Major impacts of widespread structural variation on sorghum.

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

Sugar accumulation enhancement in sorghum stem is associated with reduced reproductive sink strength and increased phloem unloading activity.

Sweet sorghum has emerged as a promising source of bioenergy mainly due to its high biomass and high soluble sugar yield in stems. Studies have shown that loss-of-function Dry locus alleles have been selected during sweet sorghum domestication, and decapitation can further boost sugar accumulation in sweet sorghum, indicating that the potential for improving sugar yields is yet to be fully realized. To maximize sugar accumulation, it is essential to gain a better understanding of the mechanism underlying the massive accumulation of soluble sugars in sweet sorghum stems in addition to the Dry locus. We performed a transcriptomic analysis upon decapitation of near-isogenic lines for mutant (d, juicy stems, and green leaf midrib) and functional (D, dry stems and white leaf midrib) alleles at the Dry locus. Our analysis revealed that decapitation suppressed photosynthesis in leaves, but accelerated starch metabolic processes in stems. SbbHLH093 negatively correlates with sugar levels supported by genotypes (DD vs. dd), treatments (control vs. decapitation), and developmental stages post anthesis (3d vs.10d). D locus gene SbNAC074A and other programmed cell death-related genes were downregulated by decapitation, while sugar transporter-encoding gene SbSWEET1A was induced. Both SbSWEET1A and Invertase 5 were detected in phloem companion cells by RNA in situ assay. Loss of the SbbHLH093 homolog, AtbHLH093, in Arabidopsis led to a sugar accumulation increase. This study provides new insights into sugar accumulation enhancement in bioenergy crops, which can be potentially achieved by reducing reproductive sink strength and enhancing phloem unloading.

UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.

Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secure vulnerable food systems. This study investigated the versatility of UAV-borne multisensory data fusion within a framework of multi-task deep learning for high-throughput phenotyping in maize. UAVs equipped with a set of miniaturized sensors including hyperspectral, thermal, and LiDAR were collected in an experimental corn field in Urbana, IL, USA during the growing season. A full suite of eight phenotypes was in situ measured at the end of the season for ground truth data, specifically, dry stalk biomass, cob biomass, dry grain yield, harvest index, grain nitrogen utilization efficiency (Grain NutE), grain nitrogen content, total plant nitrogen content, and grain density. After being funneled through a series of radiometric calibrations and geo-corrections, the aerial data were analytically processed in three primary approaches. First, an extended version normalized difference spectral index (NDSI) served as a simple arithmetic combination of different data modalities to explore the correlation degree with maize phenotypes. The extended NDSI analysis revealed the NIR spectra (750-1000 nm) alone in a strong relation with all of eight maize traits. Second, a fusion of vegetation indices, structural indices, and thermal index selectively handcrafted from each data modality was fed to classical machine learning regressors, Support Vector Machine (SVM) and Random Forest (RF). The prediction performance varied from phenotype to phenotype, ranging from R2 = 0.34 for grain density up to R2 = 0.85 for both grain nitrogen content and total plant nitrogen content. Further, a fusion of hyperspectral and LiDAR data completely exceeded limitations of single data modality, especially addressing the vegetation saturation effect occurring in optical remote sensing. Third, a multi-task deep convolutional neural network (CNN) was customized to take a raw imagery data fusion of hyperspectral, thermal, and LiDAR for multi-predictions of maize traits at a time. The multi-task deep learning performed predictions comparably, if not better in some traits, with the mono-task deep learning and machine learning regressors. Data augmentation used for the deep learning models boosted the prediction accuracy, which helps to alleviate the intrinsic limitation of a small sample size and unbalanced sample classes in remote sensing research. Theoretical and practical implications to plant breeders and crop growers were also made explicit during discussions in the studies.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.

Transformation and gene editing in the bioenergy grass Miscanthus.

