A Quantitative Chemical Proteomic Strategy for Profiling Phosphoprotein Phosphatases from Yeast to Humans*

A “tug-of-war” between kinases and phosphatases establishes the phosphorylation states of proteins. While serine and threonine phosphorylation can be catalyzed by more than 400 protein kinases, the majority of serine and threonine dephosphorylation is carried out by seven phosphoprotein phosphatases (PPPs). We have developed a chemical proteomic strategy for the systematic and quantitative interrogation of endogenous PPP catalytic subunits and their interacting proteins, including regulatory and scaffolding subunits (the “PPPome”) in human cancer cell lines, mouse tissues, and yeast species. Graphical Abstract Highlights Chemical proteomics strategy for quantitative profiling of phosphoprotein phosphatases. Compatible with quantitative multiplexing approaches. Applicable to many samples types including tissues from human to yeast. A “tug-of-war” between kinases and phosphatases establishes the phosphorylation states of proteins. While serine and threonine phosphorylation can be catalyzed by more than 400 protein kinases, the majority of serine and threonine dephosphorylation is carried out by seven phosphoprotein phosphatases (PPPs). The PPP family consists of protein phosphatases 1 (PP1), 2A (PP2A), 2B (PP2B), 4 (PP4), 5 (PP5), 6 (PP6), and 7 (PP7). The imbalance in numbers between serine- and threonine-directed kinases and phosphatases led to the early belief that PPPs are unspecific and that kinases are the primary determinants of protein phosphorylation. However, it is now clear that PPPs achieve specificity through association with noncatalytic subunits to form multimeric holoenzymes, which expands the number of functionally distinct signaling entities to several hundred. Although there has been great progress in deciphering signaling by kinases, much less is known about phosphatases. We have developed a chemical proteomic strategy for the systematic interrogation of endogenous PPP catalytic subunits and their interacting proteins, including regulatory and scaffolding subunits (the “PPPome”). PP1, PP2A, PP4, PP5, and PP6 were captured using an immobilized, specific but nonselective PPP inhibitor microcystin-LR (MCLR), followed by protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a single analysis. Here, we combine this approach of phosphatase inhibitor bead profiling and mass spectrometry (PIB-MS) with label-free and tandem mass tag (TMT) quantification to map the PPPome in human cancer cell lines, mouse tissues, and yeast species, through which we identify cell- and tissue-type-specific PPP expression patterns and discover new PPP interacting proteins.


In Brief Statement
A "tug-of-war" between kinases and phosphatases establishes the phosphorylation states of proteins. While serine and threonine phosphorylation can be catalyzed by more than 400 protein kinases, the majority of serine and threonine dephosphorylation is carried out by seven phosphoprotein phosphatases (PPPs). We have developed a chemical proteomic strategy for the systematic and quantitative interrogation of endogenous PPP catalytic subunits and their interacting proteins, including regulatory and scaffolding subunits (the "PPPome") in human cancer cell lines, mouse tissues, and yeast species.

PPT
More than three-quarters of all proteins are phosphorylated at one or more sites in human cells (1). A "tug of war" between protein kinases and protein phosphatases establishes the phosphorylation states of proteins to control their function. Proper temporal and spatial coordination of these opposing activities is essential to maintain appropriate phosphorylation site occupancy and regulate cellular signaling. Deregulation of either kinase or phosphatase activities disrupts this balance and is commonly observed in human diseases, including cancer.
Serine/threonine phosphorylation is catalyzed by over 400 protein kinases (2). Intriguingly, the majority (Ͼ 90%) of serine/ threonine dephosphorylation is carried out by the small family of phosphoprotein phosphatases (PPP) 1 , most prominently PP1 and PP2A (3)(4)(5). The PPP family also includes PP2B, PP4 -7, PPT, and PPZ (supplemental Fig. 1). PPP catalytic subunits are among the proteins with the highest degree of sequence conservation from yeast to human: they share the same catalytic mechanism, and the residues required for catalysis are 100% conserved among PPP family members (6,7). The early belief that PPPs are unspecific and that kinases are the primary determinants of protein phosphorylation derived from two observations: the imbalance in numbers of serine and threonine directed protein kinases and phosphatases and the fact that kinases, compared with free PPP catalytic subunits, exhibit greater site selectivity in vitro (7)(8)(9). However, it has recently become clear that PPP specificity and regulation is achieved when catalytic subunits associate with noncatalytic subunits to form multimeric holoenzymes (7). In mammals, the PPP family consists of seven enzymes (PP1, PP2A, PP2B, PP4, PP5, PP6, and PP7). PPP catalytic subunits interact with a diverse array of regulatory and scaffolding subunits (Ͼ200), endogenous protein inhibitors, and substrates to modulate their activity and establish substrate specificity (3). PP1 forms dimeric holoenzymes with more than 150 regulatory subunits (3), while PP2A, PP4, and PP6 mostly form trimeric holoenzymes with greater than 30, five, and six regulatory and scaffolding subunits, respectively (7).
While the combinatorial approach to PPP complexity closes the gap in numbers between phosphorylation and dephosphorylation signaling entities, it also creates an analytical challenge for investigating PPP signaling and its responses to external cues and stresses, drug treatment, and pathological rewiring. To do so, system-wide approaches are needed that capture PPP catalytic subunits and their interacting proteins, the PPPome, as a whole. Indeed, many PPP-associated proteins participate in more than one protein complex, and their associations are spatially and temporally regulated by a plethora of factors (10). Thus, to understand PPP signaling it is essential to investigate the interactions of the catalytic and noncatalytic subunits that constitute the PPP holoenzymes, rather than their protein abundances.
