Tim Conrad

Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated With Pancreatic Cancer

Purpose: Mass spectrometry–based serum peptidome profiling is a promising tool to identify novel disease-associated biomarkers, but is limited by preanalytic factors and the intricacies of complex data processing. Therefore, we investigated whether standardized sample protocols and new bioinformatic tools combined with external data validation improve the validity of peptidome profiling for the discovery of pancreatic cancer–associated serum markers. Experimental Design: For the discovery study, two sets of sera from patients with pancreatic cancer (n = 40) and healthy controls (n = 40) were obtained from two different clinical centers. For external data validation, we collected an independent set of samples from patients (n = 20) and healthy controls (n = 20). Magnetic beads with different surface functionalities were used for peptidome fractionation followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). Data evaluation was carried out by comparing two different bioinformatic strategies. Following proteome database search, the matching candidate peptide was verified by MALDI-TOF MS after specific antibody-based immunoaffinity chromatography and independently confirmed by an ELISA assay. Results: Two significant peaks (m/z 3884; 5959) achieved a sensitivity of 86.3% and a specificity of 97.6% for the discrimination of patients and healthy controls in the external validation set. Adding peak m/z 3884 to conventional clinical tumor markers (CA 19-9 and CEA) improved sensitivity and specificity, as shown by receiver operator characteristics curve analysis (AUROCcombined = 1.00). Mass spectrometry–based m/z 3884 peak identification and following immunologic quantitation revealed platelet factor 4 as the corresponding peptide. Conclusions: MALDI-TOF MS-based serum peptidome profiling allowed the discovery and validation of platelet factor 4 as a new discriminating marker in pancreatic cancer.

Human Parechovirus Infections Associated with Seizures and Rash in Infants and Toddlers.

Background: Systematic investigations assessing the clinical impact of human parechovirus (HPeV) disease are sparse. Noninvasive stool samples may be useful for targeted hospital-based surveillance. Methods: In the context of a quality management program, all hospitalized children fulfilling predefined case criteria for central nervous system (CNS) infection/inflammation underwent standardized neurologic examinations. Stool samples were collected for HPeV and enterovirus (EV) polymerase chain reaction and molecular typing at the National Reference Center. Results: From October 2010 to December 2012, stool samples of 284 patients with suspected CNS infection/inflammation were tested yielding 12 (4.2%) HPeV+ samples and 43 (15.1%) EV+ samples. HPeV-positive samples included HPeV-1, HPeV-3 and HPeV-6. No additional pathogens were identified in routine care. HPeV-positive patients were significantly younger (P < 0.001) and more likely to present with seizures (P = 0.001) and rash (P < 0.0001) when compared with HPeV-negative patients. Conclusions: In hospitalized children younger than 4 years presenting with suspected CNS infection/inflammation, seizures and/or rash, HPeV should be considered in the differential diagnosis. Large-scale public health surveillance may be indicated.

Beating the noise: new statistical methods for detecting signals in MALDI-TOF spectra below noise level

Background: The computer-assisted detection of small molecules by mass spectrometry in biological samples provides a snapshot of thousands of peptides, protein fragments and proteins in biological samples. This new analytical technology has the potential to identify disease associated proteomic patterns in blood serum. However, the presently available bioinformatic tools are not sensitive enough to identify clinically important low abundant proteins as hormons or tumor markers with only low blood concentrations. Aim: Find, analyze and compare serum proteom patterns in groups of human subjects having different properties such as disease status with a new workflow to enhance sensitivity and specificity. Problems: Mass data acquired from high-throughput platforms frequently are blurred and noisy. This complicates the reliable identification of peaks in general and very small peaks even below noise level in particular. However, this statement is only valid for single or few spectra. If the algorithm has access to a large number of spectra (e.g. N > 1000), new possibilities arise, one of such being a statistical approach. Approach: Apply signal preprocessing steps followed by statistical analyses of the blurred data and the region below the typical noise threshold to identify signals usually hidden below this “barrier”. Results: A new analysis workflow has been developed that is able to accurately identify, analyze and determine peaks and their parameters even below noise level which other tools can not detect. A Comparison to commercial software has clearly proven this gain in sensitivity. These additional peaks can be used in subsequent steps to build better peak patterns for proteomic pattern analysis. We belive that this new approach will foster identification of new biomarkers having not been detectable by most algorithms currently available.

