| | Proteomic profiling of urine identifies specific fragments of SERPINA1 and albumin as biomarkers of preeclampsiaReceived 15 December 2007; received in revised form 12 April 2008; accepted 3 July 2008.
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Preeclampsia: new approaches but the same old problems
James M. Roberts
American Journal of Obstetrics & Gynecology
November 2008 (Vol. 199, Issue 5, Pages 443-444)
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ObjectiveThe cause of preeclampsia remains unknown and the diagnosis can be uncertain. We used proteomic-based analysis of urine to improve disease classification and extend the pathophysiologic understanding of preeclampsia. Study DesignUrine samples from 284 women were analyzed by surface-enhanced laser desorption/ionization. In the exploratory phase, 59 samples were used to extract the proteomic fingerprint characteristic of severe preeclampsia requiring mandated delivery and to develop a diagnostic algorithm. In the challenge phase, we sought to prospectively validate the algorithm in 225 women screened for a variety of high- and low-risk conditions, including preeclampsia. Of these, 19 women were followed longitudinally throughout pregnancy. The presence of biomarkers was interpreted relative to clinical classification, need for delivery, and other urine laboratory measures (ratios of protein to creatinine and soluble fms-like tyrosine kinase-1 to placental growth factor). In the translational phase, biomarker identification by tandem mass spectrometry and validation experiments in urine, serum, and placenta were used to identify, quantify, and localize the biomarkers or related proteins. ResultsWe report that women with preeclampsia appear to present a unique urine proteomic fingerprint that predicts preeclampsia in need of mandated delivery with highest accuracy. This characteristic proteomic profile also has the ability to distinguish preeclampsia from other hypertensive or proteinuric disorders in pregnancy. Pregnant women followed longitudinally who developed preeclampsia displayed abnormal urinary profiles more than 10 weeks before clinical manifestation. Tandem mass spectrometry and de novo sequencing identified the biomarkers as nonrandom cleavage products of SERPINA1 and albumin. Of these, the 21 amino acid C-terminus fragment of SERPINA1 was highly associated with severe forms of preeclampsia requiring early delivery. In preeclampsia, increased and aberrant SERPINA1 immunoreactivity was found in urine, serum, and placenta, in which it localized predominantly to placental villi and placental vascular spaces adherent to the endothelium. In addition, significant perivascular deposits of misfolded SERPINA1 aggregates were exclusively identified in preeclamptic placentae. ConclusionProteomics-based characterization of urine in preeclampsia identified a proteomic fingerprint composed of SERPINA1 and albumin fragments, which can accurately diagnose preeclampsia and shows promise to discriminate it from other hypertensive proteinuric diseases. These findings provide insight into a novel pathophysiological mechanism of preeclampsia related to SERPINA1 misfolding, which may offer new therapeutic opportunities in the future. Preeclampsia complicates 5-8% of pregnancies in the United States.1 Geographic, social, economic, and racial differences are thought to be responsible for incidence rates up to 3 times higher in some populations.2, 3 Preeclampsia has been implicated in 20% of pregnancy-related maternal deaths and continues to be the leading cause of a mandated preterm delivery.4, 5, 6 As a pregnancy-specific hypertensive disorder, preeclampsia is defined as new-onset elevated blood pressure accompanied by proteinuria after 20 weeks of gestation.7 Compelling evidence suggests that key disturbances at the fetal–maternal interface (endothelial cell dysfunction, oxidative stress, circulatory, and metabolic alterations) play a central role in the etiology and pathogenesis of the clinical manifestations of this syndrome, leading to progressive deterioration of maternal and fetal condition, with early delivery as the only definitive treatment.8 For Editors' Commentary, see Table of Contents See related editorial, page 443 Because delivery of the fetus and the placenta is the only intervention proven to prevent high maternal morbidity and mortality, management involves balancing the risks of prematurity against severity of preeclampsia and response to treatment.9 This decision is frequently complicated by the difficulty in discriminating preeclampsia from other conditions that are also characterized by hypertension and proteinuria, such as chronic hypertension or glomerulonephropathies. Therefore, the discovery of relevant biomarkers to aid with the accurate prediction, rapid confirmation of the diagnosis, and treatment monitoring of preeclampsia remains crucial. Proteomics seems to have all the necessary attributes to provide the needed breakthrough in understanding the pathophysiology of preeclampsia by discovery of biomarkers that may also point to novel inroads into prevention or treatment of this syndrome.10 Because renal pathology is a hallmark of preeclampsia11 and the degree of proteinuria frequently reflects disease severity,12 we turned to proteomic profiling of urine, aiming to develop a rapid and noninvasive diagnostic and prognostic modality for preeclampsia based on qualitative rather than quantitative changes in urinary proteins. Furthermore, by identifying the aberrantly excreted proteins (biomarkers), we hoped to gain new insights into the mechanisms responsible for this condition. Materials and Methods  Patients and samples We studied urine samples from 284 women. Pregnant subjects were enrolled at Yale-New Haven Hospital from March 2004 to December 2006. The nonpregnant women were enrolled at Fletcher Allen Health Care Hospital. The research protocols were approved by both the Yale University and University of Vermont Human Investigational Committees. All women provided written informed consent. In all instances, gestational age was established based on last menstrual period and/or ultrasonographic examination before 20 weeks of gestation. At enrollment, women were clustered into clinical categories based on well recognized clinical and laboratory criteria.7 Severe preeclampsia (sPE) was defined as systolic blood pressure of > 160 mm Hg or diastolic > 110 mm Hg on at least 2 occasions 6 hours apart, > 5 g protein excretion per 24-hour urine collection, and/or persistent > 3 proteinuria on dipstick testing. Other elements of the sPE definition included in utero growth restriction (IUGR) < 10th percentile; persistent neurologic symptoms (headache, visual disturbances); epigastric pain; oliguria (< 500 mL per 24 hours); serum creatinine > 1.0 mg/dL; or any elements of HELLP syndrome: hemolysis, elevated liver enzymes (> 2 times the normal), low platelet count (< 100,000 cells/μL). Chronic hypertension (crHTN) was defined as a sustained elevation in blood pressure before pregnancy or before 20 weeks' gestational age.7 Superimposed preeclampsia (spPE) was diagnosed in the context of crHTN accompanied by new onset proteinuria, a sudden increase in proteinuria (if present in early pregnancy), a sudden increase in blood pressure that met severe criteria and did not respond to medical therapy, or the presence of other sPE criteria, with the exclusion of isolated IUGR. 7 Mild PE (mPE) was defined as a blood pressure of at least 140/90 mm Hg and urinary protein excretion of at least 300 mg per 24-hour urine collection (or at least > 1 on dipstick testing), each on 2 occasions 4-6 hours apart and no diagnosis of either sPE or spPE. A cutoff of 150 mg per 24-hour urine collection was used to judge proteinuria for nonpregnant women.13 Serum and urine samples were collected contemporaneously, as previously described.14 In select cases, placental tissue was collected at delivery for histology and messenger ribonucleic acid (mRNA) extraction. Placental tissues from gestational age–matched women with spontaneous preterm birth in the absence of histologic evidence of infection or inflammation were used as the appropriate control for preeclamptic placentae. Study design The study was undertaken in 3 phases: exploratory, challenge, and translational.15 The purpose of the exploratory phase was to extract the urinary proteomic profile characteristic