First-trimester preterm preeclampsia prediction with metabolite biomarkers: differential prediction according to maternal body mass index.

BACKGROUND: Prediction of preeclampsia risk is key to informing effective maternal care. Current screening for preeclampsia at 11 to 13 weeks of gestation using maternal demographic characteristics and medical history with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor can identify approximately 75% of women who develop preterm preeclampsia with delivery at <37 weeks of gestation. Further improvements to preeclampsia screening tests will likely require integrating additional biomarkers. Recent research suggests the existence of distinct maternal risk profiles. Therefore, biomarker evaluation should account for the possibility that a biomarker only predicts preeclampsia in a specific maternal phenotype. OBJECTIVE: This study aimed to verify metabolite biomarkers as preterm preeclampsia predictors early in pregnancy in all women and across body mass index groups. STUDY DESIGN: Observational case-control study drawn from a large prospective study on the early prediction of pregnancy complications in women attending their routine first hospital visit at King's College Hospital, London, United Kingdom, in 2010 to 2015. Pregnant women underwent a complete first-trimester assessment, including the collection of blood samples for biobanking. In 11- to 13-week plasma samples of 2501 singleton pregnancies, the levels of preselected metabolites implicated in the prediction of pregnancy complications were analyzed using a targeted liquid chromatography-mass spectrometry method, yielding high-quality quantification data on 50 metabolites. The ratios of amino acid levels involved in arginine biosynthesis and nitric oxide synthase pathways were added to the list of biomarkers. Placental growth factor and pregnancy-associated plasma protein A were also available for all study subjects, serving as comparator risk predictors. Data on 1635 control and 106 pregnancies complicated by preterm preeclampsia were considered for this analysis, normalized using multiples of medians. Prediction analyses were performed across the following patient strata: all subjects and the body mass index classes of <25, 25 to <30, and ≥30 kg/m2. Adjusted median levels were compared between cases and controls and between each body mass index class group. Odds ratios and 95% confidence intervals were calculated at the mean ±1 standard deviation to gauge clinical prediction merits. RESULTS: The levels of 13 metabolites were associated with preterm preeclampsia in the entire study population (P<.05) with particularly significant (P<.01) associations found for 6 of them, namely, 2-hydroxy-(2/3)-methylbutyric acid, 25-hydroxyvitamin D3, 2-hydroxybutyric acid, alanine, dodecanoylcarnitine, and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphocholine. Fold changes in 7 amino acid ratios, all involving glutamine or ornithine, were also significantly different between cases and controls (P<.01). The predictive performance of some metabolites and ratios differed according to body mass index classification; for example, ornithine (P<.001) and several ornithine-related ratios (P<.0001 to P<.01) were only strongly associated with preterm preeclampsia in the body mass index of <25 kg/m2 group, whereas dodecanoylcarnitine and 3 glutamine ratios were particularly predictive in the body mass index of ≥30 kg/m2 group (P<.01). CONCLUSION: Single metabolites and ratios of amino acids related to arginine bioavailability and nitric oxide synthase pathways were associated with preterm preeclampsia risk at 11 to 13 weeks of gestation. Differential prediction was observed according to body mass index classes, supporting the existence of distinct maternal risk profiles. Future studies in preeclampsia prediction should account for the possibility of different maternal risk profiles to improve etiologic and prognostic understanding and, ultimately, clinical utility of screening tests.

Glycerophospholipid and detoxification pathways associated with small for gestation age pathophysiology: discovery metabolomics analysis in the SCOPE cohort.

