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. 2022 Aug 11;236(3):893–910. doi: 10.1111/nph.18404

Integration of multi‐omics data reveals interplay between brassinosteroid and Target of Rapamycin Complex signaling in Arabidopsis

Christian Montes 1, Ping Wang 2, Ching‐Yi Liao 2, Trevor M Nolan 2,3, Gaoyuan Song 1, Natalie M Clark 1, J Mitch Elmore 1,4, Hongqing Guo 2, Diane C Bassham 2, Yanhai Yin 2,5, Justin W Walley 1,5,✉
PMCID: PMC9804314  PMID: 35892179

Summary

  • Brassinosteroids (BRs) and Target of Rapamycin Complex (TORC) are two major actors coordinating plant growth and stress responses. Brassinosteroids function through a signaling pathway to extensively regulate gene expression and TORC is known to regulate translation and autophagy. Recent studies have revealed connections between these two pathways, but a system‐wide view of their interplay is still missing.

  • We quantified the level of 23 975 transcripts, 11 183 proteins, and 27 887 phosphorylation sites in wild‐type Arabidopsis thaliana and in mutants with altered levels of either BRASSINOSTEROID INSENSITIVE 2 (BIN2) or REGULATORY ASSOCIATED PROTEIN OF TOR 1B (RAPTOR1B), two key players in BR and TORC signaling, respectively.

  • We found that perturbation of BIN2 or RAPTOR1B levels affects a common set of gene‐products involved in growth and stress responses. Furthermore, we used the multi‐omic data to reconstruct an integrated signaling network. We screened 41 candidate genes identified from the reconstructed network and found that loss of function mutants of many of these proteins led to an altered BR response and/or modulated autophagy activity.

  • Altogether, these results establish a predictive network that defines different layers of molecular interactions between BR‐ or TORC‐regulated growth and autophagy.

Keywords: BIN2, brassinosteroids, integrative, multi‐omics, network, RAP, TOR, TORAutophagy

Introduction

Organisms are frequently affected by environmental challenges. When responding to stress, specific molecular and cellular processes are triggered, and growth is often compromised. These responses to both biotic and abiotic stresses rely heavily on modulating hormonal signaling pathways, and plants need to allocate resources between their growth and stress response machinery efficiently. Therefore, well‐coordinated hormonal crosstalk is fundamental for a successful response to stress (Huot et al., 2014; Verma et al., 2016; Bürger & Chory, 2019). The growth‐promoting hormone brassinosteroid (BR) has been shown as a critical element in this balance. Plants with altered levels of BR signaling or biosynthesis genes exhibit deficient growth (Li et al., 1996; Li & Chory, 1997; Li & Nam, 2002; Yin et al., 2002; Chung et al., 2010; Guo et al., 2013) and abnormal response to various stresses (Che et al., 2010; T. M. Nolan et al., 2017; Ye et al., 2017; Fàbregas et al., 2018; Gruszka, 2018; Planas‐Riverola et al., 2019; Xie et al., 2019; Gupta et al., 2020; Liang et al., 2020).

The GLYCOGEN SYNTHASE KINASE 3 (GSK3)‐like kinase BRASSINOSTEROID INSENSITIVE 2 (BIN2) is a critical negative regulator of BR signaling (Li & Nam, 2002; Kim et al., 2009). In the absence of BRs, BIN2 phosphorylates the bri1‐EMS‐SUPPRESSOR1/BRASSINAZOLE RESISTANT1 (BES1/BZR1) family of transcription factors (TFs), which reduces their protein level, lowers DNA binding, and promotes cytoplasmic sequestration by 14‐3‐3 proteins, thereby preventing the activation of downstream BR response genes (Yin et al., 2002; Gampala et al., 2007; Ryu et al., 2007, 2010). Brassinosteroid signals through the receptor BRI1, coreceptor BAK1, and other components to inhibit BIN2, allowing BES1/BZR1 accumulation in the nucleus to regulate thousands of BR genes for various BR responses (Li & Chory, 1997; Wang et al., 2001; Nam & Li, 2002; Kim et al., 2009; Zhu et al., 2017; Nolan et al., 2020). Besides regulating BES1 and BZR1, increasing evidence position BIN2 as a hub for regulation of the balance between stress and growth (Youn & Kim, 2015; Nolan et al., 2020). BIN2 is involved in BR‐regulation of diverse processes such as drought and abscisic acid (ABA) signaling (Cai et al., 2014; Hu & Yu, 2014; Ye et al., 2017; Jiang et al., 2019), cold stress response (Ye et al., 2019), salt‐stress response (J. Li et al., 2020), root development in conjunction with auxin signaling (Cho et al., 2014; T. Li et al., 2020) as well as chloroplast development (Zhang et al., 2021). Despite the increasing number of reports with BIN2 acting as an essential regulator in growth/stress balance, no multi‐omics studies on this kinase have been reported so far.

In Arabidopsis thaliana, the Target of Rapamycin Complex (TORC) is an important regulator that integrates nutrient and energy sensing into cell proliferation and growth (Xiong & Sheen, 2014; Fu et al., 2020). Activation of TORC signaling induces the expression of ribosomal proteins, increases protein translation, stimulates photosynthesis, and upregulates (transcriptionally and translationally) plant growth‐promoting genes (Ren et al., 2012; Xiong et al., 2013; Dong et al., 2015; Van Leene et al., 2019; Scarpin et al., 2020). Conversely, TORC actively represses autophagy, a central recycling system of cytoplasmic components that is essential for rerouting nutrients and other raw materials when needed for plant growth, development, or stress responses (Noda & Ohsumi, 1998; Pu et al., 2017; Marshall & Vierstra, 2018). Target of Rapamycin Complex is comprised of TOR kinase, LETHAL WITH SEC THIRTEEN PROTEIN 8 (LST8), and REGULATORY ASSOCIATED PROTEIN OF TOR (RAPTOR). TOR is the catalytic component of TORC, LST8 provides stability, and RAPTOR interacts with and recruits substrates to the complex (Hara et al., 2002; Mahfouz et al., 2006; Yang et al., 2013). In Arabidopsis, null mutants in TOR are embryo lethal (Menand et al., 2002). Arabidopsis has two RAPTOR homologs, RAPTOR1A and RAPTOR1B, with RAPTOR1B being the predominantly expressed paralog (Anderson et al., 2005; Deprost et al., 2005). Loss of RAPTOR1A has no impact on plant growth and development while raptor1b plants have reduced TORC activity, impaired morphogenesis, and increased basal autophagy (Anderson et al., 2005; Pu et al., 2017; Salem et al., 2018; Wang et al., 2018). The combined loss of raptor1a raptor1b double mutant embryos are viable and plants maintain embryonic development, unlike tor mutants, but lack post‐embryonic growth (Anderson et al., 2005).

