Niklaas Colaert

Proteomics analyses reveal the evolutionary conservation and divergence of N-terminal acetyltransferases from yeast and humans

Nα-terminal acetylation is one of the most common protein modifications in eukaryotes. The COmbined FRActional DIagonal Chromatography (COFRADIC) proteomics technology that can be specifically used to isolate N-terminal peptides was used to determine the N-terminal acetylation status of 742 human and 379 yeast protein N termini, representing the largest eukaryotic dataset of N-terminal acetylation. The major N-terminal acetyltransferase (NAT), NatA, acts on subclasses of proteins with Ser-, Ala-, Thr-, Gly-, Cys- and Val- N termini. NatA is composed of subunits encoded by yARD1 and yNAT1 in yeast and hARD1 and hNAT1 in humans. A yeast ard1-Δ nat1-Δ strain was phenotypically complemented by hARD1 hNAT1, suggesting that yNatA and hNatA are similar. However, heterologous combinations, hARD1 yNAT1 and yARD1 hNAT1, were not functional in yeast, suggesting significant structural subunit differences between the species. Proteomics of a yeast ard1-Δ nat1-Δ strain expressing hNatA demonstrated that hNatA acts on nearly the same set of yeast proteins as yNatA, further revealing that NatA from humans and yeast have identical or nearly identical specificities. Nevertheless, all NatA substrates in yeast were only partially N-acetylated, whereas the corresponding NatA substrates in HeLa cells were mainly completely N-acetylated. Overall, we observed a higher proportion of N-terminally acetylated proteins in humans (84%) as compared with yeast (57%). N-acetylation occurred on approximately one-half of the human proteins with Met-Lys- termini, but did not occur on yeast proteins with such termini. Thus, although we revealed different N-acetylation patterns in yeast and humans, the major NAT, NatA, acetylates the same substrates in both species.

Redox Proteomics of Protein-bound Methionine Oxidation

We here present a new method to measure the degree of protein-bound methionine sulfoxide formation at a proteome-wide scale. In human Jurkat cells that were stressed with hydrogen peroxide, over 2000 oxidation-sensitive methionines in more than 1600 different proteins were mapped and their extent of oxidation was quantified. Meta-analysis of the sequences surrounding the oxidized methionine residues revealed a high preference for neighboring polar residues. Using synthetic methionine sulfoxide containing peptides designed according to the observed sequence preferences in the oxidized Jurkat proteome, we discovered that the substrate specificity of the cellular methionine sulfoxide reductases is a major determinant for the steady-state of methionine oxidation. This was supported by a structural modeling of the MsrA catalytic center. Finally, we applied our method onto a serum proteome from a mouse sepsis model and identified 35 in vivo methionine oxidation events in 27 different proteins.

Analysis of Protein Processing by N-terminal Proteomics Reveals Novel Species-specific Substrate Determinants of Granzyme B Orthologs

Using a targeted peptide-centric proteomics approach, we performed in vitro protease substrate profiling of the apoptotic serine protease granzyme B resulting in the delineation of more than 800 cleavage sites in 322 human and 282 mouse substrates, encompassing the known substrates Bid, caspase-7, lupus La protein, and fibrillarin. Triple SILAC (stable isotope labeling by amino acids in cell culture) further permitted intra-experimental evaluation of species-specific variations in substrate selection by the mouse or human granzyme B ortholog. For the first time granzyme B substrate specificities were directly mapped on a proteomic scale and revealed unknown cleavage specificities, uncharacterized extended specificity profiles, and macromolecular determinants in substrate selection that were confirmed by molecular modeling. We further tackled a substrate hunt in an in vivo setup of natural killer cell-mediated cell death confirming in vitro characterized granzyme B cleavages next to several other unique and hitherto unreported proteolytic events in target cells.

In Vitro and in Vivo Protein-bound Tyrosine Nitration Characterized by Diagonal Chromatography

A new proteomics technique for analyzing 3-nitrotyrosine-containing peptides is presented here. This technique is based on the combined fractional diagonal chromatography peptide isolation procedures by which specific classes of peptides are isolated following a series of identical reverse-phase HPLC separation steps. Here dithionite is used to reduce 3-nitrotyrosine to 3-aminotyrosine peptides, which thereby become more hydrophilic. Our combined fractional diagonal chromatography technique was first applied to characterize tyrosine nitration in tetranitromethane-modified BSA and further led to a high quality list of 335 tyrosine nitration sites in 267 proteins in a peroxynitrite-treated lysate of human Jurkat cells. We then analyzed a serum sample of a C57BL6/J mouse in which septic shock was induced by intravenous Salmonella infection and identified six in vivo nitration events in four serum proteins, thereby illustrating that our technique is sufficiently sensitive to identify rare in vivo tyrosine nitration sites in a very complex background.

Complementary positional proteomics for screening substrates of endo- and exoproteases

We describe a positional proteomics approach to simultaneously analyze N- and C-terminal peptides and used it to screen for human protein substrates of granzyme B and carboxypeptidase A4 in human cell lysates. This approach allowed comprehensive proteome studies, and we report the identification of 965 database-annotated protein C termini, 334 neo–C termini resulting from granzyme B processing and 16 neo–C termini resulting from carboxypeptidase A4 processing.

compomics-utilities: an open-source Java library for computational proteomics

BackgroundThe growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool.ResultsIn order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development.ConclusionsAs a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

ms_lims, a simple yet powerful open source laboratory information management system for MS-driven proteomics.

MS‐based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. High‐throughput studies require automation of these various steps, and management of the data in association with the results obtained. We here present ms_lims (http://genesis.UGent.be/ms_lims), a freely available, open‐source system based on a central database to automate data management and processing in MS‐driven proteomics analyses.

MS-driven protease substrate degradomics

Proteolytic processing has recently received increased attention in the field of signal propagation and cellular differentiation. Because of its irreversible nature, protein cleavage has been associated with committed steps in cell function. One aspect of protease biology that boomed the past few years is the detailed characterization of protease substrates by both shotgun as well as targeted MS‐driven proteomics techniques. The most promising techniques are discussed in this review and we further elaborate on the bioinformatics challenges that accompany mainly qualitative, MS‐driven protease substrate degradome studies.

A Quantitative Proteomics Design for Systematic Identification of Protease Cleavage Events

We present here a novel proteomics design for systematic identification of protease cleavage events by quantitative N-terminal proteomics, circumventing the need for time-consuming manual validation. We bypass the singleton detection problem of protease-generated neo-N-terminal peptides by introducing differential isotopic proteome labeling such that these substrate reporter peptides are readily distinguished from all other N-terminal peptides. Our approach was validated using the canonical human caspase-3 protease and further applied to mouse cathepsin D and E substrate processing in a mouse dendritic cell proteome, identifying the largest set of protein protease substrates ever reported and gaining novel insight into substrate specificity differences of these cathepsins.

Rover: A tool to visualize and validate quantitative proteomics data from different sources

Manual validation of regulated proteins found in MS‐driven quantitative proteome studies is tedious. Here we present Rover (http://genesis.ugent.be/rover), a tool that facilitates this process. Rover accepts quantitative data from different sources such as MASCOT Distiller and MaxQuant and, in an intuitive environment, Rover visualizes these data such that the user can select and validate algorithm‐suggested regulated proteins in the frame of the whole experiment and in the context of the protein inference problem.