INTRODUCTION: Small for gestational age (SGA) may be associated with neonatal morbidity and mortality. Our understanding of the molecular pathways implicated is poor. OBJECTIVES: Our aim was to determine the metabolic pathways involved in the pathophysiology of SGA and examine their variation between maternal biofluid samples. METHODS: Plasma (Cork) and urine (Cork, Auckland) samples were collected at 20 weeks' gestation from nulliparous low-risk pregnant women participating in the SCOPE study. Women who delivered an SGA infant (birthweight < 10th percentile) were matched to controls (uncomplicated pregnancies). Metabolomics (urine) and lipidomics (plasma) analyses were performed using ultra performance liquid chromatography-mass spectrometry. Features were ranked based on FDR adjusted p-values from empirical Bayes analysis, and significant features putatively identified. RESULTS: Lipidomics plasma analysis revealed that 22 out of the 33 significantly altered lipids annotated were glycerophospholipids; all were detected in higher levels in SGA. Metabolomic analysis identified reduced expression of metabolites associated with detoxification (D-Glucuronic acid, Estriol-16-glucuronide), nutrient absorption and transport (Sulfolithocholic acid) pathways. CONCLUSIONS: This study suggests higher levels of glycerophospholipids, and lower levels of specific urine metabolites are implicated in the pathophysiology of SGA. Further research is needed to confirm these findings in independent samples.

Association between phospholipid metabolism in plasma and spontaneous preterm birth: a discovery lipidomic analysis in the cork pregnancy cohort.

INTRODUCTION: Preterm birth (PTB) is defined as birth occurring before 37 weeks' gestation, affects 5-9% of all pregnancies in developed countries, and is the leading cause of perinatal mortality. Spontaneous preterm birth (sPTB) accounts for 31-50% of all PTB, but the underlying pathophysiology is poorly understood. OBJECTIVE: This study aimed to decipher the lipidomics pathways involved in pathophysiology of sPTB. METHODS: Blood samples were taken from SCreening fOr Pregnancy Endpoints (SCOPE), an international study that recruited 5628 nulliparous women, with a singleton low-risk pregnancy. Our analysis focused on plasma from SCOPE in Cork. Discovery profiling of the samples was undertaken using liquid chromatography-mass spectrometry Lipidomics, and features significantly altered between sPTB (n = 16) and Control (n = 32) groups were identified using empirical Bayes testing, adjusting for multiple comparisons. RESULTS: Twenty-six lipids showed lower levels in plasma of sPTB compared to controls (adjusted p < 0.05), including 20 glycerophospholipids (12 phosphatidylcholines, 7 phosphatidylethanolamines, 1 phosphatidylinositol) and 6 sphingolipids (2 ceramides and 4 sphingomyelines). In addition, a diaglyceride, DG (34:4), was detected in higher levels in sPTB compared to controls. CONCLUSIONS: We report reduced levels of plasma phospholipids in sPTB. Phospholipid integrity is linked to biological membrane stability and inflammation, while storage and breakdown of lipids have previously been implicated in pregnancy complications. The contribution of phospholipids to sPTB as a cause or effect is still unclear; however, our results of differential plasma phospholipid expression represent another step in advancing our understanding of the aetiology of sPTB. Further work is needed to validate these findings in independent pregnancy cohorts.

Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood.

With less than half of patients with major depressive disorder (MDD) correctly diagnosed within the primary care setting, there is a clinical need to develop an objective and readily accessible test to enable earlier and more accurate diagnosis. The aim of this study was to develop diagnostic prediction models to identify MDD patients among individuals presenting with subclinical low mood, based on data from dried blood spot (DBS) proteomics (194 peptides representing 115 proteins) and a novel digital mental health assessment (102 sociodemographic, clinical and personality characteristics). To this end, we investigated 130 low mood controls, 53 currently depressed individuals with an existing MDD diagnosis (established current MDD), 40 currently depressed individuals with a new MDD diagnosis (new current MDD), and 72 currently not depressed individuals with an existing MDD diagnosis (established non-current MDD). A repeated nested cross-validation approach was used to evaluate variation in model selection and ensure model reproducibility. Prediction models that were trained to differentiate between established current MDD patients and low mood controls (AUC = 0.94 ± 0.01) demonstrated a good predictive performance when extrapolated to differentiate between new current MDD patients and low mood controls (AUC = 0.80 ± 0.01), as well as between established non-current MDD patients and low mood controls (AUC = 0.79 ± 0.01). Importantly, we identified DBS proteins A1AG1, A2GL, AL1A1, APOE and CFAH as important predictors of MDD, indicative of immune system dysregulation; as well as poor self-rated mental health, BMI, reduced daily experiences of positive emotions, and tender-mindedness. Despite the need for further validation, our preliminary findings demonstrate the potential of such prediction models to be used as a diagnostic aid for detecting MDD in clinical practice.

