Brieanne Vaillancourt

Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data

BackgroundRice research has been enabled by access to the high quality reference genome sequence generated in 2005 by the International Rice Genome Sequencing Project (IRGSP). To further facilitate genomic-enabled research, we have updated and validated the genome assembly and sequence for the Nipponbare cultivar of Oryza sativa (japonica group).ResultsThe Nipponbare genome assembly was updated by revising and validating the minimal tiling path of clones with the optical map for rice. Sequencing errors in the revised genome assembly were identified by re-sequencing the genome of two different Nipponbare individuals using the Illumina Genome Analyzer II/IIx platform. A total of 4,886 sequencing errors were identified in 321 Mb of the assembled genome indicating an error rate in the original IRGSP assembly of only 0.15 per 10,000 nucleotides. A small number (five) of insertions/deletions were identified using longer reads generated using the Roche 454 pyrosequencing platform. As the re-sequencing data were generated from two different individuals, we were able to identify a number of allelic differences between the original individual used in the IRGSP effort and the two individuals used in the re-sequencing effort. The revised assembly, termed Os-Nipponbare-Reference-IRGSP-1.0, is now being used in updated releases of the Rice Annotation Project and the Michigan State University Rice Genome Annotation Project, thereby providing a unified set of pseudomolecules for the rice community.ConclusionsA revised, error-corrected, and validated assembly of the Nipponbare cultivar of rice was generated using optical map data, re-sequencing data, and manual curation that will facilitate on-going and future research in rice. Detection of polymorphisms between three different Nipponbare individuals highlights that allelic differences between individuals should be considered in diversity studies.

Insights into the Maize Pan-Genome and Pan-Transcriptome

Transcriptome sequencing of diverse maize inbreds provided insights into the nature of the maize pan-genome, including identification of 8681 loci absent in the B73 reference sequence. Genome-wide association studies using single nucleotide polymorphisms and transcript abundance variants in the maize pan-genome identified loci associated with traits important for fitness and adaptation. Genomes at the species level are dynamic, with genes present in every individual (core) and genes in a subset of individuals (dispensable) that collectively constitute the pan-genome. Using transcriptome sequencing of seedling RNA from 503 maize (Zea mays) inbred lines to characterize the maize pan-genome, we identified 8681 representative transcript assemblies (RTAs) with 16.4% expressed in all lines and 82.7% expressed in subsets of the lines. Interestingly, with linkage disequilibrium mapping, 76.7% of the RTAs with at least one single nucleotide polymorphism (SNP) could be mapped to a single genetic position, distributed primarily throughout the nonpericentromeric portion of the genome. Stepwise iterative clustering of RTAs suggests, within the context of the genotypes used in this study, that the maize genome is restricted and further sampling of seedling RNA within this germplasm base will result in minimal discovery. Genome-wide association studies based on SNPs and transcript abundance in the pan-genome revealed loci associated with the timing of the juvenile-to-adult vegetative and vegetative-to-reproductive developmental transitions, two traits important for fitness and adaptation. This study revealed the dynamic nature of the maize pan-genome and demonstrated that a substantial portion of variation may lie outside the single reference genome for a species.

Maize (Zea mays L.) Genome Diversity as Revealed by RNA-Sequencing

Maize is rich in genetic and phenotypic diversity. Understanding the sequence, structural, and expression variation that contributes to phenotypic diversity would facilitate more efficient varietal improvement. RNA based sequencing (RNA-seq) is a powerful approach for transcriptional analysis, assessing sequence variation, and identifying novel transcript sequences, particularly in large, complex, repetitive genomes such as maize. In this study, we sequenced RNA from whole seedlings of 21 maize inbred lines representing diverse North American and exotic germplasm. Single nucleotide polymorphism (SNP) detection identified 351,710 polymorphic loci distributed throughout the genome covering 22,830 annotated genes. Tight clustering of two distinct heterotic groups and exotic lines was evident using these SNPs as genetic markers. Transcript abundance analysis revealed minimal variation in the total number of genes expressed across these 21 lines (57.1% to 66.0%). However, the transcribed gene set among the 21 lines varied, with 48.7% expressed in all of the lines, 27.9% expressed in one to 20 lines, and 23.4% expressed in none of the lines. De novo assembly of RNA-seq reads that did not map to the reference B73 genome sequence revealed 1,321 high confidence novel transcripts, of which, 564 loci were present in all 21 lines, including B73, and 757 loci were restricted to a subset of the lines. RT-PCR validation demonstrated 87.5% concordance with the computational prediction of these expressed novel transcripts. Intriguingly, 145 of the novel de novo assembled loci were present in lines from only one of the two heterotic groups consistent with the hypothesis that, in addition to sequence polymorphisms and transcript abundance, transcript presence/absence variation is present and, thereby, may be a mechanism contributing to the genetic basis of heterosis.

Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution

The Poaceae family, also known as the grasses, includes agronomically important cereal crops such as rice, maize, sorghum, and wheat. Previous comparative studies have shown that much of the gene content is shared among the grasses; however, functional conservation of orthologous genes has yet to be explored. To gain an understanding of the genome-wide patterns of evolution of gene expression across reproductive tissues, we employed a sequence-based approach to compare analogous transcriptomes in species representing three Poaceae subgroups including the Pooideae (Brachypodium distachyon), the Panicoideae (sorghum), and the Ehrhartoideae (rice). Our transcriptome analyses reveal that only a fraction of orthologous genes exhibit conserved expression patterns. A high proportion of conserved orthologs include genes that are upregulated in physiologically similar tissues such as leaves, anther, pistil, and embryo, while orthologs that are highly expressed in seeds show the most diverged expression patterns. More generally, we show that evolution of gene expression profiles and coding sequences in the grasses may be linked. Genes that are highly and broadly expressed tend to be conserved at the coding sequence level while genes with narrow expression patterns show accelerated rates of sequence evolution. We further show that orthologs in syntenic genomic blocks are more likely to share correlated expression patterns compared with non-syntenic orthologs. These findings are important for agricultural improvement because sequence information is transferred from model species, such as Brachypodium, rice, and sorghum to crop plants without sequenced genomes.

Development of transcriptomic resources for interrogating the biosynthesis of monoterpene indole alkaloids in medicinal plant species.

The natural diversity of plant metabolism has long been a source for human medicines. One group of plant-derived compounds, the monoterpene indole alkaloids (MIAs), includes well-documented therapeutic agents used in the treatment of cancer (vinblastine, vincristine, camptothecin), hypertension (reserpine, ajmalicine), malaria (quinine), and as analgesics (7-hydroxymitragynine). Our understanding of the biochemical pathways that synthesize these commercially relevant compounds is incomplete due in part to a lack of molecular, genetic, and genomic resources for the identification of the genes involved in these specialized metabolic pathways. To address these limitations, we generated large-scale transcriptome sequence and expression profiles for three species of Asterids that produce medicinally important MIAs: Camptotheca acuminata, Catharanthus roseus, and Rauvolfia serpentina. Using next generation sequencing technology, we sampled the transcriptomes of these species across a diverse set of developmental tissues, and in the case of C. roseus, in cultured cells and roots following elicitor treatment. Through an iterative assembly process, we generated robust transcriptome assemblies for all three species with a substantial number of the assembled transcripts being full or near-full length. The majority of transcripts had a related sequence in either UniRef100, the Arabidopsis thaliana predicted proteome, or the Pfam protein domain database; however, we also identified transcripts that lacked similarity with entries in either database and thereby lack a known function. Representation of known genes within the MIA biosynthetic pathway was robust. As a diverse set of tissues and treatments were surveyed, expression abundances of transcripts in the three species could be estimated to reveal transcripts associated with development and response to elicitor treatment. Together, these transcriptomes and expression abundance matrices provide a rich resource for understanding plant specialized metabolism, and promotes realization of innovative production systems for plant-derived pharmaceuticals.

Marker Density and Read Depth for Genotyping Populations Using Genotyping-by-Sequencing

Genotyping-by-sequencing (GBS) approaches provide low-cost, high-density genotype information. However, GBS has unique technical considerations, including a substantial amount of missing data and a nonuniform distribution of sequence reads. The goal of this study was to characterize technical variation using this method and to develop methods to optimize read depth to obtain desired marker coverage. To empirically assess the distribution of fragments produced using GBS, ∼8.69 Gb of GBS data were generated on the Zea mays reference inbred B73, utilizing ApeKI for genome reduction and single-end reads between 75 and 81 bp in length. We observed wide variation in sequence coverage across sites. Approximately 76% of potentially observable cut site-adjacent sequence fragments had no sequencing reads whereas a portion had substantially greater read depth than expected, up to 2369 times the expected mean. The methods described in this article facilitate determination of sequencing depth in the context of empirically defined read depth to achieve desired marker density for genetic mapping studies.

