Transcriptomic analyses with high temporal resolution provide substantial new insight into hormonal response networks. This study identified the kinetics of genome-wide transcript abundance changes in response to elevated levels of the plant hormone ethylene in roots from light-grown Arabidopsis () seedlings, which were overlaid on time-matched developmental changes. Functional annotation of clusters of transcripts with similar temporal patterns revealed rapidly induced clusters with known ethylene function and more slowly regulated clusters with novel predicted functions linked to root development. In contrast to studies with dark-grown seedlings, where the canonical ethylene response transcription factor, EIN3, is central to ethylene-mediated development, the roots of and single and double mutants still respond to ethylene in light-grown seedlings. Additionally, a subset of these clusters of ethylene-responsive transcripts were enriched in targets of EIN3 and ERFs. These results are consistent with EIN3-independent developmental and transcriptional changes in light-grown roots. Examination of single and multiple gain-of-function and loss-of-function receptor mutants revealed that, of the five ethylene receptors, ETR1 controls lateral root and root hair initiation and elongation and the synthesis of other receptors. These results provide new insight into the transcriptional and developmental responses to ethylene in light-grown seedlings.

The inhibition of hypocotyl elongation by ethylene in dark-grown seedlings was the basis of elegant screens that identified ethylene-insensitive Arabidopsis mutants, which remained tall even when treated with high concentrations of ethylene. This simple approach proved invaluable for identification and molecular characterization of major players in the ethylene signaling and response pathway, including receptors and downstream signaling proteins, as well as transcription factors that mediate the extensive transcriptional remodeling observed in response to elevated ethylene. However, the dark-adapted early developmental stage used in these experiments represents only a small segment of a plant’s life cycle. After a seedling’s emergence from the soil, light signaling pathways elicit a switch in developmental programming and the hormonal circuitry that controls it. Accordingly, ethylene levels and responses diverge under these different environmental conditions. In this review, we compare and contrast ethylene synthesis, perception, and response in light and dark contexts, including the molecular mechanisms linking light responses to ethylene biology. One powerful method to identify similarities and differences in these important regulatory processes is through comparison of transcriptomic datasets resulting from manipulation of ethylene levels or signaling under varying light conditions. We performed a meta-analysis of multiple transcriptomic datasets to uncover transcriptional responses to ethylene that are both light-dependent and light-independent. We identified a core set of 139 transcripts with robust and consistent responses to elevated ethylene across three root-specific datasets. This “gold standard” group of ethylene-regulated transcripts includes mRNAs encoding numerous proteins that function in ethylene signaling and synthesis, but also reveals a number of previously uncharacterized gene products that may contribute to ethylene response phenotypes. Understanding these light-dependent differences in ethylene signaling and synthesis will provide greater insight into the roles of ethylene in growth and development across the entire plant life cycle.

Gene regulatory networks (GRNs) are defined by a cascade of transcriptional events by which signals, such as hormones or environmental cues, change development. To understand these networks, it is necessary to link specific transcription factors (TFs) to the downstream gene targets whose expression they regulate. Although multiple methods provide information on the targets of a single TF, moving from groups of co-expressed genes to the TF that controls them is more difficult. To facilitate this bottom-up approach, we have developed a web application named TF DEACoN. This application uses a publicly available Arabidopsis thaliana DNA Affinity Purification (DAP-Seq) dataset to search for TFs that show enriched binding to groups of co-regulated genes. We used TF DEACoN to examine groups of transcripts regulated by treatment with the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC), using a transcriptional dataset performed with high temporal resolution. We demonstrate the utility of this application when co-regulated genes are divided by timing of response or cell-type specific information, which provides more information on TF/target relationships than when less defined and larger groups of co-regulated genes are used. This approach predicted TFs that may participate in ethylene-modulated root development including the TF NAM (NO APICAL MERISTEM). We used a genetic approach to show that a mutation in NAM reduces the negative regulation of lateral root development by ACC. The combination of filtering and TF DEACoN used here can be applied to any group of co-regulated genes to predict GRNs that control coordinated transcriptional responses.

Cytochrome P450(BM3)-F87G catalyzed the oxidative defluorination of 4-fluorophenol, followed by reduction of the resulting benzoquinone to hydroquinone via the NADPH P450-reductase activity of the enzyme. The k (cat) and K (m) for this reaction were 71 ± 5 min(-1) and 9.5 ± 1.3 mM, respectively. Co-incubation of the reaction mixture with long chain aldehydes stimulated the defluorination reaction, with the 2,3-unsaturated aldehyde, 2-decenal producing a 12-fold increase in catalytic efficiency. At 150 μM aldehyde, k (cat) increased to 158 ± 4, while K (m) decreased to 1.8 ± 0.2. The effects of catalase, glutathione and ascorbate on the reaction were all consistent with a direct oxygen insertion mechanism, as opposed to a radical mechanism. The study demonstrates the potential use of P450(BM3) mutants in oxidative defluorination reactions, and characterizes the novel stimulatory action of straight chain aldehydes on this activity.

Transcriptome studies which provide temporal information can be valuable for identifying groups of similarly-behaving transcripts and provide insight into overarching gene regulatory networks. Nevertheless, inferring meaningful biological conclusions is challenging, in part because it is difficult to holistically consider both local relationships and global structure of these complex and overlapping transcriptional responses. To address this need, we employed the recently-developed method, Partitioned Local Depth (PaLD), which reveals community structure in large data sets, to examine four time-course transcriptomic data sets generated using the model plant Arabidopsis thaliana. As this is the first paper in systems biology to implement the PaLD approach, we provide a self-contained description of the method and show how it can be used to make predictions about gene regulatory networks based on temporal responses of transcripts. The analysis provides a global network representation of the data from which graph partitioning methods and neighborhood analysis can reveal smaller, more well-defined groups of like-responding transcripts. These groups of transcripts that change in response to hormone treatment (auxin and ethylene) and salt treatment were shown to be enriched in common biological function and/or binding of transcription factors that were not identified with prior analyses of this data using other clustering methods. These results reveal the potential of PaLD to predict gene regulatory networks within large transcriptomic data sets.