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. 2015 Feb 25;16(1):124.
doi: 10.1186/s12864-015-1312-z.

Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events

Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events

Jürgen Borlak et al. BMC Genomics. .

Abstract

Background: Hepatocellular carcinoma (HCC) is on the rise and the sixth most common cancer worldwide. To combat HCC effectively research is directed towards its early detection and the development of targeted therapies. Given the fact that epidermal growth factor (EGF) is an important mitogen for hepatocytes we searched for disease regulated proteins to improve an understanding of the molecular pathogenesis of EGF induced HCC. Disease regulated proteins were studied by 2DE MALDI-TOF/TOF and a transcriptomic approach, by immunohistochemistry and advanced bioinformatics.

Results: Mapping of EGF induced liver cancer in a transgenic mouse model identified n = 96 (p < 0.05) significantly regulated proteins of which n = 54 were tumour-specific. To unravel molecular circuits linked to aberrant EGFR signalling diverse computational approaches were employed and this defined n = 7 key nodes using n = 82 disease regulated proteins for network construction. STRING analysis revealed protein-protein interactions of > 70% disease regulated proteins with individual proteins being validated by immunohistochemistry. The disease regulated network proteins were mapped to distinct pathways and bioinformatics provided novel insight into molecular circuits associated with significant changes in either glycolysis and gluconeogenesis, argine and proline metabolism, protein processing in endoplasmic reticulum, Hif- and MAPK signalling, lipoprotein metabolism, platelet activation and hemostatic control as a result of aberrant EGF signalling. The biological significance of the findings was corroborated with gene expression data derived from tumour tissues to evntually define a rationale by which tumours embark on intriguing changes in metabolism that is of utility for an understanding of tumour growth. Moreover, among the EGF tumour specific proteins n = 11 were likewise uniquely expressed in human HCC and for n = 49 proteins regulation in human HCC was confirmed using the publically available Human Protein Atlas depository, therefore demonstrating clinical significance.

Conclusion: Novel insight into the molecular pathogenesis of EGF induced liver cancer was obtained and among the 37 newly identified proteins several are likely candidates for the development of molecularly targeted therapies and include the nucleoside diphosphate kinase A, bifunctional ATP-dependent dihydroyacetone kinase and phosphatidylethanolamine-binding protein1, the latter being an inhibitor of the Raf-1 kinase.

