Christina Rau

Bio: Christina Rau is an academic researcher from GlaxoSmithKline. The author has contributed to research in topics: Chemoproteomics & Proteomics. The author has an hindex of 12, co-authored 19 publications receiving 5657 citations.

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.

Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling

Abstract: The stability of the Wnt pathway transcription factor -catenin is tightly regulated by the multi-subunit destruction complex. Deregulated Wnt pathway activity has been implicated in many cancers, making this pathway an attractive target for anticancer therapies. However, the development of targeted Wnt pathway inhibitors has been hampered by the limited number of pathway components that are amenable to small molecule inhibition. Here, we used a chemical genetic screen to identify a small molecule, XAV939, which selectively inhibits -catenin-mediated transcription. XAV939 stimulates -catenin degradation by stabilizing axin, the concentration-limiting component of the destruction complex. Using a quantitative chemical proteomic approach, we discovered that XAV939 stabilizes axin by inhibiting the poly-ADP-ribosylating enzymes tankyrase 1 and tankyrase 2. Both tankyrase isoforms interact with a highly conserved domain of axin and stimulate its degradation through the ubiquitin-proteasome pathway. Thus, our study provides new mechanistic insights into the regulation of axin protein homeostasis and presents new avenues for targeted Wnt pathway therapies.

Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors

Abstract: We describe a chemical proteomics approach to profile the interaction of small molecules with hundreds of endogenously expressed protein kinases and purine-binding proteins. This subproteome is captured by immobilized nonselective kinase inhibitors (kinobeads), and the bound proteins are quantified in parallel by mass spectrometry using isobaric tags for relative and absolute quantification (iTRAQ). By measuring the competition with the affinity matrix, we assess the binding of drugs to their targets in cell lysates and in cells. By mapping drug-induced changes in the phosphorylation state of the captured proteome, we also analyze signaling pathways downstream of target kinases. Quantitative profiling of the drugs imatinib (Gleevec), dasatinib (Sprycel) and bosutinib in K562 cells confirms known targets including ABL and SRC family kinases and identifies the receptor tyrosine kinase DDR1 and the oxidoreductase NQO2 as novel targets of imatinib. The data suggest that our approach is a valuable tool for drug discovery.

A selective inhibitor reveals PI3Kγ dependence of T H 17 cell differentiation

Abstract: We devised a high-throughput chemoproteomics method that enabled multiplexed screening of 16,000 compounds against native protein and lipid kinases in cell extracts. Optimization of one chemical series resulted in CZC24832, which is to our knowledge the first selective inhibitor of phosphoinositide 3-kinase γ (PI3Kγ) with efficacy in in vitro and in vivo models of inflammation. Extensive target- and cell-based profiling of CZC24832 revealed regulation of interleukin-17-producing T helper cell (T(H)17) differentiation by PI3Kγ, thus reinforcing selective inhibition of PI3Kγ as a potential treatment for inflammatory and autoimmune diseases.

Targeting cancer with small molecule kinase inhibitors

Abstract: Deregulation of kinase activity has emerged as a major mechanism by which cancer cells evade normal physiological constraints on growth and survival. To date, 11 kinase inhibitors have received US Food and Drug Administration approval as cancer treatments, and there are considerable efforts to develop selective small molecule inhibitors for a host of other kinases that are implicated in cancer and other diseases. Herein we discuss the current challenges in the field, such as designing selective inhibitors and developing strategies to overcome resistance mutations. This Review provides a broad overview of some of the approaches currently used to discover and characterize new kinase inhibitors.

A quantitative analysis of kinase inhibitor selectivity.

Abstract: Kinase inhibitors are a new class of therapeutics with a propensity to inhibit multiple targets. The biological consequences of multi-kinase activity are poorly defined, and an important step toward understanding the relationship between selectivity, efficacy and safety is the exploration of how inhibitors interact with the human kinome. We present interaction maps for 38 kinase inhibitors across a panel of 317 kinases representing >50% of the predicted human protein kinome. The data constitute the most comprehensive study of kinase inhibitor selectivity to date and reveal a wide diversity of interaction patterns. To enable a global analysis of the results, we introduce the concept of a selectivity score as a general tool to quantify and differentiate the observed interaction patterns. We further investigate the impact of panel size and find that small assay panels do not provide a robust measure of selectivity.

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.