Great progress has been made in deciphering protein phosphorylation by kinases (11), in large part due to a chemical proteomic strategy that utilizes kinase inhibitors immobilized on beads and MS to enrich and quantify large swaths of the human kinome (12)(13)(14)(15)(16)(17)(18)(19). By way of one example, pioneering work utilizing this technology enabled breakthrough discoveries in describing the expressed cancer kinome and its reprogramming upon kinase inhibition (16,17). However, while kinome profiling provides global insights into one aspect of phosphorylation signaling in cancer, we lack this information for the opposing dephosphorylation reaction.
We have developed a chemical proteomic strategy that utilizes an immobilized nonselective PPP inhibitor MCLR combined with MS-based proteomics for the efficient capture, identification, and quantification of endogenously expressed PPPs, including PP1, PP2A, PP4, PP5, PP6, PPT, and PPZ and their interacting proteins in a single analysis (named PIB-MS) (supplemental Fig. 1).

EXPERIMENTAL PROCEDURES
Phylogenetic Analysis-PPP protein sequences from the UniProt database for human, mouse, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Candida albicans were analyzed by http://www.phylogeny.fr/simple_phylogeny.cgi in oneclick mode using MUltiple Sequence Comparison by Log-Expectation (MUSCLE) alignment, Gblocks curation, PhyML phylogeny, and radial tree by TreeDyn tree rendering.
All cell lines were regularly tested for mycoplasma contamination. Mouse tissues from six-week-old male C57BL/6J mice were a gift from Dr. Matthew Havrda (Dartmouth College). Yeast cells were harvested by centrifugation at 8,000 g at 4°C, washed once with 200 ml of ice-cold PBS, and then centrifuged again. The pellet was resuspended in 1/3 w/v ice-cold PBS containing complete protease inhibitors (Roche, South San Francisco, CA) and 1 mM Phenylmethanesulfonyl fluoride (PMSF) and then frozen dropwise in liquid nitrogen. Frozen pellets were lysed by 2 min of grinding in a prechilled coffee bean grinder; lysis efficiency of the resulting powder was ϳ80% as judged by microscopy. S. cerevisiae powder was a gift from Dr. Charles Cole (Dartmouth College). S. pombe powder was a gift from Dr. James Moseley (Dartmouth College). N. crassa powder was a gift from Dr. Jay Dunlap (Dartmouth College). C. albicans powder was a gift from Dr. Lawrence Myers (Dartmouth College). Antibodies against PP1, PP2A, PP4C, PP5C, and PP6C were purchased from Bethyl Laboratories (Montgomery, TX).
PIB Synthesis-PIBs were synthesized as described previously (20). Briefly, 1 mg/ml MCLR in ethanol (1 vol., Millipore, Burlington, MA) was reacted with water (1.5 vol.), DMSO (2 vol., SIGMA, St. Louis, MO), 5 N NaOH (0.67 vol.), and 1 g/ml cysteamine hydrochloride (1 vol., SIGMA). The reaction mixture was briefly purged with N 2 gas and incubated at 50°C for 1 h. The solution was then cooled and mixed with an equal volume of glacial acetic acid, diluted fivefold with 0.1% v/v TFA, followed by dropwise addition of 100% v/v TFA to reduce the pH to 1.5. The sample was applied to a C 18 solid-phase extraction cartridge (ThermoFisher Scientific, Chelmsford, MA) equilibrated with 0.1% v/v TFA. The column was washed with 0.1% v/v TFA in 10% acetonitrile, followed by elution with 0.1% v/v TFA in acetonitrile. The aminoethanethiol-MC eluate was dried by vacuum centrifugation and dissolved in 0.02 ml of methanol. 1.5 g (per mg of MCLR used) of activated CNBr Sepharose beads (GE Healthcare, Marlborough, MA) were swelled in water for 15 min followed by washing with 1 mM cold HCl to make 4.5 ml of bead slurry. The aminoethanethiol-MC was coupled to the activated beads in coupling buffer (100 mM NaHCO 3 , pH 8.2) for 1.5 h at room temperature. The beads were blocked with 100 mM Tris-HCl, pH 8.0, for 1 h, followed by washing six times alternately with 50 mM Tris-HCl, pH 8.0, 0.5 M NaCl, and 50 mM sodium acetate, pH 4.0, 0.5 M NaCl. The beads were stored in 25 mM Tris-HCl, pH 7.5, at 4°C until use.
PIB Titration-Five microliters of packed PIB resin was incubated with increasing amounts of recombinant PP1 in PIB pulldown buffer (50 mM Tris-HCl, pH 7.5, (SIGMA), 500 mM NaCl (SIGMA), 5 mM beta-glycerophosphate (SIGMA), 0.5% Triton X-100 (SIGMA), 0.1 mM DTT (SIGMA), and one EDTA-free protease inhibitor tablet (Roche) per 10 ml of lysis buffer at 4°C for 1 h while rotating. Afterward, PIBs were washed with PIB pulldown buffer and eluted with 1% sodium dodecyl sulfate (SDS) overnight at room temperature. Ten percent of elutes were analyzed by SDS-PAGE gel electrophoresis and visualized by Coomassie Brilliant Blue staining.
