Occurrence of Phytoplasmas Belonging to 16SrII Group Associated with Cyanthillium cinereum Witches'-Broom Diseases in China.

Cyanthillium cinereum, which belongs to the family of Asteraceae, is an annual or perennial herbaceous plant with significant medicinal uses for treating colds and fever. During September to November of 2020, C. cinereum showing symptoms of witches'-broom were found in economic forests distributed in Ding'an, Hainan Province of China, with 20% incidence. The symptoms of the plant were consistent with infections by 'Candidatus Phytoplasma' species. To identify the pathogen, five symptomatic and three asymptomatic C. cinereum samples were collected. Total DNAs were extracted using 0.10 g fresh leaf tissues of symptomatic and asymptomatic C. cinereum through a CTAB DNA extraction method according to Doyle and Doyle (1990). PCR amplification were performed employing the primer pairs of R16mF2/R16mR1 (Gundersen and Lee, 1996) and secAfor1/secArev3 (Hodgetts et al., 2008) specific for the conserved gene fragments of 16S rRNA and secA from phytoplasma. The PCR products were purified and sequenced through Biotechnology (Shanghai) Co., Ltd. (Guangzhou, China), and the obtained sequences were deposited in GenBank. The phytoplasmal 16S rRNA and secA gene fragments obtained in the study were all identical with the length of 1325 bp (GenBank accession: PP098738) and 741 bp (PP072217), respectively. The phytoplasma strain was described as CcWB-hnda. A BLAST search based on 16S rRNA genes indicated that CcWB-hnda strain was identical to phytoplasmas belonging to 16SrII group like peanut witches'-broom phytoplasma strain T48 (OR239773) and 'Ca. Phytoplasma aurantifolia' strain TB2022 (CP120449). Virtual RFLP profiles based on 16S rRNA gene fragments obtained by iPhyClassifier (Zhao et al., 2009) showed that CcWB-hnda strain was a member of 16SrII-A subgroup with 1.00 similarity coefficient to the reference phytoplasma strain (L33765). A BLAST search based on secA genes indicated that CcWB-hnda had 100% sequence identity with phytoplasmas belonging to 16SrII group such as 'Ca. Phytoplasma aurantifolia' isolate TB2022 (CP120449), Vigna unguiculata witches'-broom phytoplasma (OR661282) and Emilia sonchifolia witches'-broom phytoplasma (MW353710). Phylogenetic analysis based on 16S rRNA and secA genes by MEGA 7.0 employing Neighbor-Joining method with 1000 bootstrap value (Kumar et al., 2016; Felsenstein, 1985) demonstrated that CcWB-hnda was clustered into one clade with the phytoplasmas belonging to 16SrII group, with 98% and 100% bootstrap value respectively. To our knowledge, this is the first report of C. cinereum infected by phytoplasmas belonging to 16SrII-A subgroup in China. Identification of the vector insects of the pathogens is necessary in future, revealing the epidemiology of the related diseases. Phytoplasmas belonging to same 16Sr group or subgroup can infect different plants and spread through them in nature. The finding in this study will be beneficial to epidemic monitoring and early warning of C. cinereum witches'-broom disease and the related plant diseases caused by the phytoplasmas belonging to 16SrII group.

Imputing missing values for genetic interaction data.

BACKGROUND: Epistatic Miniarray Profiles (EMAP) enable the research of genetic interaction as an important method to construct large-scale genetic interaction networks. However, a high proportion of missing values frequently poses problems in EMAP data analysis since such missing values hinder downstream analysis. While some imputation approaches have been available to EMAP data, we adopted an improved SVD modeling procedure to impute the missing values in EMAP data which has resulted in a higher accuracy rate compared with existing methods. RESULTS: The improved SVD imputation method adopts an effective soft-threshold to the SVD approach which has been shown to be the best model to impute genetic interaction data when compared with a number of advanced imputation methods. Imputation methods also improve the clustering results of EMAP datasets. Thus, after applying our imputation method on the EMAP dataset, more meaningful modules, known pathways and protein complexes could be detected. CONCLUSION: While the phenomenon of missing data unavoidably complicates EMAP data, our results showed that we could complete the original dataset by the Soft-SVD approach to accurately recover genetic interactions.

Widely targeted metabolomics combined with E-tongue and E-nose reveal dynamic changes of tender coconut water in responses to the infection of Ceratocystis paradoxa.

Ceratocystis paradoxa is a major cause of postharvest disease in tender coconuts worldwide. We conducted a comprehensive study using widely targeted metabolomics, electronic tongue (E-tongue), and electronic nose (E-nose) analyses to investigate the impacts of C. paradoxa invasion on the quality of tender coconut water (TCW) from fresh control (FC), uninoculated (UN), skin-inoculated (SI), and deep-inoculated (DI) nuts. DI exhibited significantly higher taste indicators associated with bitterness, saltiness, astringency aftertaste, and bitter aftertaste, as well as odor sensor values related to various compounds such as long-chain alkanes, hydrides, methane, organic sulfides, etc. Invasion of C. paradoxa into the endosperm altered the flavor characteristics of TCW mainly through the modulation of carbohydrate and secondary metabolite pathways. Furthermore, significant correlations were observed between the differentially expressed flavorful metabolites and the sensor indicators of the E-nose and E-tongue. These findings offer valuable insights into understanding the impact of C. paradoxa infection on coconuts.

Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data.

MOTIVATION: Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponential-family random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g., the density, centrality, and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM. RESULTS: EM algorithm is a classical method to solve the mixture problem, however, it will be very slow when the data size is huge in the numerous applications. We adopt an efficient novel online graph clustering algorithm to classify the graph nodes and estimate the ERGM parameters for the MixtureERGM. In comparison studies, the MixtureERGM outperforms the role analysis for the network cluster in which the mixture of exponential-family random graph model is developed for many ego-network according to their roles. One genetic interaction network of yeast and two real social networks (provided as supplemental materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2017.2743711) show the wide potential application of the MixtureERGM.

Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data.

BACKGROUND: Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology "low-rank and sparse decomposition" (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part. RESULTS: LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.