When plants encounter stress, autophagy is often triggered, and growth‐promoting pathways such as BR or TORC signaling need to be dampened (T. Nolan et al., 2017; Liao & Bassham, 2020). To enable this balanced regulation of plant growth and stress responses, hormonal pathways such as auxin (Li et al., 2017; Schepetilnikov et al., 2017) and BRs (Zhang et al., 2016; Vleesschauwer et al., 2018) can influence or be affected by TORC activity. Increasing evidence points towards TORC‐regulated autophagy as a crucial interaction point between BRs and TORC signaling when controlling this balance. For example, activation of TORC signaling promotes BR response by stabilizing BZR1, likely preventing its autophagy‐driven degradation (Zhang et al., 2016). Additionally, BIN2 knockdown lines exhibit reduced sensitivity to TOR inhibitors AZD8055 (AZD) and KU63794 (Xiong et al., 2017). Furthermore, RIBOSOMAL PROTEIN S6 KINASE 2 (S6K2) can phosphorylate BIN2 in a TOR‐dependent manner. However, the mechanism and biological implications of this interaction are not clear (Xiong et al., 2017). Under stress conditions such as drought or sucrose starvation, BES1 is ubiquitinated by SINAT2 and/or BAF1 ubiquitin ligases and targeted to selective autophagy through ubiquitin receptor DSK2 to slow down plant growth (Yang et al., 2017; T. M. Nolan et al., 2017; Wang et al., 2021). Moreover, BIN2 has been shown to phosphorylate ubiquitin receptor DSK2 to facilitate its interaction with ATG8 and promote BES1 degradation via selective autophagy (T. M. Nolan et al., 2017).

BIN2 and TORC regulate plant responses to environmental changes via phosphorylation, exerting molecular changes at many different levels (i.e. changes in gene transcription or protein activity) (Guo et al., 2013; Youn & Kim, 2015; Bozhkov, 2018; Van Leene et al., 2019; Liao & Bassham, 2020; Nolan et al., 2020). Therefore, understanding the molecular connection between BR and TORC signaling across different levels of gene expression is necessary to unravel the interplay between these pathways. Furthermore, despite BIN2 being intensively studied, proteome‐wide identification of BIN2 substrates is lacking. Here, we present a comprehensive multi‐omic profiling detailing transcriptome, proteome, and phosphoproteome changes that occur in mutants with altered levels of BIN2 or the TORC subunit RAPTOR1B. We complement these global in vivo profiles with proteome‐wide identification of direct BIN2 substrates using a multiplexed assay for kinase specificity (MAKS). Substantial overlap was found in the transcripts, proteins, and phosphosites whose accumulation is dependent on BIN2 and RAPTOR1B. Using this wealth of information, we reconstructed an integrated kinase‐signaling network and gene regulatory network (GRN). We used this integrated network to identify novel genes whose mutant lines showed either altered growth in response to BR and/or levels of autophagy. Together, these studies further our understanding of the dynamic interplay between BR and TORC signaling.

Materials and Methods

Plant material

Arabidopsis thaliana (L.) Heynh. mutant lines bin2‐1 (Li & Nam, 2002), bin2‐3 bil1 bil2 (Yan et al., 2009), and raptor1‐1 (Anderson et al., 2005; Deprost et al., 2005) were used in this research as bin2D, bin2T, and raptor1b, respectively. The full list of seed stocks used in this work are summarized in Supporting Information Dataset S1. All plants were grown in LC1 soil mix (Sungro, Agawam, MA, USA) under long day conditions (16 h : 8 h, light : dark, 22°C) unless stated otherwise. Columbia‐0 (Col‐0) ecotype was used as wild‐type (WT) control for all assays.

QuantSeq library preparation and sequencing

Four biological replicates of 20‐d‐old rosette leaves were collected from WT and each mutant (bin2T, bin2D, and raptor1b) and immediately frozen in liquid nitrogen (N2). Tissue was ground for at least 15 min under liquid N2 using mortar and pestle. Total RNA was extracted using RNAeasy Plant Mini Kit with DNaseI treatment (Qiagen). Five‐hundred nanograms of total RNA was used for QuantSeq 3′ mRNA‐seq library Prep kit FWD for Illumina (Lexogen, Vienna, Austria) (Moll et al., 2014). Library sequencing was performed on an Illumina HiSeq3000 at the Iowa State University (ISU) DNA facility.

Transcriptomic data analysis

QuantSeq 3′ mRNA‐seq Integrated Data Analysis Pipeline on Bluebee® Genomic Platform User Guide (catalog no. 090‐094; Lexogen) was followed. Reads were adapter‐ and quality‐trimmed using BBDuk v.37.36. Trimmed reads were mapped to the Arabidopsis reference transcriptome (TAIR10 annotation) using Star Aligner v.2.5.3a (Dobin et al., 2013). Finally, transcript counts were extracted using HTSeq‐count v.0.11.2 (Anders et al., 2015).

Differential expression was assessed using the poissonseq R package (Li et al., 2012). A q‐value < 0.05 and fold change (FC) > 1.3 (log2FC > 0.4) was used as cut‐off for designating differentially expressed (DE) transcripts. All data processing scripts were deposited in a github repository (see the Data availability section).

Protein extraction for global proteome and phosphoproteome profiling

Three biological replicates from the same tissue collected for transcriptome analysis were processed for (phospho)proteomic profiling based on established methods (Song et al., 2018a,b, 2020). Protein was extracted using the urea‐FASP method from 250 mg of finely ground tissue. Tandem Mass Tag (TMT lot #TC264166; Thermo Scientific, Waltham, MA, USA) labeling was performed on 330 μg of purified peptides from each sample in a 1 : 1.7 (peptide : label) ratio as previously reported (Song et al., 2020). Tandem Mass Tag labeling reaction efficiency was assessed to be at least of 98% by liquid chromatography–tandem mass spectrometry (LC–MS/MS). Labeling reaction was quenched using 5% hydroxylamine and the samples were then pooled. One hundred micrograms of labeled peptide was used for global proteome profiling and the remaining labeled sample was subjected to a second round of C18 desalting before phosphopeptide enrichment via serial metal oxide affinity chromatography (SMOAC) using high‐select TiO2 and Fe‐NTA enrichment (Thermo Scientific). Full protein extraction methods are detailed in Methods S1.

BIN2 multiplexed assay for kinase specificity

Multiplexed assay for kinase specificity was performed based on the protocol described by Jayaraman et al. (2017) using protein extracted from 1 g of 20‐d‐old leaf Col‐0 tissue using the phenol‐FASP protocol (Song et al., 2018b, 2020). Three milligrams of total purified protein was resuspended in urea resuspension buffer (8 M urea in 50 mM Tris–HCl, pH 7.0; 5 mM TCEP), re‐precipitated in ice‐cold 100 mM NH4CH3CO2 in 100% methanol. Following precipitation, the solvent was removed and the protein pellet was resuspended in kinase buffer (50 mM Tris–HCl, pH 7.7; 5 mM MgCl2; 5 mM ATP; 1× phosphatase inhibitor cocktail). Resuspended protein was divided into 600 μg aliquots and incubated with either recombinant GST or GST‐BIN2 at a 1 : 75 (enzyme : protein) ratio at 37°C with gentle shaking for 1 h. After incubation, protein solution was subjected to FASP, reduced with 2 mM TCEP, alkylated in 50 mM IAM, and digested using trypsin as described by Song et al. (2020). Three replicates were analyzed for each treatment (i.e. GST and GST‐BIN2). Two hundred micrograms of peptides from each replicate were used for TMT labeling. Phosphopeptide enrichment was performed on labeled peptides using SMOAC. GST‐BIN2 was previously cloned (Yin et al., 2002) and purified using glutathione agarose beads as described in Jiang et al. (2019).