PPP Catalytic Subunit Purification and Activity Assay-For PP1␤, PP2AB, PP4C, and PP6C enrichments, 293FT Freestyle cells were transiently transfected with p3XFlag-CMV10-PP1␤, -PP2AB, -PP4C, or -PP6C, respectively. Forty-eight hours after transfection, cells were lysed in PIB pulldown buffer and the lysate was split in a 1:4 ratio to achieve a similar yield of PPP catalytic subunit in enrichments from total lysates versus lysates depleted with PIBs. PIBs were added to the depleted condition, and lysates were incubated for 45 min at 4°C. PIBs were collected by centrifugation and supernatant was transferred to a new tube. PPP catalytic subunits were purified using anti-Flag M2 affinity gel (SIGMA) and eluted with 3xFLAG-peptide (SIGMA) (final concentration 150 ng/l) as previously described (21).
Purifications were performed in triplicates. From each preparation, 10% was removed for trichloroacetic acid (TCA) precipitation and interactome analysis. PPP catalytic subunit abundances in each sample were determined by label-free intensity-based absolute quantification (iBAQ) quantification and confirmed by Western blotting using anti-Flag antibody (SIGMA). Phosphatase activity assays were carried out according to Chattopadhyay et al. (22). Briefly, total and depleted PP6C were diluted to a final concentration of 60  PIB Pulldowns-Cell, mouse tissues, or yeast powder were lysed in PIB pulldown buffer (50 mM Tris-HCl, pH 7.5, (SIGMA), 500 mM NaCl (SIGMA), 5 mM beta-glycerophosphate (SIGMA), 0.5% Triton X-100 (SIGMA), 0.1 mM DTT (SIGMA), and one EDTA-free protease inhibitor tablet (Roche) per 10 ml of lysis buffer) at a concentration of 1 mg/ml lysates were sonicated three times for 10 s with intermittent cooling on ice and clarified by centrifuging at 4°C for 15 min at 21,000 ϫ g.
Lysates were transferred to a new tube, 10 l of solid packed PIB resin were added, and incubated at 4°C for 1 h while rotating. Afterward, PIBs were washed three times with pulldown buffer, and proteins were eluted with 1% SDS (SIGMA) overnight at room temperature. For competition with free MCLR, 1 M MCLR (Millipore) was added to lysates after clarifying for 15 min before addition of PIBs. For competition with okadaic acid (OA), 100 nM OA (LC Labs, Woburn, MA) were added. For MCLR titration, increased amounts of MCLR were added to lysates after clarifying for 15 min before addition of PIBs. Proteins were precipitated by the addition of TCA to a final concentration of 20%, incubated on ice for 15 min and spun at 4°C for 15 min at 21,000 ϫ g, followed by two washes with 10% TCA and two washes with acetone (Burdick & Jackson, Muskegon, MI). Pelleted proteins were dried, resuspended in 25 l of either 25 mM ammonium bicarbonate (SIGMA) for label-free analysis or 133 mM Hepes (SIGMA), pH 8.5, for TMT-labeling containing 50 ng trypsin (Promega, Madison, WI) and incubated at 37°C overnight. For labelfree analysis, digests were quenched with 25 l 5% formic acid (Burdick & Jackson)/50% acetonitrile (Burdick & Jackson), and dried in a vacuum centrifuge. For TMT-labeling, acetonitrile to a final concentration of 20% was added and peptides were transferred to dried, individual TMT reagent (ThermoFisher Scientific), vortexed, and mixed with the reagent. After 1 h at room temperature, each digest was quenched with 3 l of 500 mM ammonium bicarbonate solution for 10 min, mixed into the two sets of multiplexes, diluted threefold with 0.1% TFA in water, and desalted using C 18 solid-phase extraction cartridges (ThermoFisher Scientific). The desalted multiplexes were dried by vacuum centrifugation.
MCLR Titration Data Analysis-For MCLR competitive binding assays in 293FT cell lysates, increasing amounts of MCLR (10 fM to 10 M) were added to lysates and PIB pulldowns were performed. After TCA precipitation and digestion, each condition was labeled with an individual TMT channel and analyzed by LC-MS/MS as described above in technical triplicates. TMT intensities for PP1␣, PP1␤, PP1␥, PP2A, and PP4C-6C were normalized to the maximum measured amount of the respective protein. Curve fitting was performed in GraphPad Prism by least-squares nonlinear regression one site-Fit logIC 50 .