of preeclampsia requiring mandated delivery, which was defined as an intentional intervention because of a deteriorating maternal or fetal condition in the context of preeclampsia. To minimize inaccuracies at this stage, we selected the exploratory phase samples based on stringent clinical criteria. In point, we abstracted for each patient the clinical information into a semiquantitative variable named the objective clinical score of preeclampsia severity (see Appendix section I.a for details). Women with sPE (n = 38) were chosen based on a minimum of 3 criteria of severity,7 which mandated a clinically indicated delivery. Twenty-one healthy asymptomatic women at similar gestational ages who did not develop preeclampsia during the remainder of their pregnancy and delivered at term were chosen as controls (CRL). Surface-enhanced laser desorption/ionization (SELDI) profiles were generated from urine aliquots stored at -80°C. The characteristics of the women who provided urine samples for the exploratory phase are presented in Appendix section I.b. The purpose of the challenge phase was to validate the proteomic profile in a population different from that used for its development by determining the ability to identify preeclampsia requiring mandated delivery and differentiate this condition from other clinical contexts, such as mPE or crHTN. The ability of the proteomic profile to predict disease severity based on need for delivery was further compared with that of other analytes measured in a random urine specimen and proposed to have diagnostic and prognostic value for preeclampsia: specifically, the urinary soluble fms-like tyrosine kinase-1 (sFlt-1)-to-placental growth factor (PlGF) and protein-to-creatinine ratios.14, 16, 17, 18 The design of the challenge phase is illustrated in Figure 1 and was based on the rationale that test accuracy can vary with the extent, stage of the disease, and associated morbidities.19 In addition, because preeclampsia is a progressing disease and by definition a clinical diagnosis for which no acceptable gold standard is yet available,20 we chose as the outcome measure the need for delivery rather than the clinical classification at enrollment. We reasoned that an indication for mandated delivery belongs to a team and is the last management resort when all other strategies have failed, its resulting outcome is final, cannot be revoked, and thus less subject to bias. A total of 225 consecutive women were enrolled prospectively. Proteomic spectra were acquired using fresh samples of urine, and all samples were scored by investigators unaware of the clinical classification or outcome. The cross-sectional challenge cohort consisted of 206 women who had a single random sample of urine analyzed (at entry into the study). At the end of the enrollment period and for the purpose of data analysis, several subsets of women were identified based on the clinical classification at the time of their urine sampling: (1) asymptomatic pregnant controls (n = 18, gestational age 25 [7-41] weeks); (2) pregnant women with crHTN (n = 26, gestational age 33 [20-39] weeks); (3) mPE (n = 29, gestational age: 36 [24-40] weeks); (4) sPE (n = 31, gestational age 36 [24-40] weeks); (5) spPE (n = 28, gestational age 34 [16-39] weeks); (6) pregnancy-associated conditions unrelated to preeclampsia (n = 64, gestational age 28 [21-34] weeks); and (7) nonpregnant proteinuric women (n = 10). The longitudinal cohort included 19 randomly selected asymptomatic women at low (n = 4) or high risk (n = 15) for developing preeclampsia. In all, we performed serial proteomic profiling of their fresh urine samples from the first trimester until 6 weeks postpartum. Prepregnancy conditions defining the high-risk population included crHTN, history of sPE in a prior pregnancy, diabetes, diabetic nephropathy, nephrolithiasis, membranous glomerulonephritis, and sickle cell disease with history of crises. In the third phase (translational), we identified the discriminatory biomarkers and conducted validation experiments in urine, serum, and placental samples. SELDI profiling and the development of the urinary proteomic scores (UPS) of preeclampsia During the exploratory phase, SELDI conditions were first optimized by systematically screening combinations of ProteinChip array surfaces (Ciphergen Biosystems, Fremont, CA) and various binding/washing protocols, as previously described.21 We found that an on-spot application of 1.5 μg of total unfractionated urine protein provided optimal signal-to-noise (S/N) ratios. After screening more than 200 experimental conditions, 2 (reverse phase hydrophobic surfaces H4 and H50) were considered optimal, based on several peak clusters in the 2.3- to 17.5-kDa mass region unique to the sPE samples. Arrays were incubated for 1 hour, with the samples (6 μL/spot) diluted to 0.25 mg/mL total protein. Following incubation, unbound proteins were removed by washing each spot with the respective buffer. After drying, 1 μL 20% saturated α-cyano-4-hydroxycinnamic acid solution was added and the arrays read in the SELDI time of flight mass spectrometer. To extract the discriminatory peaks (biomarkers) and define which combination of biomarkers is optimal, we applied the principles of mass restricted (MR) scoring, as described earlier,21, 22 with minor modifications (see Appendix section I.c for details). The first goal of the MR scoring method is to identify the minimal combination of SELDI peaks (identified by their molecular mass) with discriminatory value. The second goal is to reduce all proteomic information into a numeric variable that characterizes each sample and can be further compared with other diagnostic modalities or tested prospectively against outcome using standard statistical methods.23 Two objective urinary proteomic scores were designed: a Boolean score (UPSb) representing the sum of Boolean indicators (1, present; 0, absent) assigned to each biomarker, and a ranked score (UPSr) with merged semiquantitative information of biomarkers present calculated as UPSr = Σx(S/N)/10+1, where x includes the biomarkers with Boolean indicators of 1 (ie, present). To determine the intra- and interobserver variability of the proteomic scores and their stability with varying conditions and times of urine storage, SELDI analyses and calculations of UPSb and UPSr were performed on 8 samples of fresh urine (3 CRL and 3 sPE) by 3 independent investigators, each performing the analysis in triplicate. In addition, the investigators performed the same SELDI analysis after aliquots of the 8 samples were maintained at either -80°C, -4°C, or -20°C for 24 hours, 3 days, and 7 days, respectively. Identification of discriminatory proteomic biomarkers Tandem mass spectrometric (MS/MS) peptide sequencing was accomplished using a quadrupole time of flight (QTOF) instrument (Q-TOF II; Micromass Ltd, Manchester, UK) equipped with a protein chip interface (PCI1000; Ciphergen, Fremont, CA), which allows for peptides to be sequenced directly from the arrays without any offline purification necessary.24 Protein identification was achieved by database searching using Mascot software (Matrix Science, London, UK). Methodologic details on identification of the discriminatory proteomic biomarker components of the UPS scores are provided in Appendix section I.d. Following identification by MS/MS of some of the peaks of the UPS score, we conducted in vitro experiments by spiking urine samples devoid of biomarkers (CRL samples with UPS scores of 0, n = 3) with purified precursors (Sigma, St Louis, MO) in the attempt to identify whether the biomarkers of the UPS profile can be recreated by the interaction of the precursor with the urine milieu. Other experimental procedures are described in Appendix section I.e. Statistical analysis All datasets were subjected to normality testing using the Kolmogorov-Smirnov method. Data are reported as either mean with SD or 95% confidence intervals (for normally distributed data) or as median with range (for nonnormally distributed data). Datasets were compared with either Student's t-test or Mann-Whitney test as appropriate. Correlation analysis was performed using Spearman's rank order correlation. Longitudinal data were compared by 2-way analysis of variance (ANOVA). Proportions were compared with Fisher exact tests. Test accuracy (cases correctly classified/total number of cases), sensitivity, specificity, and