process at high temporal resolution, resolve distinct genetic and metabolic phases during deetiolation, and reveal numerous candidate genes that may be involved in light-induced chloroplast development and thylakoid biogenesis.

Unintended effects of transgenic rice revealed by transcriptome and metabolism

Genetically modified (GM) organisms have been developed for decades. However, unintended effects are the main concerns of safety assessment that needs to be carefully investigated. Here, eight varieties of GM rice that were developed in China were selected to assess the unintended effects through transcriptome and metabolism. There are 2892-8758 differentially expressed genes (DEGs) and 7-50 metabolites at significant level between GM varieties and their isogenic counterparts, which were far fewer than that between traditional rice varieties. The function enrichment analysis showed altered transcription in stress-related pathway and starch and sucrose metabolism. DEGs shared among eight GM samples constitute less than 1% of the genes in the genome, and none of them is reported more than four times. The insertion effect on the nearby gene expression and the associated metabolism is only restricted to 50 genes. All the results provide a comprehensive analysis of unintended effects and indication of difference in Chinese transgenic rice based on their backgrounds, transformation, and insertion elements.

Progress in plant genome sequencing: research directions

Since the first plant genome of Arabidopsis thaliana has been sequenced and published, genome sequencing technologies have undergone significant changes. New algorithms, sequencing technologies and bioinformatic approaches were adopted to obtain genome, transcriptome and exome sequences for model and crop species, which have permitted deep inferences into plant biology. As a result of an improved genome assembly and analysis methods, genome sequencing costs plummeted and the number of high-quality plant genome sequences is constantly growing. Consequently, more than 300 plant genome sequences have been published over the past twenty years. Although many of the published genomes are considered incomplete, they proved to be a valuable tool for identifying genes involved in the formation of economically valuable plant traits, for marker-assisted and genomic selection and for comparative analysis of plant genomes in order to determine the basic patterns of origin of various plant species. Since a high coverage and resolution of a genome sequence is not enough to detect all changes in complex samples, targeted sequencing, which consists in the isolation and sequencing of a specific region of the genome, has begun to develop. Targeted sequencing has a higher detection power (the ability to identify new differences/variants) and resolution (up to one basis). In addition, exome sequencing (the method of sequencing only protein-coding genes regions) is actively developed, which allows for the sequencing of non-expressed alleles and genes that cannot be found with RNA-seq. In this review, an analysis of sequencing technologies development and the construction of “reference” genomes of plants is performed. A comparison of the methods of targeted sequencing based on the use of the reference DNA sequence is accomplished.

A Network-Guided Genetic Approach to Identify Novel Regulators of Adventitious Root Formation in Arabidopsis thaliana

Adventitious roots (ARs) are formed de novo during post-embryonic development from non-root tissues, in processes that are highly dependent on environmental inputs. Whole root excision from young seedlings has been previously used as a model to study adventitious root formation in Arabidopsis thaliana hypocotyls. To identify novel regulators of adventitious root formation, we analyzed adventitious rooting in the hypocotyl after whole root excision in 112 T-DNA homozygous leaf mutants, which were selected based on the dynamic expression profiles of their annotated genes during hormone-induced and wound-induced tissue regeneration. Forty-seven T-DNA homozygous lines that displayed low rooting capacity as regards their wild-type background were dubbed as the less adventitious roots (lars) mutants. We identified eight lines with higher rooting capacity than their wild-type background that we named as the more adventitious roots (mars) mutants. A relatively large number of mutants in ribosomal protein-encoding genes displayed a significant reduction in adventitious root number in the hypocotyl after whole root excision. In addition, gene products related to gibberellin (GA) biosynthesis and signaling, auxin homeostasis, and xylem differentiation were confirmed to participate in adventitious root formation. Nearly all the studied mutants tested displayed similar rooting responses from excised whole leaves, which suggest that their affected genes participate in shared regulatory pathways required for de novo organ formation in different organs.

Applying the latest advances in genomics and phenomics for trait discovery in polyploid wheat

Improving traits in wheat has historically been challenging due to its large and polyploid genome, limited genetic diversity and in-field phenotyping constraints. However, within recent years many of these barriers have been lowered. The availability of a chromosome-level assembly of the wheat genome now facilitates a step-change in wheat genetics and provides a common platform for resources, including variation data, gene expression data and genetic markers. The development of sequenced mutant populations and gene-editing techniques now enables the rapid assessment of gene function in wheat directly. The ability to alter gene function in a targeted manner will unmask the effects of homoeolog redundancy and allow the hidden potential of this polyploid genome to be discovered. New techniques to identify and exploit the genetic diversity within wheat wild relatives now enable wheat breeders to take advantage of these additional sources of variation to address challenges facing food production. Finally, advances in phenomics have unlocked rapid screening of populations for many traits of interest both in greenhouses and in the field. Looking forwards, integrating diverse data types, including genomic, epigenetic and phenomics data, will take advantage of big data approaches including machine learning to understand trait biology in wheat in unprecedented detail.

