Plant non-coding RNAs and epigenetics

A non-structural protein encoded by Rice Dwarf Virus targets to the nucleus and chloroplast and inhibits local RNA silencing

RNA silencing is a potent antiviral mechanism in plants and animals. As a counter-defense, many viruses studied to date encode one or more viral suppressors of RNA silencing (VSR). In the latter case, how different VSRs encoded by a virus function in silencing remains to be fully understood. We previously showed that the nonstructural protein Pns10 of a Phytoreovirus, Rice dwarf virus (RDV), functions as a VSR. Here we present evidence that another nonstructural protein, Pns11, also functions as a VSR. While Pns10 was localized in the cytoplasm, Pns11 was localized both in the nucleus and chloroplasts. Pns11 has two bipartite nuclear localization signals (NLSs), which were required for nuclear as well as chloroplastic localization. The NLSs were also required for the silencing activities of Pns11. This is the first report that multiple VSRs encoded by a virus are localized in different subcellular compartments, and that a viral protein can be targeted to both the nucleus and chloroplast. These findings may have broad significance in studying the subcellular targeting of VSRs and other viral proteins in viral-host interactions.

Application of deep learning in genomics

In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification, autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in complex classification and regression problems, where the intricate structure in the high-dimensional data is difficult to discover using conventional machine learning algorithms. In biology, applications of deep learning are gaining increasing popularity in predicting the structure and function of genomic elements, such as promoters, enhancers, or gene expression levels. In this review paper, we described the basic concepts in machine learning and artificial neural network, followed by elaboration on the workflow of using convolutional neural network in genomics. Then we provided a concise introduction of deep learning applications in genomics and synthetic biology at the levels of DNA, RNA and protein. Finally, we discussed the current challenges and future perspectives of deep learning in genomics.

Decoding the plant genome: From epigenome to 3D organization

The linear genome of eukaryotes is partitioned into diverse chromatin states and packaged into a three-dimensional (3D) structure, which has functional implications in DNA replication, DNA repair, and transcriptional regulation. Over the past decades, research on plant functional genomics and epigenomics has made great progress, with thousands of genes cloned and molecular mechanisms of diverse biological processes elucidated. Recently, 3D genome research has gradually attracted great attention of many plant researchers. Herein, we briefly review the progress in genomic and epigenomic research in plants, with a focus on Arabidopsis and rice, and summarize the currently used technologies and advances in plant 3D genome organization studies. We also discuss the relationships between one-dimensional linear genome sequences, epigenomic states, and the 3D chromatin architecture. This review provides basis for future research on plant 3D genomics.

Re-sequencing and transcriptome analysis reveal rich DNA variations and differential expressions of fertility-related genes in neo-tetraploid rice

Autotetraploid rice is a useful germplasm for polyploid rice breeding, however, low seed setting is the major barrier in commercial utilization of autotetraploid rice. Our research group has developed neo-tetraploid rice lines, which have the characteristics of high fertility and heterosis when crossed with autotetraploid rice. In the present study, re-sequencing and RNA-seq were employed to detect global DNA variations and differentially expressed genes (DEGs) during meiosis stage in three neo-tetraploid rice lines compared to their parents, respectively. Here, a total of 4109881 SNPs and 640592 InDels were detected in neo-tetraploid lines compared to the reference genome, and 1805 specific presence/absence variations (PAVs) were detected in three lines. Approximately 12% and 0.5% of the total SNPs and InDels identified in three lines were located in genic regions, respectively. A total of 28 genes, harboring at least one of the large-effect SNP and/or InDel which affect the integrity of the encoded protein, were identified in the three lines. Together, 324 specific mutation genes, including 52 meiosis-related genes and 8 epigenetics-related genes were detected in neo-tetraploid rice compared to their parents. Of these 324 genes, five meiosis-related and three epigenetics-related genes displayed differential expressions during meiosis stage. Notably, 498 specific transcripts, 48 differentially expressed transposons and 245 differentially expressed ncRNAs were also detected in neo-tetraploid rice. Our results suggested that genomic structural reprogramming, DNA variations and differential expressions of some important meiosis and epigenetics related genes might be associated with high fertility in neo-tetraploid rice.