All multicellular organisms, including plants, produce hormones—chemical messengers that are released in one part of an organism but act in another. The binding of hormones to receptor proteins on the surface of target cells activates signal transduction cascades, leading ultimately to changes in the transcription and translation of genes.

Ethylene is a gaseous plant hormone that acts at trace levels to stimulate or regulate a variety of processes, including the regulation of plant growth, the ripening of fruit and the shedding of leaves. Plants also produce ethylene in response to wounding, pathogen attack or exposure to environmental stresses, such as extreme temperatures or drought. Although the effects of ethylene on plants are well documented, much less is known about how its functions are controlled and coordinated at the molecular level.

Here, Chang et al. reveal how ethylene alters the transcription of DNA into messenger DNA (mRNA) in the plant model organism, Arabidopsis thaliana. Ethylene is known to exert some of its effects via a protein called EIN3, which is a transcription factor that acts as the master regulator of the ethylene signaling pathway. To identify the targets of EIN3, Chang et al. exposed plants to ethylene and then used a technique called ChIP-Seq to identify those regions of the DNA that EIN3 binds to. At the same time, they used genome-wide mRNA sequencing to determine which genes showed altered transcription.

Over the course of 24 hr, ethylene induced four distinct waves of transcription, suggesting that discrete layers of transcriptional control are present. EIN3 binding also controlled a multitude of downstream transcriptional cascades, including a major negative feedback loop. Surprisingly, many of the genes that showed altered expression in response to EIN3 binding were also influenced by hormones other than ethylene.

In addition to extending our knowledge of the role of EIN3 in coordinating the effects of ethylene, the work of Chang et al. reveals the extensive connectivity between pathways regulated by distinct hormones in plants. The results may also make it easier to identify key players involved in hormone signaling pathways in other plant species.

Despite the importance of the plant hormone ethylene, we lack a comprehensive understanding of how its linear signaling pathway mediates many different morphological responses. The dynamic ethylene physiological response, a rapid growth inhibition independent of the master transcriptional regulator ETHYLENE INSENSITIVE3 (EIN3), followed by an EIN3-dependent sustained growth inhibition, calls for a temporal study of ethylene transcriptional regulation (Binder et al., 2004a). EIN3 has been shown to be necessary and sufficient for the ethylene response and accumulates upon a duration of exogenous ethylene gas treatment (Guo and Ecker, 2003). Although hundreds of ethylene response genes have been identified, because some of the targets of EIN3 are transcription factors (e.g. ETHYLENE RESPONSE FACTOR1 [ERF1]), it is challenging to distinguish immediate early targets from those further downstream. To understand the dynamics of the EIN3-mediated ethylene transcriptional response, we performed a genome-wide study of the ethylene-induced EIN3 protein-DNA interactions using chromatin immunoprecipitation followed by sequencing (ChIP-Seq) and simultaneously determined the repertoire of target genes that are transcriptionally regulated by ethylene (mRNA-Seq). Tracing the transcriptional cascade, we asked if EIN3-mediated genes contribute to a component of the ethylene transcriptional response. For a select number of EIN3 targets that are putative transcriptional regulators, DNA-binding motifs were identified using protein binding microarrays (PBM) and the enrichment for these motifs in the promoters of ethylene response genes was determined.

We performed ChIP-Seq using a native antibody that recognizes EIN3 (Guo and Ecker, 2003) as well as mRNA-Seq in three-day-old dark grown seedlings during a timecourse of ethylene treatment (Figure 1—figure supplements 1, 2; Supplementary file 1A). By stringent analysis of the temporal ChIP-Seq data (see ‘Materials and methods’), we identified 1460 EIN3 binding regions in the Arabidopsis genome associated with 1314 genes (Supplementary file 1B). We refer to genes associated with EIN3 binding regions as EIN3 candidate targets. In the sequences of EIN3 binding regions, we found significant enrichment of the consensus TEIL motif (Hypergeometric p<10⁻⁸⁷) (Kosugi and Ohashi, 2000), and de novo motif analysis identified the known EIN3 motif (Figure 1—figure supplement 3). We detected three previously described EIN3 targets using our stringent analysis (Figure 1—figure supplements 3, 4) (Solano et al., 1998; Konishi and Yanagisawa, 2008; Chen et al., 2009; Zhong et al., 2009; Boutrot et al., 2010). One example of a known target of EIN3, EIN3-BINDING F-BOX PROTEIN 2 (EBF2), is shown in Figure 1A. EBF2 directs the proteolysis of EIN3 and exhibits ethylene-induced transcription (Figure 1A), resulting in feedback regulation of the ethylene signaling pathway. Our study identified additional distal EIN3 binding in the EBF2 promoter region (Figure 1A, Figure 1—figure supplement 4).

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The majority of studies that exist in the literature have shown that EIN3 acts as an activator, and we observed this activation at the genome-wide level (Figure 1B). We found that a majority of EIN3 candidate targets that are regulated by ethylene (referred to as EIN3-R) are induced (85%), Moreover, when compared to the regulation of all genes that respond to ethylene, we observed an over-representation of up-regulation of EIN3 candidate targets (Figure 1B,C). Interestingly, many EIN3-R are transcription factors (∼14%); EIN3 candidate targets are significantly enriched in gene ontology (GO) terms related to transcription factor regulation, confirming that EIN3 activates a transcriptional cascade (Figure 1—figure supplement 5; Supplementary file 1C) (Maere, 2005).

Numerous studies have reported that transcription factor binding does not necessarily coincide with changes in transcription (Macquarrie et al., 2011; Menet et al., 2012), especially for master regulators targeting other transcription factors or other factors involved in chromatin state regulation. Only about 30% of the EIN3 binding sites were associated with transcriptional changes, but at least two-thirds were not (Figure 1D, Figure 1—figure supplement 2). EIN3 candidate targets that are not transcriptionally activated may require cofactors to induce a change in expression for a specific environmental response or developmental program. Quantitatively, the changes in EIN3 binding and steady-state transcription upon ethylene treatment do not correlate because the temporal transcription patterns are very diverse (Figure 2—figure supplement 1). However, relatively high levels of EIN3 occupancy in etiolated seedlings treated with ethylene indeed correspond to increases in steady-state levels of transcription (Figure 2A). In fact, we were able to differentiate the characteristics of EIN3 candidate targets that exhibited a transcriptional response to ethylene from those that do not (Figure 2A). EIN3 candidate targets that exhibit increased occupancy and increased levels of transcription (EIN3-R) are functional targets, enriched in gene families with specific functions, for example BZR, TIFY, and bHLH transcription factor families, which play a role in other hormone pathways (p<0.05) (Figure 2B). The highest percentage of hormone-associated genes occurs in EIN3 candidate targets that are ethylene-regulated (EIN3-R) (Figure 2B, inset), and it is likely that these EIN3-R targets are direct and/or functional. Other EIN3 candidate targets may play roles in different developmental stages/tissue types, or may be under spatial regulation, requiring specific cofactors.