Integrated physiologic, proteomic, and metabolomic analyses of Malus halliana adaptation to saline-alkali stress

Saline-alkali stress is a severely adverse abiotic stress limiting plant growth. Malus halliana Koehne is an apple rootstock that is tolerant to saline-alkali stress. To understand the molecular mechanisms underlying the tolerance of M. halliana to saline-alkali stress, an integrated metabolomic and proteomic approach was used to analyze the plant pathways involved in the stress response of the plant and its regulatory mechanisms. A total of 179 differentially expressed proteins (DEPs) and 140 differentially expressed metabolites (DEMs) were identified. We found that two metabolite-related enzymes (PPD and PAO) were associated with senescence and involved in porphyrin and chlorophyll metabolism; six photosynthesis proteins (PSAH2, PSAK, PSBO2, PSBP1, and PSBQ2) were significantly upregulated, especially PSBO2, and could act as regulators of photosystem II (PSII) repair. Sucrose, acting as a signaling molecule, directly mediated the accumulation of D-phenylalanine, tryptophan, and alkaloid (vindoline and ecgonine) and the expression of proteins related to aspartate and glutamate (ASP3, ASN1, NIT4, and GLN1-1). These responses play a central role in maintaining osmotic balance and removing reactive oxygen species (ROS). In addition, sucrose signaling induced flavonoid biosynthesis by activating the expression of CYP75B1 to regulate the homeostasis of ROS and promoted auxin signaling by activating the expression of T31B5_170 to enhance the resistance of M. halliana to saline-alkali stress. The decrease in peroxidase superfamily protein (PER) and ALDH2C4 during lignin synthesis further triggered a plant saline-alkali response. Overall, this study provides an important starting point for improving saline-alkali tolerance in M. halliana via genetic engineering.

The Spring of Systems Biology-Driven Breeding

Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies.

Plant Bio-Wars: Maize Protein Networks Reveal Tissue-Specific Defense Strategies in Response to a Root Herbivore

In this study we examined global changes in protein expression in both roots and leaves of maize plants attacked by the root herbivore, Western corn rootworm (WCR, Diabrotica virgifera virgifera). The changes in protein expression Are indicative of metabolic changes during WCR feeding that enable the plant to defend itself. This is one of the first studies to look above- and below-ground at global protein expression patterns of maize plants grown in soil and infested with a root herbivore. We used advanced proteomic and network analyses to identify metabolic pathways that contribute to global defenses deployed by the insect resistant maize genotype, Mp708, infested with WCR. Using proteomic analysis, 4878 proteins in roots and leaves were detected and of these 863 showed significant changes of abundance during WCR infestation. Protein abundance patterns were analyzed using hierarchical clustering, protein correlation and protein-protein interaction networks. All three data analysis pipelines showed that proteins such as jasmonic acid biosynthetic enzymes, serine proteases, protease inhibitors, proteins involved in biosynthesis and signaling of ethylene, and enzymes producing reactive oxygen species and isopentenyl pyrophosphate, a precursor for volatile production, were upregulated in roots during WCR infestation. In leaves, highly abundant proteins were involved in signal perception suggesting activation of systemic signaling. We conclude that these protein networks contribute to the overall herbivore defense mechanisms in Mp708. Because the plants were grown in potting mix and not sterilized sand, we found that both microbial and insect defense-related proteins were present in the roots. The presence of the high constitutive levels of reduced ascorbate in roots and benzothiazole in the root volatile profiles suggest a tight tri-trophic interaction among the plant, soil microbiomes and WCR-infested roots suggesting that defenses against insects coexist with defenses against bacteria and fungi due to the interaction between roots and soil microbiota. In this study, which is one of the most complete descriptions of plant responses to root-feeding herbivore, we established an analysis pipeline for proteomics data that includes network biology that can be used with different types of "omics" data from a variety of organisms.

