Christian Panse

A high-quality catalog of the Drosophila melanogaster proteome.

Understanding how proteins and their complex interaction networks convert the genomic information into a dynamic living organism is a fundamental challenge in biological sciences. As an important step towards understanding the systems biology of a complex eukaryote, we cataloged 63% of the predicted Drosophila melanogaster proteome by detecting 9,124 proteins from 498,000 redundant and 72,281 distinct peptide identifications. This unprecedented high proteome coverage for a complex eukaryote was achieved by combining sample diversity, multidimensional biochemical fractionation and analysis-driven experimentation feedback loops, whereby data collection is guided by statistical analysis of prior data. We show that high-quality proteomics data provide crucial information to amend genome annotation and to confirm many predicted gene models. We also present experimentally identified proteotypic peptides matching ∼50% of D. melanogaster gene models. This library of proteotypic peptides should enable fast, targeted and quantitative proteomic studies to elucidate the systems biology of this model organism.

Qualitative and Quantitative Analyses of Protein Phosphorylation in Naive and Stimulated Mouse Synaptosomal Preparations

Activity-dependent protein phosphorylation is a highly dynamic yet tightly regulated process essential for cellular signaling. Although recognized as critical for neuronal functions, the extent and stoichiometry of phosphorylation in brain cells remain undetermined. In this study, we resolved activity-dependent changes in phosphorylation stoichiometry at specific sites in distinct subcellular compartments of brain cells. Following highly sensitive phosphopeptide enrichment using immobilized metal affinity chromatography and mass spectrometry, we isolated and identified 974 unique phosphorylation sites on 499 proteins, many of which are novel. To further explore the significance of specific phosphorylation sites, we used isobaric peptide labels and determined the absolute quantity of both phosphorylated and non-phosphorylated peptides of candidate phosphoproteins and estimated phosphorylation stoichiometry. The analyses of phosphorylation dynamics using differentially stimulated synaptic terminal preparations revealed activity-dependent changes in phosphorylation stoichiometry of target proteins. Using this method, we were able to differentiate between distinct isoforms of Ca2+/calmodulin-dependent protein kinase (CaMKII) and identify a novel activity-regulated phosphorylation site on the glutamate receptor subunit GluR1. Together these data illustrate that mass spectrometry-based methods can be used to determine activity-dependent changes in phosphorylation stoichiometry on candidate phosphopeptides following large scale phosphoproteome analysis of brain tissue.

PhosphoPep--a phosphoproteome resource for systems biology research in Drosophila Kc167 cells.

The ability to analyze and understand the mechanisms by which cells process information is a key question of systems biology research. Such mechanisms critically depend on reversible phosphorylation of cellular proteins, a process that is catalyzed by protein kinases and phosphatases. Here, we present PhosphoPep, a database containing more than 10 000 unique high‐confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. This constitutes the most comprehensive phosphorylation map of any single source to date. To enhance the utility of PhosphoPep, we also provide an array of software tools that allow users to browse through phosphorylation sites on single proteins or pathways, to easily integrate the data with other, external data types such as protein–protein interactions and to search the database via spectral matching. Finally, all data can be readily exported, for example, for targeted proteomics approaches and the data thus generated can be again validated using PhosphoPep, supporting iterative cycles of experimentation and analysis that are typical for systems biology research.

Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods.

Tandem mass spectrometry allows for fast protein identification in a complex sample. As mass spectrometers get faster, more sensitive and more accurate, methods were devised by many academic research groups and commercial suppliers that allow protein research also in quantitative respect. Since label-free methods are an attractive alternative to labeling approaches for proteomics researchers seeking for accurate quantitative results we evaluated several open-source analysis tools in terms of performance on two reference data sets, explicitly generated for this purpose. In this paper we present an implementation, T3PQ (Top 3 Protein Quantification), of the method suggested by Silva and colleagues for LC-MS(E) applications and we demonstrate its applicability to data generated on FT-ICR instruments acquiring in data dependent acquisition (DDA) mode. In order to validate this method and to show its usefulness also for absolute protein quantification, we generated a reference data set of a sample containing four different proteins with known concentrations. Furthermore, we compare three other label-free quantification methods using a complex biological sample spiked with a standard protein in defined concentrations. We evaluate the applicability of these methods and the quality of the results in terms of robustness and dynamic range of the spiked-in protein as well as other proteins also detected in the mixture. We discuss drawbacks of each method individually and consider crucial points for experimental designs. The source code of our implementation is available under the terms of the GNU GPLv3 and can be downloaded from sourceforge (http://fqms.svn.sourceforge.net/svnroot/fqms). A tarball containing the data used for the evaluation is available on the FGCZ web server (http://fgcz-data.uzh.ch/public/T3PQ.tgz).

