Ethylene receptor gain- and loss-of-function mutants reveal an ETR1-dependent transcriptional network in Arabidopsis roots

Maleana G White; Alexandria F Harkey; Joëlle K Mühlemann; Amy L Olex; Nathan J Pfeffer; Maarten Houben; Brad M Binder; Gloria K Muday

doi:10.1093/plphys/kiag074

. 2026 Feb 20;200(3):kiag074. doi: 10.1093/plphys/kiag074

Ethylene receptor gain- and loss-of-function mutants reveal an ETR1-dependent transcriptional network in Arabidopsis roots

Maleana G White ^1,^b, Alexandria F Harkey ^2,^b, Joëlle K Mühlemann ^3,^d, Amy L Olex ⁴, Nathan J Pfeffer ⁵, Maarten Houben ^6,^d, Brad M Binder ⁷, Gloria K Muday ^8,^c,^✉,^e

PMCID: PMC13023754 PMID: 41721526

Abstract

In Arabidopsis (Arabidopsis thaliana), a family of 5 receptors mediates ethylene responses in roots, with Ethylene Response 1 (ETR1) controlling increases in root hair proliferation and decreases in lateral root formation. To define the ETR1-dependent gene regulatory network (GRN) controlling root development, we profiled the root transcriptome from Col-0 and the etr1-3 gain-of-function and etr1-7 loss-of-function mutants in the presence and absence of ethylene or the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC). We identified 4,522 differentially expressed (DE) transcripts in Col-0 roots that displayed altered abundance in response to ethylene and/or ACC treatment, with larger-magnitude changes induced by ethylene. These included 553 DE transcripts that were ETR1 dependent, defined by a lack of response to treatment with ethylene and/or ACC in ethylene-insensitive etr1-3 and constitutive alteration response in etr1-7 in the presence or absence of treatment relative to time-0 Col-0. These ETR1-dependent transcripts include transcripts from genes associated with ethylene biosynthesis and those encoding transcription factors (TFs). Reporter fusions driven by promoters from ACC OXIDASE 2 (ACO2) and ACO3, which convert ACC to ethylene, were regulated by ACC in root tissues in appropriate locations to control root development, with pACO5-driven GFP detected in root hairs. We examined the abundance of ETR1-dependent transcripts predicted to encode TFs and ACOs in Col-0 and an ein3 eil1 mutant, with and without ACC treatment. Our results suggested that the ETR1 and Ethylene Insensitive 3 (EIN3)/EIN3-like 1 (EIL1) canonical ethylene signaling pathway regulates some, but not all, of these transcriptional responses. Together, these findings reveal features of an ETR1-dependent GRN that controls both ethylene biosynthesis and root growth and development.

Transcriptional responses in ethylene response 1 mutants reveal an ethylene-receptor mediated gene regulatory network that modulates ethylene signaling and biosynthesis to control root development.

Introduction

Ethylene is a gaseous hormone that modulates growth and development throughout a plant's life cycle, from seed germination to senescence, and controls responses to both biotic and abiotic stress (Abeles et al 1992; Van de Poel et al 2015). In young, etiolated seedlings, ethylene induces a triple response: shorter hypocotyls and roots, thicker hypocotyls, and exaggerated apical hooks. Triple response phenotypes have been the basis of mutant screens for ethylene-insensitive or constitutive signaling in Arabidopsis thaliana that have identified key components of the ethylene signaling pathway (Schaller and Kieber 2002; Azhar et al 2020). Ethylene also has profound effects on root development, including inhibition of primary root elongation and lateral root formation and stimulation of root hair initiation and elongation (Negi et al 2008; Lewis et al 2011; Feng et al 2017; Harkey et al 2018; Qin et al 2019). Many of the proteins that were identified based on their functions in ethylene signaling in etiolated hypocotyls also modulate root development (Khoury et al 2024b) but with distinct developmental effects downstream of each receptor (Harkey et al 2018).

Ethylene functions through direct binding to members of a family of transmembrane receptors localized to the endoplasmic reticulum and the Golgi apparatus (Schaller and Kieber 2002; Azhar et al 2020; Binder 2020). These receptors are negative regulators of the pathway, such that ethylene binding removes repression of the pathway, which initiates a signaling cascade that upregulates or downregulates genes with distinct transcriptional responses depending on the tissue type and growth conditions (Stepanova et al 2007; Chang et al 2013; Harkey et al 2018, 2019). In Arabidopsis, 5 receptor isoforms have been identified: Ethylene Response 1 (ETR1), ETR2, Ethylene Insensitive 4 (EIN4), Ethylene Response Sensor 1 (ERS1), and ERS2 (Chang et al 1993; Schaller and Bleecker 1995; Hua and Meyerowitz 1998; Hua et al 1998; Sakai et al 1998; Binder 2020). Unlike most canonical signaling pathways, ethylene receptors are active in the unbound state. In the absence of ethylene, these receptors activate the Raf-like serine/threonine protein kinase Constitutive Triple Response 1 (CTR1), destabilizing EIN3 and EIN3-like (EIL) transcription factors (TFs), and blocking changes in gene expression downstream of these TFs (Kieber et al 1993; An et al 2010; Dolgikh et al 2019; Binder 2020). When ethylene is bound, the receptors lead to stabilization of EIN3 and EIL TFs in an EIN2-dependent manner (Kieber et al 1993; Alonso et al 1999; Huang et al 2003; Qiao et al 2009; An et al 2010), resulting in genome-wide changes in transcript abundance (Chang et al 2013; Harkey et al 2018). Downstream of EIN3/EIL are ethylene response factors (ERFs) and ethylene response DNA-binding factors (EDFs) that further mediate transcription, consistent with the existence of tissue-specific gene regulatory networks (GRNs) (Wang et al 2002).

The role of ethylene signaling proteins in controlling root architecture has recently been reviewed (Khoury et al 2024b). EIN2 is required for root developmental responses to ethylene and its immediate precursor 1-aminocyclopropane-1-carboxylic acid (ACC) at 1 µM (Alonso and Stepanova 2004; Negi et al 2008), although a recent report indicates that treatment with ACC at levels between 10 and 40 µM can alter root development in ein2-5 (Mou et al 2025). The ctr1-1 mutant was demonstrated to have constitutive-ethylene-signaling root phenotypes, with this mutant having shorter primary roots and increased root hair proliferation compared to Col-0 seedlings in the absence of ethylene (Kieber et al 1993; Masucci and Schiefelbein 1996; Hua and Meyerowitz 1998; Rahman et al 2000), suggesting its presence is required to suppress the pathway in the absence of ethylene. EIN3 and EIL1 TFs function in ethylene-dependent root hair formation, with the ein3eil1 double mutant having reduced ACC-induction of root hair proliferation (Feng et al 2017). However, root developmental responses to ethylene can still be observed in the ein3 and ein3eil1 mutants (Chang et al 2013; Harkey et al 2018), suggesting that additional TFs may participate in these transcriptional responses to ethylene.

Recent studies have provided insight into the mechanisms by which ACC is converted to ethylene and identified conditions in which high concentrations of ACC plant development independent of its conversion to ethylene. ACC is synthesized by the enzyme ACC synthase from S-adenosyl methionine and then converted to ethylene via ACC oxidase (Houben and Van de Poel 2019). There are 5 ACC oxidase enzymes in Arabidopsis. The transcripts encoding these enzymes were found to be upregulated by ACC in Arabidopsis roots, thereby increasing ethylene levels in a feed-forward loop (Harkey et al 2018). Several quintuple mutants with defects in the 5 ACO genes have been used to demonstrate that ACC alters root development in the absence of ACO enzyme activity, providing evidence for ethylene-independent actions of ACC (Houben et al 2026; Mou et al 2025). The receptors that regulate ethylene synthesis by ACC and ethylene, and whether ACC might regulate ACO in an ethylene-independent manner, have not been previously reported.

The 5 ethylene receptors in Arabidopsis elicit distinct ethylene responses (Shakeel et al 2013) due to differences in receptor subunit structure and receptor dimerization (Berleth et al 2019). ETR1 was found to be the main receptor controlling inhibition of ethylene perception by silver nitrate, nutational bending of hypocotyls, and remodeling of root architecture (McDaniel and Binder 2012; Harkey et al 2018). In Arabidopsis, ETR1 positively regulates nutational bending (Binder et al 2006), while the other 4 receptors negatively regulate this process (Kim et al 2011). In roots, a series of single and multiple loss-of-function (LOF) and gain-of-function (GOF) mutants indicated that only ETR1 was required for the inhibition of lateral root formation by ACC (Harkey et al 2018). The inhibition of lateral root formation by ACC was lost in the GOF, ethylene-insensitive mutant etr1-3, which had significantly more lateral roots than the wild type in the presence and absence of ACC. The LOF, constitutive-signaling mutant etr1-7, had significantly fewer lateral roots than wild type in the presence and absence of ACC. ETR1 also controls the inhibition of primary root elongation and stimulation of root hair formation by ACC (Harkey et al 2018). The effect of elevated levels of ethylene on remodeling the root transcriptome has been reported (Stepanova et al 2007; Harkey et al 2018, 2019), but the GRN downstream of the ETR1 receptor has not yet been identified in roots.

To identify the ETR1-dependent GRN controlling ethylene- and ACC-regulated root growth and development, we performed an RNA-Seq analysis in roots from light-grown seedlings using Col-0 and ETR1 receptor GOF (etr1-3) and LOF (etr1-7) mutants. We identified a core set of genes regulated by either or both ethylene and ACC in Col-0, including a subgroup of these transcripts that we defined as ETR1-dependent due to mis-regulation in the mutants. These ETR1-dependent transcripts had constitutively altered abundance at time-0 etr1-7 (as compared to time-0 Col-0), and their transcript abundance changes in response to ACC and/or ethylene were lost in both etr1-3 and etr1-7 mutants. We identified ACC- or ethylene-dependent regulation of transcripts encoding the ACOs, which convert ACC to ethylene, revealing a subset that was ETR1-dependent. Analysis of verified ACO transcriptional reporters driven by the native ACO promoters revealed overlapping and distinct root tissue expression patterns among this protein family. Finally, we explored the ETR1-dependent GRN through the lens of ETR1-dependent transcripts predicted to encode TFs, building a network model that revealed TF-encoding transcripts with common temporal and ETR1-regulated responses. We identified those TFs that are targets of EIN3 and tested whether these transcriptional responses required functional copies of EIN3/EIL1. This work sheds light on the ETR1 and EIN3-dependent GRNs that mediate the transcriptional responses to elevated levels of ACC and its ethylene product in Arabidopsis roots.

