Qualitative and Quantitative Proteomic Profiling of Cripto

Jan 16, 2009 - MS Technologies Center, M22 5PP Manchester, United Kingdom, ... CNR, I-80131 Napoli, Italy, and Istituto di Biostrutture e Bioimmagini,...
0 downloads 0 Views 4MB Size
Download PDF
Qualitative and Quantitative Proteomic Profiling of Cripto-/Embryonic Stem Cells by Means of Accurate Mass LC-MS Analysis Angela Chambery,*,† Johannes P. C. Vissers,‡ James I. Langridge,‡ Enza Lonardo,§ Gabriella Minchiotti,§ Menotti Ruvo,| and Augusto Parente† Dipartimento di Scienze della Vita, Seconda Universita` di Napoli, I-81100 Caserta, Italy, Waters Corporation, MS Technologies Center, M22 5PP Manchester, United Kingdom, Istituto di Genetica e Biofisica “A Buzzati-Traverso”, CNR, I-80131 Napoli, Italy, and Istituto di Biostrutture e Bioimmagini, CNR, I-80134, Napoli, Italy Received July 1, 2008

Cripto is one of the key regulators of embryonic stem cells (ESCs) differentiation into cardiomyocites vs neuronal fate. Cripto-/- murine ESCs have been utilized to investigate the molecular mechanisms underlying early events of mammalian lineage differentiation. 2D/LC-MS/MS and a label-free LC-MS approaches were used to qualitatively and quantitatively profile the cripto-/- ESC proteome, providing an integral view of the alterations induced in stem cell functions by deleting the cripto gene. Keywords: Cripto • LC-MS • proteomic profiling • mass spectrometry • data independent scanning • Hsp25

Introduction During recent years, several studies have been focused on the molecular profiling of stem cells,1-7 with the aim of defining a protein “stemness” signature, to determine their molecular hallmarks, or in other words, to identify and possibly quantify those proteins involved in molecular pathways responsible for the two main characteristic of stem cells: self-renewal and pluripotency.8,9 Large-scale proteomic analysis often combined with transcriptomic studies have been performed with several stem cell populations at different developmental stages,10-12 with the aim to establish a reference protein catalogue of embryonic stem cells both for mouse10,13 and human.4,14 Other studies focused on the proteomic analysis of neural stem cells1,2 or the characterization of post-translational modifications regulating the signaling pathways of self-renewal.15 There has been considerable effort to identify molecules/ signaling pathways that control the balance between selfrenewal and differentiation, which is crucial for the prospect of controlling stem cell differentiation for biomedical applications. Based upon these studies, the EGF-CFC protein Cripto, involved both in physiological events associated with embryogenesis and pathological events linked to tumorigenesis, turned out to have a key role. Cripto is a membrane protein with all the features of an oncodevelopmental gene. It is expressed in the early phases of embryo development; in the adult, Cripto expression is almost absent in normal tissues, whereas it is reactivated in a wide range of epithelial cancers, including * To whom correspondence should be addressed. Angela Chambery, Dipartimento di Scienze della Vita, Seconda Universita` di Napoli, Via Vivaldi 43, I-81100 Caserta, Italy. Phone: +39 0823 274535. Fax: +39 0823 274571. E-mail: [email protected]. † Seconda Universita` di Napoli. ‡ Waters Corporation. § Istituto di Genetica e Biofisica “A Buzzati-Traverso”. | Istituto di Biostrutture e Bioimmagini. 10.1021/pr800485c CCC: $40.75

 2009 American Chemical Society

breast, stomach and colon carcinomas.16-19 It is also significant that Cripto has been described as a key regulator of stem cell fate. Indeed, it is strictly required to negatively regulate neural differentiation and, at the same time, to permit differentiation of ES cells to cardiomyocites.20-26 Therefore, a deeper understanding of the Cripto-mediated signaling pathways is needed as a crucial step in the ongoing efforts to employ ES cells in regenerative medicine, as well as to find a common therapeutic target for tumor treatment. The identification and quantification of expressed proteins in cells, tissue and whole organisms, is one of the greatest challenges in the postgenomic era. Mass spectrometry is a widely applied tool for proteomics studies in biological sciences and biomedicine. The more traditionally applied identification and quantification method combines protein separation and quantification using 2D electrophoresis (2DE), with subsequent mass spectrometry based identification.27,28 However, 2DE suffers from a number of limitations associated to the high heterogeneity of the components present in biological mixtures, in terms of pI, Mr, and relative abundance. Also, the analytical procedure is both laborious and highly time-consuming, requiring technological alternatives.29 Furthermore, limitations in the sample amounts available often render the technique unsuitable for the efficient separation and detection of proteins, with no opportunity to run the replicate analysis required to statistically validate the results. Liquid chromatography (LC) is often used as a complementary method to 2DE for protein or peptide separation in complex mixtures, and recent advances in the miniaturization of analytical LC-MS systems offers enormous advantages in terms of the overall sensitivity.30,31 In addition, multidimensional liquid chromatography (MDLC) increases the peak capacity compared to a single-dimension separation allowing for the more comprehensive characterization of complex protein and peptide mixtures. Therefore, MDLC Journal of Proteome Research 2009, 8, 1047–1058 1047 Published on Web 01/16/2009

research articles coupled to mass spectrometry is a frequently considered and applied technique for the characterization of whole or subset proteomes.32-35 In the present study, by performing qualitative 2D LC-MS/ MS and qualitative and quantitative multiplexed LC-MSE analysis, a proteomic profile of murine wild type (RI) and of Cripto-deficient (Cr-/-) embryonic stem cells of expressed proteins has been obtained. Comparison of the relative amounts of the proteins identified provides an integral view of the alterations induced in stem cell biology by deleting the cripto gene. This information may prove crucial in the future to understand the molecular mechanisms underlying mammalian lineage commitment and differentiation.

Experimental Section Cell Cultures. Wild type (RI) and Cripto-/- [DE736] murine embryonic stem cells (mESCs) were maintained in the undifferentiated state by culture on mitomycin C-treated mouse embryonic fibroblast (MEF) feeder layers according to standard protocols, as previously described.26,37 Briefly, the culture medium consists of high glucose Dulbecco’s modified Eagle’s medium (Invitrogen Corporation, Carlsband, CA) containing 15% fetal bovine serum (Hyclone, Logan, UT), 0.1 mM β-mercaptoethanol (Sigma-Aldrich, St. Louis), 1 mM sodium pyruvate (GIBCO), 1× nonessential amminoacids (GIBCO), 2 mM glutamine (GIBCO), 100U/mL penicillin/streptomycin (GIBCO) and 103 U/mL leukemia inhibitory factor (LIF, Chemicon International, Temecula, CA). ES cells were routinely passaged every 2 days, and the medium was changed on alternated days. Two independent cell cultures preparations were used to address biological variation for, respectively, the qualitative 2D LC-MS/MS and the combined qualitative and quantitative data independent, alternate scanning LC-MS experiments. Sample Preparation. Monolayer cultures of cell lines were harvested, after three washes in ice-cold PBS, by incubating with a solution containing trypsin (0.5 g/L) and EDTA (0.2 g/L). After centrifugation at 2000 rpm for 5 min at 4 °C in a JA14 rotor (Beckman centrifuge GS-15R), cell pellets were washed three times with PBS and resuspended in 25 mM NH4HCO3/ 0.5% RapiGest (Waters Corporation, Milford, MA) for cell lysis and protein extraction. Samples were then sonicated into an ultrasonic bath for 10-15 min and centrifuged at 13000 · g for 15 min at 4 °C to eliminate cellular debris. The supernatants were collected and protein concentration determined by the Bradford method, according to manufacturer’s instructions (Biorad, Milan, Italy). Lysates were aliquoted and stored at -80 °C until use. Preparation of Protein Digests. Total protein extracts, obtained as described above, were reduced with 2.5 mM DTT at 60 °C for 30 min and carbamidomethylated with 7.5 mM iodoacetamide at room temperature in the dark for 30 min. Digestion was performed by two subsequent additions (1:100 for 16 h at 37 °C, then 1:50 for 4 h at 37 °C) of TPCK-treated trypsin (Sigma, Milan, Italy). After digestion, 0.5% TFA was added for the hydrolysis of RapiGest and inactivation of trypsin, samples were centrifuged at 12000 · g for 10 min. The supernatants were collected and aliquots were dried in a SpeedVac Vacuum (Savant Instruments, Holbrook, NY). Tryptic digests were then resuspended in strong cation exchange (SCX) mobile phase buffer A (5 mM ammonium formate, pH 3.2/5% CH3CN) and directly injected on the multidimensional LC-MS/MS system. The final concentration of cell protein digests was 1 µg/µL. 1048

