Protein Adductomics: Methodologies for Untargeted Screening of Adducts to Serum Albumin and Hemoglobin in Human Blood Samples.

The reaction products of electrophiles in vivo can be measured as adducts to the abundant proteins, hemoglobin (Hb), and human serum albumin (HSA), in human blood samples. During the last decade, methods for untargeted screening of such adducts, called adductomics, have used liquid chromatography-mass spectrometry to detect large numbers of previously unknown Hb and HSA adducts. This review presents methodologies that were developed and used in our laboratories for Hb and HSA adductomics, respectively. We discuss critical aspects regarding choice of target protein, sample preparation, mass spectrometry, data evaluation, and strategies for identification of detected unknown adducts. With this review we give an overview of these two methodologies used for protein adductomics and the precursor electrophiles that have been elucidated from the adducts

Molecular Formula Identification using High Resolution Mass Spectrometry: Algorithms and Applications in Metabolomics and Proteomics

Wir untersuchen mehrere theoretische und praktische Aspekte der Identifikation der Summenformel von Biomolekülen mit Hilfe von hochauflösender Massenspektrometrie. Durch die letzten Forschritte in der Instrumentation ist die Massenspektrometrie (MS) zur einen der Schlüsseltechnologien für die Analyse von Biomolekülen in der Proteomik und Metabolomik geworden. Sie misst die Massen der Moleküle in der Probe mit hoher Genauigkeit, und ist für die Messdatenerfassung im Hochdurchsatz gut geeignet. Eine der Kernaufgaben in der MS-basierten Proteomik und Metabolomik ist die Identifikation der Moleküle in der Probe. In der Metabolomik unterliegen Metaboliten der Strukturaufklärung, beginnend bei der Summenformel eines Moleküls, d.h. der Anzahl der Atome jedes Elements. Dies ist der entscheidende Schritt in der Identifikation eines unbekannten Metabolits, da die festgelegte Formel die Anzahl der möglichen Molekülstrukturen auf eine viel kleinere Menge reduziert, die mit Methoden der automatischen Strukturaufklärung weiter analysiert werden kann. Nach der Vorverarbeitung ist die Ausgabe eines Massenspektrometers eine Liste von Peaks, die den Molekülmassen und deren Intensitäten, d.h. der Anzahl der Moleküle mit einer bestimmten Masse, entspricht. Im Prinzip können die Summenformel kleiner Moleküle nur mit präzisen Massen identifiziert werden. Allerdings wurde festgestellt, dass aufgrund der hohen Anzahl der chemisch legitimer Formeln in oberen Massenbereich eine exzellente Massengenaugkeit alleine für die Identifikation nicht genügt. Hochauflösende MS erlaubt die Bestimmung der Molekülmassen und Intensitäten mit hervorragender Genauigkeit. In dieser Arbeit entwickeln wir mehrere Algorithmen und Anwendungen, die diese Information zur Identifikation der Summenformel der Biomolekülen anwenden

Alternative pre-mRNA splicing as a source of cancer neoepitopes

Robust identification of neoepitopes is crucial for the efficacy and safety of immunotherapy, the most promising treatment strategy for several cancer types. Current approaches have provided limited numbers of immunogenic and tumor-specific targets, thus preventing the broad application of targeted immunotherapy. Here, the focus on somatic mutation-derived neoantigens often overlooks possible neoepitopes originating from mRNA processing events. A potential new source of tumor-specific peptides is alternative pre-mRNA splicing, a widely dysregulated process in several cancer subtypes. However, there is limited insight regarding the potential of alternative splicing to generate peptides that are also presented on the cell surface. Thus, in this thesis, I aimed to investigate how perturbation of the splicing machinery contributes to the neoepitope repertoire in tumor cells. To explore alternative splicing-derived neoantigens, I performed immunopeptidomics to determine the HLA-I ligandome of wild-type RPE-1 cells and RPE-1 cell lines carrying common cancer mutations. To facilitate the presentation of alternative splicing-derived neoepitopes, I treated these cell lines with the splicing inhibitor GEX1A. I then performed HLA-I immunopurification to recover HLA-I-bound peptides of these cells, followed by peptide identification through mass spectrometry. To be able to identify non-canonical peptides from mass spectra, I generated sample-specific custom reference databases based on matching RNA-seq data. This strategy allowed me to identify more than 8,000 unique HLA-I-presented peptides per cell line. In parallel, I specifically identified neoepitopes originating from aberrant alternative splice events. By performing differential splicing analysis between the various conditions, I obtained thousands of differentially regulated splice junction events. Particularly in cells treated with the splicing inhibitor GEX1A, alternative splicing analysis revealed numerous novel, non-annotated splice events. To examine whether these dysregulated events were translated into novel peptides, I subsequently mapped the candidate peptides to the differential splice events. With this strategy, I was able to identify and validate several alternative splicing-derived neoepitope candidates that exhibited a high immunogenic potential in in vivo immunization assays. In conclusion, my work demonstrates that pharmacological modulation of the splicing machinery has the potential to promote the presentation of neoepitopes derived from alternative splice variants. These findings have potential implications for immunotherapy of cancer types with low tumor mutational burden. Exploring the splicing-derived neopeptidome could reveal novel therapeutic targets and serve as a predictive biomarker for response to immune checkpoint blockade therapy