Results

Ethylene and ACC doses with similar ETR1-dependent effects on root hair growth

We identified an ETR1-dependent GRN in Arabidopsis roots by examining the transcriptional responses to both ethylene and its immediate precursor ACC, as previous work suggested that ACC and ethylene may act through distinct signaling pathways in some tissues (Yin et al 2019; Mou et al 2020; Houben et al 2026). We determined the concentration of ethylene that yielded moderate root hair proliferation that was similar to treatments with 0.75 µM ACC (Martin et al 2022), by examining root hairs in Col-0 seedlings treated with a range of ethylene levels for 24 h (Fig. S1) and compared these to etr1 mutants that had reduced or constitutive ethylene signaling (Fig. 1a). The 0.3 ppm ethylene concentration induced root hair proliferation to a similar degree as 0.75 µM ACC, so we selected these concentrations for this RNA-Seq analysis. The effects of a 24-h treatment with these doses of ethylene and ACC were examined in the etr1-3 and etr1-7 mutants, revealing proliferation of root hairs in the etr1-7 mutant in the absence of treatment (Fig. 1b). The ethylene-insensitive etr1-3 mutant showed reduced responses to ACC and ethylene, forming fewer and shorter root hairs in the presence of either treatment than Col-0. To identify optimal treatment times for RNA-Seq, we used our published ACC time-course microarray dataset (Harkey et al 2018) to determine the time points that had large numbers of differentially expressed (DE) genes that were not found in other time points and overlapped with the early, middle, and late root hair developmental responses, selecting 1, 4, and 24 h treatment times (Fig. S2) (for details, see Methods).

For image description, please refer to the figure legend and surrounding text. — Altered ethylene signaling in the *etr1-3* and *etr1-7* mutants results in aberrant root hair formation. a) A schematic illustrating the wild-type ethylene signaling pathway and how it is altered in *etr1-3* and *etr1-7* mutants. b) Representative images of 6-day-old Col-0, *etr1-3*, and *etr1-7* seedlings with and without treatments with 0.3 ppm ethylene or 0.75 μM ACC for 24 h, revealing similar developmental responses by these 2 doses of ethylene and ACC within the different genotypes.

The reduced effects of ACC and ethylene on transcriptional response in ETR1 mutants are revealed by principal component analysis

We performed this RNA-Seq analysis in triplicate in root samples from 3 genotypes (Col-0, etr1-3, and etr1-7), 2 treatments (0.75 µM ACC and 0.3 ppm ethylene gas), and 4 time points (0, 1, 4, and 24 h). After sequencing and quality control, principal component analysis (PCA) plots were created using 1,000 genes with the most variance between samples to identify which factors contributed most to this variance. The PCA plot for all samples (Fig. 2a) highlights differences between genotypes, showing that the etr1-7 samples (triangles) are furthest away from the Col-0 and etr1-3 samples along PC1, consistent with this genotype having constitutive ethylene signaling at all time points. There was overlap between the etr1-7 samples and one 24-h ethylene-treated Col-0 sample, consistent with enhanced root hair growth in Col-0 at this later time point, growth that resembles the constitutive etr1-7 phenotype (Fig. 1). Along PC2, samples were separated by treatment type, with ethylene-treated samples grouping toward positive values and ACC-treated samples clustering toward negative values. Moreover, we observed that while Col-0 and etr1-3 samples had significant overlap with each other along PC2, Col-0 samples displayed slight separation from etr1-7 samples along PC1, particularly the 4- and 24-h-treated samples, consistent with Col-0 having an intermediate root phenotype between the 2 genotypes after treatment. Additionally, one control (time-0) Col-0 sample was separated from other Col-0 time-0 samples along the PC2 axis, which prompted us to analyze variation within genotypes. These comparisons (Fig. S3 and Table S1; described further in Methods) led us to remove the outlier Col-0 sample from downstream analyses.

We used 3 genotype-specific PCA plots (Fig. 2b–d) to assess the contribution of treatment type and duration to variation among samples within genotype. In Col-0, the time 0 samples (green circles) cluster together, consistent with the absence of exposure to ACC or ethylene. The effect of treatment time is most evident in the separation between samples (circled color groups) along the PC2 axes, generally shifting from time 0 to 24 h from the bottom to the top of the axis. This PCA plot also emphasizes the difference between ethylene- and ACC-treated samples across the PC1 axis, explaining 51% of the variance, suggesting distinct responses to these 2 molecules.

An important point that is revealed by these PCA plots is that both etr1-3 and etr1-7 mutants have dampened responses to treatment with ethylene and ACC, consistent with reduced or enhanced signaling at all treatment doses, respectively. In Col-0, PC1, which separates ACC and ethylene treatments in all genotypes, explains 51% of the variance. In the mutants, PC1 explains only 35% and 38% of the variance, consistent with smaller effects on overall transcript abundance changes in the mutants. This dampened response is also evident in etr1-3 by the overlapping responses of time 0 and 1 h (green and blue samples) and the reduced separation along PC2 for the 4- and 24-h samples (purple and black), with especially tight clustering in the 24-h treatment. The etr-7 samples show less separation by time of treatment along the PC2 axes (comparison of color of samples), especially for ACC-treated samples (open circles), suggesting that this mutant is exhibiting ethylene and ACC responses in the absence of treatment. Plots of Pearson's correlation (Fig. S4a and b) and of Euclidean distance (Fig. S4c and d) with and without hierarchical clustering support these conclusions, as do manually arranged plots grouped by genotype and time of ACC and ethylene treatment (Fig. S4a and c; described further in Methods).

Ethylene treatment stimulated more dramatic transcriptional changes than ACC treatment in Col-0

To summarize how the abundance of transcripts was affected by ethylene and ACC treatment, we first examined the hormone-induced responses in Col-0. For both treatments, every time point was compared back to the same set of combined Col-0 controls, defined above. We created a density plot depicting the number of transcripts as a function of their log₂FC using those transcripts with a significant adjusted P-value < 0.01 as determined by DESeq2 (Fig. 3a). We observed a bimodal distribution at each time point, showing one group of transcripts that increased in abundance and another that decreased in abundance because of these treatments, with these peaks centered on or near −0.5 and 0.5 log₂FC in both ethylene and ACC. There were larger numbers of DE transcripts and greater magnitude changes in response to ethylene than ACC treatment, especially at 1 h. Both treatments resulted in more downregulated than upregulated genes at the 4- and 24-h time points. The bimodal pattern and the higher number of downregulated genes in response to ethylene and ACC at the 4- and 24-h time points were similar to our previous microarray study using ACC (Harkey et al 2018).

Using DESeq2, we identified 4,522 transcripts that were DE in response to ethylene and/or ACC in at least one time point in Col-0 with a |log₂FC| > 0.5 and adjusted P-value < 0.01 (Fig. 3b–d). These transcripts and their log₂FCs (treated Col-0 relative to time-0 Col-0) are listed in File S1. More transcripts responded to ethylene treatment and had a greater log₂FC than to ACC treatment. This log₂FC cutoff eliminated more genes whose transcripts decreased after treatment, which resulted in a similar number of up- and downregulated transcripts passing the log₂FC cutoff, despite there being more downregulated genes in the larger set of those with a P-value < 0.01 (Fig. 3a). Many genes showed similar trends in response to ACC and ethylene treatment: 1,493 and 1,657 genes were down- and up-regulated, respectively, by ethylene only, and 182 and 213 genes were down- and up-regulated, respectively, by ACC only, as summarized in Table S2. Strikingly, only 16 genes had opposite responses to the 2 treatments: 3 were downregulated in ethylene and upregulated in ACC, and 13 were upregulated in ethylene and downregulated in ACC. This very small number of oppositely regulated genes by these 2 treatments is consistent with ACC and ethylene leading to common transcriptional responses, which may differ in magnitude of response, but infrequently in the direction of responses.

This analysis also revealed that 457 and 504 transcripts were up- and downregulated, respectively, by both ACC and ethylene, with heatmaps illustrating the transcript abundance over time in Fig. 3c and d. One pattern that became apparent was that for transcripts whose abundance decreased after both treatments, the 1- and 24-h ACC-treated samples did not respond as strongly as the time-matched ethylene-treated samples, while in the 4-h ACC treatment, the number of transcripts changed more dramatically than time-matched ethylene treatment. This finding suggested the possibility that the rate of conversion of ACC to ethylene resulted in varied ethylene concentrations across this time course. Indeed, of the 457 transcripts upregulated by both ACC and ethylene, 258 had a larger magnitude change in response to ACC treatment at the 4-h time point, but only 70% of them had a larger response to ethylene treatment in the 1-h time point. Similarly, of the 504 transcripts downregulated by both ACC and ethylene, 245 had a larger magnitude change in response to ACC treatment at the 4-h time point, with 87% responding more strongly to ethylene treatment at the 1-h time point.

Transcripts regulated by ethylene and ACC were enriched in functions related to root development

To determine which biological processes were enriched among the transcripts whose abundance changed after ethylene and/or ACC treatment in Col-0, we performed Gene Ontology (GO) analysis using AgriGO (Du et al 2010; Tian et al 2017) (Table S3). Genes whose transcripts had increased abundance in both treatments were enriched in the functional annotation “negative regulation of the ethylene-mediated signaling pathway”, consistent with both ethylene and ACC transcriptionally regulating components of the ethylene signaling pathway in a negative feedback loop, and “response to auxin stimulus,” consistent with crosstalk between ethylene and auxin.

Transcripts whose abundance decreased after ethylene and/or ACC treatment were enriched in annotations pertaining to lignin biosynthesis and cell wall loosening, while transcripts that decreased after ethylene, but not ACC treatment, were enriched in pathways related to secondary cell wall biogenesis, plant-type cell wall organization, and lignin metabolism. These results are consistent with the requirement for cell wall remodeling during apical root growth, lateral root emergence, and root hair development.

Interestingly, genes whose transcripts decreased in abundance with ethylene treatment only were enriched in annotations related to root hair cell differentiation or root hair development, including 2 myosin genes (XIB and XIK), MORPHOGENESIS OF ROOT HAIR 1, 2, and 6, ROOT HAIR DEFECTIVE 4 (RHD4), BRISTLED 1, CAN OF WORMS 1 (COW1), and SHAVE 2 and 3. No annotations were significantly enriched in genes downregulated by ACC only, likely due to the smaller size of this group.

