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Plants significantly shape root-associated microbiota, making rhizosphere microbes useful environmental indicator organisms for safety assessment. Here, we report the pyrosequencing of the bacterial 16S ribosomal RNA in rhizosphere soil samples collected from transgenic cry1Ab/cry1Ac Bt rice Huahui No. 1 (GM crop) and its parental counterpart, Minghui63. We identified a total of 2579 quantifiable bacterial operational taxonomic units (OTUs). Many treatment-enriched microbial OTUs were identified, including 14 NonGM-enriched OTUs and 10 GM-enriched OTUs. OTUs belonging to the phyla Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, Nitrospirae, Chlorobi and GN04 were identified as statistically different in abundance between GM and the other two treatments. Compared with the different impacts of different rice varieties on microbiota, the impact of rice planting on microbiota is more obvious. Furthermore, Huahui No. 1 transgenic Bt rice had a greater impact on the rhizosphere bacterial communities than Minghui63. Early developmental stages of the transgenic Bt rice had a significant impact on many Bacillaceae communities. Soil chemical properties were not significantly altered by the presence of transgenic Bt rice. The peak concentration level of Bt protein products was detected during the seedling stage of transgenic Bt rice, which may be an intriguing factor for bacterial diversity variations. Based on these findings, we conclude that transgenic Bt rice has a significant impact on root-associated bacteria. This information may be leveraged in future environmental safety assessments of transgenic Bt rice varieties.

Abstract Background The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein–protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences. Results We propose the Sequence-Statistics-Content protein sequence encoding format (SSC) based on information extraction from the original sequence for further performance improvement of the convolutional neural network. The original protein sequences are encoded in the three-channel format by introducing statistical information (the second channel) and bigram encoding information (the third channel), which can increase the unique sequence features to enhance the performance of the deep learning model. On predicting protein–protein interaction tasks, the results using the 2D convolutional neural network (2D CNN) with the SSC encoding method are better than those of the 1D CNN with one hot encoding. The independent validation of new interactions from the HIPPIE database (version 2.1 published on July 18, 2017) and the validation of directly predicted results by applying a molecular docking tool indicate the effectiveness of the proposed protein encoding improvement in the CNN model. Conclusion The proposed protein sequence encoding method is efficient at improving the capability of the CNN model on protein sequence-related tasks and may also be effective at enhancing the capability of other machine learning or deep learning methods. Prediction accuracy and molecular docking validation showed considerable improvement compared to the existing hot encoding method, indicating that the SSC encoding method may be useful for analyzing protein sequence-related tasks. The source code of the proposed methods is freely available for academic research at https://github.com/wangy496/SSC-format/ .

Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.

Corn is one of the most widely grown crops throughout the world. However, many corn fields develop pest problems such as corn borers every year that seriously affect its yield and quality. Corn′s response to initial insect damage involves a variety of changes to the levels of defensive enzymes, toxins, and communicative volatiles. Such a dramatic change secondary metabolism necessitates the regulation of gene expression at the transcript level. In this paper, we summarized the datasets of the transcriptome of corn plants in response to corn stalk borers (Ostrinia furnacalis) and/or methyl jasmonate (MeJA). Altogether, 39, 636 genes were found to be differentially expressed. The sequencing data are available in the NCBI SRA database under accession number SRS965087. Our dataset will provide more scientific and valuable information for future work such as the study of the functions of important genes or proteins and develop new insect-resistant maize varieties. Keywords: RNA-seq, Asian corn borer, jasmonic acid