Unbiased plasma proteomics for novel diagnostic biomarkers in cardiovascular disease: identification of quiescin Q6 as a candidate biomarker of acutely decompensated heart failure.

AIMS: Biochemical marker testing has improved the evaluation and management of patients with cardiovascular diseases over the past decade. Natriuretic peptides (NPs), used in clinical practice to assess cardiac dysfunction, exhibit many limitations, however. We used an unbiased proteomics approach for the discovery of novel diagnostic plasma biomarkers of heart failure (HF). METHODS AND RESULTS: A proteomics pipeline adapted for very low-abundant plasma proteins was applied to clinical samples from patients admitted with acute decompensated HF (ADHF). Quiescin Q6 (QSOX1), a protein involved in the formation of disulfide bridges, emerged as the best performing marker for ADHF (with an area under the receiver operator characteristic curve of 0.86, 95% confidence interval: 0.79-0.92), and novel isoforms of NPs were also identified. Diagnostic performance of QSOX1 for ADHF was confirmed in 267 prospectively collected subjects of whom 76 had ADHF. Combining QSOX1 to B-type NP (BNP) significantly improved diagnostic accuracy for ADHF by particularly improving specificity. Using thoracic aortic constriction in rats, QSOX1 was specifically induced within both left atria and ventricles at the time of HF onset. CONCLUSION: The novel biomarker QSOX1 accurately identifies ADHF, particularly when combined with BNP. Through both clinical and experimental studies we provide lines of evidence for a link between ADHF and cardiovascular production of QSOX1.

Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models.

BACKGROUND: Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia. OBJECTIVE: This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index. STUDY DESIGN: This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate. RESULTS: First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes. CONCLUSION: Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

The novel marker LTBP2 predicts all-cause and pulmonary death in patients with acute dyspnoea.

The risk stratification in patients presenting with acute dyspnoea remains a challenge. We therefore conducted a prospective, observational cohort study enrolling 292 patients presenting to the emergency department with acute dyspnoea. A proteomic approach for antibody-free targeted protein quantification based on high-end MS was used to measure LTBP2 [latent TGF (transforming growth factor)-binding protein 2] levels. Final diagnosis and death during follow-up were adjudicated blinded to LTBP2 levels. AHF (acute heart failure) was the final diagnosis in 54% of patients. In both AHF (P<0.001) and non-AHF (P=0.015) patients, LTBP2 levels at presentation were significantly higher in non-survivors compared with survivors with differences on median levels being 2.2- and 1.5-fold respectively. When assessing the cause of death, LTBP2 levels were significantly higher in patients dying from pulmonary causes (P=0.0005). Overall, LTBP2 powerfully predicted early pulmonary death {AUC (area under the curve), 0.95 [95% CI (confidence interval), 0.91-0.98]}. In ROC (receiver operating characteristic) curve analyses for the prediction of 1-year mortality LTBP2 achieved an AUC of 0.77 (95% CI, 0.71-0.84); comparable with the predictive potential of NT-proBNP [N-terminal pro-B-type natriuruetic peptide; 0.77 (95% CI, 0.72-0.82)]. Importantly, the predictive potential of LTBP2 persisted in patients with AHF as the cause of dypnea (AUC 0.78) and was independent of renal dysfunction (AUC 0.77). In a multivariate Cox regression analysis, LTBP2 was the strongest independent predictor of death [HR (hazard ratio), 3.76 (95% CI, 2.13-6.64); P<0.0001]. In conclusion, plasma levels of LTBP2 present a novel and powerful predictor of all-cause mortality, and particularly pulmonary death. Cause-specific prediction of death would enable targeted prevention, e.g. with pre-emptive antibiotic therapy.