Experimental Design and Statistical Rationale Cell Line Analyses-PIB pulldowns were performed in triplicates or quadruplets from 0.5 mg total protein in the presence or absence of 1 M MCLR and analyzed by label-free LC-MS/MS and data analysis as described above. Common contaminants, including keratin, colla-gen, 40S and 60S ribosomal proteins, and heterogeneous nuclear ribonucleoprotein proteins, were removed from the analysis if they were not previously identified as PPP-binding proteins. For a protein to be considered for further analysis it had to be identified with total peptide count Ͼ 1 in control samples for known PPP subunits or an average total peptide count Ͼ 2 for unknowns, more than one quantification in control samples, and have a unique matches count Ͼ 0. Furthermore, the difference in iBAQ quantification in control or MCLRtreated samples had to be statistically significant (p value Ͻ 0.05; two-tailed Student's t test, unequal variance), or the protein must have been completed competed off by MCLR, and the difference in average iBAQ abundance between control and MCLR-treated samples has to be more than the smallest difference for any catalytic subunit. For comparison of OA and MCLR specificity, missing values were set to log2 of 13 in Perseus (23). To determine binding specificity, control, and MCLR-treated HeLa pulldowns were compared. To distinguish PP1 and PP5 from PP2A, PP4, and PP6 subunits, binding upon MCLR and OA treatment was compared by two-tailed Student's t test, unequal variance, and principal component analysis. For comparison of PPP catalytic subunit abundances between cell lines, the subunit had to be present in all samples. iBAQ abundances were averaged across all replicates on a per-cell-line basis. iBAQ abundances for all proteins present in all samples were summed and total abundances were normalized. Normalized iBAQ abundances were analyzed by hierarchical clustering of rows and columns using the Euclidean distance and average linkage in Perseus (23). For network analysis, proteins identified as specifically interacting with PPP catalytic subunits were compared with PP1␣, PP1␤, PP1␥, PP2A, and PP4C-6C interactors in the BioGRID database (24) and analyzed in Cytoscape (25,26). Potentially new PPP-interacting proteins were connected to known interactors using the STRING database.
Mouse Tissue Analysis-PIB pulldowns were performed in triplicates from 0.5 mg total protein in the presence or absence of 1 M MCLR, TMT-labeled, and analyzed by replicate injection by LC-MS/MS as described above. Common contaminants, including keratin, collagen, 40S and 60S ribosomal proteins, and heterogeneous nuclear ribonucleoprotein proteins, were removed from the analysis if they were not previously identified as PPP-binding proteins. For a protein to be considered for further analysis it had to be identified with a total peptide count Ͼ 1 for known PPP subunits or an average total peptide count Ͼ 2 for unknowns, in at least one of the replicate injections and have unique matches count Ͼ 0. Missing values were imputed in Perseus (23) from a normal distribution to enable visualization by Volcano plot. Statistical analysis of protein quantification was carried out in Perseus (23) by two-tailed Student's t test on a per-tissue-type basis. For comparison of PPP subunit abundances between different tissues, the same internal standard consisting of a PPP pulldown from a mixed lysate of all mouse tissues was included as a seventh channel. TMT intensities were normalized to the internal standard on a per-multiplex basis, followed by normalization to the median of the total amount of catalytic subunits in each channel across all multiplexes. Normalized TMT intensities were analyzed by hierarchical clustering of rows and columns using the Euclidean distance and average linkage in Perseus (23).
Yeast Tissue Analysis-PIB pulldowns were performed in triplicates from 1 mg total protein in the presence or absence of 1 M MCLR, TMT-labeled, and analyzed by replicate injection by LC-MS/MS as described above. Statistical significance of the difference in binding in the presence and absence of MCLR (p value Ͻ 0.05) was determined by two-tailed Student's t test, unequal variance). Protein-protein interactions of specifically identified proteins were extracted from the STRING database and analyzed in Cytoscape (25,26).
Short Linear Motif Analysis-The human, mouse, S. cerevisiae, S. pombe, N. crassa, and C. albicans proteomes were investigated for the presence of short linear interaction motifs (SLiM) in protein sequences using SLiMSearch4 (27 Label-free LC-MS/MS Analysis-PIB pulldowns were analyzed on a Q-Exactive Plus quadrupole Orbitrap mass spectrometer (Thermo-Scientific) equipped with an Easy-nLC 1000 (ThermoScientific) and nanospray source (ThermoScientific). Peptides were resuspended in 5% methanol/1.5% formic acid and loaded on to a trap column (1-cm length, 100-m inner diameter, ReproSil, C 18 AQ 5 m 120 Å pore (Dr. Maisch, Ammerbuch, Germany)) vented to waste via a microtee and eluted across a fritless analytical resolving column (35-cm length, 100-m inner diameter, ReproSil, C 18 AQ 3 m 120 Å pore) pulled in-house (Sutter P-2000, Sutter Instruments, San Francisco, CA) with a 60-min gradient of 5-30% LC-MS buffer B (LC-MS buffer A: 0.0625% formic acid, 3% acetonitrile; LC-MS buffer B: 0.0625% formic acid, 95% acetonitrile). The Q-Exactive Plus was set to perform an Orbitrap MS1 scan (r ϭ 70K; automated gain control [AGC] target ϭ 3e6) from 350 -1500 Thomson, followed by higher energy collisional dissociation (HCD) MS2 spectra on the 10 most abundant precursor ions detected by Orbitrap scanning (r ϭ 17.5K; AGC target ϭ 1e5; max ion time ϭ 75ms) before repeating the cycle. Precursor ions were isolated for HCD by quadrupole isolation at width ϭ 0.8 Thomson and HCD fragmentation at 26 normalized collision energy. Charge state 2, 3, and 4 ions were selected for MS2. Precursor ions were added to a dynamic exclusion list Ϯ 20 ppm for 20 s. Raw data were searched using COMET (release version 2014.01) in high-resolution mode (28)  proteome sequence database with a precursor mass tolerance of Ϯ 1 Da and a fragment ion mass tolerance of 0.02 Da, and requiring fully tryptic peptides (K, R; not preceding P) with up to three miscleavages. Static modifications included carbamidomethyl cysteine and variable modifications included oxidized methionine. Searches were filtered using orthogonal measures including mass measurement accuracy (Ϯ 3ppm), Xcorr for charges from ϩ2 through ϩ4, and dCn targeting a Ͻ1% false discovery rate at the peptide level. Quantification of LC-MS/MS spectra was performed using MassChroQ (30) and the iBAQ method (31).