likelihood ratios were measured on receiver operator characteristic (ROC) plots using MedCalc (Broekstraat, Belgium) statistical software. Intra- and interrater variability of the proteomic scores (ordinal scale) was estimated by the Cronbach alpha coefficient for scale reliability25 and agreement in diagnosis (nominal scale) with either the Cohen kappa method (2 raters) or by the method of Fleiss-Cuzick, an extension of the Cohen kappa method for 3 raters per subject.26 Agreement-level calculations were performed using StatsDirect software (v 2.5.7; StatsDirect Ltd, Cheshire, UK) and interpreted as very good (kappa = 0.81-1.00), good (kappa = 0.61-0.80), moderate (kappa = 0.41-0.60), fair (kappa = 0.21-0.40), or poor (kappa < 0.20). Multiple stepwise logistic regression analysis was used to adjust P values and odds ratios (ORs), respectively, for potential and combined influences of other parameters. Variables are entered into the model if P < .05 and removed for P > .1. P < .05 was considered statistically significant. Results Characterization of the urinary proteomic signature of severe preeclampsia At the end of the exploratory phase, through analysis of the SELDI-TOF spectra, we determined that the proteomic profile characteristic of preeclampsia requiring delivery includes 13 biomarker peaks (P1-P13; Figure 2). This dictates that UPSb can range from 0 (all biomarkers absent) to 13 (all 13 biomarkers present), whereas UPSr can range from 0 to infinity, depending on the sum of the S/N ratios of the biomarkers present. In Appendix section II.a, we list the observed molecular masses and the frequency with which each of the 13 biomarkers was detected in the exploratory phase. The sPE women had significantly elevated UPS scores compared with controls (sPE: UPSb, 9 [8-13] and UPSr, 29 [13-49] vs CRL: UPSb, 1 [0-6] and UPSr, 1 [0-8], P < .001). In the ROC analysis, we determined that the combination of a UPSb > 6 and a UPSr > 8 distinguished with 100% sensitivity and 100% specificity a diagnosis of sPE from CRLs at the time of initial clinical screening. Challenge phase: prospective validation of the proteomic profiling of urine to predict preeclampsia requiring mandated delivery Of the women enrolled in the cross-sectional cohort, 86 had mandated deliveries for preeclampsia, which in 73% occurred preterm and in 50% of cases at < 34 weeks (Figure 1). Test performances of the urine proteomic scores were compared with those of other tests from a random urine sample, such as protein-to-creatinine ratio and the sFlt-1-to-PlGF ratio (Figure 3). The urine sFlt-1-to-PlGF ratio predicted a medically indicated delivery for preeclampsia better than the protein-to-creatinine ratio (difference in ROC areas: 0.32 [95% confidence interval (CI), 0.22-0.42], P < .001) and also better than either urinary analyte alone (P < .001). However, the performance of the urine proteomic scores was superior to sFlt-1-to-PlGF ratio and all the other tests (difference in ROC areas between the urine sFlt-1-to-PlGF ratio and UPSr: 0.10 [95% CI, 0.04-0.17], P = .002; Figure 3). The accuracies of the urine analytes and proteomic scores to predict a preeclampsia-related indicated delivery are listed in Table 1. A subanalysis of the women enrolled in the nonhypertensive groups that delivered either at term or preterm in the absence of preeclampsia revealed that gestational age per se did nor correlate with the proteomic scores (for UPSb: Spearman's r = 0.08; P = .427, and for UPSr: r = 0.07; P = .468). | a Represents the cut-off with highest accuracy. |
We next tested whether addition of the nonreassuring proteomic status to the clinical assessment would have additive value in predicting outcome. In a stepwise multivariate logistic regression, a clinical diagnosis of sPE at enrollment and the nonreassuring proteomic urinary status were independent predictors of the need for delivery. The best model included the clinician's assessment (OR, 45.8; 95% CI, 9.2-228.5; P < .001), a nonreassuring urine pattern (OR, 12.4; 95% CI, 4.7-33.2; P < .001) and gestational age at assessment (OR, 1.2; 95% CI, 1.0-1.3; P = .004). Variables excluded from the model (P > .1) were maternal age, gravidity, parity, sFlt-1-to-PlGF ratio and protein-to-creatinine ratio. When we restricted our analysis to women screened for sPE at < 34 weeks (n = 139) of gestation, a nonreassuring urinary profile was the only variable with additive value (P < .001) to the clinical assessment to predict the severity of sPE requiring a mandated preterm delivery at < 34 weeks, independent of gestational age. Moreover, the additive predictive value was maintained at a level of P < .001 when we restricted the analysis to the women classified initially as mPE, crHTN, and spPE. At the time of initial clinical assessment, differentiation of mPE, uncontrolled crHTN, and presumed spPE from sPE are most difficult, based on clinical criteria alone, and a nonreassuring SELDI profile was the only predictor of preeclampsia requiring delivery (OR, 9.1; 95% CI, 2.8-29.6), improving disease classification to 81% from 67% based on clinical signs and symptoms alone (P < .001). Variables excluded from the model were gestational age, parity, and urine sFlt-to-PlGF and protein-to-creatinine ratios. Of the 19 women enrolled in the longitudinal cohort of the challenge phase, 3 developed sPE requiring delivery (Figure 2). We found that the subgroup of patients that ultimately developed sPE had significantly higher median UPS scores at all times, even up to 25 weeks before delivery (Figure 4). Whereas the UPS scores in patients who did not develop sPE did not change throughout pregnancy and postpartum, we found a progressive increase in UPS scores in women who ultimately developed sPE. Evolution of urinary proteomic scores of women who developed sPE was characterized by the increase both in the number of biomarkers present in the urine (2-way ANOVA: UPSb, P < .001) and their relative abundance (UPSr, P < .001) at least 10 weeks before the onset of clinical manifestations (Figure 4). We further determined that none of the nonpregnant women had the proteomic fingerprint of preeclampsia present despite significant proteinuria (median, 332 [range, 162-543] mg per 24 hours). This allowed us to conclude that our biomarkers are specific for preeclampsia and not for proteinuric conditions unrelated to pregnancy. Challenge phase: ability of the proteomic profile to distinguish preeclampsia from other hypertensive disorders The results of the challenge phase revealed that in preeclampsia the urinary proteomic profile is a dynamic entity, with an increase in both the number and abundance of biomarkers that parallels disease progression. Based on an ROC analysis, we established that pregnant women with proteomic scores of UPSb ≥ 5 or of UPSr ≥ 7 at the time of initial assessment have either sPE or spPE requiring at some point in pregnancy a medically indicated delivery. Therefore, such scores should be deemed nonreassuring. To determine whether proteomic profiling can aid clinicians to distinguish preeclamptic women who would require delivery from those with other hypertensive disorders, that can potentially be managed expectantly, we first investigated the frequency of a nonreassuring urine profile in all pregnant women with known outcomes enrolled in the cross-sectional cohort of the challenge phase (n = 196). Of all women categorized clinically as sPE, 94% (29/31) had a nonreassuring profile. Of the spPE women, 89% (25/28) had the nonreassuring pattern and thus were correctly classified based on clinical criteria. Conversely, 66% (19/29) of the cases classified as mPE also had a nonreassuring pattern, whereas crHTN and asymptomatic nonhypertensive women exhibited the proteomic profile in only 27% (7/26) and 9% (7/82) of cases, respectively. This suggests that clustering hypertensive disorders in pregnancy on clinical criteria alone can be subject to bias and supports the need of an additional test, such as proteomic profiling of the urine, for correct classification. Intra- and interobserver variability and stability of the proteomic scores Using agreement measures, we report very good Cronbach alpha values of 0.990 (intraobserver) and 0.975 (interobserver) for the nominal classification of urinary profiles as reassuring/nonreassuring and an average kappa (Fleiss and Cuzick extension) of 0.943 (intraobserver, very