Soybean (Glycine max L.) is a major crop providing an important source of protein and oil, which can also be converted into biodiesel. A major milestone in soybean research was the recent sequencing of its genome. The sequence predicts 69 145 putative soybean genes, with 46 430 predicted with high confidence. In order to examine the expression of these genes, we utilized the Illumina Solexa platform to sequence cDNA derived from 14 conditions (tissues). The result is a searchable soybean gene expression atlas accessible through a browser (http://digbio.missouri.edu/soybean_atlas). The data provide experimental support for the transcription of 55 616 annotated genes and also demonstrate that 13 529 annotated soybean genes are putative pseudogenes, and 1736 currently unannotated sequences are transcribed. An analysis of this atlas reveals strong differences in gene expression patterns between different tissues, especially between root and aerial organs, but also reveals similarities between gene expression in other tissues, such as flower and leaf organs. In order to demonstrate the full utility of the atlas, we investigated the expression patterns of genes implicated in nodulation, and also transcription factors, using both the Solexa sequence data and large‐scale qRT‐PCR. The availability of the soybean gene expression atlas allowed a comparison with gene expression documented in the two model legume species, Medicago truncatula and Lotus japonicus, as well as data available for Arabidopsis thaliana, facilitating both basic and applied aspects of soybean research.

Similar to other complex organisms, plants consist of diverse and specialized cell types. The gain of unique biological functions of these different cell types is the consequence of the establishment of cell-type-specific transcriptional programs. As a necessary step in gaining a deeper understanding of the regulatory mechanisms controlling plant gene expression, we report the use of single nucleus RNA sequencing (sNucRNA-seq) and single nucleus Assay for Transposase Accessible Chromatin sequencing (sNucATAC-seq) technologies on Arabidopsis roots. The comparison of our single nuclei transcriptomes to published protoplast transcriptomes validated the use of nuclei as biological entities to establish plant cell-type-specific transcriptomes. Furthermore, our sNucRNA-seq results uncovered the transcriptomes of additional cell subtypes not identified by single-cell RNA-seq. Similar to our transcriptomic approach, the sNucATAC-seq approach led to the distribution of the Arabidopsis nuclei into distinct clusters suggesting the differential accessibility of chromatin between groups of cells according to their identity. To reveal the impact of chromatin accessibility on gene transcription, we integrated sNucRNA-seq and sNucATAC-seq data and demonstrated that cell-type-specific marker genes display cell-type-specific patterns of chromatin accessibility. Our data suggest that the differential chromatin accessibility is a critical mechanism to regulate gene activity at the cell-type level.

Chitin, found in the cell walls of true fungi and the exoskeleton of insects and nematodes, is a well-established elicitor of plant defense responses. In this study, we analyzed the expression patterns of Arabidopsis thaliana transcription factor (TF) and ubiquitin-ligase genes in response to purified chitooctaose at different treatment times (15, 30, 60, 90, and 120 min after treatment), using both quantitative polymerase chain reaction and the Affymetrix Arabidopsis whole-genome array. A total of 118 TF genes and 30 ubiquitin-ligase genes were responsive to the chitin treatment. Among these genes, members from the following four TF families were overrepresented: APETALA2/ethylene-reponsive element binding proteins (27), C2H2 zinc finger proteins (14), MYB domain-containing proteins (11), and WRKY domain transcription factors (14). Transcript variants from a few of these genes were found to respond differentially to chitin, suggesting transcript-specific regulation of these TF genes.

Background: Small non-coding RNAs (21 to 24 nucleotides) regulate a number of developmental processes in plants and animals by silencing genes using multiple mechanisms. Among these, the most conserved classes are microRNAs (miRNAs) and small interfering RNAs (siRNAs), both of which are produced by RNase III-like enzymes called Dicers. Many plant miRNAs play critical roles in nutrient homeostasis, developmental processes, abiotic stress and pathogen responses. Currently, only 70 miRNA have been identified in soybean. Methods: We utilized Illumina's SBS sequencing technology to generate high-quality small RNA (sRNA) data from four soybean (Glycine max) tissues, including root, seed, flower, and nodules, to expand the collection of currently known soybean miRNAs. We developed a bioinformatics pipeline using in-house scripts and publicly available structure prediction tools to differentiate the authentic mature miRNA sequences from other sRNAs and short RNA fragments represented in the public sequencing data. Results: The combined sequencing and bioinformatics analyses identified 129 miRNAs based on hairpin secondary structure features in the predicted precursors. Out of these, 42 miRNAs matched known miRNAs in soybean or other species, while 87 novel miRNAs were identified. We also predicted the putative target genes of all identified miRNAs with computational methods and verified the predicted cleavage sites in vivo for a subset of these targets using the 5' RACE method. Finally, we also studied the relationship between the abundance of miRNA and that of the respective target genes by comparison to Solexa cDNA sequencing data. Conclusion: Our study significantly increased the number of miRNAs known to be expressed in soybean. The bioinformatics analysis provided insight on regulation patterns between the miRNAs and their predicted target genes expression. We also deposited the data in a soybean genome browser based on the UCSC Genome Browser architecture. Using the browser, we annotated the soybean data with miRNA sequences from four tissues and cDNA sequencing data. Overlaying these two datasets in the browser allows researchers to analyze the miRNA expression levels relative to that of the associated target genes. The browser can be accessed at http://digbio.missouri.edu/soybean_mirna/.