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      Inducible degradation-coupled phosphoproteomics identifies PP2A Rts1 as a novel eisosome regulator

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          Abstract

          Reversible protein phosphorylation is an abundant post-translational modification dynamically regulated by opposing kinases and phosphatases. Protein phosphorylation has been extensively studied in cell division, where waves of cyclin-dependent kinase activity, peaking in mitosis, drive the sequential stages of the cell cycle. Here we developed and employed a strategy to specifically probe kinase or phosphatase substrates at desired times or experimental conditions in the model organism Saccharomyces cerevisiae. We combined auxin-inducible degradation (AID) with mass spectrometry-based phosphoproteomics, which allowed us to arrest physiologically normal cultures in mitosis prior to rapid phosphatase degradation and phosphoproteome analysis. Our results revealed that protein phosphatase 2A coupled with its B56 regulatory subunit, Rts1 (PP2A Rts1), is involved in dephosphorylation of numerous proteins in mitosis, highlighting the need for phosphatases to selectively maintain certain proteins in a hypophosphorylated state in the face of high mitotic kinase activity. Unexpectedly, we observed elevated phosphorylation at many sites on several subunits of the fungal eisosome complex following rapid Rts1 degradation. Eisosomes are dynamic polymeric assemblies that create furrows in the plasma membrane important in regulating nutrient import, lipid metabolism, and stress responses, among other things. We found that PP2A Rts1-mediated dephosphorylation of eisosomes promotes their plasma membrane association and we provide evidence that this regulation impacts eisosome roles in metabolic homeostasis. The combination of rapid, inducible protein degradation with proteomic profiling offers several advantages over common protein disruption methods for characterizing substrates of regulatory enzymes involved in dynamic biological processes.

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          The Perseus computational platform for comprehensive analysis of (prote)omics data.

          A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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            STRING v10: protein–protein interaction networks, integrated over the tree of life

            The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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              The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

              The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                24 October 2023
                : 2023.10.24.563668
                Affiliations
                [1 ]Department of Biochemistry, Purdue University, West Lafayette, IN, 47907
                [2 ]Institute for Cancer Research, Purdue University, West Lafayette, IN, 47907
                Author notes
                [* ] Corresponding author: Mark C. Hall, Phone – (765) 494-0714, mchall@ 123456purdue.edu
                Author information
                http://orcid.org/0000-0002-6707-9636
                http://orcid.org/0000-0001-6429-2506
                Article
                10.1101/2023.10.24.563668
                10634780
                37961087
                d506fa07-7250-4c09-a746-bc3a7cd13a37

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.

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                Article

                eisosome,pp2a,rts1,b56,phosphatase,phosphoproteomics,inducible protein degradation,auxin-inducible degradation

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