good) and 0.720 (interobserver, good) for the UPS scores as ordinal variables. Storage of the urine in various conditions did not alter the proteomic scores (kappa ranging from 1 [perfect agreement] to 0.75 [good]). Biomarker identification Using tandem mass spectrometry, we identified corresponding sequences for 5 of the biomarker components of the profile: P1, P2, P3, P5, and P7 (Figure 2), which matched peptide fragments of human SERPINA1 (SERine Protease Inhibitor A1; SwissProt P01009) and albumin (SwissProt P02768). The results obtained from the MS/MS analysis on selected peptides are presented in Table 2. Additional results illustrating the correspondence of biomarkers between detection in the SELDI-TOF and the Q-TOF mass spectrometer are displayed in Appendix section II.b. The complex P1-P3 at 2390-2430 Da corresponded to the 21 amino acid C-terminus fragment of SERPINA-1 (amino acids 398-418) in nonoxidized form (P1) or with either 1 (P2) or 2 (P3) methionine residues (positions 398 and/or 409) in oxidized form. Although not part of the final score, the complex of peaks that accompanies P1-P3 (Figure 2, asterisk) was identified as the 22 amino acid C-terminus peptide fragment of SERPINA1 (amino acids 397-418). The biomarker peak P7 corresponded to the 24 amino acid N-terminus fragment of SERPINA1 after cleavage of the signal peptide.27 We attempted a similar identification strategy for the remaining peaks of the UPS profile, but the amount of corresponding protein was below the current technical capability for identification. Using a strategy detailed in Appendix section II.c, we determined that P6, P8, P10, P11, P12, and P13 originate from human albumin. Translational implications of presence of SERPINA1 fragments in urine of patients with sPE By studying the combinations in which the biomarker peaks occurred in patients with sPE, we found that the urinary appearance of the 21 amino acid C-terminus fragment of SERPINA1 in the urine (P1) and at least 1 of its oxidized forms (P2 and/or P3) is a specific hallmark of preeclampsia severity. We thus became interested in how SERPINA1 homeostasis is disturbed in urine, serum, and placentae of women with sPE from a quantitative and qualitative perspective. Examination of fractional excretion of SERPINA1 revealed that women with sPE had significantly elevated urinary excretions of SERPINA1 immunoreactivity (450-fold; P < .001), even after correcting for the degree of nonspecific proteinuria (275-fold; P < .001; Figure 5A) or albuminuria (3.2-fold; P < .001; Figure 5B). Despite this excessive excretion, serum levels of immunoreactive SERPINA1 remained significantly elevated (P < .001; Figure 5C). We identified by Western blot analysis that SERPINA1 immunoreactivity excreted in urine by women with sPE was heterogeneous and included multiple molecular weight forms both below (fragments) and above (supramolecular aggregates) the 52-kDa mass of monomeric SERPINA1, whereas urine samples of CRL women were devoid of SERPINA1 immunoreactivity (Figure 5, D and E). By immunohistochemistry, we found that placentae from pregnancies complicated by sPE (n = 10) had increased stromal (P < .001), endothelial (P < .001), and intravascular (P < .001) SERPINA1 staining, compared with gestational age–matched controls (n = 10; Figure 6). The most conspicuous finding was the acellular positive material sequestered within the fetal vascular spaces and adhering to the vascular endothelium of the villi, virtually absent in nonpreeclamptic placentae. In preeclamptic placentae, SERPINA1-positive acellular aggregates were also encountered more often in the intervillous space occupied by maternal blood. By using the monoclonal ATZ11 antibody,28, 30 known to recognize oligomeric misfolded SERPINA1 complexes but not the native or latent forms of monomeric SERPINA1,29 we found an intense staining pattern in sPE placentae with predominant endothelial and perivascular localization, whereas the villi of the nonpreeclamptic placentae remained entirely free of staining.30 Comment We used multidimensional proteomic technology to determine that women with preeclampsia requiring mandated delivery exhibit a urinary proteomic signature characterized by nonrandom fragments of SERPINA1 and albumin. We provided evidence that the appearance of the proteomic fingerprint in the urine precedes the onset of clinical symptoms and maintains diagnostic significance in the context of associated morbidities, thereby improving disease classification. Proteomic profiling of the urine was significantly better than clinical evaluation alone or angiogenic factors to predict severity of preeclampsia and need for delivery. This could be very important, especially for the developing world in which severe disease is associated with high mortality.31 We further provided evidence that proteomic profiling of the urine carries the potential to differentiate preeclampsia from uncontrolled hypertensive disorders when clinical criteria alone seem powerless. Last, our findings that a significant proportion of women with crHTN or mPE display the biomarkers characteristic for sPE supports the view that classification of hypertensive disorders based on clinical criteria is deficient. Proteomics may allow future identification of new molecular diseases relevant for reclassification of the preeclamptic syndrome, treatment selection, and prognosis. In addition, knowledge of the identity of the biomarkers provided us with a new mechanistic view of preeclampsia. Proteinuria is a fundamental criterion for diagnosis of preeclampsia and the result of endothelial impairment with acute atherosis at the level of renal glomeruli.11 The degree of proteinuria has been associated with the severity of preeclampsia32 and in direct relationship with perinatal mortality.33 Current analysis of proteinuria for the diagnosis and monitoring of severity of preeclampsia in clinical practice relies on quantitative determination of total protein in a 2-step approach. The urinary dipstick analysis has been shown to be inaccurate because of high false-positive rates34 and is used as screening. If positive, it leads to the measurement of total protein eliminated in a 24-hour period. However, 24-hour collections are cumbersome, often requiring hospitalization, are subject to error and compliance issues, and result in a delay in diagnosis. Several studies attempting to provide evidence in support of random proteinuria after normalization for creatinine (protein-to-creatinine ratio) as a quick surrogate measure of proteinuria of preeclampsia have yielded mixed results.35, 36 Despite this, this test is now accepted by the International Society for the Study of Hypertension in Pregnancy as a reliable measure of proteinuria of preeclampsia.37 The current study aimed to provide a comprehensive qualitative and quantitative description of proteinuria of preeclampsia to create a customized combination of biomarkers that differentiates it from proteins present in urine of normal subjects, and most importantly from proteinuria of other conditions unrelated to preeclampsia. As shown in our current study, the accuracy of diagnostic indices based on proteomic profiling to discriminate cases with significant morbidity was higher than that of protein-to-creatinine ratio and even that of sFlt-1-to-PlGF ratio in a challenge cohort of more than 100 subjects with various pathologies, as applicable to a high-risk obstetric service. Aside from the practicality of producing a noninvasive diagnostic and prognostic test, knowledge on the identity of the biomarkers has led us to interesting and novel observations related to SERPINA1 and albumin fragmentation. SERPINA1 is an abundant plasma protein and the main blood-borne serine protease inhibitor. Although its primary function is the inhibition of neutrophil elastase,38 it also has activity against cathepsin G, proteinase 3, pancreatic elastase, trypsin, chymotrypsin and collagenases, and kallikrein.39 The antiproteolytic activity is explained by the formation of an equimolar enzyme-SERPINA1 complex. This results in the proteolytic cleavage of the reactive center peptide bond between Met358 and Ser359 of the secreted form of SERPINA1.40 SERPINA1 is synthesized by the liver, macrophages neutrophils, and also by trophoblast.41 Increases in serum SERPINA1 occur in diseases such as rheumatoid arthritis, vasculitis, infections, and other diseases