TomatoNet: A Genome-wide Co-functional Network for Unveiling Complex Traits of Tomato, a Model Crop for Fleshy Fruits

Kim, Hyojin; Kim, Bong Suk; Shim, Jung Eun; Hwang, Sohyun; Yang, Sunmo; Iyer‐Pascuzzi, Anjali S.; Lee, Insuk

doi:10.1016/j.molp.2016.11.010

Cited by 18 publications

(13 citation statements)

References 11 publications

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“…Total RNA extraction was performed as above from root tissue used in the RNA-seq analysis. cDNA synthesis and qRT-PCR was performed as in (Kim et al 2017). Two biological replicates were used for validation.…”

Section: Methodsmentioning

confidence: 99%

Whole Root Transcriptomic Analysis Reveals a Role for Auxin Pathways in Resistance toRalstonia solanacearumin Tomato

French

Kim

Rivera-Zuluaga

et al. 2017

Preprint

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Section: Methodsmentioning

confidence: 99%

Whole Root Transcriptomic Analysis Reveals a Role for Auxin Pathways in Resistance toRalstonia solanacearumin Tomato

French

Kim

Rivera-Zuluaga

et al. 2017

Preprint

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“…For Arabidopsis thaliana , the phenotypes of EAR motif-containing protein coding genes were collected from Arabidopsis Hormone Database 2.0 (AHB2.0; Jiang et al, 2011 ), and microarray expression profiling of abiotic stress, biotic stress, hormone response, growth, and development (Supplementary Table 2 ) were collected from the Gene Expression Omnibus (GEO; Clough and Barrett, 2016 ) in NCBI. The gene co-expression network of Arabidopsis thaliana, Sorghum bicolor, Oryza sativa, Triticum aestivum, Glycine max, Zea mays and Solanum lycopersicum were obtained from ATTED-II (Aoki et al, 2016 ), SorghumFDB (Tian et al, 2016 ), RiceNET (Lee et al, 2015 ), wheatNET (Lee et al, 2017 ), soyNET (Kim E. et al, 2017 ), MCENet (Tian et al, 2018 ), and TomatoNET (Kim H. et al, 2017 ), respectively. The co-expression network of Gossypium arboreum and Gossypium hirsutum was obtained from ccNET (You et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

“…We obtained co-expression networks of 9 species from public platforms (Supplementary Table 1 ). The co-expression network of Oryza sativa (Lee et al, 2015 ), Triticum aestivum (Lee et al, 2017 ), Glycine max (Kim E. et al, 2017 ) and Solanum lycopersicum (Kim H. et al, 2017 ) was determined by calculating the Pearson correlation coefficient (PCC) between genes. The co-expression networks of Arabidopsis thaliana (Aoki et al, 2016 ), Zea mays (Tian et al, 2018 ), Sorghum bicolor (Tian et al, 2016 ) , Gossypium arboretum , and Gossypium hirsutum (You et al, 2017 ) were determined by calculating the Pearson correlation Coefficient (PCC) and mutual rank (MR) between genes.…”

Section: Methodsmentioning

confidence: 99%

PlantEAR: Functional Analysis Platform for Plant EAR Motif-Containing Proteins

et al. 2018

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The Ethylene-responsive element binding factor-associated Amphiphilic Repression (EAR) motifs, which were initially identified in members of the Arabidopsis ethylene response factor (ERF) family, are transcriptional repression motifs in plants and are defined by the consensus sequence patterns of either LxLxL or DLNxxP. EAR motif-containing proteins can function as transcription repressors, thus interacting with co-repressors, such as TOPLESS and AtSAP18, affecting the structure of chromatin by histone modifications and thereby repressing gene transcription. EAR motif-containing proteins are highly conserved across diverse plant species and play important roles in hormone signal transduction, stress responses and development, but they have not been identified in most plants. In this study, we identified 20,542 EAR motif-containing proteins from 71 plant species based on a Hidden Markov Model and orthologous gene search, and then we constructed a functional analysis platform for plant EAR motif-containing proteins (PlantEAR, http://structuralbiology.cau.edu.cn/plantEAR) by integrating a variety of functional annotations and processed data. Several tools were provided as functional support for EAR motif-containing proteins, such as browse, search, co-expression and protein-protein interaction (PPI) network analysis as well as cis-element analysis and gene set enrichment analysis (GSEA). In addition, basing on the identified EAR motif-containing proteins, we also explored their distribution in various species and found that the numbers of EAR motif-containing proteins showed an increasing trend in evolution from algae to angiosperms.

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“…For other plants species, high coverage and confidence gene co-functional relationships can also be predicted via efficient computational pipelines. For instance, genome-wide gene co-functional networks have recently been integratively achieved in Glycine max ( Kim E. et al, 2017 ), Solanum lycopersicum ( Kim H. et al, 2017 ), and Triticum aestivum ( Lee et al, 2017 ) by translating molecular interaction knowledge from data-rich model species (e.g., Arabidopsis thaliana and Saccharomyces cerevisiae ) in them and using their in-species gene expression data.…”

Section: A Survey Of ‘Omics’-based Network Strategies For the Identifmentioning

confidence: 99%

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

Zhang

Tai

et al. 2018

Front. Plant Sci.

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Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.

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TomatoNet: A Genome-wide Co-functional Network for Unveiling Complex Traits of Tomato, a Model Crop for Fleshy Fruits

Cited by 18 publications

References 11 publications

Whole Root Transcriptomic Analysis Reveals a Role for Auxin Pathways in Resistance toRalstonia solanacearumin Tomato

Whole Root Transcriptomic Analysis Reveals a Role for Auxin Pathways in Resistance toRalstonia solanacearumin Tomato

PlantEAR: Functional Analysis Platform for Plant EAR Motif-Containing Proteins

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

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