Liquid chromatography–tandem mass spectrometry

Two‐dimensional (2D)‐LC–MS/MS was performed on an Agilent 1260 quaternary HPLC coupled to a Thermo Scientific Q‐Exactive Plus high‐resolution quadrupole Orbitrap mass spectrometer (Song et al., 2018a; Zhang et al., 2019; Clark et al., 2021). Full LC–MS/MS methods are detailed in Methods S2.

Proteomics data analysis

Spectra for global protein abundance runs were searched using the Andromeda Search Engine (Cox et al., 2011) against the TAIR10 Arabidopsis proteome using MaxQuant software v.1.6.1.0 (Tyanova et al., 2016). Carbamidomethyl cysteine was set as a fixed modification while methionine oxidation and protein N‐terminal acetylation were set as variable modifications. Digestion parameters were set to ‘specific’ and ‘Trypsin/P;LysC’. Up to two missed cleavages were allowed. A false discovery rate < 0.01 at both the peptide spectral match and protein identification level was required. Sample loading and internal reference scaling (IRS) normalization methods were used to account for differences within and between 2D‐LC–MS/MS runs, respectively (Plubell et al., 2017).

Differential expression was assessed using the poissonseq R package (Li et al., 2012). A q‐value < 0.1 was used as cut‐off for designating DE proteins. Scripts for data analysis were deposited in a github repository (see the Data availability section).

Phosphoproteomics data analysis

Spectra for both bin2/raptor1b mutant profiling and MAKS were searched together using the same approach as for global protein abundance with exceptions. Briefly, MaxQuant software v.1.6.10.43 was used instead and ‘Phospho (STY)’ search for variable modifications was included. Sample loading and IRS normalization methods were used to account for differences within and between 2D‐LC–MS/MS runs, respectively (Plubell et al., 2017).

Differential expression was assessed using the edger R package (Robinson et al., 2010). A q‐value < 0.1 was used as cut‐off for designating differential phosphorylation. See the Data availability section for the full analysis script.

Motif enrichment analysis

Motif enrichment was performed using the motifer R package (Wang et al., 2019) with default settings: serine or threonine as the central residues, a P‐value threshold of 0.001, a search window of 15 amino acids (aa) upstream and downstream of selected phosphosite for a final 31 aa sequence window, and TAIR10 protein annotation as background reference. Enrichment P‐value was calculated by hypergeometric testing using phyper function in R.

Analysis of overlap between BIN2 MAKS and bin2 mutant datasets

To find overlapping phosphosites, we defined any two distinct phosphosites as identical if they originated from the same phosphoprotein and were < 10 aa residues apart. This approach was used to account for cases where phosphosites were not localized to a specific aa on a given peptide in the two different datasets. Overlap statistical significance was assessed by hypergeometric test.

Kinase activation loop prediction

Protein kinases were identified using a modified version of the pipeline described by Walley et al. (2013). Briefly, all 35 386 protein sequences available in the TAIR10 annotation were searched for kinase domain using The National Center for Biotechnology Information (NCBI) batch conserved domain search tool (Lu et al., 2020). From this list of 1522 proteins with identified kinase domain, 878 were also annotated with activation loop (A‐loop) coordinates by the search tool. The kinase domains of proteins lacking the A‐loop coordinates were aligned using Mafft (Katoh & Standley, 2013) and the well conserved A‐loop beginning (DFG) and end (APE) motifs were manually searched. An extra 482 A‐loop coordinates were obtained, for a total of 1360 protein kinases with A‐loop coordinates.

Kinase‐signaling network

Kinases with differential phosphorylation inside the A‐loop (activated kinases) were used as regulators to build the kinase‐substrate network. For this, the Spearman and Pearson correlation between a regulator and the rest of differentially phosphorylated peptides was calculated as described previously (Fig. S2b, see later) (Walley et al., 2013; Clark et al., 2021).

Gene regulatory network reconstruction

A curated list of transcriptional regulators was used to identify quantified TFs in our datasets (Dataset S2) For the abundance GRN, TF protein abundance (when quantified) or TF transcript abundance (when cognate protein was not quantified) was used as the ‘regulator’ value to infer their ‘target’ transcript abundance. The phosphosite GRN, uses the TF phosphorylation intensity value as ‘regulator’ to predict ‘target’ transcript abundance. In order to mix different data sources (i.e. proteomics, phosphoproteomics, and transcriptomics) into consolidated tables, expression values for each ‘omics’ were rank‐normalized using the norm.rrank function from the R package ‘demi’ (Ilmjärv et al., 2014). In both networks, transcript abundance was used to build the target tables.

Network inference was achieved using a modified version of the Genie3 random forest algorithm (Huynh‐Thu et al., 2010) in the Sc‐ion pipeline v.2.1 (10.5281/zenodo.5237310) with no clustering, as described before (Clark et al., 2021). Results were visualized in Cytoscape v.3.9.0 (Shannon et al., 2003).

Integrated value of influence (IVI) score was calculated using the ‘influential’ and ‘igraph’ R packages following developer instructions (Csardi & Nepusz, 2006; Salavaty et al., 2020). A table with all the network interactions, where the first column had the regulator's gene ID and the second column had the corresponding target's gene ID, was parsed using an in‐house script (See the Data availability section for the full analysis script).

Brassinolide response assays

Seeds were vapor‐phase sterilized in chlorine gas, stratified at 4°C for 1 wk, and germinated in on petri plates containing half‐strength Linsmaier & Skoog media (½LS, cat. no. LSP03; Caisson Labs, Smithfield, UT, USA) in 0.7% Phytoblend (PTP01; Caisson Labs), supplemented with 1% sucrose and either dimethyl sulfoxide or 100 nM brassinolide (BL). Seedlings were grown for 7 d at 22°C : 18°C, day : night, and 16 h : 8 h, light : dark, 40% relative humidity, and light intensity of 120 μmol m−2 s−1. Seedlings were imaged and hypocotyl length was measured using Fiji software (Schindelin et al., 2012). Twenty‐four seedlings per mutant were used on each treatment and this experiment was repeated at least two times for those genotypes showing significant response. A generalized linear model with treatment and genotype as factors and controlling for random effects of replicate and plate was applied using the glmmPQL function from the mass R package (Venables & Ripley, 2002), and a threshold of ‘genotype by treatment interaction’. A P‐value < 0.1 in each of two independent experiments was set as the significance cut‐off.