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Projection of the dynamic EIN3 binding (ChIP-Seq) onto the transcriptional ethylene response (mRNA-Seq) using the Dynamic Regulatory Events Miner (DREM) (Ernst et al., 2007) revealed that the ethylene response occurs in four waves of transcription significantly regulated by EIN3 (Pathway hypergeometric p<10⁻¹⁰) (Figure 2C). These waves display distinct temporal transcription behaviors (Hypergeometric p<0.001), and the reduction of transcriptional noise occurs in successive temporal waves (Figure 2C, Figure 2—figure supplement 2). Genes that were enriched in specific biological functions within these four transcriptional waves include RNA binding/translation (Wave 1, Wave 3), cell wall maintenance (Wave 2), and response to endogenous stimulus (Wave 4). The second wave is also enriched for genes involved in cell wall maintenance, and the expression of these genes steadily increases following 1 hr of ethylene treatment, consistent with kinetics of EIN3-dependent growth inhibition (Binder et al., 2004a; Vandenbussche et al., 2012).

The four waves of the ethylene transcriptional response each contain a unique subset of EIN3 candidate targets. The first wave is highly variable, lower in steady-state levels of transcription, and it also contains the lowest percentage of EIN3 candidate targets and hormone-related genes (Figure 2C). Previous ethylene growth rate inhibition studies have shown that low amounts of ethylene can result in adaptation and desensitization to subsequent ethylene stimulation (Binder et al., 2004a, 2004b) . This first wave may serve as the immediate ethylene response, activating initial ethylene response genes as well as those that serve to desensitize the plant to subsequent ethylene stimulation, but this has yet to be shown. The next three waves of transcription are successively less variable and contain higher percentages of EIN3 candidate targets and hormone-related genes. The four waves of ethylene-induced transcription account for 50% of the transcriptionally ethylene-regulated EIN3 targets (EIN3-R), and the remaining EIN3 candidate targets are distributed among other patterns of transcription that do not contain significant numbers of EIN3 candidate targets in each transcriptional trajectory (Pathway hypergeometric p<10⁻¹⁰) (Figure 2—figure supplement 3). The expression kinetics and reduction of transcriptional noise we observe in the ethylene-induced waves may be tied to distinct mechanisms of transcriptional control, or they may reflect heterogeneity of the ethylene response in different tissues, which can be resolved using single cell analysis. From the temporal ethylene transcriptional response patterns, it appears that the initial early ethylene transcriptional response is noisy and less focused functionally. During sustained exogenous ethylene application, EIN3 accumulates, and the established ethylene transcriptional response is hormone-focused and less noisy, but feed-forward and feed-back mechanisms mentioned below may serve to establish this functional specificity.

A recurring theme throughout this study is that the key players in the ethylene transcriptional response regulated by EIN3 are involved in plant hormone response pathways, and we anticipate a dense network of interconnections between the coregulated hormone pathways because hormones operate in concert, synergistically/antagonistically regulating growth and development. Although hormone pathway interconnections have been previously described by many groups (Kaufmann et al., 2009, 2010; Sun et al., 2010; Yu et al., 2011), here we show that these interconnections exist at many regulatory levels and that the targets of EIN3 may regulate genes in these responses. Among the EIN3 candidate targets, we observed the enrichment of hormone-related targets among many different categorical sets (Figure 2B, inset). These EIN3 targets include downstream effectors of the ethylene response, key ethylene signaling players, and genes involved in other hormone pathways/responses. Many of the EIN3-modulated downstream effectors are members of the AP2/ERF transcription factor family, and as expected, these transcriptional initiators are up-regulated by ethylene (Figure 3A, inset, green font).

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Given that EIN3, the master regulator of the ethylene transcriptional response, acts at the culmination of the ethylene signal transduction pathway and is the transcriptional initiator of the ethylene response, one would expect a large number of downstream effectors to coordinate the transcriptional cascade and feedback regulators to maintain the circuitry in a homeostatic state as opposed to a feed-forward runaway response. Analysis of the ethylene-regulated EIN3 targets reveals a number of sites of ethylene signaling modulation of which the majority are negative regulators, supporting the idea that EIN3 is at the end of a signal transduction pathway, and that this regulatory logic dictates a negative feedback loop for homeostatic adaptable systems. More specifically, several negative regulators of the ethylene signaling pathway (Kendrick and Chang, 2008) were targets of EIN3 (Figure 3A), including three ethylene receptors (ETHYLENE RESPONSE2 [ETR2], ETHYLENE RESPONSE SENSOR1/2 [ERS1/2]), as well as REVERSION-TO-ETHYLENE SENSITIVITY1 (RTE1), CONSTITUTIVE TRIPLE RESPONSE1 (CTR1), and the previously mentioned EBF1/2. The induction of ETR2, ERS1/2 by ethylene was previously reported and has been suggested to restore ethylene receptor activity, resensitizing the plant to ethylene (Binder et al., 2004b; Vandenbussche et al., 2012). The negative regulation of ethylene signaling by EIN3 through induction of CTR1 and ETR2 is further supported by the literature (Chen et al., 2007), suggesting that these proteins exhibit an increase in stabilization upon ethylene treatment (Gao et al., 2003).

