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Christine Gauglhofer,
Sandra Sagmeister,
Waltraud Schrottmaier,
Carina Fischer,
Chantal Rodgarkia-Dara, Thomas Mohr,
Stefan Stättner,
Christoph Bichler,
Daniela Kandioler,
Fritz Wrba,
Rolf Schulte-Hermann,
Klaus Holzmann,
Michael Grusch,
Brigitte Marian,
Walter Berger,
Bettina Grasl-Kraupp
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ABSTRACT: Fibroblast growth factors (FGFs) and their high-affinity receptors [fibroblast growth factor receptors (FGFRs)] contribute to autocrine and paracrine growth stimulation in several non-liver cancer entities. Here we report that at least one member of the FGF8 subfamily (FGF8, FGF17, and FGF18) was up-regulated in 59% of 34 human hepatocellular carcinoma (HCC) samples that we investigated. The levels of the corresponding receptors (FGFR2, FGFR3, and FGFR4) were also elevated in the great majority of the HCC cases. Overall, 82% of the HCC cases showed overexpression of at least one FGF and/or FGFR. The functional implications of the deregulated FGF/FGFR system were investigated by the simulation of an insufficient blood supply. When HCC-1.2, HepG2, or Hep3B cells were subjected to serum withdrawal or the hypoxia-mimetic drug deferoxamine mesylate, the expression of FGF8 subfamily members increased dramatically. In the serum-starved cells, the incidence of apoptosis was elevated, whereas the addition of FGF8, FGF17, or FGF18 impaired apoptosis, which was associated with phosphorylation of extracellular signal-regulated kinase 1/2 and ribosomal protein S6. In contrast, down-modulation of FGF18 by small interfering RNA (siRNA) significantly reduced the viability of the hepatocarcinoma cells. siRNA targeting FGF18 also impaired the cells' potential to form clones at a low cell density or in soft agar. With respect to the tumor microenvironment, FGF17 and FGF18 stimulated the growth of HCC-derived myofibroblasts, and FGF8, FGF17, and FGF18 induced the proliferation and tube formation of hepatic endothelial cells. CONCLUSION: FGF8, FGF17, and FGF18 are involved in autocrine and paracrine signaling in HCC and enhance the survival of tumor cells under stress conditions, malignant behavior, and neoangiogenesis. Thus, the FGF8 subfamily supports the development and progression of hepatocellular malignancy.
Hepatology 12/2010; 53(3):854-64. · 11.66 Impact Factor
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Helge Wimmer,
Nina C Gundacker,
Johannes Griss,
Verena J Haudek,
Stefan Stättner, Thomas Mohr,
Hannes Zwickl,
Verena Paulitschke,
David M Baron,
Wolfgang Trittner,
Markus Kubicek,
Editha Bayer,
Astrid Slany,
Christopher Gerner
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ABSTRACT: Interpretation of proteome data with a focus on biomarker discovery largely relies on comparative proteome analyses. Here, we introduce a database-assisted interpretation strategy based on proteome profiles of primary cells. Both 2-D-PAGE and shotgun proteomics are applied. We obtain high data concordance with these two different techniques. When applying mass analysis of tryptic spot digests from 2-D gels of cytoplasmic fractions, we typically identify several hundred proteins. Using the same protein fractions, we usually identify more than thousand proteins by shotgun proteomics. The data consistency obtained when comparing these independent data sets exceeds 99% of the proteins identified in the 2-D gels. Many characteristic differences in protein expression of different cells can thus be independently confirmed. Our self-designed SQL database (CPL/MUW - database of the Clinical Proteomics Laboratories at the Medical University of Vienna accessible via www.meduniwien.ac.at/proteomics/database) facilitates (i) quality management of protein identification data, which are based on MS, (ii) the detection of cell type-specific proteins and (iii) of molecular signatures of specific functional cell states. Here, we demonstrate, how the interpretation of proteome profiles obtained from human liver tissue and hepatocellular carcinoma tissue is assisted by the Clinical Proteomics Laboratories at the Medical University of Vienna-database. Therefore, we suggest that the use of reference experiments supported by a tailored database may substantially facilitate data interpretation of proteome profiling experiments.