associated with an inflammatory component.42 Interestingly, studies have shown that even minor increases in levels of serum SERPINA1 are associated with the development of arterial hypertension and an increased risk of cardiovascular disease.43, 44 One explanation is that by inhibiting the activity of the kallikrein-kinin system,45 an up-regulation of plasma SERPINA1 favors the renin-angiotensin system, leading to systemic vasoconstriction and hypertension. Our experiments confirmed that women with sPE have higher serum, urine, and placental SERPINA1 immunoreactivity, despite a tremendous urinary loss. Also significant may be that the C-terminus fragment of SERPINA1 corresponding to P1-P2-P3 biomarkers is part of a sequence that has been shown to activate monocytes to a heightened proinflammatory state,46 an observation consistent with the increased cytokine levels reported in preeclampsia.47 Protein misfolding diseases are a growing body of pathological entities, such as systemic amyloidosis, Alzheimer disease, and prion diseases. These conditions are characterized by aberrations in tertiary and quaternary structures of specific proteins, which result in deposits of aggregated oligomers and alterations in organ function.48 SERPINA1 stands out because of its susceptibility to oxidative stress-induced fragmentation, misfolding, polymerization, and aggregation.49 Supramolecular aggregates of misfolded SERPINA1 have been recently implicated in the pathophysiology of an emerging class of diseases known as serpinopathies, characterized by accumulation of aggregates within hepatocytes and neurons, leading to liver damage and encephalopathy.50 The high frequency of abnormal liver and neurologic findings in women with preeclampsia supports the view that fragments or aberrant forms of SERPINA1 may be direct players in the pathophysiologic manifestations of this syndrome. Our finding of ATZ11-positive staining limited to sPE villi is indicative that the placenta is also a target organ for SERPINA1 aggregates and thus may explain its altered function in this condition. It is known that normal urine contains small amounts of intact albumin and much larger quantities of low-molecular-weight albumin fragments. In this study, we have shown that, in addition to the biomarkers generated by fragmentation of SERPINA1, the urine of sPE patients also contains a consistent pattern of albumin fragments different from that of normal subjects. Although most dye-binding or antibody-based assays detect the intact form of albumin, they would not detect the fragments that we identified as specific biomarkers for preeclampsia.51 Because mass spectrometry technology can recognize these precise biomarkers, it may prove to be the ideal screening tool for women at low and high risk of preeclampsia. In support of the value of mass spectrometry is the observation that none of the albumin or SERPINA breakdown products is present in the urine of nonpregnant proteinuric women. Thus, in the future, mass spectrometry may represent the solution to the frequent clinical dilemma of differentiating PE from other preexisting hypertensive proteinuric disorders during gestation. We do not know at this time whether any of these specific SERPINA1 or albumin cleavage products have a direct or facilitating role in the endothelial and/or glomerular damage characteristic of preeclampsia. However, our proteomics approach determined that 2 abundant serum proteins serve as a reservoir of novel peptides generated through a nongenomic posttranslational and nonrandom cleavage. For example, we have confirmed that despite elevated SERPINA1 immunoreactivity in serum and placentae by immunoblot and immunohistochemistry, the mRNA levels of SERPINA1 in sPE placentae by real-time polymerase chain reaction remain unchanged compared with controls (data not shown). This suggests that placenta is not the source of the aberrantly elevated SERPINA1 but rather its deposit. Furthermore, it is well known that circulating SERPINA1 levels are elevated in not only preeclampsia but also acute inflammatory conditions.52 Therefore, it is not the elevation in SERPINA1 level that is characteristic for preeclampsia, but rather its pattern of fragmentation present in urine. Further studies are required to elucidate whether, besides being biomarkers, the fragments or misfolded forms of SERPINA1 are also direct players in the pathophysiologic manifestations of preeclampsia. Given the increasing interest in molecular and pharmacologic chaperones that have been found to reduce the severity of several neurodegenerative disorders and other protein-misfolding diseases,53 our findings set the preliminary stage to test whether drugs that stabilize thermodynamically native protein structures can also prevent or halt the progress of preeclampsia. Acknowledgment We are indebted to the fellows, residents, and nurses in the Departments of Obstetrics and Gynecology at Yale-New Haven Hospital and University of Vermont College of Medicine who assisted with patient enrollment. Appendix I.Supplementary methodsa)Contributors to the objective clinical score of preeclampsia severity (OCS-sPE) b)Details of women who provided urine samples for the exploratory phase c)Details on mass restricted (MR) scoring as applied to extract the urinary proteomic profile characteristic of severe preeclampsia d)Methodologic details on biomarker identification e)Methodologic details on other biochemical, immunologic, and molecular estimates II.Supplementary resultsa)Frequencies and experimental masses of individual biomarkers detected in the samples studied in the exploratory phase b)Results obtained from the MS/MS analysis on selected peptides on the ProteinChip tandem interface c)Strategy for identification of the origin of other biomarkers of the UPS profile III.Literature cited in supplementary appendix Methods a. Contributors to the objective clinical score of preeclampsia severity (OCS-sPE) At the time of enrollment, 1 of the investigators (C.S.B.) abstracted the clinical information entered in the medical record into a single semiquantitative variable, which we named the objective clinical score of preeclampsia severity (OCS-sPE). Briefly, Boolean indicators (1, present and 0, absent) were assigned for each of the criteria listed in Table A1, which are based on the American College of Obstetrics and Gynecology clinical criteria of diagnosis and severity of preeclampsia.1 OCS-sPE was calculated as a sum of the point indicators for each case. b.Details of women who provided urine samples for the exploratory phase are provided in Table A2 | | |  | Variables | Asymptomatic pregnant women (n = 21) | Severe preeclampsia (n = 38) | P value | |
|---|
| Demographic, clinical, and laboratory characteristics at enrollment | | | Age, ya | 26 ± 6 | 26 ± 7 | .913 | | | Parityb | 0 (0-3) | 0 (0-6) | .697 | | | Weight, kga | 87 ± 15 | 91 ± 27 | .499 | | | Gestational age, wksb | 31 (22-42) | 33 (24-41) | .629 | | | Systolic blood pressure, mm Hgb | 114 ± 11 | 168 ± 17 | <.001 | | | Diastolic blood pressure, mm Hgb | 67 ± 8 | 102 ± 9 | <.001 | | | Neurologic symptomsc | 0 (0 %) | 17 (45%) | <.001 | | | Dipstick proteinuriac | 0 (0-1) | 3 (1-4) | <.001 | | | 24-hour proteinuria, g per 24 hoursb | NA | 3.3 (0.2-13.1) | NA | | | Elevated liver enzymesc | NA | 14 (37%) | NA | | | Platelets <100,000 cells/mm3c | 0 (0 %) | 8 (21%) | <.001 | | | LDH, U/Lb | NA | 261 (206-1300) | NA | | | Uric acid, mg/dLa | NA | 6.7 ± 1.3 | NA | | | OCS-sPE | 0 (0-0) | 4 (3-7) | <.001 | | | Outcome characteristics | | | Gestational age at delivery, wksb | 39 (37-42) | 33 (28-41) | <.001 | | | Indicated deliveryc | 0 (0%) | 38 (100%) | <.001 | | | Indicated delivery < 34 wksc | 0 (0%) | 21 (55%) | <.001 | | | Cesarean deliveryc | 8 (38%) | 26 (68%) | .029 | | | Birthweight, gb | 3360 (2540-4335) | 1775 (902-4300) | <.001 | | | | |
| a Data presented as mean ± SD and analyzed by Student's t-test. bData presented as median (range) and analyzed by Mann-Whitney test. cData presented as n (%) and analyzed by Fisher exact tests. |