Journal of Proteome Research • Vol. 8, No. 2, 2009

Chambery et al. LC-MS Configurations. The online 2D LC-MS/MS set up employed a 180 µm × 23 mm SCX column (Waters Corporation) packed with Polysulfethyl-A 5 µm (Poly LC Inc., Columbia, MD) for the first dimension separation. Two µL full loop sample volumes, or solvent plugs, were loaded onto the SCX column using the auxiliary pump of a nanoACQUITY system (Waters Corporation), at a flow rate of 3 µL/min for 3 min with SCX buffer A. A combined salt and organic step gradient was applied to the SCX column by sequentially injecting a series of solvent plugs (ranging from 20 to 200 mM ammonium formate pH 3.2, with 5-20% CH3CN) onto the SCX column. The salt gradient separates peptides on the basis of overall net charge. The organic gradient gradually reduces the hydrophobic interaction with the SCX packing material for the larger, more heavily charged and hydrophobic peptides when the salt gradient is applied. A 180 µm × 20 mm Symmetry C18 5 µm (Waters Corporation) reversed phase trap column was used to collect the peptides that elute from the first dimension SCX column. Discrete fractions were sequentially eluted by injecting solvent plugs at 0, 20, 30, 40, 50, 60, 80, 100, 150 mM ammonium formate pH 3.2 containing 5% CH3CN and a final injection of 200 mM ammonium formate pH 3.2 containing 20% CH3CN. After the injection of a buffer salt plug at a given salt concentration, a fraction of the peptides was eluted from the SCX precolumn and subsequently retained on the reversed phase trap column where they are desalted and preconcentrated. Finally, the peptides are eluted from the C18 precolumn for a second dimension separation by running a reversed phase gradient with the binary solvent manager of the nanoACQUITY system at a flow rate of 300 nL/min, for separation and elution on a 75 µm × 150 mm analytical reversed phase column packed with BEH 1.7 µm stationary phase particles (Waters Corporation) into the mass spectrometer. The sample elution was performed by increasing the organic solvent concentration from 1 to 40% B in 60 min, using 0.1% formic acid in acetonitrile/water (2:98, v/v) as reversed phase solvent A and 0.1% formic acid in acetonitrile/water (80:20, v/v) as reversed phase solvent B. The separated peptides were mass analyzed by a hybrid quadrupole orthogonal acceleration time-of-flight mass spectrometer directly coupled to the chromatographic system. Electrospray MS and MS/MS data were acquired on a Q-Tof Ultima API mass spectrometer (Waters Corporation, Manchester, UK). For all measurements, the mass spectrometer was operated in the v-mode of analysis with a typical resolving power of at least 10 000 fwhm. All analyses were performed in positive ion mode using a nanoelectrospray ion source. The time-of-flight analyzer of the mass spectrometer was externally calibrated with the fragment ion spectrum of [Glu1]-Fibrinopeptide B from m/z 50 to 1600, with the data postacquisition lock mass corrected using the monoisotopic mass of the doubly charged precursor of [Glu1]-Fibrinopeptide B. The latter was delivered at 100 fmol/µL to the mass spectrometer via a NanoLockSpray interface using the auxiliary pump of a nanoACQUITY system at a flow rate of 100 nL/min. The reference sprayer was sampled every 30 s. All LC-MS/MS data were acquired with the mass spectrometer operating in data directed analysis (DDA) MS/MS mode, with a potential of approximately 2 kV applied to the spray tip. Survey scans of 1 s duration with interscan delay of 0.1 s were taken. MS/MS data were obtained for up to the five most abundant 2+ to 4+ detected ions. MS/ MS spectra were acquired at a scan rate of 1 s with a 0.1 s interscan delay and the collision cell automatically set based

Cripto-/- Embryonic Stem Cells on peptide precursor mass and charge state. A dynamic exclusion window was set to 60 s. Nanoscale LC separations of tryptic peptides for qualitative and quantitative LC-MSE analysis were performed with a similar nanoACQUITY system as described above. In this instance, however, the SCX column was omitted from the set up and a 90 min reversed phase gradient applied. The precursor ion masses and associated fragment ion spectra of the tryptic peptides were mass measured with a Q-Tof Premier mass spectrometer (Waters Corporation). All LC-MSE analyses were conducted in triplicate. The LC-MSE scanning method combines peptide MS and multiplexed, data independent peptide fragmentation MS analysis in a single LC-MS experiment for the quantitative and qualitative characterization of a peptide mixture. Briefly, the lock mass corrected spectra are first centroided, deisotoped, and charge-state-reduced to produce a single accurately mass measured monoisotopic mass for each peptide and the associated fragment ions. The correlation of a precursor and a potential fragment ion is initially achieved by means of time alignment, followed by a further correlation process during the database searches that is based on the physicochemical properties of peptides when they undergo collision induced fragmentation. The time-of-flight analyzer of the mass spectrometer was for these experiments externally calibrated with NaI from m/z 50 to 1990, with the data also postacquisition lock mass corrected using the monoisotopic mass of the doubly charged precursor of [Glu1]-Fibrinopeptide B. Accurate mass data were collected in an alternating low energy and elevated energy mode of acquisition as described previously.38 The spectral acquisition time in each mode was 1.5 s with a 0.1 s interscan delay. In the low energy MS mode, data were collected at constant collision energy of 4 eV. In elevated energy MS mode, the collision energy was ramped from 15 to 35 eV during each 1.5 s integration, with one complete cycle of low and elevated energy data acquired every 3.2 s. The RF applied to the quadrupole mass analyzer was adjusted such that ions from m/z 300 to 2000 were efficiently transmitted, ensuring that any ion with a mass below m/z 300, observed in the LC-MS data, only arose from dissociations in the collision cell. Data Processing and Protein Identification. The continuum LC-MS/MS DDA and data independent, alternate scanning LC-MS data were both processed and searched using ProteinLynx GlobalSERVER version 2.3. Protein identifications were obtained by searching a mouse species-specific Swiss-Prot database (release 52.1, March 2007; 12 577 entries). The search parameters used for the LC-MS/MS DDA data were as follows: primary digest reagent, trypsin; precursor mass tolerance, 25 ppm; fragment ion tolerance, 0.05 Da; allowed number of missed cleavage sites up to 1; fixed modification CAM-cysteine; variable modification methionine oxidation; initial minimum number of matching peptides 1. Additional protein identification reporting criteria included the number of matching peptides, peptide identification probability >95%, the presence of a consecutive y ion series of at least 3 amino acids per MS/ MS fragmentation spectrum and the removal of lower scoring and homologues (redundant) peptides and proteins. The ion detection, data clustering and normalization of the multiplexed LC-MSE data has been explained in detail in previous reports.39,40 In brief, the lock mass corrected spectra are first centroided, deisotoped, and charge-state-reduced to produce a single accurately mass measured monoisotopic mass for each peptide and the associated fragment ion. The cluster-