The EIN3 candidate targets account for more than twice the proportion of hormone genes than in the genome (46%, Hypergeometric p=10⁻⁹⁶) (Figure 2B, inset) (Alonso et al., 2003a; Nemhauser et al., 2006; Peng et al., 2008). Many of the genes were involved in more than one hormone response, highlighting the extensive hormone co-regulation in Arabidopsis (Figure 3B). Hormone co-regulation is evident in the protein-protein as well as the transcriptional regulator interactions and this network reveals interconnectivity suggestive of robust regulatory co-regulation (Figure 3C). Many detailed examples of hormone co-regulation exist in the literature, but often the mechanism(s) of co-regulation is unknown. Previous ChIP-chip or ChIP-Seq studies from plants have also revealed cross-regulation within pathways involved in flowering and in roots (Yant et al., 2010; Iyer-Pascuzzi et al., 2011; Winter et al., 2011; Immink et al., 2012) . The findings presented in our study suggest that (1) hormone co-regulation can occur through the binding of EIN3, (2) EIN3 targets hormone pathways at multiple levels, and (3) some of these events are transcriptionally regulated by ethylene (Figure 3D).

Ethylene and jasmonate co-regulation occurs at the transcriptional level, sharing a complement of genes responsive to both hormones, for example RAP2.6L, ERF1. EIN3 also targets four JAZ repressors, two of which are transcriptionally regulated by ethylene (JAZ1, JAZ6). In general, ethylene and jasmonate are known to function synergistically and in the presence of jasmonate, JAZ1 proteins bound to EIN3 are degraded, relieving the EIN3 transcriptional activation (Zhu et al., 2011). Here, the presence of an exogenous ethylene stimulus primes cells for a jasmonate response, by loading the promoters of jasmonate/ethylene response genes with EIN3 and JAZ proteins, poising the plant for a jasmonate-ethylene driven transcriptional program, as required for plant pathogen response. Reports of anticipatory binding in other organisms have been forth coming (Macquarrie et al., 2011; Lickwar et al., 2012).

Ethylene and gibberellin co-regulation through EIN3 occurs at signal reception (GID1B, GID1C) and transcription (PIF3). The regulatory logic of EIN3 binding results in an up-regulation of the gibberellin response; GID receptors target DELLA repressors for degradation, which releases PIF3 from repression, resulting in the activation of the gibberellin transcriptional response. Additional support for feed-forward transcription is provided by over-representation of the PIF3 motif in the promoter sequences of the ethylene transcriptional response genes (Supplementary file 1E, Figure 3—figure supplement 1). Hormone co-regulation may also occur bidirectionally as a recent study reported negative regulation of ethylene by FUSCA3 (FUS3), known to regulate and be regulated by gibberellin and abscisic acid in embryonic and vegetative timing (Lumba et al., 2012). FUS3 negatively regulates genes upstream and downstream of EIN3 (EIN2 and ERF1) in leaf aging (Lumba et al., 2012).

Ethylene and auxin co-regulation occurs at both the level of transport and transcriptional response, as EIN3 modulates a regulator of auxin efflux (PID) and its upstream activator (PBP1), and at least seven auxin response proteins (Supplementary file 1B). EIN3 also targets the auxin transporter (AUX1) and an auxin signaling gene (IAA29), but these candidate targets are not responsive to ethylene in etiolated seedlings (Supplementary file 1B). Ethylene has been reported to stimulate auxin transport through AUX1 away from the root apex, to decrease lateral root primordia (Lewis et al., 2011). Therefore, it is likely these binding events have functional outcomes in specific tissue types or developmental programs not addressed in this study.

The establishment of a transcriptional program tailored to result in a specific growth and development process requires multiple levels of transcriptional modulation. EIN3 was previously suggested to initiate a transcriptional cascade because it activates AP2/ERF transcription factors ERF1/EDF1 (Solano et al., 1998). To determine additional candidate downstream effectors that may modulate the ethylene transcriptional response cascade, we used in vitro protein binding microarrays to generate DNA-binding motifs for 12 transcription factors that were ethylene-regulated targets of EIN3 (see ‘Materials and methods’). We then used the in vitro DNA-binding motifs to scan the promoter sequences of all ethylene transcriptional response genes (Lam et al., 2011). EIN3 targets that may regulate a secondary transcriptional ethylene response include AP2/ERFs AT-ERF1, ERF5, and WRKY14/47, PIF3, NAC6, and RAP2.2, and the DNA-binding motifs of the aforementioned transcription factors are over-represented in the promoter regions of genes that are regulated by ethylene (Hypergeometric p<10⁻⁵) (Supplementary file 1E, Figure 3—figure supplement 1). Future in vivo analyses of the targets of these transcription factors may help elucidate their contribution to the transcriptional cascade of the ethylene response.

The extensive hormone co-regulation that occurs in waves of transcription leads to certain testable predictions regarding the key regulatory hubs and transcriptional cascades at a genome-wide level. Using a global approach, we are able to determine not only if one gene is a candidate target of EIN3, but whether its homologs are targets as well. Transcription factor targeting of genes that are homologous, with overlapping and unique functions, can add diversity to the outputs of transcriptional programs (Macquarrie et al., 2011). One of the most striking and surprising example we found was the direct regulation of the three homologs by EIN3, HOOKLESS1 (HLS1) and HLS1-LIKE HOMOLOG2 (HLH2), and to a lesser extent HLH1 (Figure 4A, HLH1 in Figure 4—figure supplement 1). This led us to experimentally test the functionality of all four members of the HLS1 gene family in etiolated seedling growth and development. HLS1 is a well-known signal integrator of ethylene, light, auxin, sugar, and brassinolide (de Grauwe et al., 2005; Hou et al., 1993; Li et al., 2004; Ohto et al., 2006) and was previously hypothesized to be a target of ERF1 because of the presence of a GCC box motif in the HLS1 promoter region sequence (Lehman et al., 1996). The binding of EIN3 to the promoters of HLS1, HLH2, and HLH1 increased upon ethylene treatment (Figure 4—figure supplement 1) and is specific to EIN3 (Figure 4B). The EIN3 binding sites in these promoters contain known EIN3 motifs (Figure 4—figure supplement 1). The functional significance of the HLS1 EIN3 binding site is supported by a previous study that identified two allelic mutations in the HLS1 promoter sufficient to yield a ‘hookless’ phenotype (Lehman et al., 1996). Previous studies have also shown that ein2 is deficient in the accumulation of EIN3 protein (Guo and Ecker, 2003) and HLS1 mRNA, (Lehman et al., 1996). We also observed HLS1 steady-state transcript levels were significantly reduced in the ein3-1 eil1-1 mutant (Figure 4—figure supplement 2).

