Ulrike Korf

German Cancer Research Center, Heidelburg, Baden-Württemberg, Germany

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Publications (54)217.44 Total impact

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    ABSTRACT: The hepatic hormone hepcidin is a key regulator of systemic iron metabolism. Its expression is largely regulated by two signaling pathways, the 'iron-regulated' bone morphogenetic protein (BMP) and the inflammatory JAK-STAT pathways. To obtain broader insights into cellular processes that modulate hepcidin transcription and to provide a resource to identify novel genetic modifiers of systemic iron homeostasis we designed an RNAi screen that monitors hepcidin promoter activity following the knock-down of 19 599 genes in hepatocarcinoma cells. Interestingly, many of the putative hepcidin activators play roles in signal transduction, inflammation or transcription and affect hepcidin transcription through BMP-responsive elements. Furthermore, our work sheds light on new components of the transcriptional machinery that maintain steady-state levels of hepcidin expression and its responses to the BMP- and IL-6-triggered signals. Notably, we discover hepcidin suppression mediated via components of Ras/RAF MAPK and mTOR signaling, linking hepcidin transcriptional control to the pathways that respond to mitogen stimulation and nutrient status. Thus, using a combination of RNAi screening, reverse phase protein arrays and small molecules testing, we identify links between the control of systemic iron homeostasis and critical liver processes such as regeneration, response to injury, carcinogenesis as well as nutrient metabolism.
    Blood 01/2014; · 9.06 Impact Factor
  • Translational Proteomics. 01/2014;
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    ABSTRACT: The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non small cell lung cancer (NSCL) cell lines and monitored the activation state of selected receptor tyrosine kinases, PI3K/AKT, MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT-, RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA was identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.
    Biochimica et Biophysica Acta 12/2013; · 4.66 Impact Factor
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    ABSTRACT: Aberrant activation of Hedgehog (HH) signaling has been identified as a key etiologic factor in many human malignancies. Signal strength, target gene specificity, and oncogenic activity of HH signaling depend profoundly on interactions with other pathways, such as epidermal growth factor receptor-mediated signaling, which has been shown to cooperate with HH/GLI in basal cell carcinoma and pancreatic cancer. Our experimental data demonstrated that the Daoy human medulloblastoma cell line possesses a fully inducible endogenous HH pathway. Treatment of Daoy cells with Sonic HH or Smoothened agonist induced expression of GLI1 protein and simultaneously prevented the processing of GLI3 to its repressor form. To study interactions between HH- and EGF-induced signaling in greater detail, time-resolved measurements were carried out and analyzed at the transcriptomic and proteomic levels. The Daoy cells responded to the HH/EGF co-treatment by downregulating GLI1, PTCH, and HHIP at the transcript level; this was also observed when Amphiregulin (AREG) was used instead of EGF. We identified a novel crosstalk mechanism whereby EGFR signaling silences proteins acting as negative regulators of HH signaling, as AKT- and ERK-signaling independent process. EGFR/HH signaling maintained high GLI1 protein levels which contrasted the GLI1 downregulation on the transcript level. Conversely, a high-level synergism was also observed, due to a strong and significant upregulation of numerous canonical EGF-targets with putative tumor-promoting properties such as MMP7, VEGFA, and IL-8. In conclusion, synergistic effects between EGFR and HH signaling can selectively induce a switch from a canonical HH/GLI profile to a modulated specific target gene profile. This suggests that there are more wide-spread, yet context-dependent interactions, between HH/GLI and growth factor receptor signaling in human malignancies.
    PLoS ONE 01/2013; 8(6):e65403. · 3.73 Impact Factor
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    ABSTRACT: BACKGROUND: Tri- and tetra-nucleotide repeats in mammalian genomes can induce formation of alternative non-B DNA structures such as triplexes and guanine (G)-quadruplexes. These structures can induce mutagenesis, chromosomal translocations and genomic instability. We wanted to determine if proteins that bind triplex DNA structures are quantitatively or qualitatively different between colorectal tumor and adjacent normal tissue and if this binding activity correlates with patient clinical characteristics. METHODS: Extracts from 63 human colorectal tumor and adjacent normal tissues were examined by gel shifts (EMSA) for triplex DNA-binding proteins, which were correlated with clinicopathological tumor characteristics using the Mann-Whitney U, Spearman's rho, Kaplan-Meier and Mantel-Cox log-rank tests. Biotinylated triplex DNA and streptavidin agarose affinity binding were used