Superoxide production in human neutrophils is enhanced by treatment with transmembrane peptides derived from human formyl peptide receptor

Formyl peptide receptor (FPR) mediates a number of important host defense functions. Although studies have been performed on the ligand binding site of FPR, FPR dynamic behavior such as receptor dimerization on the cell surface remains unknown. Recently, peptides derived from the transmembrane (TM) domains of GPCRs were shown to disrupt dimer formation by receptors and to result in specific regulation of receptor function. To reveal the function of FPR TM domains, hFPRTM peptides derived from FPR were synthesized, and their biological activities were evaluated with human neutrophils. Synthetic peptides did not exhibit agonistic or antagonistic activity toward superoxide anion production. However, Neutrophils treated with hFPRTM4 produced 4-fold superoxide anion compared with untreated cells when stimulated with FPR agonist fMLP. Short peptide fragments derived from the fourth TM region of FPR did not enhance superoxide anion production, which suggests that hFPRTM4 did not behave as a ligand. CD and fluorescence spectra suggested that hFPRTM peptides were inserted into the membrane. The addition of hFPRTM4 increased the intracellular calcium concentration, which meant the peptide activated some membrane protein on the cell surface. The present study suggests that the fourth TM domain of FPR has a function related to a priming effect.     

MS‐Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments

LC–MS/MS has become the standard platform for the characterization of immunopeptidomes, the collection of peptides naturally presented by major histocompatibility complex molecules to the cell surface. The protocols and algorithms used for immunopeptidomics data analysis are based on tools developed for traditional bottom‐up proteomics that address the identification of peptides generated by tryptic digestion. Such algorithms are generally not tailored to the specific requirements of MHC ligand identification and, as a consequence, immunopeptidomics datasets suffer from dismissal of informative spectral information and high false discovery rates. Here, a new pipeline for the refinement of peptide‐spectrum matches (PSM) is proposed, based on the assumption that immunopeptidomes contain a limited number of recurring peptide motifs, corresponding to MHC specificities. Sequence motifs are learned directly from the individual peptidome by training a prediction model on high‐confidence PSMs. The model is then applied to PSM candidates with lower confidence, and sequences that score significantly higher than random peptides are rescued as likely true ligands. The pipeline is applied to MHC class I immunopeptidomes from three different species, and it is shown that it can increase the number of identified ligands by up to 20–30%, while effectively removing false positives and products of co‐precipitation. Spectral validation using synthetic peptides confirms the identity of a large proportion of rescued ligands in the experimental peptidome.     

A new strategy to filter out false positive identifications of peptides in SEQUEST database search results

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.     

Serum apolipoproteins C-I and C-III are reduced in stomach cancer patients: results from MALDI-based peptidome and immuno-based clinical assays

Finding new peptide biomarkers for stomach cancer in human sera that can be implemented into a clinically practicable prediction method for monitoring of stomach cancer. We studied the serum peptidome from two different biorepositories. We first employed a C8-reverse phase liquid chromatography approach for sample purification, followed by mass-spectrometry analysis. These were applied onto serum samples from cancer-free controls and stomach cancer patients at various clinical stages. We then created a bioinformatics analysis pipeline and identified peptide signature discriminating stomach adenocarcinoma patients from cancer-free controls. Matrix Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) results from 103 samples revealed 9 signature peptides; with prediction accuracy of 89% in the training set and 88% in the validation set. Three of the discriminating peptides discovered were fragments of Apolipoproteins C-I and C-III (apoC-I and C-III); we further quantified their serum levels, as well as CA19-9 and CRP, employing quantitative commercial-clinical assays in 142 samples. ApoC-I and apoC-III quantitative results correlated with the MS results. We then employed apoB-100-normalized apoC-I and apoC-III, CA19-9 and CRP levels to generate rules set for stomach cancer prediction. For training, we used sera from one repository, and for validation, we used sera from the second repository. Prediction accuracies of 88.4% and 74.4% were obtained in the training and validation sets, respectively. Serum levels of apoC-I and apoC-III combined with other clinical parameters can serve as a basis for the formulation of a diagnostic score for stomach cancer patients.     