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Figures

Figure 1
Figure 1
Liver proteome mapping of healthy non-transgenic control and EGF transgenic mice. (A) Histopathology of a well-organized normal liver tissue of a non-transgenic control (image magnification 25-fold). (B) Histopathology of completely disorganized tissue of advanced HCC showing multilayered hepatocytic trabeculae besides solid areas, peliosis-like intratumourous vasectasias and focal necroses (light red) (image magnification 25-fold). (C) Zoom-in 2D gel image of healthy non-transgenic control liver extracts in the pH range of 5–8. (D) Zoom-in 2D gel image of healthy non-transgenic control liver extracts in the pH range of 7–10.Panel E to G depict examples of zoom-in 2D gels of regulated proteins. (E) Spot 1: glycine N-methyltransferase, identified in control samples only; Spot 2: peroxiredoxin 6, up-regulated in tumour samples; Spot 3: peroxiredoxin 6, down-regulated in tumour samples; Spot 4: lysophospholipase 1, down-regulated in tumour samples; Spot 5: hypothetical protein LOC68347, down-regulated in tumour samples; Spot 6: glutathione peroxidase 1, down-regulated in tumour samples. (F) Examples of up-regulated mouse liver proteins: fibrinogen β (I), vimentin (II), Cu/Zn superoxide dismutase (III), and apolipoprotein E (IV). (G) Examples of down-regulated mouse liver proteins: arginase 1 (I), Dhdh protein (II), glutathione peroxidase 1 (III) and predicted: agmatine ureohydrolase (IV).
Figure 2
Figure 2
Western blotting of serum proteins in control and EGF transgenic mice. For the commonly regulated proteins in serum and tumours their regulation in liver tissue was confirmed by 2DE and MALDI-TOF/MS (see Table 1). Depicted are Western blots for serum proteins. Note, with the exception of EGF the regulated serum proteins were already reported in our earlier publication [7]. C 1–4 = individual control animals, T 1–6 = individual tumour bearing mice. (A) alpha-fetoprotein, (B) fibrinogen gamma, (C) serum amyloid component P, (D) epidermal growth factor, (E) and apolipoprotein M which was identified in serum samples only.
Figure 3
Figure 3
Immunohistochemistry of proteins regulated in hepatocellular carcinoma of EGF transgenic mice. Shown are images of (A) Arginase II, (B) Capza1, (C) GDI 2, (D) Tubulin β, (E) hnRNP L, (F) Amphiregulin, (G) HNF4α and (H) Epiregulin. Specificity was determined by treating the specimen with washing buffer instead of primary antibody (controls); in the case of amphiregulin the specificity was confirmed with a blocking peptide.
Figure 4
Figure 4
Significantly regulated proteins categorized by GO terms. (A) Biological process, (B) Cellular components, (C) Molecular functions. The pie-charts depict the percentage of proteins involved in the various GO terms.
Figure 5
Figure 5
Molecular interaction and biological pathways networks of regulated proteins in liver tumours of EGF transgenic mice. Cytoscape 3.0.2 with plugins (see Methods section) are used to generate functionally grouped network of pathways. Grouping of significant pathway terms (p ≤ 0.05) were based on kappa score threshold of 0.4, initial group size of 2 and sharing group percentage of 50. The pathway network consisted of 47 significantly regulated proteins involved in distinct pathways which are colour-coded. Note, the three individual terms are grey-coloured. Up and down-regulated proteins are coded as orange and green small discs, respectively.
Figure 6
Figure 6
Integrated master regulatory network for proteins uniquely expressed in tumours. Based on network information obtained for the 5 different master regulators an integrated hybrid network was constructed. The network contained 82 proteins including 20 with connectivity to EGFR signalling (yellow coloured inner node). The master regulator, the connecting proteins (network elements) and regulated proteins are given as red, green and blue coloured inner node, respectively. Furthermore, each node is partitioned into four segments whereas the first segment seen from left refers to tumour specific proteins and is red-coloured. The second, third and fourth segments refer to either up- and down-regulated proteins, tumour specific gene expression changes and gene regulations in transgenic non-tumour liver tissue, respectively. Increased expression of either proteins or genes is given in red, whereas the blue colour denotes repressed expression.
Figure 7
Figure 7
Integrated master regulatory network for HCC regulated proteins. Based on network information obtained for 7 different master regulators an integrated hybrid network was constructed. The network contained 114 proteins including 34 with connectivity to EGF/EGFR signalling (yellow coloured inner node). The master regulator, the connecting proteins (network elements) and regulated proteins are given as red, green and blue coloured inner node, respectively. Furthermore, each node is partitioned into four segments whereas the first segment seen from left refers to tumour specific proteins and is red-coloured. The second, third and fourth segments refer to either up- and down-regulated proteins, tumour specific gene expression changes and gene regulations in transgenic non-tumour liver tissue, respectively. Increased expression of either proteins or genes is given in red, whereas the blue colour denotes repressed expression.
Figure 8
Figure 8
STRING protein-protein interaction network. The network consisted of 69 statistically significantly up- and down-regulated proteins and 7 regulated proteins which failed to reach statistical significance. This STRING protein-protein network is a confidence view and a required default confidence score of 0.4 was set. The protein network depicts interaction of regulated proteins including master regulators connected to EGFR signalling.
Figure 9
Figure 9
Pathways mapping of fussed network proteins. Cytoscape 3.0.2 with plugins (see Methods section) are used to generate functionally grouped network of pathways. Grouping of significant pathway terms (p ≤ 0.05) were based on kappa score threshold of 0.4, initial group size of 2 and sharing group percentage of 50. The pathway network consisted of 35 significantly and 7 non-significantly regulated proteins involved in distinct pathways which are colour-coded. Note, the two individual terms are grey-coloured. Up and down-regulated proteins are coded as orange and green small discs, respectively. Up- and down-regulated as well as non-significantly regulated proteins and connecting proteins of the network are given as orange, green, yellow and blue coloured discs, respectively. The network depicts protein-protein interactions in liver tumours of EGFR transgenic mice and their relation to various pathways under the influence of EGFR signalling. EGFR is highlighted as blue triangle in this network.

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