c.Details on MR scoring as applied to extract the urinary proteomic profile characteristic of severe preeclampsia The method of MR scoring is a stepwise strategy to extract relevant biomarkers based on filter principles applied sequentially.2, 3, 4 Peaks were selected using the centroid tool built into the SELDI software and the mass and signal-to-noise ratio (S/N ratio) for all selected peaks exported to an Excel spreadsheet (Microsoft Corp, Redmond, WA). A macro tool was constructed to further assign Boolean indicators of 1 to masses with S/N ratios above a preestablished cutoff value. Boolean indicators of 0 were assigned if the S/N ratio was below the cutoff. In this study, the cut-off was chosen as average plus 2 SD of the S/N ratio at the same masses on the profiles obtained from the control samples. The principles (Table A3) are applied in sequence to either eliminate or retain peaks in the final profile. Only peaks of the final profile are designated as biomarkers. d.Methodologic details on biomarker identification New arrays were prepared from urine samples in the exploratory phase cohort with highest UPSr scores. Tandem mass spectrometric (MS/MS) peptide sequencing was accomplished using a quadrupole time of flight instrument (Q-TOF II; Micromass Ltd, Manchester, UK) equipped with a PCI 1000 interface (Ciphergen Biosystems, Fremont, CA).5 The interface allows for peptides to be sequenced directly from the arrays without any offline purification necessary. The instrument was calibrated externally using an acquired MS/MS spectrum of ACTH (18-39) peptide at 2465.2 m/z, where 4 fragment ions and the parent were used as calibration points. All mass spectra were acquired in electrospray positive-ion mode with a collisional gas pressure of approximately 100 mTorr. Matrix conditions were identical to SELDI-TOF analysis described previously, and protein identification was achieved by database searching using Mascot software (Matrix Science, London, UK). Following identification by MS/MS of some of the peaks of the UPS score, we conducted in vitro experiments by spiking urine samples devoid of biomarkers (n = 3) with purified precursors (Sigma, St Louis, MO) in the attempt to identify whether the biomarkers of the characteristic profile can be re-created by the interaction of the precursors with the urine milieu. e.Methodologic details on other biochemical, immunologic, and molecular estimates Creatinine levels were measured using a colorimetric assay (Stanbio Laboratory, Boerne, TX) against standard curves derived from known concentrations. Total protein was measured using a bicinchoninic acid/cupric sulphate reagent (BCA kit; Pierce, Rockford, IL). ELISA assays for human unbound soluble fms-like tyrosine kinase-1 (sFlt-1)-to-placental growth factor (PlGF) were performed according to the manufacturer's instructions (R&D Systems, Minneapolis, MN). Briefly, urine samples were assayed in duplicate in a 96-well plate precoated with a capture antibody directed against free sFlt-1 or PlGF. Incubation protocols were performed, followed by washings in accordance with the procedure summary. Plates were read at 450 nm with 570-nm wavelength correction using a VERSAmax microplate reader with Softmax Pro 3.1.1 software (Molecular Devices, Sunnyvale, CA). The minimal detectable concentrations in the assays for sFlt-1 and PlGF were 5 and 7 pg/mL, respectively. The interassay and intraassay coefficients of variation varied from 3-10%. SERPINA1 immunoassays microtiter plates (Immuno MaxiSorp; Nalge Nunc, Rochester, NY) were coated with capture antibody (10 μg/mL sheep antihuman SERPINA1 antibody; Affinity Biologicals, Ancaster, Canada). Urine and serum samples were assayed in duplicate at various dilutions (urine: 1:50-1:100,000; serum: 1:250,000) against a 7 point standard curve from 0.123-90 ng/mL. Albumin immunoassays microtiter plates were coated with goat antihuman albumin antibody as capture antibody (10 μg/mL; Bethyl Laboratories, Montgomery, TX). The plates were washed, blocked, and incubated with urine (1:1000 dilution) or serum (1:150,000) human albumin calibrants (Bethyl Laboratories) in a range from 6.25-400 ng/mL. Detection was accomplished using horseradish peroxidase–conjugated secondary antibodies (sheep antihuman SERPINA1, 1:5000 dilution [Affinity Biologicals]; or antihuman albumin, 1:150,000 dilution [Bethyl Laboratories], respectively) and 3,3′,5,5′,-tetramethylbenzidine (Vector Laboratories, Burlingame, CA) as substrate. The color reaction was stopped with 2 M sulfuric acid, and plates were read at 450 nm with 650-nm wavelength correction. The intraassay coefficient of variation was < 5%. Fractional excretion calculations (amount of an analyte excreted in the urine relative to the amount filtered by the kidney) were based on the concentrations of SERPINA1, albumin, and total protein relative to creatinine in samples of blood and urine collected at the same time.6, 7 Antielastase activity was monitored by the ability of serum or urine sample to inhibit cleavage of a p-nitroanilide substrate8 by elastase in contolled kinetic conditions (α1-antiproteinase activity assay; Oxis Research, Foster City, CA). Results are reported as concentration equivalents of the elastase inactivated (micromoles). Using Western blot, gel electrophoresis was carried out on polyacrylamide separating gel and a 4% stacking gel using a BioRad Miniprotean II (Bio-Rad, Richmond, CA) gel apparatus under either denaturing (10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis) or native (7.5% PAGE) conditions. Ten micrograms of total serum or urine protein was diluted 1:2 (volume/volume) with electrophoresis sample buffer (BioRad) and reduced by boiling for 5 minutes. Two micrograms of purified serum SERPINA1 was loaded as positive control. After electrophoretic transfer to a polyvinyl difluoride membrane (Bio-Rad) at 100 V for 70 minutes and blocking with 5% milk, the blots were incubated overnight at 4°C with anti-SERPINA1 primary antibodies (1:1000) and subsequently subjected to enhanced chemiluminescence using the Western blotting detection system (Amersham, Arlington Heights, IL) with antirabbit immunoglobulin G (IgG) conjugated to horseradish peroxidase as secondary antibody. Autoradiography film was applied to the blot until satisfactory exposure was achieved. Ex vivo preparation of SERPINA1 oligomers was achieved by incubating purified plasma protein (Sigma) at 1 mg/mL in 0.015 mol/M Tris HCl containing 0.15 mol/L NaCl, pH 7.4, overnight at 60°C.9 Aggregate formation was confirmed by 7.5% nondenaturing PAGE (native PAGE) and a concurrent loss of antielastase activity. For immunohistochemistry, placental tissue sections (6 μm) were deparaffinized in xylene and rehydrated with graded ethanol to potassium-phosphate–buffered saline solution, pH 7.2. Following antigen retrieval with citrate buffer, the sections were pretreated with 1% hydrogen peroxide for 15 minutes, followed by overnight incubation (at 4°C) with rabbit antihuman SERPINA1 (1:1000; LabVision Corp, Fremont, CA) or mouse monoclonal ATZ11 antibody (Alpco Diagnostics, Salem, NH) and then a 1-hour incubation at room temperature with biotinylated goat antirabbit or antimouse IgG (1:600; Jackson ImmunoResearch, West Grove, PA), respectively. Detection was performed with avidin-biotin staining (Vectastain Elite ABC; Vector Laboratories) with 3,3′-diaminobenzidine/nickel sulfate as the chromogen solution. The tissue sections were dehydrated in graded ethanols, cleared, and mounted. Specific staining was evaluated semiquantitatively by examining 6 fields/slide and subjectively scoring on a scale from 0 (no staining) to 5 (intense blue-black staining) the intensity of the chromogen deposited in the trophoblast, villous stroma, villous endothelium, and intravascular spaces. A median score was computed for each patient. Human liver carcinoma sections (Spring Bioscience, Fremont, CA) were used as SERPINA-1–positive control and sections incubated with rabbit and mouse IgG as negative controls. Some sections were counterstained with methyl green. II.Supplementary results a.Frequencies and experimental masses of individual biomarkers detected in the samples studied in the exploratory phase are presented in Table A4 b.Correspondence of biomarker peaks between detection in the SELDI-TOF and the Q-TOF mass spectrometer c.Strategy for identification of the origin of other biomarkers of the UPS profile Figure A1 exemplifies the correspondence of peaks P1-P3 between detection in the PBSIIC SELDI-TOF mass spectrometer (Ciphergen, A) and the Q-TOF mass spectrometer (Micromass, B). The dominant monoisotopic forms of the biomarkers P1 and P2 before and after fragmentation (of P1, 2390.2 Da) in the MS/MS mode are illustrated in Figure A1, C, and