A Differential Phosphoproteomic Analysis of Retinoic Acid-Treated

A Differential Phosphoproteomic Analysis of Retinoic Acid-Treated P19 Cells Jeffrey C. Smith, Marc A. Duchesne, Pascal Tozzi, Martin Ethier, and Daniel Figeys* Ottawa Institute of Systems Biology and Biochemistry, Microbiology and Immunology Department, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada Received March 6, 2007

External stimuli trigger internal signaling events within a cell that may represent either a temporary or permanent shift in the phosphorylation state of its proteome. Numerous reports have elucidated phosphorylation sites from a variety of biological samples and more recent studies have monitored the temporal dynamics of protein phosphorylation as a given system is perturbed. Understanding which proteins are phosphorylated as well as when they are phosphorylated may indicate novel functional roles within a system and allow new therapeutic avenues to be explored. To elucidate the dynamics of protein phosphorylation within differentiating murine P19 embryonal carcinoma cells, we induced P19 cells to differentiate using all-trans-retinoic acid and developed a strategy that combines isotopically labeled methyl esterification, immobilized metal affinity chromatography, mass spectrometric analysis, and a rigorous and unique data evaluation approach. We present the largest differential phosphoproteomic analysis using isotopically labeled methyl esterification to date, identifying a total of 472 phosphorylation sites on 151 proteins; 56 of these proteins had altered abundances following treatment with retinoic acid and approximately one-third of these have been previously associated with cellular differentiation. A series of bioinformatic tools were used to extract information from the data and explore the implications of our findings. This study represents the first global gel-free analysis that elucidates protein phosphorylation dynamics during cellular differentiation. Keywords: phosphorylation • proteomics • mass spectrometry • methyl esterification • immobilized metal affinity chromatography (IMAC) • stem cells • cellular differentiation • P19 embryonal carcinoma cells • neurogenesis • reverse database search

Introduction Stem cell differentiation is a fundamental biological process that is regulated by many different genes and complex signaling mechanisms that are generally poorly understood.1 Research in this area is complicated by a number of challenges inherent in stem cell culturing; genomics and proteomics experiments often require millions of cells per experiment which can often translate into significant effort and cost. As a result, a number of cellular models have been developed to study cellular differentiation. P19 embryonal carcinoma (EC) cells have been used for several decades as a pluripotent model of cellular differentiation;2 they may be grown indefinitely in culture as undifferentiated cells3 or may be induced to differentiate into derivatives of all three germ layers.3,4 P19 cells have been used to study cardiomyocyte differentiation and muscle cell development,3,4 as well as retinoic acid (RA)-induced neuronal differentiation.5,6 The majority of these reports have focused on specific genes, proteins or protein families; however, RAresponsive genes6 as well as protein expression differences5 have also been elucidated on a global level before and after P19 neuronal differentiation. Relevant to this study is the * To whom correspondence should be addressed. E-mail, dfigeys@ uottawa.ca.

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differentiation of P19 cells into a heterogeneous population of neuroectodermal cells such as neurons and glial cells when aggregated in the presence of RA,7 offering an ideal system with which to study neural fate determination and differentiation in vitro.4,5 Moreover, evidence of similar biomolecular requirements and signaling mechanisms in neurogenesis have been reported in P19 cells and in vivo.8 Reversible protein phosphorylation is a ubiquitous protein post-translational modification (PTM) that is involved in the majority of cellular processes. It influences the functional role of a protein and is a vital component of intracellular communication. It is a reasonable assumption that the phosphorylation states of many proteins are affected during differentiation as the morphological and behavioral characteristics of the cells commonly change.9 Moreover, critical protein phosphorylation sites and kinase expression differences have been elucidated in cellular differentiation.10 Phosphorylation patterns in P19 neuronal differentiation have been investigated;11 kinase12 and phosphatase13 expression differences as well as kinase splice variation14 have been observed. Moreover, the activation of kinases15 and the inactivation of phosphatases16 have been observed during RA-induced differentiation in P19 cells and specific phosphorylation sites have been found on 10.1021/pr070122r CCC: $37.00

 2007 American Chemical Society

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Phosphoproteomic Dynamics of RA-Treated P19 Cells

