Cell Cycle-Specific Protein Phosphatase 1 (PP1) Substrates Identification Using Genetically Modified Cell Lines

Dorothee C Kommer1, Konstantinos Stamatiou1, Paola Vagnarelli2

  • 1College of Health, Medicine and Life Science, Brunel University London, London, UK.

Insights

This study introduces a novel CRISPR-Cas9 method to identify protein phosphatase 1 (PP1) substrates during the cell cycle. By tagging regulatory subunits, researchers can pinpoint PP1 targets with cell cycle specificity.

Area of Science:

  • Molecular Biology
  • Cell Biology
  • Biochemistry

Background:

  • Identifying protein phosphatase 1 (PP1) holoenzyme substrates is challenging due to diverse regulatory subunits and cell cycle regulation.
  • Existing methods lack cell cycle specificity for PP1 substrate identification.

Purpose of the Study:

  • To develop a novel strategy for investigating PP1 substrates throughout the cell cycle.
  • To generate cell lines with endogenously tagged PP1 regulatory subunits (RIPPOs) for substrate identification.

Main Methods:

  • Utilized CRISPR-Cas9 genome editing to tag RIPPOs with auxin-inducible degron (AID) or ascorbate peroxidase 2 (APEX2) modules.
  • Employed SILAC proteomic-based approaches, including mass spectrometry (MS) with APEX2 for proximity labeling and SILAC phospho-MS after RIPPO degradation via AID.
  • Performed in silico overlap analysis between proximity-labeled and differentially phosphorylated proteomes to identify putative PP1 substrates.

Main Results:

  • Successfully generated cell lines with endogenously tagged RIPPOs using CRISPR-Cas9.
  • Established a workflow combining proximity labeling and degradable protein strategies for substrate identification.
  • Identified putative PP1 substrates enriched by the overlap of proteomic datasets.

Conclusions:

  • The presented strategy enables the investigation of PP1 substrates with cell cycle specificity.
  • This approach provides a powerful tool for understanding PP1 function in dynamic cellular processes.
  • Further validation using methods like FRET, PLA, and in vitro assays is recommended.

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