RESULTS
Synthesis and Assessment of PIBs-MCLR is a naturally occurring small molecule inhibitor of PP1, PP2A, PP4 -6, PPT1, and PPZ1 that inhibits them at nanomolar concentration (31). We began by immobilizing MCLR on activated Sepharose beads (35) to generate PIBs. To assess the capacity of PIBs, we incubated them with varying amounts of recombinant PP1 (supplemental Fig. 2A), washed the beads, eluted PP1, and analyzed the eluates by SDS-PAGE and Coomassie staining (supplemental Fig. 2B), which established the binding capacity of PIBs to be up to 3 mg/ml (supplemental Fig. 2B).
To test the performance of PIBs in removing PPPs from lysates, we performed pulldown experiments from HeLa cells (Fig. 1). Cells were lysed, lysates were split in half, and either control treated or treated with MCLR for 15 min to distinguish specific from unspecific binders. Equal amounts of PIBs were incubated with each lysate, followed by washing, elution, tryptic digestion of the eluates, and analysis by LC-MS/MS, database searching, and iBAQ (supplemental Table I proteins identified in control-and MCLR-treated lysates were compared. Using this approach, we readily detected all MCLR-sensitive catalytic PPP subunits (PP1␣, PP1␤, PP1␥, PP2A, PP4C, PP5C, and PP6C) as well as associated subunits and interacting proteins (supplemental Table I). In the absence of MCLR, the log 2 iBAQ areas of proteins in replicate analyses were comparable (r ϭ 0.92) ( Fig. 2A). However, upon preincubation of lysates with free MCLR, binding of catalytic and associated subunits and PPP interacting proteins was strongly reduced or abolished (Fig. 2B). Statistical analysis of triplicate log 2 ratios of control/MCLR iBAQ areas showed a reproducible reduction in PIB binding upon MCLR addition (supplemental Table I). By comparing PIB binding in the presence and absence of MCLR, we identified 71 known PPP subunits, as well as seven proteins previously not known to be PPP interactors as specifically captured by PIB-MS in HeLa cells (supplemental Table I). In addition, we identified on average 335 proteins that were unspecifically bound to PIBs, even upon addition of MCLR (Fig. 2C). Interestingly, the summed iBAQ area of specific PIB binders was on average 8.8-times higher compared with unspecific binders and was on average 200-fold reduced upon addition of MCLR (Fig.  2D). We also confirmed our label-free MS results by Western blotting (supplemental Fig. 3).
Next, we performed quantitative profiling of PPP binding to PIBs to determine the dose-response of PPP catalytic subunit recovery to free MCLR from 10 fM to 10 M. Consistent with relative IC 50 Table II).
Compared with MCLR, OA displays a 100-fold difference in inhibition of PP1 and PP5C compared with PP2A and PP4C in vitro (36). To determine if we can detect these differences by PIB-MS, we treated HeLa cell lysates with DMSO (control), 100 nM OA or 1 M MCLR, and subsequently performed PIB-MS on these lysates. Indeed, we found that while all PPP subunits were lost upon addition of 1 M MCLR (Fig. 3A), the addition of 100 nM OA selectively disrupted PIB binding to PP2A, PP4C, and PP6C as well as their associated subunits, while PP1 and PP5C were still bound (Fig. 3B). PCA of iBAQ areas of PPP subunits in control, OA-, and MCLR-treated samples clearly distinguished the three groups (Fig. 3C). Thus, through the use of PPP inhibitors with differential PPP inhibition selectivity, it is possible to distinguish PP1 and PP5 and from PP2A-like (PP2A, PP4, PP6) PPP catalytic and associated subunits by PIB-MS.
To evaluate the performance of PIB-MS in detecting these phenomena, we performed PIB pulldowns from lysates of  Table IV). For each cell line, we performed PIB-MS on at least three biological replicates in the presence and absence of MCLR to distinguish specific from unspecific binders. Next, we performed agglomerative hierarchical clustering on averaged iBAQ areas identified for each PPP catalytic subunit and specific binder identified in each cell line (Fig. 4A). Interestingly, this analysis revealed that cell lines clustered according to their cancer type of origin, suggesting that breast cancer and glioblastoma cell lines have specific PPP expression patterns indicative of their origin.