D, respectively. Masses for peaks that are part of the P1-P3 complex (21 C-terminal amino acid of SERPINA1) are noted in red (in daltons). In blue, are the peaks not part of the UPS score that accompany the complex P1-P3 and represent the 22 amino acid C-terminal fragment of SERPINA1. We reasoned that other peaks of the preeclampsia profile may correspond to fragments derived from the interaction of albumin and/or SERPINA1 with the urinary milieu. To confirm this, we spiked urine samples devoid of biomarkers (control [CRL] urine with UPS scores of 0, n = 3) with albumin or SERPINA1 (Sigma) purified from human serum in concentrations of 0.25 mg/mL or 0.1 mg/mL, respectively (chosen concentration reflects the median immunoreactivity of albumin and SERPINA1 on urine of the sPE group) (Figure A2, experimental peak masses shown in red). We observed the appearance of P5 and P6 biomarkers in CRL urine samples spiked with albumin but not when either CRL urine or albumin alone was applied to the array. P5 has already been identified by MS/MS as the 24 amino acid N-terminus fragment of albumin. Thus, we predict the peptide sequence of P6 is DAHKSEVAHRFKDLGEENFKALVLIA (P02768, amino acids 25-50), with a computed mass of 2938.34 Da. We observed emergence of P8 and P10 in a similar pattern (not shown). This suggests that the origin of P5, P6, P8, and P10 in sPE urine is proteolytic in nature and perhaps secondary to albumin cleavage by urinary constituents. In contrast, P11, P12, and P13 also appeared when albumin alone was applied on the H50 array, suggesting these peaks are either the consequence of fragmentation of albumin precursor (66 kDa) in the mass spectrometer or are multiple charged forms of the 66-KDa parent peak. However, regardless of their origin, it is very likely that all 3 fragments also originate from human albumin. When we spiked urine with purified SERPINA1, we were unable to recreate the biomarkers identified as fragments of SERPINA1. We thus concluded that P1, P2, P3 (21 amino acid C-terminus cleavage fragment of SERPINA1), and P7 (24 amino acid N-terminus cleavage fragment of SERPINA1) originate only in vivo and, to our knowledge, only in the context of sPE. Appendix 2 Authors' contribution Irina A. Buhimschi, principal investigator and responsible author, conceived the study, optimized and developed the proteomics scores, analyzed and interpreted the data, and wrote the initial draft of the manuscript. Guomao Zhao participated with the processing of the biological samples and acquisition of biochemical, immunological, molecular, and SELDI data including blind testing of the proteomics scores. She revised the manuscript and approved its final version. Catalin S. Buhimschi participated equally with Irina Buhimschi in the conception and design of the study, supervised enrollment and consent procedures at Yale-New Haven Hospital, interpreted the data, revised critically the manuscript, and approved the final version. Edmund Funai participated in aspects of the study design, critically revised the initial draft of the manuscript, and approved its final version. Nathan Harris participated in identification of the biomarkers of the UPS score using the PCI1000 interface and Q-TOF II instrumentation. He revised the manuscript and approved its final version. Ira Bernstein participated in aspects of the study design and interpretation of the findings, supervised enrollment and consent procedures at University of Vermont, critically revised the initial draft of the manuscript, and approved its final version. George Saade participated in aspects of the study design and interpretation of the findings, critically revised the initial draft of the manuscript, and approved its final version. Isaac Sasson participated in recruitment of patients, standardization of clinical diagnoses, and collection of biological samples, critically revised the initial draft of the manuscript, and approved its final version. References 1. 1Sibai BM, Ewell M, Levine RJ, et al. Risk factors associated with preeclampsia in healthy nulliparous women: the Calcium for Preeclampsia Prevention (CPEP) Study Group. Am J Obstet Gynecol. 1997;177:1003–1010. Abstract | Full Text |
Full-Text PDF (679 KB)
|
CrossRef
2. 2Samadi AR, Mayberry RM, Zaidi AA, Pleasant JC, McGhee N, Rice RJ. Maternal hypertension and associated pregnancy complications among African-American and other women in the United States. Obstet Gynecol. 1996;87:557–563. MEDLINE |
CrossRef
3. 3World Health Organization International Collaborative Study of Hypertensive Disorders of Pregnancy. Geographic variation in the incidence of hypertension in pregnancy. Am J Obstet Gynecol. 1988;158:80–83. MEDLINE 4. 4MacKay AP, Berg CJ, Duran C, Chang J, Rosenberg H. An assessment of pregnancy-related mortality in the United States. Paediatr Perinat Epidemiol. 2005;19:206–214. MEDLINE |
CrossRef
5. 5Fronterhouse W, Christensen FC, Rayburn LA, Gilson GJ, Curet LB, Rayburn WF. Mandated preterm delivery: its prevalence and impact at a tertiary referral center. J Matern Fetal Med. 2001;10:162–165. MEDLINE 6. 6Ananth CV, Vintzileos AM. Epidemiology of preterm birth and its clinical subtypes. J Matern Fetal Neonatal Med. 2006;19:773–782. MEDLINE |
CrossRef
7. 7ACOG Committee on Practice Bulletins: Obstetrics. ACOG practice bulletin (Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002). Obstet Gynecol. 2002;99:159–167. MEDLINE |
CrossRef
8. 8Buhimschi IA, Saade GR, Chwalisz K, Garfield RE. The nitric oxide pathway in pre-eclampsia: pathophysiological implications. Hum Reprod Update. 1998;4:25–42. MEDLINE |
CrossRef
9. 9Zamorski MA, Green LA. NHBPEP report on high blood pressure in pregnancy: a summary for family physicians. Am Fam Physician. 2001;64:263–270. 10. 10Wilkins MR, Sanchez JC, Gooley AA, et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev. 1996;13:19–50. MEDLINE 11. 11Lindheimer MD, Katz AI. The normal and diseased kidney in pregnancy. In: Schrier RW editors. Diseases of the kidney and urinary tract. 7th ed.. Philadelphia: Lippincott, Williams and Wilkins; 2001;p. 2129–2165. 12. 12Ferrazzani S, Caruso A, De Carolis S, Martino IV, Mancuso S. Proteinuria and outcome of 444 pregnancies complicated by hypertension. Am J Obstet Gynecol. 1990;162:366–371. MEDLINE 13. 13Silkensen JR, Kasiske BL. Laboratory assessment of kidney disease: clearance, urinalysis, and kidney biopsy. In: Brenner BM editors. The kidney. 7th ed.. Philadelphia: WB Saunders; 2004;p. 1107. 14. 14Buhimschi CS, Norwitz ER, Funai E, et al. Urinary angiogenic factors cluster hypertensive disorders and identify women with severe preeclampsia. Am J Obstet Gynecol. 2005;192:734–741. Abstract | Full Text |
Full-Text PDF (186 KB)
|
CrossRef
15. 15Zhou XH, Obuchowski NA, McClish DK. Statistical methods in diagnostic medicine. New York: John Wiley & Sons; 2002;. 16. 16Price CP, Newall RG, Boyd JC. Use of protein:creatinine ratio measurements on random urine samples for prediction of significant proteinuria: a systematic review. Clin Chem. 2005;51:1577–1586. MEDLINE |
CrossRef
17. 17Levine RJ, Thadhani R, Qian C, et al. Urinary placental growth factor and risk of preeclampsia. JAMA. 2005;293:77–85.
CrossRef
18. 18Buhimschi CS, Magloire L, Funai E, et al. Fractional excretion of angiogenic factors in women with severe preeclampsia. Obstet Gynecol. 2006;107:1103–1113. MEDLINE 19. 19Weinstein S, Obuchowski NA, Lieber ML. Clinical evaluation of diagnostic tests. Am J Roentgenol. 2005;184:14–19. 20. 20Lindheimer MD, Davison JM. Renal biopsy during pregnancy: ‘to b... or not to b…?’. Br J Obstet Gynaecol. 1987;94:932–934. MEDLINE 21. 21Buhimschi IA, Christner R, Buhimschi CS. Proteomic biomarker analysis of amniotic fluid for identification of intra-amniotic inflammation. BJOG. 2005;112:173–181. MEDLINE |
CrossRef
22. 22Buhimschi CS, Weiner CP, Buhimschi IA. Clinical proteomics Part II (The emerging role of proteomics over genomics in spontaneous preterm labor/birth). Obstet Gynecol Survey. 2006;61:543–553. 23. 23Buhimschi IA, Buhimschi C. Proteomics of the amniotic fluid in assessment of the placenta (Relevance for preterm birth). Placenta. 2008;29(Suppl A):S95–S101. 24. 24Merchant M, Weinberger S. Recent advancements in surface enhanced laser desorption/ionization time of flight mass spectrometry. Electrophoresis. 2000;21:1164–1167. MEDLINE |
CrossRef
25. 25Cronbach L. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297–333.