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Ethylene and auxin co-regulate plant growth and development and it is likely that this co-regulation is mediated in part by EIN3 regulation of HLS1/HLHs. To understand this hormone co-regulation, we generated quadruple mutants for the HLS1 gene family and also further characterized their role as regulatory hub signal integrators (Figure 4—figure supplement 3). The pleiotropic phenotypes we observed support the role of the HLS1 gene family in auxin regulated plant growth and development (Figure 4C–F). We observed severe defects in the embryonic patterning, etiolated seedlings, adult plant morphology, and floral morphology. The adult quadruple mutants display a dwarf phenotype, similar to the auxin mutant axr1 (Leyser et al., 1993), and floral morphology of the quadruple mutants display two stigmas atop a gynoecium, similar to the arf3/ettin mutant floral phenotype (Sessions and Zambryski, 1995). Although HLS1 is known to be involved in the differential growth of the apical hook and is necessary for the accumulation of AUXIN RESPONSE FACTOR2 (ARF2) DNA-binding protein (Li et al., 2004; Ohto et al., 2006), the biochemical function of these putative N-acetyltransferases remains to be determined. Using a genome-wide approach, we found that not only HLS1, but other gene family members are targets of EIN3 and that the requirement of the HLS proteins for hormone responses extends beyond apical hook development to many other processes from embryo patterning to flowering, linking the regulation of growth and development by ethylene to many new biological processes in novel ways.

To date, few temporal transcription factor binding studies have been undertaken (Hiroi, 2004; Ni et al., 2009; Zinzen et al., 2009). Temporal protein–DNA interactions are often difficult to reconcile with gene expression profiles and the complexity of regulation that occurs transcriptionally is very challenging to characterize and interpret biologically. Here, by jointly analyzing the temporal expression and genome-wide binding data of one key transcription factor in response to hormone stimulus, we were able to reveal several important properties of the hormone responsive transcriptional program and identify new components in the signaling pathway. We found that upon a timecourse of ethylene treatment, EIN3 binding was induced, resulting in various transcriptional patterns and that the ethylene transcriptional response occurred in waves of transcription that were temporally distinct and could be attributed to different biological functions, variable in the amount of noise, and significantly regulated by EIN3. EIN3 modulated genes were over-represented in hormone co-regulation, and the specific targets in the other hormone pathways, as reported in this study, suggest these ‘cross-talk’ events may involve multiple levels of regulation. Interestingly, feedback regulation of the ethylene response by EIN3 enabled the identification of the majority of known ethylene signaling pathway components. Moreover, the temporal resolution of steady-state levels of transcription confirmed the role of these genes in the ethylene transcriptional response. Other signaling networks utilize feedback regulation for overall system control/homeostasis, and this type of study may be used to identify novel components in signaling pathways (Rosenfeld et al., 2002; Amit et al., 2007; Tsang et al., 2007; Avraham and Yarden, 2011; Fang et al., 2011; Feng et al., 2011; Yosef and Regev, 2011).

The implication that EIN3 regulates the coordination of other hormone pathways is extensive because the transcriptional control by EIN3 is likely conserved in plants. EIN3 orthologs exist in poplar, soybean, rice, maize, moss, and multicellular algae, among many others (www.phytozome.net) and, in fact, we found that the Physcomitrella patens (moss) EIN3 protein binds a very similar motif sequence to that of the Arabidopsis thaliana EIN3 (Figure 4—figure supplement 4). The role of EIN3 in the coordination of the initiation of the ethylene transcriptional cascade, the negative feedback regulation of the ethylene signaling pathway, and the orchestration of other hormone pathways suggests that adaptable system homeostasis in plants requires robust hormone co-regulation.

1. 1. Chang Katherine N
   2. Zhong Shan
   3. Weirauch Matthew T
   4. Hon Gary
   5. Pelizzola Mattia
   6. Li Hai
   7. Huang Shao-shan Carol
   8. Schmit Robert J
   9. Urich Mark A
   10. Kuo Dwight
   11. Nery Joseph
   12. Qiao Hong
   13. Yang Ally
   14. Jamali Abdullah
   15. Ideker Trey
   16. Ren Bing
   17. Bar-Joseph Ziv
   18. Hughes Timothy R
   19. Ecker Joseph R

   (2013) Temporal transcriptional response to ethylene gas drives growth hormone crossregulation in Arabidopsis

   ID SRA063695. Publicly available at the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra).

   http://www.ncbi.nlm.nih.gov/sra/?term=SRA063695

1. 1. Alonso JM
   2. Stepanova AN
   3. Leisse TJ
   4. Kim CJ
   5. Chen H
   6. Shinn P

   et al. (2003a) Genome-wide insertional mutagenesis of Arabidopsis thaliana

   Science 301:653–657.

   https://doi.org/10.1126/science.1086391

   • Google Scholar
2. 1. Alonso JM
   2. Stepanova AN
   3. Solano R
   4. Wisman E
   5. Ferrari S
   6. Ausubel FM

   et al. (2003b) Five components of the ethylene-response pathway identified in a screen for weak ethylene-insensitive mutants in Arabidopsis

   Proc Natl Acad Sci USA 100:2992–2997.

   https://doi.org/10.1073/pnas.0438070100

   • Google Scholar
3. 1. Amit I
   2. Citri A
   3. Shay T
   4. Lu Y
   5. Katz M
   6. Zhang F

   et al. (2007) A module of negative feedback regulators defines growth factor signaling

   Nat Genet 39:503–512.

   https://doi.org/10.1038/ng1987

   • Google Scholar
4. 1. Arabidopsis Interactome Mapping Consortium

   (2011) Evidence for network evolution in an Arabidopsis interactome map

   Science 333:601–607.

   https://doi.org/10.1126/science.1203877

   • Google Scholar
5. 1. Avraham R
   2. Yarden Y

   (2011) Feedback regulation of EGFR signalling: decision making by early and delayed loops

   Nat Rev Mol Cell Biol 12:104–117.