Quantitative Phosphoproteomic and Metabolomic Analyses Reveal GmMYB173 Optimizes Flavonoid Metabolism in Soybean under Salt Stress

Salinity causes osmotic stress to crops and limits their productivity. To understand the mechanism underlying soybean salt tolerance, proteomics approach was used to identify phosphoproteins altered by NaCl treatment. Results revealed that 412 of the 4698 quantitatively analyzed phosphopeptides were significantly up-regulated on salt treatment, including a phosphopeptide covering the serine 59 in the transcription factor GmMYB173. Our data showed that GmMYB173 is one of the three MYB proteins differentially phosphorylated on salt treatment, and a substrate of the casein kinase-II. MYB recognition sites exist in the promoter of flavonoid synthase gene GmCHS5 and one was found to mediate its recognition by GmMYB173, an event facilitated by phosphorylation. Because GmCHS5 catalyzes the synthesis of chalcone, flavonoids derived from chalcone were monitored using metabolomics approach. Results revealed that 24 flavonoids of 6745 metabolites were significantly up-regulated after salt treatment. We further compared the salt tolerance and flavonoid accumulation in soybean transgenic roots expressing the 35S promoter driven cds and RNAi constructs of GmMYB173 and GmCHS5, as well as phospho-mimic (GmMYB173S59D ) and phospho-ablative (GmMYB173S59A ) mutants of GmMYB173 Overexpression of GmMYB173S59D and GmCHS5 resulted in the highest increase in salt tolerance and accumulation of cyaniding-3-arabinoside chloride, a dihydroxy B-ring flavonoid. The dihydroxy B-ring flavonoids are more effective as anti-oxidative agents when compared with monohydroxy B-ring flavonoids, such as formononetin. Hence the salt-triggered phosphorylation of GmMYB173, subsequent increase in its affinity to GmCHS5 promoter and the elevated transcription of GmCHS5 likely contribute to soybean salt tolerance by enhancing the accumulation of dihydroxy B-ring flavonoids.

A Systems Biology Study in Tomato Fruit Reveals Correlations between the Ascorbate Pool and Genes Involved in Ribosome Biogenesis, Translation, and the Heat-Shock Response

Changing the balance between ascorbate, monodehydroascorbate, and dehydroascorbate in plant cells by manipulating the activity of enzymes involved in ascorbate synthesis or recycling of oxidized and reduced forms leads to multiple phenotypes. A systems biology approach including network analysis of the transcriptome, proteome and metabolites of RNAi lines for ascorbate oxidase, monodehydroascorbate reductase and galactonolactone dehydrogenase has been carried out in orange fruit pericarp of tomato (Solanum lycopersicum). The transcriptome of the RNAi ascorbate oxidase lines is inversed compared to the monodehydroascorbate reductase and galactonolactone dehydrogenase lines. Differentially expressed genes are involved in ribosome biogenesis and translation. This transcriptome inversion is also seen in response to different stresses in Arabidopsis. The transcriptome response is not well correlated with the proteome which, with the metabolites, are correlated to the activity of the ascorbate redox enzymes-ascorbate oxidase and monodehydroascorbate reductase. Differentially accumulated proteins include metacaspase, protein disulphide isomerase, chaperone DnaK and carbonic anhydrase and the metabolites chlorogenic acid, dehydroascorbate and alanine. The hub genes identified from the network analysis are involved in signaling, the heat-shock response and ribosome biogenesis. The results from this study therefore reveal one or several putative signals from the ascorbate pool which modify the transcriptional response and elements downstream.

Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data

The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

Identifying gene coexpression networks underlying the dynamic regulation of wood-forming tissues in Populus under diverse environmental conditions

Trees modify wood formation through integration of environmental and developmental signals in complex but poorly defined transcriptional networks, allowing trees to produce woody tissues appropriate to diverse environmental conditions. In order to identify relationships among genes expressed during wood formation, we integrated data from new and publically available datasets in Populus. These datasets were generated from woody tissue and include transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments. Coexpression modules were calculated, each of which contains genes showing similar expression patterns across experimental conditions, genotypes and treatments. Conserved gene coexpression modules (four modules totaling 8398 genes) were identified that were highly preserved across diverse environmental conditions and genetic backgrounds. Functional annotations as well as correlations with specific experimental treatments associated individual conserved modules with distinct biological processes underlying wood formation, such as cell-wall biosynthesis, meristem development and epigenetic pathways. Module genes were also enriched for DNase I hypersensitivity footprints and binding from four transcription factors associated with wood formation. The conserved modules are excellent candidates for modeling core developmental pathways common to wood formation in diverse environments and genotypes, and serve as testbeds for hypothesis generation and testing for future studies.

