Functional proteomics of Bcr-Abl signaling

Die konstitutiv aktive onkogene Fusions-Tyrosin-Kinase Bcr-Abl resultiert aus einer reziproken chromosomalen Translokation (t(9;22), ‚Philadelphia Chromosom’). Die konstitutive Tyrosin-Kinaseaktivität von Bcr-Abl ist Auslöser der Chronisch Myeloischen Leukämie (CML) und das Ziel von Tyrosin-Kinaseinhibitoren wie Imatinib (Gleevec®, Novartis). Trotz des Erfolges von Imatinib entwickeln Patienten Resistenzen und werden rückfällig oder sprechen nicht auf die Therapie an, wenn diese erst in fortgeschrittenen Krankheitsstadien initiiert wird. Dennoch ist die genaue Beschaffenheit des endogenen Arzneistoffziels Bcr-Abl noch unbekannt. Daher haben wir das Protein-Interaktionsnetzwerk um Bcr-Abl mit einer doppelten proteomischen Herangehensweise systematisch kartiert: Anhand eines monoklonalen Antikörpers haben wir zunächst endogene Bcr-Abl - Proteinkomplexe aus der CML-Zelllinie K562 gewonnen und massenspektrometrisch die achtzehn engsten Interaktoren charakterisiert. Nach ‚Tandem-Affinitätsreinigung’ von neun dieser Kandidaten und massenspektrometrischer Analyse haben wir ein Interaktionsnetzwerk erstellt, das eine intensive Vernetzung der sieben ‚Kernkomponenten’ (Ship2, c-Cbl, p85, Sts-1, Shc1, Grb2 und Crk-I) mit Bcr-Abl und deren Verknüpfung mit dem AP2-Adapterproteinkomplex und verschiedenen Signaltransduktionswegen offenbarte. Quantitative Analysen dieser sieben Komponenten zeigten, dass sich deren absolute Kopienzahl innerhalb einer Größenordnung bewegt und auf 200,000 - 300,000 stöchiometrische Komplexe pro Zelle schließen lässt, wobei die 5-Inositol-Phosphatase Ship2 die höchste Interaktionsstöchiometrie aufweist. Massenspektrometrische Quantifizierungen zeigten, dass Tyrosin-Kinaseinhibitoren den Bcr-Abl-’Kernkomplex’ zerstören, einige Komponenten aber in Phospho-Tyrosin-unabhängiger Weise mit Bcr-Abl verbunden bleiben. Wir kommen daher zu der Hypothese, dass Bcr-Abl und auch andere Arzneistoffziele nicht nur als einzelne Polypeptide, sondern vielmehr als komplexe Proteinverbände zu betrachten sind, die durch Arzneistoffe zerstört oder verändert werden.The constitutively active oncogenic fusion tyrosine kinase Bcr-Abl is the product of a reciprocal chromosomal translocation (t(9;22), ‘Philadelphia Chromosome’). The constitutive tyrosine kinase activity is central to Bcr-Abl’s ability to cause chronic myeloid leukemia (CML) and is the target of tyrosine kinase inhibitors like imatinib (Gleevec®, Novartis). Despite the success of imatinib, many patients either develop resistance to the drugs and eventually relapse or they do not respond, when the treatment is initiated in advanced stages of the disease. However, the actual molecular setup of the endogenous drug target Bcr-Abl is still unknown. We have therefore charted the protein-protein interaction network of Bcr-Abl by a systematic two-pronged proteomics approach: Using a monoclonal antibody we have first purified endogenous Bcr-Abl protein complexes from the CML cell line K562 and characterized a set of eighteen most tightly associated interactors by mass spectrometry. Nine interactors were subsequently subjected to tandem affinity purification/mass spectrometry analysis to obtain a molecular interaction network. The resulting network revealed a high degree of interconnection of seven 'core' components around Bcr-Abl (Ship2, c-Cbl, p85, Sts-1, Shc1, Grb2 and Crk-I), as well as their links to the AP2 adaptor protein complex and different signaling pathways. Quantitative analysis of these Bcr-Abl ‘core’ complex components showed that the absolute protein copy numbers range within one order of magnitude consistent with a stoichiometric complex of around 200,000 to 300,000 copies per cell and revealed the highest interaction stoichiometry for the 5-inositol phosphatase Ship2. Quantitative proteomics analysis showed that tyrosine kinase inhibitors disrupt this complex, while certain components still appear to interact with Bcr-Abl in a phospho-tyrosine-independent manner. The results of this study therefore motivate us to propose that Bcr-Abl and other drug targets, rather than being considered as single polypeptides, can be considered as complex protein assemblies that are disrupted or re-modeled upon drug action

Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens

The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two protein families: the ATP-Binding Cassette (ABC) transporter family, whose members are often involved in xenobiotic efflux and drug resistance, and the large and heterogeneous family of solute carriers (SLCs). We recently argued that SLCs are collectively a rather neglected gene group, with most of its members still poorly characterized, and thus likely to include many yet-to-be-discovered associations with drugs. We searched publicly available resources and literature to define the currently known set of drugs transported by ABCs or SLCs, which involved ∼500 drugs and more than 100 transporters. In order to extend this set, we then mined the largest publicly available pharmacogenomics dataset, which involves approximately 1,000 molecularly annotated cancer cell lines and their response to 265 anti-cancer compounds, and used regularized linear regression models (Elastic Net, LASSO) to predict drug responses based on SLC and ABC data (expression levels, SNVs, CNVs). The most predictive models included both known and previously unidentified associations between drugs and transporters. To our knowledge, this represents the first application of regularized linear regression to this set of genes, providing an extensive prioritization of potentially pharmacologically interesting interactions

