Modern sugarcanes are polyploid interspecific hybrids, combining high sugar content from Saccharum officinarum with hardiness, disease resistance and ratooning of Saccharum spontaneum. Sequencing of a haploid S. spontaneum, AP85-441, facilitated the assembly of 32 pseudo-chromosomes comprising 8 homologous groups of 4 members each, bearing 35,525 genes with alleles defined. The reduction of basic chromosome number from 10 to 8 in S. spontaneum was caused by fissions of 2 ancestral chromosomes followed by translocations to 4 chromosomes. Surprisingly, 80% of nucleotide binding site-encoding genes associated with disease resistance are located in 4 rearranged chromosomes and 51% of those in rearranged regions. Resequencing of 64 S. spontaneum genomes identified balancing selection in rearranged regions, maintaining their diversity. Introgressed S. spontaneum chromosomes in modern sugarcanes are randomly distributed in AP85-441 genome, indicating random recombination among homologs in different S. spontaneum accessions. The allele-defined Saccharum genome offers new knowledge and resources to accelerate sugarcane improvement.

Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein coding potential. The expanding list of lncRNAs and accumulating evidence of their functions in plants have necessitated the creation of a comprehensive database for lncRNA research. However, currently available plant lncRNA databases have some deficiencies, including the lack of lncRNA data from some model plants, uneven annotation standards, a lack of visualization for expression patterns, and the absence of epigenetic information. To overcome these problems, we upgraded our Plant Long noncoding RNA Database (PLncDB, http://plncdb.tobaccodb.org/), which was based on a uniform annotation pipeline. PLncDB V2.0 currently contains 1 246 372 lncRNAs for 80 plant species based on 13 834 RNA-Seq datasets, integrating lncRNA information from four other resources including EVLncRNAs, RNAcentral and etc. Expression patterns and epigenetic signals can be visualized using multiple tools (JBrowse, eFP Browser and EPexplorer). Targets and regulatory networks for lncRNAs are also provided for function exploration. In addition, PLncDB V2.0 is hierarchical and user-friendly and has five built-in search engines. We believe PLncDB V2.0 is useful for the plant lncRNA community and data mining studies and provides a comprehensive resource for data-driven lncRNA research in plants.

CB-1 and K326 are closely related tobacco cultivars; however, their cold tolerance capacities are different. K326 is much more cold tolerant than CB-1. We studied the transcriptomes and metabolomes of CB-1 and K326 leaf samples treated with cold stress. Totally, we have identified 14,590 differentially expressed genes (DEGs) in CB-1 and 14,605 DEGs in K326; there was also 200 differentially expressed metabolites in CB-1 and 194 in K326. Moreover, there were many overlapping genes (around 50%) that were cold-responsive in both plant cultivars, although there were also many differences in the cold responsive genes between the two cultivars. Importantly, for most of the overlapping cold responsive genes, the extent of the changes in expression were typically much more pronounced in K326 than in CB-1, which may help explain the superior cold tolerance of K326. Similar results were found in the metabolome analysis, particularly with the analysis of primary metabolites, including amino acids, organic acids, and sugars. The large number of specific responsive genes and metabolites highlight the complex regulatory mechanisms associated with cold stress in tobacco. In addition, our work implies that the energy metabolism and hormones may function distinctly between CB-1 and K326. Differences in gene expression and metabolite levels following cold stress treatment seem likely to have contributed to the observed difference in the cold tolerance phenotype of these two tobacco cultivars.

Endogenous target mimecry of an miRNA affects nicotine biosynthesis.

Switchgrass (Panicum virgatum L.) is a perennial grass species receiving significant focus as a potential bioenergy crop. In the last 5 yr the switchgrass research community has produced a genetic linkage map, an expressed sequence tag (EST) database, a set of single nucleotide polymorphism (SNP) markers that are distributed across the 18 linkage groups, 4x sampling of the P. virgatum AP13 genome in 400-bp reads, and bacterial artificial chromosome (BAC) libraries containing over 200,000 clones. These studies have revealed close collinearity of the switchgrass genome with those of sorghum [Sorghum bicolor (L.) Moench], rice (Oryza sativa L.), and Brachypodium distachyon (L.) P. Beauv. Switchgrass researchers have also developed several microarray technologies for gene expression studies. Switchgrass genomic resources will accelerate the ability of plant breeders to enhance productivity, pest resistance, and nutritional quality. Because switchgrass is a relative newcomer to the genomics world, many secrets of the switchgrass genome have yet to be revealed. To continue to efficiently explore basic and applied topics in switchgrass, it will be critical to capture and exploit the knowledge of plant geneticists and breeders on the next logical steps in the development and utilization of genomic resources for this species. To this end, the community has established a switchgrass genomics executive committee and work group (http://switchgrassgenomics.org/ [verified 28 Oct. 2011]). Copyright © 2011. . Copyright © 2011 Crop Science Society of America

The advent of single-cell sequencing opened a new era in transcriptomic and genomic research. To understand cell composition using single-cell studies, a variety of cell markers have been widely used to label individual cell types. However, the specific database of cell markers for use by the plant research community remains very limited. To overcome this problem, we developed the Plant Cell Marker DataBase (PCMDB, http://www.tobaccodb.org/pcmdb/), which is based on a uniform annotation pipeline. By manually curating over 130 000 research publications, we collected a total of 81 117 cell marker genes of 263 cell types in 22 tissues across six plant species. Tissue- and cell-specific expression patterns can be visualized using multiple tools: eFP Browser, Bar, and UMAP/TSNE graph. The PCMDB also supports several analysis tools, including SCSA and SingleR, which allows for user annotation of cell types. To provide information about plant species currently unsupported in PCMDB, potential marker genes for other plant species can be searched based on homology with the supported species. PCMDB is a user-friendly hierarchical platform that contains five built-in search engines. We believe PCMDB will constitute a useful resource for researchers working on cell type annotation and the prediction of the biological function of individual cells.