research articles ing algorithm can perform multiple binary comparisons to align close-to-identical components to each other on the basis of mass precision and retention time. In this study only a single binary comparison had to be considered, that is, a twocondition experiment. For protein quantification, the observed intensity measurements are normalized on the intensity value of peptides that do not change in intensities between replicate injections. The principle of the search algorithm for multiplexed, alternate scanning LC-MSE data has been recently described.41,42 The following search criteria were used for protein identification: peptide mass tolerance, 10 ppm; fragment ion tolerance, 20 ppm; allowed number of missed cleavage sites up to 1; fixed modification CAM-cysteine; variable modification oxidized methionine. Furthermore, the protein identifications were based on the detection of at least three fragment ions per peptide; at least 2 peptides determined per protein and identification of the protein in at least 2 out of 3 injections within the same condition. The false positive rate of the identification algorithm is typically 3-4% with a randomized database, which is five times the size of the original utilized database. However, by using replication as a filter, the false positive rate is minimized, as false positive identifications have a random nature and therefore do not replicate across injections. As such, the search criteria are more stringent and thus more reproducible compared to the above-mentioned DDA database searches and DDA search results in general. Qualitative data independent, alternate scanning LC-MSE identification results were also considered when a protein was identified in only a single injection for a given condition (with at least two peptides) and in all triplicate injections of the comparative condition. However, in these instances, the protein identification has been confirmed by a data directed analysis identification since biological variation can occur. Quantitative analyses have been performed by data independent alternate scanning expression algorithm42 by comparing normalized peak area/intensity of each peptide in a control vs a challenged sample. The entire differentially expressed proteins data set was further filtered by considering only those identifications from the alternate scanning LC-MSE data with identified peptides exhibiting good replication rate (two out of three injections) and with a low coefficient of variation (CV < 0.02) associated to the relative protein fold change. Furthermore, the significance of regulation level was determined at 30% fold change, that is, an average relative fold change between -0.30 and 0.30 on a natural log scale, which is typically 2-3 times higher than the estimated error on the intensity measurement.42 Additional data analysis was performed with Decisionsite (Spotfire, Somerville, MA) and Excel (Microsoft Corporation, Redmond, WA). RNA Extraction and Quantitative Real-Time PCR. Total RNAs from RI and Cripto-/- mESCs were isolated using RNeasy mini kit (Qiagen, Du ¨ sseldorf, Germany) according to manufacturer’s instruction. One microgram of total RNA was used for cDNA synthesis using the QuantiTect Reverse Transcription Kit (Qiagen) following the manufacturer’s protocol. Quantitative Real time PCR (RTQ-PCR) was performed using FluoCycle II SYBR Green PCR master mix (EuroClone, Siziano, Italy). The following primers were used: Oct3/4 forward 5′-TCAGCTTGGGCTAGAGAAGG-3′ and reverse 5′-TGACTGGAACAGAGGGAAAG-3′. Primers for Cripto and Nanog were purchased from Qiagen (QuantiTech Primer assay). Journal of Proteome Research • Vol. 8, No. 2, 2009 1049

research articles

Chambery et al.

Two-Dimensional (2-D) Gel Electrophoresis. Wild type (RI) and Cr-/- mESCs protein extracts (700 µg) were separated by 2-DE (performed in duplicate) as described in Chambery et al.43 Briefly, proteins were separated in the first dimension on a non linear pH 3-10 gradient and, in the second dimension, on homogeneous polyacrylamide gels (12% T, 2.5% C). Gels were stained with colloidal Coomassie blue stain (2% phosphoric acid, 10% ammonium sulfate, 20% methanol, 0.1% Coomassie brilliant blue G-250) for 4-8 h, followed by three individual 2 h washes with deionized water. Gel scanning and image analysis followed by protein identification using MALDI-TOF mass spectrometry (MALDI LR, Waters Corporation, Manchester, UK) was performed as previously described.43 The instrument was externally calibrated using a tryptic alcohol dehydrogenase digest (Waters Corporation, Milford, MA) as standard. The protonated monoisotopic mass of ACTH peptide (m/z 2465.199) was used as internal lock mass to further improve the peptide mass accuracy to within 50 ppm. All spectra were processed and analyzed using the MassLynx 4.0 software (Waters Corporation). The obtained spectra were used to identify proteins in the Swiss-Prot protein sequence database by using ProteinLynx Global Server 2.3 software. The following searching parameters were used: mass tolerance 50 ppm; allowed number of missed cleavage sites up to 1; cysteine residue modified as carbamidomethyl-Cys; minimum number of matched-peptides 3; the isotope masses were used. Gene Ontology Analysis. The classification of identified proteins was performed according to the Gene Ontology (http:// www.geneontology.org/) hierarchy using the GO Terms Classifications Counter (http://www.animalgenome.org/bioinfo/tools/ countgo/index.html). The sequence retrieval system (SRS) web interface of the European Bioinformatics Institute (www.ebi.ac.uk) was used for the GO annotation searches. The clustering of the GO annotation results was performed applying the GOA2GO classification method (http://www.geneontology.org/GO.slims. shtml). The gene ontology OBO file was downloaded from the gene ontology database and was manually edited to obtain three separate classifications (cellular component, molecular function and biological process).

Results -/-

For the qualitative proteomic profiling of Cripto embryonic stem cells (Cr-/- ES), whole-cell extracts of both wild type (RI) and Cr-/- mESCs, obtained from two independent cell cultures preparations, were subjected to tryptic digestion as described in the Methods section. For the 2D LC-MS/MS experiments, 2 µg of the digested samples were typically loaded and analyzed. The online 2D LC separation of the tryptic peptides was followed by data-dependent MS/MS analysis, which generated a large number of MS/MS spectra acquired in a fully automated fashion, subsequently used for protein identification. Recent published studies on the large-scale identification of proteins, based on similar MS approaches, aimed at increasing the number of identified proteins, by balancing the permissiveness of database search parameters (i.e., peptide mass tolerance and MS/MS tolerance) with the resulting confidence of identifications.10,11,44 Furthermore, systems are often overloaded for the same reason.45 The latter results in a bias toward the more abundant proteins and hydrophobic peptides and as such does not correctly represent the concentration or dynamic range of the (sub)proteome. This, combined with the unrepeatable nature of DDA type of MS/ MS experiments at the lower orders of detection of the dynamic 1050

Journal of Proteome Research • Vol. 8, No. 2, 2009

range of a mass spectrometer, often leads to nonreproducible qualitative and quantitative proteomics results. Stringent database search and reporting criteria were therefore applied, at the peptide and protein level as described in the Methods section, and careful optimization of sample loading was performed to ensure that the analytical LC-MS system was not overloaded. The peptides were ultimately assigned to 265 and 231 proteins for the RI and the Cr-/mESCs, respectively. A qualitative cross-section analysis revealed that the most of the identified proteins were found to be common to both RI and Cr-/- ES cell lines (Supplementary Table 1, Supporting Information). The majority of the protein identifications were found to be confident in terms of probability, despite the fact that a number of them are based on single peptide identifications (Supplementary Table 2, Supporting Information). In this case, however, the confidence of peptide identification was attested by the quality of the MS/ MS fragmentation data in terms of the afforded mass accuracy on both the precursor and product ions and the identification score (see header and footnote of Supplementary Table 2, Supporting Information for detail on peptide scoring). In order to validate the DDA identification results and to compare the two applied scanning techniques, the qualitative results of the 2D LC experiments were integrated with those obtained from the alternate scanning LC-MSE analysis. In addition, the comparison allowed for addressing biological variation, as an independent cell cultures preparation was used for alternate scanning LC-MSE experiment. In this instance, 0.5 µg of the digested samples was loaded and analyzed in triplicate to facilitate quantitative analysis with respect to experiment variability. A scatter plot of the detected monoisotopic mass and retention time measurements from the replicates of each condition detected throughout the entire LC-MSE experiment provides a visualization of the data complexity and is presented in Supplementary Figure 1, Supporting Information. This plot visualizes a total number of about 33 000 extracted accurate mass-retention time components (clusters) detected across both conditions. Proteins identifications have been filtered on the basis of peptide replication rate as described in the Methods section. Common identified proteins in both cell lines by the alternate scanning LC-MSE technique have been integrated in Supplementary Table 1, Supporting Information. Comparing the results obtained with the two techniques, a total of 71% (85 out of 119, considering the replication filter of at least two out of three injections) of proteins identified with LC-MSE analysis can be found also in the reported DDA set. This percentage slightly decreases to 58% (85 out of 146) when identifications found in triplicate in at least one condition and confirmed by DDA experiment are included in the cross-section analysis. However, due to the large differences in relative sample abundance, the large protein concentration dynamic range and the possible under-sampling of DDA experiments, uniquely reported proteins cannot be completely excluded from this analysis. Therefore, if the uniquely identified proteins to a sample in both the DDA and the LC-MSE experiments are considered too, the common identified proteins by the two applied scanning techniques for two independent biological replicates increases to 77%. An comparison between the two qualitative experiments, reveals that the LC-MSE experiment provides better analytical reproducibility, mainly in terms of

Cripto-/- Embryonic Stem Cells

research articles

Figure 1. Gene ontology distribution terms obtained for the cripto-/- cell line proteome. The common proteins identified to the RI and cripto-/- (Cr-/-) cell lines by qualitative 2D LC-MS/MS and label-free LC-MSE were clustered based on cellular localization (A), biological process (B), and molecular function (C).

