Grégoire Thomas

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.

Chromatographic Isolation of Methionine-containing Peptides for Gel-free Proteome Analysis Identification Of More Than 800 Escherichia Coli Proteins

A novel gel-free proteomic technology was used to identify more than 800 proteins from 50 million Escherichia coli K12 cells in a single analysis. A peptide mixture is first obtained from a total unfractionated cell lysate, and only the methionine-containing peptides are isolated and identified by mass spectrometry and database searching. The sorting procedure is based on the concept of diagonal chromatography but adapted for highly complex mixtures. Statistical analysis predicts that we have identified more than 40% of the expressed proteome, including soluble and membrane-bound proteins. Next to highly abundant proteins, we also detected low copy number components such as the E. coli lactose operon repressor, illustrating the high dynamic range. The method is about 100 times more sensitive than two-dimensional gel-based methods and is fully automated. The strongest point, however, is the flexibility in the peptide sorting chemistry, which may target the technique toward quantitative proteomics of virtually every class of peptides containing modifiable amino acids, such as phosphopeptides, amino-terminal peptides, etc., adding a new dimension to future proteome research.

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.

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.

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 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.

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.

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 columns 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.

OS043. Identification and validation of novel markers for the predictionof pre-eclampsia

INTRODUCTION Currently no test accurately predicts pre-eclampsia (PE) in a healthy nulliparous population. Unbiased protein biomarker discovery has the potential to identify novel markers but multimarker panels are required to achieve clinically relevant prediction of PE. To this purpose, single biomarker performances were obtained and multimarker panels developed in a significant subcohort of the international Screening fOr Pregnancy Endpoints study (SCOPE) study [1]. OBJECTIVES To identify and validate novel protein markers for PE prediction using chromatographic and mass spectrometric techniques which enable the identification and quantification of plasma proteins present in plasma at sub ng/ml concentration (Pronota, Belgium). METHODS Pre-disease plasma samples (22-26 weeks) from women who subsequently developed PE and those with uncomplicated pregnancies [2] were used to generate 30 plasma proteome profiles using the MASStermind™ pipeline. A set of novel protein candidates were validated using an antibody-free mass spectrometry method using multiple reaction monitoring (MASSterclass™) in a subcohort of the SCOPE study (NZ & Aus) [1]. Relative abundance of 40+ proteins was determined in 20week plasma samples from 100 women who developed PE and 200 women who did not develop PE (included women with other pregnancy complications). Multivariate analyses were performed to identify algorithms with predictive performance using combinations confined to a maximum of 6 parameters (protein markers and clinical parameters) to avoid overfitting. Validation of the prediction panels was performed in an independent subcohort of SCOPE (Europe) comprising 50 PE and 150 no PE. RESULTS From this large scale biomarker discovery effort a number of key results were obtained: a novel protein, i.e., Insulin-like growth factor binding protein, acid labile subunit (IGFALS), was identified. AUC for this marker for the prediction of all PE was 0.71 (CI 0.68-0.75) which was greater than both PlGF and s-Eng (respective AUCs: 0.64 and 0.61). IGFALS was also found to have predictive value for term (AUC 0.70) as well as preterm disease (AUC 0.75). Using multivariate analysis, marker panels were identified that achieved clinically relevant prediction (exemplary panel prediction of all PE cases AUC=0.79; prediction of preterm PE AUC=0.92). These multivariate models were successfully validated in the European SCOPE subcohort. In addition, predictive algorithms based on mass spectrometric read outs were largely invariant to interchanging the IGFALS mass spectrometry quantitation data with IGFALS ELISA data. CONCLUSION This study demonstrates the capability of high level LC-MS technologies to discover candidate biomarkers and execute large scale multiplex validation to develop a predictive screening test for preeclampsia.