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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Curr Opin Plant Biol. Author manuscript; available in PMC Apr 1, 2011.
Published in final edited form as:
PMCID: PMC2862083



Many genomic-scale datasets in plants have been generated over the last few years. This substantial achievement has led to impressive progress, including some of the most detailed molecular maps in any multicellular organism. Networks and pathways have been reconstructed using transcriptome, genome-wide transcription factor binding, proteome and metabolome data, and subsequently used to infer functional interactions among genes, proteins and metabolites. However, more sophisticated systems biology approaches are needed to integrate different omics data sets. Ultimately, the integration of diverse and massive datasets into coherent models will improve our understanding of the molecular networks that underlie biological processes.


In the past decades, most plant biology studies focused on identifying the function of single genes within the context of specific responses or pathways. Recent genome-scale analyses in Arabidopsis and several crop species have allowed plant scientists to globally address the function of pathways and regulatory networks. This has significantly changed our concept of how genes work together. In the era of omics, major challenges include interpretation and integration of large datasets to understand the principles underlying the regulation of networks and their relevance. In this review, we focus on how different genome-wide data sets have been and can be used to reconstruct biological networks.

Reconstructing networks from gene expression data

Understanding plant regulatory networks and the biological principles by which they are governed requires knowledge of genome-scale responses during development and to environmental stimuli. A set of studies has examined the transcriptome of different organs and developmental stages of Arabidopsis in response to about 40 conditions [1-3]. These studies constitute a major step towards the identification of gene regulatory networks in plants; however, intrinsic complexity of some of the networks might have been overlooked. Cell-type-specific transcriptomic profiling of the Arabidopsis root and of distinct functional domains in the shoot apical meristem have unveiled gene expression patterns with unprecedented resolution [4-6]. In the root, this cell-type specific data has been combined with transcriptional profiling of 13 longitudinal sections, which represent developmental stages, to create a spatiotemporal map [5]. Similarly in rice, gene expression data from 40 different cell-types at different developmental stages has been analyzed to produce a transcriptome atlas [7]. Furthermore, profiling root cell types versus whole roots in response to salt and iron deprivation has revealed additional transcriptional complexity and has generated valuable information about specific networks operating at the tissue level [8].

Mining expression datasets, such as the transcriptomic maps, usually relies on different clustering algorithms to find groups of co-expressed genes. Genes belonging to the same co-expression clusters are hypothesized to be co-regulated genes under the same internal or external cues by similar transcription factors and form a transcriptional module or subnetwork (the so called ‘guilt by association’ principle). Based on this principle, regulatory hierarchies of gene expression can be inferred (Figure 1). Conventional microarray clustering approaches are based on separating a set of elements (genes) into related subsets based on a distance metric, or by principal-component analysis. However, analyzing more complex gene expression data from large–scale microarray datasets requires more sophisticated approaches. In the root map, finding transcriptional modules has been limited to identify a set of unique expression patterns within the more variable genes in the dataset. These clusters form the dominant regulatory modules and have been shown to be biologically significant [5, 9]. In another approach, genome-scale data from time-course experiments under different environmental conditions have been processed using predefined hypotheses to identify relevant patterns associated with specific cis-regulatory elements bound by light-regulated genes and other transcription factors (TFs) [10].

Figure 1
Approaches to network inference and reconstruction. Profiling of metabolites, transcripts and proteins as well as chromatin immunoprecipitation coupled with hybridization to microarrays or sequencing can be used to infer functional interactions among ...

Transcriptome data can be used to reconstruct regulatory networks. However, in order to build models, methods that describe nodes (genes) and their regulatory interactions (edges) are required. In several single-cell organisms and human B-cells, it is possible to predict regulators for clusters of co-expressed genes that are treated as a single unit, or alternatively, to infer a network of pairwise interactions between each regulator and its putative targets [11-13]. In plants, none of these approaches has been shown to produce meaningful results, due in all likelihood to limited information about TFs and cis-regulatory motifs. However, a network predicting about 19,000 interactions (edges) among 6760 genes (nodes) has been reconstructed using about 2000 microarrays and a modified graphical Gaussian model based on partial correlations [14]. This method successfully inferred subnetworks related to metabolic functions and stress responses that are potentially useful to predict the function of novel genes. Furthermore, integration of coexpression data, gene ontology annotations, and cis-motifs has been shown to coherently link genes to specific biological functions [15]. These approaches facilitate further dissection of these novel functions by means of gain- or loss-of-function mutants or RNAi/amiRNA strategies [16-18]. However, high-throughput approaches that test the in vivo relevance of edges and nodes within a predicted network are needed to reveal the biological significance of such interactions.

