From plant gene regulatory grids to network dynamics

Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize

Elucidating gene regulatory networks (GRNs) is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. In this study, we integrated publicly available co-expression networks derived from more than 6,000 RNA-seq samples, 283 protein-DNA interaction assays, and 16 million of SNPs used to identify expression quantitative loci (eQTL), to construct TF-target networks. In total, we analyzed ~4.6M interactions to generate four distinct types of TF-target networks: co-expression, protein-DNA interaction (PDI), trans-expression quantitative loci (trans-eQTL), and cis-eQTL combined with PDIs. To improve the functional annotation of TFs based on its target genes, we implemented three different strategies to integrate these four types of networks. We subsequently evaluated the effectiveness of our method through loss-of function mutant and random networks. The multi-network integration allowed us to identify transcriptional regulators of hormone-, metabolic- and development-related processes. Finally, using the topological properties of the fully integrated network, we identified potentially functional redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.

MINI-AC: inference of plant gene regulatory networks using bulk or single-cell accessible chromatin profiles

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants.

Genomic variations combined with epigenetic modifications rewire open chromatin in rice

Cis-regulatory elements (CREs) fine-tune gene transcription in eukaryotes. CREs with sequence variations play vital roles in driving plant or crop domestication. However, how global sequence and structural variations (SVs) are responsible for multilevel changes between indica and japonica rice (Oryza sativa) is still not fully elucidated. To address this, we conducted multiomic studies using MNase hypersensitivity sequencing (MH-seq) in combination with RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and bisulfite sequencing (BS-seq) between the japonica rice variety Nipponbare (NIP) and indica rice variety 93-11. We found that differential MNase hypersensitive sites (MHSs) exhibited some distinct intrinsic genomic sequence features between NIP and 93-11. Notably, through MHS-genome-wide association studies (GWAS) integration, we found that key sequence variations may be associated with differences of agronomic traits between NIP and 93-11, which is partly achieved by MHSs harboring CREs. In addition, SV-derived differential MHSs caused by transposable element (TE) insertion, especially by noncommon TEs among rice varieties, were associated with genes with distinct functions, indicating that TE-driven gene neo- or subfunctionalization is mediated by changes of chromatin openness. This study thus provides insights into how sequence and genomic SVs control agronomic traits of NIP and 93-11; it also provides genome-editing targets for molecular breeding aiming at improving favorable agronomic properties.

Silage maize as a potent candidate for sustainable animal husbandry development-perspectives and strategies for genetic enhancement

Maize is recognized as the queen of cereals, with an ability to adapt to diverse agroecologies (from 58^oN to 55^oS latitude) and the highest genetic yield potential among cereals. Under contemporary conditions of global climate change, C₄ maize crops offer resilience and sustainability to ensure food, nutritional security, and farmer livelihood. In the northwestern plains of India, maize is an important alternative to paddy for crop diversification in the wake of depleting water resources, reduced farm diversity, nutrient mining, and environmental pollution due to paddy straw burning. Owing to its quick growth, high biomass, good palatability, and absence of anti-nutritional components, maize is also one of the most nutritious non-legume green fodders. It is a high-energy, low-protein forage commonly used for dairy animals like cows and buffalos, often in combination with a complementary high-protein forage such as alfalfa. Maize is also preferred for silage over other fodders due to its softness, high starch content, and sufficient soluble sugars required for proper ensiling. With a rapid population increase in developing countries like China and India, there is an upsurge in meat consumption and, hence, the requirement for animal feed, which entails high usage of maize. The global maize silage market is projected to grow at a compound annual growth rate of 7.84% from 2021 to 2030. Factors such as increasing demand for sustainable and environment-friendly food sources coupled with rising health awareness are fueling this growth. With the dairy sector growing at about 4%-5% and the increasing shortage faced for fodder, demand for silage maize is expected to increase worldwide. The progress in improved mechanization for the provision of silage maize, reduced labor demand, lack of moisture-related marketing issues as associated with grain maize, early vacancy of farms for next crops, and easy and economical form of feed to sustain household dairy sector make maize silage a profitable venture. However, sustaining the profitability of this enterprise requires the development of hybrids specific for silage production. Little attention has yet been paid to breeding for a plant ideotype for silage with specific consideration of traits such as dry matter yield, nutrient yield, energy in organic matter, genetic architecture of cell wall components determining their digestibility, stalk standability, maturity span, and losses during ensiling. This review explores the available information on the underlying genetic mechanisms and gene/gene families impacting silage yield and quality. The trade-offs between yield and nutritive value in relation to crop duration are also discussed. Based on available genetic information on inheritance and molecular aspects, breeding strategies are proposed to develop maize ideotypes for silage for the development of sustainable animal husbandry.

