Plants grown at high densities perceive a decrease in the red to far-red (R:FR) ratio of incoming light, resulting from absorption of red light by canopy leaves and reflection of far-red light from neighboring plants. These changes in light quality trigger a series of responses known collectively as the shade avoidance syndrome. During shade avoidance, stems elongate at the expense of leaf and storage organ expansion, branching is inhibited, and flowering is accelerated. We identified several loci in Arabidopsis, mutations in which lead to plants defective in multiple shade avoidance responses. Here we describe TAA1, an aminotransferase, and show that TAA1 catalyzes the formation of indole-3-pyruvic acid (IPA) from L-tryptophan (L-Trp), the first step in a previously proposed, but uncharacterized, auxin biosynthetic pathway. This pathway is rapidly deployed to synthesize auxin at the high levels required to initiate the multiple changes in body plan associated with shade avoidance.

Small-molecule hormones govern every aspect of the biology of plants. Many processes, such as growth, are regulated in similar ways by multiple hormones, and recent studies have revealed extensive crosstalk among different hormonal signaling pathways. These results have led to the proposal that a common set of signaling components may integrate inputs from multiple hormones to regulate growth. In this study, we tested this proposal by asking whether different hormones converge on a common set of transcriptional targets in Arabidopsis seedlings. Using publicly available microarray data, we analyzed the transcriptional effects of seven hormones, including abscisic acid, gibberellin, auxin, ethylene, cytokinin, brassinosteroid, and jasmonate. A high-sensitivity analysis revealed a surprisingly low number of common target genes. Instead, different hormones appear to regulate distinct members of protein families. We conclude that there is not a core transcriptional growth-regulatory module in young Arabidopsis seedlings.

Correct daily phasing of transcription confers an adaptive advantage to almost all organisms, including higher plants. In this study, we describe a hypothesis-driven network discovery pipeline that identifies biologically relevant patterns in genome-scale data. To demonstrate its utility, we analyzed a comprehensive matrix of time courses interrogating the nuclear transcriptome of Arabidopsis thaliana plants grown under different thermocycles, photocycles, and circadian conditions. We show that 89% of Arabidopsis transcripts cycle in at least one condition and that most genes have peak expression at a particular time of day, which shifts depending on the environment. Thermocycles alone can drive at least half of all transcripts critical for synchronizing internal processes such as cell cycle and protein synthesis. We identified at least three distinct transcription modules controlling phase-specific expression, including a new midnight specific module, PBX/TBX/SBX. We validated the network discovery pipeline, as well as the midnight specific module, by demonstrating that the PBX element was sufficient to drive diurnal and circadian condition-dependent expression. Moreover, we show that the three transcription modules are conserved across Arabidopsis, poplar, and rice. These results confirm the complex interplay between thermocycles, photocycles, and the circadian clock on the daily transcription program, and provide a comprehensive view of the conserved genomic targets for a transcriptional network key to successful adaptation.