Birgit Dümpelfeld

Bio: Birgit Dümpelfeld is an academic researcher from GlaxoSmithKline. The author has contributed to research in topics: Protein subunit & Proteome. The author has an hindex of 4, co-authored 7 publications receiving 3242 citations. Previous affiliations of Birgit Dümpelfeld include Center for Integrated Protein Science Munich.

Proteome survey reveals modularity of the yeast cell machinery

Abstract: Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.

Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes

Abstract: The development of selective histone deacetylase (HDAC) inhibitors with anti-cancer and anti-inflammatory properties remains challenging in large part owing to the difficulty of probing the interaction of small molecules with megadalton protein complexes. A combination of affinity capture and quantitative mass spectrometry revealed the selectivity with which 16 HDAC inhibitors target multiple HDAC complexes scaffolded by ELM-SANT domain subunits, including a novel mitotic deacetylase complex (MiDAC). Inhibitors clustered according to their target profiles with stronger binding of aminobenzamides to the HDAC NCoR complex than to the HDAC Sin3 complex. We identified several non-HDAC targets for hydroxamate inhibitors. HDAC inhibitors with distinct profiles have correspondingly different effects on downstream targets. We also identified the anti-inflammatory drug bufexamac as a class IIb (HDAC6, HDAC10) HDAC inhibitor. Our approach enables the discovery of novel targets and inhibitors and suggests that the selectivity of HDAC inhibitors should be evaluated in the context of HDAC complexes and not purified catalytic subunits.

Purification, Pharmacological Modulation, and Biochemical Characterization of Interactors of Endogenous Human γ-Secretase†

Abstract: γ-Secretase is a unique intramembrane-cleaving protease complex, which cleaves the Alzheimer′s disease-associated β-amyloid precursor protein (APP) and a number of other type I membrane proteins. Human γ-secretase consists of the catalytic subunit presenilin (PS) (PS1 or PS2), the substrate receptor nicastrin, APH-1 (APH-1a or APH-1b), and PEN-2. To facilitate in-depth biochemical analysis of γ-secretase, we developed a fast and convenient multistep purification procedure for the endogenous enzyme. The enzyme was purified from HEK293 cells in an active form and had a molecular mass of ∼500 kDa. Purified γ-secretase was capable of producing the major amyloid-β peptide (Aβ) species, such as Aβ40 and Aβ42, from a recombinant APP substrate in physiological ratios. Aβ generation could be modulated by pharmacological γ-secretase modulators. Moreover, the Aβ42/Aβ40 ratio was strongly increased by purified PS1 L166P, an aggressive familial Alzheimer’s disease mutant. Tandem mass spectrometry analysis revealed the...

Femtomol sensitivity post-digest 18O labeling for relative quantification of differential protein complex composition

Abstract: Stable isotope labeling (SIL) has emerged as a powerful tool to measure the relative quantitative differences between samples in many differential display-type proteomic applications. However, current SIL procedures tend to suffer from the fact that one needs to decide very early in a biochemical strategy whether or not a sample will be subjected to relative quantification. Typically, the entire strategy has to be adapted to the needs of the particular quantification method chosen which might limit the range of biochemical experiments amenable to quantification. Metabolic labeling approaches, albeit very sensitive, can only be applied to studies using appropriate cell culture systems which might not necessarily be compatible with the biological system under investigation. Chemical labeling of complex protein mixtures by, e.g., isotope-coded affinity tags (ICAT), can offer great simplification of protein mixtures but is restricted by the accessibility of the often few suitable peptides (i.e. cysteine containing peptides) for both protein identification and quantification. Here, we describe a post-digest 18O-labeling method that can circumvent some of the above limitations by separating protein identification from quantification. An aliquot of all samples in a set can be used for rapid protein ID using, e.g., matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). In a second step, relative quantification is performed using trypsin-catalyzed 18O incorporation into all tryptic peptides. This two-stage procedure introduces significant experimental flexibility because it enables postponement of the decision about which pairs of samples from a given set of experiments are to be compared until after the protein ID stage. In-gel digested protein quantities between 50 fmol and 15 pmol are amenable to this new method, with a dynamic range of 1:10 within one sample. Accuracy for measured relative abundances is similar to those reported for other SIL strategies (errors typically <20%), and the method is applicable to protein samples from all kinds of tissue or cell culture. This paper presents quantification data for a set of standard proteins, as well as a study of differential complex formation around the NFκB transcription factor p65 following stimulation with TNF-α. Copyright © 2004 John Wiley & Sons, Ltd.

Optimization of Orally Bioavailable PI3Kδ Inhibitors and Identification of Vps34 as a Key Selectivity Target.

Abstract: Optimization of a lead series of PI3Kδ inhibitors based on a dihydroisobenzofuran core led to the identification of potent, orally bioavailable compound 19. Selectivity profiling of compound 19 showed similar potency for class III PI3K, Vps34, and PI3Kδ, and compound 19 was not well-tolerated in a 7-day rat toxicity study. Structure-based design led to an improvement in selectivity for PI3Kδ over Vps34 and, a focus on oral phramacokinetics properties resulted in the discovery of compound 41, which showed improved toxicological outcomes at similar exposure levels to compound 19.

The genetic landscape of a cell.

Abstract: A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

Mass spectrometry-based proteomics turns quantitative.

Abstract: The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.

Link communities reveal multiscale complexity in networks

Abstract: Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon.

High-Quality Binary Protein Interaction Map of the Yeast Interactome Network

Abstract: Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.