the number of detected peptides that can be reliably used for a protein identification. In addition, certain proteins were uniquely identified in either the RI or Cr-/- mESC line sample (Supplementary Table 3, Supporting Information). Differently from DDA identifications, the LC-MSE unique identifications were all based on more than one peptide match. In the latter instance due to the high dynamic range of the studied proteome, unique detections could merely depend on the detection of a mass at a given retention time in a given condition and not in the other. Therefore, to minimize the analytical measurement uncertainties, only the unique clusters detected in at least two out of three replicate injections were considered. It is however intriguing that a single dimension LC-MSE experiment identified a similar number of proteins with higher sequence coverage and better reproducibility compared to the 2D LC-MS/MS experimental approach. It should be noted however that a multidimensional separation at the peptide level does not necessarily improve the dynamic range of the analytical experiment. While the complexity will be reduced, improving coverage of the identified peptides, effective dynamic range will not be improved, as high intensity peptides are spread throughout the first dimension fractions. In particular, when the number of first dimension fractions is limited, and the sample dynamic range is high, the dynamic range within the fractions will be very similar to that of the original sample.46 Moreover, the chromatographic performance of a short column in conjunction with a step gradient is compromised, leading to a bleeding tendency of the peptides across fractions, which ultimately reduces sensitivity and negatively affects the instrument duty cycle. These type of methodological issues should be considered when identifications from quantitative experiments are compared with those obtained from the qualitative ones. For the same reason, proteins classically known as “stemness” markers and expressed by ES cells, as well as cells of the inner cell mass of mouse blastocysts, have not been

detected, probably due to their low expression levels and the large sample dynamic range. These markers include Nanog and Oct-3/4, typically used to characterize undifferentiated and pluripotent cells.47-49 Likewise, Cripto was not detected in the RI cell line, most likely due to its poor extraction yield from membranes. It was therefore decided to further characterize the samples by using complementary and more selective RTQPCR techniques (Supplementary Figure 2, Supporting Information). The key pluripotent transcription factors Oct3/4 and Nanog were expressed at comparable levels in RI and Cr-/mESCs. As expected, expression of Cripto was undetectable in knock out ESCs. Although the gene expression profile does not fully correspond to the protein amounts, these data indicate that different approaches could be synergistically used to deepen a given cell line proteomic profile. The aim of this work is, however, to present reproducible qualitative and quantitative proteomics profiles on relatively small amounts of samples and not necessarily the greatest number or depth of identified proteins. The latter requires different fractionation and sampling strategies at either the peptide50-52 or protein33,34,53 level to reduce sample complexity and increase the number of candidate peptides to be selected for an MS/MS experiment and ultimately identifications. The clustering of identified proteins was performed according to the Gene Ontology (GO) hierarchy based on cellular components, molecular functions and biological processes categories. The classification for cellular localization (Figure 1A) revealed that, as already reported for other mouse and human stem cells, the membrane (54.7%), cytoplasmic (32.0%) and nuclear (2.7%) proteins represent the largest populations found in the investigated stem cell lines.4,10 The annotation of the biological processes (Figure 1B) revealed that most of the identified proteins were involved in metabolism (27.0%), regulation of biological processes (13.2%) and nucleic acid metabolism (11.32%). Furthermore, small, but representative classes of proteins involved in cell differentiation (4.0%) and Journal of Proteome Research • Vol. 8, No. 2, 2009 1051

research articles

Chambery et al.

Figure 2. Replicate LC-MSE statistical analysis to assess analytical reproducibility. (A) The mass precision measurements from all detected accurate mass - retention time clusters was typically within ( 5 ppm of the mean mass measurement (approximately 2 ppm). (B) The relative standard deviation of the measured signal intensity of the clusters. The average retention time coefficient of variation (CV) was centered at 0.5%. (C) Bar plot reporting the error distribution associated with the intensity measurements, with an average CV among the replicate injections of about 3% at the sample level and 1) < 0.05 or 0.95 < ( p > 1) < 1] illustrating the significantly regulated clusters (peptides).

T-complex 1 (TCP-1), with an active role in the folding of actin and tubulin have been previously associated with synapse maturation occurring upon the maturation of P19 neurons.54 Furthermore several protein isoforms involved into metabolism, such as phosphoglycerate and pyruvate kinase, triosephosphate isomerase, lactate dehydrogenase, and enolase, were found to be up-regulated in the Cr-/- ES cell line. Some of the

up-regulated proteins have been previously associated with cellular proliferation and/or differentiation, as in the case of heat-shock proteins (see later under Discussion). A further validation of expression results regarding some heat-shock proteins was performed following a classical proteomic approach. In Figure 6, representative subsections of 2D gels are shown, illustrating some differentially expressed proteins of Journal of Proteome Research • Vol. 8, No. 2, 2009 1053

research articles

Chambery et al.

Table 1. Significantly Regulated (>30% Fold Change; 0.3 Natural Log Scale) in RI vs Cripto Label-Free Quantitative LC-MSEa

-/-

description

P57780 P56480 Q9D2U9 Q8CGP0 P62806 Q61696 P17879 P16627 P14602 P60335 P17742 P62259 P47738 P05064 O70251 P17182 P17183 O35737 P07901 P11499 P52480 P53657 P06151 Q60817 P28656 Q99LX0 P09411 P26350 P35979 P27659 Q9D8E6 P47911 P63276 P62858 P80315 P42932 Q01853 P17751

Alpha-actinin-4 (Nonmuscle alpha-actinin 4) ATP synthase subunit beta mitochondrial Histone H2B type 3-A Histone H2B type 3-B Histone H4 Heat shock 70 kDa protein 1A Heat shock 70 kDa protein 1B Heat shock 70 kDa protein 1 L Heat-shock protein beta-1 (HSP25) Poly(rC)-binding protein 1 Peptidyl-prolyl cis-trans isomerase A 14-3-3 protein epsilon Aldehyde dehydrogenase mitochondrial Fructose-bisphosphate aldolase A Elongation factor 1-beta (EF-1-beta) Alpha-enolase (Non- neural enolase) Gamma-enolase (Neuron-specific enolase) Heterogeneous nuclear ribonucleoprotein H Heat shock protein HSP 90-alpha Heat shock protein HSP 90-beta Pyruvate kinase isozymes M1/M2 Pyruvate kinase isozymes R/L L-lactate dehydrogenase A chain Nascent polypeptide-associated complex subunit alpha Nucleosome assembly protein 1-like 1 Protein DJ-1 Phosphoglycerate kinase 1 Prothymosin alpha 60S ribosomal protein L12 60S ribosomal protein L3 60S ribosomal protein L4 60S ribosomal protein L6 40S ribosomal protein S17 40S ribosomal protein S28 T-complex protein 1 subunit delta T-complex protein 1 subunit theta Transitional endoplasmic reticulum ATPase Triosephosphate isomerase

(Cr

ES) Cell Line Identified by

log ratio RI/Cr-/-

variance

regulation

0.44 0.38 0.96 1 1.39 0.49 0.46 0.39 0.56 0.68 0.43 -0.61 -0.35 -0.52 -0.36 -0.64 -0.74 -0.42 -0.36 -0.43 -0.56 -0.50 -0.50 -0.77 -0.74 -0.69 -0.45 -0.4 -0.57 -0.49 -0.66 -0.3 -0.68 -0.36 -0.35 -0.45 -0.46 -0.30

0.012 0.007 0.011 0.011 0.009 0.012 0.011 0.007 0.011 0.008 0.007 0.009 0.012 0.004 0.009 0.004 0.008 0.011 0.003 0.002 0.003 0.008 0.005 0.012 0.014 0.021 0.010 0.006 0.006 0.012 0.007 0.014 0.008 0.012 0.008 0.011 0.011 0.008

up up up up up up up up up up up down down down down down down down down down down down down down down down down down down down down down down down down down down down

e

accession #

-/-

a The number of peptides used for quantification are provided in Supplementary Table 1, Supporting Information. Data relative to differentially expressed proteins have been filtered on the basis of identification in at least two out of three replicate injections (see Supplementary Table 1, Supporting Information, for replication detail) and variance value.