The majority of proteins identified by PIB-MS were known PPP subunits. We identified all MCLR-sensitive PPP human catalytic subunits (PP1␣, PP1␤, PP1␥, PP2AA, PP2AB, PP4C, PP5C, and PP6C). While PP1 forms heterodimeric holoenzymes with regulatory subunits that can also be substrates, PP2A, PP4, and PP6 form heterotrimeric enzymes with regu-latory and scaffolding subunits which in turn recruit substrates. This difference is reflected in the number of regulatory subunits. PP1 interacts with Ͼ150 regulatory subunits in a highly regulated and context-specific manner (3). In contrast, PP2A, PP4, and PP6 holoenzymes are formed in a more constitutive manner and the regulation occurs between regulatory subunits and substrates (51,52). In the PIB-MS analyses, we identified 82 known PPP-associated subunits or interacting proteins, the majority of which (59%) were PP1 specific (Fig. 4B). Furthermore, we detected all known PP4 and PP6 and the majority of PP2A regulatory and scaffolding subunits. We compared the results of the PIB-MS analyses with PP2A interactome datasets (51,53), which revealed that PIB-MS identifies both PP2A scaffolding (2AAA and 2AAB) subunits as well as members of the four families of regulatory B subunits (B55, B56, PR72, and striatins) but not proteins interacting with B subunits (supplemental Table IV). This is consistent with observations made in kinome profiling, where for the most part only the kinase itself or the kinase catalytic subunit in complex with regulatory subunits is detected and quantified but not substrates (15,16).
In addition to known PPP subunits, we also identified 26 proteins that are potentially new PPP-associated subunits or interacting proteins (Fig. 4B). These potentially new PPPassociated proteins included the catalytic subunit of casein kinase II alpha (CSK21), which is implicated in the regulation of many cellular processes, including DNA damage response (54), mitotic progression (55,56), and circadian rhythm (57,58). Others such as GCC1 and SNX1 function in transport through the trans-Golgi network (59 -61), while RAB6A and RAB14 contribute to the transport from Golgi to endoplasmic reticulum (ER) and endosomes (62,63). To see if these interactions were previously observed in large scale proteinprotein interaction analyses, we compared our dataset with the BioGRID (24) and STRING protein interaction databases (Fig. 4B). Indeed, some of the proteins were found in the BioGRID or STRING databases to interact with PPP subunits, including KLC1, IDH3A, CSK2, NSA2, and COX41 (Fig. 4B). To identify if these potentially new PPP-associated proteins are PP1 or PP2A interactors, we investigated their primary amino acid sequences for conserved short linear motifs (SLiM), such as RVxF, SILK, or MyPHoNE, which are required for interaction of PP1 regulatory and catalytic subunits (64), or LxxIxE indicative of PP2A-B56 binding (51, 52) (supplemental Table IV). Based on these predictions, we selected three potential PP1 interactors: KLC1, SMD1, and IDH3A, and one potential PP2A interactor GCC1 for follow-up analyses. The four genes were cloned into a Flag-tag containing vector and individually co-expressed with either myc-tagged PP1␥ or -PP2AB in 293T cells. To determine if these proteins interacted with the predicted PPP, we immunoprecipitated PP1␥ or PP2AB using anti-myc antibody and probed the eluates with antibodies against the myc or Flag epitope by Western blotting (Fig. 4C). In each immunoprecipitation, we detected either myc-PP1␥ or myc-PP2AB. Importantly, we also readily detected the respective PPP interacting, Flag-tagged protein, confirming that these proteins do interact in PPP holoenzymes.
Endogenous Inhibitors of PPPs are Absent from PIB-MS Analyses-Importantly, we did not identify known endogenous inhibitors of PPP activity such as ␣4, SET, IPP1/2, or CP1-17 (3,7,65) as specifically bound in any of our PIB-MS analyses. This was not due to their low expression, weak interaction, or problems with their MS-based detection since they are readily detectable in affinity purifications of catalytic PPP subunits (21). Endogenous PPP inhibitors function in part by displacing regulatory and scaffolding subunits, blocking or distorting the active site of the catalytic subunit, or altering the ability of the PPP holoenzyme to bind to substrates (3,7,65,66). For instance, PP2A, PP4, and PP6 catalytic subunits all bind to ␣4 (also called IGBP1 in humans, TAP42 in yeast, and Tap46 in plants) (67)(68)(69). During holoenzyme biogenesis, ␣4 binds to nascent, partially folded PP2A, PP4, and PP6 catalytic subunits, inhibits their activities and prevents aggregation and polyubiquitination (65, 70 -73). In addition, upon cellular stress (e.g. DNA damage and heat shock), PP2A holoenzymes, and potentially PP4 and PP6 holoenzymes as well, are disassembled and the catalytic subunits are bound by ␣4 (70). Thus, we hypothesized that this inactive subset of PPP complexes might not be accessible for PIB capture. To test this, we compared the relative abundances of interacting proteins in Flag-PP1␤, PP2AB, PP4C, or PP6C affinity purifications from 293FT cell lysates (total) and 