proteins that are critical to the differentiation process.17 Phosphorylation plays an eminent role in P19 neuronal differentiation; elucidation of the temporal dynamics of this PTM on a global scale would represent novel and insightful information into cellular signaling in this process. Recently, a variety of strategies to elucidate phosphoproteomic information using common analytical tools have appeared in the literature.18 This has led to the cataloguing of a variety of complicated phosphoproteomes from different biological samples using techniques such as protein microarrays19 or mass spectrometry-based approaches.9 Advances in mass spectrometry-based techniques over the past two decades have been a major contributor to the increasing prevalence of phosphoproteomic research in the literature.20 Due to the vast dynamic range of protein concentrations within a cell, and because phosphorylation events are transient, this PTM is often difficult to observe in a complicated protein sample such as a cell lysate. To overcome this, a variety of strategies have been reported that isolate and purify phosphopeptides, either through affinity methods21 or chemical derivatization,22,23 or fractionate a digested protein sample to reduce the dynamic range to a concentration that will allow phosphopeptide observation using MS.24 The use of immobilized metal affinity chromatography (IMAC) to capture and purify phosphopeptides was first described 20 years ago25 and has since been used in conjunction with MS to identify many phosphorylation sites.26 IMAC resins chelate trivalent metal ions which in turn have an affinity toward negatively charged functional groups on peptides including carboxylic acid groups as well as covalently bound phosphate groups. To increase the selectivity of IMAC toward phosphopeptides, O-methyl esterification has been employed to methylate the carboxylic acid groups in a sample and thereby enhance the purity of phosphopeptides;27 this technique has been used to identify phosphoserine (pS) and phosphothreonine (pT) containing peptides in global phosphoproteomic analyses by MS.18 Although the discovery of phosphorylation sites is a critical first step to understand cellular signaling, the dynamics of protein phosphorylation also need to be ascertained to accurately and fully comprehend the roles that this PTM plays in cellular physiology. A plethora of approaches have been used to conduct differential phosphoproteomic analyses including gelbased methods28 and methods comparing the results of separate analyses;29 however, the majority are based on chemical labeling strategies using isotopically labeled reagents. These include stable isotope labeling by amino acids in cell culture (SILAC)30 in conjunction with immunoprecipitation31 or IMAC,32 absolute quantification of abundance (AQUA),33 phosphoprotein isotope-coded affinity tagging (PhIAT),34 enzymatic digestion in isotopically labeled H2O,35 chemical labeling with dithiothreitol,36 and peptide dimethyl labeling combined with IMAC.37 Deuterated or non-deuterated methanolic HCl solutions have also been used to differentially label peptides prior to IMAC enrichment, which offers the advantage of improved IMAC efficiency, as described above; this was quickly reported as an extension of the esterification/IMAC strategy.38 Since that time however, there have been relatively few reports that have used this technique to monitor global phosphorylation changes. Several groups have detailed its use on peptides following antiphosphotyrosine immunoprecipitation to elucidate quantitative information with39 and without40 IMAC enrichment. Differential analysis by methyl esterification offers some unique advantages including a minimal cost per experiment, the

simplicity of the chemistry involved, its compatibility with and ability to enhance phosphopeptide enrichment using IMAC, and that it can be used on bodily fluids or tissues. The present study used isotopically labeled methyl esterification combined with IMAC enrichment and MS analysis on wild type and RA-treated P19 cells to elucidate phosphoproteomic signaling events implicated in neuronal and glial differentiation. We report phosphoproteomic differences in P19 cells before and after treatment with RA and highlight some novel phosphorylation sites on proteins previously known to be involved in neurogenesis, cellular differentiation, as well as RA metabolism.

Methods Cell Culturing. Unless otherwise noted, all reagents were purchased from Sigma Aldrich (Sigma-Aldrich, St. Louis, MO). Digestion, derivatization, and enrichment methods were first optimized using a 1.7 ng/µL mixture of bovine R-casein and β-casein. Figure 1 displays a graphical overview of the methods used. Briefly, murine P19 EC cells were cultured at 37 °C and 5% CO2 in DMEM medium supplemented with 10% fetal bovine serum (GIBCO-BRL, Burlington, ON). Cells were transferred to bacteriological grade Petri dishes for 48 h to allow aggregation; a fraction of them were harvested and the rest were recultured on tissue culture plates in the presence of 1 µM all-trans-RA (all at 37 °C and 5% CO2). RA-containing media was replaced every 48 h, and cells were harvested after 15 days. Cells were maintained between 70 and 80% confluency; microscopy was used to ascertain the health of the cells and monitor the progression of differentiation. Protein Extraction, Digestion, and Derivatization. Cells were lysed and proteins were extracted using Trizol (Invitrogen, Carlsbad, CA) according to manufacturer’s instructions. Proteins were resolublized in 1% SDS; their concentrations were measured using the Bradford assay (BioRad, Hercules, CA) (BSA as standard). Prior to digestion, protein solutions were diluted 5 fold with 50 mM ammonium bicarbonate (pH 7.0); samples were heated to 70 °C for 5 min, allowed to cool to room temperature and digested overnight at 37 °C using a 1:25 (w/w) ratio of sequencing grade porcine trypsin (Promega, Madison, WI). Tryptic peptides were methyl esterified as previously described,27 with modifications. Aliquots (500 µg) of the resulting peptide mixtures were lyophilized in 1.5 mL Eppendorf tubes. One milliliter of d0-methanolic HCl (created by adding 40 µL of thionyl chloride added dropwise per 1 mL anhydrous methanol) was added to each lyophilized aliquot from undifferentiated P19 cells (Day 0); differentiated P19 cells (Day 15) were methyl esterified using d3-methanolic HCl (using deuterated methanol (ACP Chemicals, St. Leonard, QC)). The addition of thionyl chloride to methanol produces a violent reaction and should only be conducted with proper personal protective equipment within the confines of a fumehood. The mixtures were vortexed for 90 s, sonicated for 15 min, and allowed to stand at room temperature for 1 h to allow methyl esterification to occur before they were again lyophilized. Aliquots were then reconstituted in 60 µL of a 1:1:1 solution of 0.1% acetic acid, methanol, and acetonitrile for immobilized metal affinity chromatography (IMAC). Phosphopeptide Enrichment. IMAC columns were constructed by adding a frit to one end of a 20 cm × 200 µm ID fused silica microcapillary (Polymicro Technologies, Phoenix, AZ) using a 1:5.5 mixture of formamide and Kasil #1 (PQ Corporation, Valley Forge, PA), packing Poros 20 MC beads Journal of Proteome Research • Vol. 6, No. 8, 2007 3175

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Figure 1. Overview of methods used: a) P19 cell culturing, aggregation, and treatment with RA; (b) protein extraction, digestion, derivatization, phosphopeptide enrichment, and analysis using LC-MS/MS; and (c) reaction mechanism schematic of peptide methyl esterification. For greater details on methods used, refer to Methods section.

