An Integrated Approach of Differential Mass Spectrometry and Gene Ontology Analysis Identified Novel Proteins Regulating Neuronal Differentiation and Survival
2009; Elsevier BV; Volume: 8; Issue: 10 Linguagem: Inglês
10.1074/mcp.m900179-mcp200
ISSN1535-9484
AutoresDaiki Kobayashi, Jiro Kumagai, Takashi Morikawa, Masayo Wilson‐Morifuji, Anthony G. Wilson, Atsushi Irie, Norie Araki,
Tópico(s)S100 Proteins and Annexins
ResumoMS-based quantitative proteomics is widely used for large scale identification of proteins. However, an integrated approach that offers comprehensive proteome coverage, a tool for the quick categorization of the identified proteins, and a standardized biological study method is needed for helping the researcher focus on investigating the proteins with biologically important functions. In this study, we utilized isobaric tagging for relative and absolute quantification (iTRAQ)-based quantitative differential LC/MS/MS, functional annotation with a proprietary gene ontology tool (Molecular Annotation by Gene Ontology (MANGO)), and standard biochemical methods to identify proteins related to neuronal differentiation in nerve growth factor-treated rat pheochromocytoma (PC12) cells, which serve as a representative model system for studying neuronal biological processes. We performed MS analysis by using both nano-LC-MALDI-MS/MS and nano-LC-ESI-MS/MS for maximal proteome coverage. Of 1,482 non-redundant proteins semiquantitatively identified, 72 were differentially expressed with 39 up- and 33 down-regulated, including 64 novel nerve growth factor-responsive PC12 proteins. Gene ontology analysis of the differentially expressed proteins by MANGO indicated with statistical significance that the up-regulated proteins were mostly related to the biological processes of cell morphogenesis, apoptosis/survival, and cell differentiation. Some of the up-regulated proteins of unknown function, such as PAIRBP1, translationally controlled tumor protein, prothymosin α, and MAGED1, were further analyzed to validate their significant functions in neuronal differentiation by immunoblotting and immunocytochemistry using each antibody combined with a specific short interfering RNA technique. Knockdown of these proteins caused abnormal cell morphological changes, inhibition of neurite formation, and cell death during each course of the differentiation, confirming their important roles in neurite formation and survival of PC12 cells. These results show that our iTRAQ-MANGO-biological analysis framework, which integrates a number of standard proteomics strategies, is effective for targeting and elucidating the functions of proteins involved in the cellular biological process being studied. MS-based quantitative proteomics is widely used for large scale identification of proteins. However, an integrated approach that offers comprehensive proteome coverage, a tool for the quick categorization of the identified proteins, and a standardized biological study method is needed for helping the researcher focus on investigating the proteins with biologically important functions. In this study, we utilized isobaric tagging for relative and absolute quantification (iTRAQ)-based quantitative differential LC/MS/MS, functional annotation with a proprietary gene ontology tool (Molecular Annotation by Gene Ontology (MANGO)), and standard biochemical methods to identify proteins related to neuronal differentiation in nerve growth factor-treated rat pheochromocytoma (PC12) cells, which serve as a representative model system for studying neuronal biological processes. We performed MS analysis by using both nano-LC-MALDI-MS/MS and nano-LC-ESI-MS/MS for maximal proteome coverage. Of 1,482 non-redundant proteins semiquantitatively identified, 72 were differentially expressed with 39 up- and 33 down-regulated, including 64 novel nerve growth factor-responsive PC12 proteins. Gene ontology analysis of the differentially expressed proteins by MANGO indicated with statistical significance that the up-regulated proteins were mostly related to the biological processes of cell morphogenesis, apoptosis/survival, and cell differentiation. Some of the up-regulated proteins of unknown function, such as PAIRBP1, translationally controlled tumor protein, prothymosin α, and MAGED1, were further analyzed to validate their significant functions in neuronal differentiation by immunoblotting and immunocytochemistry using each antibody combined with a specific short interfering RNA technique. Knockdown of these proteins caused abnormal cell morphological changes, inhibition of neurite formation, and cell death during each course of the differentiation, confirming their important roles in neurite formation and survival of PC12 cells. These results show that our iTRAQ-MANGO-biological analysis framework, which