Electrophoresis 07/2009; 30(12):2076-89. · 3.30 Impact Factor
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ABSTRACT: Interpretation of proteome profiling experiments largely relies on comparative analyses. False-positive identifications may cause fatal misinterpretation of data. On the other hand, proteome analysis may also suffer from false negatives, when proteins that are actually present are not detected. This circumstance may be as fatal as false-positive identifications and was hardly considered until now. Appropriate positive controls would facilitate quality assessment of proteome profiling experiments. Based on cell biology knowledge, our aim was to generate a list of commonly expressed proteins, which may serve as positive control. Following a pragmatic experimental strategy, we compared the cytoplasmic fractions of four largely differing kinds of cells, which were human DCs, endothelial cells, fibroblasts and keratinocytes. Proteome profiling was performed by 2D-PAGE in addition to shotgun analysis. By shotgun analysis, 665 proteins were identified, which occurred in each of the four cells types; 360 proteins of those were also detectable in the corresponding 2-D gels. We consider these proteins as common proteins. All shotgun analysis data, including mass fragmentation spectra of the corresponding peptides, are accessible via the proteomics identification database (http://www.ebi.ac.uk/pride). As expected, most of the common proteins could be clearly assigned to at least one of the following functional categories: chaperones, cytoskeleton, energy metabolism, redox regulation, nucleic acid processing, protein turnover, membrane transport, protein synthesis and signaling. We suggest that the present data may prove helpful for data assessment, quality control and interpretation of a large variety of experiments based on proteome profiling.
Electrophoresis 05/2009; 30(8):1306-28. · 3.30 Impact Factor
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ABSTRACT: Metastasis in melanoma is associated with poor prognosis. Early detection may thus substantially improve patient survival. Here we present a novel biomarker discovery strategy based on proteome profiling and secretome analysis of primary cells. Tumor associated stroma cells secrete proteins that may act as powerful tumor promoters. This cell cooperativity is reversible and may thus be directly accessible to therapeutic intervention. The onset of these characteristic events seems to precede tumor progression. Thus, proteins specifically secreted by these cells may serve as early disease biomarkers. Due to the leaky nature of newly formed blood vessels and the increased hydrostatic pressure within tumors, secreted proteins are most plausibly shed into the blood. Our analysis strategy is based on three different model systems, including established cultured cell lines, animal model systems, and clinical human samples. The feasibility is demonstrated with secretome and proteome profiles generated from normal human skin fibroblasts in comparison to melanoma-associated fibroblasts isolated from mouse xenografts and fibroblasts from bone marrow of multiple myeloma patients. Further mutual comparisons were enabled including proteome profiles of melanocytes and M24met melanoma cells. All shotgun proteomics data are accessible via the PRIDE database. Among others, the candidate biomarkers GPX5, secreted by melanoma cells, in addition to periostin and stanniocalcin-1, which are expressed by melanoma-associated fibroblasts were identified. In conclusion, this is a novel strategy to identify diagnostic marker proteins aiding early detection of metastatic melanoma and to improve our understanding of pathomechanisms involving the microenvironment to enable the design of novel therapeutic strategies.
Journal of Proteome Research 03/2009; 8(5):2501-10. · 5.11 Impact Factor
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ABSTRACT: Comparative proteome profiling, performed by two-dimensional polyacrylamide gel electrophoresis or multidimensional protein identification technology, usually relies on the relative comparison of samples of interest with respect to a reference. Currently, no standardized quantitative protein expression database of human cells, facilitating data comparisons between different laboratories, exists. Recently, we have published two-dimensional polyacrylamide gel electrophoresis-based techniques to assess absolute protein data comprising protein amounts, synthesis rates and biological half-lives (Mol. Cell. Proteomics 2002, 1, 528-537). Determination of protein amounts by fluorography of two-dimensional gels was followed by the exact quantification of the amount of incorporated (35)S radiolabel. Here we demonstrate an application of this highly standardized method to quiescent human T cells, phythaemagglutinin-stimulated T cells and Jurkat cells, a human T lymphoblast cell line. While the protein composition of quiescent T cells differed significantly compared to that of Jurkat cells, it was only slightly different compared to the activated T cells. Synthesis profile analyses demonstrated that activated T cells clearly differed from the quiescent cells, performing apparently almost like lymphoblast cells. The great sensitivity of this approach was further demonstrated with human umbilical vein endothelial cells treated for six hours with vascular endothelial growth factor. While no significant alteration of protein amounts was detected at all upon activation, the synthesis rate of several proteins was found to be more than doubled.
PROTEOMICS 06/2004; 4(5):1314-23. · 4.51 Impact Factor