Integrated data management and validation platform for phosphorylated tandem mass spectrometry data

MS/MS is a widely used method for proteome‐wide analysis of protein expression and PTMs. The thousands of MS/MS spectra produced from a single experiment pose a major challenge for downstream analysis. Standard programs, such as MASCOT, provide peptide assignments for many of the spectra, including identification of PTM sites, but these results are plagued by false‐positive identifications. In phosphoproteomic experiments, only a single peptide assignment is typically available to support identification of each phosphorylation site, and hence minimizing false positives is critical. Thus, tedious manual validation is often required to increase confidence in the spectral assignments. We have developed phoMSVal, an open‐source platform for managing MS/MS data and automatically validating identified phosphopeptides. We tested five classification algorithms with 17 extracted features to separate correct peptide assignments from incorrect ones using over 2600 manually curated spectra. The naïve Bayes algorithm was among the best classifiers with an AUC value of 97% and PPV of 97% for phosphotyrosine data. This classifier required only three features to achieve a 76% decrease in false positives as compared with MASCOT while retaining 97% of true positives. This algorithm was able to classify an independent phosphoserine/threonine data set with AUC value of 93% and PPV of 91%, demonstrating the applicability of this method for all types of phospho‐MS/MS data. PhoMSVal is available at http://csbi.ltdk.helsinki.fi/phomsval .     

Analysis of MALDI-TOF mass spectrometry data for discovery of peptide and glycan biomarkers of hepatocellular carcinoma

This paper presents computational methods to analyze MALDI-TOF mass spectrometry data for quantitative comparison of peptides and glycans in serum. The methods are applied to identify candidate biomarkers in serum samples of 203 participants from Egypt; 73 hepatocellular carcinoma (HCC) cases, 52 patients with chronic liver disease (CLD) consisting of cirrhosis and fibrosis cases, and 78 population controls. Two complementary sample preparation methods were applied prior to generating mass spectra: (1) low molecular weight (LMW) enrichment of each serum sample was carried out for MALDI-TOF quantification of peptides, and (2) glycans were enzymatically released from proteins in each serum sample and permethylated for MALDI-TOF quantification of glycans. A peak selection algorithm was applied to identify the most useful peptide and glycan peaks for accurate detection of HCC cases from high-risk population of patients with CLD. In addition to global peaks selected by the whole population based approach, where identically labeled patients are treated as a single group, subgroup-specific peaks were identified by searching for peaks that are differentially abundant in a subgroup of patients only. The peak selection process was preceded by peak screening, where we eliminated peaks that have significant association with covariates such as age, gender, and viral infection based on the peptide and glycan spectra from population controls. The performance of the selected peptide and glycan peaks was evaluated in terms of their ability in detecting HCC cases from patients with CLD in a blinded validation set and through the cross-validation method. Finally, we investigated the possibility of using both peptides and glycans in a panel to enhance the diagnostic capability of these candidate markers. Further evaluation is needed to examine the potential clinical utility of the candidate peptide and glycan markers identified in this study.     

Surface immobilization chemistry influences peptide‐based detection of lipopolysaccharide and lipoteichoic acid

Antimicrobial peptides (AMPs) have recently gained attention as potentially valuable diagnostic and therapeutic agents. The utilization of these peptides for diagnostic purposes relies on the ability to immobilize them on the surface of a detection platform in a predictable and reliable manner that facilitates target binding. The method for attachment of peptides to a solid support is guided by peptide length, amino acid composition, secondary structure, and the nature of the underlying substrate. While immobilization methods that target amine groups of amino acid sequences are widely used, they can result in heterogeneous conjugation at multiple sites on a peptide and have direct implications for peptide presentation and function. Using two types of commercial amine‐reactive microtiter plates, we described the effects of analogous immobilization chemistries on the surface attachment of AMPs and their differential binding interaction with Gram‐specific bacterial biomarkers, lipopolysaccharide and lipoteichoic acid. As might be expected, differences in overall binding affinities were noted when comparing AMPs immobilized on the two types of plates. However, the two‐amine‐targeted linking chemistries also affected the specificity of the attached peptides; lipopolysaccharide generally demonstrated a preference for peptides immobilized on one type of plate, while (when observed at all) lipoteichoic acid bound preferentially to AMPs immobilized on the other type of plate. These results demonstrate the potential for tuning not only the binding affinities but also the specificities of immobilized AMPs by simple alterations in linking strategy. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

