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.

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.

Relationship between the concentration of ergothioneine in plasma and the likelihood of developing pre-eclampsia.

Ergothioneine, an antioxidant nutraceutical mainly at present derived from the dietary intake of mushrooms, has been suggested as a preventive for pre-eclampsia (PE). We analysed early pregnancy samples from a cohort of 432 first time mothers as part of the Screening for Endpoints in Pregnancy (SCOPE, European branch) project to determine the concentration of ergothioneine in their plasma. There was a weak association between the ergothioneine levels and maternal age but none for BMI. Of these 432 women, 97 went on to develop pre-term (23) or term (74) PE. If a threshold was set at the 90th percentile of the reference range in the control population (≥462 ng/ml), only one of these 97 women (1%) developed PE, versus 96/397 (24.2%) whose ergothioneine level was below this threshold. One possible interpretation of these findings, consistent with previous experiments in a reduced uterine perfusion model in rats, is that ergothioneine may indeed prove protective against PE in humans. An intervention study of some kind now seems warranted.

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.

Cohort profile: Improved Pregnancy Outcomes via Early Detection (IMPROvED), an International Multicentre Prospective Cohort.

Background: Improved Pregnancy Outcomes via Early Detection (IMPROvED) is a multi-centre, European phase IIa clinical study. The primary aim of IMPROvED is to enable the assessment and refinement of innovative prototype preeclampsia risk assessment tests based on emerging biomarker technologies. Here we describe IMPROvED's profile and invite researchers to collaborate. Methods: A total of 4,038 low-risk nulliparous singleton pregnancies were recruited from maternity units in Ireland (N=1,501), United Kingdom (N=1,108), The Netherlands (N=810), and Sweden (N=619) between November 2013 to August 2017. Participants were interviewed by a research midwife at ~11 weeks (optional visit), ~15 weeks, ~20 weeks, ~34 weeks' gestation (optional visit), and postpartum (within 72-hours following delivery). Findings to date: Clinical data included information on maternal sociodemographic, medical history, and lifestyle factors collected at ~15 weeks' gestation, and maternal measurements, collected at each study visit. Biobank samples included blood, urine, and hair collected at each study visit throughout pregnancy in all units plus umbilical cord/blood samples collected at birth in Ireland and Sweden. A total of 74.0% (N=2,922) had an uncomplicated pregnancy, 3.1% (N=122) developed preeclampsia, 3.6% (N=143) had a spontaneous preterm birth, and 10.5% (N=416) had a small for gestational age baby. We evaluated a panel of metabolite biomarkers and a panel of protein biomarkers at 15 weeks and 20 weeks' gestation for preeclampsia risk assessment. Their translation into tests with clinical application, as conducted by commercial entities, was hampered by technical issues and changes in test requirements. Work on the panel of proteins was abandoned, while work on the use of metabolite biomarkers for preeclampsia risk assessment is ongoing. Future plans: In accordance with the original goals of the IMPROvED study, the data and biobank are now available for international collaboration to conduct high quality research into the cause and prevention of adverse pregnancy outcomes.

Early cost-effectiveness analysis of screening for preeclampsia in nulliparous women: A modelling approach in European high-income settings.