(Applied Biosystems, Foster City, CA) to a length of 8 cm and adding a frit to the open end. The IMAC columns were then rinsed and charged (subsequent 200 µL washes of 100 mM EDTA pH 8.0, water, 100 mM FeCl3, and 0.1% acetic acid) at a flow rate of 20 µL/min using a high-pressure bomb loader (Proxeon Biosystems, Odense, Denmark). One Day 0 aliquot was mixed with one Day 15 aliquot and the peptides were passed over the column at a flow rate of 2 µL/min followed by a rinse solution at the same flow rate for 10 min composed of 100 mM NaCl, 25% acetonitrile, and 0.1% acetic acid. The flow rate was then increased to 20 µL/min for 5 min followed by an additional 5 min in the reverse direction. The column was washed with 0.1% acetic acid for 10 min and the peptides were eluted into a 7 cm × 100 µm ID trap column, fritted, and packed in-house with 5 cm of YMC ODS A reversed phase packing material (Waters, Milford, MA), using 250 mM Na2HPO4 pH 8.0. The trap column was then connected to an Agilent 1100 HPLC (Agilent, Santa Clara, CA) and washed for 10 min at 2 µL/min with 5% acetonitrile, 0.1% formic acid. The trap column was then connected to a 6 cm × 75 µm Picofrit analytical column with a tip opening of 10 µm (New Objective, Woburn, MA), packed with 5 cm of YMC ODS A, for mass spectrometric analysis as well as a diverter valve to split the solvent flow prior to the columns. The steps described above were optimized using d0-methyl esterified samples of Day 0 peptides; high quality data found uniquely using this method were added to the overall results. Chromatography and Mass Spectrometry. Phosphopeptides were eluted from the trap and analytical columns at a flow rate of ∼250 nL/min provided by an Agilent 1100 HPLC (Agilent, Santa Clara, CA) and were analyzed on a QSTAR Pulsar QqTOF mass spectrometer (Applied Biosystems, Foster City, CA) in 3176

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information-dependent acquisition mode. Mass analysis included a 1 s survey scan followed by four 3 s tandem mass spectrometric scans on the most intense peaks in the spectrum; masses could be sequenced twice before being added to an exclusion list for 90 s. The HPLC pumped 0.1% formic acid in water with the following percentage gradient of acetonitrile: 0 min: 5%, 3 min: 15%, 50 min: 40%, 55 min: 50%, and 60 min: 80%. The IMAC/LC-MS/MS experiment described above was repeated 13 times, and the data from all of these experiments were concatenated into one Mascot Generic File (.mgf) file. MS/MS data were searched against the mouse taxonomy of the NCBI nr database using Mascot (Matrixscience Ltd, Boston, MA) with a peptide mass tolerance of ( 25 ppm, a fragment ion tolerance of ( 0.1 Da, and a possibility of 2 missed cleavages. Phosphorylation of S, T, or Y residues, d0- and d3-methyl esterification of D and E residues, as well as d0- and d3-methyl esterification of the C-terminus were selected as a variable modifications. Data Analysis. The Mascot output was formatted to display a peptide summary report using standard scoring for as many proteins that would show up using an ion score cutoff of 0 and the requirement of a bold red peptide in each protein (see http://www.matrixscience.com/help/results_help. html#FORMAT for descriptions of Mascot results terms). The html output was copied to MS Excel (Microsoft, Redmond, WA) and parsed using logical functions. Peptides were deleted from the list if they were not phosphopeptides or if they contained both d0- and d3-methyl esterification modifications. The MS/MS data of matching peptides were then manually inspected; data were deemed acceptable if at least 3 successive y- or b-ions were present (or y-++ or b-++ ions if the charge state of the peptide was greater than 2+); matching b1+ or b1++

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ions were ignored.41 The MS/MS data were then manually reanalyzed to ensure the rms error was less than 250 ppm; data with error levels above this were discarded. The degree of esterification on each peptide was then assessed by comparing the number of sites that were methyl esterified to the number of potential sites (i.e., the number of D and E residues as well as the C-terminus); a simple arbitrary scoring system was created as criteria for spectral acceptance (termed “ascore” to differentiate between Mascot ion scores). MS/MS spectra that matched with only 3 y- or b-ions in a row were assigned an ascore of 0. For every additional b- or y-ion that matched in a row, an ascore of 1 was added (e.g., a spectrum with 5 y-ions in a row would be given an ascore of 2). Additionally, an ascore of 1 was added for every multiple series of 3 or more y- or b-ions a spectrum contained (e.g., a spectrum with 4 y-ion and 3 b-ions would be given an ascore of 1 for the fourth y-ion as well as 1 for the second series of ions (b-series) for a total of 2). For example, if a spectrum had 3 y-ions in a row and 4 y++ions in a row it would be given an ascore of 2. As a final example, if a spectrum matched ions b3, b4, b5, b8, b9, b10, b11, and b12, it would be given an ascore of 3 (2 for having 5 ions in a row, plus one for matching another series of 3 elsewhere). The final ascore for each peptide was compared to the number of missed methyl esterification sites that were allowable; a peptide that had more unmodified possible esterification sites than the ascore assigned to its spectrum was discarded. This entire process was automated and is available for download at www.oisb.ca/downloads.htm. Following the curation processes described above, the extracted ion chromatograms (XICs) of the light and heavy labeled peptides were isolated and their relative abundances were measured by observing the maximum absolute abundance determined from the MS spectrum taken at the maximum of each ion’s XIC. It should also be noted that the elution times differed between the light and heavy labeled peptides; XIC maxima were determined independently for every ion. Peptides with Mascot scores less than 10 were discarded if both the heavy and light versions were not present in the experiment. A “reverse” database was created by reversing the sequences for all mouse proteins in the NCBI nr database and was installed into Mascot; the concatenated differential MS/MS data was run against this and the output was treated as described above. The curation process described above was optimized by monitoring the results from both the reverse and forward database searches. The number of y- or b-ions in a row, the rms error tolerance level, the score below which both isotopic forms of the peptide had to be present in the experiment, and the method used to calculate the ascore value were manipulated to minimize false positive results (i.e., peptides identified in the reverse database) relative to the true positive results (i.e., peptides identified in the forward database). Sequences from spectra that passed all of the criteria were submitted to a BLAST homology search to ensure they were not found in vivo. The MS/MS data of the reverse hit was also compared to its match in the forward database; if the forward database had a better match than the reverse, the reverse hit was removed from the false positive list. Three 1 mg samples of Day 15 peptides were each split into two equal fractions and were methyl esterified using light and heavy reagents, respectively. The two fractions were recombined, and each milligram of differentially labeled peptides was analyzed as described above. The relative abundances of