Structural Determinants for the Interaction of Formyl Peptide Receptor 2 with Peptide Ligands

Unlike formyl peptide receptor 1 (FPR1), FPR2/ALX (FPR2) interacts with peptides of diverse sequences but has low affinity for the Escherichia coli-derived chemotactic peptide fMet-Leu-Phe (fMLF). Using computer modeling and site-directed mutagenesis, we investigated the structural requirements for FPR2 to interact with formyl peptides of different length and composition. In calcium flux assay, the N-formyl group of these peptides is necessary for activation of both FPR2 and FPR1, whereas the composition of the C-terminal amino acids appears more important for FPR2 than FPR1. FPR2 interacts better with pentapeptides (fMLFII, fMLFIK) than tetrapeptides (fMLFK, fMLFW) and tripeptide (fMLF) but only weakly with peptides carrying negative charges at the C terminus (e.g. fMLFE). In contrast, FPR1 is less sensitive to negative charges at the C terminus. A CXCR4-based homology model of FPR1 and FPR2 suggested that Asp-281^7.32 is crucial for the interaction of FPR2 with certain formyl peptides as its negative charge may be repulsive with the terminal COO- group of fMLF and negatively charged Glu in fMLFE. Asp-281^7.32 might also form a stable interaction with the positively charged Lys in fMLFK. Site-directed mutagenesis was performed to remove the negative charge at position 281 in FPR2. The D281^7.32G mutant showed improved affinity for fMLFE and fMLF and reduced affinity for fMLFK compared with wild type FPR2. These results indicate that different structural determinants are used by FPR1 and FPR2 to interact with formyl peptides.     

Integrated approach for manual evaluation of peptides identified by searching protein sequence databases with tandem mass spectra

Quantitative proteomics relies on accurate protein identification, which often is carried out by automated searching of a sequence database with tandem mass spectra of peptides. When these spectra contain limited information, automated searches may lead to incorrect peptide identifications. It is therefore necessary to validate the identifications by careful manual inspection of the mass spectra. Not only is this task time-consuming, but the reliability of the validation varies with the experience of the analyst. Here, we report a systematic approach to evaluating peptide identifications made by automated search algorithms. The method is based on the principle that the candidate peptide sequence should adequately explain the observed fragment ions. Also, the mass errors of neighboring fragments should be similar. To evaluate our method, we studied tandem mass spectra obtained from tryptic digests of E. coli and HeLa cells. Candidate peptides were identified with the automated search engine Mascot and subjected to the manual validation method. The method found correct peptide identifications that were given low Mascot scores (e.g., 20-25) and incorrect peptide identifications that were given high Mascot scores (e.g., 40-50). The method comprehensively detected false results from searches designed to produce incorrect identifications. Comparison of the tandem mass spectra of synthetic candidate peptides to the spectra obtained from the complex peptide mixtures confirmed the accuracy of the evaluation method. Thus, the evaluation approach described here could help boost the accuracy of protein identification, increase number of peptides identified, and provide a step toward developing a more accurate next-generation algorithm for protein identification.     

Recognition of Bacterial Signal Peptides by Mammalian Formyl Peptide Receptors: A NEW MECHANISM FOR SENSING PATHOGENS*