Enabling Precision Medicine With Digital Case Classification at the Point-of-Care.

Infectious and inflammatory diseases of the central nervous system are difficult to identify early. Case definitions for aseptic meningitis, encephalitis, myelitis, and acute disseminated encephalomyelitis (ADEM) are available, but rarely put to use. The VACC-Tool (Vienna Vaccine Safety Initiative Automated Case Classification-Tool) is a mobile application enabling immediate case ascertainment based on consensus criteria at the point-of-care. The VACC-Tool was validated in a quality management program in collaboration with the Robert-Koch-Institute. Results were compared to ICD-10 coding and retrospective analysis of electronic health records using the same case criteria. Of 68,921 patients attending the emergency room in 10/2010–06/2013, 11,575 were hospitalized, with 521 eligible patients (mean age: 7.6 years) entering the quality management program. Using the VACC-Tool at the point-of-care, 180/521 cases were classified successfully and 194/521 ruled out with certainty. Of the 180 confirmed cases, 116 had been missed by ICD-10 coding, 38 misclassified. By retrospective application of the same case criteria, 33 cases were missed. Encephalitis and ADEM cases were most likely missed or misclassified. The VACC-Tool enables physicians to ask the right questions at the right time, thereby classifying cases consistently and accurately, facilitating translational research. Future applications will alert physicians when additional diagnostic procedures are required.

Educating parents about the vaccination status of their children: A user-centered mobile application.

Parents are often uncertain about the vaccination status of their children. In times of vaccine hesitancy, vaccination programs could benefit from active patient participation. The Vaccination App (VAccApp) was developed by the Vienna Vaccine Safety Initiative, enabling parents to learn about the vaccination status of their children, including 25 different routine, special indication and travel vaccines listed in the WHO Immunization Certificate of Vaccination (WHO-ICV). Between 2012 and 2014, the VAccApp was validated in a hospital-based quality management program in Berlin, Germany, in collaboration with the Robert Koch Institute. Parents of 178 children were asked to transfer the immunization data of their children from the WHO-ICV into the VAccApp. The respective WHO-ICV was photocopied for independent, professional data entry (gold standard). Demonstrating the status quo in vaccine information reporting, a Recall Group of 278 parents underwent structured interviews for verbal immunization histories, without the respective WHO-ICV. Only 9% of the Recall Group were able to provide a complete vaccination status; on average 39% of the questions were answered correctly. Using the WHO-ICV with the help of the VAccApp resulted in 62% of parents providing a complete vaccination status; on average 95% of the questions were answered correctly. After using the VAccApp, parents were more likely to remember key aspects of the vaccination history. User-friendly mobile applications empower parents to take a closer look at the vaccination record, thereby taking an active role in providing accurate vaccination histories. Parents may become motivated to ask informed questions and to keep vaccinations up-to-date.

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

BackgroundHigh-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible.ResultsWe present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.

Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials

ABSTRACT Introduction: Influenza-Like Illness is a leading cause of hospitalization in children. Disease burden due to influenza and other respiratory viral infections is reported on a population level, but clinical scores measuring individual changes in disease severity are urgently needed. Areas covered: We present a composite clinical score allowing individual patient data analyses of disease severity based on systematic literature review and WHO-criteria for uncomplicated and complicated disease. The 22-item ViVI Disease Severity Score showed a normal distribution in a pediatric cohort of 6073 children aged 0–18 years (mean age 3.13; S.D. 3.89; range: 0 to 18.79). Expert commentary: The ViVI Score was correlated with risk of antibiotic use as well as need for hospitalization and intensive care. The ViVI Score was used to track children with influenza, respiratory syncytial virus, human metapneumovirus, human rhinovirus, and adenovirus infections and is fully compliant with regulatory data standards. The ViVI Disease Severity Score mobile application allows physicians to measure disease severity at the point-of care thereby taking clinical trials to the next level.

