An altered pattern of circulating apolipoprotein E3 isoforms is implicated in preeclampsia
2008; Elsevier BV; Volume: 50; Issue: 1 Linguagem: Inglês
10.1194/jlr.m800296-jlr200
ISSN1539-7262
AutoresKelly R. Atkinson, Marion Blumenstein, Michael A. Black, Steven Wu, Nikola Kasabov, Rennae S. Taylor, Garth J. S. Cooper, Robyn A. North,
Tópico(s)MicroRNA in disease regulation
ResumoPreeclampsia is a common pregnancy complication that is an important cause of preterm birth and fetal growth restriction. Because there is no diagnostic test yet available for preeclampsia, we used a proteomic approach to identify novel serum/plasma biomarkers for this condition. We conducted case control studies comparing nulliparous women who developed preeclampsia at 36–38 weeks of gestation with healthy nulliparous women matched by gestational age at sampling. Serum/plasma was depleted of six abundant proteins and analyzed by two-dimensional gel electrophoresis (n = 12 per group) and difference gel electrophoresis (n = 12 per group). Differences in abundance of protein spots were detected by univariate and multivariate statistical analyses. Proteins were identified by mass spectrometry and expression of selected proteins was validated by immunoblotting. Proteins whose concentrations were selectively associated with preeclampsia included apolipoprotein E (apoE), apoC-II, complement factor C3c, fibrinogen, transthyretin, and complement factor H-related protein 2. An increase in a deglycosylated isoform of apoE3 and concomitantly decreased amounts of one apoE3 glycoisoform were identified in preeclamptic plasma and confirmed by immunoblotting. Altered production of these preeclampsia-related apoE3 isoforms might impair reverse cholesterol transport, contributing to arterial damage. These findings point to a novel mechanistic link between preeclampsia and subsequent cardiovascular disease. Preeclampsia is a common pregnancy complication that is an important cause of preterm birth and fetal growth restriction. Because there is no diagnostic test yet available for preeclampsia, we used a proteomic approach to identify novel serum/plasma biomarkers for this condition. We conducted case control studies comparing nulliparous women who developed preeclampsia at 36–38 weeks of gestation with healthy nulliparous women matched by gestational age at sampling. Serum/plasma was depleted of six abundant proteins and analyzed by two-dimensional gel electrophoresis (n = 12 per group) and difference gel electrophoresis (n = 12 per group). Differences in abundance of protein spots were detected by univariate and multivariate statistical analyses. Proteins were identified by mass spectrometry and expression of selected proteins was validated by immunoblotting. Proteins whose concentrations were selectively associated with preeclampsia included apolipoprotein E (apoE), apoC-II, complement factor C3c, fibrinogen, transthyretin, and complement factor H-related protein 2. An increase in a deglycosylated isoform of apoE3 and concomitantly decreased amounts of one apoE3 glycoisoform were identified in preeclamptic plasma and confirmed by immunoblotting. Altered production of these preeclampsia-related apoE3 isoforms might impair reverse cholesterol transport, contributing to arterial damage. These findings point to a novel mechanistic link between preeclampsia and subsequent cardiovascular disease. Preeclampsia is a syndrome that complicates 5% of first pregnancies and is characterized by the presence of hypertension and proteinuria, and in severe cases, can include coagulopathy, renal and liver dysfunction, and eclamptic seizures. A third of the resulting babies are premature and a quarter are growth restricted, with associated increased morbidity and mortality. Women who develop preeclampsia are at increased risk of cardiovascular disease in later life (1Bellamy L. Casas J.P. Hingorani A.D. Williams D.J. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis..BMJ. 2007; 335: 974Crossref PubMed Scopus (1846) Google Scholar). The complex pathophysiology of preeclampsia involves aberrant placentation triggering a systemic maternal response that involves widespread endothelial dysfunction, activated coagulation, and inflammation, leading to multi-organ damage (2Redman C.W Sargent I.L. Latest advances in understanding preeclampsia..Science. 