Comprehensive and systematic transcriptome profiling provides valuable insight into biological and developmental processes that occur throughout the life cycle of a plant. We have enhanced our previously published microarray-based gene atlas of maize (Zea mays L.) inbred B73 to now include 79 distinct replicated samples that have been interrogated using RNA sequencing (RNAseq). The current version of the atlas includes 50 original arraybased gene atlas samples, a time-course of 12 stalk and leaf samples postflowering, and an additional set of 17 samples from the maize seedling and adult root system. The entire dataset contains 4.6 billion mapped reads, with an average of 20.5 million mapped reads per biological replicate, allowing for detection of genes with lower transcript abundance. As the new root samples represent key additions to the previously examined tissues, we highlight insights into the root transcriptome, which is represented by 28,894 (73.2%) annotated genes in maize. Additionally, we observed remarkable expression differences across both the longitudinal (four zones) and radial gradients (cortical parenchyma and stele) of the primary root supported by fourfold differential expression of 9353 and 4728 genes, respectively. Among the latter were 1110 genes that encode transcription factors, some of which are orthologs of previously characterized transcription factors known to regulate root development in Arabidopsis thaliana (L.) Heynh., while most are novel, and represent attractive targets for reverse genetics approaches to determine their roles in this important organ. This comprehensive transcriptome dataset is a powerful tool toward understanding maize development, physiology, and phenotypic diversity.

Plants regenerated from tissue culture and their progenies are expected to be identical clones, but often display heritable molecular and phenotypic variation. We characterized DNA methylation patterns in callus, primary regenerants, and regenerantderived progenies of maize using immunoprecipitation of methylated DNA (meDIP) to assess the genome-wide frequency, pattern, and heritability of DNA methylation changes. Although genome-wide DNA methylation levels remained similar following tissue culture, numerous regions exhibited altered DNA methylation levels. Hypomethylation events were observed more frequently than hypermethylation following tissue culture. Many of the hypomethylation events occur at the same genomic sites across independent regenerants and cell lines. The DNA methylation changes were often heritable in progenies produced from self-pollination of primary regenerants. Methylation changes were enriched in regions upstream of genes and loss of DNA methylation at promoters was associated with altered expression at a subset of loci. Differentially methylated regions (DMRs) found in tissue culture regenerants overlap with the position of naturally occurring DMRs more often than expected by chance with 8% of tissue culture hypomethylated DMRs overlapping with DMRs identified by profiling natural variation, consistent with the hypotheses that genomic stresses similar to those causing somaclonal variation may also occur in nature, and that certain loci are particularly susceptible to epigenetic change in response to these stresses. The consistency of methylation changes across regenerants from independent cultures suggests a mechanistic response to the culture environment as opposed to an overall loss of fidelity in the maintenance of epigenetic states. C LONAL propagation of plants and animals is expected to produce individuals identical to the donor. However, this is often not the case. Plant tissue culture involves dedifferentiation, or return to a "stem-cell-like" state, which involves dynamic reprogramming at the chromatin level to induce the formation of callus. Subsequently, proliferating cells start to redifferentiate when specific changes in the balance of growth regulators are introduced in the culture medium, ultimately leading to organogenesis or regeneration into whole plants (Grafi et al. 2011;Miguel and Marum 2011). This process represents a traumatic stress to plant cells and often provokes an array of genetic and epigenetic instabilities that are somatically and meiotically heritable (Phillips et al. 1994;Kaeppler et al. 2000). This suite of molecular and phenotypic phenomena is collectively termed somaclonal variation (Larkin and Scowcroft 1981).At the cellular and molecular levels, somaclonal variation is composed of chromosome rearrangements, polyploidy and aneuploidy, DNA sequence changes, activation of quiescent transposable elements (TEs), and epigenetic variation reflected by altered DNA methylation patterns (Karp and Maddock 1984;Brettell et al. 1986;Dennis et al. 1987;Pe...