BACKGROUND: Preeclampsia causes substantial maternal and perinatal morbidity and mortality and significant societal economic impact. Effective screening would facilitate timely and appropriate prevention and management of preeclampsia. OBJECTIVES: To develop an early cost-effectiveness analysis to assess both costs and health outcomes of a new screening test for preeclampsia from a healthcare payer perspective, in the United Kingdom (UK), Ireland, the Netherlands and Sweden. METHODS: A decision tree over a 9-month time horizon was developed to explore the cost-effectiveness of the new screening test for preeclampsia compared to the current screening strategy. The new test strategy is being developed so that it can stratify healthy low risk nulliparous women early in pregnancy to either a high-risk group with a risk of 1 in 6 or more of developing preeclampsia, or a low-risk group with a risk of 1 in 100 or less. The model simulated 25 plausible scenarios in a hypothetical cohort of 100,000 pregnant women, in which the sensitivity and specificity of the new test were varied to set a benchmark for the minimum test performance that is needed for the test to become cost-effective. The input parameters and costs were mainly derived from published literature. The main outcome was incremental costs per preeclampsia case averted, expressed as an incremental cost-effectiveness ratio (ICER). Deterministic and probabilistic sensitivity analyses were conducted to assess uncertainty. RESULTS: Base case results showed that the new test strategy would be more effective and less costly compared to the current situation in the UK. In the Netherlands, the majority of scenarios would be cost-effective from a threshold of €50,000 per preeclampsia case averted, while in Ireland and Sweden, the vast majority of scenarios would be considered cost-effective only when a threshold of €100,000 was used. In the best case analyses, ICERs were more favourable in all four participating countries. Aspirin effectiveness, prevalence of preeclampsia, accuracy of the new screening test and cost of regular antenatal care were identified as driving factors for the cost-effectiveness of screening for preeclampsia. CONCLUSION: The results indicate that the new screening test for preeclampsia has potential to be cost-effective. Further studies based on proven accuracy of the test will confirm whether the new screening test is a cost-effective additional option to the current situation.

Toward clinical proteomics on a next-generation sequencing platform.

We report the first next generation sequencing (NGS) application to identify and quantify proteins. Customization of protein specific aptamers enabled direct conversion of serum protein information into NGS read outs. The intrinsic ability of aptamer sequencing to highly multiplex protein detection and quantification, together with the prospect of DNA sequencing further evolving into a commodity technology, could constitute the core of a novel, universal diagnostics paradigm.

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.

Revealing of endogenous Marinobufagin by an ultra-specific and sensitive UHPLC-MS/MS assay in pregnant women.

Marinobufagenin (MBG) is a bufadienolide cardiac inotrope implicated in volume expansion-mediated hypertensive states including essential hypertension and preeclampsia (PE). Endogenous MBG is an inhibitor of the α1-isoform of Na+,K+-ATPase with vasoconstrictive and cardiotonic properties, causing hypertension and natriuresis. Elevated endogenous MBG-like material levels have been described by immunoassays in salt-sensitive pregnant and preeclamptic rats as well as in preeclamptic human patients. The rise of endogenous MBG-like material appears prior the development of the main symptoms of PE, leading us to consider MBG as one of the potential biomarkers for PE. The weak specificity and the high variability of the published immunoassays gives no certification about endogenous MBG existence. This led us to set-up a highly specific and sensitive analytical method to detect MBG in plasma at low levels relying on liquid chromatography combined to mass spectrometry (UHPLC-MS/MS) with recording of 7 highly specific MRM transitions for MBG. Pure MBG standard used in the method development was obtained by purification from the Bufo marinus toad venom. d3-25-hydroxyvitamin D3 was used as internal standard. An increasing organic gradient with mobile phase A and B composed of 97:3 (v/v) H2O: MeOH and 50:45:5 (v/v/v) MeOH:IPA:H2O at pH 4.5 respectively was used on a Pursuit 3 PFP column (100 mm × 3 mm; 3 µm) to allow elution and separation of the plasmatic compounds. Chromatographic analyses of plasma samples were preceded by a precipitation of proteins pretreatment. The developed UHPLC-MS/MS assay has been applied to early-pregnant women plasma samples allowing us to investigate MBG plasma levels. Thanks to the high specificity of the assay we were able to authenticate and certify the presence of endogenous MBG in early-pregnant women plasma with the use of the 7 selected specific mass transitions. These pioneering preliminary results are giving a promising perspective for early preeclampsia risk assessment in pregnant women.

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.

In-source CID of guanosine: gas phase ion-molecule reactions.