The Transcription Factor ATHB5 Affects GA-Mediated Plasticity in Hypocotyl Cell Growth during Seed Germination

Gibberellic acid (GA)-mediated cell expansion initiates the seed-to-seedling transition in plants and is repressed by DELLA proteins. Using digital single-cell analysis, we identified a cellular subdomain within the midhypocotyl, whose expansion drives the final step of this developmental transition under optimal conditions. Using network inference, the transcription factor ATHB5 was identified as a genetic factor whose localized expression promotes GA-mediated expansion specifically within these cells. Both this protein and its putative growth-promoting target EXPANSIN3 are repressed by DELLA, and coregulated at single-cell resolution during seed germination. The cellular domains of hormone sensitivity were explored within the Arabidopsis (Arabidopsis thaliana) embryo by putting seeds under GA-limiting conditions and quantifying cellular growth responses. The middle and upper hypocotyl have a greater requirement for GA to promote cell expansion than the lower embryo axis. Under these conditions, germination was still completed following enhanced growth within the radicle and lower axis. Under GA-limiting conditions, the athb5 mutant did not show a phenotype at the level of seed germination, but it did at a cellular level with reduced cell expansion in the hypocotyl relative to the wild type. These data reveal that the spatiotemporal cell expansion events driving this transition are not determinate, and the conditional use of GA-ATHB5-mediated hypocotyl growth under optimal conditions may be used to optionally support rapid seedling growth. This study demonstrates that multiple genetic and spatiotemporal cell expansion mechanisms underlie the seed to seedling transition in Arabidopsis.

Effects of MeJA on Arabidopsis metabolome under endogenous JA deficiency

Jasmonates (JAs) play important roles in plant growth, development and defense. Comprehensive metabolomics profiling of plants under JA treatment provides insights into the interaction and regulation network of plant hormones. Here we applied high resolution mass spectrometry based metabolomics approach on Arabidopsis wild type and JA synthesis deficiency mutant opr3. The effects of exogenous MeJA treatment on the metabolites of opr3 were investigated. More than 10000 ion signals were detected and more than 2000 signals showed significant variation in different genotypes and treatment groups. Multivariate statistic analyses (PCA and PLS-DA) were performed and a differential compound library containing 174 metabolites with high resolution precursor ion-product ions pairs was obtained. Classification and pathway analysis of 109 identified compounds in this library showed that glucosinolates and tryptophan metabolism, amino acids and small peptides metabolism, lipid metabolism, especially fatty acyls metabolism, were impacted by endogenous JA deficiency and exogenous MeJA treatment. These results were further verified by quantitative reverse transcription PCR (RT-qPCR) analysis of 21 related genes involved in the metabolism of glucosinolates, tryptophan and α-linolenic acid pathways. The results would greatly enhance our understanding of the biological functions of JA.

Computational inference of gene regulatory networks: Approaches, limitations and opportunities

Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Gene coexpression network analysis of oil biosynthesis in an interspecific backcross of oil palm

Global demand for vegetable oils is increasing at a dramatic rate, while our understanding of the regulation of oil biosynthesis in plants remains limited. To gain insights into the mechanisms that govern oil synthesis and fatty acid (FA) composition in the oil palm fruit, we used a multilevel approach combining gene coexpression analysis, quantification of allele-specific expression and joint multivariate analysis of transcriptomic and lipid data, in an interspecific backcross population between the African oil palm, Elaeis guineensis, and the American oil palm, Elaeis oleifera, which display contrasting oil contents and FA compositions. The gene coexpression network produced revealed tight transcriptional coordination of fatty acid synthesis (FAS) in the plastid with sugar sensing, plastidial glycolysis, transient starch storage and carbon recapture pathways. It also revealed a concerted regulation, along with FAS, of both the transfer of nascent FA to the endoplasmic reticulum, where triacylglycerol assembly occurs, and of the production of glycerol-3-phosphate, which provides the backbone of triacylglycerols. Plastid biogenesis and auxin transport were the two other biological processes most tightly connected to FAS in the network. In addition to WRINKLED1, a transcription factor (TF) known to activate FAS genes, two novel TFs, termed NF-YB-1 and ZFP-1, were found at the core of the FAS module. The saturated FA content of palm oil appeared to vary above all in relation to the level of transcripts of the gene coding for β-ketoacyl-acyl carrier protein synthase II. Our findings should facilitate the development of breeding and engineering strategies in this and other oil crops.

Are We There Yet? Reliably Estimating the Completeness of Plant Genome Sequences

Genome sequencing is becoming cheaper and faster thanks to the introduction of next-generation sequencing techniques. Dozens of new plant genome sequences have been released in recent years, ranging from small to gigantic repeat-rich or polyploid genomes. Most genome projects have a dual purpose: delivering a contiguous, complete genome assembly and creating a full catalog of correctly predicted genes. Frequently, the completeness of a species' gene catalog is measured using a set of marker genes that are expected to be present. This expectation can be defined along an evolutionary gradient, ranging from highly conserved genes to species-specific genes. Large-scale population resequencing studies have revealed that gene space is fairly variable even between closely related individuals, which limits the definition of the expected gene space, and, consequently, the accuracy of estimates used to assess genome and gene space completeness. We argue that, based on the desired applications of a genome sequencing project, different completeness scores for the genome assembly and/or gene space should be determined. Using examples from several dicot and monocot genomes, we outline some pitfalls and recommendations regarding methods to estimate completeness during different steps of genome assembly and annotation.