integrates a number of standard proteomics strategies, is effective for targeting and elucidating the functions of proteins involved in the cellular biological process being studied. MS-based quantitative proteomics strategies such as iTRAQ 1The abbreviations used are:iTRAQisobaric tagging for relative and absolute quantitationNGFnerve growth factorGOgene ontologyPC12PRSPC12 proteome reference setICCimmunocytochemistrysiRNAshort interfering RNATCTPtranslationally controlled tumor proteinProTαprothymosin αTrkAtropomyosin-related kinase A2-Dtwo-dimensionalRPreverse phaseGOAgene ontology annotationPCNAproliferating cell nuclear antigenPIpropidium iodidePAIRBP1plasminogen activator inhibitor 1 RNA-binding proteinPAIplasminogen activator inhibitormESCmouse embryonic stem cellMAGEmelanoma antigenp75NTRp75 neurotrophin receptorQqTOFquadrupole/quadrupole/time-of-flight mass spectrometers. (1Ross P.L. Huang Y.N. 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The following four main issues are typically the sources of such difficulties. 1) Quantitative identification by one type of MS system may fail to cover the total proteome because of ionization efficiency differences, such as those between ESI and MALDI, for certain peptides, leading to theoretical limitations in proteome coverage. 2) The public protein databases are often insufficient for searching non-human species because of the limited available genomic information. 3) The identification of the functions and biological processes of thousands of proteins is a formidable task because of the lack of simple and user-friendly software to automate gene ontology (GO) annotation. Furthermore it is difficult to convert large lists of taxonomically diverse proteins into their human orthologs to obtain the richest GO information available. 4) Lastly biological validation strategies for identified proteins have not been standardized. Therefore, we believe an analysis framework that provides (a) comprehensive proteome data; (b) a simple and quick tool for organizing, enriching, and sorting those data to reveal candidate molecules for relation to certain processes; and (c) a standardized biological validation technique would greatly benefit this field. We therefore designed a concise, three-step, sequential proteomics strategy that addresses the above concerns and utilized it successfully in studying the mechanism of neuronal differentiation in PC12 cells. isobaric tagging for relative and absolute quantitation nerve growth factor gene ontology PC12 proteome reference set immunocytochemistry short interfering RNA translationally controlled tumor protein prothymosin α tropomyosin-related kinase A two-dimensional reverse phase gene ontology annotation proliferating cell nuclear antigen propidium iodide plasminogen activator inhibitor 1 RNA-binding protein plasminogen activator inhibitor mouse embryonic stem cell melanoma antigen p75 neurotrophin receptor quadrupole/quadrupole/time-of-flight mass spectrometers. PC12 cells (9Greene L.A. Tischler A.S. Establishment of a noradrenergic clonal line of rat adrenal pheochromocytoma cells which respond to nerve growth factor.Proc. Natl. Acad. Sci. 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NGF is one member of a family of structurally and functionally related dimeric polypeptides, neurotrophins, that are essential for the development and maintenance of distinct neuronal populations in the central and peripheral nervous systems (17Davies A.M. The role of neurotrophins in the developing nervous system.J. Neurobiol. 1994; 25: 1334-1348Crossref PubMed Scopus (430) Google Scholar). The initial signaling cascades in the neuronal cells right after NGF stimulation have been subjected to thorough investigation and characterization by using PC12 cells. After binding of extracellular NGF to the cell membrane-localized tropomyosin-related kinase A (TrkA) receptor, TrkA receptors dimerize and subsequently autophosphorylate each other. Then the phosphorylated receptors recruit a complex of signaling molecules and induce a number of intracellular signaling cascades involving the signaling molecules, such as phosphoinositide 3-kinase, phospholipase C-γ, and Ras (18Chao M.V. 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Neurosci. 1990; 2: 163-174Crossref PubMed Scopus (10) Google Scholar). Even currently available PC12 cell 2-D databases include merely a few proteins related to NGF stimulation (26Huang C.M. Shui H.A. Wu Y.T. Chu P.W. Lin K.G. Kao L.S. Chen S.T. Proteomic analysis of proteins in PC12 cells before and after treatment with nerve growth factor: increased levels of a 43-kDa chromogranin B-derived fragment during neuronal differentiation.Brain Res. Mol. Brain Res. 2001; 92: 181-192Crossref PubMed Scopus (28) Google Scholar, 27Huang Y.H. Chang A.Y. Huang C.M. Huang S.W. Chan S.H. Proteomic analysis of lipopolysaccharide-induced apoptosis in PC12 cells.Proteomics. 