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The Continuous Automated Model EvaluatiOn (CAMEO) platform complements the biennial CASP experiment by conducting fully automated blind evaluations of three-dimensional protein prediction servers based on the weekly prerelease of sequences of those structures, which are going to be published in the upcoming release of the Protein Data Bank. While in CASP14, significant success was observed in predicting the structures of individual protein chains with high accuracy, significant challenges remain in correctly predicting the structures of complexes. By implementing fully automated evaluation of predictions for protein-protein complexes, as well as for proteins in complex with ligands, peptides, nucleic acids, or proteins containing noncanonical amino acid residues, CAMEO will assist new developments in those challenging areas of active research.

Proteomics has allowed the discovery and validation of a massive number of biomarkers. However most of them suffer from insufficient specificity and sensitivity and therefore didn't translate to clinical practice. Combining biomarkers with different properties into panels can be an efficient way to bypass these limitations and facilitate the translation of biomarkers into clinical practice.

Sweat is one of the less employed biofluids for discovery of markers in spite of its increased application in medicine for detection of drugs or for diagnostic of cystic fibrosis. In this research, human sweat was used as clinical sample to develop a screening tool for lung cancer, which is the carcinogenic disease with the highest mortality rate owing to the advanced stage at which it is usually detected. In this context, a method based on the metabolite analysis of sweat to discriminate between patients with lung cancer versus smokers as control individuals is proposed. The capability of the metabolites identified in sweat to discriminate between both groups of individuals was studied and, among them, a trisaccharide phosphate presented the best independent performance in terms of the specificity/sensitivity pair (80 and 72.7%, respectively). Additionally, two panels of metabolites were configured using the PanelomiX tool as an attempt to reduce false negatives (at least 80% specificity) and false positives (at least 80% sensitivity). The first panel (80% specificity and 69% sensitivity) was composed by suberic acid, a tetrahexose, and a trihexose, while the second panel (69% specificity and 80% sensitivity) included nonanedioic acid, a trihexose, and the monoglyceride MG(22:2). Thus, the combination of the five metabolites led to a single panel providing 80% specificity and 79% sensitivity, reducing the false positive and negative rates to almost 20%. The method was validated by estimation of within-day and between-days variability of the quantitative analysis of the five metabolites.

Network medicine aims at unraveling cell signaling networks to propose personalized treatments for patients suffering from complex diseases. In this short review, we show the relevance of network medicine to cancer treatment by outlining the potential convergence points of the most recent technological and scientific developments in both drug design and signaling network analysis. Clinical Pharmacology & Therapeutics (2013); 94 6, 646–650. doi:10.1038/clpt.2013.171

PURPOSE: Accurate early anticipation of long-term irreversible brain damage during the acute phase of patients with aneurysmal subarachnoid hemorrhage (aSAH) remains difficult. Using a combination of clinical scores together with brain injury-related biomarkers (H-FABP, NDKA, UFD1 and S100beta), this study aimed at developing a multiparameter prognostic panel to facilitate early outcome prediction following aSAH. METHODS: Blood samples of 141 aSAH patients from two separated cohorts (sets of 28 and 113 patients) were prospectively enrolled and analyzed with 14 months of delay. Patients were admitted within 48 h following aSAH onset. A venous blood sample was withdrawn within 12 h after admission. H-FABP, NDKA, UFD1, S100beta and troponin I levels were determined using classical immunoassays. The World Federation of Neurological Surgeons (WFNS) at admission and the Glasgow Outcome Score (GOS) at 6 months were evaluated. RESULTS: In the two cohorts, blood concentration of H-FABP, S100beta and troponin I at admission significantly predicted unfavorable outcome (GOS 1-2-3). A multivariate analysis identified a six-parameter panel, including WFNS, H-FABP, S100beta, troponin I, NDKA and UFD-1; when at least three of these parameters were simultaneously above cutoff values, prediction of unfavorable outcome reached around 70% sensitivity in both cohorts for 100% specificity. CONCLUSION: The use of this panel, including four brain injury-related proteins, one cardiac marker and a clinical score, could be a valuable tool to identify aSAH patients at risk of poor outcome.