Figure 5. Gene ontology distribution terms obtained for the up- and down-regulated proteins in the RI vs Cr-/- ES cell line. The proteins significantly up- and down-regulated on the basis of >30% fold change (see Table 1) and clustered based on molecular function are reported in A and B, respectively.

interest. In particular, image analysis representing spots corresponding to Hsp70 and Hsp25 are reported in Figure 6A and Figure 6B, respectively. As can be seen, 2D PAGE data confirmed the reduced expression in Cr-/- ES vs the RI cell line. Furthermore, the obtained relative fold changes (on a natural log scale) for Hsp70 and Hsp25 expression levels by 2D PAGE, 1054

Journal of Proteome Research • Vol. 8, No. 2, 2009

0.6 and 0.5, respectively, were found to be in good agreement with those derived from the LC-MSE analysis (see Table 1). Although a complete comparison of LC-MSE with 2D PAGE data is beyond the scope of the present work, a good agreement of identified LC-MSE regulation trends for other differentially expressed protein spots has been generally observed. These

Cripto-/- Embryonic Stem Cells

research articles

Figure 6. Detail of differential expression of heat shock proteins by 2D SDS/PAGE. Representative subsections of two 2D gels showing the regulation detail of two differentially expressed proteins. (A) Hsp70 and (B) Hsp25 spots are circled for both the RI and cripto-/- cell lines. The data used for protein identification are reported in Supplementary Table 4, Supporting Information.

comparative studies are however the subject of further investigations.

Discussion In the present study, by performing qualitative 2D LC-MS/ MS and qualitative and quantitative LC-MSE analysis, a proteomic profile of murine wild type (RI) and of Cripto-deficient (Cr-/-) embryonic stem cells of expressed proteins has been obtained. Cripto has been identified as one of the key mediators of cardiac vs neuronal ES cell-differentiation,21,26 therefore the Cr-/- mESC represent a very useful tool to investigate the molecular mechanisms and related factors underlying these differentiation processes. Elucidation of these mechanisms and the knowledge of the molecular networks associated to cell differentiation constitute an important step toward the possibility of controlling neuronal differentiation and thus to eventually generate specific neuronal subtypes from ES cells.55 Although great efforts have been undertaken to gain molecular insights on the role played by Cripto in the early events leading to cardiac or neuronal destiny, the biochemical stimuli regulating these processes are still largely unknown. To address this issue, qualitative and quantitative proteomic profiling of Cripto-/- has been performed. The qualitative analysis has provided a snapshot view of the Cr-/- mESC main cellular components and an estimation of their relative distribution along the different functions and subcellular compartments. The evaluation of protein expression levels compared to the wild type counterparts has provided a contribution to the comprehension of molecular pathways likely altered by the deletion of the cripto gene and thus potentially involved in the neuronal commitment of ES cells. Factors known to be associated to neuronal commitment/ differentiation were identified and shown to be differentially expressed. These included proteins such as, the heat-shock proteins Hsp25 and Hsp70, which are up-regulated in Cr-/- mESC. Interestingly, recent studies reported a key role of the heat-shock Hsp25 in mammalian development, both in response to cellular stress56-58 and following the differentiation of embryonic stem cells.59 Indeed, using an embryonal carcinoma cell line (P19), which can be induced to differentiate in vitro into either cardiomyocytes or neurons, it has been demonstrated that Hsp25 is essential for the functional differentiation of P19 into beating cardiomyocytes, but it is not required for neuronal differentiation.60

Furthermore, Hsp25 has been found up-regulated in P19 embryonal carcinoma cells in response to retinoic acid-induced differentiation.61 These findings correlate well with the presented results, since higher Hsp25 expression in wild type RI compared to Cr-/- mESC was found. While RI mESC spontaneously differentiate into cardiomyocites, ablation of cripto leads to the loss of competence of these cells to acquire a cardiac phenotype and, at the same time, to spontaneous neuronal differentiation. Furthermore, tissue specific expression of Hsp25 during mouse development has also been reported for the nervous system.62,63 Other Hsp proteins, such as Hsp70, have also been found to be highly expressed on the surface of human embryonic stem cells and down-regulated upon cell differentiation.64,65 Also a correlation between the expression levels of different Hsp, including Hsp25 and Hsp7066 has been reported. Similarly, a correlation was observed between Hsp25 and Hsp70 regulation. Along with the down-regulation of Hsp25, a significant decrease of the expression levels was noticed for several Hsp70 subunits in Cr-/- mESC. Another ubiquitous Hsp, which is up-regulated in Cr-/- mESC is the molecular chaperone Hsp90.67 As discussed for Hsp25 and Hsp70, also for Hsp90 a differential expression in glial and neuronal cells, as well as in the different structures of the brain has been reported.63 Furthermore, Hsp90 exists as two isoforms, Hsp90 alpha (or Hsp84) and beta (or Hsp86), coded by related but separate genes.65,68 It has been reported that the down-regulation of Hsp90 is a physiologically critical event in the differentiation of human and mouse embryonal carcinoma cells and that specific Hsp90 isoforms may be involved in differentiation into specific cell lineages.69,70 Indeed, only the level of Hsp86, but not Hsp84, decreases upon cellular differentiation.71,72 Despite several evidence suggest a strong involvement of proteins belonging to the Hsp family in differentiation processes, to date their function, mechanism of action and role remain largely unknown. A comprehensive proteomic study reports the differentially expression of several heat shock proteins, including members of the Hsp40/DNAJ homologue family, in both human and mouse ESCS compared to the differentiated cells.3 These findings strongly suggest that these proteins might have specific roles, such as intracellular transport, distinct from their chaperone activity.63 Notably, a recent study reports that Cripto associates to 78 kDa glucose-regulated protein (GRP78), belonging to the heat shock protein 70 family.73 It has been Journal of Proteome Research • Vol. 8, No. 2, 2009 1055

research articles demonstrated that GRP78 and Cripto associate to form a cell surface complex that inhibits TGF-β signaling and promotes tumor cell growth.73 Although in the presented results, GRP78 has been identified in both the RI and Cr-/- cell lines, it would be interesting to investigate its expression profile upon differentiation. Another class of molecular chaperones, the hetero-oligomeric T-complex protein 1, has been found up-regulated in Cr-/- mESC. These proteins, involved in the folding of actin, tubulin, and other cytosolic proteins, show a characteristic temporal and spatial regulation during embryogenesis, especially in neural and somitic development.74 Notably, a recent proteomic study focused on cellular processes responsible of retinoic acid-induced neuronal differentiation of P19 cell line, similarly reports the differential expression of TCP-1 and of several chaperones of the Hsp70 and Hsp90 families, suggesting that coordinated remodelling of the cytoskeleton and modulations in chaperone activity underlie the neuronal commitment and differentiation.54 Many other proteins up-regulated in Cr-/ES are involved, directly or indirectly, in neuronal development. Among these, 14-3-3 proteins are a family of highly conserved cellular proteins that play key roles in the regulation of central physiological pathways by interacting with several target proteins, including proteins involved in mitogenic and cell survival signaling, cell cycle control and apoptotic cell death.75 Binding of 14-3-3 proteins is necessary for the cytoplasmic retention of histone deacetylase 4 (HDAC4) by both inhibiting its nuclear import and stimulating the nuclear export.76 Histone acetylation and deacetylation is a major mechanism of chromatin transcriptional regulation, allowing or blocking the access of transcriptional factors to DNA sequences. The involvement of histone acetylation in the earliest steps of differentiation has been demonstrated for mouse embryonic stem cells,77 while chromatin structure modulation by HDACs has a proven implication in the transcriptional regulation of the neuronal differentiation of embryonic neural stem cells.78 Beyond its role on regulation of HDAC4 cellular localization, a high expression of 14-3-3, and in particular of 14-3-3 epsilon, has been found in the nervous system.79,80 Most interestingly, during neuronal differentiation, 14-3-3 epsilon remains at a high level in the neuronal cytoplasm and mutations in the Drosophila 14-3-3 genes disrupt neuronal differentiation, thus confirming a direct implication of these proteins in the development and function of the nervous system.80 The pyruvate kinase M2 isozyme has also been found up-regulated in Cr-/- mESC. Using embryonic P19 cells, it has been demonstrated that the specific activity of pyruvate kinase and its isozyme composition is correlated with the differentiation state of neurons.81 The up-regulation of pyruvate kinase M2 isozyme has also been confirmed by array analysis of the genes regulated during neuronal differentiation of human embryonal cells.82 Among the intriguing observations in our proteomic analysis, is the up-regulation in Cr-/- mESC of the nucleosome assembly protein 1 (NAP1), which belongs to a protein family with a structurally conserved fold.83 Although initially identified as histone chaperones and chromatin-assembly factors, additional functions have been ascribed to NAP1,84 including an active role in neuronal differentiation.85 In particular, it has been observed that the neuronal Nap1l2 variant, by controlling histone acetylation, can strongly affect the transcription regulation during neuronal differentiation. Furthermore, the phenotype of knockout Nap1l2 gene in mice correlates with an increased maintenance of the neural stem cell stage, thus 1056

Journal of Proteome Research • Vol. 8, No. 2, 2009

Chambery et al. leading to an impairment of neural tube formation. Furthermore, by ex vivo differentiation studies of Nap1l2-/- ES cells, it has been demonstrated that this protein regulates the kinetic of neuronal differentiation, increasing neural precursor renewal, maintenance, and apoptosis. Notably, among the Nap1l2 downstream target genes, cdkn1c has been identified. Cdkn1c is a negative regulator of cell proliferation that promotes neuronal differentiation and is found down-regulated in neural precursors and neurons upon Nap1l2 deletion.86,87 These findings suggest that Nap1l2 is implicated in the epigenetic regulation of gene expression occurring during neuronal differentiation and that it could work by mediating cell-typespecific mechanisms of establishment/modification of a chromatin-permissive state that, in turn, can affect neurogenesis and neuronal survival.

