Network Analysis Reveals a Role for Salicylic Acid Pathway Components in Shade Avoidance.

Plants have sophisticated mechanisms for sensing neighbor shade. To maximize their ability to compete for light, plants respond to shade through enhanced elongation and physiological changes. The shade avoidance response affects many different organs and growth stages, yet the signaling pathways underlying this response have mostly been studied in seedlings. We assayed transcriptome changes in response to shade across a 2-d time course in the wild type and 12 Arabidopsis (Arabidopsis thaliana) mutants. The resulting temporal map of transcriptional responses to shade defines early and late responses in adult plants, enabling us to determine connections between key signaling genes and downstream responses. We found a pervasive and unexpectedly strong connection between shade avoidance and genes related to salicylic acid, suggesting salicylic acid signaling to be an important shade avoidance growth regulator. We tested this connection and found that several mutants disrupting salicylic acid levels or signaling were defective in shade avoidance. The effect of these mutations on shade avoidance was specific to petiole elongation; neither hypocotyl nor flowering time responses were altered, thereby defining important stage-specific differences in the downstream shade avoidance signaling pathway. Shade treatment did not change salicylic acid levels, indicating that the mediation of shade avoidance by salicylic acid is not dependent on the modulation of salicylic acid levels. These results demonstrate that salicylic acid pathway genes also are key components of petiole shade avoidance.

Shade avoidance components and pathways in adult plants revealed by phenotypic profiling.

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.

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/.