Towards a Personalised Approach to Managing Influenza Infections in Infants and Children – Food for Thought and a Note on Oseltamivir

Acute respiratory infections represent common diseases in childhood and a challenge to infection control, public heath, and the clinical management of patients and their families. Children are avid spreaders of respiratory viruses, and seasonal outbreaks of influenza create additional disease burden and healthcare cost. Infants under the age of two and children with chronic conditions are at high risk. The absence of pre-defined risk factors however, does not protect from serious disease. Immunisation rates remain low, and physical interventions are of limited value in young children. Children with influenza may be contagious prior to the onset of symptoms, and school closures have been shown to have a temporary effect at most. The timely detection of influenza in at-risk patients is important to prevent hospital-based transmission and influenza-associated morbidity and mortality. Guidelines issued by professional associations and public health agencies need to be translated into everyday clinical practice. Antiviral therapy should be initiated early and monitored closely, including virologic and clinical outcomes. The duration of treatment and the decision to readmit children to schools and kindergartens should be adjusted to the individual child patient using evidence-based clinical and virologic criteria. This article presents lessons learnt from a quality management program for infants and children with influenza-like illness at the Charite Department of Paediatrics in collaboration with the National Reference Centre for Influenza at the Robert Koch Institute, in Berlin, Germany. The Charité Influenza-Like Disease (ChILD) Cohort was established during the 2009 influenza pandemic and encompasses nearly 4000 disease episodes to date.

Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs

Background Proteases play an essential part in a variety of biological processes. Besides their importance under healthy conditions they are also known to have a crucial role in complex diseases like cancer. In recent years, it has been shown that not only the fragments produced by proteases but also their dynamics, especially ex vivo, can serve as biomarkers. But so far, only a few approaches were taken to explicitly model the dynamics of proteolysis in the context of mass spectrometry. Results We introduce a new concept to model proteolytic processes, the degradation graph. The degradation graph is an extension of the cleavage graph, a data structure to reconstruct and visualize the proteolytic process. In contrast to previous approaches we extended the model to incorporate endoproteolytic processes and present a method to construct a degradation graph from mass spectrometry time series data. Based on a degradation graph and the intensities extracted from the mass spectra it is possible to estimate reaction rates of the underlying processes. We further suggest a score to rate different degradation graphs in their ability to explain the observed data. This score is used in an iterative heuristic to improve the structure of the initially constructed degradation graph. Conclusion We show that the proposed method is able to recover all degraded and generated peptides, the underlying reactions, and the reaction rates of proteolytic processes based on mass spectrometry time series data. We use simulated and real data to demonstrate that a given process can be reconstructed even in the presence of extensive noise, isobaric signals and false identifications. While the model is currently only validated on peptide data it is also applicable to proteins, as long as the necessary time series data can be produced.

Finding Modules in Networks with Non-modular Regions

Most network clustering methods share the assumption that the network can be completely decomposed into modules, that is, every node belongs to (usually exactly one) module. Forcing this constraint can lead to misidentification of modules where none exist, while the true modules are drowned out in the noise, as has been observed e.g. for protein interaction networks. We thus propose a clustering model where networks contain both a modular region consisting of nodes that can be partitioned into modules, and a transition region containing nodes that lie between or outside modules. We propose two scores based on spectral properties to determine how well a network fits this model. We then evaluate three (partially adapted) clustering algorithms from the literature on random networks that fit our model, based on the scores and comparison to the ground truth. This allows to pinpoint the types of networks for which the different algorithms perform well.