identical peptides from these samples were measured and used to determine the ratio at which the differences were statistically significant at a 95% confidence interval. The logarithms of the ratios were calculated and were plotted according to their abundance and normalized to a non-logarithmic ratio of 1:1. Unique peptides identified in these experiments were curated as described above and added to the overall results. The logarithms of the Day 0:Day 15 peptide ratios were then calculated and plotted according to their abundance for each IMAC experiment to reveal and account for experimental determinate error; statistically significant Day 0:Day 15 peptide ratios were highlighted and copied to a separate file for interaction analysis (vide infra). All phosphoproteins were then searched on the Phospho.Elm database42 (http://phospho.elm.eu.org/) to probe for previously identified phosphorylation sites. If a protein was not found in the database, synonyms (as suggested by the Swiss-Prot Protein Knowledgebase (http://www.expasy.org/sprot/)) were probed. Previous phosphorylation site data for each protein was compared to the sites identified in the present study. Proteins that contained significantly different abundances of a phosphopeptide between the Day 0 and Day 15 samples were compiled in a list and annotated by their LocusLink identifier using the Ariadne Genomics Protein ID Mapping Search website (http://www.ariadnegenomics.com/services/ idmap.html). The LocusLink identifiers were then loaded into PathwayStudio Version 4.0 (Ariadne Genomics, Rockville, MD) and analyzed using the MSSQL:PS Database of known protein interactions. All proteins that were known to bind to, regulate, or be regulated by the input list of proteins were selected and used to elucidate known signaling pathways in the data. Finally, the flanking residues around each identified phosphorylation site were parsed out of the data (6 N-terminal and 6 C-terminal amino acids around residue). The sequences were submitted to the online motif extractor, motif-X43 (http:// motif-x.med.harvard.edu/) to elucidate overrepresented patterns in the sequences and thereby recognize motifs in the sequences that were being phosphorylated. Recognized motifs were compared to known kinase recognition motifs using the Human Protein Reference Database PhosphoMotif Finder (http://www.hprd.org/PhosphoMotif_finder). All of the peptides were examined to find those that contained motifs known to be recognized by specific kinases.

Results and Discussion It was hypothesized that measurable changes in protein phosphorylation between wild and RA-treated P19 cells would illuminate proteins and signaling pathways that are involved in or affected by the process of differentiation. Accurate measurement of these differences initially required optimization of phosphopeptide identification and quantitation techniques. Here we describe a novel and robust method to classify Mascot output specifically for post-translationally modified peptides, the performance of methyl esterification in phosphopeptide quantitation, and the application of these approaches to study P19 cellular differentiation. Cutoff Scores and False Positive Rates. The tenability of protein/peptide identifications made using global mass spectrometry-based proteomic approaches is currently under intense scrutiny.44 The reality that mass spectrometers with lower mass accuracies lead to significant false positive and false negative rates is emerging in large-scale proteomics studies and, in particular, those involving PTMs.45 In phosphoproJournal of Proteome Research • Vol. 6, No. 8, 2007 3177

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Figure 2. Determination of false positive levels for each Mascot peptide score. The triangular (yellow) and “X” (light blue) hashed lines represent the fraction of peptides that were assigned each Mascot score out of the total number of MS/MS spectra acquired (7614) searching the forward and reverse databases, respectively. The square (red) and diamond (dark blue) hashed lines represent the fraction of MS/MS spectra that passed the curation criteria and were chosen as true and false positive results for the forward and reverse databases, respectively, out of the total number of MS/MS data in a particular Mascot score bin. (Inset) Region of lower Mascot peptide scores (0-17) enlarged.

teomic studies, it is essential that the false positive rate be measured and controlled; therefore, it was closely monitored, and a new phosphopeptide scoring approach was developed to further reduce the false positive rate. Briefly, 13 separate differential IMAC experiments were conducted with d0-labeled Day 0 samples and d3-labeled Day 15 samples. Following data acquisition, the peak lists from all 7614 MS/MS spectra were concatenated into one file and searched with Mascot against forward (normal) and reverse databases to indicate the false positive rate.46 The output was curated in steps, as described in the methods, until the false positive rate was optimized. Figure 2 reveals four different plots that demonstrate the efficacy of these methods to reduce the false positive identifications to an acceptable level. The majority of reports to date have calculated false positive rates on entire results as a whole, grouping all peptide scores above a specific threshold;47 using a QqTOF instrument and Mascot, scores of approximately 15 have been suggested to lead to a 1% false positive rate.48 One would expect that the false positive rate for peptides with scores of 15 would be higher than peptides with scores of 50; therefore, we opted to bin the peptides based on their Mascot scores and calculate false positive rates therein. Interestingly, the forward 3178