Conclusion By using a 2D LC-MS/MS approach in combination with a multiplexed, alternate scanning LC-MSE experiment, a qualitative proteomic profiling of the cripto-/- embryonic mouse stem cells has been obtained that represents a very useful method to probe the molecular mechanisms underlying cardiomyocites vs neuronal differentiation. The quantitative proteomic profiling analysis, performed by LC-MSE experiments, has allowed a quantitative comparison of proteins differentially expressed giving a greater insight into the proteomes of the two cell lines. The increased neural induction in Cr-/- ES and the evidence that the suppression of Cripto allows cells to spontaneously differentiate to neural cells have led to hypothesize that suppression of Cripto plays an active role in “neural fate” commitment. The data presented here support this hypothesis, showing a good correlation between the phenotype of the Cripto-/- mESC, i.e. enhanced neurogenesis and their proteomic signature. Among the proteins whose expression levels are affected by cripto ablation, there are many already described as involved in neuronal differentiation/survival. In addition, Hsp25, Hsp70, Hsp90, 14-3-3, and proteins of the NAP family have been identified. More interestingly, for the first time to the knowledge of the authors, the presented data indicate that a hallmark of neuronal differentiation is already established in undifferentiated murine ES cells, in the absence of Cripto, and thus open new insights into the understanding of the molecular mechanisms that control the balance between self-renewal and differentiation of stem cells.

Acknowledgment. We gratefully acknowledge Dr. Simona Scarpella of Waters S.p.A (Milan, Italy) for useful suggestions and support. We are also grateful to Dr. Emily Dimmer and Dr. Evelyn Camon of the European Bioinformatics Institute (Hinxton, United Kingdom) who are kindly acknowledged for their help with the GO annotation searches. This study was supported by funds from the Second University of Naples, by grants from the CNR (short-term mobility program 2006) and partially by the project FIRB2003, N° RBNE03PX83_005 to MR and AIRC to GM. Supporting Information Available: Supplementary Figures 1 and 2 and Supplementary Tables 1-4. This material is available free of charge via the Internet at http://pubs.acs.org.

Cripto-/- Embryonic Stem Cells

References (1) Skalnikova, H.; Halada, P.; Vodicka, P.; Motlik, J.; Rehulka, P.; Horning, O.; Chmelik, J.; Norregaard Jensen, O.; Kovarova, H. A proteomic approach to studying the differentiation of neural stem cells. Proteomics 2007, 7, (11), 1825–38. (2) Hoffrogge, R.; Mikkat, S.; Scharf, C.; Beyer, S.; Christoph, H.; Pahnke, J.; Mix, E.; Berth, M.; Uhrmacher, A.; Zubrzycki, I. Z.; Miljan, E.; Volker, U.; Rolfs, A. 2-DE proteome analysis of a proliferating and differentiating human neuronal stem cell line (ReNcell VM). Proteomics 2006, 6 (6), 1833–47. (3) Van Hoof, D.; Passier, R.; Ward-Van Oostwaard, D.; Pinkse, M. W.; Heck, A. J.; Mummery, C. L.; Krijgsveld, J. A quest for human and mouse embryonic stem cell-specific proteins. Mol. Cell Proteomics 2006, 5 (7), 1261–73. (4) Baharvand, H.; Hajheidari, M.; Ashtiani, S. K.; Salekdeh, G. H. Proteomic signature of human embryonic stem cells. Proteomics 2006, 6 (12), 3544–9. (5) Vodicka, P.; Skalnikova, H.; Kovarova, H. The characterization of stem cell proteomes. Curr. Opin. Mol. Ther. 2006, 8 (3), 232–9. (6) Hoffrogge, R.; Beyer, S.; Volker, U.; Uhrmacher, A. M.; Rolfs, A. 2-DE proteomic profiling of neuronal stem cells. Neurodegener. Dis. 2006, 3 (1-2), 112–21. (7) Blonder, J.; Xiao, Z.; Veenstra, T. D. Proteomic profiling of differentiating osteoblasts. Expert Rev. Proteomics 2006, 3 (5), 483– 96. (8) Hayman, M. W.; Christie, V. B.; Keating, T. S.; Przyborski, S. A. Following the differentiation of human pluripotent stem cells by proteomic identification of biomarkers. Stem Cells Dev. 2006, 15 (2), 221–31. (9) Prudhomme, W.; Daley, G. Q.; Zandstra, P.; Lauffenburger, D. A. Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling. Proc. Natl. Acad. Sci. U.S.A. 2004, 101 (9), 2900–5. (10) Nagano, K.; Taoka, M.; Yamauchi, Y.; Itagaki, C.; Shinkawa, T.; Nunomura, K.; Okamura, N.; Takahashi, N.; Izumi, T.; Isobe, T. Large-scale identification of proteins expressed in mouse embryonic stem cells. Proteomics 2005, 5 (5), 1346–61. (11) Wang, D.; Gao, L. Proteomic analysis of neural differentiation of mouse embryonic stem cells. Proteomics 2005, 5 (17), 4414–26. (12) Guo, X.; Ying, W.; Wan, J.; Hu, Z.; Qian, X.; Zhang, H.; He, F. Proteomic characterization of early-stage differentiation of mouse embryonic stem cells into neural cells induced by all-trans retinoic acid in vitro. Electrophoresis 2001, 22 (14), 3067–75. (13) Elliott, S. T.; Crider, D. G.; Garnham, C. P.; Boheler, K. R.; Van Eyk, J. E. Two-dimensional gel electrophoresis database of murine R1 embryonic stem cells. Proteomics 2004, 4 (12), 3813–32. (14) Hayman, M. W.; Przyborski, S. A. Proteomic identification of biomarkers expressed by human pluripotent stem cells. Biochem. Biophys. Res. Commun. 2004, 316 (3), 918–23. (15) Puente, L. G.; Borris, D. J.; Carriere, J. F.; Kelly, J. F.; Megeney, L. A. Identification of candidate regulators of embryonic stem cell differentiation by comparative phosphoprotein affinity profiling. Mol. Cell. Proteomics 2006, 5 (1), 57–67. (16) Gong, Y. P.; Yarrow, P. M.; Carmalt, H. L.; Kwun, S. Y.; Kennedy, C. W.; Lin, B. P.; Xing, P. X.; Gillett, D. J. Overexpression of Cripto and its prognostic significance in breast cancer: A study with longterm survival. Eur. J. Surg. Oncol. 2007, 33 (4), 438–43. (17) Carmalt, H. L.; Gong, Y. P.; Yarrow, P. M.; Lin, B. P.; Xing, P. X.; Gillett, D. J. Bs10 the prognostic significance of the overexpression of the growth factor cripto in patients with breast cancer. ANZ J. Surg. 2007, 77 (1), A3. (18) Shen, M. M. Decrypting the role of Cripto in tumorigenesis. J. Clin. Invest. 2003, 112 (4), 500–2. (19) Strizzi, L.; Bianco, C.; Normanno, N.; Salomon, D. Cripto-1: a multifunctional modulator during embryogenesis and oncogenesis. Oncogene 2005, 24 (37), 5731–41. (20) Persico, M. G.; Liguori, G. L.; Parisi, S.; D’Andrea, D.; Salomon, D. S.; Minchiotti, G. Cripto in tumors and embryo development. Biochim. Biophys. Acta 2001, 1552 (2), 87–93. (21) Minchiotti, G. Nodal-dependant Cripto signaling in ES cells: from stem cells to tumor biology. Oncogene 2005, 24 (37), 5668–75. (22) Minchiotti, G.; Parisi, S.; Persico, M. G. Cripto signaling in differentiating embryonic stem cells. Methods Mol. Biol. 2006, 329, 151–69. (23) Gray, P. C.; Shani, G.; Aung, K.; Kelber, J.; Vale, W. Cripto binds transforming growth factor beta (TGF-beta) and inhibits TGF-beta signaling. Mol. Cell. Biol. 2006, 26 (24), 9268–78. (24) Bianco, C.; Normanno, N.; Salomon, D. S.; Ciardiello, F. Role of the cripto (EGF-CFC) family in embryogenesis and cancer. Growth Factors 2004, 22 (3), 133–9.