Comparative expression analysis in three Brassicaceae species revealed compensatory changes of the underlying gene regulatory network

Trichomes are regularly distributed on the leaves of Arabidopsis thaliana. The gene regulatory network underlying trichome patterning involves more than 15 genes. However, it is possible to explain patterning with only five components. This raises the questions about the function of the additional components and the identification of the core network. In this study, we compare the relative expression of all patterning genes in A. thaliana, A. alpina and C. hirsuta by qPCR analysis and use mathematical modelling to determine the relative importance of patterning genes. As the involved proteins exhibit evolutionary conserved differential complex formation, we reasoned that the genes belonging to the core network should exhibit similar expression ratios in different species. However, we find several striking differences of the relative expression levels. Our analysis of how the network can cope with such differences revealed relevant parameters that we use to predict the relevant molecular adaptations in the three species.

Prediction of Transcription Factor Regulators and Gene Regulatory Networks in Tomato Using Binding Site Information

Gene regulatory networks (GRNs) represent the regulatory links between transcription factors (TF) and their target genes. In plants, they are essential to understand transcriptional programs that control important agricultural traits such as yield or (a)biotic stress response. Although several high- and low-throughput experimental methods have been developed to map GRNs in plants, these are sometimes expensive, come with laborious protocols, and are not always optimized for tomato, one of the most important horticultural crops worldwide. In this chapter, we present a computational method that covers two protocols: one protocol to map gene identifiers between two different tomato genome assemblies, and another protocol to predict putative regulators and delineate GRNs given a set of functionally related or coregulated genes by exploiting publicly available TF-binding information. As an example, we applied the motif enrichment protocol on tomato using upregulated genes in response to jasmonate, as well as upregulated and downregulated genes in plants with genotypes OENAM1 and nam1, respectively. We found that our protocol accurately infers the expected TFs as top enriched regulators and identifies GRNs functionally enriched in biological processes related with the experimental context under study.

Stomatal development and genetic expression in Arabidopsis thaliana L

Stomata are turgor-driven microscopic epidermal valves of land plants. The controlled opening and closing of the valves are essential for regulating the gas exchange and minimizing the water loss and eventually regulating the internal temperatures. Stomata are also a major site of pathogen/microbe entry and plant defense system. Maintaining proper stomatal density, distribution, and development are pivotal for plant survival. Arabidopsis is a model plant to study molecular basis including signaling pathways, transcription factors, and key components for the growth and development of specific organs as well as the whole plant. It has intensively been studied and found out the driver for the development and patterning of stomata. In this review, we have explained how the MAPK signaling cascade is controlled by TOO MANY MOUTHS (TMM) receptor-like protein and the Erecta (ER) receptor-like kinase family. We have also summarized how this MAPK cascade affects primary transcriptional regulators to finally activate the main three basic Helix-Loop-Helix (bHLH) principal transcription factors, which are required for the development and patterning of stomata. Moreover, regulatory activity and cellular connections of polar proteins and environmentally mediated ligand-receptor interactions in the stomatal developmental pathways have extensively been discussed in this review.

Diversifying maize genomic selection models

Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.

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.

Challenges of Translating Gene Regulatory Information into Agronomic Improvements

Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.

TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

A gene regulatory network (GRN) is a collection of regulatory interactions between transcription factors (TFs) and their target genes. GRNs control different biological processes and have been instrumental to understand the organization and complexity of gene regulation. Although various experimental methods have been used to map GRNs in Arabidopsis thaliana, their limited throughput combined with the large number of TFs makes that for many genes our knowledge about regulating TFs is incomplete. We introduce TF2Network, a tool that exploits the vast amount of TF binding site information and enables the delineation of GRNs by detecting potential regulators for a set of co-expressed or functionally related genes. Validation using two experimental benchmarks reveals that TF2Network predicts the correct regulator in 75–92% of the test sets. Furthermore, our tool is robust to noise in the input gene sets, has a low false discovery rate, and shows a better performance to recover correct regulators compared to other plant tools. TF2Network is accessible through a web interface where GRNs are interactively visualized and annotated with various types of experimental functional information. TF2Network was used to perform systematic functional and regulatory gene annotations, identifying new TFs involved in circadian rhythm and stress response.

Recent advances in gene function prediction using context-specific coexpression networks in plants

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

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.

Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.

A Maize Gene Regulatory Network for Phenolic Metabolism

The translation of the genotype into phenotype, represented for example by the expression of genes encoding enzymes required for the biosynthesis of phytochemicals that are important for interaction of plants with the environment, is largely carried out by transcription factors (TFs) that recognize specific cis-regulatory elements in the genes that they control. TFs and their target genes are organized in gene regulatory networks (GRNs), and thus uncovering GRN architecture presents an important biological challenge necessary to explain gene regulation. Linking TFs to the genes they control, central to understanding GRNs, can be carried out using gene- or TF-centered approaches. In this study, we employed a gene-centered approach utilizing the yeast one-hybrid assay to generate a network of protein-DNA interactions that participate in the transcriptional control of genes involved in the biosynthesis of maize phenolic compounds including general phenylpropanoids, lignins, and flavonoids. We identified 1100 protein-DNA interactions involving 54 phenolic gene promoters and 568 TFs. A set of 11 TFs recognized 10 or more promoters, suggesting a role in coordinating pathway gene expression. The integration of the gene-centered network with information derived from TF-centered approaches provides a foundation for a phenolics GRN characterized by interlaced feed-forward loops that link developmental regulators with biosynthetic genes.

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.

Combinatorial control of plant gene expression

Combinatorial gene regulation provides a mechanism by which relatively small numbers of transcription factors can control the expression of a much larger number of genes with finely tuned temporal and spatial patterns. This is achieved by transcription factors assembling into complexes in a combinatorial fashion, exponentially increasing the number of genes that they can target. Such an arrangement also increases the specificity and affinity for the cis-regulatory sequences required for accurate target gene expression. Superimposed on this transcription factor combinatorial arrangement is the increasing realization that histone modification marks expand the regulatory information, which is interpreted by histone readers and writers that are part of the regulatory apparatus. Here, we review the progress in these areas from the perspective of plant combinatorial gene regulation, providing examples of different regulatory solutions and comparing them to other metazoans. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

Lessons from Domestication: Targeting Cis-Regulatory Elements for Crop Improvement

Domestication of wild plant species has provided us with crops that serve our human nutritional needs. Advanced DNA sequencing has propelled the unveiling of underlying genetic changes associated with domestication. Interestingly, many changes reside in cis-regulatory elements (CREs) that control the expression of an unmodified coding sequence. Sequence variation in CREs can impact gene expression levels, but also developmental timing and tissue specificity of expression. When genes are involved in multiple pathways or active in several organs and developmental stages CRE modifications are favored in contrast to mutations in coding regions, due to the lack of detrimental pleiotropic effects. Therefore, learning from domestication, we propose that CREs are interesting targets for genome editing to create new alleles for plant breeding.