We further examined the identities of transcripts that were significantly regulated by ACC but not by ethylene, as these transcripts might reveal ethylene-independent, ACC-mediated signaling mechanisms in roots. Among the 213 transcripts that were uniquely upregulated by ACC were transcripts encoding 2 mitogen-activated protein (MAP) kinases MAPKKK13 and MEK1, enzymes that remodel the cell wall, such as EXPANSIN A17 (EXPA17) and PECTIN METHYLESTERASE 3 (PME3), and transcripts encoding proteins that function in root hair development, such as RHO OF PLANTS GUANINE NUCLEOTIDE EXCHANGE FACTOR 12 (ROPGEF12) and ROOT HAIR DEFECTIVE 6-LIKE 5 (RSL5). Among the 182 transcripts that were downregulated by ACC were EXPANSIN A4 (EXPA4) and EXPANSIN-LIKE A2 (EXLA3). These genes suggest testable hypotheses on mechanisms for ACC-regulated root development, including cell wall remodeling, MAP kinase signaling, and membrane trafficking.

Transcriptional responses are accentuated in constitutive-signaling etr1-7 and reduced in ethylene-insensitive etr1-3

We asked how the transcriptional responses to ethylene and ACC varied in the etr1-3 and etr1-7 mutants compared to Col-0. Using a cutoff of |log₂FC| > 0.5 and adjusted P-value < 0.01, we compared each time point for each treatment and genotype to the Col-0 control and found 8,323 transcripts that were DE in at least one of these genotype comparisons, and these transcripts and their log₂FC values relative to time-0 Col-0 are listed in File S1. A heatmap displaying the changes in abundance of these transcripts in all 3 genotypes and both treatments is shown in Fig. 4. All log₂FC values represented here were relative to Col-0 at time 0 to accurately reflect the changes in baseline levels in etr1-7. This heat map reveals that almost all the DE genes show the same directional change in response to both ACC and ethylene. The magnitude of the response is larger in response to ethylene than to ACC for most genes, and the treatment response is muted in etr1-3, whereas the enhanced transcript abundance is independent of treatment in etr1-7.

The heatmap in Fig. 4 suggested that the log₂FC filter eliminated transcripts that responded to ACC but had smaller magnitude changes in response to ACC than ethylene. As illustrated in the density plots, many transcripts had a |log₂FC| between 0.25 and 0.5, so we examined transcriptional responses using a less stringent cutoff as detailed in the methods. Regardless of cutoff, the number of transcripts that only responded to ethylene was greater than those only responding to ACC, as shown in Table S2, where the |log₂FC| > 0.25. We therefore used the more stringent cutoff in our experiments.

We compared baseline levels of expression in time-0 samples between genotypes (Fig. 5a), identifying 2,562 genes that were DE (P-value < 0.01) in at least one of these comparisons (these transcripts and their log₂FC values relative to other genotypes are listed in File S1). Consistent with our expectations, etr1-3 and Col-0 were nearly identical in untreated samples; only 49 out of 2,562 genes were found to be DE between them. However, when we compared etr1-7 with either Col-0 or etr1-3, we found a dramatic difference: 1,941 genes were DE between etr1-7 and Col-0, and 2,078 genes were DE between etr1-7 and etr1-3. A large number (1,458) of the genes were DE in both comparisons with etr1-7, including genes enriched in the “response to ethylene” annotation (Fig. 5b).

One very interesting group of transcripts was the 1,941 transcripts that were DE in etr1-7 as compared to Col-0, in the absence of hormone treatment. The large number and magnitude change of these genes are consistent with the etr1-7 mutation being sufficient to lead to large changes in abundance of transcripts in the presence of endogenous levels of ethylene due to enhanced ethylene signaling. Under these conditions, the etr1-7 mutant has increased root hair formation relative to Col-0. Consistent with this group of genes having an important function in ethylene response, this group was enriched in annotations pertaining to the ethylene signaling pathway. Similar annotations were also found in the group of transcripts that responded to ethylene and/or ACC treatment in Col-0, including “response to ethylene” (Table S3). These results are consistent with etr1-7 having nearly constitutive ethylene signaling and therefore a different transcriptional landscape than the other genotypes in the absence of exogenous ethylene or ACC and with transcript abundance changes that overlap with Col-0 after treatment with ACC or ethylene.

A subset of ACC- and ethylene-regulated transcript responses is dependent on ETR1

We developed a rigorous set of criteria to determine which ethylene- and/or ACC-regulated transcripts were dependent on ETR1 for transcriptional response. For this analysis, we included transcripts that were DE in response to ACC or ethylene designated as a |log₂FC| > 0.5 and a P-value < 0.01 and defined transcripts as ETR1-dependent based on the following criteria: (i) the transcript was DE with ethylene and/or ACC treatment in at least one time point in Col-0; (ii) the transcript showed no significant change in any etr1-3 samples compared to Col-0 time 0; and (iii) the transcript was DE in etr1-7 relative to Col-0 in the baseline and showed similar abundance in ACC- and ethylene-treated etr1-7 samples. Since etr1-7 has constitutive ethylene signaling, it should have had transcriptional responses that differed from Col-0 in the absence of either ACC or ethylene treatment and were unchanged by ACC or ethylene treatment. Therefore, we compared etr1-7-treated samples to etr1-7 control samples, and only those that did not change with treatment in etr1-7 were qualified as ETR1-dependent.

With this strict criteria, 553 transcripts were identified as ETR1-dependent, which are shown in the heat map in Fig. 6 (and File S1) with their log₂FC values relative to time-0 Col-0). A subset of 292 transcripts met these criteria in ethylene treatment only, another 191 in ACC only, and 70 genes were classified as ETR1-dependent in both treatments. The patterns in this heat map are striking, leading to 4 major conclusions. First, it is clear that the etr1-7 mutant leads to profound changes in transcript abundance (relative to time-0 Col-0) even without treatment, which is evident in the time-0 etr1-7 sample (labeled C), which is normalized to the Col-0 time-0 sample) and that there are profound increases that are independent of treatment. Second, there is a muted response in etr1-3 that is evident for treatment with either ethylene or ACC. Third, the response of these ETR1-dependent transcripts to ethylene is generally greater than to ACC, which may be tied to the potency of doses of these 2 molecules, although ACC induced a larger effect at the 4-h time point in Col-0. Fourth, the larger number of DE genes in response to ethylene than ACC may be tied to a smaller magnitude of ACC response that was below the log₂FC cutoffs, suggesting that these transcripts are regulated by ETR1 in both treatments. We also examined the transcripts filtered to have a |log₂FC| > 0.25, which yielded many more transcripts in each group but did not change the overall ratios of transcripts in those groups.

Out of the 553 genes that qualified as ETR1-dependent in 1 or both treatments, 31 overlapped with a set of core ethylene-responsive genes identified by a comparison of 3 transcriptome studies of ethylene- or ACC-treated roots (Harkey et al 2019). These genes included ACC OXIDASE 2 (ACO2), ETHYLENE RESPONSE SENSOR (ERS)1 & 2, and ETHYLENE AND SALT INDUCIBLE 3 (ESE3). This finding suggested there were ETR1-dependent transcriptional responses to ACC and ethylene in this core group. Other ethylene-related genes that did not overlap with this group but were identified as ETR1-dependent here included ACO3, several ERFs, and PINOID, a kinase that is a positive regulator of auxin transport (Sukumar et al 2009) and a negative regulator of root hairs (Lee and Cho 2008). The 553 ETR1-dependent transcripts were enriched in GO annotations, including lipid transport, response to ABA, and regulation of transcription (Table S4).

We tested an additional set of less stringent criteria, where response to treatment was evaluated by comparing treated samples to the control samples within each genotype, but only required the presence or absence of treatment response regardless of the control sample abundance. In this case, ETR1 dependence was defined by a response in Col-0 but not in the mutants, and ETR1 independence was defined as a response in all 3 genotypes. This analysis allowed for a larger set of transcripts to be included in both groups (Fig. S5), but ultimately, the changes in abundance of the additional transcripts were not as strong as those seen with the more stringent criteria, so we focused our analysis on the transcripts that met the more stringent criteria.

Other transcripts appear to be regulated by ACC and/or ethylene in an ETR1-independent or more complex manner

We defined ETR1-independent responses for each treatment as having a response to treatment in all genotypes and no altered response in the mutants at baseline (compared to time-0 Col-0). We compared the etr1-7 treated samples to the etr1-7 control samples to determine whether a gene responded to treatment in this genotype relative to baseline transcript levels, but in this case, a response to treatment was required to qualify as ETR1-independent.

The search for ETR1-independent genes resulted in a slightly smaller set of genes: 481 genes qualified as ETR1-independent in ethylene only, 95 in ACC only, and 117 qualified in both treatments (listed in File S1 with their log₂FC values relative to time-0 Col-0). These ETR1-independent transcripts are shown in the heatmap in Fig. 7, where the log₂FC relative to time-0 Col-0 was reported. What is very striking in this figure is the consistent pattern of the genes that are DE in response to both ethylene and ACC show no difference in response in all 3 genotypes. This trend is also true in the ethylene-only DE group, but the ACC response is muted. The ACC-only group had a very sporadic pattern with multiple different responses.

After grouping of genes into ETR1-dependent and -independent responses, there were an additional 7,077 genes that were DE in response to ethylene- or ACC treatment in at least one sample but did not meet the strict criteria to fit in either group (listed in File S1 with their log₂FC values relative to time-0 Col-0) (Fig. S6). We also compared our ACC-regulated genes to a recent dataset where ein2-5 seedlings were treated with 10 µM ACC for 40 h to identify ACC-regulated transcripts (Mou et al 2025). Mou et al. identified 211 DE genes that were downregulated and 147 that were upregulated by ACC. We asked whether these DE genes were also regulated by ACC in our root-specific dataset, finding that, indeed, a majority of the upregulated genes were (108 out of 147), although only 17 out of 212 downregulated genes were ACC-regulated in our dataset, but over 100 of these were ethylene-regulated. We also asked if their DE genes were found in our ETR-independent dataset, as the phenotypes of etr1-3 and ein2-5 mutants are similar. None of their upregulated genes were DE in response to ACC treatment in the ETR1-independent dataset, while 32 of their upregulated genes (22%) were ACC-regulated in our ETR1-independent dataset. The limited overlap between these 2 ACC treatment datasets was unexpected, but these differences could be due to seedling age, genotype, growth conditions, time of treatment, and isolation of RNA from whole seedling versus roots.

