Article pubs.acs.org/jpr
Secretome-Derived Isotope Tags (SDIT) Reveal Adipocyte-Derived Apolipoprotein C-I as a Predictive Marker for Cardiovascular Disease Rong-Xia Li,†,⊥ Yu-Bo Ding,‡,⊥ Shi-Lin Zhao,†,⊥ Yuan-Yuan Xiao,‡ Qing-run Li,† Fang-Ying Xia,† Liang Sun,§ Xu Lin,§ Jia-Rui Wu,† Kan Liao,*,‡ and Rong Zeng*,† †
Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China ‡ State Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Graduate School, Chinese Academy of Sciences, Shanghai 200031, China § Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China S Supporting Information *
ABSTRACT: We developed a quantitative strategy, named secretome-derived isotopic tag (SDIT), to concurrently identify and quantify the adipocyte-secreted plasma proteins from normal and high-fat-diet (HFD) induced obese mice, based on the application of isotope-labeled secreted proteins from cultured mouse adipocytes as internal standards. We detected 197 proteins with significant changes between normal and obese mice plasma. Importantly, a novel adipocyte-secreted plasma protein, apolipoprotein C-I (apoC-I), significantly increased in the obese mice plasma. The expression and secretion of adipocyte apoC-I was detected in differentiated 3T3-L1 and primary rat adipocytes. Our in vitro experiments proved that functional Golgi apparatus was required for apoC-I secretion. Additionally, obese mice had increased apoC-I production in adipose tissue. Population survey of 367 participants showed that the plasma level of apoC-I was significantly increased in obese individuals compared with healthy individuals. After multiple adjustments for age and sex, the odds ratios for risk factors of cardiovascular disease including high LDL cholesterol, hypercholesterolemia, and hypertriglyceridemia, respectively, were used to compare the highest with the lowest apoC-I quartile. Taken together, our studies provide a novel strategy to concurrently identify and quantify tissue-specific secreted proteins. This strategy can be used to identify the largest global characterization of adipocyte-derived plasma proteome and provides a potential disease-related biomarker for clinical diagnoses. By selectively analyzing adipocytesecreted proteins in plasma from obese vs lean murine and/or human subjects, we discovered that apoC-I is an adipocytesecreted plasma protein and a predictive marker for cardiovascular disease. KEYWORDS: secretome-derived isotope tags, adipocyte-secreted proteins, obesity, apolipoprotein C-I, cardiovascular risk factors
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INTRODUCTION Obesity is characterized by an excess of body fat mass, which is mostly stored in adipose tissue.1,2 White adipose tissue (WAT) represents the vast majority of adipose tissue in an organism and plays a crucial active role in the development of obesity.3 © 2012 American Chemical Society
Recent data suggest that WAT is not only a passive energy store but also an active endocrine organ.4 It has been clearly Received: December 19, 2011 Published: March 12, 2012 2851
dx.doi.org/10.1021/pr201224e | J. Proteome Res. 2012, 11, 2851−2862
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Article
room temperature for 1.5 min. The plasma glucose was measured using the Amplex Red Glucose/Glucose Oxidase Assay Kit (A22189; Molecular Probes, Eugene, OR, USA). All procedures were performed in accordance with the experimental guidelines for animal care. Protein quantitation was performed using the Bradford protein assay (Biorad).
demonstrated that human WAT produces a variety of secretory factors that exert multiple effects at both local and systemic levels.2 However, until now, there has been limited systematic and integrative characterization of secretory products released by WAT. Currently, the applications of stable isotope labeling by amino acids in cell culture (SILAC) strategy in quantitative proteome analysis include two major aspects. One application uses means of quantitative MS data is to compare totally differential expression of whole cell lysates between two variable conditions.5,6 Moreover, the whole lysates from normal and cancer tissue can also be quantified using corresponding cell type cultured in 13C-labeled leucine-rich medium as an internal standard. This approach, named culture-derived isotope tags (CDITs), is useful to explore differential expression proteins between normal and cancer tissue.7 In another aspect, most applications of the SILAC strategy are used to quantify subgroup proteins derived from whole-cell lysates, such as protein complexes (protein−protein interactions), protein posttranslation modification (phosphoproteome) and organelles.7 These approaches all focus on cellular research that combines SILAC with traditional biochemistry technologies. Few published studies have reported the application of SILACbased quantitative strategies to the comprehensive investigation of certain tissue-derived plasma proteins. These strategies are only able to produce an overview of the protein changes in plasma; the proteins secreted by adipose tissue remain indistinguishable. In this study, we quantified the adipocyte-derived plasma proteins of obese and normal mice using heavy isotope-lysinelabeled adipocyte-secreted proteins as internal standards. An in-depth investigation of ApoC-I, a novel adipocyte-secreted protein, was conducted at both the cell and tissue level. Identification and quantitation of novel secreted molecules using this proteomic methodology will allow for functional classification of such proteins in adipose biology and in various metabolic pathways in general.