Background & Aims Algorithms for diagnosis of malignant common bile duct (CBD) stenoses are complex and lack accuracy. Malignant tumors secrete large numbers of extracellular vesicles (EVs) into surrounding fluids; EVs might therefore serve as biomarkers for diagnosis. We investigated whether concentrations of EVs in bile could discriminate malignant from nonmalignant CBD stenoses. Methods We collected bile and blood samples from 50 patients undergoing therapeutic endoscopic retrograde cholangiopancreatography at university hospitals in Europe for CBD stenosis of malignant (pancreatic cancer, n = 20 or cholangiocarcinoma, n = 5) or nonmalignant (chronic pancreatitis [CP], n = 15) origin. Ten patients with CBD obstruction due to biliary stones were included as controls. EV concentrations in samples were determined by nanoparticle tracking analyses. The discovery cohort comprised the first 10 patients with a diagnosis of pancreatic cancer, based on tissue analysis, and 10 consecutive controls. Using samples from these subjects, we identified a threshold concentration of bile EVs that could best discriminate between patients with pancreatic cancer from controls. We verified the diagnostic performance of bile EV concentration by analyzing samples from the 30 consecutive patients with a diagnosis of malignant (pancreatic cancer or cholangiocarcinoma, n = 15) or nonmalignant (CP, n = 15) CBD stenosis. Samples were compared using the Mann-Whitney test and nonparametric Spearman correlation analysis. Receiver operating characteristic area under the curve was used to determine diagnostic accuracy. Results In both cohorts, the median concentration of EVs was significantly higher in bile samples from patients with malignant CBD stenoses than controls or nonmalignant CBD stenoses (2.41 × 10 15 vs 1.60 × 10 14 nanoparticles/L in the discovery cohort; P 15 vs 1.26 × 10 14 nanoparticles/L in the verification cohort; P 14 nanoparticles/L in bile best distinguished patients with malignant CBD from controls in the discovery cohort. In the verification cohort, this threshold discriminated malignant from nonmalignant CBD stenoses with 100% accuracy. Serum concentration of EVs distinguished patients with malignant vs patients with nonmalignant CBD stenoses with 63.3% diagnostic accuracy. Conclusions Concentration of EVs in bile samples discriminates between patients with malignant vs nonmalignant CBD stenosis with 100% accuracy. Further studies are needed to confirm these findings. Clinical Trial registration no: ISRCTN66835592.

The outcome after severe traumatic brain injury (TBI) is largely unfavorable, with approximately two thirds of patients suffering from severe disabilities or dying during the first 6 months. Existing predictive models displayed only limited utility for outcome prediction in individual patients. Time courses of heart-fatty acidic binding protein (H-FABP) and their association with outcome were investigated and compared with S100b. Forty-nine consecutive patients with severe TBI (sTBI; Head component of the Abbreviated Injury Scale [HAIS]3) with mono and multiple trauma were enrolled in this study. Enzyme-linked immunosorbent assay measured blood concentrations of H-FABP and S100b at 6, 12, 24, and 48 h after TBI. Outcome measures were conscious state at 14 days (Glasgow Coma Scale), disability (Glasgow Outcome Scale Extended; GOSE), and mortality at 3 months. Univariate logistic regression analysis and receiver operating characteristic curves analysis were carried out. Maximal H-FABP and S100b concentrations were observed at 6 h after TBI (34.4±34.0 and 0.64±0.99 ng/mL, respectively). Patients with multi-trauma had significantly higher H-FABP concentrations at 24 and 48 h (22.6±25.6 and 12.4±18.2 ng/mL, respectively), compared to patients with mono trauma (6.9±5.1 and 3.7±4.2 ng/mL, respectively). In the first 48 h, H-FABP and S100b were inversely correlated with the GOSE at 3 months; H-FABP at 48 h predicted mortality with 75% sensitivity and 93% specificity. Early blood levels of H-FABP after sTBI have prognostic significance for survival and disability.

International audience; Accurate stage determination is crucial in the choice of treatment for patients suffering from sleeping sickness, also known as human African trypanosomiasis (HAT). Current staging methods, based on the counting of white blood cells (WBC) and the detection of parasites in the cerebrospinal fluid (CSF) have limited accuracy. We hypothesized that immune mediators reliable for staging T. b. gambiense HAT could also be used to stratify T. b. rhodesiense patients, the less common form of HAT.A population comprising 85 T. b. rhodesiense patients, 14 stage 1 (S1) and 71 stage 2 (S2) enrolled in Malawi and Uganda, was investigated. The CSF levels of IgM, MMP-9, CXCL13, CXCL10, ICAM-1, VCAM-1, neopterin and B2MG were measured and their staging performances evaluated using receiver operating characteristic (ROC) analyses.IgM, MMP-9 and CXCL13 were the most accurate markers for stage determination (partial AUC 88%, 86% and 85%, respectively). The combination in panels of three molecules comprising CXCL13-CXCL10-MMP-9 or CXCL13-CXCL10-IgM significantly increased their staging ability to partial AUC 94% (p value < 0.01).The present study highlighted new potential markers for stage determination of T. b. rhodesiense patients. Further investigations are needed to better evaluate these molecules, alone or in panels, as alternatives to WBC to make reliable choice of treatment.

