From Single Genes to Co-Expression Networks: Extracting Knowledge from Barley Functional Genomics

Faccioli, Primetta; Provero, Paolo; Herrmann, Carl; Stanca, A. M.; Morcia, Caterina; Terzi, Valeria

doi:10.1007/s11103-005-8159-7

Cited by 23 publications

(12 citation statements)

References 33 publications

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“…Networks constructed from mixed sample sets represent a "global" or meta-analysis view of gene coexpression and have been obtained for many species (Faccioli et al, 2005;Lee et al, 2009;Ficklin et al, 2010;Schaefer et al, 2014). However, targeted networks, focusing on a specific condition, organ, or developmental phase, allow the dissection of fine regulatory switches that will otherwise be obscured through global comparisons (Usadel et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

Righetti¹,

Vu²,

Pelletier³

et al. 2015

Plant Cell

118

191

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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.

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Section: Introductionmentioning

confidence: 99%

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

Righetti¹,

Vu²,

Pelletier³

et al. 2015

Plant Cell

118

191

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“…Many co-expression networks have been constructed in plants, such as Arabidopsis [1, 3, 6–11], barley [12], rice [13, 14], poplar [15], tobacco [16], and maize [17]. Several of these efforts have been implemented as web-based tools, e.g.…”

Section: Introductionmentioning

confidence: 99%

An integrated genomic and metabolomic framework for cell wall biology in rice

et al. 2014

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BackgroundPlant cell walls are complex structures that full-fill many diverse functions during plant growth and development. It is therefore not surprising that thousands of gene products are involved in cell wall synthesis and maintenance. However, functional association for the majority of these gene products remains obscure. One useful approach to infer biological associations is via transcriptional coordination, or co-expression of genes. This approach has proved useful for several biological processes. Nevertheless, combining co-expression with other large-scale measurements may improve the biological inferences.ResultsIn this study, we used a combined approach of co-expression and cell wall metabolomics to obtain new insight into cell wall synthesis in rice. We initially created a weighted gene co-expression network from publicly available datasets, and then established a comprehensive cell wall dataset by determining cell wall compositions from 29 tissues that almost cover the whole life cycle of rice. We subsequently combined the datasets through the conversion of co-expressed gene modules into eigen-vectors, representing expression profiles for the genes in the modules, and performed comparative analyses against the cell wall contents. Here, we made three major discoveries. First, we confirmed our approach by finding primary and secondary wall cellulose biosynthesis modules, respectively. Second, we found co-expressed modules that strongly correlated with re-organization of the secondary cell walls and with modifications and degradation of hemicellulosic structures. Third, we inferred that at least one module is likely to play a regulatory role in the production of G-rich lignification.ConclusionsHere, we integrated transcriptomic associations and cell wall metabolism and found that certain co-expressed gene modules are positively correlated with distinct cell wall characteristics. We propose that combining multiple data-types, such as coordinated transcription and cell wall analyses, may be a useful approach to glean new insight into biological processes. The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets. In addition, our data extend the typical co-expression approach to allow deeper insight into cell wall biology in rice.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-596) contains supplementary material, which is available to authorized users.

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“…Examples include those constructed to identify functional gene modules in humans (Lee et al, 2004), identification of genes involved with cellulose synthase in Arabidopsis (Arabidopsis thaliana; Persson et al, 2005), identification of biomarkers for glycerol kinase-deficient mice (MacLennan et al, 2009), identification of cis-regulatory elements in gene clusters for budding yeast (Mariñ o-Ramírez et al, 2009), construction of a regulatory network of iron response in Shewanella oneidensis , and identification of conserved gene clusters across several species (Stuart et al, 2003). For plants, global coexpression networks have been constructed for Arabidopsis (Persson et al, 2005;Wei et al, 2006;Mentzen et al, 2008;Atias et al, 2009;Mao et al, 2009;Wang et al, 2009), barley (Hordeum vulgare; Faccioli et al, 2005), rice (Jupiter et al, 2009;Lee et al, 2009), and tobacco (Nicotiana tabacum; Edwards et al, 2010).…”

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confidence: 99%

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks

2010

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Discovering gene sets underlying the expression of a given phenotype is of great importance, as many phenotypes are the result of complex gene-gene interactions. Gene coexpression networks, built using a set of microarray samples as input, can help elucidate tightly coexpressed gene sets (modules) that are mixed with genes of known and unknown function. Functional enrichment analysis of modules further subdivides the coexpressed gene set into cofunctional gene clusters that may coexist in the module with other functionally related gene clusters. In this study, 45 coexpressed gene modules and 76 cofunctional gene clusters were discovered for rice (Oryza sativa) using a global, knowledge-independent paradigm and the combination of two network construction methodologies. Some clusters were enriched for previously characterized mutant phenotypes, providing evidence for specific gene sets (and their annotated molecular functions) that underlie specific phenotypes.

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From Single Genes to Co-Expression Networks: Extracting Knowledge from Barley Functional Genomics

Cited by 23 publications

References 33 publications

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

An integrated genomic and metabolomic framework for cell wall biology in rice

The Association of Multiple Interacting Genes with Specific Phenotypes in Rice Using Gene Coexpression Networks

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