293FT cell lysates depleted with PIBs (depleted) (Fig. 5A) ANKRD52  SMEK2  SMEK1  PPP4R1  ANKRD28   TOX4   TPRN  URI1   WBP11   FDPS  PP4C  LTF  GRB2  ITGB1  PP6C  PPP6R2  ANKRD44   LYZ  PP5C  NUDT21 POLDIP3  HSPA8  PPP6R3  HSPA4  PPP4R2  PPP6R1  PPP4R4   NUP153   TOX2   TOX3   MKI67   TOX   Quantification of end-point measurement of PPP catalytic subunit activity purified from total and PIB depleted 293FT cell lysates. (C) Relative quantification of iBAQ abundances of regulatory/scaffolding subunits and endogenous inhibitors (IPP2 and ␣4/IGBP1) from catalytic subunit purifications from total and PIB depleted 293FT lysates. * p value less than 0.05, ** p value less than 0.01, and *** p value less than 0.005. split the lysate from Flag-PPP catalytic subunit transfected 293FT cells in a 1:4 ratio and depleted PPPs from the larger portions of the lysate using PIBs (Fig. 5A). After depletion, Flag-PPP catalytic subunits that did not bind to PIBs were immunoprecipitated from the PIB-depleted lysates. Separately, Flag-PPP catalytic subunits were immunoprecipitated from the remaining nondepleted, total lysate. We then compared the phosphatase activities of equal amounts of PP1, PP2AB, PP4C, and PP6C in enzyme activity assays before and after PIB depletion. Strikingly, phosphatase activity was reduced in the samples after PIB depletion by two-to sixfold (Fig. 5B), supporting the notion that the PPP catalytic subunits not captured by PIBs are likely in inhibitory complexes that block access to the active site and also make them inaccessible to PIB capture. To further investigate this, we compared the interactomes of PPP catalytic subunits from total and PIB-depleted lysates (Fig. 5C). In these analyses, we found that the interactome from PP1␤ catalytic subunits after PIB depletion were enriched in Inhibitor 2 (IPP2), while the interactomes of PP2AB, PP4C, and PP6C were enriched in ␣4/ IGBP1 after PIB depletion. Conversely, the PP4C and PP6C interactomes after PIB depletion were significantly reduced in all regulatory subunit binding. For PP2AB, B55, PR72, and striatin regulatory subunits were decreased, while surprisingly the scaffold 2AAA and B56 subunits were increased. In contrast to IPP2 and ␣4, we do detect 2AAA and B56 subunits by PIB-MS. These observations suggested to us that there might be a population of PP2A-2AAA-B56 complexes for which access for MCLR-Sepharose to the active site is hindered. To further explore this possibility, we compared the structures of PP2A-2AAA-B55, PP2A-2AAA-B56, and PP2A-␣4 (65,74,75). Closer inspection of the PP2A-2AAA-B56 structure showed that the amino acids 92 to 118 are part of an extended loop that is in close contact with the PP2A active site. A similar interaction does not exist in the PP2A-2AAA-B55 structure. Based on these observations, we surmise that under certain conditions, the 92-118 loop in B56 might block access to the active site, which would result in PIB inaccessible PP2A-2AAA-B56 complexes, although future experiments are required to formally test this hypothesis. We could not determine the subunit composition of PIB-bound PPP catalytic complexes because elution of PIBs requires denaturing conditions that result in the disassembly of PPP holoenzymes. Taken together, these results suggest that PIB-MS profiles the catalytically active, MCLR-accessible portion of the PPPome that is not bound to endogenous inhibitors.
Quantitative Profiling of the PPPome in Tissue Samples-A major advantage of PIB-MS is the ability to profile endogenous PPP catalytic and interacting proteins without the need to introduce tags into endogenous PPP loci or overexpress PPPs. To evaluate the performance of PIB pulldown in tissues, we performed PIB pulldowns on six mouse organs (brain, heart, kidney, liver, spleen, and testis). To increase throughput, we combined PIB-MS with isobaric labeling (Fig.  6A). Organs were resected from mice, lysed, and controltreated or treated with MCLR. To compare PPP subunit abundances between different tissues, we included an internal standard composed of a mixed lysate of all mouse tissues. PIB eluates were digested with trypsin per normal, followed by labeling with TMT reagents. The resulting labeled peptides were combined and analyzed by single-shot LC-MS/MS; proteins were identified and quantified based on the intensity of the TMT-reporter ion. Specificity of binding was determined by comparing summed TMT-reporter ion intensities for each protein in biological triplicates treated or untreated with MCLR for each organ type (supplemental Fig. 5, supplemental Table  VI). Using this approach, we were able to identify and quantify MCLR-sensitive PPP catalytic subunits, 76 known associated PPP subunits, and 16 proteins that specifically bound to PIBs but were not previously identified as PPP interacting proteins (Fig. 6B, supplemental Table VI). We observed significant tissue-type-specific expression of PPP subunits and interacting proteins. Taken together, these analyses demonstrated that PIB-MS can easily be performed in tissues and combined with TMT-labeling to achieve quantitative analyses of endogenous PPP expression.