research articles (25) Minchiotti, G.; Parisi, S.; Liguori, G. L.; D’Andrea, D.; Persico, M. G. Role of the EGF-CFC gene cripto in cell differentiation and embryo development. Gene 2002, 287 (1-2), 33–7. (26) Parisi, S.; D’Andrea, D.; Lago, C. T.; Adamson, E. D.; Persico, M. G.; Minchiotti, G. Nodal-dependent Cripto signaling promotes cardiomyogenesis and redirects the neural fate of embryonic stem cells. J. Cell Biol. 2003, 163 (2), 303–14. (27) Gygi, S. P.; Corthals, G. L.; Zhang, Y.; Rochon, Y.; Aebersold, R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. U.S.A. 2000, 97 (17), 9390–5. (28) Gygi, S. P.; Aebersold, R. Mass spectrometry and proteomics. Curr. Opin. Chem. Biol. 2000, 4 (5), 489–94. (29) Steel, L. F.; Haab, B. B.; Hanash, S. M. Methods of comparative proteomic profiling for disease diagnostics. J. Chromatogr., B: Anal. Technol. Biomed. Life Sci. 2005, 815 (1-2), 275–84. (30) Wickremsinhe, E. R.; Singh, G.; Ackermann, B. L.; Gillespie, T. A.; Chaudhary, A. K. A review of nanoelectrospray ionization applications for drug metabolism and pharmacokinetics. Curr. Drug Metab. 2006, 7 (8), 913–28. (31) Vissers, J. P. Recent developments in microcolumn liquid chromatography. J. Chromatogr. A 1999, 856 (1-2), 117–43. (32) Lee, H. J.; Lee, E. Y.; Kwon, M. S.; Paik, Y. K. Biomarker discovery from the plasma proteome using multidimensional fractionation proteomics. Curr. Opin. Chem. Biol. 2006, 10 (1), 42–9. (33) Chen, E. I.; Hewel, J.; Felding-Habermann, B.; Yates, J. R. 3rd Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT). Mol. Cell Proteomics 2006, 5 (1), 53–6. (34) Sheng, S.; Chen, D.; Van Eyk, J. E. Multidimensional liquid chromatography separation of intact proteins by chromatographic focusing and reversed phase of the human serum proteome: optimization and protein database. Mol. Cell Proteomics 2006, 5 (1), 26–34. (35) Bonneil, E.; Tessier, S.; Carrier, A.; Thibault, P. Multiplex multidimensional nanoLC-MS system for targeted proteomic analyses. Electrophoresis 2005, 26 (24), 4575–89. (36) Xu, C.; Liguori, G.; Persico, M. G.; Adamson, E. D. Abrogation of the Cripto gene in mouse leads to failure of postgastrulation morphogenesis and lack of differentiation of cardiomyocytes. Development 1999, 126 (3), 483–94. (37) Williams, R. L.; Hilton, D. J.; Pease, S.; Willson, T. A.; Stewart, C. L.; Gearing, D. P.; Wagner, E. F.; Metcalf, D.; Nicola, N. A.; Gough, N. M. Myeloid leukaemia inhibitory factor maintains the developmental potential of embryonic stem cells. Nature 1988, 336 (6200), 684–7. (38) Bateman, R. H.; Carruthers, R.; Hoyes, J. B.; Jones, C.; Langridge, J. I.; Millar, A.; Vissers, J. P. A novel precursor ion discovery method on a hybrid quadrupole orthogonal acceleration time-of-flight (QTOF) mass spectrometer for studying protein phosphorylation. J Am. Soc. Mass. Spectrom. 2002, 13 (7), 792–803. (39) Silva, J. C.; Denny, R.; Dorschel, C. A.; Gorenstein, M.; Kass, I. J.; Li, G. Z.; McKenna, T.; Nold, M. J.; Richardson, K.; Young, P.; Geromanos, S. Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 2005, 77 (7), 2187–200. (40) Hughes, M. A.; Silva, J. C.; Geromanos, S. J.; Townsend, C. A. Quantitative proteomic analysis of drug-induced changes in mycobacteria. J. Proteome Res. 2006, 5 (1), 54–63. (41) Li, G. Z.; Golick, D.; Gorenstein, M.; Silva, J. C.; Vissers, J. P.; Geromanos, S. In A novel ion accounting algorithm for protein database searches; Poster W079 presented at the 5th HUPO Annual World Congress, Long Beach, CA, 2006. (42) Vissers, J. P.; Langridge, J. I.; Aerts, J. M. Analysis and quantification of diagnostic serum markers and protein signatures for Gaucher disease. Mol. Cell. Proteomics 2007, 6 (5), 755–66. (43) Chambery, A.; Farina, A.; Di Maro, A.; Rossi, M.; Abbondanza, C.; Moncharmont, B.; Malorni, L.; Cacace, G.; Pocsfalvi, G.; Malorni, A.; Parente, A. Proteomic analysis of MCF-7 cell lines expressing the zinc-finger or the proline-rich domain of retinoblastomainteracting-zinc-finger protein. J. Proteome Res. 2006, 5 (5), 1176– 85. (44) Ma, L.; Sun, B.; Hood, L.; Tian, Q. Molecular profiling of stem cells. Clin. Chim. Acta 2007, 378 (1-2), 24–32. (45) Liu, H.; Sadygov, R. G.; Yates, J. R. 3rd A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 2004, 76 (14), 4193–201. (46) Fenyo, D. Holistic therapy for proteomics experiments. Computer simulation predicts whether a particular shotgun proteomics setup will be successful. Anal. Chem. 2007, 79, 7237. (47) Palmieri, S. L.; Peter, W.; Hess, H.; Scholer, H. R. Oct-4 transcription factor is differentially expressed in the mouse embryo during

Journal of Proteome Research • Vol. 8, No. 2, 2009 1057

research articles (48) (49)

(50) (51) (52)

(53)

(54)

(55) (56) (57) (58) (59)

(60) (61)

(62) (63)

(64)

(65) (66)