database contained roughly twice as many peptides per score bin than the reverse database below scores of 15, which would suggest that hits below these scores are not entirely random false positives or else their frequency of occurrence would be the same. At scores between 17 and 36, an increasing fraction of the spectra passed the acceptance criteria in the forward results while none passed in the reverse results. Similarly, spectra with scores above 36 all passed the acceptance criteria in the forward direction while none passed in the reverse. A decreasing fraction of proposed peptide identifications passed the acceptance criteria as the Mascot score decreased from 16 to 0 in the forward database, as expected, as many identifications are of poor quality at these scores (few matching fragment ions in each spectra). There was, however, a significant portion of the spectra at every score in the forward database that did pass the acceptance criteria, in accordance with previous reports that search algorithm scores are suppressed for phosphorylated peptides.27,40,49-53 Significantly, the acceptance criteria removed all passing spectra in the reverse database results between scores of 0 and 6 and lowered the percentage of passing spectra to less than 10% of the forward database results between scores of 7 to 11 and 13 to 16; the only score that had

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Figure 3. Determination of level of statistical significance for isotopic methyl esterification IMAC/LC-MS/MS measurements at the 95% CI. The frequency of the logarithm of the H/D ratio was plotted against the logarithm of the H/D ratio for Day 15 samples divided into 2, methyl esterified with light and heavy methyl groups, and analyzed. Raw data points are illustrated as red triangles and are connected by a red line. Data was fitted using a Lorentzian equation (solid black line); the error associated with the fitting is shown as a dotted black line. SD and 95% CI calculations (logarithmic and non-logarithmic) are shown in the gray box.

a higher percentage than this was a score of 12 at 15%. Because false positive results have an equal opportunity to randomly

match peptide entries in a forward or reverse database and the reverse database was the same size as the forward database, the number of false positive identifications in the forward and reverse databases were assumed to be equal.54 Therefore, the false positive rate for each Mascot score may be considered to be the same as the percentage of passing spectra in the reverse database compared to the forward database, as described above. It was also observed that the majority of spectra were excluded, particularly at scores below 10, due to Mascot assigning both light and heavy methyl esterification sites on a peptide. Although the search output could have been simplified had it been possible to input constraints that a peptide may only have one or the other modification (either heavy or light, not both), having two isotopes of the modifications available in the search seemed to be effective at filtering out non-peptide MS/MS data and removed the vast majority of low scoring hits in the reverse database. Peptides with scores under 10 that remained after employing this strategy were removed when their corresponding light or heavy counterparts were not observed in the experiment. This approach identifies phosphopeptides without the need for choosing a cutoff score for the output of the search engine. Moreover, these methods reduce the false negative rate by considering data with scores below traditional cutoff thresholds. The concept of considering low scoring data is consistent with approaches currently being pursued in other research groups (Ron Beavis, personal communication). In all, 4544 of the 7614 MSMS spectra were assigned to a peptide by Mascot; following our filtering strategy described above only 587 peptides were determined to be veritable phosphopeptides identified in this study.

Table 1. Proteins Previously Shown to Be Associated with Differentiation NCBI gi number

protein name

differential phosphorylation observed?

31807869 56971256 33243985 18203772 13277669 82885797 25955477 13626040 1731448 14198134 27695358 42475974 13529464 84043961 1575290 55391456 9828173 6272692 82907567 23297191 6678946 13432200 22095015 82883611 745064 1480114 4160288 6755849 6449470 3599511 21618685 66794561 3702844 82932526

Tcof1 protein PDGFA associated protein 1 Minichromosome maintenance deficient 2 mitotin Hepatoma-derived growth factor Hdgfrp2 protein PREDICTED: similar to Paf1/RNA polymerase II complex component Calnexin A kinase (PRKA) anchor protein (gravin) 12 Zuotin-related factor 1 Hmga1 protein Lens epithelium-derived growth factor phosphoinositide-3-kinase, class 3 Nucleolin eukaryotic translation initiation factor 5B p34 cdc2 kinase Eukaryotic translation initiation factor 2B, subunit 5 epsilon actin-associated protein palladin short microtubule-associated protein 1A; short MAP1A PREDICTED: dynein, axonemal, heavy chain 3 modifier of cell adhesion microtubule-associated protein 1 B Microtubule-associated protein tau longevity assurance homolog 2 PREDICTED: similar to centromere protein F (350/400kD) isoform 3 Janus kinase TIF1 beta protein lactosylceramide alpha-2,3-sialyltransferase topoisomerase (DNA) II alpha DNA cytosine-5 methyltransferase 3B1 leukemia/lymphoma related factor LRF P450 (cytochrome) oxidoreductase Ssrp1 protein pituitary tumor transforming gene protein PREDICTED: similar to WNT1 inducible signaling pathway protein 3 precursor (WISP-3)

Yes Yes Yes Yes Yes No No Yes Yes Yes No No No No Yes No Yes Yes Yes Yes Yes Yes No Yes Yes No No Yes Yes No No No No No

reference(s) 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 77 80 81 82 83-85 86 87 88 89 90 91 92 93 94

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Figure 4. Binding interaction network of proteins that showed altered abundance following RA treatment. Shown are proteins that either bound directly to another identified protein or to another identified protein via one other protein. Proteins identified in this study are illustrated as green rectangles (see Table 2 for details); those that have been previously associated with differentiation are indicated with an asterisk (see Table 1 for details). An interactive version of this figure may be viewed at www.oisb.ca/people/jeff/ binding.htm.

