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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.

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 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.

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.

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 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.

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.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.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.

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.

We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007. The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis. We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections. We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis’ workflows in these studies impossible to follow and therefore replicate or even imitate. While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

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

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.

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.

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.

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. Copyright © 2007 John Wiley & Sons, Ltd.

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.

We report upon a novel procedure to specifically isolate cysteine-containing peptides from a complex peptide mixture. Cysteines are converted to hydrophobic residues by mixed disulfide formation with Ellman's reagent. Proteins are subsequently digested with trypsin and the generated peptide mixture is a first time fractionated by reverse-phase high-performance liquid chromatography. Cysteinyl-peptides are isolated out of each primary fraction by a reduction step followed by a secondary peptide separation on the same column, performed under identical conditions as for the primary separation. The reducing agent removes the covalently attached group from the cysteine side chain, making cysteine-peptides more hydrophilic and, thereby, such peptides can be specifically collected during the secondary separation and are finally used to identify their precursor proteins using automated liquid chromatography tandem mass spectrometry. We show that this procedure efficiently isolates cysteine-peptides, making the sample mixture less complex for further analysis. This method was applied for the analysis of the proteomes of human platelets and enriched human plasma. In both proteomes, a significant number of low abundance proteins were identified next to extremely abundant ones. A dynamic range for protein identification spanning 4-5 orders of magnitude is demonstrated.

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.