to purify triplex-binding proteins in RKO cells. Western blotting and reverse-phase protein array were used to measure protein expression in tissue extracts. RESULTS: Increased triplex DNA-binding activity in tumor extracts correlated significantly with lymphatic disease, metastasis, and reduced overall survival. We identified three multifunctional splicing factors with biotinylated triplex DNA affinity: U2AF65 in cytoplasmic extracts, and PSF and p54nrb in nuclear extracts. Super-shift EMSA with anti-U2AF65 antibodies produced a shifted band of the major EMSA H3 complex, identifying U2AF65 as the protein present in the major EMSA band. U2AF65 expression correlated significantly with EMSA H3 values in all extracts and was higher in extracts from Stage III/IV vs. Stage I/II colon tumors (p = 0.024). EMSA H3 values and U2AF65 expression also correlated significantly with GSK3 beta, beta-catenin, and NF- B p65 expression, whereas p54nrb and PSF expression correlated with c-Myc, cyclin D1, and CDK4. EMSA values and expression of all three splicing factors correlated with ErbB1, mTOR, PTEN, and Stat5. Western blots confirmed that full-length and truncated beta-catenin expression correlated with U2AF65 expression in tumor extracts. CONCLUSIONS: Increased triplex DNA-binding activity in vitro correlates with lymph node disease, metastasis, and reduced overall survival in colorectal cancer, and increased U2AF65 expression is associated with total and truncated beta-catenin expression in high-stage colorectal tumors.
    Molecular Cancer 06/2012; 11(1):38. · 5.13 Impact Factor
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    ABSTRACT: The supporting role of urokinase-type plasminogen activator (uPA) and its inhibitor plasminogen activator inhibitor 1 (PAI-1) in migration and invasion is well known. In addition, both factors are key components in cancer cell-related signaling. However, little information is available for uPA and PAI-1-associated signaling pathways in primary cancers and corresponding lymph node metastases. The aim of this study was to compare the expression of uPA and PAI-1-associated signaling proteins in 52 primary breast cancers and corresponding metastases. Proteins were extracted from formalin-fixed paraffin-embedded tissue samples of the primary tumors and metastases. Protein lysates were subsequently analyzed by reverse phase protein array for the expression of members of the PI3K/AKT (FAK, GSK3-β, ILK, pGSK3-β, PI3K, and ROCK) and the MAPK pathways (pp38, pSTAT3, and p38). A solid correlation of uPA expression existed between primary tumors and metastases, whereas PAI-1 expression did not significantly correlate between them. The correlations of uPA and PAI-1 with signaling pathways found in primary tumors did not persist in metastases. Analysis of single molecules revealed that some correlated well between tumors and metastases (FAK, pGSK3-β, ILK, Met, PI3K, ROCK, uPA, p38, and pp38), whereas others did not (PAI-1 and GSK3-β). Whether the expression of a protein correlated between tumor and metastasis or not was independent of the pathway the protein is related to. These findings hint at a complete deregulation of uPA and PAI-1-related signaling in metastases, which might be the reason why uPA and PAI-1 reached clinical relevance only for lymph node-negative breast cancer tissues.
    Translational oncology 04/2012; 5(2):98-104. · 3.40 Impact Factor
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    ABSTRACT: The present study aimed to investigate the proteome profiling of surgically treated prostate cancers. Hereto, 2D-DIGE and mass spectrometry were performed for protein identification, and data validation for peroxiredoxin 3 and 4 (PRDX3 and PRDX4) was accomplished by reverse phase protein arrays (RPPA). The Formal Concept Analysis (FCA) method was applied to assess whether the TMPRSS2-ERG gene fusion could influence the degree of overexpression of PRDX3 and PRDX4 in prostate cancer. Lastly, we performed an in vitro functional characterization of both PRDX3 and PRDX4 using the classical human prostate cancer cell lines DU145 and LNCaP. Reverse phase protein arrays verified that the overexpression of both PRDX3 and PRDX4 in tumor samples is negatively correlated with the presence of the TMPRSS2-ERG gene fusion. Functional characterization of PRDX3 and PRDX4 activity in PCa cell lines suggests a role of these members of the peroxiredoxin family in the pathophysiology of this tumor entity.
    Journal of Proteome Research 03/2012; 11(4):2452-66. · 5.06 Impact Factor
  • Molecular Systems Biology 02/2012; 8:570. · 11.34 Impact Factor