   https://doi.org/10.1038/nrm3048

   • Google Scholar
6. 1. Badis G
   2. Berger MF
   3. Philippakis AA
   4. Talukder S
   5. Gehrke AR
   6. Jaeger SA

   et al. (2009) Diversity and complexity in DNA recognition by transcription factors

   Science 324:1720–1723.

   https://doi.org/10.1126/science.1162327

   • Google Scholar
7. 1. Berger MF
   2. Bulyk ML

   (2009) Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors

   Nat Protoc 4:393–411.

   https://doi.org/10.1038/nprot.2008.195

   • Google Scholar
8. 1. Berger MF
   2. Badis G
   3. Gehrke AR
   4. Talukder S
   5. Philippakis AA
   6. Peña-Castillo L

   et al. (2008) Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences

   Cell 133:1266–1276.

   https://doi.org/10.1016/j.cell.2008.05.024

   • Google Scholar
9. 1. Berger MF
   2. Philippakis AA
   3. Qureshi AM
   4. He FS
   5. Estep PW
   6. Bulyk ML

   (2006) Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities

   Nat Biotechnol 24:1429–1435.

   https://doi.org/10.1038/nbt1246

   • Google Scholar
10. 1. Binder BM
    2. Mortimore LA
    3. Stepanova AN
    4. Ecker JR
    5. Bleecker AB

    (2004a) Short-term growth responses to ethylene in Arabidopsis seedlings are EIN3/EIL1 independent

    Plant Physiol 136:2921–2927.

    https://doi.org/10.1104/pp.104.050393

    • Google Scholar
11. 1. Binder BM
    2. O’Malley RC
    3. Wang W
    4. Moore JM
    5. Parks BM
    6. Spalding EP

    et al. (2004b) Arabidopsis seedling growth response and recovery to ethylene. A kinetic analysis

    Plant Physiol 136:2913–2920.

    https://doi.org/10.1104/pp.104.050369

    • Google Scholar
12. 1. Boutrot F
    2. Segonzac C
    3. Chang KN
    4. Qiao H
    5. Ecker JR
    6. Zipfel C

    et al. (2010) Direct transcriptional control of the Arabidopsis immune receptor FLS2 by the ethylene-dependent transcription factors EIN3 and EIL1

    Proc Natl Acad Sci USA 107:14502–14507.

    https://doi.org/10.1073/pnas.1003347107

    • Google Scholar
13. 1. Chao Q
    2. Rothenberg M
    3. Solano R
    4. Roman G
    5. Terzaghi W
    6. Ecker JR

    (1997) Activation of the ethylene gas response pathway in Arabidopsis by the nuclear protein ETHYLENE-INSENSITIVE3 and related proteins

    Cell 89:1133–1144.

    https://doi.org/10.1016/S0092-8674(00)80300-1

    • Google Scholar
14. 1. Chen H
    2. Xue L
    3. Chintamanani S
    4. Germain H
    5. Lin H
    6. Cui H

    et al. (2009) ETHYLENE INSENSITIVE3 and ethylene INSENSITIVE3-LIKE1 repress salicylic acid induction DEFICIENT2 expression to negatively regulate plant innate immunity in Arabidopsis

    Plant Cell 21:2527–2540.

    https://doi.org/10.1105/tpc.108.065193

    • Google Scholar
15. 1. Chen Y-F
    2. Shakeel SN
    3. Bowers J
    4. Zhao X-C
    5. Etheridge N
    6. Schaller GE

    (2007) Ligand-induced degradation of the ethylene receptor ETR2 through a proteasome-dependent pathway in Arabidopsis

    J Biol Chem 282:24752–24758.

    https://doi.org/10.1074/jbc.M704419200

    • Google Scholar
16. 1. Chenna R

    (2003) Multiple sequence alignment with the clustal series of programs

    Nucleic Acids Res 31:3497–3500.

    https://doi.org/10.1093/nar/gkg500

    • Google Scholar
17. 1. de Grauwe L
    2. Vandenbussche F
    3. Tietz O
    4. Palme K
    5. van der Straeten D

    (2005) Auxin, ethylene and brassinosteroids: tripartite control of growth in the Arabidopsis hypocotyl

    Plant Cell Physiol 46:827–836.

    https://doi.org/10.1093/pcp/pci111

    • Google Scholar
18. 1. Deng W
    2. Ying H
    3. Helliwell CA
    4. Taylor JM
    5. Peacock WJ
    6. Dennis ES

    (2011) FLOWERING LOCUS C (FLC) regulates development pathways throughout the life cycle of Arabidopsis

    Proc Natl Acad Sci USA 108:6680–6685.

    https://doi.org/10.1073/pnas.1103175108

    • Google Scholar
19. 1. Eddy SR

    (2009) A new generation of homology search tools based on probabilistic inference

    Genome Inform 23:205–211.

    https://doi.org/10.1142/9781848165632_0019

    • Google Scholar
20. 1. Ernst J
    2. Vainas O
    3. Harbison CT
    4. Simon I
    5. Bar-Joseph Z

    (2007) Reconstructing dynamic regulatory maps

    Mol Syst Biol 3:74.

    https://doi.org/10.1038/msb4100115

    • Google Scholar
21. 1. Fang X
    2. Yoon J-G
    3. Li L
    4. Yu W
    5. Shao J
    6. Hua D

    et al. (2011) The SOX2 response program in glioblastoma multiforme: an integrated ChIP-seq, expression microarray, and microRNA analysis

    BMC Genomics 12:11.

    https://doi.org/10.1186/1471-2164-12-11

    • Google Scholar
22. 1. Feng Z
    2. Lin M
    3. Wu R

    (2011) The regulation of aging and longevity: a new and complex role of p53

    Genes Cancer 2:443–452.

    https://doi.org/10.1177/1947601911410223

    • Google Scholar
23. 1. Finn RD
    2. Mistry J
    3. Tate J
    4. Coggill P
    5. Heger A
    6. Pollington JE

    et al. (2009) The Pfam protein families database

    Nucleic Acids Res 38:D211–D222.

    https://doi.org/10.1093/nar/gkp985

    • Google Scholar
24. 1. Foat BC
    2. Morozov AV
    3. Bussemaker HJ

    (2006) Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE

    Bioinformatics 22:e141–e149.