A novel cis-acting element in the GmERF3 promoter contributes to inducible gene expression in soybean and tobacco after wounding

KEY MESSAGE

Using in silico and functional analyses, we cloned and validated the expression profile of an inducible soybean promoter (GmERF3) along with its novel wound-induced and delayed expression (WIDE) element. Promoters and their contributing promoter elements are the main regulators of gene expression at the transcriptional level. Although the Ethylene Response Factor (ERF) gene family is one of the most well-studied stress-responsive gene families in plants, their promoter regions have received little attention. In this study, we investigated the expression patterns driven by the soybean (Glycine max) GmERF3 promoter and its cis-acting elements in soybean and tobacco. Transcriptomic data revealed that the native GmERF3 gene was differentially expressed in organs and tissues of plants. In transgenic soybeans containing a 1.3 kb GmERF3 promoter fused to the green fluorescent protein (gfp) gene, organ- and tissue-specificity was observed in untreated plants while mechanical wounding led to induction of GFP expression. Further in silico and in planta analyses of the GmERF3 promoter sequence in soybean revealed different cis-acting elements, including a novel cis-acting element, which contributed to increased expression, 1-2 days after mechanical wounding. We have named this DNA motif the wound-induced and delayed expression element (GGATTCAAGTTTAACC). A synthetic promoter containing a tetrameric repeat of this element showed high but late wound-induced GFP expression in leaves of transgenic tobacco. Our study expands the toolbox of inducible promoters and promoter elements for potential use in basic and applied research.

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.

Arabidopsis Motif Scanner

BACKGROUND

The major mechanism driving cellular differentiation and organism development is the regulation of gene expression. Cis-acting enhancers and silencers have key roles in controlling gene transcription. The genomic era allowed the transition from single gene analysis to the investigation of full transcriptomes. This transition increased the complexity of the analyses and the difficulty in the interpretation of the results. In this context, there is demand for new tools aimed at the creation of gene networks that can facilitate the interpretation of Next Generation Sequencing (NGS) data.

RESULTS

Arabidopsis Motif Scanner (AMS) is a Windows application that runs on local computers. It was developed to build gene networks by identifying the positions of cis-regulatory elements in the model plant Arabidopsis thaliana and by providing an easy interface to assess and evaluate gene relationships. Its major innovative feature is to combine the cis-regulatory element positions, NGS and DNA Chip Arrays expression data, Arabidopsis annotations and gene interactions for the identification of gene networks regulated by transcription factors. In studies focused on transcription factors function, the software uses the expression data and binding site motifs in the regulative gene regions to predict direct target genes. Additionally, AMS utilizes DNA-protein and protein-protein interaction data to facilitate the identification of the metabolic pathways regulated by the transcription factor of interest.

CONCLUSIONS

Arabidopsis Motif Scanner is a new tool that helps researchers to unravel gene relations and functions. In fact, it facilitates studies focused on the effects and the impact that transcription factors have on the transcriptome by correlating the position of cis-acting elements, gene expression data and interactions.

Upstream regulatory architecture of rice genes: summarizing the baseline towards genus-wide comparative analysis of regulatory networks and allele mining

Dissecting the upstream regulatory architecture of rice genes and their cognate regulator proteins is at the core of network biology and its applications to comparative functional genomics. With the rapidly advancing comparative genomics resources in the genus Oryza, a reference genome annotation that defines the various cis-elements and trans-acting factors that interface each gene locus with various intrinsic and extrinsic signals for growth, development, reproduction and adaptation must be established to facilitate the understanding of phenotypic variation in the context of regulatory networks. Such information is also important to establish the foundation for mining non-coding sequence variation that defines novel alleles and epialleles across the enormous phenotypic diversity represented in rice germplasm. This review presents a synthesis of the state of knowledge and consensus trends regarding the various cis-acting and trans-acting components that define spatio-temporal regulation of rice genes based on representative examples from both foundational studies in other model and non-model plants, and more recent studies in rice. The goal is to summarize the baseline for systematic upstream sequence annotation of the rapidly advancing genome sequence resources in Oryza in preparation for genus-wide functional genomics. Perspectives on the potential applications of such information for gene discovery, network engineering and genomics-enabled rice breeding are also discussed.