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    ABSTRACT: Increasing the efficacy of targeted cancer therapies requires the identification of robust biomarkers suitable for patient stratification. This study focused on the identification of molecular mechanisms causing resistance against the anti-ERBB2-directed therapeutic antibodies trastuzumab and pertuzumab presently used to treat patients with ERBB2-amplified breast cancer. Immunohistochemistry and clinical data were evaluated and yielded evidence for the existence of ERBB2-amplified breast cancer with high-level epidermal growth-factor receptor (EGFR) expression as a separate tumor entity. Because the proto-oncogene EGFR tightly interacts with ERBB2 on the protein level, the hypothesis that high-level EGFR expression might contribute to resistance against ERBB2-directed therapies was experimentally validated. SKBR3 and HCC1954 cells were chosen as model systems of EGFR-high/ERBB2-amplified breast cancer and exposed to trastuzumab, pertuzumab and erlotinib, respectively, and in combination. Drug impact was quantified in cell viability assays and on the proteomic level using reverse-phase protein arrays. Phosphoprotein dynamics revealed a significant downregulation of AKT signaling after exposure to trastuzumab, pertuzumab or a coapplication of both antibodies in SKBR3 cells but no concomitant impact on ERK1/2, RB or RPS6 phosphorylation. On the other hand, signaling was fully downregulated in SKBR3 cells after coinhibition of EGFR and ERBB2. Inhibitory effects in HCC1954 cells were driven by erlotinib alone, and a significant upregulation of RPS6 and RB phosphorylation was observed after coincubation with pertuzumab and trastuzumab. In summary, proteomic data suggest that high-level expression of EGFR in ERBB2-amplified breast cancer cells attenuates the effect of anti-ERBB2-directed antibodies. In conclusion, EGFR expression may serve as diagnostic and predictive biomarker to advance personalized treatment concepts of patients with ERBB2-amplified breast cancer.
    Oncogenesis. 01/2012; 1:e16.
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    ABSTRACT: The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.
    Molecular Systems Biology 01/2012; 8:570. · 11.34 Impact Factor
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    ABSTRACT: Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction, and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g., the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid, and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor. Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger cohort of patients and matched controls will confirm our preliminary findings presented here.
    Frontiers in Physiology 01/2012; 3:339.
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    ABSTRACT: TMPRSS2-ERG gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although TMPRSS2-ERG fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear. We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays. Comparison of gene expression levels among TMPRSS2-ERG fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like CRISP3 were found to be associated with the gene fusion status. WNT and TGF-β/BMP signaling pathways were significantly associated with genes upregulated in TMPRSS2-ERG fusion-positive tumors. The TMPRSS2-ERG gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.
    BMC Cancer 12/2011; 11:507. · 3.33 Impact Factor
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    ABSTRACT: Reverse phase protein arrays (RPPAs) emerged as a very useful tool for high-throughput screening of protein expression in large numbers of small specimen. Similar to other protein chemistry methods, antibody specificity is also a major concern for RPPA. Currently, testing antibodies on Western blot for specificity and applying serial dilution curves to determine signal/concentration linearity of RPPA signals are most commonly employed to validate antibodies for RPPA applications. However, even the detection antibodies fulfilling both requirements do not always give the expected result. Chemically synthesized small interfering RNAs (siRNAs) are one of the most promising and time-efficient tools for loss-of-function studies by specifically targeting the gene of interest resulting in a reduction at the protein expression level, and are therefore used to dissect biological processes. Here, we report the utilization of siRNA-treated sample lysates for the quantification of a protein of interest as a useful and reliable tool to validate antibody specificity for RPPAs. As our results indicate, we recommend the use of antibodies which give the highest dynamic range between the control siRNA-treated samples and the target protein (here: EGFR) siRNA-treated ones on RPPAs, to be able to quantify even small differences of protein abundance with high confidence.
    Methods in molecular biology (Clifton, N.J.) 01/2011; 785:45-54.
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    ABSTRACT: To expedite the development of personalized medicine, new and reliable biomarkers are required to facilitate early diagnosis, to determine prognosis, predict response or resistance to different therapies, and to monitor disease progression or recurrence. Human body fluids, such as blood, present a promising resource for biomarker discovery, in every sense. Microspot immunoassays allow the simultaneous quantification of multiple analytes from a minute amount of samples in a single measurement. The experimental design of microspot immunoassays is based on antibody pairs recognizing different epitopes of the analyte. The first antibody is used to capture the analyte from the complex sample, and the second antibody is used for detection. As with traditional enzyme-linked immunosorbent assays, highly reliable and reproducible results are obtained.