Methyl Esterification Variability. The use of methyl esterification in MS-based proteomics studies was introduced in 198655 and used in differential proteomics experiments in 2001.56 As mentioned above, its ability to enhance IMAC phosphopeptide capture has been reported;27 however, this principle has not been universally accepted in the scientific community.57,58 Methyl esterification using chemically identical yet isotopically distinct reagents to aid IMAC phosphopeptide capture and allow relative quantification in phosphoproteomic studies has been reported relatively few times, and only once involving primarily pS and pT residues.38 Our elucidation of 472 phosphorylation sites is thus the largest differential phosphoproteomic study using isotopically labeled methyl esterification to date. One of the drawbacks of methyl esterification chemistry is that the reaction does not go to 100% completion and that the presence of water quenches the reaction. Esterification with anhydrous as well as HPLC grade methanol was performed on protein standards at the advent of this study revealing no significant differences in the completeness of the reaction (data not shown). Moreover, esterification was deemed to be complete on the vast majority of peptides; those that were 3180


not fully reacted likely had concentrations well below the limit of detection and did not play a role in this analysis. Observed peptides that were not fully reacted were often accompanied by a fully reacted counterpart that was of relatively high abundance in the sample (see Supplementary Table S1, Supporting Information). The variability of this labeling technique was estimated to be approximately 5% (based on preliminary esterification experiments with standard phosphoproteins (data not shown)), much lower than the biological variability of the proteins being examined as well as the threshold of significance (vide infra); to this end, error introduced through esterification was not considered significant. Relative Quantitation using Methyl Esterification. Although quantitative phosphoproteomic studies have been reported using isotopically labeled methyl esterification and IMAC,38,39,59 a control experiment to determine the ratio at which differences become statistically significant using these methods has not been conducted. To address this, three 1 mg samples of Day 15 peptides were each split in two, methyl esterified with light and heavy reagents, respectively, and recombined. Each milligram of differentially labeled peptides was analyzed as

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Phosphoproteomic Dynamics of RA-Treated P19 Cells Table 2. Identified Proteins That Bind to One Another Either Directly or via One Other Protein symbol

NCBI gi number

protein name

cellular localization

TCOF1 SRRM2 MCM2 HIRIP3 AKAP12 DEK ZRF1 HMGY EEF1D CDC2 MAP1A PRPF4B DOCK3 MAP1B MAPT CENPF

31807869 30424862 33243985 26337095 13626040 26329115 1731448 14198134 10442752 1575290 6272692 1399464 23297191 6678946 13432200 82883611

Nucleus Cytoplasm Nucleus Nucleus Plasma membrane Nucleus Nucleus Nucleus Cytoplasm Nucleus Cytoplasm Nucleus Cytoplasm Cytoplasm Plasma membrane Nucleus

JAK3 Dnajc5 TOP2A DNMT3B

745064 13162361 6755849 6449470

Tcof1 protein Serine/arginine repetitive matrix 2 Minichromosome maintenance deficient 2 mitotin unnamed protein product A kinase (PRKA) anchor protein (gravin) 12 unnamed protein product Zuotin-related factor 1 Hmga1 protein eukaryotic translation elongation factor 1-delta p34 cdc2 kinase short microtubule-associated protein 1A; short MAP1A serine/threonine-protein kinase PRP4 m modifier of cell adhesion microtubule-associated protein 1 B Microtubule-associated protein tau PREDICTED: similar to centromere protein F (350/ 400kD) isoform 3 Janus kinase cysteine string protein topoisomerase (DNA) II alpha DNA cytosine-5 methyltransferase 3B1

described above to determine the extent of indeterminate error associated with the entire experimental process. Figure 3 reveals a plot of the number of times a particular ratio was observed versus the logarithm of the light to heavy ratio measured in the experiment. The maximum of the curve is centered at 0 (a ratio of 1:1), as one would expect, however there are a number of outliers that tail off above and below 0. This distribution was best fitted using a Lorentzian algorithm, revealing a standard deviation (SD, full width of the distribution at half of the maximum height (fwhm)) of 0.1; the 95% confidence interval (CI) was thus calculated by multiplying the SD by a factor of 3. Therefore, it was determined that 95% of the data falls within a factor of 2 from the maximum of a distribution; ratios that are therefore greater than 2 or less than 0.5 are statistically significant at the 95% CI. To discern the ratios in a sample that were statistically significant, the maxima of the number of times a ratio was observed was determined for each individual IMAC/LC-MS/ MS experiment. Once maxima were determined by plotting the number of times a ratio was observed in the experiment versus the logarithm of the light to heavy ratio, the distribution was normalized to a non-logarithmic ratio of 1:1 so that ratios with differences greater than a factor of 2 could be labeled as veritable experimental outliers. Phosphopeptides/phosphoproteins with outlying ratios were separated for additional analysis as candidate signaling markers for neurogenesis (vide infra). Analysis of the Phosphoproteome of P19 Cells. Our analyses successfully observed the phosphoproteomic dynamics of 151 proteins in P19 cells as they differentiate into neuronal and glial phenotypes. In total, 472 phosphorylation sites were identified on 340 uniquely phosphorylated and esterified peptides, translating to an average of 1.4 phosphorylation sites per peptide. Ignoring differences in the degree of esterification, 273 peptides with unique phosphorylation sites were observed (1.7 sites per peptide). The number of pS, pT, and phosphotyrosine (pY) sites equaled 394, 64, and 14 respectively; 84.5% of them were novel when searched in the Phospho.Elm database42 (see Supplementary Table S1 for details on all results). Fifty-six proteins were observed to have altered phosphorylation states following RA treatment and are detailed in Supplementary Table S2 (see Supporting Information).