Evidence for selection on gene expression in cultivated rice (Oryza sativa)

Artificial selection has been used throughout plant domestication and breeding to develop crops that are adapted to diverse environments. Here, we investigate whether gene regulatory changes have been widespread targets of lineage-specific selection in cultivated lines Minghui 63 and Zhenshan 97 of rice, Oryza sativa. A line experiencing positive selection for either an increase or a decrease in genes' transcript abundances is expected to have an overabundance of expression quantitative trait locus (eQTL) alleles that increase or decrease those genes' expression, respectively. Results indicate that several genes that share Gene Ontology terms or are members of the same coexpression module have eQTL alleles from one parent that consistently increase gene expression relative to the second parent. A second line of evidence for lineage-specific selection is an overabundance of cis-trans pairs of eQTL alleles that affect gene expression in the same direction (are reinforcing). Across all cis-trans pairs of eQTL, including pairs that both weakly and strongly affect gene expression, there is no evidence for selection. However, the frequency of genes with reinforcing eQTL increases with eQTL strength. Therefore, there is evidence that eQTL with strong effects were positively selected during rice cultivation. Among 41 cis-trans pairs with strong trans eQTL, 31 have reinforcing eQTL. Several of the candidate genes under positive selection accurately predict phenotypic differences between Minghui 63 and Zhenshan 97. Overall, our results suggest that positive selection for regulatory alleles may be a key factor in plant improvement.

Regulatory networks controlling nitrogen sensing and uptake in Candida albicans

Nitrogen is one of the key nutrients for microbial growth. During infection, pathogenic fungi like C. albicans need to acquire nitrogen from a broad range of different and changing sources inside the host. Detecting the available nitrogen sources and adjusting the expression of genes for their uptake and degradation is therefore crucial for survival and growth as well as for establishing an infection. Here, we analyzed the transcriptional response of C. albicans to nitrogen starvation and feeding with the infection-relevant nitrogen sources arginine and bovine serum albumin (BSA), representing amino acids and proteins, respectively. The response to nitrogen starvation was marked by an immediate repression of protein synthesis and an up-regulation of general amino acid permeases, as well as an up-regulation of autophagal processes in its later stages. Feeding with arginine led to a fast reduction in expression of general permeases for amino acids and to resumption of protein synthesis. The response to BSA feeding was generally slower, and was additionally characterized by an up-regulation of oligopeptide transporter genes. From time-series data, we inferred network interaction models for genes relevant in nitrogen detection and uptake. Each individual network was found to be largely specific for the experimental condition (starvation or feeding with arginine or BSA). In addition, we detected several novel connections between regulator and effector genes, with putative roles in nitrogen uptake. We conclude that C. albicans adopts a particular nitrogen response network, defined by sets of specific gene-gene connections for each environmental condition. All together, they form a grid of possible gene regulatory networks, increasing the transcriptional flexibility of C. albicans.

AthaMap web tools for the analysis of transcriptional and posttranscriptional regulation of gene expression in Arabidopsis thaliana

The AthaMap database provides a map of verified and predicted transcription factor (TF) and small RNA-binding sites for the A. thaliana genome. The database can be used for bioinformatic predictions of putative regulatory sites. Several online web tools are available that address specific questions. Starting with the identification of transcription factor-binding sites (TFBS) in any gene of interest, colocalizing TFBS can be identified as well as common TFBS in a set of user-provided genes. Furthermore, genes can be identified that are potentially targeted by specific transcription factors or small inhibitory RNAs. This chapter provides detailed information on how each AthaMap web tool can be used online. Examples on how this database is used to address questions in circadian and diurnal regulation are given. Furthermore, complementary databases and databases that go beyond questions addressed with AthaMap are discussed.

Navigating the transcriptional roadmap regulating plant secondary cell wall deposition

The current status of lignocellulosic biomass as an invaluable resource in industry, agriculture, and health has spurred increased interest in understanding the transcriptional regulation of secondary cell wall (SCW) biosynthesis. The last decade of research has revealed an extensive network of NAC, MYB and other families of transcription factors regulating Arabidopsis SCW biosynthesis, and numerous studies have explored SCW-related transcription factors in other dicots and monocots. Whilst the general structure of the Arabidopsis network has been a topic of several reviews, they have not comprehensively represented the detailed protein-DNA and protein-protein interactions described in the literature, and an understanding of network dynamics and functionality has not yet been achieved for SCW formation. Furthermore the methodologies employed in studies of SCW transcriptional regulation have not received much attention, especially in the case of non-model organisms. In this review, we have reconstructed the most exhaustive literature-based network representations to date of SCW transcriptional regulation in Arabidopsis. We include a manipulable Cytoscape representation of the Arabidopsis SCW transcriptional network to aid in future studies, along with a list of supporting literature for each documented interaction. Amongst other topics, we discuss the various components of the network, its evolutionary conservation in plants, putative modules and dynamic mechanisms that may influence network function, and the approaches that have been employed in network inference. Future research should aim to better understand network function and its response to dynamic perturbations, whilst the development and application of genome-wide approaches such as ChIP-seq and systems genetics are in progress for the study of SCW transcriptional regulation in non-model organisms.