    Methods in molecular biology (Clifton, N.J.) 01/2011; 785:237-45.
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    ABSTRACT: Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition) and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.
    BMC Bioinformatics 01/2011; 12:291. · 3.02 Impact Factor
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    ABSTRACT: Protein microarrays are an ideal technology platform which allow for a robust and standardized profiling of the cellular proteome. Many cellular functions are not simply controlled by the presence of certain proteins, especially the propagation of external stimuli, which depend on transient post-translational modifications that determine whether a protein is in its active or inactive state. Thus, complex biological processes require the availability of a sound set of quantitative and time-resolved measurements to be understood. For this reason, new assay platforms which allow for the investigation of several proteins in parallel are necessary. The current best understood mode of cellular regulation occurs via phosphorylation and dephosphorylation processes, which are mediated via a large panel of kinases and phosphatases. The microspot immunoassay technique described here allows for an exact determination of several different phosphorylated proteins in parallel, as well as from small sample amounts, and is therefore an appropriate system to deepen the understanding of the complex regulatory networks implicated in health and disease.
    Methods in molecular biology (Clifton, N.J.) 01/2011; 785:191-201.
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    ABSTRACT: Reverse phase protein array (RPPA) techniques allow the quantitative analysis of signal transduction events in a high-throughput format. Sensitivity is important for RPPA-based detection approaches, since numerous signaling proteins or posttranslational modifications are present at low levels. Especially, the proteomic analysis of clinical samples exposes its own challenges with respect to sensitivity. Antibody-mediated signal amplification (AMSA) is a novel strategy relying on sequential incubation steps with fluorescently labeled secondary antibodies reactive against each other. AMSA is a simple extension of the standard quantification in the near-infrared range and is highly specific and robust. In this chapter, we present the amplification protocol and application examples for the time-resolved analysis of signaling pathways as well as protein profiling of clinical samples.
    Methods in molecular biology (Clifton, N.J.) 01/2011; 785:55-64.
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    ABSTRACT: Protein microarrays are well-established as sensitive tools for proteomics. Particularly, the microspot immunoassay (MIA) platform enables a quantitative analysis of (phospho-) proteins in complex solutions (e.g. cell lysates or blood plasma) and with low consumption of samples and reagents. Despite numerous biological and clinical applications of MIAs there is currently no user-friendly open source data analysis software available with versatile options for data analysis and data visualization. Here, we introduce the open source software QuantProReloaded that is specifically designed for the analysis of data from MIA experiments. Availability and implementation: QuantProReloaded is written in R and Java and is open for download under the BSB license at http://code.google.com/p/quantproreloaded/.
    Bioinformatics 10/2010; 26(19):2480-1. · 5.47 Impact Factor
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    ABSTRACT: To identify new interactions as well as diagnostically, prognostically and therapeutically relevant differences in the regulation of gene expression in gastrointestinal stromal tumors (GISTs), we analyzed the methylation status, mRNA expression, microRNA expression, protein expression and protein phosphorylation in parallel in identical tumor tissue samples. The data were analyzed in a multilayer approach and were correlated to each other and to clinico-pathological parameters. Differentially regulated genes were mapped to signal transduction pathways which are already known to play a major role in GISTs. A functionally orientated overview of the different data layers was constructed, which enabled new insights into gene regulation in GISTs.
    Der Pathologe 10/2010; 31 Suppl 2:134-7. · 0.62 Impact Factor
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    ABSTRACT: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays. Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. Dynamic deterministic effects propagation networks is implemented in the R programming language and available at http://www.dkfz.de/mga2/ddepn/.
    Bioinformatics 09/2010; 26(18):i596-602. · 5.47 Impact Factor

Publication Stats

644 Citations
393 Downloads
4k Views
217.44 Total Impact Points

Institutions

  • 2004–2014
    • German Cancer Research Center
      • Division of Molecular Genome Analysis
      Heidelburg, Baden-Württemberg, Germany
  • 2010
    • Universitätsklinikum Freiburg
      Freiburg an der Elbe, Lower Saxony, Germany