GFP‐ATG8e protoplast assay

Protoplasts were isolated from 20‐d‐old leaves and transformed as described previously (Wu et al., 2009). Protoplasts were observed by epifluorescence microscopy (Carl Zeiss Axio Imager.A2; Carl Zeiss) using a fluorescein isothiocyanate (FITC) filter, and protoplasts with more than three visible autophagosomes were counted as active for autophagy as previously described (Yang et al., 2016; Pu et al., 2017). One hundred protoplasts were analyzed per treatment per genotype and the experiment was repeated three times. For sucrose starvation, transformed protoplasts were incubated in W5 solution without sucrose or with 0.5% (w/v) sucrose as control at room temperature for 36 h in dark before assessing autophagy.

Significance of basal autophagy levels was assessed by two‐sample t‐test whereas a generalized linear model with treatment and genotype as factors and controlling for random effects of replicates was used for autophagy levels under sucrose starvation. A P‐value < 0.05 was used as a cut‐off on both cases.

Western blotting

For BES1 Western blot, 10‐d‐old seedlings were grown on ½LS plates under constant light and then transferred to either liquid ½LS or ½LS with 100 nM BL for 2 h. Seedlings were collected, dabbed dry, and flash frozen in liquid N2. Samples were ground in 2× sodium dodecyl sulfate (SDS) buffer (100 mM Tris–HCl pH 6.8, 4% (w/v) SDS, 20% (v/v) glycerol, 0.2% (w/v) bromophenol blue, 0.2 M β‐mercaptoethanol) and resolved on 8% sodium dodecyl sulfate–polyacrylamide gel electrophoresis followed by Western blotting with anti‐BES1 antibody.

S6K Western blot was done similar to BES1, with the exception that 7‐d‐old seedlings were grown on ½LS plates under constant light and then transferred to either liquid ½LS or ½LS with 1 μM AZD8055 for 2 h. It was then blotted using anti‐S6K1/2 antibody (AS12 1855; Agrisera, Vännäs, Sweden).

Results

Multi‐omics profiling of bin2 and raptor1b mutants provides insights into known and new regulatory roles

To discover novel molecular components linked to BR and/or TORC signaling we performed quantitative multi‐omics. We performed transcriptome, proteome, and phosphoproteome profiling on rosette leaves of 20‐d‐old WT, bin2D (gain‐of‐function), bin2T (bin2 bil1 bil2 triple loss of function), and raptor1b loss of function plants. We quantified transcript levels using 3′ QuantSeq (Moll et al., 2014) and measured protein abundance and phosphorylation state using 2D‐LC–MS/MS on TMT labeled peptides (McAlister et al., 2012; Hogrebe et al., 2018; Song et al., 2018a) (Fig. 1a). From these samples, we detected 23 975 transcripts, 11 183 proteins, and up to 27 887 phosphosites from 5675 phosphoproteins (Fig. 1b; Dataset S3). We found 5653 transcripts and 4001 protein groups (hereafter referred to as proteins) that were DE in at least one mutant when compared to WT (Figs 1c, S1a,b). Gene ontology (GO) analysis of DE transcripts and proteins in bin2D, bin2T, and raptor1b mutants showed enrichment of many terms from similar processes including growth, hormones, stimuli sensing, and stress (Fig. 2a,b; Datasets [Link], [Link]), which is consistent with the known roles of BIN2 and RAPTOR in growth/stress balance and hormonal crosstalk.

Fig. 1.

Fig. 1

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Experimental design, workflow, and data overview. (a) Schematic representation of the multi‐omics processing pipeline for bin2 and raptor1b mutants. (b) Number of total detected transcripts, proteins, phosphoproteins, or phosphosites. na, not applicable. (c) Differentially expressed transcripts, proteins, and phosphorylated amino acids for each analyzed mutant compared to wild‐type.

Fig. 2.

Fig. 2

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Gene ontology (GO) analysis on datasets. Selection of significant GO biological processes among differential expressed transcripts (a), proteins (b), and phosphosites (c), on bin2D (filled squares), bin2T (filled circles), and raptor1b (filled triangles). For transcripts and protein expression, significant terms were selected from both, upregulated and downregulated genes. For phosphosites, terms were selected from those differentially expressed in the directionality of the respective kinase mutant (i.e. upregulated in bin2D, or downregulated in bin2T or raptor1b).

Our phosphoproteomics analysis identified 4153 differentially phosphorylated sites in at least one mutant (Figs 1c, S1c; Dataset S3c). Gene ontology analysis for potential BIN2 target proteins (i.e. those with increased phosphorylation in bin2D or decreased phosphorylation in bin2T) revealed enrichment of terms related to plant growth and development, as well as response to stress and defense, processes in accordance with known functions of BIN2 and its homologs. In addition, response to BR, ABA, and auxin terms were also significant, highlighting once more the close relationship between BIN2 activity and these hormones. Transcriptional regulation‐related terms were significantly enriched in the bin2D dataset, consistent with known impacts of BIN2 on TFs (Fig. 2c; Dataset S6). Finally, we assessed GO enrichment for proteins with decreased phosphorylation in raptor1b. We found that most of the enriched terms were related to growth, autophagy, starvation, auxin, and BR response. This is consistent with the known biological role of RAPTOR and suggests a cross‐regulation between BR and TORC pathways via phospho‐signaling (Fig. 2c; Dataset S6).

Phosphoproteomic analysis of bin2 mutants shows enrichment of BIN2 direct targets

Because BIN2 is a kinase, we hypothesized that phosphosites increased in the bin2D gain‐of‐function mutant or decreased in bin2T may be direct BIN2 substrates. To test this hypothesis, we generated a proteome‐wide dataset of BIN2 direct targets using the MAKS (Brumbaugh et al., 2014; Jayaraman et al., 2017) (See the Materials and Methods section for details). We quantified a total of 10 375 phosphosites accounting for 3628 phosphoproteins from this assay (Dataset S3c). As expected, the obtained phosphoproteome was heavily skewed toward increased phosphosites, with 1343 phosphosites increasing following incubation with GST‐BIN2 (Fig. 3a; Dataset S3c). Among proteins with increased phosphorylation we observed YDA and BSK1, two known BIN2 targets (Kim et al., 2012; Sreeramulu et al., 2013). To evaluate this set of phosphorylation sites as BIN2 kinase‐substrates, we performed motif enrichment analysis and found a significant enrichment of the well‐known GSK3 motif ‘S/T‐X‐X‐X‐S/T’ (Fiol et al., 1987; Youn & Kim, 2015) among the increased phosphosites (P < 0.01, Fig. 3b; Dataset S7a). Additionally, another highly enriched motif found in the analysis was ‘S/T‐P’, which is reported as a motif for GSK3, CDK, and MAPK families (Amanchy et al., 2007; Lin et al., 2015) (Dataset S7a). Some previously unreported length variations of the GSK3 motif were also significantly enriched (i.e. S/T‐X‐X‐S/T, S/T‐X‐S/T, and S/T‐S/T, Dataset S7a). These results support the robustness of our BIN2 kinase dataset and suggest a more flexible substrate recognition motif for BIN2 as a GSK3‐like kinase.