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

In recent years, rapid developments in several omics platforms and next generation sequencing technology have generated a huge amount of biological data about plants. Systems biology aims to develop and use well-organized and efficient algorithms, data structure, visualization, and communication tools for the integration of these biological data with the goal of computational modeling and simulation. It studies crop plant systems by systematically perturbing them, checking the gene, protein, and informational pathway responses; integrating these data; and finally, formulating mathematical models that describe the structure of system and its response to individual perturbations. Consequently, systems biology approaches, such as integrative and predictive ones, hold immense potential in understanding of molecular mechanism of agriculturally important complex traits linked to agricultural productivity. This has led to identification of some key genes and proteins involved in networks of pathways involved in input use efficiency, biotic and abiotic stress resistance, photosynthesis efficiency, root, stem and leaf architecture, and nutrient mobilization. The developments in the above fields have made it possible to design smart crops with superior agronomic traits through genetic manipulation of key candidate genes.

Mapping Transcriptional Networks in Plants: Data-Driven Discovery of Novel Biological Mechanisms

In plants, systems biology approaches have led to the generation of a variety of large data sets. Many of these data are created to elucidate gene expression profiles and their corresponding transcriptional regulatory mechanisms across a range of tissue types, organs, and environmental conditions. In an effort to map the complexity of this transcriptional regulatory control, several types of experimental assays have been used to map transcriptional regulatory networks. In this review, we discuss how these methods can be best used to identify novel biological mechanisms by focusing on the appropriate biological context. Translating network biology back to gene function in the plant, however, remains a challenge. We emphasize the need for validation and insight into the underlying biological processes to successfully exploit systems approaches in an effort to determine the emergent properties revealed by network analyses.

PP2A-3 interacts with ACR4 and regulates formative cell division in the Arabidopsis root

In plants, the generation of new cell types and tissues depends on coordinated and oriented formative cell divisions. The plasma membrane-localized receptor kinase ARABIDOPSIS CRINKLY 4 (ACR4) is part of a mechanism controlling formative cell divisions in the Arabidopsis root. Despite its important role in plant development, very little is known about the molecular mechanism with which ACR4 is affiliated and its network of interactions. Here, we used various complementary proteomic approaches to identify ACR4-interacting protein candidates that are likely regulators of formative cell divisions and that could pave the way to unraveling the molecular basis behind ACR4-mediated signaling. We identified PROTEIN PHOSPHATASE 2A-3 (PP2A-3), a catalytic subunit of PP2A holoenzymes, as a previously unidentified regulator of formative cell divisions and as one of the first described substrates of ACR4. Our in vitro data argue for the existence of a tight posttranslational regulation in the associated biochemical network through reciprocal regulation between ACR4 and PP2A-3 at the phosphorylation level.

Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses

Differentially evolved responses to various stress conditions in plants are controlled by complex regulatory circuits of transcriptional activators, and repressors, such as transcription factors (TFs). To understand the general and condition-specific activities of the TFs and their regulatory relationships with the target genes (TGs), we have used a homogeneous stress gene expression dataset generated on ten natural ecotypes of the model plant Arabidopsis thaliana, during five single and six combined stress conditions. Knowledge-based profiles of binding sites for 25 stress-responsive TF families (187 TFs) were generated and tested for their enrichment in the regulatory regions of the associated TGs. Condition-dependent regulatory sub-networks have shed light on the differential utilization of the underlying network topology, by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions. Clustering patterns of transcription factor binding sites (TFBS) have reflected the combinatorial nature of transcriptional regulation, and suggested the putative role of the homotypic clusters of TFBS towards maintaining transcriptional robustness against cis-regulatory mutations to facilitate the preservation of stress response processes. The Gene Ontology enrichment analysis of the TGs reflected sequential regulation of stress response mechanisms in plants.