Formyl peptide receptors (FPRs) are G-protein-coupled receptors that function as chemoattractant receptors in innate immune responses. Here we perform systematic structure-function analyses of FPRs from six mammalian species using structurally diverse FPR peptide agonists and identify a common set of conserved agonist properties with typical features of pathogen-associated molecular patterns. Guided by these results, we discover that bacterial signal peptides, normally used to translocate proteins across cytoplasmic membranes, are a vast family of natural FPR agonists. N-terminally formylated signal peptide fragments with variable sequence and length activate human and mouse FPR1 and FPR2 at low nanomolar concentrations, thus establishing FPR1 and FPR2 as sensitive and broad signal peptide receptors. The vomeronasal receptor mFpr-rs1 and its sequence orthologue hFPR3 also react to signal peptides but are much more narrowly tuned in signal peptide recognition. Furthermore, all signal peptides examined here function as potent activators of the innate immune system. They elicit robust, FPR-dependent calcium mobilization in human and mouse leukocytes and trigger a range of classical innate defense mechanisms, such as the production of reactive oxygen species, metalloprotease release, and chemotaxis. Thus, bacterial signal peptides constitute a novel class of immune activators that are likely to contribute to mammalian immune defense against bacteria. This evolutionarily conserved detection mechanism combines structural promiscuity with high specificity and enables discrimination between bacterial and eukaryotic signal sequences. With at least 175,542 predicted sequences, bacterial signal peptides represent the largest and structurally most heterogeneous class of G-protein-coupled receptor agonists currently known for the innate immune system.     

A high confidence, manually validated human blood plasma protein reference set

Background

The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods

To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results

Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion

Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.     

蛋白质组学肽段鉴定可信度评价方法

蛋白质组学基于质谱数据鉴定肽段和蛋白质，从而给出基因表达的直接证据，帮助解析蛋白质的结构和功能，研究蛋白质与疾病的关系，提供靶向治疗方案，而这些都取决于鉴定的肽段和蛋白质的准确性。蛋白质组学常采用目标-诱饵库方法（target-decoy approach，TDA）对鉴定的肽段和蛋白质进行质量控制，并对其进行改进演化后应用到子类肽段（比如突变肽段和修饰肽段等）和交联肽段等特殊鉴定结果的可信度评价中。然而，TDA存在两个局限，即错误率估计值不够准确以及不能评价单个鉴定结果的可信度，经过TDA质量控制后的结果还需要进一步检验，因此领域内也提出了一系列其他方法（本文统称为Beyond-TDA方法），协同加强肽段的可信度评价。本文对数据依赖模式下采集的质谱数据肽段层面的TDA常规方法和特殊方法进行了综述，对Beyond-TDA方法进行了分类阐述，并总结了各种方法的优势与不足。     

Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC‐MS phosphopeptide analysis

LC combined with MS/MS analysis of complex mixtures of protein digests is a reliable and sensitive method for characterization of protein phosphorylation. Peptide retention times (RTs) measured during an LC‐MS/MS run depend on both the peptide sequence and the location of modified amino acids. These RTs can be predicted using the LC of biomacromolecules at critical conditions model (BioLCCC). Comparing the observed RTs to those obtained from the BioLCCC model can provide additional validation of MS/MS‐based peptide identifications to reduce the false discovery rate and to improve the reliability of phosphoproteome profiling. In this study, energies of interaction between phosphorylated residues and the surface of RP separation media for both “classic” alkyl C18 and polar‐embedded C18 stationary phases were experimentally determined and included in the BioLCCC model extended for phosphopeptide analysis. The RTs for phosphorylated peptides and their nonphosphorylated analogs were predicted using the extended BioLCCC model and compared with their experimental RTs. The extended model was evaluated using literary data and a complex phosphoproteome data set distributed through the Association of Biomolecular Resource Facilities Proteome Informatics Research Group 2010 study. The reported results demonstrate the capability of the extended BioLCCC model to predict RTs which may lead to improved sensitivity and reliability of LC‐MS/MS‐based phosphoproteome profiling.     