Cytoplasm Nucleus Nucleus Nucleus

Quantitative analysis revealed significant differences in 90 uniquely phosphorylated and esterified phosphopeptides (26.4%) or 82 phosphopeptides if esterification differences are ignored (30.0%). Only 1.8% of the potential methyl esterification sites (peptide D residues, E residues and C-termini) were not modified (77 out of 4181). The 151 identified phosphoproteins were grouped according to their GO functional classifications, as assigned by PathwayStudio (based on http://www.geneontology.org/), and are displayed in Supplementary Figure S1a (see Supporting Information). The subset of these that were found to have differential phosphorylation before and after treatment with RA (56 proteins) are displayed in Supplementary Figure S1b (see Supporting Information). A comparison of Supplementary Figures S1a and S1b reveals a great deal of similarity in the number and relative percentages of each category indicating that phosphoproteins from a diverse range of functional categories are affected during differentiation. The majority of the proteins identified had regulatory roles and a significant percentage of them were involved in signal transduction (kinases or phosphatases). A total of 22% of the phosphoproteins identified in this study have been previously linked with cellular differentiation and are listed in Table 1. Of the 56 phosphoproteins that showed a response to RA treatment, 32% have been previously associated with differentiation suggesting that many of the others identified in this study may also play a role in this process. Variation in protein expression during cellular differentiation has been reported for many of the proteins identified in this study. For example, the expression levels of HMGA69 as well as the microtubule-associated proteins (1A,77 1B,77 and tau80) are greatly affected during differentiation. Although the abundance differences of other proteins identified in this study may be explained in this way, a few specific phosphorylation sites have been elucidated that are modified following RA treatment. Five proteins were identified in this study that contained peptides that were significantly different between the wild and RAtreated samples as well as peptides that had equal abundance. This would indicate that the differences observed were either not due to changes in expression but changes in the degree of phosphorylation, or if expression differences were present the degree of phosphorylation was modified at particular sites so Journal of Proteome Research • Vol. 6, No. 8, 2007 3181

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Figure 5. Twelve of the most common sequence motifs surrounding pS residues, parsed out of the flanking sequences surrounding all of the identified phosphorylation sites using motif-X43 (http://motif-x.med.harvard.edu/). Six amino acids were selected before and after the pS residue; the frequency of occurrence is indicated by the size of the residue at each position. To the right of each is the kinase that recognizes the motif as well as the fold increase. (* - the fold increase was calculated by comparing the number of times a motif appeared in the input data versus the mouse proteome).

that the abundance of the phosphopeptide was the same between both samples. Proteins that had both different and non-changing phosphopeptides may be viewed in Supplementary Table S1 and include Tcof1 protein (31807869), Serine/ arginine repetitive matrix 2 (30424862), minichromosome maintenance deficient 2 mitotin (33243985), Hdgfrp2 protein (13277669), and PREDICTED: similar to thioredoxin domain containing 9 (82942320). A protein-protein interaction network was constructed for the proteins that were observed to have altered phosphorylation states following RA treatment using PathwayStudio, (Figure 4, an interactive version may be viewed at www.oisb.ca/people/ jeff/binding.htm). Interestingly, 20 of the 56 proteins are linked within one degree of separation based on known proteinprotein interactions reported in the literature and are listed in Table 2; the remaining 36 proteins did not show any connectivity to each other either directly or via another protein. The number of interacting proteins (35.7%) is much larger than expected by random chance alone and illustrates an enrichment for proteins that have altered phosphorylation states for 3182


specific pathways and processes. Upon closer observation, Figure 4 can be viewed as two distinct groups: a larger group of interacting proteins (in the upper left portion of the figure), as well as a smaller group of 7 interacting proteins (in the bottom right of the figure); proteins identified in this study are illustrated as green rectangles. Fourteen of the 20 proteins in green rectangles (70%) are involved in differentiation (indicated by asterisks); considering the larger group of proteins alone, 82.4% are involved in differentiation. This large enrichment may suggest novel roles in differentiation for the three other proteins that have no known connection to cellular differentiation in the literature as well. The smaller group of interacting proteins in Figure 4 (bottom right) are all involved in mRNA processing (see interactive version of Figure 4 for references www.oisb.ca/people/jeff/binding.htm); our results would suggest that those identified (green rectangles) also play a role in cellular differentiation and implicates that this complex as a whole may be involved in the differentiation process. Because a variety of proteins are affected during differentiation (Supplementary Figure S1a, b, see Supporting Informa-