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Cell Culture and Protein Preparation
3T3-L1 preadipocytes were grown at 37 °C in Dulbecco’s modified Eagle’s medium (deficient in L-leucine, L-lysine, and L-methionine, Sigma) supplemented with 10% dialyzed fetal bovine serum (Gibco 26400−044), antibiotics, and 13C615N2 L-lysine (98% purity, Cambridge Isotope Laboratories, Andover, MA, USA). 3T3-L1 preadipocytes were cultured and induced to differentiate following the protocol previously described.8,9 On day 10 of differentiation, 3T3-L1 cells cultured in 100-mm dishes were supplemented with 1 μg/mL of porcine insulin at 37 °C for 4 h and washed six times with 5 mL of serum-free medium. The washed cells were then incubated in 4 mL of phenol red-free and serum-free medium containing 1 μg/mL of porcine insulin. After 4 h, the media were harvested and centrifuged at 3000 rpm at 4 °C for 60 min. The supernatants were filtered through 0.22-μm filters (Millipore) and then transferred to superfilteration tubes (centriprep YM-3; Millipore) and superfiltered to concentrate the sample at 4 °C. The concentrated medium samples were freeze-dried and dissolved in fresh rehydration buffer (8 M urea, 4% CHAPS, 65 mM dithiothreitol, 40 mM Tris). Protein quantitation was performed using the Bradford protein assay. Aliquots were stored at −80 °C. 1D Nano LC−MS/MS Analysis
A Surveyor liquid chromatography system (Thermo Finnigan, San Jose, CA, USA) consisting of a degasser, MS Pump, and autosampler and equipped with a C18 trap column (RP, 320 μm × 20 mm, Column Technology Inc., CA, USA) and an analytical C18 column (RP, 75 μm × 150 mm, Column Technology Inc., CA, USA). The HPLC solvents used were 0.1% formic acid (v/v) in ddH2O (A) and 0.1% formic acid (v/v) in acetonitrile (B). The samples were first loaded into the trap column at a 3 μL/min flow rate after the split. The reversed-phase gradient was varied between 2 and 40% mobile phase B in 180 min at a 120 μL/min flow rate before the split and at a 250 nL/min flow rate after the split. A linear ion trap/ Orbitrap (LTQ-Orbitrap) hybrid mass spectrometer (Thermo Finnigan, San Jose, CA, USA) equipped with an NSI nanospray source was used for the MS/MS experiment with an ion transfer capillary of 200 °C and an NSI voltage of 1.80 kV. The normalized collision energy was 35.0. The mass spectrometer was set for the acquisition of one full MS scan (m/z 400−1800) in the Orbitrap parallel to three MS/MS scans in the linear ion trap of the three most intense ions from the full MS spectrum with the following Dynamic Exclusion settings: repeat count 2, repeat duration 30 s, and exclusion duration 90 s. The resolving power of the Orbitrap mass analyzer was set at 60 000 for the precursor ion scans (m/Δm 50% at m/z 400). The m/z (445.120025) was used as an internal lock mass, and calibrating ions were used in the full MS scan.
MATERIALS AND METHODS
Mice Breeding and Plasma Preparation
Female C57BL/6J mice were housed at 22 ± 2 °C and 55 ± 5% relative humidity, with a 12-h light/dark cycle. One week after arrival, mice at 6 weeks of age were divided into two groups and were fed either a high-fat diet (HFD) or a standard chow diet for up to 34 weeks. On a caloric basis, the highfat diet consisted of 47% fat from lard, 37% carbohydrate, and 16% protein (total 19.1 kJ/g), whereas the normal diet contained 12.2% fat, 62% carbohydrate, and 25.8% protein (total 14.2 kJ/g). Body weight was measured once a week. Body composition measurements were performed at 27 weeks with the Bruker Minispec, which used nuclear magnetic resonance (NMR) technology to estimate the fat, lean and fluid mass of the animals. At 39 weeks, three obese mice and four normal mice were sacrificed, and plasma samples were collected for subsequent analysis. Blood samples were collected with ∼10 IU/mL of heparin, incubated at room temperature for 20 min, and centrifuged at 5000g at 4 °C for 20 min. An initial protein concentration of ∼100 mg/mL of plasma was determined using the Bradford method, after which the sample was diluted to 20 mg/mL with a 100 mM NaCl buffer (pH 7.4, containing 10 mM HEPES). The plasma samples were filtered through 0.22-μm filters (Millipore) by spinning at 16000g at
Data Processing and Quantification
The acquired MS/MS spectra were searched against the Mouse International Protein Index protein sequence database (version 3.07, www.ebi.ac.uk/IPI) using the TurboSEQUEST program in the BioWorks 3.2 software suite (Thermo, San Jose, CA, USA). The database contained 86 600 protein entries and was 2852
