title: An integrated genomic and metabolomic framework for cell wall biology in rice.
abstract: BACKGROUND
  Plant 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  Here, 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.
authors.forename: Kai
authors.initials: K
authors.lastname: Guo
authors.forename: Weihua
authors.initials: W
authors.lastname: Zou
authors.forename: Yongqing
authors.initials: Y
authors.lastname: Feng
authors.forename: Mingliang
authors.initials: M
authors.lastname: Zhang
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Fen
authors.initials: F
authors.lastname: Tu
authors.forename: Guosheng
authors.initials: G
authors.lastname: Xie
authors.forename: Lingqiang
authors.initials: L
authors.lastname: Wang
authors.forename: Yangting
authors.initials: Y
authors.lastname: Wang
authors.forename: Sebastian
authors.initials: S
authors.lastname: Klie
authors.forename: Staffan
authors.initials: S
authors.lastname: Persson
authors.affiliation: National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei 430070, P, R, China. lpeng@mail.hzau.edu.cn.
authors.forename: Liangcai
authors.initials: L
authors.lastname: Peng
mesh_terms: D002473:Cell Wall / Q000378:metabolism*; D016000:Cluster Analysis; D018745:Genome, Plant; D023281:Genomics; D055432:Metabolomics; D012275:Oryza / Q000235:genetics*; D010940:Plant Proteins / Q000235:genetics / Q000378:metabolism; D059467:Transcriptome
chemical_list: D010940:Plant Proteins