Identifying Gene Regulatory Networks in Arabidopsis by In Silico Prediction, Yeast-1-Hybrid, and Inducible Gene Profiling Assays

A system-wide understanding of gene regulation will provide deep insights into plant development and physiology. In this chapter we describe a threefold approach to identify the gene regulatory networks in Arabidopsis thaliana that function in a specific tissue or biological process. Since no single method is sufficient to establish comprehensive and high-confidence gene regulatory networks, we focus on the integration of three approaches. First, we describe an in silico prediction method of transcription factor-DNA binding, then an in vivo assay of transcription factor-DNA binding by yeast-1-hybrid and lastly the identification of co-expression clusters by transcription factor induction in planta. Each of these methods provides a unique tool to advance our understanding of gene regulation, and together provide a robust model for the generation of gene regulatory networks.

AtRAV1 and AtRAV2 overexpression in cotton increases fiber length differentially under drought stress and delays flowering

There is a longstanding problem of an inverse relationship between cotton fiber qualities versus high yields. To better understand drought stress signaling and adaptation in cotton (Gossypium hirsutum) fiber development, we expressed the Arabidopsis transcription factors RELATED_TO_ABA-INSENSITIVE3/VIVIPAROUS1/(RAV1) and AtRAV2, which encode APETALA2-Basic3 domain proteins shown to repress transcription of FLOWERING_LOCUS_T (FT) and to promote stomatal opening cell-autonomously. In three years of field trials, we show that AtRAV1 and AtRAV2-overexpressing cotton had ∼5% significantly longer fibers with only marginal decreases in yields under well-watered or drought stress conditions that resulted in 40-60% yield penalties and 3-7% fiber length penalties in control plants. The longer transgenic fibers from drought-stressed transgenics could be spun into yarn which was measurably stronger and more uniform than that from well-watered control fibers. The transgenic AtRAV1 and AtRAV2 lines flowered later and retained bolls at higher nodes, which correlated with repression of endogenous GhFT-Like (FTL) transcript accumulation. Elevated expression early in development of ovules was observed for GhRAV2L, GhMYB25-Like (MYB25L) involved in fiber initiation, and GhMYB2 and GhMYB25 involved in fiber elongation. Altered expression of RAVs controlling critical nodes in developmental and environmental signaling hierarchies has the potential for phenotypic modification of crops.

Transgenic Rice Expressing Ictb and FBP/Sbpase Derived from Cyanobacteria Exhibits Enhanced Photosynthesis and Mesophyll Conductance to CO2

To find a way to promote the rate of carbon flux and further improve the photosynthetic rate in rice, two CO2-transporting and fixing relevant genes, Ictb and FBP/Sbpase, which were derived from cyanobacteria with the 35SCaMV promotor in the respective constructs, were transformed into rice. Three homologous transgenic groups with Ictb, FBP/Sbpase and the two genes combined were constructed in parallel, and the functional effects of these two genes were investigated by physiological, biochemical and leaf anatomy analyses. The results indicated that the mesophyll conductance and net photosynthetic rate were higher at approximately 10.5-36.8% and 13.5-34.6%, respectively, in the three groups but without any changes in leaf anatomy structure compared with wild type. Other physiological and biochemical parameters increased with the same trend in the three groups, which showed that the effect of FBP/SBPase on improving photosynthetic capacity was better than that of ICTB and that there was an additive effect in ICTB+FBP/SBPase. ICTB localized in the cytoplasm, whereas FBP/SBPase was successfully transported to the chloroplast. The two genes might show a synergistic interaction to promote carbon flow and the assimilation rate as a whole. The multigene transformation engineering and its potential utility for improving the photosynthetic capacity and yield in rice were discussed.

Genes and networks regulating root anatomy and architecture

The root is an excellent model for studying developmental processes that underlie plant anatomy and architecture. Its modular structure, the lack of cell movement and relative accessibility to microscopic visualization facilitate research in a number of areas of plant biology. In this review, we describe several examples that demonstrate how cell type-specific developmental mechanisms determine cell fate and the formation of defined tissues with unique characteristics. In the last 10 yr, advances in genome-wide technologies have led to the sequencing of thousands of plant genomes, transcriptomes and proteomes. In parallel with the development of these high-throughput technologies, biologists have had to establish computational, statistical and bioinformatic tools that can deal with the wealth of data generated by them. These resources provide a foundation for posing more complex questions about molecular interactions, and have led to the discovery of new mechanisms that control phenotypic differences. Here we review several recent studies that shed new light on developmental processes, which are involved in establishing root anatomy and architecture. We highlight the power of combining large-scale experiments with classical techniques to uncover new pathways in root development.