CrossRef
26. 26Fleiss JL, Cuzick J. The reliability of dichotomous judgements: unequal numbers of judges per subject. Applied Psychological Measurement. 1979;3:537–542. 27. 27Carrell RW, Jeppsson JO, Laurell CB, et al. Structure and variation of human alpha 1-antitrypsin. Nature. 1982;298:329–334. MEDLINE |
CrossRef
28. 28Callea F, Brisigotti M, Faa G, Lucini L, Eriksson S. Identification of PiZ gene products in liver tissue by a monoclonal antibody specific for the Z mutant of alpha 1-antitrypsin. J Hepatol. 1991;12:372–376. MEDLINE |
CrossRef
29. 29Janciauskiene S, Dominaitiene R, Sternby NH, Piitulainen E, Eriksson S. Detection of circulating and endothelial cell polymers of Z and wild type alpha 1-antitrypsin by a monoclonal antibody. J Biol Chem. 2002;277:26540–26546. MEDLINE |
CrossRef
30. 30James EL, Whisstock JC, Gore MG, Bottomley SP. Probing the unfolding pathway of alpha 1-antitrypsin. J Biol Chem. 1999;274:9482–9488. MEDLINE |
CrossRef
31. 31Duley L. Maternal mortality associated with hypertensive disorders of pregnancy in Africa, Asia, Latin America and the Caribbean. Br J Obstet Gynaecol. 1992;99:547–553. MEDLINE 32. 32Chan P, Brown M, Simpson JM, Davis G. Proteinuria in pre-eclampsia: how much matters?. BJOG. 2005;112:280–285. MEDLINE |
CrossRef
33. 33Ferrazzani S, Caruso A, De Carolis S, Martino IV, Mancuso S. Proteinuria and outcome of 444 pregnancies complicated by hypertension. Am J Obstet Gynecol. 1990;162:366–371. MEDLINE 34. 34Brown MA, Buddle ML. Inadequacy of dipstick proteinuria in hypertensive pregnancy. Aust N Z J Obstet Gynaecol. 1995;35:366–369. MEDLINE |
CrossRef
35. 35Rodriguez-Thompson D, Lieberman ES. Use of a random urinary protein to creatinine ratio for the diagnosis of significant proteinuria during pregnancy. Am J Obstet Gynecol. 2001;185:808–811. Abstract | Full Text |
Full-Text PDF (87 KB)
|
CrossRef
36. 36Meyer NL, Mercer BM, Friedman SA, Sibai BM. Urinary dipstick protein: a poor predictor of absent or severe proteinuria. Am J Obstet Gynecol. 1994;170:137–141. Abstract | Full Text 37. 37Brown MA, Lindheimer MD, de Swiet M, Van Assche A, Moutquin JM. The classification and diagnosis of the hypertensive disorders of pregnancy: statement from the International Society for the Study of Hypertension in Pregnancy (ISSHP). Hypertens Pregnancy. 2001;20:IX–XIV. MEDLINE |
CrossRef
38. 38Coakley RJ, Taggart C, O'Neill S, McElvaney NG. Alpha 1-antitrypsin deficiency: biological answers to clinical questions. Am J Med Sci. 2001;321:33–41. MEDLINE |
CrossRef
39. 39Lisowska-Myjak B. AAT as a diagnostic tool. Clin Chim Acta. 2005;352:1–13. MEDLINE |
CrossRef
40. 40Carrell RW. Alpha 1-antitrypsin: molecular pathology, leukocytes, and tissue damage. J Clin Invest. 1986;78:1427–1431. MEDLINE |
CrossRef
41. 41Bergman D, Kadner SS, Cruz MR, et al. Synthesis of alpha 1-antichymotrypsin and alpha 1-antitrypsin by human trophoblast. Pediatr Res. 1993;34:312–317. MEDLINE 42. 42Ritchie RF, Palomaki GE, Neveux LM, Navolotskaia O. Glycoprotein (orosomucoid), alpha 1-antitrypsin, and haptoglobin: a comparison of a large cohort to the world's literature. J Clin Lab Anal. 2000;14:265–270. MEDLINE |
CrossRef
43. 43Engstrom G, Janzon L, Berglund G, et al. Blood pressure increase and incidence of hypertension in relation to inflammation-sensitive plasma proteins. Arterioscler Thromb Vasc Biol. 2002;22:2054–2058.
CrossRef
44. 44Engstrom G, Lind P, Hedblad B, Stavenow L, Janzon L, Lindgarde F. Long-term effects of inflammation-sensitive plasma proteins and systolic blood pressure on incidence of stroke. Stroke. 2002;33:2744–2749.
CrossRef
45. 45Schmaier AH. The plasma kallikrein-kinin system counterbalances the renin-angiotensin system. J Clin Invest. 2002;109:1007–1009. MEDLINE |
CrossRef
46. 46Janciauskiene S, Moraga F, Lindgren S. C-terminal fragment of alpha 1-antitrypsin activates human monocytes to a pro-inflammatory state through interactions with the CD36 scavenger receptor and LDL receptor. Atherosclerosis. 2001;158:41–51. Abstract | Full Text |
Full-Text PDF (177 KB)
|
CrossRef
47. 47Kupferminc MJ, Peaceman AM, Wigton TR, Rehnberg KA, Socol ML. Tumor necrosis factor-alpha is elevated in plasma and amniotic fluid of patients with severe preeclampsia. Am J Obstet Gynecol. 1994;170:1752–1757. Abstract | Full Text |
Full-Text PDF (823 KB)
48. 48Aguzzi A, Haass C. Games played by rogue proteins in prion disorders and Alzheimer's disease. Science. 2003;302:814–818.
CrossRef
49. 49Matheson NR, Wong PS, Travis J. Enzymatic inactivation of human alpha-1-proteinase inhibitor by neutrophil myeloperoxidase. Biochem Biophys Res Commun. 1979;88:402–409.
CrossRef
50. 50Lomas DA, Belorgey D, Mallya M, et al. Polymerisation underlies alpha 1-antitrypsin deficiency, dementia and other serpinopathies. Front Biosci. 2004;9:2873–2891.
CrossRef
51. 51Gudehithlu KP, Pegoraro AA, Dunea G, Arruda JA, Singh AK. Degradation of albumin by the renal proximal tubule cells and the subsequent fate of its fragments. Kidney Int. 2004;65:2113–2122. MEDLINE |
CrossRef
52. 52Travis J, Shieh BH, Potempa J. The functional role of acute phase plasma proteinase inhibitors. Tokai J Exp Clin Med. 1988;13:313–320. MEDLINE 53. 53Muchowski PJ, Wacker JL. Modulation of neurodegeneration by molecular chaperones. Nat Rev Neurosci. 2005;6:11–22. III. Literature cited in supplementary appendix 1. 1ACOG Committee on Practice Bulletins—Obstetrics. ACOG practice bulletin (Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002). Obstet Gynecol. 2002;99:59–67. 2. 2Buhimschi IA, Buhimschi CS, Christner R, Weiner CP. Proteomics technology for the accurate diagnosis of inflammation in twin pregnancies. BJOG. 2005;112:250–255. 3. 3Buhimschi CS, Weiner CP, Buhimschi IA. Clinical proteomics part II (The emerging role of proteomics over genomics in spontaneous preterm labor/birth). Obstet Gynecol Survey. 2006;61:543–553. 4. 4Buhimschi IA, Buhimschi C. Proteomics of the amniotic fluid in assessment of the placenta (Relevance for preterm birth). Placenta. 2008;29(Suppl A):S95–S101. 5. 5Merchant M, Weinberger S. Recent advancements in surface enhanced laser desorption/ionization time of flight mass spectrometry. Electrophoresis. 2000;21:1164–1167. 6. 6Buhimschi CS, Magloire L, Funai E, et al. Fractional excretion of angiogenic factors in women with severe preeclampsia. Obstet Gynecol. 2006;107:1103–1113. 7. 7Hamar BD, Buhimschi IA, Sfakianaki AK, et al. Serum and urine inhibin A but not free activin A are endocrine biomarkers of severe pre-eclampsia. Am J Obstet Gynecol. 2006;195:1636–1645. 8. 8Bieth J, Wermuth CG. The action of elastase on p-nitroanilide substrates. Biochem Biophys Res Commun. 1973;53:383–390. 9. 9Janciauskiene S, Eriksson S, Callea F, et al. Differential detection of PAS-positive inclusions formed by the Z, Siiyama, and M malton variants of alpha1-antitrypsin. Hepatology. 2004;40:1203–1210. a Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine, New Haven, CT b Ciphergen Biosystems, Inc, Fremont, CA c Department of Obstetrics and Gynecology, University of Vermont College of Medicine, Burlington, VT d Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, TX  Reprints: Irina A. Buhimschi, MD, Department of Obstetrics, Gynecology, and Reproductive Science, Yale University School of Medicine, 333 Cedar St, LCI 804, PO Box 208063, New Haven, CT 06520-8063
This study was supported by the Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine, and partially supported by funds from the Department of Health and Human Services Grant R01 HD 047321-01 (to I.A.B.). Financial disclosure statement: Drs Irina and Catalin Buhimschi are joint inventors on patent applications filed by Yale University regarding the use of urinary angiogenic factors and proteomic profiling for diagnosis of preeclampsia. Dr Nathan Harris is an employee of Ciphergen Biosystems. Drs Irina Buhimschi and Catalin Buhimschi have no financial relationship with Ciphergen Biosystems and have collaborated with Mr Harris on a solely scientific basis. None of the other authors has any financial relationships regarding the content of this manuscript. Role of the funding source: none of the funding sources had any involvement in study design, interpretation of data, writing of the report, or the decision to submit the paper for publication. Cite this article as: Buhimschi IA, Zhao G, Funai EF, et al. Proteomic profiling of urine identifies specific fragments of SERPINA1 and albumin as biomarkers of preeclampsia. Am J Obstet Gynecol 2008;199:551.e1-551.e16. PII: S0002-9378(08)00790-4 doi:10.1016/j.ajog.2008.07.006 © 2008 Mosby, Inc. All rights reserved. | |
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