PPP catalytic subunits are highly conserved from yeast to human (7). In addition, fungi express PPT1 and PPZ1, which have similarity with PP5C (76) and PP1 (77) in higher eukaryotes, respectively (supplemental Fig. 1). To extend PPP profiling beyond mammals, we performed PIB-MS experiments in four different yeast species (S. cerevisiae, S. pombe, C. albicans, and N. crassa). Indeed, we were able to capture the homologous catalytic subunits of PP1, PP2A, PP4C, and PP6C, as well as the yeast-specific subunits PPT1 and PPZ1 (Fig. 7, supplemental Table VII). Furthermore, we identified known and new PPP interacting proteins (Fig. 7) supporting our assertion of PIB-MS as a general strategy for PPP profiling across eukaryotes. DISCUSSION In this work, we present a chemical proteomics strategy we call PIB-MS for quantitative profiling of endogenous PPP catalytic and associated noncatalytic subunits from cells and tissues of diverse eukaryotes in a single analysis. Using this approach, we are able to identify and quantify PP1, PP2A, PP4, PP5, and PP6 subunits as well as the yeast-specific PPP family members PPT and PPZ, but not PP2B and PP7 (supplemental Fig. 1). PIB-MS is easily implemented, compatible with quantitative multiplexing approaches such as isobaric labeling, and widely applicable to investigations into phosphorylation signaling by phosphoprotein phosphatases from yeast to humans. In the future, it would be exciting to extend this approach to PP2B and PP7 to profile the whole PPP family using a multiplexed inhibitor bead-based approach, as has been done for human kinases (15,16).   PP4C   SKP1  CC85A  CC85C  FXL16  P2R3A  2AAA  STRP1  FBLN5  STRP2  2ABA  2AAB  2ABD  2ABG  2ABB  HEMH  2A5E  CT2NL  LRRC1  PHOCN  SIKE1  DYH17  RPAP3  2A5A  RAI14  LAP2  TARA  RASF8  2A5G  STRN4  STRN3  STRN  FGOP2  T3JAM   PP2A   PP4C  PPP5  PPP6  PP2AA  PP1G  PP1B  PP1A   catalytic   RRP1B  RIF1  PPR1A  WDR92  TOX4  PPR3A  PPR37  TSKS  PPR18  YLPM1  WDR82  LMTK2  TPRN  CDCA2  RMP  PP16B  CNST  CA114  CASC5  ASPP2  LMTK3  WBP11  PP16A  SETD7  TOX3  TOX  KI18A  RPAB1  ZFY16  CYTSB  IASPP  RAIN  OGT1  MYPT1  PP1R8  MYPT2  PP12C  ASPP1  SH24A

Phosphatase Inhibitor Beads and Mass Spectrometry (PIB-MS)
PPPs achieve substrate specificity through the formation of multimeric holoenzyme complexes of the catalytic subunit with other noncatalytic scaffolding and regulatory subunits. While this strategy greatly expands the number of unique PPP signaling entities, it requires mapping of protein-protein interactions to investigate PPP signaling and its rewiring in cells and tissues upon drug treatments and cellular stresses. Proteome-wide protein expression analyses do not discriminate among the total amount of PPP associated subunits from those in complex with catalytic subunits, missing crucial information essential for deciphering PPP signaling.
PIB-MS bridges this gap by allowing for the facile identification and quantification of endogenous, MCLR-sensitive PPP holoenzymes. Using this approach, we can reproducibly identify and quantify PP1, PP2A, PP4, PP5, and PP6 catalytic subunits and on average 80 known associated subunits from 0.5 mg of total protein lysate (Figs. 1, 4, and 5). A major advantage of this strategy is that it does not require the introduction of tags to PPP catalytic subunits, exogenous expression of tagged PPPs, or the use of PPP-specific antibodies. In-depth analyses of phosphatase protein-protein interactions in protein-centric or proteome-wide analyses have comprehensively annotated associated proteins (3, 7, 40, 78 -81), allowing us to link proteins identified by PIB-MS to their respective PPP catalytic subunits. PIB-MS can also be performed on many samples types, including tissues and tumors. In addition, combining PIB-MS with postdigestion labeling approaches such as multiplexes isobaric labeling allows for the quantitative comparison of PPP expression across many samples types.
As observed with other affinity enrichment strategies that utilize chemical compounds immobilized to Sepharose resin, we detected a large number of unspecifically bound proteins (supplemental Fig. 2). However, pretreatment of lysates with MCLR results in a 200-fold reduction in PIB binding for specific interactors, allowing us to effectively distinguish them from unspecific contaminants. Furthermore, the summed iBAQ area of specific binders is 8.8-times higher compared with unspecific ones. Thus, statistical analysis of replicate analyses readily identifies specifically bound proteins that are potentially new PPP interactors. In addition, we found that comparison of protein binding behavior to PIBs in the presence of MCLR or OA allows us to distinguish PP1 and PP5 from PP2A, PP4, and PP6 subunits (Fig. 3).
Intriguingly, in our analysis of different cell lines, we failed to specifically observe endogenous inhibitors such as ␣4, SET, IPP1/2, CP1-17 (3,7,65) by PIB-MS (Fig. 5). We hypothesized that this was likely due to the ability of these inhibitory proteins to block the PPP catalytic subunit active site. Consistent with this hypothesis, we found that PPP catalytic subunits not bound by PIBs displayed reduced activity in phosphatase activity assays. Furthermore, the interactomes of catalytic subunits not bound by PIBs showed increased binding to endogenous inhibitors (Fig. 5). This suggests that PIB-MS profiles a subproteome of PPP holoenzymes that are actively engaged in phosphorylation signaling.
PPP catalytic subunits are among the most conserved proteins from yeast to human (7). We demonstrate that PIB-MS is not restricted to human cells but can be employed to investigate PPP signaling in mouse and yeast tissues. Thus, PIB-MS is a general strategy for PPP profiling across the eukaryotic kingdom.
To understand how protein phosphorylation regulates complex biological processes, we must investigate both forward and reverse reactions. Although there has been great progress in deciphering signaling by kinases, much less is known about phosphatases. Approaches similar to PIB-MS for kinases have greatly expanded our understanding of the kinome and its rewiring in cancer (13)(14)(15)(16)(17)(18)(19). We anticipate that PIB-MS will yield similar information and greatly complement kinome profiling by providing the to-date missing insights into the dephosphorylation reaction and its regulation and dysregulation in basic biology and disease.