CressInt: a user-friendly web resource for genome-scale exploration of gene regulation in Arabidopsis thaliana

The thale cress Arabidopsis thaliana is a powerful model organism for studying a wide variety of biological processes. Recent advances in sequencing technology have resulted in a wealth of information describing numerous aspects of A. thaliana genome function. However, there is a relative paucity of computational systems for efficiently and effectively using these data to create testable hypotheses. We present CressInt, a user-friendly web resource for exploring gene regulatory mechanisms in A. thaliana on a genomic scale. The CressInt system incorporates a variety of genome-wide data types relevant to gene regulation, including transcription factor (TF) binding site models, ChIP-seq, DNase-seq, eQTLs, and GWAS. We demonstrate the utility of CressInt by showing how the system can be used to (1) Identify TFs binding to the promoter of a gene of interest; (2) identify genetic variants that are likely to impact TF binding based on a ChIP-seq dataset; and (3) identify specific TFs whose binding might be impacted by phenotype-associated variants. CressInt is freely available at http://cressint.cchmc.org.

What the transcriptome does not tell - proteomics and metabolomics are closer to the plants' patho-phenotype

The proteome and metabolome of the plant provide a wealth of additional information on plant-microbe interactions since they not only represent additional levels of regulation, but often they harbor the end products of regulatory processes. Proteomics has contributed to our understanding of plant-microbe research by increasing the spatial resolution of the analysis within the infected tissue, because components of the basal immunity were uncovered in the apoplast. Metabolomics has developed into a powerful approach to discover the role of small molecules during plant-microbe interactions in non-model plants since it does not depend on the availability of genome or transcriptome data. Moreover, novel molecules involved in systemic acquired resistance and the precursors for the formation of molecules that provide physical barriers to prevent spreading of pathogens were identified.

Genetic Determinants of the Network of Primary Metabolism and Their Relationships to Plant Performance in a Maize Recombinant Inbred Line Population

Deciphering the influence of genetics on primary metabolism in plants will provide insights useful for genetic improvement and enhance our fundamental understanding of plant growth and development. Although maize (Zea mays) is a major crop for food and feed worldwide, the genetic architecture of its primary metabolism is largely unknown. Here, we use high-density linkage mapping to dissect large-scale metabolic traits measured in three different tissues (leaf at seedling stage, leaf at reproductive stage, and kernel at 15 d after pollination [DAP]) of a maize recombinant inbred line population. We identify 297 quantitative trait loci (QTLs) with moderate (86.2% of the mapped QTL, R(2) = 2.4 to 15%) to major effects (13.8% of the mapped QTL, R(2) >15%) for 79 primary metabolites across three tissues. Pairwise epistatic interactions between these identified loci are detected for more than 25.9% metabolites explaining 6.6% of the phenotypic variance on average (ranging between 1.7 and 16.6%), which implies that epistasis may play an important role for some metabolites. Key candidate genes are highlighted and mapped to carbohydrate metabolism, the tricarboxylic acid cycle, and several important amino acid biosynthetic and catabolic pathways, with two of them being further validated using candidate gene association and expression profiling analysis. Our results reveal a metabolite-metabolite-agronomic trait network that, together with the genetic determinants of maize primary metabolism identified herein, promotes efficient utilization of metabolites in maize improvement.

A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes

As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

Combinatorial code governing cellular responses to complex stimuli

Cells adapt to their environment through the integration of complex signals. Multiple signals can induce synergistic or antagonistic interactions, currently considered as homogenous behaviours. Here, we use a systematic theoretical approach to enumerate the possible interaction profiles for outputs measured in the conditions 0 (control), signals X, Y, X+Y. Combinatorial analysis reveals 82 possible interaction profiles, which we biologically and mathematically grouped into five positive and five negative interaction modes. To experimentally validate their use in living cells, we apply an original computational workflow to transcriptomics data of innate immune cells integrating physiopathological signal combinations. Up to 9 of the 10 defined modes coexisted in context-dependent proportions. Each interaction mode was preferentially used in specific biological pathways, suggesting a functional role in the adaptation to multiple signals. Our work defines an exhaustive map of interaction modes for cells integrating pairs of physiopathological and pharmacological stimuli.

Cells constantly integrate information from multiple stimuli. By considering every possible means by which two stimuli can interact, Cappuccio et al. define 10 interaction modes and demonstrate their preferential use by dendritic cells responding to different combinations of microbial and host inflammatory cues.