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Prediction of preeclampsia risk in first time pregnant women: Metabolite biomarkers for a clinical test.

Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. Accurate prediction of preeclampsia risk would enable more effective, risk-based prenatal care pathways. Current risk assessment algorithms depend on clinical risk factors largely unavailable for first-time pregnant women. Delivering accurate preeclampsia risk assessment to this cohort of women, therefore requires for novel biomarkers. Here, we evaluated the relevance of metabolite biomarker candidates for their selection into a prototype rapid, quantitative Liquid Chromatography-tandem Mass Spectrometry (LC-MS/MS) based clinical screening assay. First, a library of targeted LC-MS/MS assays for metabolite biomarker candidates was developed, using a medium-throughput translational metabolomics workflow, to verify biomarker potential in the Screening-for-Pregnancy-Endpoints (SCOPE, European branch) study. A variable pre-selection step was followed by the development of multivariable prediction models for pre-defined clinical use cases, i.e., prediction of preterm preeclampsia risk and of any preeclampsia risk. Within a large set of metabolite biomarker candidates, we confirmed the potential of dilinoleoyl-glycerol and heptadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine to effectively complement Placental Growth Factor, an established preeclampsia biomarker, for the prediction of preeclampsia risk in first-time pregnancies without overt risk factors. These metabolites will be considered for integration in a prototype rapid, quantitative LC-MS/MS assay, and subsequent validation in an independent cohort.

Application of a combined weak cation-exchange/crown ether column: first demonstrations of a versatile tool for proteome subselection.

The present paper introduces the use of a weak cation-exchange/crown ether column in the proteomics field. The 18-crown-6 ether functionality is well-known to selectively complex ammonium and monoalkylammonium ions, which should make this column highly suitable to trap peptides with free alpha-NH(2) or free epsilon-NH(2) groups from lysine side chains. This unique selection mechanism was put to the test in an N-teromics setup which aims for the enrichment of deliberately acetylated protein N-terminal peptides from a serum digest. It was demonstrated that peptides with free alpha-NH(2) groups and peptides with alpha-amino-acetylated groups can be separated from each other using this weak cation-exchange/crown ether column. The peptides of interest, bearing no free primary amines, were found to be significantly enriched in the column's flow through. At the same time a favorable coenrichment of N-glycosylated peptides was observed. To obtain more insight in the contributions of the two distinct column functionalities, i.e., the weak cation exchanger and the crown ether, the experimental data were checked against a theoretical prediction of the outcome.

Exploring proteomes and analyzing protein processing by mass spectrometric identification of sorted N-terminal peptides.

Current non-gel techniques for analyzing proteomes rely heavily on mass spectrometric analysis of enzymatically digested protein mixtures. Prior to analysis, a highly complex peptide mixture is either separated on a multidimensional chromatographic system or it is first reduced in complexity by isolating sets of representative peptides. Recently, we developed a peptide isolation procedure based on diagonal electrophoresis and diagonal chromatography. We call it combined fractional diagonal chromatography (COFRADIC). In previous experiments, we used COFRADIC to identify more than 800 Escherichia coli proteins by tandem mass spectrometric (MS/MS) analysis of isolated methionine-containing peptides. Here, we describe a diagonal method to isolate N-terminal peptides. This reduces the complexity of the peptide sample, because each protein has one N terminus and is thus represented by only one peptide. In this new procedure, free amino groups in proteins are first blocked by acetylation and then digested with trypsin. After reverse-phase (RP) chromatographic fractionation of the generated peptide mixture, internal peptides are blocked using 2,4,6-trinitrobenzenesulfonic acid (TNBS); they display a strong hydrophobic shift and therefore segregate from the unaltered N-terminal peptides during a second identical separation step. N-terminal peptides can thereby be specifically collected for further liquid chromatography (LC)-MS/MS analysis. Omitting the acetylation step results in the isolation of non-lysine-containing N-terminal peptides from in vivo blocked proteins.

