Integrated omics networks reveal the temporal signaling events of brassinosteroid response inArabidopsis

Abstract

Brassinosteroids (BRs) are plant steroid hormones that are known to regulate cell division and stress response. We used a systems biology approach to integrate multi-omic datasets and unravel the molecular signaling events of BR response in Arabidopsis. We profiled the levels of 32,549 transcripts, 9,035 protein groups, and 26,950 phosphorylation sites from Arabidopsis seedlings treated with brassinolide (BL, most active BR) for six different lengths of time. We then constructed a network inference pipeline called Spatiotemporal Clustering and Inference of Omics Networks (SC-ION) that was able to integrate these multi-omic data into one, cohesive regulatory network. Our network illustrates the signaling cascade of BR response, starting with kinase-substrate phosphorylation and ending with transcriptional regulation. We used our network predictions to identify putative, relevant phosphorylation sites on the TF BRI1-EMSSUPPRESSOR 1 (BES1); the importance of which we experimentally validated. Additionally, we identified an uncharacterized TF, which we named BRONTOSAURUS (BRON), that regulates cell division. Further, we show that bron mutant roots are hypersensitive to BL. Together, this work demonstrates the power of integrative network analysis applied to multiomic data and provides fundamental insights into the molecular signaling events occurring during BR response.

Quantitative proteomics reveals extensive lysine ubiquitination in the Arabidopsis root proteome and uncovers novel transcription factor stability states

“…Statistical analyses. Statistical analyses on the protein abundance and ubiquitination data were performed using TMT-NEAT Analysis Pipeline version 1.3 (https://github.com/nmclark2/TMT-Analysis-Pipeline) (10). An expanded description of statical analyses is provided in the supplemental methods (Supplemental File 1).…”

To the proteome and beyond: advances in single-cell omics profiling for plant systems

“…Thus, recent work has sought to integrate transcriptomics and proteomics data into one comprehensive regulatory network ( Figure 3 ). Specifically, we developed a computational method named Spatiotemporal Clustering and Inference of Omics Networks (SC-IONs), which allows one to construct integrative omics networks from bulk-tissue transcriptome and proteome data ( Clark et al, 2020b ). Additionally, integrative Dynamic Regulatory Events Miner combines static protein–DNA interaction data with time series expression data including transcriptomics, proteomics, epigenomics, and/or single-cell RNA-Seq to generate dynamic regulatory networks ( Ding et al, 2018 ).…”

Library-free BoxCarDIA solves the missing value problem in label-free quantitative proteomics

“… Abstract The last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. This has included the use of both labelled and label-free quantitative liquid-chromatography mass spectrometry (LC-MS) strategies in model 1,2 and non-model plants 3 . While chemical labelling-based workflows (e.g.…”