Integration of gene expression and transcription factor binding data

For a comprehensive reconstruction of transcriptional networks it is essential to determine if a transcription factor regulates a gene in a direct or an indirect fashion (Figure 1). However, it is not possible to determine such regulatory interactions with high confidence on the basis of transcriptome data alone. Chromatin immunoprecipitation coupled with hybridization to whole genome arrays (ChIP-chip) or deep sequencing (ChIPseq) promise to fill this gap.

Despite the assumption that ChIP-chip data would enable the complete reconstruction of transcriptional networks, new complexities arose with those data sets. Previous studies identified varying numbers of binding sites for different transcription factors, ranging from less than 100 [19] to several hundred [20-22] and even up to several thousand [23, 24]. Interestingly, only a very small fraction of the genes that were bound responded transcriptionally to altered levels of the respective transcription factor. In particular, most of the cases reported that less than 10% of the directly bound genes were among genes with significant transcriptional response [21, 23, 24].

The integration of data from transcriptome and ChIP-chip experiments was typically attempted by classifying transcriptionally responsive genes into a high-confidence direct target set and another set that included indirect target genes [20, 22-24]. A more sophisticated approach was undertaken to study transcriptional networks of trichome development. In this case, transcriptome profiles of mutant plants and inducible versions of the transcription factors GLABRA3 (GL3) and GLABRA1 (GL1) were integrated via a meta-analysis. This enabled the detection of a minimal set of genes that were uniquely associated with the formation of trichomes. This gene set, interpreted with results from individual ChIP-chip experiments for both transcription factors led to a multi-tiered reconstruction of the GL1/GL3 regulated network of trichome formation. [21].

It is startling that almost a decade after the first introduction of the ChIP-chip methodology, only a very limited number of genome wide transcription factor occupancy studies have been successfully conducted in the plant field. Typically each study confirmed known binding motifs and showed that most binding seemed to take place within the 2000 bp proximal upstream region of a gene and identified the enrichment of GO categories that were consistent with prior biological knowledge [20-24]. Because whole genome transcription factor binding data should, in theory, explain a massive part of transcriptional regulation, the low amount of correlation between directly bound genes and genes that are responding to alterations in transcription factor levels is puzzling. However, similar low level agreement between ChIP-chip and expression data was also frequently found in other organisms [25]. This may indicate that many binding events are silent most of the time, but they may play a crucial role in dynamic transient regulatory interactions. Since this often cannot be easily assayed, more sophisticated approaches to monitor transient expression changes of target genes, or elucidation of the function of a transcription factor in different developmental or environmental contexts should increase the agreement of these data sets. A more complete understanding of transcription factor function is obligatory to define transcriptional modules with a high confidence and refine the networks that were originally reconstructed through ‘guilt by association’ principles.

Interpretation and integration of omics data

Proteomic studies use Mass Spectometry (MS) based methods to profile the proteome, phosphoproteome or plasma membrame proteome, while metabolomic studies use MS or Nuclear Magnetic Resonance to profile the metabolites of whole plants, organs, cells or organelles [26-29]. Remarkably, analyses of the proteome of six organs plus undifferentiated cultured cells identified around 13,000 distinct proteins to construct the first Arabidopsis organ proteome map [30]. The correlation of these data with transcriptome profiles suggests that protein accumulation in Arabidopsis is primarily regulated at the transcriptional level. Notably, profiling specific cell-types or enriching certain sub-cellular components seems to increase the resolution of these studies and provides deeper biological insight. MS analysis of isolated guard cells has detected 336 novel proteins and has identified a novel glucosinolate-myrosinase system required for ABA signaling [31]. Moreover, isolation of plasma membrane proteins revealed that a small set of kinases, called BSKs, transduce brassinosteroid signaling downstream of the BRI1 receptor that had been overlooked when total proteins were analyzed [32].