    https://doi.org/10.1093/bioinformatics/btl223

    • Google Scholar
25. 1. Gao Z
    2. Chen Y-F
    3. Randlett MD
    4. Zhao X-C
    5. Findell JL
    6. Kieber JJ

    et al. (2003) Localization of the Raf-like kinase CTR1 to the endoplasmic reticulum of Arabidopsis through participation in ethylene receptor signaling complexes

    J Biol Chem 278:34725–34732.

    https://doi.org/10.1074/jbc.M305548200

    • Google Scholar
26. 1. Guo H
    2. Ecker JR

    (2003) Plant responses to ethylene gas are mediated by SCF(EBF1/EBF2)-dependent proteolysis of EIN3 transcription factor

    Cell 115:667–677.

    https://doi.org/10.1016/S0092-8674(03)00969-3

    • Google Scholar
27. 1. Hertz GZ
    2. Stormo GD

    (1999) Identifying DNA and protein patterns with statistically significant alignments of multiple sequences

    Bioinformatics 15:563–577.

    https://doi.org/10.1093/bioinformatics/15.7.563

    • Google Scholar
28. 1. Hiroi H

    (2004) Temporal and spatial changes in transcription factor binding and histone modifications at the steroidogenic acute regulatory protein (stAR) locus associated with stAR transcription

    Mol Endocrinol 18:791–806.

    https://doi.org/10.1210/me.2003-0305

    • Google Scholar
29. 1. Hou Y
    2. Von Arnim AG
    3. Deng XW

    (1993) A new class of Arabidopsis constitutive photomorphogenic genes involved in regulating cotyledon development

    Plant Cell 5:329–339.

    https://doi.org/10.1105/tpc.5.3.329

    • Google Scholar
30. 1. Immink RG
    2. Posé D
    3. Ferrario S
    4. Ott F
    5. Kaufmann K
    6. Valentim FL

    et al. (2012) Characterization of SOC1’s central role in flowering by the identification of its upstream and downstream regulators

    Plant Physiol 160:433–449.

    https://doi.org/10.1104/pp.112.202614

    • Google Scholar
31. 1. Iyer-Pascuzzi AS
    2. Jackson T
    3. Cui H
    4. Petricka JJ
    5. Busch W
    6. Tsukagoshi H

    et al. (2011) Cell identity regulators link development and stress responses in the Arabidopsis root

    Dev Cell 21:770–782.

    https://doi.org/10.1016/j.devcel.2011.09.009

    • Google Scholar
32. 1. Kaufmann K
    2. Muiño JM
    3. Jauregui R
    4. Airoldi CA
    5. Smaczniak C
    6. Krajewski P

    et al. (2009) Target genes of the MADS transcription factor SEPALLATA3: integration of developmental and hormonal pathways in the Arabidopsis flower

    PLOS Biol 7:e90.

    https://doi.org/10.1371/journal.pbio.1000090

    • Google Scholar
33. 1. Kaufmann K
    2. Wellmer F
    3. Muino JM
    4. Ferrier T
    5. Wuest SE
    6. Kumar V

    et al. (2010) Orchestration of floral initiation by APETALA1

    Science 328:85–89.

    https://doi.org/10.1126/science.1185244

    • Google Scholar
34. 1. Kendrick M
    2. Chang C

    (2008) Ethylene signaling: new levels of complexity and regulation

    Curr Opin Plant Biol 11:479–485.

    https://doi.org/10.1016/j.pbi.2008.06.011

    • Google Scholar
35. 1. Kharchenko PV
    2. Tolstorukov MY
    3. Park PJ

    (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins

    Nat Biotechnol 26:1351–1359.

    https://doi.org/10.1038/nbt.1508

    • Google Scholar
36. 1. Konishi M
    2. Yanagisawa S

    (2008) Ethylene signaling in Arabidopsis involves feedback regulation via the elaborate control of EBF2 expression by EIN3

    Plant J 5:821–831.

    https://doi.org/10.1111/j.1365-313X.2008.03551.x

    • Google Scholar
37. 1. Kosugi S
    2. Ohashi Y

    (2000) Cloning and DNA-binding properties of a tobacco ethylene-insensitive3 (EIN3) homolog

    Nucleic Acids Res 28:960–967.

    https://doi.org/10.1093/nar/28.4.960

    • Google Scholar
38. 1. Lam KN
    2. van Bakel H
    3. Cote AG
    4. van der Ven A
    5. Hughes TR

    (2011) Sequence specificity is obtained from the majority of modular C2H2 zinc-finger arrays

    Nucleic Acids Res 39:4680–4690.

    https://doi.org/10.1093/nar/gkq1303

    • Google Scholar
39. 1. Lehman A
    2. Black R
    3. Ecker JR

    (1996)

    HOOKLESS1, an ethylene response gene, is required for differential cell elongation in the Arabidopsis hypocotyl

    Cell 85:183–194.

    • Google Scholar
40. 1. Lewis DR
    2. Negi S
    3. Sukumar P
    4. Muday GK

    (2011) Ethylene inhibits lateral root development, increases IAA transport and expression of PIN3 and PIN7 auxin efflux carriers

    Development 138:3485–3495.

    https://doi.org/10.1242/dev.065102

    • Google Scholar
41. 1. Leyser H
    2. Lincoln C
    3. Timpte C
    4. Lammer D

    (1993) Arabidopsis auxin-resistance gene AXR1 encodes a protein related to ubiquitin-activating enzyme E1

    Nature 364:161–164.

    https://doi.org/10.1038/364161a0

    • Google Scholar
42. 1. Li H
    2. Johnson P
    3. Stepanova A
    4. Alonso JM
    5. Ecker JR

    (2004) Convergence of signaling pathways in the control of differential cell growth in Arabidopsis

    Dev Cell 7:193–204.

    https://doi.org/10.1016/j.devcel.2004.07.002

    • Google Scholar
43. 1. Lickwar CR
    2. Mueller F
    3. Hanlon SE
    4. McNally JG
    5. Lieb JD

    (2012) Genome-wide protein–DNA binding dynamics suggest a molecular clutch for transcription factor function

    Nature 484:251–255.

