Martijn M. VanDuijn

Sequencing and quantifying IgG fragments and antigen-binding regions by mass spectrometry.

In cancer and autoimmune diseases, immunoglobulins with a specific molecular signature that could potentially be used as diagnostic or prognostic markers are released into body fluids. An immunomics approach based on this phenomenon relies on the ability to identify the specific amino acid sequences of the complementarity-determining regions (CDR) of these immunoglobulins, which in turn depends on the level of accuracy, resolution, and sensitivity that can be achieved by advanced mass spectrometry. Reproducible isolation and sequencing of antibody fragments (e.g., Fab) by high-resolution mass spectrometry (MS) from seven healthy donors revealed 43 217 MS signals: 225 could be associated with CDR1 peptides, 513 with CDR2 peptides, and 19 with CDR3 peptides. Seventeen percent of the 43 217 MS signals did not overlap between the seven donors. The Fab isolation method used is reproducible and fast, with a high yield. It provides only one Fab sample fraction for subsequent characterization by high-resolution MS. In 17% and 4% of these seven healthy donors, qualitative (presence/absence) and quantitative (intensity) differences in Fab fragments could be demonstrated, respectively. From these results, we conclude that the identification of a CDR signature as biomarker for autoimmune diseases and cancer without prior knowledge of the antigen is feasible.

De Novo Protein Sequencing by Combining Top-Down and Bottom-Up Tandem Mass Spectra

There are two approaches for de novo protein sequencing: Edman degradation and mass spectrometry (MS). Existing MS-based methods characterize a novel protein by assembling tandem mass spectra of overlapping peptides generated from multiple proteolytic digestions of the protein. Because each tandem mass spectrum covers only a short peptide of the target protein, the key to high coverage protein sequencing is to find spectral pairs from overlapping peptides in order to assemble tandem mass spectra to long ones. However, overlapping regions of peptides may be too short to be confidently identified. High-resolution mass spectrometers have become accessible to many laboratories. These mass spectrometers are capable of analyzing molecules of large mass values, boosting the development of top-down MS. Top-down tandem mass spectra cover whole proteins. However, top-down tandem mass spectra, even combined, rarely provide full ion fragmentation coverage of a protein. We propose an algorithm, TBNovo, for de novo protein sequencing by combining top-down and bottom-up MS. In TBNovo, a top-down tandem mass spectrum is utilized as a scaffold, and bottom-up tandem mass spectra are aligned to the scaffold to increase sequence coverage. Experiments on data sets of two proteins showed that TBNovo achieved high sequence coverage and high sequence accuracy.

Immune responses are characterized by specific shared immunoglobulin peptides that can be detected by proteomic techniques

In the adaptive immune response, immunoglobulins develop that bind specifically to the antigens to which the organism was exposed. Immunoglobulins may bind to known or unknown antigens in a variety of diseases and have been used in the past to identify novel antigens for use as a biomarker. We propose that the immunoglobulins themselves could also be used as biomarkers in antibody-mediated disease. In this proteomic study, rats were immunized with one of two purified antigens, and immunoglobulins from pre- and postimmune sera were analyzed with nano-LC coupled mass spectrometry. It was found that the two treatment groups could be distinguished based on cluster analysis of the immunoglobulin peptides from the immune sera. In addition, we identified 684 specific peptides that were differentially present in one of the two treated groups. We could find an amino acid sequence for 44% of the features in the mass spectra by combining database-driven and de novo sequencing techniques. The latter were essential for sequence identification, as the more common database-driven approach suffers from a poor representation of immunoglobulins in the available databases. Our data show that the development of immunoglobulins during an immune response is not a fully random process, but that instead selection pressures exist that favor the best binding amino acid sequences, and that this selection is shared between different animals. This finding implies that immunoglobulin peptides could indeed be a powerful and easily accessible class of biomarkers.

De Novo Sequencing of Peptides from Top-Down Tandem Mass Spectra.

De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for protein analysis and characterization, implying a need for efficient algorithms to process this kind of MS/MS data. Here, we describe a method that allows for the retrieval, from a set of top-down MS/MS spectra, of long and accurate sequence fragments of the proteins contained in the sample. To this end, we outline a strategy for generating high-quality sequence tags from top-down spectra, and introduce the concept of a T-Bruijn graph by adapting to the case of tags the notion of an A-Bruijn graph widely used in genomics. The output of the proposed approach represents the set of amino acid strings spelled out by optimal paths in the connected components of a T-Bruijn graph. We illustrate its performance on top-down data sets acquired from carbonic anhydrase 2 (CAH2) and the Fab region of alemtuzumab.

Top-down analysis of protein samples by de novo sequencing techniques

MOTIVATION Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data. RESULTS We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns. AVAILABILITY AND IMPLEMENTATION Freely available on the web at http://bioinf.spbau.ru/en/twister CONTACT vyatkina@spbau.ru or ppevzner@ucsd.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Survivin autoantibodies are not elevated in lung cancer when assayed controlling for specificity and smoking status

Reports of autoantibodies to survivin in lung cancer sera lead to suggestions of roles as biomarkers. The authors tested patient serum with two approaches, controlling for specificity and using controls stratified for smoking habits. No autoreactivity was found. The high mortality rate in lung cancer is largely attributable to late diagnosis. Case–control studies suggest that autoantibodies to the survivin protein are potential biomarkers for early diagnosis. We tested the hypothesis that sandwich ELISA can detect autoantibodies to survivin before radiologic diagnosis in patients with early-stage non–small cell lung cancer (NSCLC). Because previous studies assayed survivin autoantibodies with the direct antigen-coating ELISA (DAC-ELISA), we first compared that assay with the sandwich ELISA. Based on the more robust results from the sandwich ELISA, we used it to measure survivin autoantibodies in the serum of 100 individuals from a well-controlled population study [the Dutch–Belgian Lung Cancer Screening Trial (NELSON) trial] composed of current and former smokers (50 patients with NSCLC, both before and after diagnosis, and 50 matched, smoking-habit control subjects), and another 50 healthy nonsmoking control subjects. We found no difference in specific autoantibodies to survivin in NSCLC patients, although nonspecific median optical densities were 24% higher (P < 0.001) in both NSCLC patients and smokers, than in healthy nonsmokers. Finally, we confirmed the ELISA results with Western blot analysis of recombinant and endogenous survivin (HEK-293), which showed no anti-survivin reactivity in patient sera. We conclude that specific anti-survivin autoantibody reactivity is most likely not present in sera before or after diagnosis. Autoantibody studies benefit from a comparison to a well-controlled population, stratified for smoking habit. Cancer Immunol Res; 4(2); 165–72. ©2015 AACR.

De Novo Sequencing of Peptides from High-Resolution Bottom-Up Tandem Mass Spectra using Top-Down Intended Methods

Despite high‐resolution mass spectrometers are becoming accessible for more and more laboratories, tandem (MS/MS) mass spectra are still often collected at a low resolution. And even if acquired at a high resolution, software tools used for their processing do not tend to benefit from that in full, and an ability to specify a relative mass tolerance in this case often remains the only feature the respective algorithms take advantage of. We argue that a more efficient way to analyze high‐resolution MS/MS spectra should be with methods more explicitly accounting for the precision level, and sustain this claim through demonstrating that a de novo sequencing framework originally developed for (high‐resolution) top‐down MS/MS data is perfectly suitable for processing high‐resolution bottom‐up datasets, even though a top‐down like deconvolution performed as the first step will leave in many spectra at most a few peaks.