Utility of RNA Sequencing for Analysis of Maize Reproductive Transcriptomes

Transcriptome sequencing is a powerful method for studying global expression patterns in large, complex genomes. Evaluation of sequence‐based expression profiles during reproductive development would provide functional annotation to genes underlying agronomic traits. We generated transcriptome profiles for 12 diverse maize (Zea mays L.) reproductive tissues representing male, female, developing seed, and leaf tissues using high throughput transcriptome sequencing. Overall, ∼80% of annotated genes were expressed. Comparative analysis between sequence and hybridization‐based methods demonstrated the utility of ribonucleic acid sequencing (RNA‐seq) for expression determination and differentiation of paralagous genes (∼85% of maize genes). Analysis of 4975 gene families across reproductive tissues revealed expression divergence is proportional to family size. In all pairwise comparisons between tissues, 7 (pre‐ vs. postemergence cobs) to 48% (pollen vs. ovule) of genes were differentially expressed. Genes with expression restricted to a single tissue within this study were identified with the highest numbers observed in leaves, endosperm, and pollen. Coexpression network analysis identified 17 gene modules with complex and shared expression patterns containing many previously described maize genes. The data and analyses in this study provide valuable tools through improved gene annotation, gene family characterization, and a core set of candidate genes to further characterize maize reproductive development and improve grain yield potential.

An Expanded Maize Gene Expression Atlas based on RNA Sequencing and its Use to Explore Root Development

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 (RNA‐seq). The current version of the atlas includes 50 original array‐based 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.

Spud DB: A resource for mining sequences, genotypes, and phenotypes to accelerate potato breeding

Potato is the worlds third most important crop, and is becoming increasingly important in developing countries. Cultivated potato is a highly heterozygous tetraploid (2n = 4x = 48) and suffers from significant inbreeding depression when selfed. As potato can be vegetatively propagated, breeding has been based primarily on phenotypic selection in F1 populations. However, recent advances in genome sequencing and genotyping methods have resulted in the development of large genomic, genetic, and phenotypic datasets that will enable more efficient and rapid breeding approaches. We have developed Spud DB (http://potato.plantbiology.msu.edu/) for the community to access the potato genome sequence and associated annotation datasets, along with phenotypic and genotypic data from a diversity panel of 250 potato clones. The Breeders Assistant is a web tool to retrieve pertinent phenotypic and genotypic data in a user‐guided manner, and query polymorphic markers such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) to identify custom sets of markers for a gene or region of interest. To browse and query the potato genome, a genome browser with 94 tracks of genome annotation, sequence variants, and expression abundance has been deployed. Spud DB also provides a comprehensive search page to data mine the potato genome through tools that query sequence identifiers, functional annotation, gene ontology (GO), InterPro domains, and basic local alignment search tool (BLAST) databases. Collectively, this resource links potato genomic data with phenotypic and genotypic data from a large collection of potato lines for use by the potato community, especially breeders and geneticists.

mRNA-Seq Analysis of the Pseudoperonospora cubensis Transcriptome During Cucumber (Cucumis sativus L.) Infection

Pseudoperonospora cubensis, an oomycete, is the causal agent of cucurbit downy mildew, and is responsible for significant losses on cucurbit crops worldwide. While other oomycete plant pathogens have been extensively studied at the molecular level, Ps. cubensis and the molecular basis of its interaction with cucurbit hosts has not been well examined. Here, we present the first large-scale global gene expression analysis of Ps. cubensis infection of a susceptible Cucumis sativus cultivar, ‘Vlaspik’, and identification of genes with putative roles in infection, growth, and pathogenicity. Using high throughput whole transcriptome sequencing, we captured differential expression of 2383 Ps. cubensis genes in sporangia and at 1, 2, 3, 4, 6, and 8 days post-inoculation (dpi). Additionally, comparison of Ps. cubensis expression profiles with expression profiles from an infection time course of the oomycete pathogen Phytophthora infestans on Solanum tuberosum revealed similarities in expression patterns of 1,576–6,806 orthologous genes suggesting a substantial degree of overlap in molecular events in virulence between the biotrophic Ps. cubensis and the hemi-biotrophic P. infestans. Co-expression analyses identified distinct modules of Ps. cubensis genes that were representative of early, intermediate, and late infection stages. Collectively, these expression data have advanced our understanding of key molecular and genetic events in the virulence of Ps. cubensis and thus, provides a foundation for identifying mechanism(s) by which to engineer or effect resistance in the host.