2005; 308: 1592-1594Crossref PubMed Scopus (2067) Google Scholar). Diagnosis of preeclampsia is currently based on a set of clinical symptoms and signs, along with routine laboratory tests assessing platelet count and liver and renal function. Recently, the ratio of soluble vascular endothelial growth factor receptor 1 (sFlt-1) to placental growth factor has been proposed as a late predictive or diagnostic test (3Levine R.J Maynard S.E. Qian C. Lim K.H. England L.J. Yu K.F. Schisterman E.F. Thadhani R. Sachs B.P. Epstein F.H. et al.Circulating angiogenic factors and the risk of preeclampsia..N. Engl. J. Med. 2004; 350: 672-683Crossref PubMed Scopus (2870) Google Scholar). Given the multiple pathogenic processes that culminate in preeclampsia, however, it is likely that a set of biomarkers will be required to achieve clinical utility in a diagnostic test. It is increasingly recognized that the systematic application of proteomic approaches in pregnancy research will be needed to discover novel biomarkers for preeclampsia (4Shankar R. Gude N. Cullinane F. Brennecke S. Purcell A.W. Moses E.K. An emerging role for comprehensive proteome analysis in human pregnancy research..Reproduction. 2005; 129: 685-696Crossref PubMed Scopus (38) Google Scholar, 5Robinson J.M Ackerman IV, W.E Kniss D.A. Takizawa T. Vandre D.D. Proteomics of the human placenta: promises and realities..Placenta. 2008; 29: 135-143Crossref PubMed Scopus (25) Google Scholar). Recently, two-dimensional gel electrophoresis (2DE) proteomic techniques have been used to investigate changes in the plasma proteome in preeclampsia (6Watanabe H. Hamada H. Yamada N. Sohda S. Yamakawa-Kobayashi K. Yoshikawa H. Arinami T. Proteome analysis reveals elevated serum levels of clusterin in patients with preeclampsia..Proteomics. 2004; 4: 537-543Crossref PubMed Scopus (74) Google Scholar, 7Heitner J.C Koy C. Kreutzer M. Gerber B. Reimer T. Glocker M.O. Differentiation of HELLP patients from healthy pregnant women by proteome analysis—on the way towards a clinical marker set..J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2006; 840: 10-19Crossref PubMed Scopus (36) Google Scholar). A 2DE study of HELLP syndrome, a severe form of preeclampsia, identified one downregulated and five upregulated plasma proteins (7Heitner J.C Koy C. Kreutzer M. Gerber B. Reimer T. Glocker M.O. Differentiation of HELLP patients from healthy pregnant women by proteome analysis—on the way towards a clinical marker set..J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2006; 840: 10-19Crossref PubMed Scopus (36) Google Scholar). In another study, serum clusterin was reportedly increased in preeclampsia with fetal growth restriction (6Watanabe H. Hamada H. Yamada N. Sohda S. Yamakawa-Kobayashi K. Yoshikawa H. Arinami T. Proteome analysis reveals elevated serum levels of clusterin in patients with preeclampsia..Proteomics. 2004; 4: 537-543Crossref PubMed Scopus (74) Google Scholar). Although these studies indicated the potential of proteomic approaches, only small numbers of women with severe disease were included. Severe-disease subsets are not representative of the majority of women with preeclampsia. Furthermore, steroids may have been given to promote fetal lung maturation, modifying the plasma proteome, and limited statistical approaches were employed. Combinations of univariate statistical methods and multivariate classification methodologies have recently been proposed for the analysis of complex proteomic datasets (8Broadhurst D.I Kell D.B. Statistical strategies for avoiding false discoveries in metabolomics and related experiments..Metabolomics. 2006; 2: 171-196Crossref Scopus (621) Google Scholar). This approach pinpoints protein combinations that may be able to distinguish between healthy and disease states (9Nedenskov Jensen K. Jessen F. Jorgensen B.M. Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples..J. Proteome Res. 2008; 7: 1288-1296Crossref PubMed Scopus (24) Google Scholar). Here, we employed two different two-dimensional (2-D) gel-based proteomic approaches, conventional 2DE and difference gel electrophoresis (DIGE), to identify preeclampsia-specific markers in serum/plasma of women presenting with disease at 36–38 weeks of gestation in comparison with healthy pregnant women. Our comprehensive bioinformatic analysis identified several serum/plasma candidates in preeclampsia. Specifically, altered glycosylation of circulating apolipoprotein E (apoE) isoforms was a novel finding, confirmed by Western blot analysis. Furthermore, multivariate classification methods revealed that apoE and fibrinogen β, in combination with other proteins, could serve as discriminators between preeclampsia and healthy pregnancy. For 2DE analysis, serum from nulliparous women presenting with preeclampsia at 36–38 weeks (n = 12) and gestational age-matched healthy control participants (n = 12) was used. Similarly, n = 12 cases and controls were used for the DIGE study, analyzing plasma from women who were recruited into the SCOPE study (SCreening fOr Pregnancy Endpoints, Australian and New Zealand Clinical Trials Registry ACTRN12607000551493), a prospective screening study of nulliparous women. Specimen collection at this gestational age avoided any steroid exposure for fetal lung maturation prior to sampling. Study protocols were approved by the Auckland Regional Ethics Committee (2000/157 and AKX/02/00/364), and written informed consent was obtained from each woman. Preeclampsia was defined as systolic blood pressure (BP) ≥140 mm Hg and/or diastolic BP ≥90 mm Hg on two or more occasions after 20 weeks of gestation but prior to the onset of labor, or postpartum systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg postpartum on at least two occasions 4 h apart, combined with either proteinuria (spot protein:creatinine ratio ≥30 mg/mmol, or 24 h urinary protein ≥0.3 g per 24 h, or dipstick proteinuria ≥2+) or any multi-organ complication (10Brown M.A Hague W.M. Higgins J. Lowe S. McCowan L. Oats J. Peek M.J. Rowan J.A. Walters B.N. The detection, investigation and management of hypertension in pregnancy: full consensus statement..Aust. N. Z. J. Obstet. Gynaecol. 2000; 40: 139-155Crossref PubMed Scopus (237) Google Scholar). Severe preeclampsia was defined by the presence of one or more of the following additional findings: coagulopathy, hemolysis, liver impairment, acute renal insufficiency, imminent eclampsia, or eclampsia. Babies who were small for gestational age had a birth weight less than the 10th customized centile (adjusted for infant sex and maternal ethnicity, height, and weight) (11Gardosi J. Mongelli M. Wilcox M. Chang A. An adjustable fetal weight standard..Ultrasound Obstet. Gynecol. 1995; 6: 168-174Crossref PubMed Scopus (471) Google Scholar). For serum samples, blood was collected by venipuncture into plain tubes, allowed to clot on ice, and centrifuged (2,400 g, 10 min, 4°C). The resulting serum was centrifuged again (3,200 g, 15 min, 4°C) and stored at −80°C. For plasma samples, blood was collected into EDTA tubes, centrifuged (2,400 g, 10 min, 4°C), and stored at −80°C. All samples were processed within 3 h of collection. To remove the six most abundant proteins (albumin, transferrin, IgG, IgA, haptoglobin, and α-1-antitrypsin), serum/plasma was immunodepleted using the Multiple Affinity Removal System (MARS; Agilent, Santa Clara, CA) according to the manufacturer's instructions. Before depletion, a protease inhibitor cocktail was added (Roche Applied Science, Auckland, New Zealand). Depleted samples were buffer exchanged using 5 kDa molecular mass cutoff centrifugal filters into the respective 2DE and DIGE rehydration solutions. Protein content was determined using the 2D Quant protein assay (GE Healthcare, Auckland, New Zealand). Depleted serum (150 μg) was diluted in rehydration solution (9 M urea, 2% CHAPS, 65 mM DTT, 0.5% immobilized pH gradient (IPG) Buffer pH 4–7, 0.002% bromophenol blue) and passively rehydrated overnight using 18 cm pH 4–7 IPG strips (GE Healthcare). IPG strips were focused on a PROTEAN cell (Bio-Rad, Hercules, CA), followed by SDS-PAGE using ExcelGel XL 12–14% polyacrylamide gels (GE Healthcare). Gels were stained with SYPRO® Ruby (Invitrogen, Carlsbad, CA) and imaged on a FLA–2000 phosphorimager (Fujifilm, Tokyo, Japan). Digitized 2DE images were analyzed by ImageMaster™ 2D Platinum v5.0 (GE Healthcare). Spot volume data for each gel were exported from ImageMaster