DNA methylation can contribute to the maintenance of genome integrity and regulation of gene expression. In most situations, DNA methylation patterns are inherited quite stably. However, changes in DNA methylation can occur at some loci as a result of tissue culture resulting in somaclonal variation. To investigate heritable epigenetic changes as a consequence of tissue culture, a sequence-capture bisulfite sequencing approach was implemented to monitor context-specific DNA methylation patterns in ∼15 Mb of the maize genome for a population of plants that had been regenerated from tissue culture. Plants that have been regenerated from tissue culture exhibit gains and losses of DNA methylation at a subset of genomic regions. There was evidence for a high rate of homozygous changes to DNA methylation levels that occur consistently in multiple independent tissue culture lines, suggesting that some loci are either targeted or hotspots for epigenetic variation. The consistent changes inherited following tissue culture include both gains and losses of DNA methylation and can affect CG, CHG, or both contexts within a region. Only a subset of the tissue culture changes observed in callus plants are observed in the primary regenerants, but the majority of DNA methylation changes present in primary regenerants are passed onto offspring. This study provides insights into the susceptibility of some loci and potential mechanisms that could contribute to altered DNA methylation and epigenetic state that occur during tissue culture in plant species.

Summary Plant secondary cell walls constitute the majority of plant biomass. They are predominantly found in xylem cells, which are derived from vascular initials during vascularization. Little is known about these processes in grass species despite their emerging importance as biomass feedstocks. The targeted biofuel crop Sorghum bicolor has a sequenced and well‐annotated genome, making it an ideal monocot model for addressing vascularization and biomass deposition.Here we generated tissue‐specific transcriptome and DNA methylome data from sorghum shoots, roots and developing root vascular and nonvascular tissues.Many genes associated with vascular development in other species show enriched expression in developing vasculature. However, several transcription factor families varied in vascular expression in sorghum compared with Arabidopsis and maize. Furthermore, differential expression of genes associated with DNA methylation were identified between vascular and nonvascular tissues, implying that changes in DNA methylation are a feature of sorghum root vascularization, which we confirmed using tissue‐specific DNA methylome data. Roots treated with a DNA methylation inhibitor also showed a significant decrease in root length.Tissues and organs can be discriminated based on their genomic methylation patterns and methylation context. Consequently, tissue‐specific changes in DNA methylation are part of the normal developmental process.

BackgroundImbibition (uptake of water by a dry seed) initiates the germination process. An automated method for quantifying imbibition would enable research on the genetic elements that influence the underlying hydraulic and biochemical processes. In the case of crop research, a high throughput imbibition assay could be used to investigate seed quality topics or to improve yield by selecting varieties with superior germination characteristics.ResultsAn electronic force transducer measured imbibition of single maize kernels with very high resolution but low throughput. An image analysis method was devised to achieve high throughput and sufficient resolution. A transparent fixture held 90 maize kernels in contact with water on the imaging window of a flatbed document scanner that produced an image of the kernels automatically every 10 min for 22 h. Custom image analysis software measured the area A of each indexed kernel in each image to produce imbibition time courses. The ultimate change in area (ΔA) ranged from 19.3 to 23.4% in a population of 72 hybrids derived from 9 inbred parents. Kernel area as a function of time was fit well by where Af is the final kernel area. The swelling coefficient, k, ranged from 0.098 to 0.159 h−1 across the genotypes. The full diallel structure of the population enabled maternal genotype effects to be assessed. In a separate experiment, measurements of kernels of the same 25 inbreds produced in three different years demonstrated that production and storage variables affected imbibition much less than genotype. In a third experiment, measurements of 30 diverse inbred lines showed that k varied inversely with germination time (r = − 0.7) and directly with germination percentage (r = 0.7).ConclusionsNonspecialized imaging hardware and custom analysis software running on public cyber infrastructure form a low-cost platform for measuring seed imbibition with high resolution and throughput. We measured imbibition of thousands of kernels to determine that genotype influenced imbibition of maize kernels much more than seed production and storage environments. In some hybrids, k depended on which inbred parent was maternal. Quantitative relationships between k and germination traits were discovered.Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0383-7) contains supplementary material, which is available to authorized users.

CG−only CHG−only 0 25 50 75 100 0 25 50 75 100 CG.ratio_control CG.ratio_sample CHG.ratio_control CHG.ratio_sample CHH.ratio_control CHH.ratio_sample CG.ratio_control CG.ratio_sample CHG.ratio_control CHG.ratio_sample CHH.ratio_control CHH.ratio_sample ratio sample.contex ratio_context CG CHG CHH