Fig. 3.

Fig. 3

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Phosphoproteomic analysis on bin2 mutants shows significant enrichment of BIN2 direct targets. (a) Volcano plot of phosphorylation sites from a multiplexed assay for kinase specificity (MAKS) on Arabidopsis leaf protein extracts incubated with recombinant GST or GST‐BIN2. Significantly increased phosphosites are colored blue (q < 0.1). (b) De novo motif enrichment analysis showed high enrichment for the GSK3 motif on BIN2‐related phosphoproteomic datasets. Motif score and FoldEnrich values are calculated by motifer, while P‐value was calculated using hypergeometric testing. (c) Venn diagrams show overlap between BIN2 direct targets (i.e. those upregulated in BIN2 MAKS) and phosphosites (upper) or phosphoproteins (lower) upregulated in bin2D (left) or downregulated in bin2T (right) mutants. Numbers below each Venn diagram represent the overlapping percent for that mutant (purple = bin2D, orange = bin2T). Phosphosite overlaps were calculated using a 20 amino acid window, centered on the differentially regulated phosphosite (for details see the Materials and Methods section). Statistical significance was calculated using hypergeometric testing (*, P < 0.05; **, P < 0.01).

We next assessed the prevalence of BIN2 direct targets present in our in vivo profiling of bin2D and bin2T mutants. For this, we initially performed motif enrichment analysis on phosphosites perturbed in the expected direction for BIN2 targets (i.e. either upregulated in bin2D or downregulated in bin2T). A significant enrichment for the GSK3 motif was found in both bin2D up (P < 0.01) and bin2T down (P < 0.01) phosphosites (Fig. 3b; Dataset S7b,c). Next, we looked at the overlap with BIN2 direct targets identified in the MAKS experiment. For bin2D, 17.0% (41/241; P < 0.01) of the total differentially upregulated phosphosites and 23.6% (38/161; P < 0.01) of the upregulated phosphoproteins were also BIN2 direct targets. For bin2T, 13.8% (77/558; P < 0.05) of the downregulated phosphosites and 23.3% (117/503; P < 0.01) of the downregulated phosphoproteins were also part of our BIN2 direct substrate list (Fig. 3c; Dataset S8). These results indicate that a subset of the BIN2‐dependent phosphosites identified by in vivo mutant profiling may be direct BIN2 substrates.

Kinase‐signaling network inference on bin2 and raptor1b mutants

Since both BIN2 and RAPTOR1B (TORC) participate in phosphorylation‐based signaling, we reconstructed the molecular relationships of these signaling networks. To do so, we used our data to infer a kinase‐signaling network for each mutant (i.e. bin2D, bin2T, and raptor1b). To build these networks, we inferred the activation state of kinases in our dataset. The A‐loop is a well‐conserved region inside the kinase domain whose phosphorylation is necessary for kinase activation (Adams, 2003; Ahiri, 2019). Thus, phosphosite intensity level of the A‐loop can be used as a proxy for kinase activity quantification (Walley et al., 2013; Beekhof et al., 2019; Schmidlin et al., 2019; Clark et al., 2021). Initially, we performed a whole‐proteome Arabidopsis in silico A‐loop prediction and were able to identify this region on 1360 proteins (Dataset S9a). Subsequently, we identified kinases whose A‐loop phosphosite intensity was differentially regulated in at least one of the profiled mutants (Fig. S2a; Dataset S9b).

We found 27, 21, and 24 kinases exhibiting an altered activation state in the bin2D, bin2T, and raptor1b mutants, respectively (Dataset S9b). Using this information, we inferred a kinase‐signaling network by correlating phosphosite level with kinase activation state (Fig. S2b). A network containing 4138 nodes, representing 33 activated kinases and 2284 target sites arising from 1853 possible substrate proteins was obtained (Fig. 4a; Dataset S10). To evaluate this kinase‐signaling network, predicted BIN2 targets were obtained (i.e. nodes connected by edges directed outward of BIN2), and motif enrichment analysis was performed. As expected, the GSK3 motif was enriched among BIN2 targets (P = 1.96e‐06). Additionally, the MAPK consensus motif P‐X‐[pS/pT]‐P was overrepresented among MPK6 targets (P < 0.01). Several variants of the proline‐directed phosphorylation motif [pS/pT]‐P were significantly enriched among targets of MPK4 (P < 0.001), MPK10 (P < 0.001), and BIN2 (P < 0.001) (Amanchy et al., 2007; Lin et al., 2015; Rayapuram et al., 2021) (Fig. 4b; Dataset S11). Finally, 70% (7/10) of known BIN2 targets reported in the literature and present in our network were correctly predicted as BIN2 targets (either as direct targets or direct downstream second neighbor, Dataset S10b). These results support the target prediction value of our inferred kinase signaling network.

Fig. 4.

Fig. 4

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Kinase signaling network. (a) A signaling network was inferred using phosphoproteomic data from bin2D, bin2T, and raptor1b mutants. Activated kinases are shown as named circles with their size representing the number of predicted targets (i.e. node outdegree). Target proteins are represented as small, purple circles. (b) De novo motif enrichment analysis among predicted direct targets (i.e. node first neighbors) for BIN2, MPK4, and MPK10 showed high enrichment for the GSK3 motif ([S/T]‐X‐X‐X‐[S/T]) and GSK3/MPK3 motif ([pS/pT]‐P). Analysis on MPK6 predicted direct targets showed significant enrichment of MPK3/6 motif (P‐X‐[pS/pT]‐P). Enrichment analysis was done on a 14 amino acids window, centered on target phosphosites. Motif enrichment significance was assessed through hypergeometric test.

Integrative multi‐dimensional signaling network reconstruction reveals proteins required for normal BR response and autophagy

We have previously shown that using multiple omics datasets can increase the predictive power of GRN inference (Walley et al., 2016). We inferred two separate TF‐centered GRNs using the SC‐ION pipeline (Fig. 5) (Clark et al., 2021). In the ‘abundance GRN’, TF protein abundance (when quantified) or TF transcript abundance (when cognate protein was not quantified) was used as the ‘regulator’ value to infer their ‘target’ transcript abundance. (Fig. 5, blue line). In the ‘phosphosite GRN’, the quantified TF phosphorylation intensity value was used as a ‘regulator’ to predict ‘target’ transcript abundance (Fig. 5, green line). These two GRNs were integrated with the kinase‐signaling network to provide a multi‐layered portrait of signaling cascades dependent on BR and TOR (Fig. 5; Dataset S12). In this network, there are 2272 BIN2‐responsive nodes (kinases, regulatory TFs, or targets) that are present based on regulatory inference being made using information from mutants with altered BIN2 levels (Fig. 5, left side) and 2370 RAPTOR1B/TORC‐dependent nodes (Fig. 5, right side). Additionally, 1044 nodes are linked to both BIN2 and RAPTOR1B, elucidating novel molecular connections between both pathways (Fig. 5, center). To identify important network regulators, we calculated the IVI score for each node, which integrates network centrality measurements into one normalized value to account for each node's ranked importance in the analyzed network (Salavaty et al., 2020) (Table 1; Dataset S12b). Among the top 10% most influential nodes, we observed well‐known BR signaling regulators such as BAK1, BEH1, BES1, BIM1, BIN2, and BSK1, as well as previously reported TOR targets such as VIP1, RBR1, and HAG1 (Van Leene et al., 2019).