for bioinformatic analyses. Depleted and buffer-exchanged plasma samples were labeled with 200 pmol per 50 μg of protein each with CyDye minimal dyes (GE Healthcare) according to the manufacturer's protocol. For the pooled internal standard, equal amounts of representative specimens (n = 6 cases and n = 6 controls) were combined. Samples were labeled with their respective CyDyes: the internal standard with Cy2, and controls and cases with either Cy3 or Cy5, with each case and control group being dye-balanced. IPG strips (11 cm, pH 4–7) were rehydrated with a multiplexed plasma sample comprising 50 μg protein for each CyDye-labeled case, control, and the pooled internal standard in rehydration buffer (7 M urea, 2 M thiourea, 1% C7BzO detergent, 1% IPG buffer, pH 4–7, 65 mM DTT, and 0.002% bromophenol blue). First-dimension separation was performed on a Multiphor II flatbed (GE Healthcare) followed by SDS-PAGE using Criterion 8–16% Tris-HCl midigels run in a Dodeca cell (Bio-Rad) for 30 min at 15 mA per gel, and 90 min at 30 mA per gel. All twelve DIGE gels were run simultaneously. Typhoon 9410 (GE Healthcare) digitized images were imported into DeCyder 2-D Differential Analysis software v6.5 (GE Healthcare) for spot detection and matched to an automatically chosen master gel using an estimated number of spots of 10,000 and applying a spot volume filter of >26,000. Standardized abundance data were exported from DeCyder for bioinformatic analyses. ApoE and apoC-II were measured in native (undepleted) plasma using a multiplexed immunoassay (LINCOplex; Millipore, Billerica, MA) according to the manufacturer's instructions. Assays were read on a Luminex 100 instrument (Luminex, Austin, TX). First-dimension separation of depleted serum (200 μg) was performed on 7 cm pH 4–7 IPG strips followed by SDS-PAGE using NuPAGE 4–12% Bis-Tris minigels (Invitrogen), which were blotted onto Immobilon-FL membranes (Millipore) according to the manufacturer's instructions. Membranes were blocked with 5% BSA in PBS, 0.1% Tween 20 overnight at 4°C. Detection of apoE (Abcam #ab7620, Cambridge, UK, at 1:5,000) was achieved with Qdot® 655-conjugated IgG (Invitrogen, 1:1,000). Blots were imaged on a Typhoon 9410 imager. Spots were quantified using Phoretix 2D Evolution v2004 software (Nonlinear Dynamics, Newcastle upon Tyne, UK). Continuous clinical variables, immunoassay data, and 2-D Western blot data were compared using Student's t-test. Categorical clinical data were compared using Fisher's exact test. A P value of less than 0.05 was considered significant. Analyses were performed using GraphPad Prism version 4.03 for Windows (GraphPad Software, San Diego, CA). Standardized abundances exported from DeCyder 2D were transformed (log base 2). 2DE and DIGE data were normalized by centering each gel at zero using median subtraction. Box plots demonstrated no outlying gels. Logged data were then used for univariate and multivariate analyses. 2DE data were analyzed by Student's t-test and DIGE data by the modified t-test implemented in Limma (linear models for microarray data) to determine differential expression between healthy and preeclamptic samples. A false discovery rate (FDR) correction (12Benjamini Y. Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing..J. R. Stat. Soc. B. 1995; 57: 289-300Google Scholar) was applied to correct for multiple comparisons. An FDR-corrected P value of less than 0.05 in either of the above tests was considered significant. 2DE data were analyzed by three different classifier generation methods: nearest shrunken centroids (NSC), recursive feature elimination (RFE), and evolving connectionist functions (ECF). Missing values were imputed using the minimum nonzero spot volume for the 2DE data set. DIGE data were analyzed by the NSC method alone after imputing the median spot volume for missing values. The NSC approach uses a variant of discriminant analysis to classify samples with a modified t-statistic used to rank spots according to the degree of separation exhibited between data classes (preeclamptic and healthy pregnant) (13Tibshirani R. Hastie T. Narasimhan B. Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression..Proc. Natl. Acad. Sci. USA. 2002; 99: 6567-6572Crossref PubMed Scopus (2183) Google Scholar). The scores for each spot were shrunk toward zero using an approach designed to eliminate noninformative spots from the analysis and determine sets of spots that best discriminate disease classes. The RFE method used an approach based on support vector machine methodology coupled with an iterative method for variable selection (14Ambroise C. McLachlan G.J. Selection bias in gene extraction on the basis of microarray gene-expression data..Proc. Natl. Acad. Sci. USA. 2002; 99: 6562-6566Crossref PubMed Scopus (1100) Google Scholar). At each iteration, the predictive ability of the model was assessed with cross-validation. An ECF model (15Kasabov N.K. Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines. Springer, London and New York2003Crossref Google Scholar) was used in conjunction with 24-fold leave-one-out cross-validation to generate a classifier able to distinguish sample groups. The number of spots used in the final model was chosen as the number of spots that achieved the best overall cross-validated classification rate. Protein spots of interest were selected with the following criteria: 1) protein spots that were significantly upregulated or downregulated (P < 0.05 with FDR correction) by univariate methods; or 2) protein spots that were selected by a classifier method. Spots of interest were manually inspected and any gel artifacts excluded. The resulting spots were identified by liquid chromatography tandem mass spectrometry (LC-MS/MS). Spots were excised from gels and digested with trypsin according to published methods (16Hardt M. Thomas L.R. Dixon S.E. Newport G. Agabian N. Prakobphol A. Hall S.C. Witkowska H.E. Fisher S.J. Toward defining the human parotid gland salivary proteome and peptidome: identification and characterization using 2D SDS-PAGE, ultrafiltration, HPLC, and mass spectrometry..Biochemistry. 2005; 44: 2885-2899Crossref PubMed Scopus (147) Google Scholar), and submitted for LC-MS/MS analysis on a QSTAR XL ESI-qTOF (Applied Biosystems, Foster City, CA) at the Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland. Tryptic peptides of spots corresponding to apoE isoforms were analyzed by LC-MS/MS (LTQ FT-ICR; Thermo, San Jose, CA) at the Maurice Wilkins Centre. The SALSA algorithm was used to search for posttranslational modifications (17Hansen B.T Jones J.A. Mason D.E. Liebler D.C. SALSA: a pattern recognition algorithm to detect electrophile-adducted peptides by automated evaluation of CID spectra in LC-MS-MS analyses..Anal. Chem. 2001; 73: 1676-1683Crossref PubMed Scopus (92) Google Scholar). MS/MS data were extracted from raw spectra using Mascot Distiller (Matrix Science, London, UK). Data were searched against the Swiss-Prot database (version 52.2, date 14 April 2007) using the Mascot search engine v2.2.0 with the following parameters. Taxonomy: human, semitrypsin cleavage with up to 1 missed cleavage allowed; fixed modification: carbamidomethylation (serum) or propionamidation (plasma); variable modification: oxidation (M), mass tolerances ±0.1 Da, peptide charges 2+ and 3+. Positive identifications reported here had at least three unique peptides match the database entry. Two separate case control studies were conducted using either serum (2DE) or plasma (DIGE) collected from women diagnosed with preeclampsia compared with healthy pregnant women matched by gestational age at sampling. The clinical characteristics of participants and the maternal and fetal outcomes are detailed in Table 1.TABLE 1Maternal and neonatal clinical characteristics for 2DE and DIGE samples2DEDIGEStudy CharacteristicPreeclamptic (n = 12)Healthy Pregnant (n = 12)PPreeclamptic (n = 12)Healthy Pregnant (n = 12)PMaternal Gestational age at sampling (weeks)37.6 (0.8)37.3 (1.0)0.3837.3 (1.0)37.0 (1.1)0.44 Systolic blood pressure at sampling (mm Hg)138 (14)116 (9)0.0001137 (9)113 (12)<0.0001 Diastolic blood pressure at sampling (mm Hg)94 (11)72 (7)<0.000195 (8)70 (8)<0.0001 Age (years)31.4 (4.2)31.8 (3.4)0.8330.2 (5.0)31.6 (3.6)0.43 Ethnicity Caucasian7879 Maori or Pacific Islander300.20400.10 Other2413 Body mass index at booking (kg/m2)26.4 (4.1)23.2 (4.4)0.0926.8 (4.0)25.2 (5.1)0.39 Gestational age at first visit (weeks)12.2 (2.7)11.9 (4.1)0.8813.8 (2.7)13.6 (3.3)0.91 Systolic blood