BACKGROUND: Miscanthus, a C4 member of Poaceae, is a promising perennial crop for bioenergy, renewable bioproducts, and carbon sequestration. Species of interest include nothospecies M. x giganteus and its parental species M. sacchariflorus and M. sinensis. Use of biotechnology-based procedures to genetically improve Miscanthus, to date, have only included plant transformation procedures for introduction of exogenous genes into the host genome at random, non-targeted sites. RESULTS: We developed gene editing procedures for Miscanthus using CRISPR/Cas9 that enabled the mutation of a specific (targeted) endogenous gene to knock out its function. Classified as paleo-allopolyploids (duplicated ancient Sorghum-like DNA plus chromosome fusion event), design of guide RNAs (gRNAs) for Miscanthus needed to target both homeologs and their alleles to account for functional redundancy. Prior research in Zea mays demonstrated that editing the lemon white1 (lw1) gene, involved in chlorophyll and carotenoid biosynthesis, via CRISPR/Cas9 yielded pale green/yellow, striped or white leaf phenotypes making lw1 a promising target for visual confirmation of editing in other species. Using sequence information from both Miscanthus and sorghum, orthologs of maize lw1 were identified; a multi-step screening approach was used to select three gRNAs that could target homeologs of lw1. Embryogenic calli of M. sacchariflorus, M. sinensis and M. x giganteus were transformed via particle bombardment (biolistics) or Agrobacterium tumefaciens introducing the Cas9 gene and three gRNAs to edit lw1. Leaves on edited Miscanthus plants displayed the same phenotypes noted in maize. Sanger sequencing confirmed editing; deletions in lw1 ranged from 1 to 26 bp in length, and one deletion (433 bp) encompassed two target sites. Confocal microscopy verified lack of autofluorescence (chlorophyll) in edited leaves/sectors. CONCLUSIONS: We developed procedures for gene editing via CRISPR/Cas9 in Miscanthus and, to the best of our knowledge, are the first to do so. This included five genotypes representing three Miscanthus species. Designed gRNAs targeted all copies of lw1 (homeologous copies and their alleles); results also confirmed lw1 made a good editing target in species other than Z. mays. The ability to target specific loci to enable endogenous gene editing presents a new avenue for genetic improvement of this important biomass crop.

Small RNAs mediate transgenerational inheritance of genome-wide trans-acting epialleles in maize.

BACKGROUND: Hybridization and backcrossing are commonly used in animal and plant breeding to induce heritable variation including epigenetic changes such as paramutation. However, the molecular basis for hybrid-induced epigenetic memory remains elusive. RESULTS: Here, we report that hybridization between the inbred parents B73 and Mo17 induces trans-acting hypermethylation and hypomethylation at thousands of loci; several hundreds (~ 3%) are transmitted through six backcrossing and three selfing generations. Notably, many transgenerational methylation patterns resemble epialleles of the nonrecurrent parent, despite > 99% of overall genomic loci are converted to the recurrent parent. These epialleles depend on 24-nt siRNAs, which are eliminated in the isogenic hybrid Mo17xB73:mop1-1 that is defective in siRNA biogenesis. This phenomenon resembles paramutation-like events and occurs in both intraspecific (Mo17xB73) and interspecific (W22xTeosinte) hybrid maize populations. Moreover, siRNA abundance and methylation levels of these epialleles can affect expression of their associated epigenes, many of which are related to stress responses. CONCLUSION: Divergent siRNAs between the hybridizing parents can induce trans-acting epialleles in the hybrids, while the induced epigenetic status is maintained for transgenerational inheritance during backcross and hybrid breeding, which alters epigene expression to enhance growth and adaptation. These genetic and epigenetic principles may apply broadly from plants to animals.

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Genome biology of the paleotetraploid perennial biomass crop Miscanthus.

Miscanthus is a perennial wild grass that is of global importance for paper production, roofing, horticultural plantings, and an emerging highly productive temperate biomass crop. We report a chromosome-scale assembly of the paleotetraploid M. sinensis genome, providing a resource for Miscanthus that links its chromosomes to the related diploid Sorghum and complex polyploid sugarcanes. The asymmetric distribution of transposons across the two homoeologous subgenomes proves Miscanthus paleo-allotetraploidy and identifies several balanced reciprocal homoeologous exchanges. Analysis of M. sinensis and M. sacchariflorus populations demonstrates extensive interspecific admixture and hybridization, and documents the origin of the highly productive triploid bioenergy crop M. × giganteus. Transcriptional profiling of leaves, stem, and rhizomes over growing seasons provides insight into rhizome development and nutrient recycling, processes critical for sustainable biomass accumulation in a perennial temperate grass. The Miscanthus genome expands the power of comparative genomics to understand traits of importance to Andropogoneae grasses.

