Computational approaches to unravel leaf growth
High-throughput technologies generate huge amounts of data, representing the active components of the cell, e.g. genes, proteins, metabolites and the interactions between them in particular developmental stages, tissues or environments.
Focusing on leaf growth, we develop and apply computational approaches to construct networks of the players that are relevant to growth and development of the leaf to better understand the regulatory processes involved.
Transcriptome profiling and data analysis
The advent of massive parallel sequencing technologies has opened a new realm of possibilities in transcriptome analysis. Using RNA-seq (Fig. 1), instead of hybridization-based technologies, we are able to widen the scope of transcriptomics studies beyond reference-based analyses. Next to transgenic approaches, we also study natural variation, such as different accessions of Arabidopsis or inbred lines and RILs in maize. This will lead to a better understanding of the core processes regulating growth as well as give us insight in the different regulatory processes selected for in the natural population.
We have implemented an analysis pipeline for RNA-seq data using state-of-the-art tools, incorporating the available information on genetic diversity from public sources, as well as the sequencing data itself.
Data integration and gene prioritization
We are applying state-of-the-art computational approaches for the analysis and integration of transcriptomics, interactomics and functional genomics data. Through data integration and the construction of molecular networks, we aim to mine specific in house-generated data on leaf growth, as well as publicly available data sources to unravel the molecular mechanisms of leaf growth. In addition, we try to identify new leaf growth regulators through gene prioritization in these molecular networks.
Bioinformatics tool development: CORNET - a user-friendly tool for data mining and integration
We have developed the tool CORNET which allows the interrogation of currently available microarray, protein-protein interaction, regulatory interaction, gene-gene association and functional annotation data (De Bodt et al. 2010, 2012). Different search options are implemented to enable the construction of integrated networks, centered around multiple input genes or proteins. Networks and associated evidence of the majority of currently available data types are visualized in Cytoscape (see Fig. 2). CORNET is currently available for Arabidopsis and maize.
|Figure 1. Transcript profiling using RNA-seq
|Figure 2. CORNET co-expression and protein-protein interaction network|
People involved: Frederik Coppens, Dorota Herman, Steven Maere, Bram Slabbinck, Michiel Van Bel
De Bodt, S., Carvajal, D., Hollunder, J., Van den Cruyce, J., Movahedi, S., Inzé, D. (2010) CORNET: a user-friendly tool for data mining and integration. Plant Physiology 152:1167-1179
De Bodt, S., Hollunder, J., Nelissen, H., Meulemeester, N., Inzé, D. (2012) CORNET 2.0: integrating plant coexpression, protein-protein interactions, regulatory interactions, gene associations and functional annotations. New Phytologist 195:707-720
Andriankaja, M., Dhondt, S., De Bodt, S., Vanhaeren, H., Coppens, F., De Milde, L., Mühlenbock, P., Skirycz, A., Gonzalez, N., Beemster G.T., Inzé, D. (2012) Exit from proliferation during leaf development in Arabidopsis thaliana: a not-so-gradual process. Development Cell 22:64-78
Skirycz, A., Claeys, H., De Bodt, S., Oikawa, A., Shinoda, S., Andriankaja, M., Maleux, K., Eloy, N.B., Coppens, F., Yoo, S.D., Saito, K., Inzé, D. (2011) Pause-and-stop: the effects of osmotic stress on cell proliferation during early leaf development in Arabidopsis and a role for ethylene signaling in cell cycle arrest. Plant Cell 23:1876-1888
Gonzalez, N., De Bodt, S., Sulpice, R., Jikumaru, Y., Chae, E., Dhondt, S., Van Daele, T., De Milde, L., Weigel, D., Kamiya, Y., Stitt, M., Beemster, G.T., Inzé, D. (2010) Increased leaf size: different means to an end. Plant Physiology 153:1261-1279
Van Leene, J., Hollunder, J., Eeckhout, D., Persiau, G., Van De Slijke, E., Stals, H., Van Isterdael, G., Verkest, A., Neirynck, S., Buffel, Y., De Bodt, S., Maere, S., Laukens, K., Pharazyn, A., Ferreira, P.C., Eloy, N.B., Renne, C., Meyer, C., Faure, J.D., Steinbrenner, J., Beynon, J., Larkin, J.C., Van de Peer, Y., Hilson, P., Kuiper, M., De Veylder, L., Van Onckelen, H., Inzé, D., Witters, E., De Jaeger, G. (2010) Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana. Molecular Systems Biology 6:397