pressure at <20 weeks (mm Hg)114 (14)116 (12)0.73111 (15)112 (11)0.84 Diastolic blood pressure at <20 weeks (mm Hg)70 (11)63 (7)0.0967 (10)63 (7)0.33 Maximum systolic blood pressure (mm Hg)165 (19)125 (7)<0.0001155 (13)127 (8)<0.0001 Maximum diastolic blood pressure (mm Hg)106 (6)77 (8)<0.0001103 (6)77 (9)<0.0001 Proteinuria (g per 24 h; median, range, n)0.87 (0.39–6.98, n = 10)01.05 (0.39–8.94, n = 9)0 Severe preeclampsia2 (16.6%)aImminent eclampsia (n = 1), HELLP (n = 1).03 (25%)bHELLP (n = 1), acute renal insufficiency (n = 2).0Neonatal Gestational age at delivery (weeks)38.1 (0.6)40.1 (1.5)0.000337.8 (0.8)40.2 (1.4)<0.0001 Infant birth weight (g)2,943 (340)3,628 (329)<0.00013,107 (333)3,671 (399)0.0001 Small for gestational age4 (33.3%)02 (17%)02DE, two-dimensional gel electrophoresis; DIGE, difference gel electrophoresis. Values are reported as mean (SD) or n (%) unless otherwise noted.a Imminent eclampsia (n = 1), HELLP (n = 1).b HELLP (n = 1), acute renal insufficiency (n = 2). Open table in a new tab 2DE, two-dimensional gel electrophoresis; DIGE, difference gel electrophoresis. Values are reported as mean (SD) or n (%) unless otherwise noted. Univariate analysis of our 2DE data revealed 23 differentially expressed spots that were up- or downregulated (P < 0.05) in the serum of preeclamptic women. After correcting for multiple comparisons, no differences remained significant. Using multivariate classification methods, six spots were identified as part of a set of proteins capable of separating preeclampsia from healthy pregnancy (Table 2). Spot members of these classification models were identified by LC-MS/MS as apoC-II (spot 28), retinol binding protein (spot 125), apoE basic isoform (spot 161), apoE acidic isoform (spot 168), complement factor C3c (spot 194), and inter-α-trypsin inhibitor H4 (spot 428) (Fig. 1 and Table 3). Levels of the two apoE isoforms varied oppositely between preeclampsia and healthy pregnancy. Specifically, the basic isoform (spot 161, pI 5.7) was found to be upregulated in preeclampsia, whereas the more acidic isoform (spot 168, pI 5.3) was downregulated.TABLE 2Results from multivariate classifier analysis of 2DE dataMethodSpot NumberNearest Shrunken CentroidsRecursive Feature EliminationFeature Selection Using Evolving Connectionist Functions28xxx125x161xxx168xx194xx428xxx Open table in a new tab TABLE 3Proteins identified from 2DE and DIGE classifier spotsStudySpot NumberFold Change (PE/Healthy)ProteinaProteins are listed within each gel spot number in descending order of Mascot match score.AccessionSequence CoveragebSequence coverage was calculated as part of the Mascot search process using the entire chain of the protein's Swiss-Prot database entry.Peptides (Unique)Calculated Molecular MassGel Molecular MassGel pI%kDacMolecular mass was calculated on Swiss-Prot entries from the main chain or appropriate chain only using the Compute MW/pI tool (http://www.expasy.org/tools/pi_tool.html).2DE282.5Apolipoprotein C-IIP02655718 (6)8.9104.62DE1252.1Plasma retinol binding proteinP027535611 (8)21.1245.32DE1612.0Apolipoprotein E (basic isoform)P026494011 (10)34.2345.72DE168−1.7Apolipoprotein E (acidic isoform)P02649215 (5)34.2355.32DE194−2.5Complement c3c, C-terminal fragment of α′ chaincMolecular mass was calculated on Swiss-Prot entries from the main chain or appropriate chain only using the Compute MW/pI tool (http://www.expasy.org/tools/pi_tool.html).P0102429cMolecular mass was calculated on Swiss-Prot entries from the main chain or appropriate chain only using the Compute MW/pI tool (http://www.expasy.org/tools/pi_tool.html).8 (7)39.5404.82DE428−3.6Inter-α-trypsin inhibitor, heavy chain H4Q146241715 (14)70.6904.9DIGE3551.9Fibrinogen, β chainP026755939 (22)50.7506.1β-2-glycoprotein 1P027494514 (10)36.2TransferrinP027872512 (12)75.1HemopexinP027903010 (8)49.3Fibrinogen, α chainP026711612 (10)91.3DIGE3561.9Fibrinogen, β chainP026756436 (20)50.7506.3Fibrinogen, α chainP026712520 (15)91.3Fibrinogen, γ chainP02679195 (5)48.5Complement C3 (peptides map to the β chain)dComplement C3 is a multi-chain protein derived from a single polypeptide precursor; sequence coverage, molecular mass, and pI were calculated from the C3 β chain only.P0102414 vs. β chaindComplement C3 is a multi-chain protein