2002; 2: 1220-1228Crossref PubMed Scopus (38) Google Scholar, 28Zhou B. Yang W. Ji J.G. Ru B.G. Differential display proteome analysis of PC-12 cells transiently transfected with metallothionein-3 gene.J. Proteome Res. 2004; 3: 126-131Crossref PubMed Scopus (8) Google Scholar, 29Yang W. Liu P. Liu Y. Wang Q. Tong Y. Ji J. Proteomic analysis of rat pheochromocytoma PC12 cells.Proteomics. 2006; 6: 2982-2990Crossref PubMed Scopus (31) Google Scholar). There is thus a paucity of functional proteomic information related to PC12 cell biological processes that may be attributed to technical limitations such as those listed above. In this study, we performed the first proteomics survey of proteins differentially expressed in PC12 cells during NGF treatment by using a semiquantitative differential LC shotgun method, namely isobaric tagging for relative and absolute quantitation (iTRAQ) coupled with concurrent use of two tandem MS/MS systems, namely nano-LC-MALDI-TOF-TOF and nano-LC-ESI-Quadrupole/quadrupole/time-of-flight mass spectrometers. The total list of proteins identified was converted into a new file linked to the GO database by our proprietary GO analysis tool for proteomes (MANGO) and categorized by biological process and function using specific classification methods. Thereafter we classified the subset of proteins that were up- or down-regulated during neurite formation into specific molecular categories by combining the differential data obtained by iTRAQ with the proteomic GO analysis results. We then attempted to characterize the functional mechanism of NGF-induced PC12 cell neuronal differentiation. Interestingly the specific up-regulated groups classified in this study were related to apoptosis/cell survival in addition to cell motility, differentiation, stress stimulation, and morphogenesis. To investigate the molecular functions of the up-regulated proteins in relation to both PC12 cell differentiation and apoptosis/survival during neurite formation, some of them were further analyzed with a biochemical and cellular biological strategy using a combined antibody and siRNA technique. Lastly we demonstrated the advantages that our concise, sequential proteomics strategy offers for studying the molecular mechanisms of cellular biological events such as cell differentiation and survival/apoptosis. PC12 cells were cultured under 5% CO2 at 37 °C in Dulbecco's modified Eagle's medium supplemented with 10% horse serum and 5% fetal bovine serum. We performed four independent cell cultures for a fourplex iTRAQ analysis. Two of them were used as duplicated samples for controls, and the other two samples were used as NGF-treated cells. For NGF stimulation, the cells were cultured onto collagen-coated culture dishes (Iwaki) in the same medium and stimulated with 50 ng/ml 2.5 S NGF (Wako) at 48 h. For preparation of cell lysate, cells were solubilized with the lysis buffer containing 8 m urea, 2% CHAPS, 2 mm Na2VO4, 10 mm NaF, 1 µm okadaic acid, and 1% (v/v) protease inhibitor mixture (Sigma) and passed through a 25-gauge syringe 15 times. Lysates were centrifuged at 13,000 × g for 20 min at 4 °C, and the protein concentration of the supernatants was determined using the Bio-Rad protein assay. One hundred micrograms of each protein sample was precipitated using a 2-D Clean-Up kit (Amersham Biosciences), and the precipitants were dissolved in 10 µl of 6 m urea. iTRAQ sample labeling was performed according to the manufacturer's protocol with minimum modification. For the fourplex iTRAQ labeling, the four lysates of PC12 cells separately cultured were treated with iTRAQ reagents in parallel. Twenty microliters of dissolution buffer and 1 µl of denaturant reagent were added to the samples. The samples were reduced by addition of 2 µl of reducing reagent and incubation at 60 °C for 1 h. Reduced cysteine residues were then blocked by addition of 1 µl of cysteine blocking reagent and incubated at room temperature for a further 10 min. Tryptic digestion was initiated by the addition of 12.5 µl of trypsin solution (Promega; prepared as 1 µg/µl in water solution) and incubated at 37 °C for 16 h. To label the peptides with iTRAQ reagents, one vial of labeling reagent was thawed and reconstituted in 80 µl of ethanol. The reagents 114 and 115 for two samples from untreated cells and the reagents 116 and 117 for two samples from NGF-stimulated cells were added to the digests and incubated for 1 h at room temperature. The labeled samples were then mixed together before fractionation using a cation exchange column. To remove excess, unbound iTRAQ reagent and to simplify the peptide mixture, the labeled peptide mixture was purified and fractionated using a GE Healthcare AKTA