Several ACOs are transcriptionally regulated by ACC and ethylene and dependent on the ETR1 receptor

An important action of both ACC and ethylene is to positively regulate the synthesis of more ethylene. The ACC oxidase (ACO) enzyme catalyzes the oxidation of ACC to ethylene as the final step of ethylene biosynthesis using both endogenously synthesized and exogenously applied ACC as a substrate. Included in the ETR1-dependent dataset were transcripts encoding several of the 5 ACOs (ACO1 through ACO5). The transcript levels of ACO1, ACO2, and ACO3 in each genotype and treatment are shown in Fig. 8, revealing that transcripts encoding ACO enzymes are positively regulated by ACC and ethylene levels but with a stronger response to ACC. This ACC-driven increase in ACO transcript synthesis has the potential to increase conversion of exogenous ACC to ethylene. The abundance of transcripts from these genes varied in time-0 Col-0 roots, with higher transcript abundance of ACO2 and ACO5 (313 and 315 TPM, respectively), while ACO1, ACO3, and ACO4 had low TPM values between 6 and 39. We found that both ethylene and ACC treatment of Col-0 significantly increased the transcript abundance of 4 ACOs (ACO1, ACO2, ACO3, and ACO5) in at least one time point after treatment. For these genes, there was a greater magnitude increase in transcript abundance in response to ACC than to ethylene treatment in at least one time point for each transcript. This difference in response is striking, as most of the upregulated DE transcripts in our dataset had larger magnitude changes in response to ethylene than to ACC treatment. This finding suggests that exogenous ACC may be a stronger signal to promote transcript accumulation of ACOs than ethylene gas itself.

According to our strict criteria for ETR1 dependence, detailed above, ACO2 and ACO3 were ETR1-dependent in response to ethylene treatment, with uniform reductions in transcript abundance in etr1-3 and increases in etr1-7 compared to Col-0 (Fig. 8d, f). While ACO2 and ACO3 passed the criteria for ETR1 dependence in Col-0 and etr1-3 in response to ethylene treatment, ACC induced significant increases in transcript abundance at one time point in the etr1-7 mutant relative to the etr1-7 time-0 control, and therefore, these ACOs did not pass the criteria for ETR1 dependence in the presence of ACC. ACO1, ACO4, and ACO5 did not meet our criteria for ETR1 dependence in the presence of either ACC or ethylene (Fig. 8a, b; Fig. S7; significant differences determined by DESeq2). ACO5 appeared to be ETR1-dependent based on P-value, but it failed the log₂FC cutoff for having a smaller magnitude change than required for our stringent requirement. It is clear from the graphs that reduced or constitutive ethylene signaling in the etr1-3 and etr1-7 mutants, respectively, altered the transcriptional response to ACC and/or ethylene for each ACO, consistent with the importance of the ETR1 receptor in regulating ACO transcript abundance, even for ACOs that do not meet our stringent ETR1-dependent criteria.

ACC-induced transcript accumulation of ACO1, ACO2, and ACO4 is mediated by EIN3/EIL1

To extend our understanding of the regulation of ACOs, we asked if their changes in transcript abundance in roots were regulated by both ETR1- and EIN3-mediated GRNs. We quantified the transcript abundance of the ACOs in roots of Col-0 and ein3eil1 using RT-qPCR in the presence and absence of 0.75 µM ACC. We treated these samples with ACC for 4 h, as this was the time point that yielded the largest increases in ACO transcript abundance (relative to time-0 Col-0) in our RNA-Seq data. For ACO1, ACO2, and ACO4, we found significant increases in transcript levels with ACC treatment in Col-0, including 2-fold increases in ACO1 and ACO2 and a 1.5-fold increase in ACO4 (Fig. 8g–i). These ACC-induced increases were lost in the ein3eil1 mutant, consistent with these transcripts being ACC-regulated through the canonical ethylene signaling pathway (Fig. 8g–i). Consistent with our RT-qPCR results, the promoters of ACO2 and ACO4 were found to be directly bound by EIN3 in a DAP-Seq (O’Malley et al 2016) and/or ChIP-Seq dataset (Chang et al 2013) as summarized in Harkey et al (2019), but the absence of ACO1 as an EIN3 target is surprising and is consistent with an EIN3-regulated TF binding to and controlling ACO1 transcription, rather than being a direct EIN3 target.

In contrast, we did not detect significant increases in ACO3 and ACO5 transcripts in response to ACC treatment in Col-0 in our RT-qPCR (Fig. S8). ACO3 transcript levels in ein3eil1 did not differ from those in Col-0 in the presence or absence of ACC, while ACO5 transcript levels in Col-0 ACC-treated samples were significantly higher than those in ein3eil1 in the presence and absence of ACC (Fig. S8). For both ACOs, these findings were consistent with localized changes in abundance in specific tissues that cannot be consistently detected by RT-qPCR using whole roots.

ACO transcription increases in distinct root cell types in response to ACC treatment

To determine which of the ACOs might control ACC- and ethylene-induced changes in root development, we queried the ACOs in a root cell-type-specific dataset (Brady et al 2007) to identify the tissues where these transcripts were most highly enriched. ACO5 was identified as a candidate for the control of root hair growth because, like ETR1, its transcripts were highly enriched in cells expressing root hair markers, while the other ACOs had low transcript abundance in this cell type (Fig. 9a).

To provide additional insight into the effect of ACC treatment on tissue-specific localization and abundance of ACO transcripts, we examined a fluorescent reporter driven by each of the native ACO promoters (Houben et al 2026). To validate the root hair cell-specific ACO5 accumulation in the Brady et al 2007 dataset described above, we examined fluorescence of a pACO5::GFP-GUS reporter in roots. Comparison of root tip with and without a 4-h ACC treatment reveals low levels of GFP signal at the root tip with higher levels in the elongation and differentiation zones. The GFP signal is evident in hair cells and root hairs, which are increased in number after ACC treatment (Fig. 9b). Laser scanning confocal microscope images of root tips of 5-day-old seedlings revealed brighter fluorescence in hair cells than non-hair cells, consistent with cell-type-specific expression patterns (Figs 9b and 10). Treatment with ACC for 4 and 24 h (Fig. 9c) or 5 days (Fig. S9) led to an increased number and elongation of root hairs, further illustrating the presence of pACO5 driving GFP fluorescence in these cells (Fig. 9b). These results indicate that ACO5 is expressed in the appropriate position for its protein product to synthesize ethylene to drive root hair elongation.

We found shared and distinct ACO expression patterns in roots of 10-day-old seedlings in the presence and absence of 0.75 µM ACC after 5 days of treatment (Fig. 10). The pACO1::GFP-GUS reporter had a fluorescent signal in the lateral root cap and the columella of primary and lateral root tips, where this signal was increased in response to ACC treatment. The pACO3::GFP-GUS reporter exhibited a fluorescent signal in the primary root columella and the inner tissues of primary and lateral roots, where this signal was increased in response to ACC treatment. Interestingly, the pACO4::GFP-GUS reporter had a fluorescent signal in the columella of elongated lateral roots that was increased after ACC treatment, but this signal was not detected in the columella of primary root tips in the presence or absence of ACC. We identified weak ACC-induced changes for some of the reporters in the root tissues of interest, although there appeared to be moderate basal levels of expression consistent with the cell type data (Fig. 10). The fluorescence of the pACO2::GFP-GUS reporter was low and did not show induction by ACC in root tissues of interest (Fig. S10). These diverse expression patterns of the ACO reporters suggest that the conversion of ACC to ethylene may be controlled at the tissue level to locally elevate ethylene signaling.

Identification of EIN3-regulated transcripts predicted to encode TFs within the ETR1-dependent GRN

One goal of this study was to gain additional insight into the transcriptional regulators downstream of ETR1 whose abundance changed in response to ACC and ethylene treatment. Consistent with a global remodeling of the transcriptome, a GO analysis revealed that the ETR1-dependent transcripts were significantly enriched in genes that function in the regulation of transcription (Table S4). We identified 60 ETR1-dependent transcripts predicted to encode TFs using the AGRIS database (Palaniswamy et al 2006). The expression patterns of these TF-encoding transcripts were calculated, and the log₂FC relative to time-0 Col-0 is shown in Fig. 11a. Note the larger number of downregulated than upregulated transcripts and the distinct temporal responses, with some transcripts changing rapidly after ACC and/or ethylene treatment and other transcripts responding more slowly to these treatments.

To better visualize groups of TFs that have similar expression patterns in response to treatments and in the etr1 mutants, we employed a PaLD analysis (Berenhaut et al 2022; Khoury et al 2024a) using the log₂FC values relative to Col-0 time 0 for each transcript to generate a network of the 60 ETR1-dependent TFs (Fig. 11b). This analysis results in a network with each node representing one of the 60 TF-encoding transcripts and the edges connecting transcripts with the most similar responses to ACC and ethylene over time in the different genotypes.

To sort these transcripts into distinct groups with the most similar responses, we used the Louvain method, which identifies communities in large networks (Blondel et al 2008). The network yielded a structure consisting of 6 groups containing 2 or more transcripts (Fig. 11b). Transcripts belonging to the same group as indicated by their matching colors are those with the most similar transcript abundance patterns across the time course of ACC and ethylene treatment in Col-0, etr1-3, and etr1-7. As EIN3 is a TF that is downstream of ETR1 in the canonical ethylene signaling pathway (Dolgikh et al 2019), we used publicly available data on the genes to which EIN3 binds, revealed by DAP-Seq (O’Malley et al 2016) or ChIP-Seq (Chang et al 2013). We highlighted in large, bold font nodes in the PaLD network that are the direct targets of EIN3. Consistent with EIN3 functioning to primarily increase transcript abundance, most of the targets were found in the upregulated groups of genes (Fig. 11b). We also calculated the percentage of genes in each Louvain group that are direct EIN3 targets. The pink and yellow groups making up the upregulated transcripts consisted of 71% and 31% EIN3 targets, respectively. These numbers are substantially higher than the 4% of the whole genome containing EIN3 targets. In contrast, the green, blue, orange, and red-orange groups consisting of downregulated transcripts, included 25%, 8%, 0%, and 0% EIN3 targets, respectively. The upregulated EIN3 targets had diverse expression patterns, with some being rapidly induced (eg, ERF2) or repressed (eg AT1G08320) by ACC/ethylene treatment and others being more slowly upregulated (eg, RSL5) or downregulated (eg MYB9 and MYB107).