Similar to transcriptional networks, protein and metabolic networks can be reconstructed using edges and nodes. In protein networks, nodes are the proteins and edges normally represent protein-protein interactions (interactome) or a functional modification, whereas in metabolic networks, nodes are considered to be metabolites and edges enzymatic reactions or biochemical modifications (Figure 1). The aforementioned studies can identify nodes of protein networks and be potentially used to infer functional relationships; however, edges are usually identified through different approaches. In several model organisms protein-protein interactions have been mined to produce proteome-scale interactome networks, while in plants this information is still missing. Recently, on a smaller scale, protein microarrays have revealed the target network of several Arabidopsis calmodulins [33]; and 10 mitogen-activated protein kinases (MPK) that are in turn phosphorylated by 9 MPK kinases (MKK) [34]. The MKK/MPK network contains 589 nodes and 1331 edges representing predicted phosphorylation events. Interestingly, in vivo assays link this network to developmental and pathogen responses [34].

Metabolome studies have the potential to reveal biochemical networks, but integration of unknown metabolites into networks cannot be achieved by correlation analyses as in transcriptional networks. One alternative is combining quantitative trait locus analysis with quantitative metabolite profiling to generate de novo biochemical network models [35]. However, most of the work inferring biochemical pathways is based on the principle that under conditions that modulate the accumulation of a certain metabolite, gene expression patterns can then be correlated to decode the function of genes involved in that metabolic pathway. By using this strategy, R2R3-MYB TFs were correctly inferred as involved in glucosinolate biosynthesis [36] and enzymes responsible for variation of chain length of methionine-derived glucosinolates were identified [37]. Moreover, combined transcriptomic and metabolomic profiling of plants over-expressing glucosinolate regulators has revealed connections to other metabolic pathways and primary metabolism [38]. Using similar methods, other studies reported the decoding of novel gene functions in the flavonoid [39] and other biochemical [40] pathways, as well as the possible coupling of the circadian system in Arabidopsis with chloroplast and mitochondrion functions [41].

The combination of different omics data will facilitate the functional identification of networks controlling development, growth and response to biotic and abiotic stresses. For instance, it was recently reported that phosphorylation of enzymes involved in metabolic processes correlates with plant growth [42]. Databases have the potential to facilitate hypothesis generation for plant biology by integrating omic datasets. Currently, there are different data repositories, such as ArrayExpress, GEO, CYBEX, PRIDE, Golm Metabolome DataBase and others [43]; while GabiPD [44] intends to be an integrative omics database. However, new computational approaches and interfaces that allow a joint interpretation of massive datasets are needed to form coherent models of biological networks and their function.

Conclusions and future perspectives

Our comprehension of complex molecular networks that underlie biological processes has grown dramatically in the last few years. In addition to the progress discussed above, development in other areas of research has contributed to our understanding of molecular networks, like protein-DNA binding microarrays [45] and genome-wide profiling of histone modifications and DNA methylation [46, 47]. However, for future understanding and reconstruction of complex molecular networks, experimental design and technical sophistication of the experiments should be brought to a level that suits the complexity of the networks that are being studied (Figure 1). Whereas chromatin binding maps of transcription factors and whole-genome interactome networks explore a general parameter space for biological networks, cell-type specific data such as transcriptional data, nucleosome occupancy, epigenetic chromatin-landscapes, proteomics and metabolomics are needed to address specific topologies of regulatory networks. In addition, new technologies, such as medium to high throughput microscopy, are emerging and promise to obtain sufficient spatiotemporal resolution for observing the dynamics of aspects of complex systems.

One major challenge of performing system approaches in Arabidopsis is transferring such knowledge to crop species. Translational research seems feasible and approaches similar to those used in Arabidopsis have been applied in rice and other crops, e.g. maize and barley [7, 48-50]. These achievements potentially enable the reconstruction of regulatory pathways and networks in crops, which will lead to new biotechnological applications in the future.


We thank members of the Benfey lab for critical reading of the manuscript and helpful suggestions; and apologize to those, whose work we could not cover due to space limitations. Work in the Benfey lab on regulatory networks in plants is funded by grants from the NIH, NSF and DARPA.


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