Diagnostic model of saliva peptide finger print analysis of primary Sj?gren's syndrome patients by using weak cation exchange magnetic beads

Saliva diagnostics has become an attractive field utilizing nanotechnology and molecular technologies for pSS (primary Sjögren''s syndrome). However, no specific methods have been established. To refine the diagnostic power of the saliva peptide finger print for the early detection of pSS, we screened the expression spectrum of salivary peptides in pSS patients by using mass spectrometry MALDI-TOF-MS (matrix-assisted laser-desorption ionization-time-of-flight MS) combined with magnetic bead. The present study was comprised 12 pSS patients and 13 healthy controls and broken down to two different phases. In the initial ‘exploratory phase’, we enrolled seven pSS patients with eight age- and sex-matched healthy volunteers. Proteomics analysis of the unstimulated salivary samples was conducted to generate proportional peptide mass fingerprints. A diagnostic model was established. The testing cohort of the second ‘validation phase’ was represented by five pSS patients and five age- and sex-matched healthy controls. The diagnostic power of this diagnostic panel was then validated. The results showed seven m/z (mass-to-charge) ratio peaks with significant differences. Five peptides were up-regulated and two down-regulated in the pSS patients compared with matched healthy subjects. In the validation phase, four out of five pSS patients were diagnosed as pSS, and four of the five healthy controls were diagnosed as healthy controls, respectively. Potential biomarkers were also primarily predicted. The novel diagnostic proteomic model with m/z peaks 1068.1 Da, 1196.2 Da, 1738.4 Da, 3375.3 Da, 3429.3 Da, 3449.7 Da and 3490.6 Da is of certain value for early diagnosis of pSS.     

Peptide reranking with protein-peptide correspondence and precursor peak intensity information

Searching tandem mass spectra against a protein database has been a mainstream method for peptide identification. Improving peptide identification results by ranking true Peptide-Spectrum Matches (PSMs) over their false counterparts leads to the development of various reranking algorithms. In peptide reranking, discriminative information is essential to distinguish true PSMs from false PSMs. Generally, most peptide reranking methods obtain discriminative information directly from database search scores or by training machine learning models. Information in the protein database and MS1 spectra (i.e., single stage MS spectra) is ignored. In this paper, we propose to use information in the protein database and MS1 spectra to rerank peptide identification results. To quantitatively analyze their effects to peptide reranking results, three peptide reranking methods are proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak Intensity (PPI) information, and MIRanker employs both PPM information and PPI information. According to our experiments on a standard protein mixture data set, a human data set and a mouse data set, PPMRanker and MIRanker achieve better peptide reranking results than PetideProphet, PeptideProphet+NSP (number of sibling peptides) and a score regularization method SRPI. The source codes of PPMRanker, PPIRanker, and MIRanker, and all supplementary documents are available at our website: http://bioinformatics.ust.hk/pepreranking/. Alternatively, these documents can also be downloaded from: http://sourceforge.net/projects/pepreranking/.     

Feature selection in validating mass spectrometry database search results

Tandem mass spectrometry (MS/MS) combined with protein database searching has been widely used in protein identification. A validation procedure is generally required to reduce the number of false positives. Advanced tools using statistical and machine learning approaches may provide faster and more accurate validation than manual inspection and empirical filtering criteria. In this study, we use two feature selection algorithms based on random forest and support vector machine to identify peptide properties that can be used to improve validation models. We demonstrate that an improved model based on an optimized set of features reduces the number of false positives by 58% relative to the model which used only search engine scores, at the same sensitivity score of 0.8. In addition, we develop classification models based on the physicochemical properties and protein sequence environment of these peptides without using search engine scores. The performance of the best model based on the support vector machine algorithm is at 0.8 AUC, 0.78 accuracy, and 0.7 specificity, suggesting a reasonably accurate classification. The identified properties important to fragmentation and ionization can be either used in independent validation tools or incorporated into peptide sequencing and database search algorithms to improve existing software programs.     

Partially sequenced organisms, decoy searches and false discovery rates

Tandem mass spectrometry is commonly used to identify peptides, typically by comparing their product ion spectra with those predicted from a protein sequence database and scoring these matches. The most reported quality metric for a set of peptide identifications is the false discovery rate (FDR), the fraction of expected false identifications in the set. This metric has so far only been used for completely sequenced organisms or known protein mixtures. We have investigated whether FDR estimations are also applicable in the case of partially sequenced organisms, where many high-quality spectra fail to identify the correct peptides because the latter are not present in the searched sequence database. Using real data from human plasma and simulated partial sequence databases derived from two complete human sequence databases with different levels of redundancy, we could demonstrate that the mixture model approach in PeptideProphet is robust for partial databases, particularly if used in combination with decoy sequences. We therefore recommend using this method when estimating the FDR and reporting peptide identifications from incompletely sequenced organisms.     

