Plant Phosphoproteomics: Known Knowns, Known Unknowns, and Unknown Unknowns of an Emerging Systems Science Frontier

Quantitative phosphoproteomic analysis of chitin-triggered immune responses in the plasma membrane of Arabidopsis

Plant diseases seriously damage crop production, and most plant diseases are caused by fungi. Fungal cell walls contain chitin, a highly conserved component that is widely recognised by plants as a PAMP (pathogen-associated molecular pattern) to induce defence responses. The molecular mechanisms that function downstream of chitin-triggered intracellular phosphorylation remain largely unknown. In this study, we performed quantitative phosphoproteomics analysis to study protein phosphorylation changes in the plasma membrane after chitin treatment in Arabidopsis thaliana L. seedlings. Proteins with altered phosphorylation status after chitin treatment participated in biological processes ranging from signalling, localisation, and transport, to biogenesis, processing, and metabolism, suggesting that PAMP signalling targets multiple processes to coordinate the immune response. These results provide important insights into the molecular mechanism of chitin-induced plant immunity.

A Data-Driven Signaling Network Inference Approach for Phosphoproteomics

Proteins are rapidly and dynamically post-transcriptionally modified as cells respond to changes in their environment. For example, protein phosphorylation is mediated by kinases while dephosphorylation is mediated by phosphatases. Quantifying and predicting interactions between kinases, phosphatases, and target proteins over time will aid the study of signaling cascades under a variety of environmental conditions. Here, we describe methods to statistically analyze label-free phosphoproteomic data and infer posttranscriptional regulatory networks over time. We provide an R-based method that can be used to normalize and analyze label-free phosphoproteomic data using variance stabilizing normalization and a linear mixed model across multiple time points and conditions. We also provide a method to infer regulator-target interactions over time using a discretization scheme followed by dynamic Bayesian modeling computations to validate our conclusions. Overall, this pipeline is designed to perform functional analyses and predictions of phosphoproteomic signaling cascades.