Model systems of protein-misfolding diseases reveal chaperone modifiers of proteotoxicity

Chaperones and co-chaperones enable protein folding and degradation, safeguarding the proteome against proteotoxic stress. Chaperones display dynamic responses to exogenous and endogenous stressors and thus constitute a key component of the proteostasis network (PN), an intricately regulated network of quality control and repair pathways that cooperate to maintain cellular proteostasis. It has been hypothesized that aging leads to chronic stress on the proteome and that this could underlie many age-associated diseases such as neurodegeneration. Understanding the dynamics of chaperone function during aging and disease-related proteotoxic stress could reveal specific chaperone systems that fail to respond to protein misfolding. Through the use of suppressor and enhancer screens, key chaperones crucial for proteostasis maintenance have been identified in model organisms that express misfolded disease-related proteins. This review provides a literature-based analysis of these genetic studies and highlights prominent chaperone modifiers of proteotoxicity, which include the HSP70-HSP40 machine and small HSPs. Taken together, these studies in model systems can inform strategies for therapeutic regulation of chaperone functionality, to manage aging-related proteotoxic stress and to delay the onset of neurodegenerative diseases

Proteostasis network deregulation signatures as biomarkers for pharmacological disease intervention

Protein homeostasis, or proteostasis, is fundamental to cellular and organismal health. Proteostasis collapse is linked to diverse diseases, including neurodegeneration and cancers. The proteostasis network (PN) comprises the intricately regulated interplay of signaling processes and molecular machines involved in the synthesis, folding, and clearance of the diverse spectrum of proteins comprising the folded, native proteome. Human disease biomarkers are important tools for early detection, individualized phenotyping, and patient stratification and for companion diagnostic use during therapy. With the increasing knowledge and understanding of PN disease alterations, various strategies, such as the modulation of chaperone levels or interference with proteasomal activity, for the therapeutic adjustment of proteostasis deregulation have been devised. To complement the tool kit of therapeutic strategies through chemical chaperones or proteostasis regulator drugs, context-specific biomarkers of PN deregulation will provide important guidance for precise pharmacological proteostasis regulation. Here, we summarize representative studies contributing to our understanding of proteostasis deregulation in age-onset neurodegeneration and cancers, with a focus on the chaperome. We call for a systematic mapping and assessment of the global PN interactome network as a resource for the elucidation of diagnostic and prognostic proteostasis biomarkers

A core transcriptional network for early mesoderm development in Drosophila melanogaster

Embryogenesis is controlled by large gene-regulatory networks, which generate spatially and temporally refined patterns of gene expression. Here, we report the characteristics of the regulatory network orchestrating early mesodermal development in the fruitfly Drosophila, where the transcription factor Twist is both necessary and sufficient to drive development. Through the integration of chromatin immunoprecipitation followed by microarray analysis (ChIP-on-chip) experiments during discrete time periods with computational approaches, we identified >2000 Twist-bound cis-regulatory modules (CRMs) and almost 500 direct target genes. Unexpectedly, Twist regulates an almost complete cassette of genes required for cell proliferation in addition to genes essential for morophogenesis and cell migration. Twist targets almost 25% of all annotated Drosophila transcription factors, which may represent the entire set of regulators necessary for the early development of this system. By combining in vivo binding data from Twist, Mef2, Tinman, and Dorsal we have constructed an initial transcriptional network of early mesoderm development. The network topology reveals extensive combinatorial binding, feed-forward regulation, and complex logical outputs as prevalent features. In addition to binary activation and repression, we suggest that Twist binds to almost all mesodermal CRMs to provide the competence to integrate inputs from more specialized transcription factors

In silico Prioritization of Transporter–Drug Relationships From Drug Sensitivity Screens

The interplay between drugs and cell metabolism is a key factor in determining both compound potency and toxicity. In particular, how and to what extent transmembrane transporters affect drug uptake and disposition is currently only partially understood. Most transporter proteins belong to two protein families: the ATP-Binding Cassette (ABC) transporter family, whose members are often involved in xenobiotic efflux and drug resistance, and the large and heterogeneous family of solute carriers (SLCs). We recently argued that SLCs are collectively a rather neglected gene group, with most of its members still poorly characterized, and thus likely to include many yet-to-be-discovered associations with drugs. We searched publicly available resources and literature to define the currently known set of drugs transported by ABCs or SLCs, which involved 500 drugs and more than 100 transporters. In order to extend this set, we then mined the largest publicly available pharmacogenomics dataset, which involves approximately 1,000 molecularly annotated cancer cell lines and their response to 265 anti-cancer compounds, and used regularized linear regression models (Elastic Net, LASSO) to predict drug responses based on SLC and ABC data (expression levels, SNVs, CNVs). The most predictive models included both known and previously unidentified associations between drugs and transporters. To our knowledge, this represents the first application of regularized linear regression to this set of genes, providing an extensive prioritization of potentially pharmacologically interesting interactions.(VLID)470350