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

OBJECTIVES: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Identification and characterization of a novel stay-green QTL that increases yield in maize.

Functional stay-green is a valuable trait that extends the photosynthetic period, increases source capacity and biomass and ultimately translates to higher grain yield. Selection for higher yields has increased stay-green in modern maize hybrids. Here, we report a novel QTL controlling functional stay-green that was discovered in a mapping population derived from the Illinois High Protein 1 (IHP1) and Illinois Low Protein 1 (ILP1) lines, which show very different rates of leaf senescence. This QTL was mapped to a single gene containing a NAC-domain transcription factor that we named nac7. Transgenic maize lines where nac7 was down-regulated by RNAi showed delayed senescence and increased both biomass and nitrogen accumulation in vegetative tissues, demonstrating NAC7 functions as a negative regulator of the stay-green trait. More importantly, crosses between nac7 RNAi parents and two different elite inbred testers produced hybrids with prolonged stay-green and increased grain yield by an average 0.29 megagram/hectare (4.6 bushel/acre), in 2 years of multi-environment field trials. Subsequent RNAseq experiments, one employing nac7 RNAi leaves and the other using leaf protoplasts overexpressing Nac7, revealed an important role for NAC7 in regulating genes in photosynthesis, chlorophyll degradation and protein turnover pathways that each contribute to the functional stay-green phenotype. We further determined the putative target of NAC7 and provided a logical extension for the role of NAC7 in regulating resource allocation from vegetative source to reproductive sink tissues. Collectively, our findings make a compelling case for NAC7 as a target for improving functional stay-green and yields in maize and other crops.

Transcriptome Profiling and Genome-Wide Association Studies Reveal GSTs and Other Defense Genes Involved in Multiple Signaling Pathways Induced by Herbicide Safener in Grain Sorghum.

Herbicide safeners protect cereal crops from herbicide injury by inducing genes and proteins involved in detoxification reactions, such as glutathione S-transferases (GSTs) and cytochrome P450s (P450s). Only a few studies have characterized gene or protein expression profiles for investigating plant responses to safener treatment in cereal crops, and most transcriptome analyses in response to safener treatments have been conducted in dicot model species that are not protected by safener from herbicide injury. In this study, three different approaches were utilized in grain sorghum (Sorghum bicolor (L.) Moench) to investigate mechanisms involved in safener-regulated signaling pathways. An initial transcriptome analysis was performed to examine global gene expression in etiolated shoot tissues of hybrid grain sorghum following treatment with the sorghum safener, fluxofenim. Most upregulated transcripts encoded detoxification enzymes, including P450s, GSTs, and UDP-dependent glucosyltransferases (UGTs). Interestingly, several of these upregulated transcripts are similar to genes involved with the biosynthesis and recycling/catabolism of dhurrin, an important chemical defense compound, in these seedling tissues. Secondly, 761 diverse sorghum inbred lines were evaluated in a genome-wide association study (GWAS) to determine key molecular-genetic factors governing safener-mediated signaling mechanisms and/or herbicide detoxification. GWAS revealed a significant single nucleotide polymorphism (SNP) associated with safener-induced response on chromosome 9, located within a phi-class SbGST gene and about 15-kb from a different phi-class SbGST. Lastly, the expression of these two candidate SbGSTs was quantified in etiolated shoot tissues of sorghum inbred BTx623 in response to fluxofenim treatment. SbGSTF1 and SbGSTF2 transcripts increased within 12-hr after fluxofenim treatment but the level of safener-induced expression differed between the two genes. In addition to identifying specific GSTs potentially involved in the safener-mediated detoxification pathway, this research elucidates a new direction for studying both constitutive and inducible mechanisms for chemical defense in cereal crop seedlings.

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets.

OBJECTIVES: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. DATA DESCRIPTION: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Transposable elements, mRNA expression level and strand-specificity of small RNAs are associated with non-additive inheritance of gene expression in hybrid plants.