A novel method for interrogating receiver operating characteristic curves for assessing prognostic tests.

BACKGROUND: Disease prevalence is rarely explicitly considered in the early stages of the development of novel prognostic tests. Rather, researchers use the area under the receiver operating characteristic (AUROC) as the key metric to gauge and report predictive performance ability. Because this statistic does not account for disease prevalence, proposed tests may not appropriately address clinical requirements. This ultimately impedes the translation of prognostic tests into clinical practice. METHODS: A method to express positive- and/or negative predictive value criteria (PPV, NPV) within the ROC space is presented. Equations are derived for so-called equi-PPV (and equi-NPV) lines. Herewith it is possible, for any given prevalence, to plot a series of sensitivity-specificity pairs which meet a specified PPV (or NPV) criterion onto the ROC space.This concept is introduced by firstly reviewing the well-established "mechanics", strengths and limitations of the ROC analysis in the context of developing prognostic models. Then, the use of PPV (and/or) NPV criteria to augment the ROC analysis is elaborated.Additionally, an interactive web tool was also created to enable people to explore the dynamics of lines of equi-predictive value in function of prevalence. The web tool also allows to gauge what ROC curve shapes best meet specific positive and/or negative predictive value criteria (http://d4ta.link/ppvnpv/). RESULTS: To illustrate the merits and implications of this concept, an example on the prediction of pre-eclampsia risk in low-risk nulliparous pregnancies is elaborated. CONCLUSIONS: In risk stratification, the clinical usefulness of a prognostic test can be expressed in positive- and negative predictive value criteria; the development of novel prognostic tests will be facilitated by the possibility to co-visualise such criteria together with ROC curves. To achieve clinically meaningful risk stratification, the development of separate tests to meet either a pre-specified positive value (rule-in) or a negative predictive value (rule-out) criteria should be considered: the characteristics of successful rule-in and rule-out tests may markedly differ.

Mid-trimester maternal ADAM12 levels differ according to fetal gender in pregnancies complicated by preeclampsia.

An overrepresentation of adverse pregnancy outcomes has been observed in pregnancies associated with a male fetus. We investigated the association between fetal gender and candidate biomarkers for preeclampsia. Proteins were quantified in samples taken at 20 weeks from women recruited to the SCreening fOr Pregnancy Endpoints (SCOPE) study (preeclampsia n = 150; no preeclampsia n = 450). In contrast to placental growth factor, soluble endoglin, and insulin-like growth factor acid labile subunit, levels of metallopeptidase domain 12 (ADAM12) at 20 weeks were dependent on fetal gender in pregnancies complicated by preeclampsia, for male (n = 73) fetuses the multiples of the median (MoM; interquartile range [IQR] 1.1-1.5) was 1.3, whereas for female fetuses (n = 75) MoM was 1.1 (1.0-1.3); P < .01. Prediction of preeclampsia using ADAM12 levels was improved for pregnancies associated with a male fetus (area under receiver-operator curve [AUC] 0.73 [95% confidence interval [CI] 0.67-0.80]) than that of a female fetus (AUC 0.62 [0.55-0.70]); P = .03. The data presented here fit a contemporary hypothesis that there is a difference between the genders in response to an adverse maternal environment and suggest that an alteration in ADAM12 may reflect an altered placental response in pregnancies subsequently complicated by preeclampsia.

Soluble CD146, a new endothelial biomarker of acutely decompensated heart failure.