Identification of combinatorial patterns of post-translational modifications on individual histones in the mouse brain.

Post-translational modifications (PTMs) of proteins are biochemical processes required for cellular functions and signalling that occur in every sub-cellular compartment. Multiple protein PTMs exist, and are established by specific enzymes that can act in basal conditions and upon cellular activity. In the nucleus, histone proteins are subjected to numerous PTMs that together form a histone code that contributes to regulate transcriptional activity and gene expression. Despite their importance however, histone PTMs have remained poorly characterised in most tissues, in particular the brain where they are thought to be required for complex functions such as learning and memory formation. Here, we report the comprehensive identification of histone PTMs, of their combinatorial patterns, and of the rules that govern these patterns in the adult mouse brain. Based on liquid chromatography, electron transfer, and collision-induced dissociation mass spectrometry, we generated a dataset containing a total of 10,646 peptides from H1, H2A, H2B, H3, H4, and variants in the adult brain. 1475 of these peptides carried one or more PTMs, including 141 unique sites and a total of 58 novel sites not described before. We observed that these PTMs are not only classical modifications such as serine/threonine (Ser/Thr) phosphorylation, lysine (Lys) acetylation, and Lys/arginine (Arg) methylation, but also include several atypical modifications such as Ser/Thr acetylation, and Lys butyrylation, crotonylation, and propionylation. Using synthetic peptides, we validated the presence of these atypical novel PTMs in the mouse brain. The application of data-mining algorithms further revealed that histone PTMs occur in specific combinations with different ratios. Overall, the present data newly identify a specific histone code in the mouse brain and reveal its level of complexity, suggesting its potential relevance for higher-order brain functions.

Comparing the Lipid Membrane Affinity and Permeation of Drug-like Acids: The Intriguing Effects of Cholesterol and Charged Lipids

PurposeLipid bilayers regulate the passage of solutes into and between cellular compartments. A general prerequisite for this passage is the partitioning of the solute into the bilayer. We investigated the relationship between bilayer partitioning and permeation of three drug-like acids in liposomal systems consisting of phosphatidylcholine alone or mixed with cholesterol or charged lipids.Materials and MethodsBilayer partitioning was determined by equilibrium dialysis. Bilayer permeation was studied with a luminescence assay which is based on the energy transfer of the permeant to intraliposomal terbium(III).ResultsThe influence of the lipid composition on the pH-dependent membrane affinity was in accordance with the membrane rigidity and possible electrostatic interactions between the acids and the lipids. However, there was no direct relationship between membrane affinity and permeation. This seeming discrepancy was closer analyzed with numerical simulations of the permeation process based on the single rate constants for partitioning and translocation. The simulations were in line with our experimental findings.ConclusionsDepending on the single rate constants and on the geometry of the system, lipid bilayer permeation may positively, negatively or not correlate with the bilayer affinity of the permeant.

Combining Higher-Energy Collision Dissociation and Electron-Transfer/Higher-Energy Collision Dissociation Fragmentation in a Product-Dependent Manner Confidently Assigns Proteomewide ADP-Ribose Acceptor Sites

