Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/

Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/

Polymorphism Identification and Improved Genome Annotation of Brassica rapa

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes—R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)—using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/

Shade Avoidance Components and Pathways in Adult Plants Revealed by Phenotypic Profiling

<div><p>Shade from neighboring plants limits light for photosynthesis; as a consequence, plants have a variety of strategies to avoid canopy shade and compete with their neighbors for light. Collectively the response to foliar shade is called the shade avoidance syndrome (SAS). The SAS includes elongation of a variety of organs, acceleration of flowering time, and additional physiological responses, which are seen throughout the plant life cycle. However, current mechanistic knowledge is mainly limited to shade-induced elongation of seedlings. Here we use phenotypic profiling of seedling, leaf, and flowering time traits to untangle complex SAS networks. We used over-representation analysis (ORA) of shade-responsive genes, combined with previous annotation, to logically select 59 known and candidate novel mutants for phenotyping. Our analysis reveals shared and separate pathways for each shade avoidance response. In particular, auxin pathway components were required for shade avoidance responses in hypocotyl, petiole, and flowering time, whereas jasmonic acid pathway components were only required for petiole and flowering time responses. Our phenotypic profiling allowed discovery of seventeen novel shade avoidance mutants. Our results demonstrate that logical selection of mutants increased success of phenotypic profiling to dissect complex traits and discover novel components.</p></div

Phenotypic profiling of 59 mutants/overexpressors.

<p>For hypocotyl phenotype, plants were grown under continuous simulated sun conditions (R/FR = 1.3) for four days and further grown for three days under either simulated sun or simulated shade (R/FR = 0.5). For leaf phenotypes, plants were grown under long day conditions (16 hour light/8 hour dark) with approximately 90 μE PAR (R/FR = 1.9). Two week old plants were further grown for 12 days under either simulated sun (R/FR = 1.9) or simulated shade (R/FR = 0.5). For flowering time phenotype plants were grown in the same condition with leaf phenotyping and days after stratification at bolted was used for flowering time index. For phenotype clustering heatmap, differences between shade and sun values (except flowering time) were normalized and centered on Col (i.e., Col value = 0) and visualized with color coding (magenta indicates larger response than Col while green indicates reduced response relative to Col). For flowering time, responses to shade were normalized against flowering time under sun condition to eliminate strong dependencies of response on flowering time under sun condition (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.s002" target="_blank">S2H Fig</a> for normalized data and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.s002" target="_blank">S2G Fig</a> for non normalized data). Asterisks (*) from hypocotyl.length to flowering.time indicate a significant difference from corresponding wild type (p-value<0.05). Although SPT_ox appeared to be a SAS mutant in flowering time (residual method, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.s003" target="_blank">S3H Fig</a>), it was eliminated from SAS mutants in flowering time (residual method) because its background genotype (L<i>er</i>) also showed a similar shift from the regression line. Colors of mutant names correspond to groups found in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.g001" target="_blank">Fig 1</a>. Clustering of mutants according to its phenotype is shown in the dendgrogram on the right. Clustering of traits is show on the top dendrogram. Known phenotypes for hypocotyl, petiole, or flowering time are shown in colored boxes (yellow-green for less response, blue for normal response, and magenta for exaggerated response).</p

Mutants used in SAS phenotypic profiling.

<p>Shade-induced genes are shown in magenta bold and shade-repressed are shown in green. Background genotype for almost all mutants is Col (other genotypes are show in parentheses).</p

GO category analysis of 1 hour shade-responsive genes in hypocotyl.

<p>Same as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.t001" target="_blank">Table 1</a> except 1 hour shade treatment in hypocotyl instead of 1 hour shade treatment in leaf. Expression data is from [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.ref007" target="_blank">7</a>]. Terms with p-value<1e-5 were selected from amiGO analysis.</p><p>GO category analysis of 1 hour shade-responsive genes in hypocotyl.</p

Schematic representation of proposed signal transduction for shade-avoidance syndrome.

<p>PIFs represent PIF3, PIF4, PIF5, PIF7, PIL1, and SPT. JAZs represents JAZ5 and JAZ10. Black lines represent genetic interactions, blue lines show direct interactions, and yellow lines show hormone biosynthesis/metabolism. Lines without arrowheads are for interactions where directionality is unknown. Dashed lines show hypothetical interactions. SCL13 is omitted because of its unknown function within this context.</p

GO category analysis of 4 hour shade-responsive genes in leaf/apical region.

<p>Same as <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004953#pgen.1004953.t001" target="_blank">Table 1</a> except 4 hour shade treatment instead of 1 hour shade treatment</p><p>GO category analysis of 4 hour shade-responsive genes in leaf/apical region.</p