    https://doi.org/10.1038/nature10985

    • Google Scholar
44. 1. Lippman Z
    2. Gendrel A
    3. Colot V

    (2005) Profiling DNA methylation patterns using genomic tiling microarrays

    Nat Methods 2:219–224.

    https://doi.org/10.1038/nmeth0305-219

    • Google Scholar
45. 1. Lister R
    2. Omalley R
    3. Tontifilippini J
    4. Gregory B
    5. Berry C
    6. Millar A

    et al. (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis

    Cell 133:523–536.

    https://doi.org/10.1016/j.cell.2008.03.029

    • Google Scholar
46. 1. Lumba S
    2. Tsuchiya Y
    3. Delmas F
    4. Hezky J
    5. Provart NJ
    6. Lu QS

    et al. (2012) The embryonic leaf identity gene FUSCA3 regulates vegetative phase transitions by negatively modulating ethylene-regulated gene expression in Arabidopsis

    BMC Biol 10:8.

    https://doi.org/10.1186/1741-7007-10-8

    • Google Scholar
47. 1. Macquarrie KL
    2. Fong AP
    3. Morse RH
    4. Tapscott SJ

    (2011) Genome-wide transcription factor binding: beyond direct target regulation

    Trends Genet 27:141–148.

    https://doi.org/10.1016/j.tig.2011.01.001

    • Google Scholar
48. 1. Maere S

    (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks

    Bioinformatics 21:3448–3449.

    https://doi.org/10.1093/bioinformatics/bti551

    • Google Scholar
49. 1. Mahony S
    2. Benos PV

    (2007) STAMP: a web tool for exploring DNA-binding motif similarities

    Nucleic Acids Res 35:W253–W258.

    https://doi.org/10.1093/nar/gkm272

    • Google Scholar
50. 1. Mahony S
    2. Golden A
    3. Smith TJ
    4. Benos PV

    (2005) Improved detection of DNA motifs using a self-organized clustering of familial binding profiles

    Bioinformatics 21:i283–i291.

    https://doi.org/10.1093/bioinformatics/bti1025

    • Google Scholar
51. 1. Mathieu O
    2. Jasencakova Z
    3. Vaillant I
    4. Gendrel A-V
    5. Colot V
    6. Schubert I

    et al. (2003) Changes in 5S rDNA chromatin organization and transcription during heterochromatin establishment in Arabidopsis

    Plant Cell 15:2929–2939.

    https://doi.org/10.1105/tpc.017467

    • Google Scholar
52. 1. Menet JS
    2. Rodriguez J
    3. Abruzzi KC
    4. Rosbash M

    (2012) Nascent-Seq reveals novel features of mouse circadian transcriptional regulation

    eLife 1:e00011.

    https://doi.org/10.7554/eLife.00011.026

    • Google Scholar
53. 1. Nemhauser JL
    2. Hong F
    3. Chory J

    (2006) Different plant hormones regulate similar processes through largely nonoverlapping transcriptional responses

    Cell 126:467–475.

    https://doi.org/10.1016/j.cell.2006.05.050

    • Google Scholar
54. 1. Ni L
    2. Bruce C
    3. Hart C
    4. Leigh-Bell J
    5. Gelperin D
    6. Umansky L

    et al. (2009) Dynamic and complex transcription factor binding during an inducible response in yeast

    Genes Dev 23:1351–1363.

    https://doi.org/10.1101/gad.1781909

    • Google Scholar
55. 1. Ohto M-A
    2. Hayashi S
    3. Sawa S
    4. Hashimoto-Ohta A
    5. Nakamura K

    (2006) Involvement of HLS1 in sugar and auxin signaling in Arabidopsis leaves

    Plant Cell Physiol 47:1603–1611.

    https://doi.org/10.1093/pcp/pcl027

    • Google Scholar
56. 1. Peng Z-Y
    2. Zhou X
    3. Li L
    4. Yu X
    5. Li H
    6. Jiang Z

    et al. (2008) Arabidopsis hormone database: a comprehensive genetic and phenotypic information database for plant hormone research in Arabidopsis

    Nucleic Acids Res 37:D975–D982.

    https://doi.org/10.1093/nar/gkn873

    • Google Scholar
57. 1. Rosenfeld N
    2. Elowitz MB
    3. Alon U

    (2002) Negative autoregulation speeds the response times of transcription networks

    J Mol Biol 323:785–793.

    https://doi.org/10.1016/S0022-2836(02)00994-4

    • Google Scholar
58. 1. Rozowsky J
    2. Euskirchen G
    3. Auerbach RK
    4. Zhang ZD
    5. Gibson T
    6. Bjornson R

    et al. (2009) PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls

    Nat Biotechnol 27:66–75.

    https://doi.org/10.1038/nbt.1518

    • Google Scholar
59. 1. Sessions RA
    2. Zambryski PC

    (1995)

    Arabidopsis gynoecium structure in the wild and in ettin mutants

    Development 121:1519–1532.

    • Google Scholar
60. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D

    et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • Google Scholar
61. 1. Solano R
    2. Stepanova A
    3. Chao Q
    4. Ecker JR

    (1998) Nuclear events in ethylene signaling: a transcriptional cascade mediated by ETHYLENE-INSENSITIVE3 and ETHYLENE-RESPONSE-FACTOR1

    Genes Dev 12:3703–3714.

    https://doi.org/10.1101/gad.12.23.3703

    • Google Scholar
62. 1. Sun Y
    2. Fan X-Y
    3. Cao D-M
    4. Tang W
    5. He K
    6. Zhu J-Y

    et al. (2010) Integration of brassinosteroid signal transduction with the transcription network for plant growth regulation in Arabidopsis

    Dev Cell 19:765–777.

    https://doi.org/10.1016/j.devcel.2010.10.010

    • Google Scholar
63. 1. Thibaud-Nissen F
    2. Wu H
    3. Richmond T
    4. Redman JC
    5. Johnson C
    6. Green R

    et al. (2006) Development of Arabidopsis whole-genome microarrays and their application to the discovery of binding sites for the TGA2 transcription factor in salicylic acid-treated plants

    Plant J 47:152–162.