Integrated platform for manual and high‐throughput statistical validation of tandem mass spectra

As proteomic data sets increase in size and complexity, the necessity for database‐centric software systems able to organize, compare, and visualize all the proteomic experiments in a lab grows. We recently developed an integrated platform called high‐throughput autonomous proteomic pipeline (HTAPP) for the automated acquisition and processing of quantitative proteomic data, and integration of proteomic results with existing external protein information resources within a lab‐based relational database called PeptideDepot. Here, we introduce the peptide validation software component of this system, which combines relational database‐integrated electronic manual spectral annotation in Java with a new software tool in the R programming language for the generation of logistic regression spectral models from user‐supplied validated data sets and flexible application of these user‐generated models in automated proteomic workflows. This logistic regression spectral model uses both variables computed directly from SEQUEST output in addition to deterministic variables based on expert manual validation criteria of spectral quality. In the case of linear quadrupole ion trap (LTQ) or LTQ‐FTICR LC/MS data, our logistic spectral model outperformed both XCorr (242% more peptides identified on average) and the X!Tandem E‐value (87% more peptides identified on average) at a 1% false discovery rate estimated by decoy database approach.     

Novel Prostate Cancer Biomarkers Derived from Autoantibody Signatures

BACKGROUND: Due to the low specificity of the prostate-specific antigen (PSA) assay and a high false positive rate, a large number of prostate cancer (PCA) biopsies are performed unnecessarily. Consequently, there is a need for new biomarkers that can identify PCA at any stage of progression while limiting the number of false positives. The use of autoantibody signature–developed biomarkers has proven to be an effective method to solve this problem. RESULTS: Using T7 phage–peptide detection, we identified a panel of eight biomarkers for PCA on a training set. The estimated receiver-operating characteristic (ROC) curve had an area under the ROC curve of 0.69 when applied to the validation set. Spearman correlations were high, within 0.7 to 0.9, indicating that the biomarkers have a degree of inter-relatedness. The identified biomarkers play a role in processes such as androgen response regulation and cellular structural integrity and are proteins that are thought to play a role in prostate tumorigenesis. CONCLUSIONS: Autoantibodies against PCA can be developed as biomarkers for detecting PCA. The scores from the algorithm developed here can be used to indicate a relative high or low risk of PCA, particularly for patients with intermediate (4.0 to 10 ng/ml) PSA levels. Since most commercially available assays test for PSA or have a PSA component, this novel approach has the potential to improve diagnosis of PCA using a biologic measure independent of PSA.     

An extracellular vesicle epitope profile is associated with acute myocardial infarction

The current standard biomarker for myocardial infarction (MI) is high‐sensitive troponin. Although powerful in clinical setting, search for new markers is warranted as early diagnosis of MI is associated with improved outcomes. Extracellular vesicles (EVs) attracted considerable interest as new blood biomarkers. A training cohort used for diagnostic modelling included 30 patients with STEMI, 38 with stable angina (SA) and 30 matched‐controls. Extracellular vesicle concentration was assessed by nanoparticle tracking analysis. Extracellular vesicle surface‐epitopes were measured by flow cytometry. Diagnostic models were developed using machine learning algorithms and validated on an independent cohort of 80 patients. Serum EV concentration from STEMI patients was increased as compared to controls and SA. EV levels of CD62P, CD42a, CD41b, CD31 and CD40 increased in STEMI, and to a lesser extent in SA patients. An aggregate marker including EV concentration and CD62P/CD42a levels achieved non‐inferiority to troponin, discriminating STEMI from controls (AUC = 0.969). A random forest model based on EV biomarkers discriminated the two groups with 100% accuracy. EV markers and RF model confirmed high diagnostic performance at validation. In conclusion, patients with acute MI or SA exhibit characteristic EV biomarker profiles. EV biomarkers hold great potential as early markers for the management of patients with MI.