LIK1, a CERK1-interacting kinase, regulates plant immune responses in Arabidopsis.

Chitin, an integral component of the fungal cell wall, is one of the best-studied microbe-associated molecular patterns. Previous work identified a LysM receptor-like kinase (LysM-RLK1/CERK1) as the primary chitin receptor in Arabidopsis. In order to identify proteins that interact with CERK1, we conducted a yeast two-hybrid screen using the intracellular kinase domain of CERK1 as the bait. This screen identified 54 putative CERK1-interactors. Screening mutants defective in 43 of these interacting proteins identified only two, a calmodulin like protein (At3g10190) and a leucine-rich repeat receptor like kinase (At3g14840), which differed in their response to pathogen challenge. In the present work, we focused on characterizing the LRR-RLK gene where mutations altered responses to chitin elicitation. This LRR-RLK was named LysM RLK1-interacting kinase 1 (LIK1). The interaction between CERK1 and LIK1 was confirmed by co-immunoprecipitation using protoplasts and transgenic plants. In vitro experiments showed that LIK1 was directly phosphorylated by CERK1. In vivo phosphorylation assays showed that Col-0 wild-type plants have more phosphorylated LIK1 than cerk1 mutant plants, suggesting that LIK1 may be directly phosphorylated by CERK1. Lik1 mutant plants showed an enhanced response to both chitin and flagellin elicitors. In comparison to the wild-type plants, lik1 mutant plants were more resistant to the hemibiotrophic pathogen Pseudomonas syringae, but more susceptible to the necrotrophic pathogen Sclerotinia sclerotiorum. Consistent with the enhanced susceptibility to necrotrophs, lik1 mutants showed reduced expression of genes involved in jasmonic acid and ethylene signaling pathways. These data suggest that LIK1 directly interacts with CERK1 and regulates MAMP-triggered innate immunity.

Evolutionary dynamics of protein domain architecture in plants.

BACKGROUND: Protein domains are the structural, functional and evolutionary units of the protein. Protein domain architectures are the linear arrangements of domain(s) in individual proteins. Although the evolutionary history of protein domain architecture has been extensively studied in microorganisms, the evolutionary dynamics of domain architecture in the plant kingdom remains largely undefined. To address this question, we analyzed the lineage-based protein domain architecture content in 14 completed green plant genomes. RESULTS: Our analyses show that all 14 plant genomes maintain similar distributions of species-specific, single-domain, and multi-domain architectures. Approximately 65% of plant domain architectures are universally present in all plant lineages, while the remaining architectures are lineage-specific. Clear examples are seen of both the loss and gain of specific protein architectures in higher plants. There has been a dynamic, lineage-wise expansion of domain architectures during plant evolution. The data suggest that this expansion can be largely explained by changes in nuclear ploidy resulting from rounds of whole genome duplications. Indeed, there has been a decrease in the number of unique domain architectures when the genomes were normalized into a presumed ancestral genome that has not undergone whole genome duplications. CONCLUSIONS: Our data show the conservation of universal domain architectures in all available plant genomes, indicating the presence of an evolutionarily conserved, core set of protein components. However, the occurrence of lineage-specific domain architectures indicates that domain architecture diversity has been maintained beyond these core components in plant genomes. Although several features of genome-wide domain architecture content are conserved in plants, the data clearly demonstrate lineage-wise, progressive changes and expansions of individual protein domain architectures, reinforcing the notion that plant genomes have undergone dynamic evolution.

A protein domain co-occurrence network approach for predicting protein function and inferring species phylogeny.

Protein Domain Co-occurrence Network (DCN) is a biological network that has not been fully-studied. We analyzed the properties of the DCNs of H. sapiens, S. cerevisiae, C. elegans, D. melanogaster, and 15 plant genomes. These DCNs have the hallmark features of scale-free networks. We investigated the possibility of using DCNs to predict protein and domain functions. Based on our experiment conducted on 66 randomly selected proteins, the best of top 3 predictions made by our DCN-based aggregated neighbor-counting method achieved a semantic similarity score of 0.81 to the actual Gene Ontology terms of the proteins. Moreover, the top 3 predictions using neighbor-counting, χ(2), and a SVM-based method achieved an accuracy of 66%, 59%, and 61%, respectively, when used to predict specific Gene Ontology terms of human target domains. These predictions on average had a semantic similarity score of 0.82, 0.80, and 0.79 to the actual Gene Ontology terms, respectively. We also used DCNs to predict whether a domain is an enzyme domain, and our SVM-based and neighbor-inference method correctly classified 79% and 77% of the target domains, respectively. When using DCNs to classify a target domain into one of the six enzyme classes, we found that, as long as there is one EC number available in the neighboring domains, our SVM-based and neighboring-counting method correctly classified 92.4% and 91.9% of the target domains, respectively. Furthermore, we benchmarked the performance of using DCNs to infer species phylogenies on six different combinations of 398 single-chromosome prokaryotic genomes. The phylogenetic tree of 54 prokaryotic taxa generated by our DCNs-alignment-based method achieved a 93.45% similarity score compared to the Bergey's taxonomy. In summary, our studies show that genome-wide DCNs contain rich information that can be effectively used to decipher protein function and reveal the evolutionary relationship among species.