Open Access Highly Accessed Research article

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

Kai Guo124, Weihua Zou123, Yongqing Feng123, Mingliang Zhang123, Jing Zhang123, Fen Tu124, Guosheng Xie123, Lingqiang Wang123, Yangting Wang123, Sebastian Klie5, Staffan Persson2356 and Liangcai Peng1234*

Author Affiliations

1 National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei 430070, P. R. China

2 Biomass and Bioenergy Research Centre, Huazhong Agricultural University, Wuhan, Hubei 430070, P. R. China

3 College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, P. R. China

4 College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, P. R. China

5 Max-Planck-Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Germany

6 School of Botany, University of Melbourne, Melbourne, VIC 3010, Australia

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BMC Genomics 2014, 15:596  doi:10.1186/1471-2164-15-596

Published: 15 July 2014

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.

Keywords:
Rice; Cell wall; Co-expression network; Metabolomics