Expression of flavonoid 3'-hydroxylase is controlled by P1, the regulator of 3-deoxyflavonoid biosynthesis in maize

BACKGROUND

The maize (Zea mays) red aleurone1 (pr1) encodes a CYP450-dependent flavonoid 3'-hydroxylase (ZmF3'H1) required for the biosynthesis of purple and red anthocyanin pigments. We previously showed that Zmf3'h1 is regulated by C1 (Colorless1) and R1 (Red1) transcription factors. The current study demonstrates that, in addition to its role in anthocyanin biosynthesis, the Zmf3'h1 gene also participates in the biosynthesis of 3-deoxyflavonoids and phlobaphenes that accumulate in maize pericarps, cob glumes, and silks. Biosynthesis of 3-deoxyflavonoids is regulated by P1 (Pericarp color1) and is independent from the action of C1 and R1 transcription factors.

RESULTS

In maize, apiforol and luteoforol are the precursors of condensed phlobaphenes. Maize lines with functional alleles of pr1 and p1 (Pr1;P1) accumulate luteoforol, while null pr1 lines with a functional or non-functional p1 allele (pr1;P1 or pr1;p1) accumulate apiforol. Apiforol lacks a hydroxyl group at the 3'-position of the flavylium B-ring, while luteoforol has this hydroxyl group. Our biochemical analysis of accumulated compounds in different pr1 genotypes showed that the pr1 encoded ZmF3'H1 has a role in the conversion of mono-hydroxylated to bi-hydroxylated compounds in the B-ring. Steady state RNA analyses demonstrated that Zmf3'h1 mRNA accumulation requires a functional p1 allele. Using a combination of EMSA and ChIP experiments, we established that the Zmf3'h1 gene is a direct target of P1. Highlighting the significance of the Zmf3'h1 gene for resistance against biotic stress, we also show here that the p1 controlled 3-deoxyanthocyanidin and C-glycosyl flavone (maysin) defence compounds accumulate at significantly higher levels in Pr1 silks as compared to pr1 silks. By virtue of increased maysin synthesis in Pr1 plants, corn ear worm larvae fed on Pr1; P1 silks showed slower growth as compared to pr1; P1 silks.

CONCLUSIONS

Our results show that the Zmf3'h1 gene participates in the biosynthesis of phlobaphenes and agronomically important 3-deoxyflavonoid compounds under the regulatory control of P1.

Conserved non-coding regulatory signatures in Arabidopsis co-expressed gene modules

Complex traits and other polygenic processes require coordinated gene expression. Co-expression networks model mRNA co-expression: the product of gene regulatory networks. To identify regulatory mechanisms underlying coordinated gene expression in a tissue-enriched context, ten Arabidopsis thaliana co-expression networks were constructed after manually sorting 4,566 RNA profiling datasets into aerial, flower, leaf, root, rosette, seedling, seed, shoot, whole plant, and global (all samples combined) groups. Collectively, the ten networks contained 30% of the measurable genes of Arabidopsis and were circumscribed into 5,491 modules. Modules were scrutinized for cis regulatory mechanisms putatively encoded in conserved non-coding sequences (CNSs) previously identified as remnants of a whole genome duplication event. We determined the non-random association of 1,361 unique CNSs to 1,904 co-expression network gene modules. Furthermore, the CNS elements were placed in the context of known gene regulatory networks (GRNs) by connecting 250 CNS motifs with known GRN cis elements. Our results provide support for a regulatory role of some CNS elements and suggest the functional consequences of CNS activation of co-expression in specific gene sets dispersed throughout the genome.