Technologies for discovering new peptide signals

As indicated from the above examples, peptide signals regulate a variety of important biological processes in plants. However, compared to the hundreds of peptide signals in animals, the number of known plant peptide signals is low. Growing evidence indicates that many more peptide signals in plants are yet to be discovered. For example, plants possess a large number of receptor-like kinases (RLKs). Six hundred ten RLKs have been identified through analysis of the Arabidopsis genome (Shiu & Bleecker, 2001). Among these, 216 contain extracellular LRR domains typical of peptide-binding motifs (Kobe & Deisenhofer, 1994, 1995). The receptors for CLV3, PSK, and systemin are all the members of LRR-containing RLKs. However, to date, ligands have only been identified for very few of these plant RLKs.

0ne of the hurdles in identifying peptides that function as signal molecules is their low abundance. For example, over 60 lb of tomato leaves was used in purifying the tomato systemin for sequence determination and characterization (Pearce et al., 1991). Another hurdle is the lack of an assay for their biological activity. The discovery of the TomHypSys and TobHypSys systemins was facilitated by the availability of the alkalinization assay. Without a convenient assay, it would be very difficult to isolate a new peptide signal using a biochemical approach.

Two of the peptide signals, SCR and CLAVATA3, were identified through genetic approaches. Mutational analysis will continue to contribute to discovery of new peptide signals. However, most genes encoding peptide signals are likely small. This small target size reduces the chance to introduce a mutation in those genes. Even if some of them do encode large precursors, the active form of a peptide signal could be a short peptide such as PSK. Therefore, a large portion of mutations in those genes will be silent. As a result, a relatively large mutagenized population needs to be screened in order to isolate a loss-of-function mutation in a peptide signal-encoding gene.

Gain-of-function mutagenesis such as activation tagging (Weigel et al., 2000) could be a powerful alternative genetic approach to isolate genes that encode peptide signals. Such an approach has led to the recent identification of an Arabidopsis gene (DEVIL1) encoding a novel 51-amino acid polypeptide whose gain-of-function mutation causes changes in a variety of developmental processes, including the leaf shape, plant stature, and silique development (Wen et al., 2004). The Arabidopsis genome encodes 20 DVL1-like proteins, many of which appear to play a biological role similar to DVL1. The exact biological functions of the members of this gene family have yet to be determined. Besides, further studies are needed to prove that DVL1 and its homologues act as signal molecules.

The availability of whole genome sequences for a growing list of plant species as well as advances in functional genomics and proteomics technology provide a unique opportunity to identify new peptide signals in a high-throughput fashion. An in silico search of putative peptide signal-encoding genes is the first step toward this goal. However, the commonly used gene prediction algorithms predict genes on the basis of presence of a significant 0RF of at least 100 amino acids (Harrison et al., 2002). Similar gene prediction algorithms are also used to predict genes from the Arabidopsis and rice genomes (MacIntosh et al., 2001; Goff et al., 2002). Undoubtedly, a large number of genuine genes with small 0RFs could not be predicted from those genomes. Therefore, many small 0RFs that have been predicted as noncoding sequences could encode peptide signals.

A systemic analysis of small ORFs in Arabidopsis that encode putative secreted peptides has identified a family of 34 genes, many of which contain the RALF-like sequence (Olsen et al., 2002). Biological functions of RALFs have not been well understood. The existence of the larger number of putative secreted RALF-like molecules suggests that many of them may be signals involved in a variety of biological pathways. Another search for CLV3 homologues has identified 42 CLV3-related (CLE) sequences in plants (Cock & McCormick, 2001). Among them, 24 are in the Arabidopsis genome. Speculatively, these CLE genes could encode the ligands for many RLKs. A similar search has identified a large number of putative genes that encode peptides homologous to SCR (Vanoosthuyse et al., 2001).

One of the challenges of this approach is to determine that these in silico genes do encode polypeptides. The peptidomics approach may offer a way to aid the systematic discovery of peptides (Schulz-Knappe et al., 2001). The approach takes advantage of high sensitivity of mass spectrometry technology in identification of proteins and improvements in separating and isolating peptides to systematically identify a complement of peptide molecules produced by particular tissues, organs, or whole organisms. Such an approach has recently led to the discovery of a large number of new animal neuropeptides and peptide hormones that had eluded identification through classical methods (Takahashi et al., 1997; Sweedler et al., 2000; Clynen et al., 2001; Schrader & Schulz-Knappe, 2001; Svensson et al., 2003). For instance, Svensson et al. (2003) simultaneously detected more than 550 endogenous neuropeptides from hypothalamic extracts, which include previously described and many novel neuropeptides. These structural peptidomics approaches can be explored to identify the major complement of peptides produced by plant cells.

A more challenging task is to determine whether a peptide really functions as a signal because many small proteins have other regulatory or nonregulatory functions. A method to address this question is to determine if a peptide binds to a receptor. Binding of a peptide to a cell surface receptor will provide strong evidence of function as a signal molecule. Several high-throughput protein-protein interaction technologies have been developed in recent years (Drewes & Bouwmeester, 2003). It should be feasible to apply them in generating interaction maps between peptide molecules and the putative receptor kinases encoded by the plant genomes. Such information will not only provide important clues as to whether a peptide functions as a signal but also facilitate identification of its receptors and downstream signaling components.

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