dx.doi.org/10.1021/pr201224e | J. Proteome Res. 2012, 11, 2851−2862
Journal of Proteome Research
Article
built using forward-oriented normal sequences and the same sequences with their amino acid sequences reversed. Database searches were performed with the following parameters: trypsin was designated as the protease with one missed cleavage site allowed. The precursor mass tolerance was 500 ppm (Orbitrap), and the mass tolerance for fragment ions was ±0.5 Da (LTQ). Carbamidomethylation was searched as a fixed modification, while isotope-labeled lysine (+8.014199 Da) and oxidized methionine (+15.99492 Da) were regarded as variable modifications. All output results were filtered and combined using an in-house software named BuildSummary to process the redundant data.10 If all identified peptides assigned to protein group X were not unique, the in-house software would remove protein group X (the peptides were also assigned to another protein group).10 The peptides identified were filtered using precursor ions with m/z ≤ 10 ppm, and all accepted SEQUEST results required a deltaCn score of at least 0.1 regardless of charge state, as deltaCn ≥ 0.1 is significant for determining the first candidate peptide from the second candidate peptide.11 The peptides were calculated separately and filtered to ensure a false-discovery rate (FDR) ≤ 1%. The false-discovery rate was calculated on the basis of the following formula: FDR = nrev/nfor × 100%. The nrev is the number of peptide hits matched to the “reverse” protein, and nfor is the number of peptide hits matched to the “forward” protein.12 If the identified peptide sequences of one protein was equal to or contained another protein’s peptide set, the two proteins were grouped together and reported as one protein group. For labeled quantitative analysis, the lysine-containing peptides were input to the software Census (version 1.7)13 to obtain the peptide ratio from ion chromatograms. The following parameters in Census were used: a max scan of 100 and a mass accuracy of 30 ppm. Any determination factor larger than 0.5 was selected for the filtration of high-quality peptides. The biweight algorithm14 was then used to eliminate the outliers and to determine the ratio of light isotope-sample/heavy isotopesample proteins. The sequential KNN imputation method was used to fill in the missing values.15
transitions were selected and monitored. The best transition was used to quantify the corresponding peptide. For quantitation analysis, 5 μg of digested protein from samples was resolubilized in 0.1% formic acid spiked with 0.25 pmol internal standard peptide. The peak area was calculated using Xcalibur software (ThermoFisher Scientific). The production of apoC-I in the whole adipose mass was calculated as the apoC-I concentration multiplied by the whole adipose mass weight. Study Population
To reduce the bias of the sample source, a larger multicenter survey was used to detect the plasma level of apoC-I. In this study, 150 individual human plasma samples were obtained from a population-based study16 that was approved by the Institutional Review Board of the Institute for Nutritional Sciences. Written informed consent was obtained from each participant.16 Two hundred seventeen individual human serum samples were obtained from Shanghai No. 6 People’s Hospital. Written informed consent was obtained from each person and approved by Shanghai No. 6 People’s Hospital Review Committee.17 All serum samples were prepared from whole blood using routine protocols and subsequently stored at −80 °C.16,17 Definition of the Diseases
Features of the metabolic disorders considered for the present report include obesity (BMI ≥ 24.0 kg/m2), blood pressure elevation (≥130/85 mmHg), low HDL cholesterol (70.14 (μg/mL)
37/92 1.00 67/92 1.00 22/92 1.00 16/92 1.00 22/92 1.00 33/92 1.00 36/92 1.00
39/92 1.08 (0.6−1.96) 60/92 0.73 (0.38−1.39) 38/92 2.24 (1.18−4.25) 20/92 1.36 (0.65−2.84) 38/92 2.21 (1.17−4.2) 37/92 1.22 (0.65−2.28) 29/92 0.67 (0.36−1.25)
50/91 1.78 (0.98−3.22) 67/91 1.13 (0.58−2.22) 53/91 4.55 (2.38−8.68) 26/91 1.98 (0.97−4.03) 42/91 2.69 (1.42−5.1) 32/91 0.94 (0.49−1.77) 25/91 0.52 (0.27−0.99)
53/92 1.98 (1.1−3.59) 78/92 2.25 (1.07−4.75) 66/92 8.59 (4.38−16.87) 49/92 5.66 (2.85−11.23) 46/92 3.21 (1.69−6.06) 38/92 1.22 (0.65−2.28) 28/92 0.6 (0.32−1.13)
p for Trends 0.047 0.017
