Lukas N. Mueller

Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics

The rise of systems biology implied a growing demand for highly sensitive techniques for the fast and consistent detection and quantification of target sets of proteins across multiple samples. This is only partly achieved by classical mass spectrometry or affinity-based methods. We applied a targeted proteomics approach based on selected reaction monitoring (SRM) to detect and quantify proteins expressed to a concentration below 50 copies/cell in total S. cerevisiae digests. The detection range can be extended to single-digit copies/cell and to proteins undetected by classical methods. We illustrate the power of the technique by the consistent and fast measurement of a network of proteins spanning the entire abundance range over a growth time course of S. cerevisiae transiting through a series of metabolic phases. We therefore demonstrate the potential of SRM-based proteomics to provide assays for the measurement of any set of proteins of interest in yeast at high-throughput and quantitative accuracy.

An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data.

Over the past decade, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of the resulting data have been generated. We provide here an overview of the main quantification principles and available software solutions for the analysis of data generated by liquid chromatography coupled to mass spectrometry (LC-MS). Three conceptually different methods to perform quantitative LC-MS experiments have been introduced. In the first, quantification is achieved by spectral counting, in the second via differential stable isotopic labeling, and in the third by using the ion current in label-free LC-MS measurements. We discuss here advantages and challenges of each quantification approach and assess available software solutions with respect to their instrument compatibility and processing functionality. This review therefore serves as a starting point for researchers to choose an appropriate software solution for quantitative proteomic experiments based on their experimental and analytical requirements.

Identification of cross-linked peptides from large sequence databases

NOTE: In the version of this Brief Communication initially published, an author name (Lukas Mueller) was incorrect. The correct author name is Lukas N Mueller. The error has been corrected in the HTML and PDF versions of the article.We describe a method to identify cross-linked peptides from complex samples and large protein sequence databases by combining isotopically tagged cross-linkers, chromatographic enrichment, targeted proteomics and a new search engine called xQuest. This software reduces the search space by an upstream candidate-peptide search before the recombination step. We showed that xQuest can identify cross-linked peptides from a total Escherichia coli lysate with an unrestricted database search.

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.

An integrated mass spectrometric and computational framework for the analysis of protein interaction networks

Biological systems are controlled by protein complexes that associate into dynamic protein interaction networks. We describe a strategy that analyzes protein complexes through the integration of label-free, quantitative mass spectrometry and computational analysis. By evaluating peptide intensity profiles throughout the sequential dilution of samples, the MasterMap system identifies specific interaction partners, detects changes in the composition of protein complexes and reveals variations in the phosphorylation states of components of protein complexes. We use the complexes containing the human forkhead transcription factor FoxO3A to demonstrate the validity and performance of this technology. Our analysis identifies previously known and unknown interactions of FoxO3A with 14-3-3 proteins, in addition to identifying FoxO3A phosphorylation sites and detecting reduced 14-3-3 binding following inhibition of phosphoinositide-3 kinase. By improving specificity and sensitivity of interaction networks, assessing post-translational modifications and providing dynamic interaction profiles, the MasterMap system addresses several limitations of current approaches for protein complexes.

Proteome Analysis of Legionella Vacuoles Purified by Magnetic Immunoseparation Reveals Secretory and Endosomal GTPases