BACKGROUND: The present study involved both human cohorts and animal experiments to explore the performance of soluble CD146 (sCD146), a marker of endothelial function, as a diagnostic marker of acutely decompensated heart failure (ADHF), to determine the influence of patients' characteristics on that performance and to explore the potential application of CD146 in the pathophysiology of ADHF. METHODS AND RESULTS: NT-proBNP and sCD146 were measured in three hundred ninety-one patients admitted to the emergency department for acute dyspnea. ROC curve analysis demonstrated that AUCs for ADHF diagnosis in dyspneic patients were 0.86 (95% CI: 0.82-0.90) for sCD146 and 0.90 (95% CI: 0.86-0.92) for NT-proBNP. Subgroup analyses demonstrated that adding sCD146 to NT-proBNP improved the diagnostic performance for patients lying in the gray zone of NT-proBNP (p=0.02) and could be especially useful for ruling-out ADHF. An experimental model of ADHF in rats using thoracic aortic constriction suggests that CD146 is expressed in the intima of large arteries and associated with both left ventricular function and organ congestion. CONCLUSIONS: sCD146, a marker of endothelial function, seems to be as powerful as NT-proBNP is used to detect the cardiac origin of an acute dyspnea. The combination of sCD146 and NT-proBNP may have better performance than NT-proBNP used alone in particular for patients underlying in the "gray" zone and could therefore be an improved option for ruling-out ADHF. Both experimental and human data suggest that CD146 is related to systolic left ventricular function and to organ congestion.

Integrated proteomics pipeline yields novel biomarkers for predicting preeclampsia.

Preeclampsia, a hypertensive pregnancy complication, is largely unpredictable in healthy nulliparous pregnant women. Accurate preeclampsia prediction in this population would transform antenatal care. To identify novel protein markers relevant to the prediction of preeclampsia, a 3-step mass spectrometric work flow was applied. On selection of candidate biomarkers, mostly from an unbiased discovery experiment (19 women), targeted quantitation was used to verify and validate candidate biomarkers in 2 independent cohorts from the SCOPE (SCreening fOr Pregnancy Endpoints) study. Candidate proteins were measured in plasma specimens collected at 19 to 21 weeks' gestation from 100 women who later developed preeclampsia and 200 women without preeclampsia recruited from Australia and New Zealand. Protein levels (n=25), age, and blood pressure were then analyzed using logistic regression to identify multimarker models (maximum 6 markers) that met predefined criteria: sensitivity ≥50% at 20% positive predictive value. These 44 algorithms were then tested in an independent European cohort (n=300) yielding 8 validated models. These 8 models detected 50% to 56% of preeclampsia cases in the training and validation sets; the detection rate for preterm preeclampsia cases was 80%. Validated models combine insulin-like growth factor acid labile subunit and soluble endoglin, supplemented with maximally 4 markers of placental growth factor, serine peptidase inhibitor Kunitz type 1, melanoma cell adhesion molecule, selenoprotein P, and blood pressure. Predictive performances were maintained when exchanging mass spectrometry measurements with ELISA measurements for insulin-like growth factor acid labile subunit. In conclusion, we demonstrated that biomarker combinations centered on insulin-like growth factor acid labile subunit have the potential to predict preeclampsia in healthy nulliparous women.

A strategy for the prior processing of high-resolution mass spectral data obtained from high-dimensional combined fractional diagonal chromatography.

Combined fractional diagonal chromatography (COFRADIC) is a novel suite of gel-free technologies for the identification of biomarkers in complex peptide mixtures. For this purpose, reversed-phase high performance liquid chromatography (HPLC) technology and, in this case, matrix assisted laser desorption /ionization- time of flight (MALDI-TOF) mass spectrometers are extensively used. The particular characteristic of COFRADIC mass spectrometry data is the high number of chromatographic fractions, over which a peptide can be scattered. This can obstruct the quantification of the peptide abundance in the biological sample, which is required for statistical analysis. On the other hand, because of the superior peptide sorting properties of the methodology, the mass spectra become less crowded. Consequently, each peptide appears in a mass spectrum as a series of peaks with peak heights proportional to the probability of occurrence of the isotopic variants of the peptide. In this manuscript, we propose an analysis strategy concerned with the preprocessing of COFRADIC mass spectra prior to a downstream statistical analysis. The preprocessing algorithm produces for each mass spectrum a peptide list by exploiting the characteristic features that should be associated with peaks corresponding to an isotopically resolved cluster of peptide peaks. This reduction step is necessary to facilitate the clustering used in a next step to assemble the validated monoisotopic peptide peaks found over several fractions into a single peptide abundance. To assess the performance of the algorithm, two technical experiments were conducted. The proposed strategy is memory and computationally efficient.