system. The mixed sample was diluted in loading buffer (20% (v/v) ACN and 10 mm potassium phosphate, pH 3.0) and loaded onto a Mono S column (GE Healthcare) equilibrated with loading buffer. Peptides were eluted with a gradient of solvent B (10 mm potassium phosphate, pH 3.0, and 1 m KCl in 20% (v/v) ACN) as follows: 0–2 min, 0–7% B; at 6 min, to 14% B; at 8 min, to 32% B; at 13 min, to 70% B; and at 21 min, to 100% B. Twenty-five fractions that included the iTRAQ-labeled peptides were used for analysis. The fractions were dried in a vacuum centrifuge and rehydrated with solution containing 2% ACN and 0.1% TFA. The samples were desalted with ZipTipTM µ-C18 pipette tips (Millipore). The desalted peptides were divided into two fractions to analyze the same samples by using nano-LC-MALDI-TOF-TOF and nano-LC-ESI-QqTOF systems. Samples were separated by C18 nano-LC using DiNa Map (KYA Tech Corp.) equipped with a device spotting eluted fractions on a MALDI plate. Sample was injected onto a C18 column (0.5-mm inner diameter × 1-mm length, KYA Tech Corp.) equilibrated with solvent A (2% ACN and 0.1% TFA) and resolved on a C18 nanocolumn (0.15-mm inner diameter × 100-mm length; KYA Tech Corp.) at a flow rate of 300 nl/min with a 90-min gradient of solvent B (70% ACN and 0.1% TFA) as follows: 0–20% B from 0 to 10 min, to 50% B at 65 min, and to 100% B at 75 min. Column effluent was mixed with matrix (2 mg/ml α-cyano-4-hydroxycinnamic acid in 50% ACN and 0.1% TFA) at a flow rate of 1.4 µl/min. Fractions were spotted at 30-s intervals onto a stainless steel MALDI target plate (192 wells/plate; Applied Biosystems). Mass spectra of the peptides were acquired on a 4700 Proteomics Analyzer (Applied Biosystems) using 4000 Series Explorer software (Version 3.6). Mass spectra from m/z 800 to 4,000 were acquired for each fraction with 1,500 laser shots. To analyze the less abundant peptides, all of the peaks with a signal to noise ratio threshold from 50 to 75 and from 75 to 100 in each MS spectrum were selected for MS/MS analysis with 5,000 and 4,000 laser shots, respectively. Next all of the peaks above a signal to noise ratio threshold of 100 were selected for MS/MS analysis with 3,000 laser shots. Fragmentation of the labeled peptides was induced by the use of atmosphere as a collision gas with a pressure of 1 × 10−6 Torr and a collision energy of 1 kV. Samples were analyzed by nano-LC-ESI-MS/MS using the LC Packings Ultimate instrument fitted with a 20-µl sample loop. Samples were loaded onto a 5-mm RP C18 precolumn (LC Packings) at 30 µl/min and washed for 10 min before switching the precolumn in line with the separation column. The separation column used was a 75-µm internal diameter × 150-mm length PepMap RP column from LC Packings packed with 3-µm C18 beads with 100-Å pores. The flow rate used for separation on the RP column was 200 nl/min with a 90-min gradient of solvent B (85% ACN and 0.1% formic acid) as follows: 0–40% B from 0–60 min to 100% B at 70 min. The samples were divided into two fractions beforehand, and the first analysis was performed on a QSTAR Pulsar i mass spectrometer (Applied Biosystems/MDS Sciex), and the software used for data acquisition was Analyst QS 1.1 (Applied Biosystems/MDS Sciex) with the scan cycles set up to perform a 1-s MS scan followed by three MS/MS scans of the three most abundant peaks for 3 s each. Data acquisition was performed with an exclusion of 60 s for previous target ions. To analyze the less abundant peptides, the second analysis was performed under the same condition except for input of the m/z list to exclude the analyses of peptide ions already analyzed in the first run. The labeled peptides were fragmented under CID conditions designed to give iTRAQ reporter ions. Data from MALDI or ESI analysis were analyzed using the ParagonTM algorithm (30Shilov I.V. Seymour S.L. Patel A.A. Loboda A. Tang W.H. Keating S.P. Hunter C.L. Nuwaysir L.M. Schaeffer D.A. The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.Mol. Cell. Proteomics. 2007; 6: 1638-1655Abstract Full Text Full Text PDF PubMed Scopus (1073) Google Scholar) of ProteinPilot Version 2.0 (Applied Biosystems), and the database searched was the Swiss-Prot database with all taxonomy (Revision number 53, 269,293 sequence entries, updated on May 29, 2007). Identified proteins were grouped by the Paragon algorithm of the software to minimize redundancy. This software has a function of automatic grouping of identified proteins according to the identified peptide sequence. The identified proteins were automatically grouping by the Paragon algorithm. Peptides used for the quantification of proteins were chosen by this algorithm of ProteinPilot software. All peptides used for the calculation of protein ratios were unique
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