Micraspis discolor (Fabricius) (Coleoptera: Coccinellidae) is a widely distributed coleoptera predator in southern Asia in rice ecosystem, and adult M. discolor feed on both rice pollen and soft-bodied arthropods. Bitrophic bioassay and tritrophic bioassay were conducted to evaluate the potential impact of Cry1Ac/Cry1Ab-expressing rice Huahui 1 and its non-transgenic counterpart Minghui 63 on fitness parameters of adult M. discolor. The results showed that the survival, and fecundity of this beetle' adults were not different when they fed on Bt rice or non-Bt rice pollen or Nilaparvata lugens (Stål) reared on Bt rice or non-Bt rice. Toxicity assessment to ensure M. discolor adults were not sensitive to Cry1Ab or Cry1Ac protein independent from the pollen background, M. discolor adults were fed with an artificial diet containing Cry1Ac, Cry1Ab or both protein approximately 10 times higher concentration than in Huahui 1 rice pollen. No difference was detected for any of the life-table parameters tested between Cry protein-containing and pure diet. Artificial diet containing E-64 (N-(trans-Epoxysuccinyl)-L-leucine 4-guanidinobutylamide) was included as a positive control. In contrast, the pre-oviposition and fecundity of M. discolor were significantly adversely affected by feeding on E-64-containing diet. In both bioassays, the uptakes of Cry protein by adult M. discolor were tested by ELISA measurements. These results indicated that adults of M. discolor are not affected by Cry1Ab- or Cry1Ac-expressing rice pollen and are not sensitive to Cry protein at concentrations exceeding the levels in rice pollen in Huahui1. This suggests that M. discolor adults would not be harmed by Cry1Ac/Cry1Ab rice if Bt rice Huahui 1 were commercialized.

WRKY proteins comprise one of the largest transcription factor families in plants and form key regulators of many plant processes. This study presents the characterization of 58 WRKY genes from the castor bean (Ricinus communis L., Euphorbiaceae) genome. Compared with the automatic genome annotation, one more WRKY-encoding locus was identified and 20 out of the 57 predicted gene models were manually corrected. All RcWRKY genes were shown to contain at least one intron in their coding sequences. According to the structural features of the present WRKY domains, the identified RcWRKY genes were assigned to three previously defined groups (I-III). Although castor bean underwent no recent whole-genome duplication event like physic nut (Jatropha curcas L., Euphorbiaceae), comparative genomics analysis indicated that one gene loss, one intron loss and one recent proximal duplication occurred in the RcWRKY gene family. The expression of all 58 RcWRKY genes was supported by ESTs and/or RNA sequencing reads derived from roots, leaves, flowers, seeds and endosperms. Further global expression profiles with RNA sequencing data revealed diverse expression patterns among various tissues. Results obtained from this study not only provide valuable information for future functional analysis and utilization of the castor bean WRKY genes, but also provide a useful reference to investigate the gene family expansion and evolution in Euphorbiaceus plants.

Aquaporins (AQPs) are a class of integral membrane proteins that facilitate the passive transport of water and other small solutes across biological membranes. Castor bean (Ricinus communis L., Euphobiaceae), an important non-edible oilseed crop, is widely cultivated for industrial, medicinal and cosmetic purposes. Its recently available genome provides an opportunity to analyze specific gene families. In this study, a total of 37 full-length AQP genes were identified from the castor bean genome, which were assigned to five subfamilies, including 10 plasma membrane intrinsic proteins (PIPs), 9 tonoplast intrinsic proteins (TIPs), 8 NOD26-like intrinsic proteins (NIPs), 6 X intrinsic proteins (XIPs) and 4 small basic intrinsic proteins (SIPs) on the basis of sequence similarities. Functional prediction based on the analysis of the aromatic/arginine (ar/R) selectivity filter, Froger's positions and specificity-determining positions (SDPs) showed a remarkable difference in substrate specificity among subfamilies. Homology analysis supported the expression of all 37 RcAQP genes in at least one of examined tissues, e.g., root, leaf, flower, seed and endosperm. Furthermore, global expression profiles with deep transcriptome sequencing data revealed diverse expression patterns among various tissues. The current study presents the first genome-wide analysis of the AQP gene family in castor bean. Results obtained from this study provide valuable information for future functional analysis and utilization.