Fig. 5.

Fig. 5

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Multi‐dimensional integrative network. Kinase‐signaling network (purple lines), abundance gene regulatory network (GRN) (blue lines), and phosphosite GRN (green lines) were reconstructed for each mutant (i.e. bin2D, bin2T, and raptor1b) using transcriptomic, proteomic and phosphoproteomic information and merged into an integrative network (see the Materials and Methods section).

Table 1.

Top ranked Arabidopsis thaliana regulator and target genes co‐regulated by BIN2 and RAPTOR1B.

Gene ID Gene symbol Outdegree Indegree IVI score Node type Mutant Network
AT3G48360 BT2 1024 63 66.51 TF CoReg GRN
AT3G02830 ZFN1 1414 37 62.48 TF CoReg GRN; KS
AT2G13800 SERK5 825 36 58.79 Kinase CoReg GRN; KS
AT1G42990 BZIP60 911 84 56.04 TF CoReg GRN
AT4G23810 WRKY53 1010 87 54.60 TF CoReg GRN
AT1G15340 MBD10 857 99 54.06 TF CoReg GRN; KS
AT5G22380 NAC090 854 83 53.73 TF CoReg GRN
AT2G17040 NAC036 897 87 48.92 TF CoReg GRN
AT5G07100 WRKY26 554 70 44.24 TF CoReg GRN
AT3G48430 REF6 907 78 43.79 TF CoReg GRN; KS
AT2G35050 AT2G35050 684 50 43.18 Kinase CoReg GRN; KS
AT4G20400 JMJ14 946 63 42.14 TF CoReg GRN; KS
AT5G59010 BSK5 687 56 39.85 Kinase CoReg GRN; KS
AT4G12610 RAP74 758 69 38.98 TF CoReg GRN; KS
AT3G02380 COL2 772 78 37.18 TF CoReg GRN
AT1G71692 AGL12 917 12 37.17 TF CoReg GRN
AT5G18620 CHR17 785 68 37.04 TF CoReg GRN
AT2G36350 KIPK2 710 53 36.47 Kinase CoReg GRN; KS
AT4G34290 AT4G34290 634 43 35.10 TF CoReg GRN
AT4G35570 HMGB5 813 7 32.75 TF CoReg GRN
AT5G46710 AT5G46710 618 84 30.60 TF CoReg GRN
AT4G23740 AT4G23740 594 55 29.82 Kinase CoReg GRN; KS
AT5G58140 PHOT2 0 92 2.281 Target CoReg GRN; KS
AT1G72150 PATL1 0 84 2.160 Target CoReg GRN; KS
AT5G16880 TOL1 0 82 2.133 Target CoReg GRN; KS
AT4G39680 AT4G39680 0 93 2.104 Target CoReg GRN; KS
AT2G19910 RDR3 0 93 2.029 Target CoReg GRN
AT5G26860 LON1 0 74 2.022 Target CoReg GRN; KS
AT4G31430 AT4G31430 0 69 1.990 Target CoReg GRN; KS
AT4G02510 TOC159 0 81 1.988 Target CoReg GRN; KS
AT3G03960 CCT8 0 59 1.986 Target CoReg GRN; KS
AT1G22530 PATL2 0 66 1.984 Target CoReg GRN; KS
AT1G62390 Phox2 0 67 1.984 Target CoReg GRN; KS
AT3G21060 RBL 0 72 1.979 Target CoReg GRN; KS
AT3G25130 AT3G25130 0 78 1.976 Target CoReg GRN
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Next, we examined the integrative signaling network to determine whether these predictions identified proteins involved in BR response and/or TORC‐response. To assess this question, we enlisted those proteins being differentially phosphorylated simultaneously in either of the BIN2 mutants (i.e. bin2D or bin2T) and in raptor1b (Fig. 6). When selecting candidates, we focused our attention on the 1044 ‘co‐regulated’ genes present in the integrative network (Fig. 5, center). We next filtered to keep those proteins up‐phosphorylated in bin2D and those down‐phosphorylated in bin2T since this phosphorylation ‘directionality’ could pinpoint those proteins direct or indirectly affected by BIN2 activity. Finally, we fine‐tuned this selection to identify possible BIN2/RAPTOR1B co‐regulated genes by keeping only those proteins exhibiting differential phosphorylation in raptor1b (Fig. 6). This selection criteria gave us a total of 48 candidate genes. We were able to obtain viable mutants for 41 of these genes to perform a reverse genetic screen for altered BR responses and autophagy phenotypes (Dataset S13). In BR induced hypocotyl elongation assays mutants in 31.7% (13/41) of the genes showed a significantly altered BL response (Fig. 7; Dataset S13a). To place the 13 genes with altered hypocotyl growth as upstream or downstream of BES1 we measured BES1 phosphorylation state in these mutants by Western blotting. BES1 accumulation in the unphosphorylated form in response to BL still happens in most tested mutants and no obvious relationship between BL effect on hypocotyl growth and BES1 phosphorylation changes upon BL treatment were noticed, suggesting that perturbations on these mutants happen downstream of or in parallel with BES1. The mechanisms by which these genes affect BR responses remain to be determined in future studies (Fig. S3). We next measured autophagy levels as a readout of TORC activity, a total of 29 candidate genes that showed significantly altered hypocotyl elongation in response to BL and/or exhibited decreased phosphorylation in raptor1b mutant were examined for autophagy activity by transient expression of a GFP‐ATG8e marker, which labels autophagosomal membranes, in protoplasts obtained from mutant lines (Contento et al., 2005). Twenty genes (71.4% of assayed candidate genes) showed significantly altered basal autophagy levels when mutated, with 15 of them being higher than WT and five lower than WT (Fig. 8; Dataset S13b). GFP‐ATG8e was also assessed under sucrose starvation for the same genotypes. Nineteen genes (67.9% of assayed genes) showed significant changes in autophagosome number under sucrose starvation conditions. Interestingly, there was little to no increase in autophagy upon sucrose starvation in the five genotypes with low basal autophagy (Fig. 8; Dataset S13b). We further examined the mutants showing altered autophagy by assessing phosphorylation of S6K, a known TORC substrate, by Western blot (Fig. S4). Most of the mutants showed no clear difference in S6K phosphorylation when compared to WT. However, 3 out of the 20 assayed genotypes (ref6‐1, at3g01160‐1, and pin4‐3) showed a noticeable reduction in phospho‐S6K which was consistent with their corresponding GFP‐ATG8e autophagy levels. This suggests that most of these genes act downstream of TORC. Nevertheless, three of our mutants seem to work upstream of TORC to negatively regulate autophagy.

Fig. 6.

Fig. 6

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Phosphosite levels in bin2D, bin2T, and raptor1b. Scatterplot showing phosphosite intensity log2‐fold change (FC) (mutant/wild‐type) for raptor1b on the x‐axis and bin2D (a) or bin2T (b) on the y‐axis. Red dots indicate phosphosites from selected candidate genes.

Fig. 7.

Fig. 7

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Hypocotyl response to brassinolide (BL) treatment in selected mutant lines. (Upper) Average hypocotyl length on assayed mutants upon BL treatment. Bar plot showing average (n = 24) hypocotyl length on mock (blue bars) or BL (green bars). Wild‐type (WT) hypocotyl measurement in figure is the average across all experiments and is used only as visual reference. Error bars show standard error. (Lower) Heatmap showing hypocotyl length response to BL treatment. Values shown are the log2 fold change (FC) in hypocotyl length (BL/mock); n = 24. *, P < 0.1 was observed in each of two independent experiments using a generalized linear model regression (Supporting Information Dataset S13a).

Fig. 8.

Fig. 8

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Autophagy levels on selected mutants. (a) Percentage of protoplast with high autophagy activity under normal conditions (basal autophagy, blue bars) and upon sucrose starvation (orange bars). (b) Heatmap showing fold change (FC) in protoplasts with active autophagy between mutants and wild‐type (WT) protoplasts. (Upper) Basal autophagy (mutant/WT) log2FC. *, P ≤ 0.05, two‐sample t‐test. (Lower) Mutant autophagy response to sucrose starvation (starvation/mock) vs WT response to sucrose starvation (starvation/mock). Values are log2FC. *, P ≤ 0.05, generalized linear model. For all measurements, 100 protoplast were assessed in triplicate. Error bars show standard error. ╪, Cells from these genotypes did not survive protoplasting. ‡, Cells from these genotypes did not survive sucrose starvation.

In summary, we found a total of 26 genes out of the 41 selected candidates (63.4%) showing an altered response to BR and/or level of autophagy with 11 of them presenting both BR and autophagy phenotypes. We, moreover, found three genes whose mutation can affect TORC activity. These results confirm the robustness of our integrative multi‐omics approach as a way of selecting candidate proteins related to the BR and/or autophagy pathways.

Discussion

Brassinosteroid and TORC have emerged as two key signaling pathways coordinating growth and stress responses. An outstanding question in the field is the interplay between these two pathways across levels of gene expression. By quantifying multi‐omics data across key genotypes, we generated a kinase‐signaling network and two TF‐centric GRNs. These networks were then merged into one integrative multi‐dimensional network, which predicted novel genes that function in both shared and unique BR‐TORC pathways. These data were validated with several reverse genetic screens to uncover novel players of BR, TOR and BR‐TORC responses in vivo.

Our work supports previous transcriptome profiling studies of BR (Wang et al., 2014; Kim et al., 2019; Liu et al., 2020) and TOR signaling (Ren et al., 2012; Xiong et al., 2013; Dong et al., 2015). In addition, this study provides comprehensive phosphoproteomic data underpinning BR signaling via BIN2. Furthermore, a global catalog of potential direct BIN2 substrates was generated using MAKS. In terms of TORC signaling, we substantially expand on the work of Salem et al. (2018), which provided an initial description of proteins that are mis‐expressed in raptor1b as well as the proteins and phosphorylation sites that respond to TOR inhibition via treatment with Torin 2, AZD8055, or rapamycin (Van Leene et al., 2019; Scarpin et al., 2020). Most importantly, through the generation and analysis of these multi‐omics data we found a large overlap of gene‐products (i.e. transcript, protein, or phosphosites) whose level is altered in response to the misexpression of both BIN2 and RAPTOR1B. Additionally, 22 of the potential BIN2 substrates identified in the MAKS experiment were previously identified by Van Leene et al. (2019) as TORC targets. Together our data suggest extensive interplay between BR‐ and TORC‐dependent signaling pathways.

Using our multi‐omics data, we reconstructed an integrated gene regulatory and kinase‐signaling network. By focusing on the 1044 regulators and targets predicted by this regulatory network to be co‐regulated by BIN2 and RAPTOR1B (Fig. 5, center), and accounting for the phosphosites FC ‘directionality’ on each mutant (Fig. 6), we identified and tested a set of 41 candidate genes for their involvement in BR/TORC signaling pathways.

To summarize these phenotyping results, we divided our gene set into groups according to their different phenotypes in BR‐regulated hypocotyl elongation and autophagy levels as a readout of TORC activity (Fig. 9). TOR signaling is known to be positively regulated by auxin and glucose availability (Xiong et al., 2013; Li et al., 2017; Schepetilnikov et al., 2017). Here, we found that loss of the auxin efflux carrier PIN4 (Friml et al., 2002) exhibited increased autophagy and reduced TOR activity (Fig. 9, purple). In additional, we discovered homologs of proteins involved in autophagy and mTOR signaling in human. Homo sapiens (Hsa) PTEN has been shown to negatively regulate both mTOR signaling and autophagy through independent pathways (Errafiy et al., 2013). In agreement, our results show that Arabidopsis PTEN3 mutant plants have increased autophagy (Fig. 9, purple). Conversely, HsaHMGB1 can translocate to the cytoplasm and induce autophagy upon perception of reactive oxygen species (Tang et al., 2010). Here, AtHMGB1 mutants show reduced sensitivity to BR and increased autophagy levels (Fig. 9, dark green), suggesting an opposite function in plants.

Fig. 9.

Fig. 9

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Proposed model of interaction for significant genes. Genes with significant response to brassinolide or altered autophagy levels are organized into groups according to their mutants' phenotype. Solid, blunt‐ended, lines represent the possibility of negative regulation whereas solid arrows are possible positive regulation. Dashed arrows depict possible upstream regulation of Target of Rapamycin Complex (TORC).

Finally, our results identified novel components of BR and autophagy pathways in planta. For example, loss of MPK6 leads to reduced BR sensitivity and increased autophagy levels (Fig. 9, dark green). Although no direct role as a BR‐induced growth has been established, MPK6 kinase is involved in a myriad of processes and has been shown to directly phosphorylate and activate BES1 to increase immune response (Kang et al., 2015). Furthermore, BIN2 can phosphorylate and inhibit MKK4, a direct MPK6 activator (Khan et al., 2013). Moreover, our signaling network prediction situates MPK6 as potentially being upstream of RAPTOR1B.