To validate whether both upregulated and downregulated ETR1-dependent transcripts that are EIN3 targets by ChIP-Seq and DAP-Seq showed altered transcript abundance in the ein3eil1 mutant, we quantified the transcript abundance of several genes in roots of 5-day-old Col-0 and ein3eil1 seedlings in the presence and absence of ACC after 4 h. We were interested in ROOT HAIR DEFECTIVE 6-LIKE 5 (RSL5), as this TF was previously demonstrated to be involved in root hair development (Pires et al 2013). Intriguingly, in our RNA-Seq dataset, RSL5 transcript levels were significantly up-regulated by ACC at the 4- and 24-h time points but were not significantly regulated by ethylene (Fig. 11c, d). Consistent with these data, we found a significant increase in RSL5 transcripts in the presence of ACC and demonstrated the dependence of this change on EIN3, as this induction was lost in the ein3eil1 mutant (Fig. 11e). This result suggested that although RSL5 was more strongly regulated by ACC, this regulation depended upon the canonical ethylene signaling pathway. We also observed a significant increase in RAP2.6L transcript levels and a significant decrease in MYB9 transcript levels in the presence of ACC, and both changes were lost in the ein3eil1 mutant (Fig. 11f, g). We also examined transcript abundance of an additional gene (LRP1) whose promoter was targeted by EIN3, and 3 genes (ANAC058, MYB52, and MYB93) that were not identified as direct targets of EIN3 by ChIP-Seq and DAP-Seq (Chang et al 2013; O’Malley et al 2016). However, the levels of these transcripts were not significantly altered by ACC in our RT-qPCR data, although there were trends consistent with the patterns observed in the RNA-Seq data (Fig. S11). This example further validates the ability of this RNA-Seq dataset to identify additional root-specific genes that are dependent upon ETR1 and EIN3 for altered transcript accumulation in response to ACC and/or ethylene treatment.

The proLRP1::GUS reporter exhibited increased amounts of GUS product in lateral root primordia in the presence of ACC

We were interested in exploring the roles of several of these ETR1-dependent TFs in root development and how they were modulated by ACC. We examined LRP1 since it had previously been demonstrated to function in lateral root primordium (LRP) development, as LRP1 overexpression lines had increased numbers of stages I, IV, and V LRP but reduced numbers of emerged lateral roots (Singh et al 2020). Since LRP1 transcripts were induced in whole roots in response to ethylene and ACC treatment (Fig. 12a, b), we next asked in which root tissues these transcripts were synthesized. We used the proLRP1::GUS transcriptional reporter (Estornell et al 2018) treated with ACC for 5 days and visualized GUS product in roots using a stereomicroscope. In 10-day-old seedlings, we found the GUS product accumulated in the central root tissues in both the presence and absence of ACC, beginning at the root elongation zone (Fig. 12c). We observed increased GUS activity in the presence of ACC in lateral root primordia at all primordia stages and in emerged lateral roots, with darkest staining evident in the stele layers, consistent with LRP1 transcripts being synthesized in these tissues and having increased expression in response to ACC treatment.

We also quantified the number of lateral roots and the lateral root density in the region of new root growth in the lrp1-1 and lrp1-2 mutants in the presence and absence of ACC, with representative images shown in Fig. S12a. Based on a prior report that examined LRP1 overexpression lines and identified a 2.5-fold decrease in lateral root density in these lines compared to Col-0 (Singh et al 2020), we expected an increase in lateral roots in lrp mutants. We quantified the number of lateral roots in the region of root formation after transfer to control or ACC media, as we previously demonstrated a negative effect of ACC on lateral root formation in this region (Negi et al 2008). We found that in time-0 roots, there was a small and significant increase in the number of emerged lateral roots in lrp1-1 with a nonsignificant increase in lrp1-2 (Fig. 12e). We found that these small differences were lost in the presence of ACC, which had equivalent inhibition of lateral root formation as Col-0. We also quantified lateral root density in the region of new root growth (5 days of ACC treatment) and found no significant difference in lateral root density in lrp1-1 and lrp1-2 in the absence of ACC (Fig. S12b). The reduction in lateral root density by ACC significantly differed from Col-0 in lrp1-2 and not lrp1-1, with less inhibition of lateral root formation in lrp1-2 compared to Col-0. Consistent with the weak accumulation and the absence of changes with ACC of the proLRP1::GUS reporter in the primary root tip (Fig. 12c), there were subtle changes in primary root elongation in the mutants (Fig. 12d). We observed a small but statistically significant increase in primary root growth in lrp1-1 in the absence of ACC and no significant increase in lrp1-2 compared to Col-0.

Several ETR1-dependent TF mutants show subtle differences in root hair initiation, lateral root formation, and root elongation

We screened mutants in a small set of ETR1-dependent TFs for root phenotypes in the presence and absence of ACC. We selected several genes that were also regulated by ACC over a time course of ACC treatment (Harkey et al 2018). These included RAP2.6-L, ANA058, MYB9, and MYB52, some of which were also found to be direct targets of EIN3. To illustrate how these genes responded to ethylene and ACC treatment in Col-0, etr1-3, and etr1-7 in this RNA-Seq study, we reported the fold change in transcript abundance relative to time-0 Col-0 (Fig. S13). These transcripts were also expressed in various root tissues, including lateral roots, pericycle cells, xylem pole pericycle cells, hair cells, and non-hair cells to varying degrees (Fig. S14a).

We found that in the region of new root growth, myb52-1 had significantly fewer lateral roots than Col-0 in the absence of ACC, but it showed wild-type inhibition of lateral root formation in the presence of ACC (Fig. S14b). The anac058-1, myb52-1, and rap2.6l-1 mutants all had lower lateral root densities in this region in the presence of ACC compared to Col-0, suggesting they may function to promote lateral root formation (Fig. S14c). These results were consistent with MYB52 and ANAC058 being downregulated by ACC but not with RAP2.6L being upregulated by ACC. The myb9-1 mutant was also found to have a longer primary root growth phenotype in the absence of ACC compared to Col-0, and no difference was observed in the presence of ACC (Fig. S14d), suggesting MYB9 may inhibit primary root growth, although this was not consistent with MYB9 being down-regulated by ACC. We also examined the effects of these mutants on root hair number in the absence and presence of 4 hours of ACC treatment. Only the myb52-1 mutant had aberrant root hair formation with reduced root hairs compared to Col-0 in both the presence and absence of ACC, suggesting it functions to promote root hair formation (Fig. S15). These subtle phenotypes were likely due to functional redundancy, as many of the proteins encoded by these transcripts belonged to extensive TF families.

We also compared the growth response of the light-grown seedlings described above to dark-grown seedlings (Fig. S16). In 10-day-old light-grown seedlings, the lrp1-1 and myb9-1 mutants exhibited more primary root growth, yet the ability of ACC to inhibit elongation was like that observed in Col-0, with other mutants having Col-0 levels of root elongation in the absence of treatment (Figs. S12b and S14b). When these same mutants were etiolated on ACC (or untreated) plates for 4 days, we found that lrp1-1 had significantly shorter roots than Col-0 at 0.2 µM ACC, while myb9-1 behaved like Col-0 at all doses of ACC we tested. We also found that rap2.6l-1, which did not have a primary root growth phenotype when grown under continuous light, displayed shorter roots than Col-0 at multiple lower doses of ACC (<0.5 µM) after etiolation. However, lrp1-1 and rap2.6l-1 did not differ from Col-0 at ACC doses of 0.5 µM and higher. The myb52-1 mutant displayed longer roots than Col-0 under control conditions at 5 days of age, although the primary root growth did not differ from Col-0 at 10 days of age when lateral roots were measured. In contrast, etiolated myb52-1 seedlings displayed longer roots than Col-0 in the absence of ACC treatment but shorter roots than Col-0 in the presence of 0.2 µM ACC. The anac058-1 mutant was not significantly different from Col-0 at any of the doses of ACC we tested. These differences in light- and dark-grown seedlings suggested that perhaps some of the ETR1-dependent TFs were more important for light-dependent root responses than light-independent ones (Harkey et al 2019).

Discussion

The Arabidopsis genome encodes 5 ethylene receptors that have distinct and overlapping functions in the regulation of plant growth. These receptors negatively regulate the ethylene signaling pathway and are turned off by ethylene binding, inducing downstream changes in transcription. The specific roles of individual receptors in controlling ethylene-regulated development have been examined in only a few tissues (Cancel and Larsen 2002; Harkey et al 2018; Binder 2020; Ma and Dong 2021). In roots, ETR1 is the ethylene receptor that is most strongly linked to the inhibition of both lateral root formation and primary root elongation and the stimulation of root hair growth (Harkey et al 2018). To identify the transcripts whose synthesis is regulated by ETR1 to control root development, we identified genome-wide changes in transcript abundance in roots in response to treatment with ethylene or its precursor, ACC, using Col-0 and LOF and GOF etr1 receptor mutants. We examined the GRNs that are turned on by ethylene and ACC and are dependent on ETR1 signaling, revealing transcripts encoding enzymes of ethylene synthesis and TFs that have the potential to drive transcriptional responses downstream of ethylene.

To identify the transcriptional changes in the presence and absence of ethylene and ACC in Col-0, we treated seedlings with ethylene or ACC, and the transcript abundance in roots was quantified at several time points after treatment (0, 1, 4, and 24 h), with the transcript abundance relative to control (time-0) Col-0 used to identify DE transcripts and the direction and magnitude change of abundance of these transcripts. Ethylene induced a greater number of DE genes than ACC at all time points after treatment, and the magnitude change of these DE genes was larger (Fig. 3). The most likely explanation for this result was that the ethylene gas used in our treatments elevated ethylene levels more than this dose of ACC. Most transcripts that responded to either ethylene or ACC treatment decreased in abundance, especially at the 4- and 24-h time points. This observation is in alignment with other reports that ACC treatment led to a greater number of transcripts with decreased abundance than increased abundance relative to time-0 controls in Col-0 and in the ethylene-insensitive ein2-5 mutant (Harkey et al 2018; Mou et al 2025).

