Chitin, a polymer of N-acetyl-d-glucosamine, is found in fungal cell walls but not in plants. Plant cells can perceive chitin fragments (chitooligosaccharides) leading to gene induction and defense responses. We identified a LysM receptor-like protein (LysM RLK1) required for chitin signaling in Arabidopsis  thaliana. The mutation in this gene blocked the induction of almost all chitooligosaccharide-responsive genes and led to more susceptibility to fungal pathogens but had no effect on infection by a bacterial pathogen. Additionally, exogenously applied chitooligosaccharides enhanced resistance against both fungal and bacterial pathogens in the wild-type plants but not in the mutant. Together, our data indicate that LysM RLK1 is essential for chitin signaling in plants (likely as part of the receptor complex) and is involved in chitin-mediated plant innate immunity. The LysM RLK1-mediated chitin signaling pathway is unique, but it may share a conserved downstream pathway with the FLS2/flagellin- and EFR/EF-Tu–mediated signaling pathways. Additionally, our work suggests a possible evolutionary relationship between the chitin and Nod factor perception mechanisms due to the similarities between their potential receptors and between the signal molecules perceived by them.

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, χ2, 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.

Nature 463, 178-183 (2010) During resubmission of this work, a paper was published 1 that used a comparative genomics approach between soybean and maize to show that a single-base mutation in chromosome 19 accounts for the duplicate recessive epistasis needed to greatly reduce phytate production in soybean seed.In this Article, the statement that: ''31,264 high-confidence soybean genes have recent paralogues with K s < 0.13 synonymous substitutions per site and 4dTv < 0.0566 synonymous transversions per site'' is inadvertently incorrect, and instead the correct statement is that ''26,501 high-confidence soybean genes have recent paralogues with K s < 0.13 synonymous substitutions per site and 4dTv < 0.0566 synonymous transversions per site''. This change does not affect the overall conclusions.

BackgroundProtein 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.ResultsOur 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.ConclusionsOur 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.

Prognostic indices are commonly used in the context of brain metastases radiotherapy to guide patient decision-making and clinical trial stratification. This study is to choose an appropriate prognostic index (PI) for non-small cell lung cancer (NSCLC) patients with brain metastases (BM) who underwent radiosurgery. A total of 103 patients with BM from NSCLC receiving radiosurgery were analyzed retrospectively. There are six prognostic factors were analyzed, including age, primary tumor control, extracranial metastasis, KPS score, number of lesions, max lesion volume; and four prognostic indices were compared, include Recursive Partitioning Analysis (RPA),Graded Prognostic Assessment (GPA), Score Index for Radiosurgery (SIR), Basic Score for Brain Metastases (BSBM). Survival curves were estimated with the Kaplan-Meier method and compared with a log-rank test stratified according to the PIs. Univariate and multivariate analysis was performed using the Cox regression analysis. The PI's predictive capacity was compared in terms of Akaike information criterion (AIC), Log-rank × 2, Concordance index (C-index) and calibration curve. The median survival time was 8 months, and the 6-months and 12-months survival rate were 61% and 26% respectively. All four prognostic indices were correlated with prognosis (P<0.005).The AIC for BSBM (686.317) was the minimum in the four PIs(range,686.317-739.113).The Log-rank × 2 value for BSBM (77.62) was the maximum in the four PIs (range,23.32-77.62).The C-index for BSBM (0.758)was superior than the other PIs predictive capacity (range,0.611-0.758). The calibration curve showed that the BSBM was able to predict 6-months and 12-months overall survival accurately. In conclusion, the BSBM may be the most accurate prognostic index for patients with BM from NSCLC who underwent radiosurgery.