1058

establishment of the first two extraembryonic cell lineages involved in implantation. Dev. Biol. 1994, 166 (1), 259–67. Niwa, H. How is pluripotency determined and maintained. Development 2007, 134 (4), 635–46. Bhattacharya, B.; Miura, T.; Brandenberger, R.; Mejido, J.; Luo, Y.; Yang, A. X.; Joshi, B. H.; Ginis, I.; Thies, R. S.; Amit, M.; Lyons, I.; Condie, B. G.; Itskovitz-Eldor, J.; Rao, M. S.; Puri, R. K. Gene expression in human embryonic stem cell lines: unique molecular signature. Blood 2004, 103 (8), 2956–64. Wolters, D. A.; Washburn, M. P.; Yates, J. R. 3rd An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73 (23), 5683–90. Washburn, M. P.; Wolters, D.; Yates, J. R. 3rd Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19 (3), 242–7. Shen, Y.; Jacobs, J. M.; Camp, D. G., 2nd.; Fang, R.; Moore, R. J.; Smith, R. D.; Xiao, W.; Davis, R. W.; Tompkins, R. G. Ultra-highefficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. Anal. Chem. 2004, 76 (4), 1134–44. Sharma, S.; Simpson, D. C.; Tolic, N.; Jaitly, N.; Mayampurath, A. M.; Smith, R. D.; Pasa-Tolic, L. Proteomic profiling of intact proteins using WAX-RPLC 2-D separations and FTICR mass spectrometry. J. Proteome Res. 2007, 6 (2), 602–10. Inberg, A.; Bogoch, Y.; Bledi, Y.; Linial, M. Cellular processes underlying maturation of P19 neurons: Changes in protein folding regimen and cytoskeleton organization. Proteomics 2007, 7 (6), 910–20. Parish, C. L.; Parisi, S.; Persico, M. G.; Arenas, E.; Minchiotti, G. Cripto as a target for improving embryonic stem cell-based therapy in Parkinson’s disease. Stem Cells 2005, 23 (4), 471–6. Christians, E. S.; Zhou, Q.; Renard, J.; Benjamin, I. J. Heat shock proteins in mammalian development. Semin. Cell Dev. Biol. 2003, 14 (5), 283–90. Arrigo, A. P. In search of the molecular mechanism by which small stress proteins counteract apoptosis during cellular differentiation. J. Cell Biochem. 2005, 94 (2), 241–6. Arrigo, A. P. Heat shock proteins as molecular chaperones. Med. Sci. (Paris) 2005, 21 (6-7), 619–25. Battersby, A.; Jones, R. D.; Lilley, K. S.; McFarlane, R. J.; Braig, H. R.; Allen, N. D.; Wakeman, J. A. Comparative proteomic analysis reveals differential expression of Hsp25 following the directed differentiation of mouse embryonic stem cells. Biochim. Biophys. Acta 2007, 1773 (2), 147–56. Davidson, S. M.; Morange, M. Hsp25 and the p38 MAPK pathway are involved in differentiation of cardiomyocytes. Dev. Biol. 2000, 218 (2), 146–60. Stahl, J.; Wobus, A. M.; Ihrig, S.; Lutsch, G.; Bielka, H. The small heat shock protein hsp25 is accumulated in P19 embryonal carcinoma cells and embryonic stem cells of line BLC6 during differentiation. Differentiation 1992, 51 (1), 33–7. Gernold, M.; Knauf, U.; Gaestel, M.; Stahl, J.; Kloetzel, P. M. Development and tissue-specific distribution of mouse small heat shock protein hsp25. Dev. Genet. 1993, 14 (2), 103–11. Loones, M. T.; Chang, Y.; Morange, M. The distribution of heat shock proteins in the nervous system of the unstressed mouse embryo suggests a role in neuronal and non-neuronal differentiation. Cell Stress Chaperones 2000, 5 (4), 291–305. Son, Y. S.; Park, J. H.; Kang, Y. K.; Park, J. S.; Choi, H. S.; Lim, J. Y.; Lee, J. E.; Lee, J. B.; Ko, M. S.; Kim, Y. S.; Ko, J. H.; Yoon, H. S.; Lee, K. W.; Seong, R. H.; Moon, S. Y.; Ryu, C. J.; Hong, H. J. Heat shock 70-kDa protein 8 isoform 1 is expressed on the surface of human embryonic stem cells and downregulated upon differentiation. Stem Cells 2005, 23 (10), 1502–13. Calabrese, V.; Scapagnini, G.; Ravagna, A.; Giuffrida Stella, A. M.; Butterfield, D. A. Molecular chaperones and their roles in neural cell differentiation. Dev. Neurosci. 2002, 24 (1), 1–13. Seo, H. R.; Chung, D. Y.; Lee, Y. J.; Lee, D. H.; Kim, J. I.; Bae, S.; Chung, H. Y.; Lee, S. J.; Jeoung, D.; Lee, Y. S. Heat shock protein 25 or inducible heat shock protein 70 activates heat shock factor 1: dephosphorylation on serine 307 through inhibition of ERK1/2 phosphorylation. J. Biol. Chem. 2006, 281 (25), 17220–7.

Journal of Proteome Research • Vol. 8, No. 2, 2009

Chambery et al. (67) Caplan, A. J.; Mandal, A. K.; Theodoraki, M. A. Molecular chaperones and protein kinase quality control. Trends Cell Biol. 2007, 17 (2), 87–92. (68) Dale, E. C.; Yang, X.; Moore, S. K.; Shyamala, G. Murine 86-kDa heat shock protein gene and promoter. Cell Stress Chaperones 1997, 2 (2), 87–93. (69) Kohda, T.; Kondo, K.; Oishi, M. Cellular HSP90 (HSP86) mRNA level and in vitro differentiation of mouse embryonal carcinoma (F9) cells. FEBS Lett. 1991, 290 (1-2), 107–10. (70) Yamada, T.; Hashiguchi, A.; Fukushima, S.; Kakita, Y.; Umezawa, A.; Maruyama, T.; Hata, J. Function of 90-kDa heat shock protein in cellular differentiation of human embryonal carcinoma cells. In Vitro Cell Dev. Biol. Anim. 2000, 36 (2), 139–46. (71) Barnier, J. V.; Bensaude, O.; Morange, M.; Babinet, C. Mouse 89 kD heat shock protein. Two polypeptides with distinct developmental regulation. Exp. Cell Res. 1987, 170 (1), 186–94. (72) Yufu, Y.; Nishimura, J.; Takahira, H.; Ideguchi, H.; Nawata, H. Down-regulation of a Mr 90,000 heat shock cognate protein during granulocytic differentiation in HL-60 human leukemia cells. Cancer Res. 1989, 49 (9), 2405–8. (73) Shani, G.; Fischer, W. H.; Justice, N. J.; Kelber, J. A.; Vale, W.; Gray, P. C. GRP78 and Cripto form a complex at the cell surface and collaborate to inhibit transforming growth factor beta signaling and enhance cell growth. Mol. Cell. Biol. 2008, 28 (2), 666–77. (74) Sun, H. B.; Neff, A. W.; Mescher, A. L.; Malacinski, G. M. Expression of the axolotl homologue of mouse chaperonin t-complex protein-1 during early development. Biochim. Biophys. Acta 1995, 1260 (2), 157–66. (75) Tzivion, G.; Gupta, V. S.; Kaplun, L.; Balan, V. 14-3-3 proteins as potential oncogenes. Semin. Cancer Biol. 2006, 16 (3), 203–13. (76) Wang, A. H.; Yang, X. J. Histone deacetylase 4 possesses intrinsic nuclear import and export signals. Mol. Cell. Biol. 2001, 21 (17), 5992–6005. (77) McCool, K. W.; Xu, X.; Singer, D. B.; Murdoch, F. E.; Fritsch, M. K. The role of histone acetylation in regulating early gene expression patterns during early embryonic stem cell differentiation. J. Biol. Chem. 2007, 282 (9), 6696–706. (78) Balasubramaniyan, V.; Boddeke, E.; Bakels, R.; Kust, B.; Kooistra, S.; Veneman, A.; Copray, S. Effects of histone deacetylation inhibition on neuronal differentiation of embryonic mouse neural stem cells. Neuroscience 2006, 143 (4), 939–51. (79) Skoulakis, E. M.; Davis, R. L. 14-3-3 proteins in neuronal development and function. Mol. Neurobiol. 1998, 16 (3), 269–84. (80) Tien, A. C.; Hsei, H. Y.; Chien, C. T. Dynamic expression and cellular localization of the drosophila 14-3-3epsilon during embryonic development. Mech. Dev. 1999, 81 (1-2), 209–12. (81) van Erp, H. E.; Rijksen, G.; van der Saag, P. T.; Staal, G. E. Phosphofructokinase and pyruvate kinase in mouse embryonal carcinoma P19 cells in relation to growth and differentiation. Differentiation 1990, 45 (3), 199–205. (82) Bani-Yaghoub, M.; Felker, J. M.; Ozog, M. A.; Bechberger, J. F.; Naus, C. C. Array analysis of the genes regulated during neuronal differentiation of human embryonal cells. Biochem. Cell Biol. 2001, 79 (4), 387–98. (83) Park, Y. J.; Luger, K. Structure and function of nucleosome assembly proteins. Biochem Cell Biol. 2006, 84 (4), 549–58. (84) Zlatanova, J.; Seebart, C.; Tomschik, M. Nap1: taking a closer look at a juggler protein of extraordinary skills. Faseb J. 2007, 21 (7), 1294–310. (85) Attia, M.; Rachez, C.; De Pauw, A.; Avner, P.; Rogner, U. C. Nap1l2 promotes histone acetylation activity during neuronal differentiation. Mol. Cell. Biol. 2007, 27 (17), 6093–102. (86) Joseph, B.; Wallen-Mackenzie, A.; Benoit, G.; Murata, T.; Joodmardi, E.; Okret, S.; Perlmann, T. p57(Kip2) cooperates with Nurr1 in developing dopamine cells. Proc. Natl. Acad. Sci. U.S.A. 2003, 100 (26), 15619–24. (87) Rothschild, G.; Zhao, X.; Iavarone, A.; Lasorella, A. E Proteins and Id2 converge on p57Kip2 to regulate cell cycle in neural cells. Mol. Cell. Biol. 2006, 26 (11), 4351–61.

PR800485C