Genome‐wide analysis of Nilaparvata lugens nymphal responses to high‐density and low‐quality rice hosts

The brown planthopper (BPH) Nilaparvata lugens is an economically important pest on rice plants. In this study, the higher population density and yellow‐ripe stage of rice plants were used to construct adverse survival conditions (ASC) against BPH nymphs. Simultaneously, the low population density and tillering stage of rice plants were used to establish a suitable survival condition (SSC) as a control. Solexa/Illumina sequencing was used to identify genes of BPH nymphs responding to ASC. Significantly longer duration development of BPH nymphs and significantly lower brachypterous ratio of BPH adults were observed by ASC compared with SSC. A total of 2 544 differentially expressed genes (DEGs) were obtained and analyzed by BLASTx, Gene Ontology and KEGG Orthology. Gene ontology analysis revealed that the DEGs were mainly involved in categories of cell, cell part, cellular process, binding, catalytic, organelle and metabolic processes. 1 138 DEGs having enzyme commission numbers were assigned to different metabolic pathways. The largest clusters were neurodegenerative diseases (137, 12.0%), followed by carbohydrate metabolism (113, 9.9%), amino acid metabolism (94, 8.3%), nucleotide metabolism (76, 6.7%), energy metabolism (64, 5.6%), translation (60, 5.3%), lipid metabolism (58, 5.1%), and folding, sorting and degradation (52, 4.6%). Expressing profile of 11 DEGs during eight nymphal developmental stages of BPH were analyzed by quantitative real‐time polymerase chain reaction. The 11 genes exhibited differential expression between ASC and SSC during at least one developmental stage. The DEGs identified in this study provide molecular proof of how BPH reconfigures its gene expression profile to adapt to overcrowding and low‐quality hosts.     

Morphogenesis of T-tubules in heart cells: the role of junctophilin-2

The T-tubule (TT) system forms the structural basis for excitation-contraction coupling in heart and muscle cells. The morphogenesis of the TT system is a key step in the maturation of heart cells because it does not exist in neonatal cardiomyocytes. In the present study, we quantified the morphological changes in TTs during heart cell maturation and investigated the role of junctophilin-2 (JP2), a protein known to anchor the sarcoplasmic reticulum (SR) to TT, in changes to TT morphological parameters. Analysis of confocal images showed that the transverse elements of TTs increased, while longitudinal elements decreased during the maturation of TTs. Fourier transform analysis showed that the power of ~2 m spatial components increased with cardiomyocytes maturation. These changes were preceded by increased expression of JP2, and were reversed by JP2 knockdown. These findings indicate that JP2 is required for the morphogenesis of TTs during heart development.     

Secretory/releasing proteome-based identification of plasma biomarkers in HBV-associated hepatocellular carcinoma

For successful therapy, hepatocellular carcinoma (HCC) must be detected at an early stage. Herein, we used a proteomic approach to analyze the secretory/releasing proteome of HCC tissues to identify plasma biomarkers. Serum-free conditioned media (CM) were collected from primary cultures of cancerous tissues and surrounding noncancerous tissues. Proteomic analysis of the CM proteins permitted the identification of 1365 proteins. The enriched molecular functions and biological processes of the CM proteins, such as hydrolase activity and catabolic processes, were consistent with the liver being the most important metabolic organ. Moreover, 19% of the proteins were characterized as extracellular or membrane-bound. For validation, secretory proteins involved in transforming growth factor-β signaling pathways were validated in plasma samples. Alphafetoprotein (AFP), metalloproteinase (MMP)1, osteopontin (OPN), and pregnancy-specific beta-1-glycoprotein (PSG)9 were significantly increased in HCC patients. The overall performance of MMP1 and OPN in the diagnosis of HCC remained greater than that of AFP. In addition, this study represents the first report of MMP1 as a biomarker with a higher sensitivity and specificity than AFP. Thus, this study provides a valuable resource of the HCC secretome with the potential to investigate serological biomarkers. MMP1 and OPN could be used as novel biomarkers for the early detection of HCC and to improve the sensitivity of biomarkers compared with AFP.     

Overexpression of Arabidopsis ABF3 gene confers enhanced tolerance to drought and heat stress in creeping bentgrass

The Arabidopsis, abscisic acid responsive element-binding factor 3, ABF3 is known to play an important role in stress responses via regulating the expression of stress-responsive genes. In this study, we introduced pCAMBIA3301 vector harboring the ABF3 gene into creeping bentgrass (Agrostis stolonifera) through Agrobacterium-mediated transformation in order to develop a stress-tolerant variety of turfgrass. After transformation, putative transgenic plants were selected using the herbicide resistance assay. Genomic integration of the transgene was confirmed by genomic PCR and Southern blot analysis, and gene expression was validated by northern blot analysis. Under drought-stressed condition, the transgenic plants overexpressing ABF3 displayed significantly enhanced drought tolerance with higher water content and slower water loss rate than the control plants. Furthermore, the stomata of the ABF3 transgenic plants closed more than those of wild-type creeping bentgrass plants, under both non-stressed and ABA treatment conditions. In addition, the transgenic plants showed enhanced tolerance to heat stress. These results suggest that the overexpression of the ABF3 gene in creeping bentgrass might enhance survival in water-limiting and high temperature environments through increased stomatal closure and reduced water losses.     

An Efficient Hierarchical Generalized Linear Mixed Model for Mapping QTL of Ordinal Traits in Crop Cultivars

Many important phenotypic traits in plants are ordinal. However, relatively little is known about the methodologies for ordinal trait association studies. In this study, we proposed a hierarchical generalized linear mixed model for mapping quantitative trait locus (QTL) of ordinal traits in crop cultivars. In this model, all the main-effect QTL and QTL-by-environment interaction were treated as random, while population mean, environmental effect and population structure were fixed. In the estimation of parameters, the pseudo data normal approximation of likelihood function and empirical Bayes approach were adopted. A series of Monte Carlo simulation experiments were performed to confirm the reliability of new method. The result showed that new method works well with satisfactory statistical power and precision. The new method was also adopted to dissect the genetic basis of soybean alkaline-salt tolerance in 257 soybean cultivars obtained, by stratified random sampling, from 6 geographic ecotypes in China. As a result, 6 main-effect QTL and 3 QTL-by-environment interactions were identified.     

An Ensemble Method for Predicting Subnuclear Localizations from Primary Protein Structures

Background

Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods.

Methodology/Principal Findings

A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis.

Conclusions

It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method. It is freely available at http://bioinformatics.awowshop.com/snlpred_page.php.