Dynamics of Protein Synthesis and Degradation in FLG22-Stimulated Arabidopsis

Gene expression is a multi-step process that shapes the most central aspects of a cell including its physiology and metabolism. As such, it is governed by precise regulatory mechanisms so that the protein levels can respond swiftly and precisely to stimuli. The regulatory cascade includes transcription, translation and protein degradation, yet the relative contributions of these regulation levels are not well studied on a systems-wide level, especially for plants. In this study, I quantified four aspects of gene expression on a time-resolved, genome-wide scale using RNA sequencing (RNA-Seq, transcription), ribosome footprint profiling (Ribo-Seq, translation), quantitative mass spectrometry with spike-in stable isotopic labelling with amino acids in cell culture (SILAC MS, protein abundance) and dynamic SILAC MS (protein degradation). All datasets were obtained from the same Arabidopsis suspension cell cultures after stimulation with the pathogen-associated molecular pattern (PAMP) flg22, the elicitor-active epitope of bacterial flagellin, or under control conditions. In the process of obtaining these datasets, I developed an improved SILAC method for plant cell cultures, created an optimized Ribo-Seq protocol for plant samples, identified gene-specific differences in translational efficiency, showed that protein turnover is modelled more accurately with a logistic decay model compared to an exponential decay model, and determined the baseline half-lives for over 2500 Arabidopsis proteins. This is the largest plant protein turnover dataset obtained so far and the first one acquired with dynamic SILAC. Statistical analyses of the data revealed that transcription showed the largest response to flg22 stimulation both in size of the fold-changes and the number of regulated genes. The translational response reflected the transcriptional response. In fact, I found only few examples with some regulation of translational efficiency upon flg22 treatment. Analysis of the quantitative proteomics data revealed that some protein abundances did not follow the transcriptional and translational changes. For the proteins that maintained constant protein levels despite transcriptional and translational upregulation, I could show that the protein degradation rates increased correspondingly, counteracting the raised protein synthesis. Downregulation of gene expression is determined by a more gradual reduction in transcription, which slowly affects the protein abundance. The speed in which the protein level responds to the transcriptional downregulation presumably depends on the turnover rate of the individual protein