Background Post-therapeutic follow-up is essential to confirm cure and to detect early treatment failures in patients affected by sleeping sickness (HAT). Current methods, based on finding of parasites in blood and cerebrospinal fluid (CSF) and counting of white blood cells (WBC) in CSF, are imperfect. New markers for treatment outcome evaluation are needed. We hypothesized that alternative CSF markers, able to diagnose the meningo-encephalitic stage of the disease, could also be useful for the evaluation of treatment outcome. Methodology/Principal findings Cerebrospinal fluid from patients affected by Trypanosoma brucei gambiense HAT and followed for two years after treatment was investigated. The population comprised stage 2 (S2) patients either cured or experiencing treatment failure during the follow-up. IgM, neopterin, B2MG, MMP-9, ICAM-1, VCAM-1, CXCL10 and CXCL13 were first screened on a small number of HAT patients (n = 97). Neopterin and CXCL13 showed the highest accuracy in discriminating between S2 cured and S2 relapsed patients (AUC 99% and 94%, respectively). When verified on a larger cohort (n = 242), neopterin resulted to be the most efficient predictor of outcome. High levels of this molecule before treatment were already associated with an increased risk of treatment failure. At six months after treatment, neopterin discriminated between cured and relapsed S2 patients with 87% specificity and 92% sensitivity, showing a higher accuracy than white blood cell numbers. Conclusions/Significance In the present study, neopterin was highlighted as a useful marker for the evaluation of the post-therapeutic outcome in patients suffering from sleeping sickness. Detectable levels of this marker in the CSF have the potential to shorten the follow-up for HAT patients to six months after the end of the treatment., Author Summary The reduction of the number of lumbar punctures performed during the follow-up of patients affected by sleeping sickness (HAT) is considered a research priority. Follow-up, consisting of the examination of cerebrospinal fluid (CSF) for presence of parasites and for the number of leukocytes, is necessary to assess treatment outcome. However, diagnosis of treatment failure is still imperfect and WHO encourages improvements in defining criteria. Many studies have attempted to standardize actual methods and to define a cut-off for the number of white blood cells in CSF to define relapses, while only few have proposed alternatives to current practice. Here we show that neopterin, already proven to be a powerful marker for staging T. b. gambiense HAT, is also useful in evaluating post-therapeutic outcome. The measurement of neopterin concentration in CSF during the follow-up may allow reduction in the number of lumbar punctures from five to three for the majority of cured patients.

High throughput protein identification and quantification analysis based on mass spectrometry are fundamental steps in most proteomics projects. Here, we present EasyProt (available at http://easyprot.unige.ch), a new platform for mass spectrometry data processing, protein identification, quantification and unexpected post-translational modification characterization. EasyProt provides a fully integrated graphical experience to perform a large part of the proteomic data analysis workflow. Our goal was to develop a software platform that would fulfill the needs of scientists in the field, while emphasizing ease-of-use for non-bioinformatician users. Protein identification is based on OLAV scoring schemes and protein quantification is implemented for both, isobaric labeling and label-free methods. Additional features are available, such as peak list processing, isotopic correction, spectra filtering, charge-state deconvolution and spectra merging. To illustrate the EasyProt platform, we present two identification and quantification workflows based on isobaric tagging and label-free methods.

BACKGROUND: Ability to accurately determine time of stroke onset remains challenging. We hypothesized that an early biomarker characterized by a rapid increase in blood after stroke onset may help defining better the time window during which an acute stroke patient may be candidate for intravenous thrombolysis or other intravascular procedures. METHODS: The blood level of 29 proteins was measured by immunoassays on a prospective cohort of stroke patients (N = 103) and controls (N = 132). Mann-Whitney U tests, ROC curves and diagnostic odds ratios were applied to evaluate their clinical performances. RESULTS: Among the 29 molecules tested, GST-π concentration was the most significantly elevated marker in the blood of stroke patients (p3 h after onset). According to goal-oriented distinct cut-offs (sensitivity(Se)-oriented: 17.7 or specificity(Sp)-oriented: 65.2 ug/L), the GST-π test obtained 91%Se/50%Sp and 50%Se/91%Sp, respectively. Moreover, GST-π showed also the highest AUC (0.83) and performances for detecting patients treated with tPA (N = 12) compared to ineligible patients (N = 103). CONCLUSIONS: This study demonstrates that GST-π can accurately predict the time of stroke onset in over 50% of early stroke patients. The GST-π test could therefore complement current guidelines for tPA administration and potentially increase the number of patients accessing thrombolysis.