In-source collision induced dissociation was applied to access second generation ions of protonated guanosine. The in-source gas-phase behavior of [BH2]+-NH3 (m/z 135, C5H3N4O+) was investigated. Adduct formation and reactions with available solvent molecules (H2O and CH3OH) were demonstrated. Several addition/elimination sequences were observed for this particular ion and solvent molecules. Dissociation pathways for the newly formed ions were developed using a QqTOF mass spectrometer, permitting the assignment of elemental compositions of all product ions produced. Reaction schemes were suggested arising from the ring-opened intermediate of the protonated base moiety [BH2]+, obtained from fragmentation of guanosine. The mass spectral data revealed that the in-source CH3OH-reaction product underwent more complex fragmentations than the comparable ion following reaction with H2O. A rearrangement and a parallel radical dissociation pathway were discerned. Apart from the mass spectrometric evidence, the fragmentation schemes are supported by density functional theory calculations, in which the reaction of the ring-opened protonated guanine intermediate with CH3OH and a number of subsequent fragmentations were elaborated. Additionally, an in-source transition from the ring-opened intermediate of protonated guanine to the ring-opened intermediate of protonated xanthine was suggested. For comparison, a low-energy collision induced dissociation study of xanthosine was performed. Its dissociation pathways agreed with our assumption.

Intriguing mass spectrometric behavior of guanosine under low energy collision-induced dissociation: H2O adduct formation and gas-phase reactions in the collision cell.

An in-depth study of the fragmentation pathway of guanosine was conducted by using an in-source collision-induced dissociation high-mass accuracy tandem mass spectrometry experiment. The equivalent of MS4 data, a level of information normally achieved on ion trap instruments, was obtained on a Q-TOF mass spectrometer. The combination of the features of high-resolution, accuracy, and in-source CID permitted the unambiguous elucidation of the different fragmentation pathways. Furthermore the elemental compositions of the product ions generated were assigned and their mutual genealogical relationships established. Formerly proposed dissociation pathways of guanosine were revisited and elaborated on more deeply. Furthermore, the presence of H2O in the collision cell of several tandem MS instruments was demonstrated and its effect on the product ion spectra investigated. The neutral gain of H2O by particular fragments of guanosine was experimentally proven by using argon, saturated with H2(18)O, as the collision gas. Data indicating the occurrence of more complex reactions in the collision cell as a result of the presence of H2O were produced, specifically relating to neutral gain/neutral loss sequences. In silico calculations supported the experimental observation of neutral gain by guanosine fragments and predicted a similar behavior for adenosine. The latter was subsequently experimentally confirmed.

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.

Investigation of the use of immobilised metal affinity chromatography for the on-line sample clean up and pre-concentration of nucleotides prior to their determination by ion pair liquid chromatography-electrospray mass spectrometry: a pilot study.

This study explored an alternative way to enrich and pre-purify biological samples containing nucleoside mono-, di- and triphosphates. These compounds were trapped by immobilised metal affinity chromatography (IMAC) on a Poros 20 MC IMAC-column, which was conditioned with Fe3+. The IMAC-column was implemented in a column switching set-up separating nucleoside mono-, di- and triphosphates on a Hypersil ODS 35 mm x 0.3 mm capillary column hyphenated to electrospray mass spectrometry resulting in the first miniaturised column switching liquid chromatography-mass spectrometry (LC-MS) system for nucleotides.

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.

Highly efficient peptide separations in proteomics. Part 2: bi- and multidimensional liquid-based separation techniques.

Multidimensional liquid-based separation techniques are described for maximizing the resolution of the enormous number of peptides generated upon tryptic digestion of proteomes, and hence, reduce the spatial and temporal complexity of the sample to a level that allows successful mass spectrometric analysis. This review complements the previous contribution on unidimensional high performance liquid chromatography (HPLC). Both chromatography and electrophoresis will be discussed albeit with reversed-phase HPLC (RPLC) as the final separation dimension prior to MS analysis.