Systems Approaches to Identifying Gene Regulatory Networks in Plants

1Department of Biology, Duke University, Durham, North Carolina 27708, USA.
Annual Review of Cell and Developmental Biology (Impact Factor: 16.66). 08/2008; 24(1):81-103. DOI: 10.1146/annurev.cellbio.24.110707.175408
Source: PubMed


Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed.

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Available from: Philip N Benfey, Oct 11, 2015
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    • "ns , representing a binary phase transition study , in nongerminating ( or dormant ) and germinating Arabidopsis thaliana seeds ( Bassel et al . , 2011 ) . Integration of the ex - pression data at the resolution of developmental stages and in different environmental contexts is expected to generate a high confidence dynamic gene regulatory model ( Long et al . , 2008 ) ."
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    ABSTRACT: Seed longevity, the maintenance of viability during storage, is a crucial factor for preservation of genetic resources and ensuring proper seedling establishment and high crop yield. We used a systems biology approach to identify key genes regulating the acquisition of longevity during seed maturation of Medicago truncatula. Using 104 transcriptomes from seed developmental time courses obtained in five growth environments, we generated a robust, stable coexpression network (MatNet), thereby capturing the conserved backbone of maturation. Using a trait-based gene significance measure, a coexpression module related to the acquisition of longevity was inferred from MatNet. Comparative analysis of the maturation processes in M. truncatula and Arabidopsis thaliana seeds and mining Arabidopsis interaction databases revealed conserved connectivity for 87% of longevity module nodes between both species. Arabidopsis mutant screening for longevity and maturation phenotypes demonstrated high predictive power of the longevity cross-species network. Overrepresentation analysis of the network nodes indicated biological functions related to defense, light, and auxin. Characterization of defense-related wrky3 and nf-x1-like1 (nfxl1) transcription factor mutants demonstrated that these genes regulate some of the network nodes and exhibit impaired acquisition of longevity during maturation. These data suggest that seed longevity evolved by co-opting existing genetic pathways regulating the activation of defense against pathogens.
    The Plant Cell 09/2015; DOI:10.1105/tpc.15.00632 · 9.34 Impact Factor
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    • "To fully understand the transcriptional regulation of a developmental process, it is necessary to determine the binding of individual transcription factors to their target genes. Transcription factor binding data can be used in modeling of transcriptional regulatory networks, which provide precise specifications of complex interdependencies underpinning these biological systems (Ideker et al., 2001; Long et al., 2008; Van de Poel et al., 2014). The putative target genes for a given transcription factor can be estimated using a variety of techniques. "
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    ABSTRACT: Identifying transcription factor target genes is essential for modeling the transcriptional networks underlying developmental processes. Here we report a chromatin immunoprecipitation sequencing (ChIP-seq) resource consisting of genome-wide binding regions and associated putative target genes for four Populus homeodomain transcription factors expressed during secondary growth and wood formation. Software code (programs and scripts) for processing the Populus ChIP-seq data are provided within a publically-available iPlant image, including tools for ChIP-seq data quality control and evaluation adapted from the human ENCODE project. Basic information for each transcription factor (including members of Class I KNOX, Class III HD ZIP, BEL1-like families) binding are summarized, including the number and location of binding regions, distribution of binding regions relative to gene features, associated putative target genes, and enriched functional categories of putative target genes. These ChIP-seq data have been integrated within the Populus Genome Integrative Explorer (PopGenIE) where they can be analyzed using a variety of web-based tools. We present an example analysis that shows preferential binding of transcription factor ARBORKNOX1 to the nearest neighbor genes in a pre-calculated co-expression network module, and enrichment for meristem-related genes within this module including multiple orthologs of Arabidopsis KNOTTED-like Arabidopsis 2/6. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    The Plant Journal 04/2015; 82(5). DOI:10.1111/tpj.12850 · 5.97 Impact Factor
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    • "The research on construction of genetic regulatory network of photosynthesis using high throughput transcriptomic and genomic data are mostly lacking. This is in sharp contrast to the rapid progresses in construction of regulatory networks related to other plant processes, e.g., the circadian clock and flowering control (Keurentjes et al., 2007; Ma et al., 2007; Thai et al., 2007; Long et al., 2008; Lee et al., 2010). "
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    ABSTRACT: Photosynthesis is one of the most important biological processes on the earth. So far, though the molecular mechanisms underlying photosynthesis is well understood, however, the regulatory networks of photosynthesis are poorly studied. Given the current interest in improving photosynthetic efficiency for greater crop yield, elucidating the detailed regulatory networks controlling the construction and maintenance of photosynthetic machinery is not only scientifically significant but also holding great potential in agricultural application. In this study, we first identified transcription factors (TFs) related to photosynthesis through the TRAP approach using position weight matrix information. Then, for TFs related to photosynthesis, interactions between them and their targets were also determined by the ARACNE approach. Finally, a gene regulatory network was established by combining TF-targets information generated by these two approaches. Topological analysis of the regulatory network suggested that (a) the regulatory network of photosynthesis has a property of "small world"; (b) there is substantial coordination mediated by transcription factors between different components in photosynthesis.
    Frontiers in Plant Science 06/2014; 5:273. DOI:10.3389/fpls.2014.00273 · 3.95 Impact Factor
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