A large number of biomarkers have been discovered over the past few years using proteomics techniques. Unfortunately, most of them are neither specific nor sensitive enough to be translated into in vitro diagnostics and routine clinical practice. From this observation, the idea of combining several markers into panels to improve clinical performances has emerged. In this article, we present a discussion of the bioinformatics aspects of biomarker panels and the concomitant challenges, including high dimensionality and low patient number and reproducibility.

The HUPO Proteomics Standards Initiative (PSI) defines standards for data representation in proteomics to facilitate data exchange and comparison, and quality assessment. A set of minimum reporting requirements, called MIAPE (for Minimum Information About a Proteomics Experiment) is provided to ensure consistency of data set annotation. Like the MIAME reporting requirements for transcriptomics, it is anticipated that journal editors will soon require such annotation for published data sets, simplifying further mining of data. Therefore, tools for data entry and public repositories for long-term storage will be needed. MIAPEGelDB is a public repository and a web-based data entry tool for documents conforming to the MIAPE gel electrophoresis guidelines. It aims to guide authors through the publication of the minimal set of information for their proteomics experiments using a clear, sequential interface. After publication by their author, documents in MIAPEGelDB can be viewed in HTML or plain text formats, and further used through stable URL links from remote resources. MIAPEGelDB is accessible at: http://miapegeldb.expasy.org/.

In order to increase their predictive power, medical biomarkers can be combined into panels. However, the lack of ready-to-use tools generating interpretable results and implementing rigorous validation standards hampers the more widespread application of panels and their translation into clinical practice.The computational toolbox we present here – PanelomiX – uses the iterative combination of biomarkers and thresholds (ICBT) method. This method combines biomarkers and clinical scores by selecting thresholds that provide optimal classification performance. To speed up the calculation for a large number of biomarkers, PanelomiX selects a subset of thresholds and parameters based on the random forest method. The panels’ robustness and performance are analysed by cross-validation (CV) and receiver operating characteristic (ROC) analysis.Using 8 biomarkers, we compared this method against classic combination procedures in the determination of outcome for 113 patients with an aneurysmal subarachnoid haemorrhage. The panel classified the patients better than the best single biomarker (p

Bioinformatics tools may assist scientists in all steps of a typical 2-DE gel analysis workflow, that is, from the description of the sample preparation protocols, going through the gel image analysis and protein identification, to the publication of Internet-ready 2-DE gel databases. This short communication highlights in a single and summarised view, this workflow and the current bioinformatics solutions developed by the Proteome Informatics Group at the Swiss Institute of Bioinformatics.

Background The relationship between severe traumatic brain injury (TBI) and blood levels of matrix metalloproteinase-9 (MMP-9) or cellular fibronectin (c-Fn) has never been reported. In this study, we aimed to assess whether plasma concentrations of MMP-9 and c-Fn could have predictive values for the composite endpoint of intensive care unit (ICU) length of stay (LOS) of survivors and mortality after severe TBI. Secondary outcomes were the state of consciousness measured with the Glasgow Coma Scale (GCS) of survivors at 14 days and Glasgow Outcome Scale Extended (GOSE) at 3 months. Methods Forty-nine patients with abbreviated injury scores of the head region ≥ 4 were included. Blood was sampled at 6, 12, 24 and 48 hours after injury. MMP-9 and c-Fn concentrations were measured by ELISA. The values of MMP-9 and c-Fn, and, for comparison, the value of the GCS on the field of the accident (fGCS), as predictors of the composite outcome of ICU LOS and death were assessed by logistic regression. Results There was a linear relationship between maximal MMP-9 concentration, measured during the 6-12-hour period, and maximal c-Fn concentration, measured during the 24-48-hour period. The risk of staying longer than 9 days in the ICU or of dying was increased in patients with a maximal early MMP-9 concentration ≥ 21.6 ng/ml (OR = 5.0; 95% CI: 1.3 to 18.6; p = 0.02) or with a maximal late c-Fn concentration ≥ 7.7 μg/ml (OR = 5.4; 95% CI: 1.4 to 20.8; p = 0.01). A similar risk association was observed with fGCS ≤8 (OR, 4.4; 95% CI, 1.2-15.8; p = 0.02). No relationship was observed between MMP-9, c-Fn concentrations or fGCS and the GCS at 14 days of survivors and GOSE at 3 months. Conclusions Plasma MMP-9 and c-Fn concentrations in the first 48 hours after injury are predictive for the composite endpoint of ICU LOS and death after severe TBI but not for consciousness at 14 days and outcome at 3 months.