Digital Single-Cell Analysis of Plant Organ Development Using 3DCellAtlas

Diverse molecular networks underlying plant growth and development are rapidly being uncovered. Integrating these data into the spatial and temporal context of dynamic organ growth remains a technical challenge. We developed 3DCellAtlas, an integrative computational pipeline that semiautomatically identifies cell types and quantifies both 3D cellular anisotropy and reporter abundance at single-cell resolution across whole plant organs. Cell identification is no less than 97.8% accurate and does not require transgenic lineage markers or reference atlases. Cell positions within organs are defined using an internal indexing system generating cellular level organ atlases where data from multiple samples can be integrated. Using this approach, we quantified the organ-wide cell-type-specific 3D cellular anisotropy driving Arabidopsis thaliana hypocotyl elongation. The impact ethylene has on hypocotyl 3D cell anisotropy identified the preferential growth of endodermis in response to this hormone. The spatiotemporal dynamics of the endogenous DELLA protein RGA, expansin gene EXPA3, and cell expansion was quantified within distinct cell types of Arabidopsis roots. A significant regulatory relationship between RGA, EXPA3, and growth was present in the epidermis and endodermis. The use of single-cell analyses of plant development enables the dynamics of diverse regulatory networks to be integrated with 3D organ growth.

Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.

Genetic basis and detection of unintended effects in genetically modified crop plants

In January 2014, an international meeting sponsored by the International Life Sciences Institute/Health and Environmental Sciences Institute and the Canadian Food Inspection Agency titled “Genetic Basis of Unintended Effects in Modified Plants” was held in Ottawa, Canada, bringing together over 75 scientists from academia, government, and the agro-biotech industry. The objectives of the meeting were to explore current knowledge and identify areas requiring further study on unintended effects in plants and to discuss how this information can inform and improve genetically modified (GM) crop risk assessments. The meeting featured presentations on the molecular basis of plant genome variability in general, unintended changes at the molecular and phenotypic levels, and the development and use of hypothesis-driven evaluations of unintended effects in assessing conventional and GM crops. The development and role of emerging “omics” technologies in the assessment of unintended effects was also discussed. Several themes recurred in a number of talks; for example, a common observation was that no system for genetic modification, including conventional methods of plant breeding, is without unintended effects. Another common observation was that “unintended” does not necessarily mean “harmful”. This paper summarizes key points from the information presented at the meeting to provide readers with current viewpoints on these topics.

ocsESTdb: a database of oil crop seed EST sequences for comparative analysis and investigation of a global metabolic network and oil accumulation metabolism

BACKGROUND

Oil crop seeds are important sources of fatty acids (FAs) for human and animal nutrition. Despite their importance, there is a lack of an essential bioinformatics resource on gene transcription of oil crops from a comparative perspective. In this study, we developed ocsESTdb, the first database of expressed sequence tag (EST) information on seeds of four large-scale oil crops with an emphasis on global metabolic networks and oil accumulation metabolism that target the involved unigenes.

DESCRIPTION

A total of 248,522 ESTs and 106,835 unigenes were collected from the cDNA libraries of rapeseed (Brassica napus), soybean (Glycine max), sesame (Sesamum indicum) and peanut (Arachis hypogaea). These unigenes were annotated by a sequence similarity search against databases including TAIR, NR protein database, Gene Ontology, COG, Swiss-Prot, TrEMBL and Kyoto Encyclopedia of Genes and Genomes (KEGG). Five genome-scale metabolic networks that contain different numbers of metabolites and gene-enzyme reaction-association entries were analysed and constructed using Cytoscape and yEd programs. Details of unigene entries, deduced amino acid sequences and putative annotation are available from our database to browse, search and download. Intuitive and graphical representations of EST/unigene sequences, functional annotations, metabolic pathways and metabolic networks are also available. ocsESTdb will be updated regularly and can be freely accessed at http://ocri-genomics.org/ocsESTdb/ .

CONCLUSION

ocsESTdb may serve as a valuable and unique resource for comparative analysis of acyl lipid synthesis and metabolism in oilseed plants. It also may provide vital insights into improving oil content in seeds of oil crop species by transcriptional reconstruction of the metabolic network.

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported. Besides recovering 1182 known regulatory interactions, cis-regulatory motifs and coherent functionalities of target genes corresponded with the predicted transcription factors. We provide a valuable resource of 572 abiotic stress modules of coregulated genes with functional and regulatory information, from which we deduced functional relationships for 1966 uncharacterized genes and many regulators. Using gain- and loss-of-function mutants of seven transcription factors grown under control and salt stress conditions, we experimentally validated 141 out of 271 predictions (52% precision) for 102 selected genes and mapped 148 additional transcription factor-gene regulatory interactions (49% recall). We identified an intricate core oxidative stress regulatory network where NAC13, NAC053, ERF6, WRKY6, and NAC032 transcription factors interconnect and function in detoxification. Our work shows that ensemble reverse-engineering can generate robust biological hypotheses of gene regulation in a multicellular eukaryote that can be tested by medium-throughput experimental validation.

Machine learning for Big Data analytics in plants

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences.