2021
DOI: 10.1038/s41467-021-26165-3
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Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis

Abstract: Brassinosteroids (BRs) are plant steroid hormones that regulate cell division and stress response. Here we use a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profile the levels of 26,669 transcripts, 9,533 protein groups, and 26,617 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL) for six different lengths of time. We then construct a network inference pipeline called Spatiotemporal Clusteri… Show more

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Cited by 53 publications
(66 citation statements)
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“…In order to infer regulatory relationships between auxin responsive root transcription factors (TFs) and their targets we generated a gene regulatory network (GRN). To reconstruct the predictive GRN we implemented our network inference pipeline, SC-ION, which is an extension of RTP-STAR and has been shown previously to successfully identify novel TF roles in response to hormone treatment (Clark et al, 2019; Broeck et al, 2021; Clark et al, 2021). The resulting GRN consisted of 15,856 nodes (genes) with a total of 86,461 directed edges (Figure 4 and Supplemental Table 3).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In order to infer regulatory relationships between auxin responsive root transcription factors (TFs) and their targets we generated a gene regulatory network (GRN). To reconstruct the predictive GRN we implemented our network inference pipeline, SC-ION, which is an extension of RTP-STAR and has been shown previously to successfully identify novel TF roles in response to hormone treatment (Clark et al, 2019; Broeck et al, 2021; Clark et al, 2021). The resulting GRN consisted of 15,856 nodes (genes) with a total of 86,461 directed edges (Figure 4 and Supplemental Table 3).…”
Section: Resultsmentioning
confidence: 99%
“…Transcription factor (TF)-centered gene regulatory networks (GRNs) were generated using SC-ION version 2.1 (Clark et al, 2021) and annotated maize TFs from Grassius (Yilmaz et al, 2009). We first clustered the transcript data by root region (MZ, EZ, C, or S) using Independent Component Analysis (ICA) (Nascimento et al, 2017) implemented in SC-ION.…”
Section: Methodsmentioning
confidence: 99%
“…transcriptomic, proteomic, and phosphoproteomic data) can increase the predictive power of Gene Regulatory Network (GRN) inference (Walley et al, 2016). With this in mind, we also inferred two separate transcription factor (TF)-centered GRNs, for each of our mutants ( bin2D, bin2T , and raptor1b ), using the SC-ION pipeline (Figure 5) (Clark et al, 2021). In the first network, called “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.…”
Section: Resultsmentioning
confidence: 99%
“…Protein kinases were identified using a modified version of the pipeline described by (Walley et al, 2013;Clark et al, 2021). Briefly, all 35,386 protein sequences available in the TAIR10 annotation (https://www.arabidopsis.org/download_files/Proteins/TAIR10_protein_lists/TAIR10_pep _20101214) were searched for kinase domain using The National Center for Biotechnology Information batch conserved domain search tool (Lu et al, 2020).…”
Section: Kinase Activation Loop Predictionmentioning
confidence: 99%
“…BIN2 may regulate TOR substrate recruitment efficiency or substrate selectivity through direct phosphorylation at the RAPTOR1B Ser916 site. Analysis of a recently published BL time-series dataset 47 indicated that Ser916 phosphorylation status varies in response to different BL treatment times. Consistent with our observations in Figure S2, Ser916 phosphorylation rapidly increased in the beginning of BL treatment and decreased at the later time points.…”
Section: Discussionmentioning
confidence: 99%