Protein adenosine diphosphate (ADP)-ribosylation is a physiologically and pathologically important post-translational modification. Recent technological advances have improved analysis of this complex modification and have led to the discovery of hundreds of ADP-ribosylated proteins in both cultured cells and mouse tissues. Nevertheless, accurate assignment of the ADP-ribose acceptor site(s) within the modified proteins identified has remained a challenging task. This is mainly due to poor fragmentation of modified peptides. Here, using an Orbitrap Fusion Tribrid mass spectrometer, we present an optimized methodology that not only drastically improves the overall localization scores for ADP-ribosylation acceptor sites but also boosts ADP-ribosylated peptide identifications. First, we systematically compared the efficacy of higher-energy collision dissociation (HCD), electron-transfer dissociation with supplemental collisional activation (ETcaD), and electron-transfer/higher-energy collision dissociation (EThcD) fragmentation methods when determining ADP-ribose acceptor sites within complex cellular samples. We then tested the combination of HCD and EThcD fragmentation, which were employed in a product-dependent manner, and the unique fragmentation properties of the ADP-ribose moiety were used to trigger targeted fragmentation of only the modified peptides. The best results were obtained with a workflow that included initial fast, high-energy HCD (Orbitrap, FT) scans, which produced intense ADP-ribose fragmentation ions. These potentially ADP-ribosylated precursors were then selected and analyzed via subsequent high-resolution HCD and EThcD fragmentation. Using these resulting high-quality spectra, we identified a xxxxxxKSxxxxx modification motif where lysine can serve as an ADP-ribose acceptor site. Due to the appearance of serine within this motif and its close presence to the lysine, further analysis revealed that serine serves as a new ADP-ribose acceptor site across the proteome.

A new metaphor for projection-based visual analysis and data exploration

In many important application domains such as Business and Finance, Process Monitoring, and Security, huge and quickly increasing volumes of complex data are collected. Strong efforts are underway developing automatic and interactive analysis tools for mining useful information from these data repositories. Many data analysis algorithms require an appropriate definition of similarity (or distance) between data instances to allow meaningful clustering, classification, and retrieval, among other analysis tasks. Projection-based data visualization is highly interesting (a) for visual discrimination analysis of a data set within a given similarity definition, and (b) for comparative analysis of similarity characteristics of a given data set represented by different similarity definitions. We introduce an intuitive and effective novel approach for projection-based similarity visualization for interactive discrimination analysis, data exploration, and visual evaluation of metric space effectiveness. The approach is based on the convex hull metaphor for visually aggregating sets of points in projected space, and it can be used with a variety of different projection techniques. The effectiveness of the approach is demonstrated by application on two well-known data sets. Statistical evidence supporting the validity of the hull metaphor is presented. We advocate the hull-based approach over the standard symbol-based approach to projection visualization, as it allows a more effective perception of similarity relationships and class distribution characteristics.

PTM MarkerFinder, a software tool to detect and validate spectra from peptides carrying post-translational modifications

Mass spectrometry (MS) analysis of peptides carrying post‐translational modifications is challenging due to the instability of some modifications during MS analysis. However, glycopeptides as well as acetylated, methylated and other modified peptides release specific fragment ions during CID (collision‐induced dissociation) and HCD (higher energy collisional dissociation) fragmentation. These fragment ions can be used to validate the presence of the PTM on the peptide. Here, we present PTM MarkerFinder, a software tool that takes advantage of such marker ions. PTM MarkerFinder screens the MS/MS spectra in the output of a database search (i.e., Mascot) for marker ions specific for selected PTMs. Moreover, it reports and annotates the HCD and the corresponding electron transfer dissociation (ETD) spectrum (when present), and summarizes information on the type, number, and ratios of marker ions found in the data set. In the present work, a sample containing enriched N‐acetylhexosamine (HexNAc) glycopeptides from yeast has been analyzed by liquid chromatography‐mass spectrometry on an LTQ Orbitrap Velos using both HCD and ETD fragmentation techniques. The identification result (Mascot .dat file) was submitted as input to PTM MarkerFinder and screened for HexNAc oxonium ions. The software output has been used for high‐throughput validation of the identification results.

Targeted proteomics coming of age ‐ SRM, PRM and DIA performance evaluated from a core facility perspective

Quantitative mass spectrometry is a rapidly evolving methodology applied in a large number of omics‐type research projects. During the past years, new designs of mass spectrometers have been developed and launched as commercial systems while in parallel new data acquisition schemes and data analysis paradigms have been introduced. Core facilities provide access to such technologies, but also actively support the researchers in finding and applying the best‐suited analytical approach. In order to implement a solid fundament for this decision making process, core facilities need to constantly compare and benchmark the various approaches. In this article we compare the quantitative accuracy and precision of current state of the art targeted proteomics approaches single reaction monitoring (SRM), parallel reaction monitoring (PRM) and data independent acquisition (DIA) across multiple liquid chromatography mass spectrometry (LC‐MS) platforms, using a readily available commercial standard sample. All workflows are able to reproducibly generate accurate quantitative data. However, SRM and PRM workflows show higher accuracy and precision compared to DIA approaches, especially when analyzing low concentrated analytes.