De novo sequencing of MS/MS spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field. © 2011 Expert Reviews Ltd.The Turkish Academy of Science (TÜBA

Exploiting fragment-ion complementarity for peptide de novo sequencing from collision induced dissociation tandem mass spectra

Thesis (Master)--Izmir Institute of Technology, Molecular Biology and Genetics, Izmir, 2011Includes bibliographical references (leaves: 58-64)Text in English; Abstract: Turkish and Englishx, 64 leavesPeptide identification from mass spectrometric data is a key step in proteomics because this field provides sequence, quantitative, and modification data of actually expressed proteins. Two approaches are generally deployed to interpret experimental MS/MS data, database searching and de novo sequencing. Database search method has been used successfully in proteomics projects for organisms with well-studied genomes. However, it is not applicable in situations where a target sequence is not in the protein database. This can happen for a number of reasons, including novel proteins, protein mutations and post-translational modifications. Because of the disadvantages of database searching method, a lot of research has focused on de novo sequencing method which assigns amino acid sequences to MS/MS spectra without the need for a database. The aim of this study is to enhance the accuracy of de novo sequencing tools. One step commonly employed in all de novo sequencing tools is naming of fragment ions. It is essential to know which peak represents which ion type in order to traverse a spectrum graph to find an amino acid sequence that best explains the MS/MS spectrum. Different approaches have been tried to name ions and some success has been achieved in naming b-type ions and y-type ions. We have presented a new approach which enables the naming of not only b- and y-type ions but other arbitrary ion types as well. This enabled the detection of b-ion ladder. In the latter case, missing fragments were determined by using other named ion types. Furthermore, unexplained data in tandem mass spectra were reduced as much as possible. Therefore, a complete sequence will be derived by the new approach

Antilope - A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem

Peptide sequencing from mass spectrometry data is a key step in proteome research. Especially de novo sequencing, the identification of a peptide from its spectrum alone, is still a challenge even for state-of-the-art algorithmic approaches. In this paper we present Antilope, a new fast and flexible approach based on mathematical programming. It builds on the spectrum graph model and works with a variety of scoring schemes. Antilope combines Lagrangian relaxation for solving an integer linear programming formulation with an adaptation of Yen's k shortest paths algorithm. It shows a significant improvement in running time compared to mixed integer optimization and performs at the same speed like other state-of-the-art tools. We also implemented a generic probabilistic scoring scheme that can be trained automatically for a dataset of annotated spectra and is independent of the mass spectrometer type. Evaluations on benchmark data show that Antilope is competitive to the popular state-of-the-art programs PepNovo and NovoHMM both in terms of run time and accuracy. Furthermore, it offers increased flexibility in the number of considered ion types. Antilope will be freely available as part of the open source proteomics library OpenMS

De novo sequencing of heparan sulfate saccharides using high-resolution tandem mass spectrometry

Heparan sulfate (HS) is a class of linear, sulfated polysaccharides located on cell surface, secretory granules, and in extracellular matrices found in all animal organ systems. It consists of alternately repeating disaccharide units, expressed in animal species ranging from hydra to higher vertebrates including humans. HS binds and mediates the biological activities of over 300 proteins, including growth factors, enzymes, chemokines, cytokines, adhesion and structural proteins, lipoproteins and amyloid proteins. The binding events largely depend on the fine structure - the arrangement of sulfate groups and other variations - on HS chains. With the activated electron dissociation (ExD) high-resolution tandem mass spectrometry technique, researchers acquire rich structural information about the HS molecule. Using this technique, covalent bonds of the HS oligosaccharide ions are dissociated in the mass spectrometer. However, this information is complex, owing to the large number of product ions, and contains a degree of ambiguity due to the overlapping of product ion masses and lability of sulfate groups; as a result, there is a serious barrier to manual interpretation of the spectra. The interpretation of such data creates a serious bottleneck to the understanding of the biological roles of HS. In order to solve this problem, I designed HS-SEQ - the first HS sequencing algorithm using high-resolution tandem mass spectrometry. HS-SEQ allows rapid and confident sequencing of HS chains from millions of candidate structures and I validated its performance using multiple known pure standards. In many cases, HS oligosaccharides exist as mixtures of sulfation positional isomers. I therefore designed MULTI-HS-SEQ, an extended version of HS-SEQ targeting spectra coming from more than one HS sequence. I also developed several pre-processing and post-processing modules to support the automatic identification of HS structure. These methods and tools demonstrated the capacity for large-scale HS sequencing, which should contribute to clarifying the rich information encoded by HS chains as well as developing tailored HS drugs to target a wide spectrum of diseases

Expanding the immune self : impact of non-canonical translation on the repertoire of MHC I-associated peptides