In summary, this study builds upon previous findings that connect BR and TORC in the regulation of plant growth and stress responses (Zhang et al., 2016; T. M. Nolan et al., 2017, 2020; Xiong et al., 2017; Vleesschauwer et al., 2018; Liao & Bassham, 2020). Our multi‐omics studies provide genome‐wide evidence for extensive interactions between BR and TORC signaling pathways across different gene expression levels. These results establish an integrative signaling network that defines molecular interactions between BR‐ or TORC‐regulated growth and autophagy.

Author contributions

CM, TMN, HG, DCB, YY and JWW planned and designed the research. CM, PW, C‐YL, TMN, GS, NMC and JME performed experiments and analyzed data. CM, TMN, DCB, YY and JWW wrote the manuscript.

Supporting information

Dataset S1 Mutant lines used for this study.

Click here for additional data file. (24KB, xlsx)

Dataset S2 Curated list of Arabidopsis transcription factors and other TRs.

Click here for additional data file. (183KB, xlsx)

Dataset S3 Transcriptomic, proteomic, and phosphoproteomic datasets.

Click here for additional data file. (38.2MB, xlsx)

Dataset S4 Gene ontology analysis for bin2D, bin2T, and raptor1b DE transcripts.

Click here for additional data file. (212KB, xlsx)

Dataset S5 Gene ontology analysis for bin2D, bin2T, and raptor1b DE proteins.

Click here for additional data file. (333.1KB, xlsx)

Dataset S6 Gene ontology analysis for bin2D, bin2T, and raptor1b differentially phosphorylated proteins.

Click here for additional data file. (191.5KB, xlsx)

Dataset S7 Motif enrichment analysis on BIN2 maks and bin2 mutants.

Click here for additional data file. (75.3KB, xlsx)

Dataset S8 BIN2 direct targets (BIN2 MAKS) present in bin2D and bin2T phosphoproteome.

Click here for additional data file. (228.5KB, xlsx)

Dataset S9 Activation loop coordinates for Arabidopsis thaliana kinases and list of kinases with activation state being modified in bin2D/WT, bin2TWT, or raptor1b/WT.

Click here for additional data file. (64.1KB, xlsx)

Dataset S10 Kinase signaling network.

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Dataset S11 Motif enrichment analysis on BIN2 and MAPKs targets predicted by signaling network.

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Dataset S12 Integrative network.

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Dataset S13 Brassinosteroid and autophagy response phenotype on mutants from selected candidate genes.

Click here for additional data file. (35.9KB, xlsx)

Fig. S1 Differential expression analysis.

Fig. S2 Workflow of signaling network reconstruction.

Fig. S3 BES1 Western blot.

Fig. S4 S6K1/2 Western blot.

Methods S1 Protein extraction for global proteome and phosphoproteome profiling.

Methods S2 LC–MS/MS.

Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Click here for additional data file. (1.7MB, pdf)

Acknowledgements

This work was supported by the Iowa State University (ISU) Plant Science Institute, Ames, IA, USA (YY and JWW), NIH R01GM120316 (YY, DCB, JWW), NSF IOS‐1818160 (YY and JWW), and USDA NIFA Hatch project IOW3808 funds to JWW. NMC is supported by a USDA NIFA Postdoctoral Research Fellowship (2019‐67012‐29712) and TMN is supported by the National Science Foundation Postdoctoral Research Fellowships in Biology Program (grant no. IOS‐2010686). The authors thank Peng Liu (ISU Department of Statistics) for help in determining statistical analysis for the BL and autophagy phenotype assays. Open access funding provided by the Iowa State University Library.

Data availability

The RNA‐sequencing data generated by this work has been deposited at the National Center for Biotechnology Information (NCBI) Short Read Archive (SRA) as BioProject accession no. PRJNA678744. The original MS proteomics raw data, as well as the MaxQuant output files, can be downloaded from MassIVE (http://massive.ucsd.edu) using the identifier MSV000086460 for bin2D, bin2T, and raptor1b mutants profiling and MSV000086462 for the BIN2 MAKS. All the scripts used for this work are available on the following github repository: https://github.com/chrisfmontes/BIN2_RAPTOR1B_MULTIOMICS.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Dataset S1 Mutant lines used for this study.

Click here for additional data file. (24KB, xlsx)

Dataset S2 Curated list of Arabidopsis transcription factors and other TRs.

Click here for additional data file. (183KB, xlsx)

Dataset S3 Transcriptomic, proteomic, and phosphoproteomic datasets.

Click here for additional data file. (38.2MB, xlsx)

Dataset S4 Gene ontology analysis for bin2D, bin2T, and raptor1b DE transcripts.

Click here for additional data file. (212KB, xlsx)

Dataset S5 Gene ontology analysis for bin2D, bin2T, and raptor1b DE proteins.

Click here for additional data file. (333.1KB, xlsx)

Dataset S6 Gene ontology analysis for bin2D, bin2T, and raptor1b differentially phosphorylated proteins.

Click here for additional data file. (191.5KB, xlsx)

Dataset S7 Motif enrichment analysis on BIN2 maks and bin2 mutants.

Click here for additional data file. (75.3KB, xlsx)

Dataset S8 BIN2 direct targets (BIN2 MAKS) present in bin2D and bin2T phosphoproteome.

Click here for additional data file. (228.5KB, xlsx)

Dataset S9 Activation loop coordinates for Arabidopsis thaliana kinases and list of kinases with activation state being modified in bin2D/WT, bin2TWT, or raptor1b/WT.

Click here for additional data file. (64.1KB, xlsx)

Dataset S10 Kinase signaling network.

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Dataset S11 Motif enrichment analysis on BIN2 and MAPKs targets predicted by signaling network.

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Dataset S12 Integrative network.

Click here for additional data file. (8.3MB, xlsx)

Dataset S13 Brassinosteroid and autophagy response phenotype on mutants from selected candidate genes.

Click here for additional data file. (35.9KB, xlsx)

Fig. S1 Differential expression analysis.

Fig. S2 Workflow of signaling network reconstruction.

Fig. S3 BES1 Western blot.

Fig. S4 S6K1/2 Western blot.

Methods S1 Protein extraction for global proteome and phosphoproteome profiling.

Methods S2 LC–MS/MS.

Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

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Data Availability Statement

The RNA‐sequencing data generated by this work has been deposited at the National Center for Biotechnology Information (NCBI) Short Read Archive (SRA) as BioProject accession no. PRJNA678744. The original MS proteomics raw data, as well as the MaxQuant output files, can be downloaded from MassIVE (http://massive.ucsd.edu) using the identifier MSV000086460 for bin2D, bin2T, and raptor1b mutants profiling and MSV000086462 for the BIN2 MAKS. All the scripts used for this work are available on the following github repository: https://github.com/chrisfmontes/BIN2_RAPTOR1B_MULTIOMICS.

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