bioRxivPlant Biology
Parallel global profiling of plant TOR dynamics reveals a conserved role for LARP1 in protein translation
Abstract
TARGET OF RAPAMYCIN (TOR) is a deeply conserved protein kinase that coordinates eukaryotic metabolism with nutrient availability. In mammals, TOR specifically promotes translation of ribosomal protein mRNAs when amino acids are available to support protein synthesis. The mechanisms controlling translation downstream from TOR remain contested, however, and are largely unexplored in plants. Here, we took parallel global profiling approaches to define the in planta TOR-regulated transcriptome, translatome, proteome, and phosphoproteome. We found that TOR regulates ribosome biogenesis in plants at multiple levels, but through mechanisms that do not directly depend on the canonical 5' oligopyrimidine tract motif (5'TOP) found in mammalian ribosomal protein mRNAs. To investigate this further, we focused on a putative TOR substrate identified in our phosphoproteome: LARP1, a eukaryotic RNA-binding protein that is proposed to mediate TOR translational control of 5'TOP mRNAs in humans and that has gained increased interest because it associates with SARS-CoV-2. By conducting parallel global profiling experiments with larp1 mutants, we discovered that the TOR-LARP1 signaling axis controls 5'TOP mRNA translation in plants and defined a set of conserved eukaryotic 5'TOP mRNAs that encode cyclins, importins/karyopherins, translation elongation factors, and TCTP1, among others. We then identified novel, plant-specific 5'TOP mRNAs involved in critical biological processes, including ribosome biogenesis, chromatin remodeling, and auxin signaling. Our study illuminates the ancestral roles of the TOR-LARP1-5'TOP metabolic regulatory network and provides evolutionary context for ongoing debates about the molecular function of LARP1 in eukaryotic cells.
- organism: plant (other)
- assay: bulk RNA-seq
- assay: proteomics
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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