Les molécules du complexe majeur d’histocompatibilité de classe I (MHC I) sont des glycoprotéines de surface exprimées par la majorité des cellules nucléés de notre organisme. Ces molécules servent à exposer une vue intégrative de l’état interne de nos cellules (soi immunitaire) via la présentation de courts peptides (MAPs) générés lors de la dégradation des protéines cytosoliques par le protéasome. Le répertoire des MAPs de chaque cellule, véritable carte d’identité peptidique, est constamment passé en revue par nos lymphocytes T CD8+, cellules centrales du système immunitaire, afin de débusquer et d’éliminer toute cellule anormale, e.g., celles présentant des MAPs d’origine virale ou tumorale (TSAs). Au vu du nombre grandissant d’articles démontrant que des ARNs autres que les ARNs messagers peuvent être traduits, nous avons décidé d’évaluer l’impact de ces mécanismes de traduction non-canonique sur le répertoire des MAPs présentés par des cellules B. En développant une approche protéogénomique, i.e., combinant spectrométrie de masse et séquençage d’ARN à haut-débit, nous avons pu démontrer qu’environ 10 % des MAPs présentés par nos cellules B dérivent d’évènements de traduction non-canonique incluant (i) la traduction d’ARNs messagers dans un cadre de lecture alternatif ou (ii) la traduction de régions ou ARNs supposés non-codants. L’analyse subséquente des caractéristiques de ces MAPs dits « cryptiques » suggère que leur biogenèse diffère de celle des MAPs conventionnels, les MAPs cryptiques étant principalement encodés par des ARNs instables produisant de courtes protéines dont la dégradation ne semble pas exiger l’intervention du protéasome. Sachant que la déméthylation globale du génome des cellules cancéreuses permet l’expression d’un plus grand bassin d’ARNs non-codants, nous avons supposé que ces cellules pourraient présenter de nombreux MAPs (et TSAs) cryptiques. En adaptant notre approche protéogénomique, nous avons pu analyser le répertoire des MAPs de cellules cancéreuses, incluant celui de deux lignées tumorales de souris (EL4 et CT26) et sept échantillons primaires humains (quatre leucémies aigues lymphoblastiques B et trois biopsies de cancer du poumon). Cette analyse nous a permis de découvrir qu’environ 90% des TSAs sont des TSAs cryptiques. Ayant observé que la plupart de ces TSAs dérivent de séquences normales dont l’expression est restreinte aux cellules tumorales, comme les retroéléments endogènes, il est plausible que ces TSAs soient partagés par plusieurs patients. Enfin, nos études chez la souris nous ont permis de démontrer qu’au moins deux facteurs influencent positivement le potentiel protectif d’un TSA in vivo : l’expression de cet antigène par les cellules cancéreuses et la fréquence des lymphocytes T capables de le reconnaître. En conclusion, le recours à la protéogénomique pour analyser les MAPs présentés par les cellules normales et cancéreuses nous a permis de démontrer que les MAPs cryptiques contribuent significativement au bassin de peptides constituant le soi immunitaire et qu’ils permettent aux lymphocytes T CD8+ d’effectuer une surveillance immunitaire plus efficace.On their surface, nucleated cells present major histocompatibility complex class I (MHC I) molecules in complex with short peptides, that we will refer to as MHC I-associated peptides (MAPs). These MAPs derive from the degradation of cytosolic proteins by the proteasome and provide an integrative view of the inner state of cells to CD8+ T cells, which can, in turn, eliminate abnormal cells, e.g., those presenting viral MAPs or tumor-specific antigens (TSAs). With the growing body of evidence suggesting that translation does occur outside of protein-coding transcripts, we tried to evaluate the impact of non-canonical translation on the repertoire of MAPs. Combining RNA-sequencing and mass spectrometry to analyze the MAP repertoire of B-lymphoblastoid cell lines, we uncovered that ~ 10 % of the MAP repertoire derives from such non-canonical translation events, including (i) the out-of-frame translation of protein-coding transcripts or (ii) the translation of non-coding regions (UTRs, introns, etc.) or transcripts (antisense, pseudogene, etc.). Interestingly, our data suggest that the biogenesis of cryptic and conventional MAPs differs, as cryptic MAPs derive from unstable transcripts generating short proteins that might be degraded in a proteasome-independent fashion. Because the global DNA hypomethylation observed in cancer cells tend to de-repress non-coding transcripts, we developed another proteogenomic approach to probe the cryptic MAP repertoire of two murine cancer cell lines (EL4 and CT26) and seven humor primary tumor samples (four B-lineage acute lymphoblastic leukemias and three lung tumor biopsies). This second analysis revealed that ~ 90% of TSAs are cryptic TSAs. Interestingly, most of those TSAs derived from cancer-restricted yet non-mutated sequences, such as endogenous retroelements, thereby suggesting that such TSAs could be shared between patients. Lastly, our validation study in mice demonstrated that at least two parameters can influence the in vivo protective effect of TSAs, namely TSA expression in cancer cells and the frequency of TSA-specific T cells. Altogether, our proteogenomic studies on the MAP repertoire of normal and cancer cells demonstrate that cryptic MAPs significantly expand the immune self and, consequently, the scope of CD8+ T cell immunosurveillance