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tion), the motifs surrounding the phosphorylation sites on phosphopeptides that showed altered abundances were investigated to elucidate common kinases that may play a role in neuronal differentiation. The most common sequence motifs in all of the data were elucidated through parsing 6 amino acids before and after each phosphorylation site and submitting these sequences to motif-X (as described above). There were insufficient pT and pY residues elucidated in our experiments to decipher motifs; however, a number of pS motifs were found with high confidence, the top 12 of which are illustrated in Figure 5. The kinases that are known to act on these motifs are also indicated in Figure 5; therefore, the data was combed to find which phosphopeptides contained one or more of the motifs to gain perspective on the kinases that were impacted during RA treatment. Interestingly, a number of the most common motifs illustrated in Figure 5 do not match any of the known kinase motifs currently reported in the Human Protein Reference Database (www.hprd.org/PhosphoMotif_ finder), suggesting a great deal of previously uncharacterized kinase activity during differentiation. Supplementary Table S3 (see Supporting Information) contains a list of all differentially phosphorylated peptides that contained a discernible motif, indicating which kinase may be responsible for the phosphorylation changes observed; casein kinase was omitted from Supplementary Table S3 due to its ubiquitous behavior and diverse substrate recognition. Several of the proteins illustrated in Figure 4 were more closely scrutinized within the literature in an attempt to develop a hypothetical relationship between the dynamics of their abundance as a consequence of the addition of RA. Investigation into the cellular biochemistry of Janus kinase (Jak3) revealed that it is both responsive to RA and is involved in cellular differentiation. Jak3 plays a determinant role in Interleukin-2-dependent signal transduction in T lymphocytes; this signaling is influenced by the addition of RA.95 Moreover, RA has been shown to potentiate the proliferation of these cells in vitro96 through the activation of the Jak/STAT pathway.97 In addition to playing a role in RA-dependent cellular proliferation, Jak3 is also directly involved in myeloid differentiation.84 The Jak3 pathway is stimulated in both granulocyte and monocyte differentiation in response to granulocyte-macrophage colony-stimulating factor (GM-CSF); Jak3 expression has been demonstrated to increase via activation of the Jak/STAT pathway in response to GM-CSF leading to growth arrest and terminal differentiation.84 The Jak3 pathway is also involved in osteogenesis as well as the dedifferentiation of adipocytes in response to the multifunctional cytokine Oncostatin M (OSM).85 Finally, the Jak/STAT pathway was implicated in mammalian forebrain development nearly a decade ago,83 and recent findings have demonstrated that Jak3 is directly involved in process outgrowth in human neural precursor cells in response to platelet derived growth factor (PDGF).98 Our results elucidated a >7.5-fold abundance decrease in a single Jak3 phosphopeptide following the addition of RA to the P19 cells. This peptide is doubly phosphorylated on two serine residues, neither of which have been previously reported to be modified. The two residues are located on the exterior of the kinase domain of Jak3 and, more specifically, at the initiation of a recently discovered helix that is unique to Jak tyrosine kinases and may be involved in intramolecular regulatory interactions with the FERM domain (which mediates the cytoplasmic region of the protein that interacts with cytokines).99 Taken together, it is conceivable that our results implicate Jak3 in RA-induced

neuronal differentiation. Jak3 has been demonstrated to be involved in the RA response pathway as well as differentiation in several cell types; in all cases, Jak3 abundance increased after the addition of RA (following differentiation). Our results may suggest that the decrease observed in the pS-containing Jak3 peptide occurred as a result of Jak3 activation and that these two phosphorylation sites are in fact inhibitory modifications that are removed during protein activation. Alternatively, it is conceivable that the Jak/STAT pathway was activated by RA, increasing the expression of Jak3 until differentiation ensued, at which point it decreased lower than its pre-differentiation level (i.e., measurements in the middle of the 15 Day time course may have detected elevated Jak3 levels). It is also possible that the Jak/STAT pathway or the expression of Jak3 is inactivated by RA in P19 cells. Due to the lack of commercially available antibodies specific to the Jak3 phosphorylation sites that were detected, these latter hypotheses cannot be presently tested.

Conclusion We have shown for the first time the use of isotopically labeled methyl esterification on tryptic peptides prior to an IMAC/LC-MS/MS analysis to reveal the temporal phosphoproteomic dynamics of P19 cells before and after being treated with RA. This has also been the first time that a global gel-free analysis has been used to elucidate the dynamics of protein phosphorylation during cellular differentiation. A total of 472 phosphorylation sites were identified on 151 proteins, making this the largest differential phosphoproteomic study of its kind to date. A series of data curation steps were developed to lower the false positive and false negative rates to optimal levels, employing a reverse database and considering MS/MS data with lower Mascot scores. Thirty-seven percent of the proteins identified in this study contained one or more phosphopeptide that was of significantly different abundance following RA treatment; it was shown that these proteins belong to a diverse variety of different functional classification groups based on GO ontology. Thirty-two percent of the phosphoproteins that showed altered abundance following RA treatment have also been previously associated with cellular differentiation and a significant grouping has been shown to interact in vivo. Twelve dominant motifs were also parsed from the data, suggesting a number of kinases that may be actively involved in cellular differentiation and highlighting several motifs with unknown kinase recognition sequences.

Acknowledgment. We thank Fred Elisma for making the automated parser and PathwayStudio binding interaction results accessible online and acknowledge financial support from the Canadian Foundation for Innovation, the Province of Ontario, NSERC, CIHR, the University of Ottawa, MDS Inc., and the Foundation Louis-Le´vesque. D.F. acknowledges a Canada Research Chair in Proteomics and Systems Biology. Supporting Information Available: Supplementary Table S1, the complete analysis of all phosphoproteomic data obtained in this work. Supplementary Table S2, a complete summary of the phosphoproteins that had statistically significantly phosphorylation differences following RA treatment. Supplementary Table S3, a complete summary of the phosphopeptides that contained one or more kinase recognition motifs. Supplementary Figure S1, the functional classification of proteins based on GO ontology (http://www.geneontologyJournal of Proteome Research • Vol. 6, No. 8, 2007 3183

research articles .org/) and displayed as a fraction of all identified proteins; (a) all 151 proteins identified and (b) only considering the 56 proteins that contained a phosphopeptide that had altered abundance following RA treatment. This information is available free of charge via the Internet at http://pubs.acs.org.

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