Legionella pneumophila, the causative agent of Legionnaires’ disease, replicates in macrophages and amoebae within ‘Legionella‐containing vacuoles’ (LCVs), which communicate with the early secretory pathway and the endoplasmic reticulum. Formation of LCVs requires the bacterial Icm/Dot type IV secretion system. The Icm/Dot‐translocated effector protein SidC selectively anchors to LCVs by binding the host lipid phosphatidylinositol‐4‐phosphate (PtdIns(4)P). Here, we describe a novel and simple approach to purify intact vacuoles formed by L. pneumophila within Dictyostelium discoideum by using magnetic immunoseparation with an antibody against SidC, followed by density gradient centrifugation. To monitor LCV purification by fluorescence microscopy, we used Dictyostelium producing the LCV marker calnexin‐GFP and L. pneumophila labeled with the red fluorescent protein DsRed. A proteome analysis of purified LCVs by liquid chromatography coupled to tandem mass spectrometry revealed 566 host proteins, including known LCV components, such as the small GTPases Arf1, Rab1 and Rab7. Rab8, an endosomal regulator of the late secretory pathway originating from the trans Golgi network, and the endosomal GTPase Rab14 were identified as novel LCV components, which were found to be present on vacuoles harboring wild‐type but not Icm/Dot‐deficient L. pneumophila. Thus, LCVs also communicate with the late secretory and endosomal pathways. Depletion of Rab8 or Arf1 by RNA interference reduced the amount of SidC on LCVs, indicating that the GTPases promote the recruitment of Legionella effectors by regulating the level of PtdIns(4)P.

An Integrated, Directed Mass Spectrometric Approach for In-depth Characterization of Complex Peptide Mixtures

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.

Analysis of Cell Surface Proteome Changes via Label-free, Quantitative Mass Spectrometry

We present a mass spectrometry-based strategy for the specific detection and quantification of cell surface proteome changes. The method is based on the label-free quantification of peptide patterns acquired by high mass accuracy mass spectrometry using new software tools and the cell surface capturing technology that selectively enriches glycopeptides exposed to the cell exterior. The method was applied to monitor dynamic protein changes in the cell surface glycoproteome of Drosophila melanogaster cells. The results led to the construction of a cell surface glycoprotein atlas consisting of 202 cell surface glycoproteins of D. melanogaster Kc167 cells and indicated relative quantitative changes of cell surface glycoproteins in four different cellular states. Furthermore we specifically investigated cell surface proteome changes upon prolonged insulin stimulation. The data revealed insulin-dependent cell surface glycoprotein dynamics, including insulin receptor internalization, and linked these changes to intracellular signaling networks.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

BackgroundQuantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.ResultsWe have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.ConclusionThe Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

Interactome of the Amyloid Precursor Protein APP in Brain Reveals a Protein Network Involved in Synaptic Vesicle Turnover and a Close Association with Synaptotagmin-1

Knowledge of the protein networks interacting with the amyloid precursor protein (APP) in vivo can shed light on the physiological function of APP. To date, most proteins interacting with the APP intracellular domain (AICD) have been identified by Yeast Two Hybrid screens which only detect direct interaction partners. We used a proteomics-based approach by biochemically isolating tagged APP from the brains of transgenic mice and subjecting the affinity-purified complex to mass spectrometric (MS) analysis. Using two different quantitative MS approaches, we compared the protein composition of affinity-purified samples isolated from wild-type mice versus transgenic mice expressing tagged APP. This enabled us to assess truly enriched proteins in the transgenic sample and yielded an overlapping set of proteins containing the major proteins involved in synaptic vesicle endo- and exocytosis. Confocal microscopy analyses of cotransfected primary neurons showed colocalization of APP with synaptic vesicle proteins in vesicular structures throughout the neurites. We analyzed the interaction of APP with these proteins using pulldown experiments from transgenic mice or cotransfected cells followed by Western blotting. Synaptotagmin-1 (Stg1), a resident synaptic vesicle protein, was found to directly bind to APP. We fused Citrine and Cerulean to APP and the candidate proteins and measured fluorescence resonance energy transfer (FRET) in differentiated SH-SY5Y cells. Differentially tagged APPs showed clear sensitized FRET emission, in line with the described dimerization of APP. Among the candidate APP-interacting proteins, again only Stg1 was in close proximity to APP. Our results strongly argue for a function of APP in synaptic vesicle turnover in vivo. Thus, in addition to the APP cleavage product Aβ, which influences synaptic transmission at the postsynapse, APP interacts with the calcium sensor of synaptic vesicles and might thus play a role in the regulation of synaptic vesicle exocytosis.