Several reports have suggested that ACC may act as a signal independent of its conversion to ethylene, especially at high concentrations where ACO-dependent conversion of ACC to ethylene becomes rate limiting (Xu et al 2008; Tsuchisaka et al 2009; Tsang et al 2011; Van de Poel and Van Der Straeten 2014; Houben and Van de Poel 2019; Polko and Kieber 2019; Vanderstraeten et al 2019; Yin et al 2019; Li et al 2020, 2022; Mou et al 2020, 2025; Van de Poel 2020; Althiab-Almasaud et al 2021). At concentrations of 10 µM and above, ACC inhibits rosette size and growth across all tissues in seedlings (Vanderstraeten et al 2019) and alters lateral root initiation and emergence (Mou et al 2025). However, in roots at lower doses of ACC (less than or equal to 1 µM), both the ethylene-insensitive ein2-1 and ein2-5 mutants do not respond to treatment with 1 µM ACC (Růžička et al 2007; Ivanchenko et al 2008; Negi et al 2008; Muday et al 2012; Harkey et al 2018), suggesting that ACC is either largely converted to ethylene at this dose or that in these tissues under these growth conditions ACC predominantly acts through its conversion to ethylene. Our finding that most of the transcripts that were DE in response to this low dose of ACC were also DE in response to ethylene is consistent with these possibilities. Yet the presence of transcripts in this dataset whose abundance changed after ACC treatment, but not ethylene treatment, even though ethylene generally resulted in more robust transcript abundance changes, is consistent with ethylene-independent transcriptional effects of ACC.

We found that several transcripts regulated by only ACC (and not by ethylene) encoded MAP kinases, enzymes that remodel the cell wall, and proteins involved in root hair development, as well as ACO enzymes whose regulation we characterized. We found that ACC downregulated ACS7, which suggests negative feedback of ACC synthesis in response to elevated levels of ACC, while ACO transcripts increase to facilitate conversion of ACC to ethylene. A recent article described a mechanism by which ACC-regulated transcripts may result from direct ACC signaling to TFs rather than signaling after its conversion to ethylene (Mou et al 2025). ACC has been shown to regulate WOX5 and LBD18 to control meristem growth and lateral root development, respectively, independent of ethylene signaling (Mou et al 2025). The majority of the positively ACC-regulated DE transcripts identified in their dataset when ethylene-insensitive ein2-5 seedlings were treated with ACC were also DE in response to ACC in our dataset, consistent with conserved transcriptional responses between our datasets.

An important goal of these experiments was to take the large number (8,323) of DE genes that are altered in response to ethylene and/or ACC (Fig. 4) and refine this dataset by identifying the genes whose regulation depended on functional ETR1. We identified ETR1-regulated genes using 2 etr1 mutant alleles: etr1-3, which is a GOF receptor mutant with a constitutively active receptor that turns off ethylene signaling, resulting in an ethylene-insensitive phenotype, and etr1-7, which is an LOF receptor mutant that has an inactive receptor, resulting in constitutive ethylene signaling. The combination of GOF and LOF receptor mutants served as a powerful approach to identify ETR1-dependent transcriptional responses. This is clear from the earliest steps of the analysis in which comparison of all samples by a PCA plot led to the separation of etr1-7 samples from Col-0 and etr1-3 on the PC1 axis (Fig. 2a).

We used strict criteria to define the ETR1-dependent genes, requiring that genes were DE with either ethylene or ACC, significantly altered in abundance in the etr1-7 allele, with constitutive ethylene response, and transcripts were not significantly changed by ethylene or ACC in the ethylene-insensitive etr1-3 mutant. The pattern of response evident in Fig. 6 shows enhanced transcript abundance in etr1-7 that is not changed by either ethylene or ACC treatment, consistent with constitutive ethylene signaling in this mutant. An unexpected finding in this study was that there were nearly as many transcripts that responded in an ETR1-independent fashion as those that were ETR1-dependent (Fig. 7). Transcripts were defined as ETR1-independent if they responded to ACC or ethylene treatment in all 3 genotypes. If the abundance of these transcripts increased and decreased in etr1-7 and etr1-3, respectively, and changed with hormone treatment, this might suggest 2 receptors control their activity, with hormone-dependent changes being mediated by a second receptor.

In a prior study, we found ETR1 to be essential in root developmental processes and identified roles for other ethylene receptors in other processes (Harkey et al 2018). ACC's effect on lateral root development was completely lost in the ethylene-insensitive mutant etr1-3 and not induced by the addition of ACC in the constitutively signaling mutant etr1-7, suggesting inhibition of lateral root formation by ethylene signaling had strong dependence on ETR1. In contrast, inhibition of primary root elongation and stimulation of root hair growth by ACC were less dependent on ETR1 as they showed attenuated responses to ACC in etr1-7 (Harkey et al 2018), suggesting a role of other ethylene receptors in these root processes. Perhaps some of the transcripts which were ETR1 independent were involved in root elongation or root hair initiation. Indeed, when we examined genes that were ETR1-independent by the strictest criteria, they were enriched in the annotation “root hair cell differentiation” and several other root-related annotations.

We were interested in further exploring the ETR1 regulatory mechanisms of the transcripts that encode ACC oxidases, the enzymes that convert ACC to ethylene, to reveal their regulation by ETR1 and their activity in different root tissues. In our dataset, we found that ACO transcripts were positively regulated by ACC and ethylene, where application of ethylene and/or ACC-induced transcript accumulation of all ACOs in at least one time point after treatment (Fig. 8). We found that ACO2 and ACO3 met our strict criteria for ETR1 dependence in response to ethylene but not ACC. This suggests that ACC may be able to regulate transcripts in an ETR1-independent signaling pathway. However, for ACO2, ACC-mediated changes in transcript abundance were completely lost in ein3eil1, suggesting that ACC activates the canonical ethylene signaling pathway to control ACO2 levels. A likely explanation for this finding is that ACO2 is regulated by a different ethylene receptor. Another possible explanation is that ACC is working through a noncanonical pathway, as might be required when ACC levels become too high, and the plant needs a mechanism to rapidly convert ACC to ethylene. Glucose can signal to EIN2 and EIN3/EIL1 through the target of rapamycin pathway (Fu et al 2021), resulting in transcriptional changes that are not dependent on ETR1 but require the EIN3/EIL1 machinery and revealing that direct regulation of the signaling components downstream of the ethylene receptors in receptor mutants is possible.

To provide more information on the localization of changes in ACO transcript abundance, we examined the root cell-type-specific localization of ACO transcripts in untreated roots using publicly available transcriptome data and in planta using ACO promoter-driven fluorescent protein reporters to inform our understanding of the tissues that express the machinery to convert ACC to ethylene under our treatment conditions (Figs 9 and 10). We found that each ACO had a distinct tissue expression pattern, and many of these were enhanced in the presence of ACC treatment in 10-day-old seedlings. These findings were consistent with ACOs being expressed in root hairs, lateral roots, pericycle cells, and the root elongation zone, where their protein products convert ACC to ethylene to signal root responses in these tissues. The most striking observation was that ACO5 transcripts were the only ACO accumulating to high levels in hair cells prior to and after root hairs initiated, and this root hair expression was evident in ACC-treated seedlings that formed more and longer root hairs, consistent with a role for ACO5 in driving root hair elongation. The other pACO::GFP reporters were not detected or had low fluorescence in root hairs.

To map the GRN downstream of ETR1 and EIN3/EIL1, we used the Agris database (Davuluri et al 2003) and identified 60 transcripts predicted to encode TFs. These transcripts change with a range of different kinetic responses to ACC and ethylene treatments (Fig. 11). We also generated a network using PaLD, which groups transcripts together with the most similar temporal response to visualize connections among transcripts with the most similar responses to ACC and ethylene across time and genotypes. We also partitioned transcripts into groups of similarly responding genes with upregulated genes clustered into 2 groups with both groups significantly enriched in targets of EIN3, one consisting of transcripts with rapid and strong induction in response to treatment and another consisting of transcripts with more muted upregulatory patterns.

We asked whether 3 of the TF-encoding genes that were found to be EIN3 targets in DAP-Seq or ChIP-Seq datasets (Chang et al 2013; O’Malley et al 2016) lost their ACC-regulated expression in the ein3eil1 mutant, focusing on ROOT HAIR DEFECTIVE 6-LIKE 5 (RSL5), RELATED TO AP2 6L (RAP26.L), and MYB9 (Fig. 11). We found that the ACC-mediated increase in RSL5 and RAP2.6L transcripts and the decrease in MYB9 transcripts were lost in ein3eil1. However, not all the ETR1-dependent genes were found to be regulated by EIN3/EIL1. Therefore, an interesting question that arises from this work is how ETR1 regulates genes through mechanisms other than the canonical EIN3/EIL1 pathway. One possible mechanism involves the AHP and ARR families (Binder 2020), with previous work demonstrating a direct interaction and phosphorylation between ETR1 and AHP1 in vitro (Scharein and Groth 2011). We also examined root phenotypes in mutants of other ETR1-dependent genes predicted to encode TFs: LRP1, ANAC058, MYB9, MYB52, and RAP2.6L, finding weak phenotypes in mutants in these genes, consistent with the redundant function of TFs driving ethylene-dependent root development.

This study reveals similarities and differences in the root transcriptome in response to ethylene and ACC and identifies which transcriptional targets are regulated by the ETR1 receptor in Arabidopsis roots. We used network analysis to characterize a GRN of the ETR1-dependent genes, identifying groups of ETR1-dependent genes with consistent kinetic responses that are enriched in targets of the EIN3 TF. This study provides a rich dataset that can be further dissected to reveal the roles of ETR1-regulated genes in controlling root responses to changes in both ethylene and ACC.

Materials and methods

Genotypes and plant growth conditions

All mutants used in this study were in the Col-0 background. Both the etr1-3 and etr1-7 alleles have been described previously (Guzmán and Ecker 1990; Hua and Meyerowitz 1998; Harkey et al 2018). The etr1-7 (AT1G66340) mutant was obtained from Elliot Meyerowitz (Hua and Meyerowitz 1998). T-DNA insertion mutants were obtained from the Arabidopsis Biological Resource Center for AT3G18400 (SALK_049205C), AT5G12330 (SAIL_402_G06/CS873836 and SALK_201247C), AT5G16770 (SALK_149765C), AT1G17950 (SALK_138624C), and AT5G13330 (SALK_051006C); see Table S5 for more information on these lines. The pACO::GFP-GUS reporters were prepared by Dr. John Vaughan-Hirsh and shared by Dr. Bram Van de Poel (Houben et al 2026). The proLRP1::GUS reporter was obtained from ABRC (Estornell et al 2018).