Using a Poisson approximation to predict the isotopic distribution of sulphur-containing peptides in a peptide-centric proteomic approach.

Breen et al. (Electrophoresis 2000; 21: 2243) proposed a method for finding monoisotopic peptide peaks in mass spectra based on an approximation of the distribution of different isotopic variants of a peptide by a Poisson distribution. They developed the method using all protein sequences from the SWISS-PROT database. We investigate the suitability of this method to predict the isotopic distribution in an environment which enriches for peptides carrying sulphur. More specifically, we focus on mass spectra obtained by a COmbined FRActional DIagonal Chromatography (COFRADIC) approach, developed by Gevaert et al. (Nature Biotechnology 2003; 21: 566), targeting a specific subset of peptides, in this case the N-terminal peptides. One can therefore ask whether the original results of Breen et al. apply to spectra generated by the particular COFRADIC method. We investigate whether the proposed approximation holds for N-terminal peptides. We also evaluate whether ignoring sulphur atoms while developing the approximation, as proposed by Breen et al., does not increase the risk of missing monoisotopic peaks corresponding to sulphur-containing peptides. Finally, we check the sensitivity of the quality of the approximation to optimization criteria used in the development process. The results are not simply restricted to a COFRADIC setting but are also applicable more generally, for any method which enriches for sulphur-containing peptides.

Combination of COFRADIC and high temperature-extended column length conventional liquid chromatography: a very efficient way to tackle complex protein samples, such as serum.

The previously reported COmbined FRActional DIagonal Chromatography (COFRA-DIC) methodology, in which a subset of peptides representative for their parent proteins are sorted, is particularly powerful for whole proteome analysis. This peptide-centric technology is built around diagonal chromatography, where peptide separations are crucial. This paper presents high efficiency peptide separations, in which four 250 x 2.1 mm, 5 microm Zorbax 300SB-C18 columns (total length 1 m) were coupled at operating temperatures of 60'C using a dedicated LC oven and conventional LC equipment. The high efficiency separations were combined with the COFRADIC procedure. This extremely powerful combination resulted, for the analysis of serum, in an increase in the uniquely identified peptide sequences by a factor of 2.6, compared to the COFRADIC procedure on a 25 cm column. This is a reflection of the increased peak capacity obtained on the 1 m column, which was calculated to be a factor 2.7 higher than on the 25 cm column. Besides more efficient sorting, less ion suppression was noticed.

The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile.

Several studies have been published in which holistic approaches were used to characterise the proteome and transcriptome of human platelets. The key intent being that a deeper understanding of the normal and aberrant physiological functions of platelets can only be achieved if most biomolecular building blocks are mapped. Here we present the application of recently developed novel technologies that overcome some of the shortcomings of gel-based proteomics. Central in our approach is the so-called combined fractional diagonal chromatography (COFRADIC)-technology in which sets of representative peptides are sorted in a diagonal RP chromatographic system through a specific modification of their side chain. In this study we combined three different COFRADIC sorting techniques to analyse the proteome of human platelets. Methionyl, cysteinyl and amino terminal peptides were isolated and analysed by MS/MS. Merging the peptide identifications obtained after database searching resulted in a core set of 641 platelet proteins, which comprises the largest set identified today. In comparison to previously published platelet proteomes, we identified 404 novel platelet proteins containing a high number of hydrophobic membrane proteins and hypothetical proteins. Furthermore we discuss the observed characteristics and potential benefits of each of the different COFRADIC technologies for proteome analysis and highlight important issues that need to be considered when searching sequence databases using data obtained in peptide-centric, non-gel proteomics studies.