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Exploring tomato gene functions based on coexpression modules using graph clustering and differential coexpression
Atsushi Fukushima1, Tomoko Nishizawa, Mariko Hayakumo
1RIKEN Plant Science Center, Yokohama, Kanagawa 230-0045, Japan. a-fukush@psc.riken.jp
Plant Physiology
|February 7, 2012
Summary
Tomato gene coexpression networks reveal functional modules and differential expression patterns. This study aids in predicting gene functions and understanding metabolic pathways in tomato (Solanum lycopersicum).
Area of Science:
- Plant genomics and bioinformatics
- Functional genomics in Solanum lycopersicum
Background:
- Gene coexpression analysis is crucial for predicting gene functions in plants.
- Understanding tomato (Solanum lycopersicum) gene expression patterns is essential for crop improvement.
Purpose of the Study:
- To construct and analyze tomato coexpression networks and modules to predict unknown gene functions.
- To investigate differential coexpression in tomato across different tissues (leaf, fruit, root).
- To identify key regulatory steps in metabolic pathways using coexpression analysis.
Main Methods:
- Analysis of over 300 tomato microarrays from public and in-house data.
- Construction of gene coexpression networks and identification of modules using graph clustering.
- Differential coexpression analysis across leaf, fruit, and root transcriptomes.
- Validation of selected gene findings using quantitative real-time polymerase chain reaction (qRT-PCR).
Main Results:
- 465 coexpression modules were identified, with 88% assigned Gene Ontology terms, revealing biologically relevant functions.
- Duplicated and metabolic genes showed significant differential coexpressions.
- Reversal of gene coexpression was observed in lycopene and flavonoid biosynthesis pathways.
Conclusions:
- Coexpression network analysis effectively identifies functional modules in the tomato transcriptome.
- Differential coexpression analysis provides insights into regulatory mechanisms of metabolic pathways.
- The findings facilitate prioritization of candidate genes for tomato functional genomics and metabolic studies.
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