derived from a single polypeptide precursor; sequence coverage, molecular mass, and pI were calculated from the C3 β chain only.5 (5)71.3dComplement C3 is a multi-chain protein derived from a single polypeptide precursor; sequence coverage, molecular mass, and pI were calculated from the C3 β chain only.DIGE5214.7Apolipoprotein EP026494714 (12)34.2345.6DIGE5691.9TransthyretinP027666410 (5)13.7285.4Mannose binding protein CP11226317 (5)24.0Serum amyloid P componentP02743245 (5)23.2Complement H-related protein 2P36980347 (7)28.7Fibrinogen, γ chainP02679176 (5)48.5apolipoprotein EP02649245 (5)34.2PE, preeclampsia.a Proteins are listed within each gel spot number in descending order of Mascot match score.b Sequence coverage was calculated as part of the Mascot search process using the entire chain of the protein's Swiss-Prot database entry.c Molecular mass was calculated on Swiss-Prot entries from the main chain or appropriate chain only using the Compute MW/pI tool (http://www.expasy.org/tools/pi_tool.html).d Complement C3 is a multi-chain protein derived from a single polypeptide precursor; sequence coverage, molecular mass, and pI were calculated from the C3 β chain only. Open table in a new tab PE, preeclampsia. Because conventional 2DE has an inherently high technical variability (18Schröder S. Zhang H. Yeung E.S. Jänsch L. Zabel C. Wätzig H. Quantitative gel electrophoresis: sources of variation..J. Proteome Res. 2008; 7: 1226-1234Crossref PubMed Scopus (30) Google Scholar), we also employed a DIGE approach in a separate case control study. In contrast to the 2DE study, in which 42 gels per group were necessary to achieve an 80% power to detect three-quarters of the proteins with a 2-fold difference between cases and controls, the DIGE protocol proved to be more efficient, requiring a sample size of 12 cases and 12 controls. DIGE plasma analysis (n = 12 per group) revealed 63 spots that had significantly different (P < 0.05) expression ratios by univariate statistical methods. None of these spots remained significant after FDR correction for multiple comparisons. NSC analysis identified a set of four spots associated with preeclampsia (Fig. 2). Based on their high Mascot scores and the large number of unique peptides per spot, these proteins were identified by LC-MS-MS as fibrinogen β (spots 355, 356), apoE (spot 521), and transthyretin (spot 569) (Table 3). Three of these spots (spots 355, 356, and 569) contained several additional protein species with more than two unique peptides (Table 3), likely to be due to comigration of plasma proteins on 2-D gels (19Campostrini N. Areces L.B. Rappsilber J. Pietrogrande M.C. Dondi F. Pastorino F. Ponzoni M. Righetti P.G. Spot overlapping in two-dimensional maps: a serious problem ignored for much too long..Proteomics. 2005; 5: 2385-2395Crossref PubMed Scopus (130) Google Scholar). Taking into account their molecular weights, other possible protein components were hemopexin (spot 355), fibrinogen γ (spot 356), and complement H-related protein 2 (spot 569). The set of the above six proteins correctly classified between preeclampsia and healthy pregnancy with 80% accuracy. It is noteworthy that the upregulation of the basic isoform of apoE (spot 521) was significant upon univariate analysis prior to FDR correction, and had also been identified in our prior 2DE-based study, further supporting its association with preeclampsia. To further explore the different production of preeclampsia-specific apoE isoforms revealed by 2DE and DIGE, we utilized 2-D Western blot analysis of depleted serum. Western blot analysis (Fig. 3A,B) confirmed our 2DE data and showed the upregulation of a basic apoE isoform (corresponding to spot 161 in Fig. 1A) in serum from women with preeclampsia, whereas an acidic isoform of apoE (corresponding to spot 168 in Fig. 1A) was downregulated in disease. The basic apoE isoform (spot 161) was found in eleven of twelve women with preeclampsia, of whom two had severe disease and four had a baby that was small for gestational age, and in only three of twelve healthy pregnant women, indicating that disease severity is an unlikely explanation for this abnormality.Fig. 3Specific detection and quantitat
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