Plants were grown on 1× Murashige and Skoog medium (Caisson Laboratories), pH 5.6, Murashige and Skoog vitamins, and 0.8% agar, buffered with 0.05% MES (Sigma), and supplemented with 1% sucrose. After stratification for 72 h at 4 °C, plants were grown under 100–130 µmol m⁻² s⁻¹ continuous cool-white light.

For selecting a dose of ethylene for the RNA-Seq, Col-0 seedlings were treated on day 5 after germination. For ACC treatment, the seedlings were transferred to a growth medium containing 0 or 0.75 µM ACC for 24 h before imaging. For ethylene treatment, plates were placed in clear treatment tanks with constant flow-through of the indicated concentration of ethylene for 24 h before imaging.

Method for selecting time points for RNA-Seq analysis using a previous microarray study

We developed an analysis approach to identify the subset of time points that contained the most unique (ie, not found in other time points) DE genes in response to ACC treatment using our previously published time-course microarray study with ACC-treated Col-0 roots (Harkey et al 2018). In that prior study, we examined transcriptional responses to 1 µM ACC after 0, 0.5, 1, 2, 4, 8, 12, and 24 h of treatment, which overlaid the timeline for ACC-induced root hair initiation. To inform the selection of time points for the present study, we first analyzed the prior microarray data to find DE genes for each individual time point compared to time 0. We then performed a pairwise comparison of time points to determine the number of overlapping DE genes between each time point (Fig. S2b) and the percent of DE genes in a time point that overlapped with another time point (Fig. S2a). These results revealed that treatment times of 1, 4, and 24 h contained the most unique sets of transcripts while also overlapping with the timing of root hair formation to capture early, middle, and late developmental responses. Therefore, we chose to perform RNA-Seq using samples treated with ACC or ethylene at 3 time points: 1 h, to monitor rapid transcriptional responses that precede developmental changes; 4 h, where the most unique transcripts are DE and root hair initiation and primary root elongation are increasing and decreasing, respectively, in response to treatment; and 24 h to identify transcripts linked to ethylene-modulated lateral root regulation.

Microarray data (Harkey et al 2018) were analyzed using limma (Ritchie et al 2015). Probes were first filtered for interquartile range, with anything less than 0.1 excluded from further analysis. Time points were then compared to their time-matched controls; P-values were adjusted using a Benjamini–Hochberg correction. Genes with an absolute log₂FC greater than 0.5 and an adjusted P-value less than 0.05 were considered to be DE at that time point. An overlap matrix was created by comparing the DE genes across the time points in a pair-wise manner. Fig. S2a graphically illustrates the results of this analysis. For each column, the percentage of DE genes at that time point (column) that are also DE in the time point to which it was compared (row) is shown. The number of genes in each comparison was used to calculate the percentages of overlap between time points, which can be seen in Fig. S2b. For example, the 2-h time point showed considerable overlap with the 4-h time point; 87% of all 2-h DE genes were also DE at 4 h. The reverse comparison of the genes that were DE after 4 h of treatment with those that were also DE at 2 h yielded only 32% of the 4-h genes that were DE at 2 hours because there were more DE genes in this 4-h sample. Therefore, the 4-h time point identified nearly all the same DE genes as the 2-h time point, as well as many more unique genes, so we selected the 4-h time point over the 2-h for this RNA-Seq analysis. We also examined the overlaps of the 4- and 8-h time points and the 12- and 24-h time points, finding more unique DE transcripts in the 8- and 24-h time points for each of these overlaps. However, as root hairs had already begun to initiate by 4 h (Harkey et al 2018), and the ethylene-dependent inhibition of lateral root initiation had begun by 24 h, these time points also represented important developmental milestones. Ultimately, this analysis informed our choice to do the present experiment with time points 0, 1, 4, and 24 h.

ACC and ethylene treatment of seedlings for RNA isolation for sequencing

Plants were grown on media as described above on top of a nylon filter (03-100/32; Sefar Filtration) pressed against the plate, as described previously (Levesque et al 2006; Harkey et al 2018). Approximately 100 sterilized seeds were placed on each filter; 2 plates were combined for each biological replicate. ACC-treated samples were grown and harvested at Wake Forest University, and ethylene-treated samples were grown and harvested at the University of Tennessee Knoxville, utilizing a gas flow-through system to treat with constant levels of ethylene gas. The same researchers prepared both sets of samples, and identical reagents and light treatments were used in both laboratories. Time-0 controls were prepared in both locations to account for any location-specific effects on baseline expression.

Plants were treated on day 5 after germination. For time-0 samples, the nylon filter containing 5-day-old plants was transferred to a new control medium, and root tissue was immediately harvested. For ACC treatment, the plants on nylon were transferred to growth medium with 1 µM ACC for the given treatment time (1, 4, and 24 h), and then the root tissue was harvested. For ethylene treatment, the nylon was transferred to a new control medium, and plants were placed in clear treatment tanks with constant flow-through of 0.3 ppm ethylene gas for the given treatment time (1, 4, and 24 h), and then the root tissue was harvested. At the time of harvesting, roots were cut from seedlings and flash frozen in liquid nitrogen. Frozen samples were ground in liquid nitrogen, and RNA isolation was performed according to the Qiagen plant RNeasy kit protocol, with the addition of the Qiagen RNase-free DNase treatment (Qiagen). After RNA isolation, samples were quantified by A260 using a Nanodrop spectrophotometer (Nanodrop Technologies). Each sample yielded at least 3 µg, and on average 15 µg, of RNA. One Col-0 sample, which had a 4-h ethylene treatment, had an RNA integrity number (RIN) less than 6 and was not sequenced. Sequencing was performed by GENEWIZ, LLC using the Illumina HiSeq platform.

RNA-Seq preprocessing and quality control

FastQC v0.11.8 (Wingett and Andrews 2018) and MultiQC v1.7 (Ewels et al 2016) were used for assessing read quality, which identified adaptor contamination and a bimodal GC Content distribution. Adaptors were trimmed from reads using CutAdapt v1.18 (Martin 2011), and reads trimmed to less than 25 bp were removed. Trimmed reads were then processed with the BBMap tool BBDuk (Bushnell 2014) to identify and remove rRNA contamination. Running FastQC/MultiQC over the trimmed and filtered reads revealed that the identified quality issues had been mitigated. Read quantification was performed directly on the processed fastq files with the Salmon v0.12.0 (Patro et al 2017) “quant” algorithm in mapping mode using the “–validateMapping” flag, setting the library type to “IU”, and using the TAIR10 reference transcriptome (NCBI ID: GCF_000001735.3). Counts were imported into R v3.6.0 using the tximport R package v1.12.3 (Soneson et al 2016) and summarized to the gene level.

Differential expression and visualization

For visualization and sample comparisons, raw read counts were normalized using the Variant Stabilizing Transformation method from the R package DESeq2 v1.24.0 (Love et al 2014). Sample-wise comparison heatmaps were generated using Pearson's correlation and Euclidean distance as the distance metrics, respectively. PCA analyses were performed by DESeq2's plotPCA method using the top 1,000 most variable genes for all genotypes together, and on a per-genotype basis.

All sample-wise comparisons were performed using the DESeq2 package and ashr v2.2-47 (Stephens et al 2020). Density plots were generated using ggplot2 v3.3.0 (Wickham 2016), and heatmaps of sample-wise comparisons were generated using ComplexHeatmap v.2.3.4 (Gu et al 2016). For DE transcripts used in heatmaps and GO analyses, we considered a transcript to be DE if it had a P-value < 0.01, |logFC| > 0.5, and baseMean > 10.

Identification of outliers and pooling of control samples

We performed ACC treatments at Wake Forest University and ethylene treatments at the University of Tennessee. Therefore, to determine if these samples were similar, we first compared the controls from each location for each genotype (Table S1). Since the controls for Col-0 and etr1-3 were well matched between the 2 locations, and the PCA plots suggested differences in etr1-7 were not location dependent (Fig. 2d), we combined the 6 control (0-h) samples for each genotype, or the 5 control samples for Col-0, excluding the outlier sample described above, to use as the baseline for comparisons for the remaining analyses.

To determine if all the Col-0 control (time-0) samples could be pooled for downstream analyses, we generated a PCA plot using only the Col-0 samples (Fig. S3). This plot revealed that one sample (in the ethylene time-0 treatment group) did not cluster with the other control samples. This sample showed substantial separation from the rest along the PC1 axis, which represents over 50% of the variance across all the Col-0 samples. This outlier sample was found to have far lower RNA yield than the other 71 samples, so we removed it from downstream analyses. We generated a new PCA plot without this sample, revealing much greater consistency.

To further validate this method, we performed 2 DE analyses to examine batch effects on samples collected in the 2 locations, one with all 6 Col-0 control samples included (3 from each location) and one with the outlier sample removed. When all 6 samples were included, 693 genes were found to be DE (adjusted P-value < 0.01, |log₂FC| > 0.5) between the ACC and ethylene controls performed at these 2 locations (Table S1). When the outlier sample identified by the PCA plot was removed from the analysis, only 34 genes were found to be DE between the Col-0 samples (Table S1). This removal also affected the model generated by DESeq2, resulting in subtle effects on the etr1-3 and etr1-7 comparisons (Table S1). In the model with the outlier Col-0 removed, 121 and 763 genes were DE between the controls compared at each location in etr1-3 and etr1-7, respectively (Table S1). The time-0 etr1-7 samples had more DE genes between locations than had been expected, but there were 2 controls that were quite different than other samples (as evident in the PCA analysis in Fig. 2d), which suggested that this is a characteristic of 2 samples rather than a difference in samples because of location. We ultimately decided to combine the control samples for each genotype from both locations.

PaLD analysis

A recently developed tool called partitioned local depths (PaLD) was used to generate networks of the 553 ETR1-dependent transcripts and the 60 ETR1-dependent TFs, with application of this algorithm to gene expression and time course datasets described previously (Berenhaut et al 2022; Khoury et al 2024a). We sorted genes based on their transcript abundance patterns in response to ACC and ethylene over time in Col-0, etr1-3, and etr1-7. We used the log₂FC values relative to time-0 Col-0 as input for the PaLD analysis. We further divided the networks using the Louvain method for community detection (Blondel et al 2008). Relevant R code can be found at https://github.com/moorekatherine/pald.

