Bioinformatic analysis of the CLE signaling peptide family
1 School of Biochemistry and Molecular Biology, The Australian National University, Canberra, ACT, Australia
2 Research School of Biological Sciences, The Australian National University, Canberra, ACT, Australia
3 The University of Queensland, Brisbane, QLD, Australia
4 The Australian Research Council Centre of Excellence for Integrative Legume Research
BMC Plant Biology 2008, 8:1 doi:10.1186/1471-2229-8-1Published: 3 January 2008
Plants encode a large number of leucine-rich repeat receptor-like kinases. Legumes encode several LRR-RLK linked to the process of root nodule formation, the ligands of which are unknown. To identify ligands for these receptors, we used a combination of profile hidden Markov models and position-specific iterative BLAST, allowing us to detect new members of the CLV3/ESR (CLE) protein family from publicly available sequence databases.
We identified 114 new members of the CLE protein family from various plant species, as well as five protein sequences containing multiple CLE domains. We were able to cluster the CLE domain proteins into 13 distinct groups based on their pairwise similarities in the primary CLE motif. In addition, we identified secondary motifs that coincide with our sequence clusters. The groupings based on the CLE motifs correlate with known biological functions of CLE signaling peptides and are analogous to groupings based on phylogenetic analysis and ectopic overexpression studies. We tested the biological function of two of the predicted CLE signaling peptides in the legume Medicago truncatula. These peptides inhibit the activity of the root apical and lateral root meristems in a manner consistent with our functional predictions based on other CLE signaling peptides clustering in the same groups.
Our analysis provides an identification and classification of a large number of novel potential CLE signaling peptides. The additional motifs we found could lead to future discovery of recognition sites for processing peptidases as well as predictions for receptor binding specificity.