BACKGROUND: Gene expression inheritance patterns in Arabidopsis hybrid plants were investigated for correlation with the presence of transposable elements (TEs) and small RNA profile. RESULTS: The presence of TEs in a gene and the expression of small RNA matching a gene were both found to be associated with non-additive mRNA inheritance patterns in hybrids. Expression levels below mid-parent values in the hybrids were associated with low mRNA expression in parents, with the presence of small RNA from both strands, and with the presence of TEs. High-parent dominance of mRNA levels was found to be associated with high parental mRNA expression levels, the absence of TEs, and for some genes, with small RNA fragments that are predominantly from the sense strand. These small RNAs exhibit a broader size distribution than siRNA and reduced nucleotide end bias, which are consistent with an origin from degraded mRNA. Thus, increased as well as decreased gene expression in hybrids relative to the parental mean is associated with gene expression levels, TE presence and small RNA fragments with differing characteristics. CONCLUSIONS: The data presented here is consistent with a role for differential mRNA decay kinetics as one mechanism contributing to high-parent dominance in gene expression. Our evidence is also consistent with trans repression by siRNA and TEs as the cause of low-parent dominance.

Transcriptional responses indicate maintenance of photosynthetic proteins as key to the exceptional chilling tolerance of C4 photosynthesis in Miscanthus × giganteus.

Miscanthus × giganteus is exceptional among C4 plants in its ability to acclimate to chilling (≤14 °C) and maintain a high photosynthetic capacity, in sharp contrast to maize, leading to very high productivity even in cool temperate climates. To identify the mechanisms that underlie this acclimation, RNA was isolated from M × giganteus leaves in chilling and nonchilling conditions and hybridized to microarrays developed for its close relative Zea mays. Among 21 000 array probes that yielded robust signals, 723 showed significant expression change under chilling. Approximately half of these were for annotated genes. Thirty genes associated with chloroplast membrane function were all upregulated. Increases in transcripts for the lhcb5 (chlorophyll a/b-binding protein CP26), ndhF (NADH dehydrogenase F, chloroplast), atpA (ATP synthase alpha subunit), psbA (D1), petA (cytochrome f), and lhcb4 (chlorophyll a/b-binding protein CP29), relative to housekeeping genes in M. × giganteus, were confirmed by quantitative reverse-transcription PCR. In contrast, psbo1, lhcb5, psbA, and lhcb4 were all significantly decreased in Z. mays after 14 days of chilling. Western blot analysis of the D1 protein and LHCII type II chlorophyll a/b-binding protein also showed significant increases in M. × giganteus during chilling and significant decreases in Z. mays. Compared to other C4 species, M. × giganteus grown in chilling conditions appears to counteract the loss of photosynthetic proteins and proteins protecting photosystem II typically observed in other species by increasing mRNA levels for their synthesis.

A detailed gene expression study of the Miscanthus genus reveals changes in the transcriptome associated with the rejuvenation of spring rhizomes.

BACKGROUND: The Miscanthus genus of perennial C4 grasses contains promising biofuel crops for temperate climates. However, few genomic resources exist for Miscanthus, which limits understanding of its interesting biology and future genetic improvement. A comprehensive catalog of expressed sequences were generated from a variety of Miscanthus species and tissue types, with an emphasis on characterizing gene expression changes in spring compared to fall rhizomes. RESULTS: Illumina short read sequencing technology was used to produce transcriptome sequences from different tissues and organs during distinct developmental stages for multiple Miscanthus species, including Miscanthus sinensis, Miscanthus sacchariflorus, and their interspecific hybrid Miscanthus × giganteus. More than fifty billion base-pairs of Miscanthus transcript sequence were produced. Overall, 26,230 Sorghum gene models (i.e., ~ 96% of predicted Sorghum genes) had at least five Miscanthus reads mapped to them, suggesting that a large portion of the Miscanthus transcriptome is represented in this dataset. The Miscanthus × giganteus data was used to identify genes preferentially expressed in a single tissue, such as the spring rhizome, using Sorghum bicolor as a reference. Quantitative real-time PCR was used to verify examples of preferential expression predicted via RNA-Seq. Contiguous consensus transcript sequences were assembled for each species and annotated using InterProScan. Sequences from the assembled transcriptome were used to amplify genomic segments from a doubled haploid Miscanthus sinensis and from Miscanthus × giganteus to further disentangle the allelic and paralogous variations in genes. CONCLUSIONS: This large expressed sequence tag collection creates a valuable resource for the study of Miscanthus biology by providing detailed gene sequence information and tissue preferred expression patterns. We have successfully generated a database of transcriptome assemblies and demonstrated its use in the study of genes of interest. Analysis of gene expression profiles revealed biological pathways that exhibit altered regulation in spring compared to fall rhizomes, which are consistent with their different physiological functions. The expression profiles of the subterranean rhizome provides a better understanding of the biological activities of the underground stem structures that are essentials for perenniality and the storage or remobilization of carbon and nutrient resources.

crw1--A novel maize mutant highly susceptible to foliar damage by the western corn rootworm beetle.