RT-qPCR

Roots from 5-day-old Arabidopsis seedlings grown on mesh were harvested using sterile razor blades then immediately frozen in liquid N₂. RNA was extracted from the roots using the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Briefly, frozen root tissue was homogenized in microcentrifuge tubes using pestles that were pre-dipped in liquid N₂. After RNA extraction, DNA was removed using the RapidOut DNA Removal Kit (ThermoScientific, Waltham, MA, USA) according to the manufacturer's instructions. Complementary DNA was synthesized using the Superscript III First-Strand Synthesis System for RT-PCR (Invitrogen, Carlsbad, CA, USA) using a total of 500 ng of input RNA for each sample. PowerUp SYBR Green Master Mix (Applied Biosystems, Foster City, CA, USA) was used for quantitative PCR on a QuantStudio 3 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Standard curves were generated by combining equal volumes of cDNA from each sample, then creating 10-fold serial dilutions. The standard curves were used to calculate primer efficiency and transcript abundance. Target transcripts were normalized to UBQ10, and fold change relative to Col-0 control was calculated using the ratio of target and reference genes and is reported in graphs. Primer sequences are reported in Table S6.

Fluorescence and brightfield imaging

For high-resolution imaging of all pACO::GFP-GUS reporters, a Zeiss LSM 880 microscope was used. Roots were first stained with propidium iodide (PI) diluted in water to a final concentration of 0.01 mg/mL for 2 min before being washed briefly in DI water and mounted on slides in DI water with #1.5 coverslips. Confocal images were acquired with a 488 nm excitation laser for imaging GFP and the 561 nm laser for imaging PI with emission ranges of 490 to 552 nm and 56 to 696 nm, respectively. To minimize light scatter and improve image resolution, the pinholes for imaging GFP and PI were adjusted to slightly less than 1 Airy Unit. Images were taken with a Plan-Apo 20×/0.8NA objective and a field size of 2,048 × 2,048 pixels. For Z-stacks, pixel dwell time was 0.26 µs and line averaging was set to 4. Z-stacks were obtained with 2.9 µm intervals between optical slices. For single-slice, high-resolution images, pixel dwell time was 1.02 µs and line averaging was set to 8.

For epifluorescence imaging of the pACO5::GFP-GUS reporter, a Zeiss Axio Zoom.V16 equipped with a PlanNeoFluar Z 1.0× (0.25NA) objective was used. An excitation range of 430 to 495 nm and emission range of 495 to 575 nm was employed with a Zeiss 38HE fluorescence filter cube. Images were captured with an Axiocam 506 monochrome camera with an exposure time of around 500 ms.

For imaging proLRP1::GUS, the Zeiss Axio Zoom.V16 equipped with a PlanNeoFluar Z 2.3× (0.75NA) objective was used. Images were captured with an AxioCam HR R3 color camera with an exposure time of around 10 ms.

GUS staining procedure

Fresh X-Gluc (GoldBio) was dissolved on ice in N,N-Dimethylformamide immediately before incubating samples. For GUS staining, 10-day-old seedlings were vacuum infiltrated with GUS buffer containing X-Gluc (100 mM phosphate buffer pH 7, 0.5 mM K₄Fe(CN)₆, 0.5 mM, K₃Fe(CN)₆, 0.5 mM, 10 mM EDTA, 0.1% Triton X-100, and 2 mM X-Gluc) for 5 min, then incubated in the buffer and X-Gluc mixture for 1.5 h at 37 °C. Following product formation, seedlings were washed in 70% EtOH for 10 min then fixed overnight in 3:1 EtOH:acetic acid at 4 °C in the dark prior to imaging.

PCR genotyping

The primers used for genotyping are reported in Table S5. Young leaf tissue was homogenized in a microcentrifuge tube using a micropestle that was dipped in liquid nitrogen. Edwards Solution (Edwards et al 1991) was added to the homogenized leaf tissue, and samples were incubated at room temperature for at least 1 hour before being spun down. Supernatant was carefully transferred to a new tube containing an equal volume of isopropanol. Samples were washed with EtOH and air dried in a sterile hood for approximately 30 min before being resuspended in molecular biology grade H₂O. Genotype was determined using wild-type primers that flanked the T-DNA insert (the wild-type reaction) to amplify the gene, and right border primers with T-DNA primers (the T-DNA reaction) to amplify the T-DNA insert (O’Malley et al 2015), with primer sequences listed in Table S5. DreamTaq DNA polymerase (ThermoFisher) was used according to the manufacturer's instructions.

Root development analysis

For analysis of root hair and lateral root phenotypes, 5-day-old seedlings were transferred to plates containing 0 or 0.75 µM ACC for 4 h or 5 days before imaging root hairs and lateral roots, respectively. For the root length analysis of dark-grown seedlings, seeds were germinated in the dark for 4 days on untreated plates or plates containing 0.1, 0.2, 0.5, 1, or 2 µM ACC, then root lengths were measured. Statistical tests (2-way ANOVA and multiple comparisons tests) were performed in GraphPad Prism 8.0.2. Outliers were identified using ROUT (Q = 1%).

Accession numbers

Sequence data from this article can be found in the GenBank/EMBL data libraries under accession numbers AT2G19590 (ACO1), AT1G62380 (ACO2), AT1G12010 (ACO3), AT1G05010 (ACO4), AT1G77330 (ACO5), AT3G18400 (ANAC058), AT3G20770 (EIN3), AT2G27050 (EIL1), AT1G66340 (ETR1), AT5G12330 (LRP1), AT5G16770 (MYB9), AT1G17950 (MYB52), AT1G34670 (MYB93), and AT5G13330 (RAP2.6L).

Supplementary Material

kiag074_Supplementary_Data

kiag074_supplementary_data.zip^{(6.7MB, zip)}

Acknowledgments

We appreciate the help and mentorship of Megan Gerber, particularly with the comparison of transcriptomic datasets.

Contributor Information

Maleana G White, Wake Forest University, Department of Biology and Center for Molecular Signaling, Winston-Salem, NC, United States.

Alexandria F Harkey, Wake Forest University, Department of Biology and Center for Molecular Signaling, Winston-Salem, NC, United States.

Joëlle K Mühlemann, Wake Forest University, Department of Biology and Center for Molecular Signaling, Winston-Salem, NC, United States.

Amy L Olex, Virginia Commonwealth University, C. Kenneth and Dianne Wright Center for Clinical and Transtional Research, Richmond, VA, United States.

Nathan J Pfeffer, Wake Forest University, Department of Biology and Center for Molecular Signaling, Winston-Salem, NC, United States.

Maarten Houben, Wake Forest University, Department of Biology and Center for Molecular Signaling, Winston-Salem, NC, United States.

Brad M Binder, University of Tennessee, Biochemistry & Cellular and Molecular Biology, Knoxville, TN, United States.

Gloria K Muday, Wake Forest University, Department of Biology and Center for Molecular Signaling, Winston-Salem, NC, United States.

Author contributions

Maleana White, Alexandria Harkey, and Amy Olex helped design the research, performed and analyzed data, and wrote and edited the manuscript. Joelle Muhlemann, Nathan Pfeffer, and Maarten Houben performed experiments, analyzed data, and edited the manuscript. Brad Binder helped design and perform experiments and edited the manuscript. Gloria Muday designed experiments and wrote and edited the manuscript.

Supplementary material

Supplementary material is available at Plant Physiology online.

Funding

This work was supported by the National Science Foundation (MCB-1716279 to G.K.M. and B.M.B.) and the Center for Molecular Signaling Graduate Research Fellowship to M.G.W. and a WFU Provost’s Pilot Research grant (to G.K.M. and M.G.W.).

Data availability

The data underlying this article are available in Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE193833, and can be accessed with GSE193833. The analyzed data are available in the article itself and the online supplemental dataset.

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Data Availability Statement

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PERMALINK

Ethylene receptor gain- and loss-of-function mutants reveal an ETR1-dependent transcriptional network in Arabidopsis roots

Maleana G White

Alexandria F Harkey

Joëlle K Mühlemann

Amy L Olex

Nathan J Pfeffer

Maarten Houben

Brad M Binder

Gloria K Muday

Abstract

Introduction

Results

Ethylene and ACC doses with similar ETR1-dependent effects on root hair growth

Figure 1.

The reduced effects of ACC and ethylene on transcriptional response in ETR1 mutants are revealed by principal component analysis

Figure 2.

Ethylene treatment stimulated more dramatic transcriptional changes than ACC treatment in Col-0

Figure 3.

Transcripts regulated by ethylene and ACC were enriched in functions related to root development

Transcriptional responses are accentuated in constitutive-signaling etr1-7 and reduced in ethylene-insensitive etr1-3

Figure 4.

Figure 5.

A subset of ACC- and ethylene-regulated transcript responses is dependent on ETR1

Figure 6.

Other transcripts appear to be regulated by ACC and/or ethylene in an ETR1-independent or more complex manner

Figure 7.

Several ACOs are transcriptionally regulated by ACC and ethylene and dependent on the ETR1 receptor

Figure 8.

ACC-induced transcript accumulation of ACO1, ACO2, and ACO4 is mediated by EIN3/EIL1

ACO transcription increases in distinct root cell types in response to ACC treatment

Figure 9.

Figure 10.

Identification of EIN3-regulated transcripts predicted to encode TFs within the ETR1-dependent GRN

Figure 11.

The proLRP1::GUS reporter exhibited increased amounts of GUS product in lateral root primordia in the presence of ACC

Figure 12.

Several ETR1-dependent TF mutants show subtle differences in root hair initiation, lateral root formation, and root elongation

Discussion

Materials and methods

Genotypes and plant growth conditions

Method for selecting time points for RNA-Seq analysis using a previous microarray study

ACC and ethylene treatment of seedlings for RNA isolation for sequencing

RNA-Seq preprocessing and quality control

Differential expression and visualization

Identification of outliers and pooling of control samples

PaLD analysis

RT-qPCR

Fluorescence and brightfield imaging

GUS staining procedure

PCR genotyping

Root development analysis

Accession numbers

Supplementary Material

Acknowledgments

Contributor Information

Author contributions

Supplementary material

Funding

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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