    https://doi.org/10.1111/j.1365-313X.2006.02770.x

    • Google Scholar
64. 1. Tsang J
    2. Zhu J
    3. van Oudenaarden A

    (2007) MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals

    Mol Cell 26:753–767.

    https://doi.org/10.1016/j.molcel.2007.05.018

    • Google Scholar
65. 1. Vandenbussche F
    2. Vaseva I
    3. Vissenberg K
    4. van der Straeten D

    (2012) Ethylene in vegetative development: a tale with a riddle

    New Phytol 194:895–909.

    https://doi.org/10.1111/j.1469-8137.2012.04100.x

    • Google Scholar
66. 1. Winter CM
    2. Austin RS
    3. Blanvillain-Baufumé S
    4. Reback MA
    5. Monniaux M
    6. Wu MF

    et al. (2011) LEAFY target genes reveal floral regulatory logic, cis motifs, and a link to biotic stimulus response

    Dev Cell 20:430–443.

    https://doi.org/10.1016/j.devcel.2011.03.019

    • Google Scholar
67. 1. Yant L
    2. Mathieu J
    3. Dinh TT
    4. Ott F
    5. Lanz C
    6. Wollmann H

    et al. (2010) Orchestration of the floral transition and floral development in Arabidopsis by the bifunctional transcription factor APETELA2

    Plant Cell 22:2156–2170.

    https://doi.org/10.1105/tpc.110.075606

    • Google Scholar
68. 1. Yilmaz A
    2. Mejia-Guerra MK
    3. Kurz K
    4. Liang X
    5. Welch L
    6. Grotewold E

    (2010) AGRIS: the Arabidopsis gene regulatory information server, an update

    Nucleic Acids Res 39:D1118–D1122.

    https://doi.org/10.1093/nar/gkq1120

    • Google Scholar
69. 1. Yu X
    2. Li L
    3. Zola J
    4. Aluru M
    5. Ye H
    6. Foudree A

    et al. (2011) A brassinosteroid transcriptional network revealed by genome-wide identification of BESI target genes in Arabidopsis thaliana

    Plant J 65:634–646.

    https://doi.org/10.1111/j.1365-313X.2010.04449.x

    • Google Scholar
70. 1. Yosef N
    2. Regev A

    (2011) Impulse control: temporal dynamics in gene transcription

    Cell 144:886–896.

    https://doi.org/10.1016/j.cell.2011.02.015

    • Google Scholar
71. 1. Zhang Y
    2. Liu T
    3. Meyer CA
    4. Eeckhoute J
    5. Johnson DS
    6. Bernstein BE

    et al. (2008) Model-based analysis of chip-seq (MACS)

    Genome Biol 9:R137.

    https://doi.org/10.1186/gb-2008-9-9-r137

    • Google Scholar
72. 1. Zhao Y
    2. Stormo GD

    (2011) Quantitative analysis demonstrates most transcription factors require only simple models of specificity

    Nat Biotechnol 29:480–483.

    https://doi.org/10.1038/nbt.1893

    • Google Scholar
73. 1. Zhong S
    2. Zhao M
    3. Shi T
    4. Shi H
    5. An F
    6. Zhao Q

    et al. (2009) EIN3/EIL1 cooperate with PIF1 to prevent photo-oxidation and to promote greening of Arabidopsis seedlings

    Proc Natl Acad Sci USA 106:21431–21436.

    https://doi.org/10.1073/pnas.0907670106

    • Google Scholar
74. 1. Zhu Z
    2. An F
    3. Feng Y
    4. Li P
    5. Xue L
    6. Mu A

    et al. (2011) Derepression of ethylene-stabilized transcription factors (EIN3/EIL1) mediates jasmonate and ethylene signaling synergy in Arabidopsis

    Proc Natl Acad Sci USA 108:12539–12544.

    https://doi.org/10.1073/pnas.1103959108

    • Google Scholar
75. 1. Zinzen RP
    2. Girardot C
    3. Gagneur J
    4. Braun M
    5. Furlong EEM

    (2009) Combinatorial binding predicts spatio-temporal cis-regulatory activity

    Nature 461:65–70.

    https://doi.org/10.1038/nature08531

    • Google Scholar

Author details

1. Katherine Noelani Chang

   Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   KNC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

2. Shan Zhong

   Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States

   Contribution

   SZ, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Matthew T Weirauch

   Department of Molecular Genetics and Banting and Best Department of Medical Research, University of Toronto, Ontario, Canada

   Contribution

   MTW, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

4. Gary Hon

   Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, United States

   Contribution

   GH, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Mattia Pelizzola

   Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Present address

   Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia (IIT), Milan, Italy

   Contribution

   MP, Conception and design, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Hai Li

   Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   HL, Conception and design, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

7. Shao-shan Carol Huang

   1. Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States
   2. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   S-sCH, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Robert J Schmitz

   Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   RJS, Conception and design, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

9. Mark A Urich

   Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

   Contribution

   MAU, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Dwight Kuo

    Department of Bioengineering, Department of Medicine, The Institute for Genomic Medicine, University of California, San Diego, La Jolla, United States

    Contribution

    DK, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Joseph R Nery

    Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

    Contribution

    JRN, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Hong Qiao

    Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

    Contribution

    HQ, Conception and design, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

13. Ally Yang

    Department of Molecular Genetics and Banting and Best Department of Medical Research, University of Toronto, Ontario, Canada

    Contribution

    AY, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. Abdullah Jamali

    Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

    Contribution

    AJ, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. Huaming Chen

    Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States

    Contribution

    HC, Conception and design, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

16. Trey Ideker

    Departments of Medicine and Bioengineering, University of California, San Diego, San Diego, United States

    Contribution

    TI, Conception and design, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

17. Bing Ren

    1. Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, United States
    2. Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, San Diego, United States

    Contribution

    BR, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

18. Ziv Bar-Joseph

    1. Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
    2. Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States

    Contribution

    ZB-J, Conception and design, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

19. Timothy R Hughes

    Department of Molecular Genetics and Banting and Best Department of Medical Research, University of Toronto, Ontario, Canada

    Contribution

    TRH, Conception and design, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

20. Joseph R Ecker

    1. Plant Biology Laboratory, and Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, United States
    2. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, United States

    Contribution

    JRE, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    ecker@salk.edu

    Competing interests

    The authors declare that no competing interests exist.

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