Western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is the most destructive insect pest of corn (Zea mays L.) in the United States. The adult WCR beetles derive their nourishment from multiple sources including corn pollen and silks as well as the pollen of alternate hosts. Conversely, the corn foliage is largely neglected as a food source by WCR beetles, leading to a perception of a passive interaction between the two. We report here a novel recessive mutation of corn that was identified and named after its foliar susceptibility to corn rootworm beetles (crw1). The crw1 mutant under field conditions was exceptionally susceptible to foliar damage by WCR beetles in an age-specific manner. It exhibits pleiotropic defects on cell wall biochemistry, morphology of leaf epidermal cells and lower structural integrity via differential accumulation of cell wall bound phenolic acids. These findings indicate that crw1 is perturbed in a pathway that was not previously ascribed to WCR susceptibility, as well as implying the presence of an active mechanism(s) deterring WCR beetles from devouring corn foliage. The discovery and characterization of this mutant provides a unique opportunity for genetic analysis of interactions between maize and adult WCR beetles and identify new strategies to control the spread and invasion of this destructive pest.

The inheritance pattern of 24 nt siRNA clusters in arabidopsis hybrids is influenced by proximity to transposable elements.

Hybrids often display increased size and growth, and thus are widely cultivated in agriculture and horticulture. Recent discoveries demonstrating the important regulatory roles of small RNAs have greatly improved our understanding of many basic biological questions, and could illuminate the molecular basis for the enhanced growth and size of hybrid plants. We profiled small RNAs by deep sequencing to characterize the inheritance patterns of small RNA levels in reciprocal hybrids of two Arabidopsis thaliana accessions, Columbia and Landsberg erecta. We find 24-nt siRNAs predominate among those small RNAs that are differentially expressed between the parents. Following hybridization, the transposable element (TE)-derived siRNAs are often inherited in an additive manner, whereas siRNAs associated with protein-coding genes are often down-regulated in hybrids to the levels observed for the parent with lower relative siRNA levels. Among the protein-coding genes that exhibit this pattern, genes that function in pathogen defense, abiotic stress tolerance, and secondary metabolism are significantly enriched. Small RNA clusters from protein-coding genes where a TE is present within one kilobase show a different predominant inheritance pattern (additive) from those that do not (low-parent dominance). Thus, down-regulation in the form of low-parent dominance is likely the default pattern of inheritance for genic siRNA, and a different inheritance mechanism for TE siRNA is suggested.

Repeat associated small RNAs vary among parents and following hybridization in maize.

Small RNAs (sRNAs) are hypothesized to contribute to hybrid vigor because they maintain genome integrity, contribute to genetic diversity, and control gene expression. We used Illumina sequencing to assess how sRNA populations vary between two maize inbred lines (B73 and Mo17) and their hybrid. We sampled sRNAs from the seedling shoot apex and the developing ear, two rapidly growing tissues that program the greater growth of maize hybrids. We found that parental differences in siRNAs primarily originate from repeat regions. Although the maize genome contains greater number and complexity of repeats compared with Arabidopsis or rice, we confirmed that, like these simpler plant genomes, 24-nt siRNAs whose abundance differs between maize parents also show a trend of down-regulation following hybridization. Surprisingly, hybrid vigor is fully maintained when 24-nt siRNAs are globally reduced by mutation of the RNA-dependent RNA polymerase 2 encoded by modifier of paramutation1 (mop1). We also discovered that 21-22-nt siRNAs derived from a number of distinct retrotransposon families differentially accumulate between B73 and Mo17 as well as their hybrid. Thus, maize possesses a unique source of genetic variation for regulating transposons and genes at a genomic scale, which may contribute to its high degree of observed heterosis.