Peptide Growth Factors and Their Receptors I

Growth factor
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The cells were passaged upon reaching confluence with 0. Protein concentration was determined by Bradford assay [ 23 ]. The levels of cyclinD1, cdk4 Nox1, Nox2, Nox4, p47 phox , c-Src and growth factor receptors were determined by Western blotting using specific antibodies as described in detail earlier [ 19 , 24 ]. The antibody-antigen complexes were detected by second antibody horseradish peroxidase-conjugated goat anti-mouse, donkey anti-goat and goat anti-mouse.

Protein bands were visualized by enhanced-chemiluminescence ECL. Western blotting detection reagents were from Santa Cruz Biotechnology. Quantitative analysis of specific bands was performed by densitometric scanning of the autoradiographs with an enhanced laser densitometer LKB Ultroscan XL, Pharmacia, Dorval, Qc, Canada and quantified by using gel-scan XL evaluation software version 2. The average luminescence value was estimated, the background value subtracted and the result was divided by the total wet weight of tissue in each sample.

Basal superoxide- induced luminescence was then subtracted from the luminescence value induced by NADH. DNA synthesis was evaluated by incorporation of [ 3 H] thymidine into cells as described in detail earlier [ 17 , 19 ]. After being washed twice with ice-cold water, the cells were incubated with 0. The number of independent experiments is reported.

Each experiment was conducted at least five times using separate cell population. On the other hand, this treatment did not have any significant effect on the phosphorylation of c-Src in WKY rats.

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The proteins were quantified by densitometric scanning as described in materials and methods. Since the enhanced activation of growth factor receptors reported in VSMC from SHR [ 22 ] was shown to contribute to increased proliferation of VSMC from SHR [ 11 ], it was of interest to investigate if the antiproliferative effect of in vivo C-ANP 4—23 treatment is also attributed to its ability to attenuate the enhanced activation of growth factor receptors. Since oxidative stress has been shown to contribute to hyperproliferation of VSMC from SHR [ 11 ] and C-ANP attenuates hyperproliferation, therefore, it was of interest to investigate if the antiproliferative effect of C-ANP is attributed to its ability to decrease enhanced oxidative stress.

Dynein was used as a loading control. Since C-ANP inhibits the enhanced oxidative stress, c-Src and growth factor receptor activation, it was of interest to investigate the contribution of these signaling molecules in the overexpression of cell cycle proteins, implicated in the hyperproliferation of VSMC from SHR. Hypertension is associated with vascular remodeling which is attributed to the hyperproliferation and hypertrophy of VSMC.

Earlier studies showed that NPR-C activation exerts antiproliferative effects in several cell types including vascular smooth muscle cells [ 17 , 26 — 30 ]. Hashim et. However, in the present study, we demonstrate for the first time that the inhibition of the enhanced oxidative stress, enhanced activation of c-Src and growth factor receptor activation by C-ANP attenuates the overexpression of cyclin D1 and cdk4 in VSMC from SHR and result in the attenuation of hyperproliferation.

Furthermore, Caniffi et. However, there have been no studies yet to delineate the role of NPR-C in the attenuation of BP in hypertensive patients. Oxidative stress is now widely recognized as being a critical player in the pathogenesis of cardiovascular disease including hypertension [ 24 , 25 , 34 ]. However, in this study, we demonstrate that the overexpression of cell cycle proteins cyclin D1 and cdk4 in VSMC from SHR is attributed to the enhanced oxidative stress because the treatment of these cells with NAC, a scavenger of O 2 - attenuated the enhanced expression of these proteins.

Earlier studies have demonstrated the role of non-receptor tyrosine kinase c-Src, a downstream signaling molecule of oxidative stress [ 11 , 25 ] in the hyperproliferation of VSMC from SHR [ 11 ]. Here, we demonstrate for the first time the role of c-Src in enhanced expression of cell cycle proteins cyclin D1 and cdk4 because inhibitor of c-Src, PP2, attenuated the overexpression of these proteins. Taken together, it may be suggested that the antiproliferative effect of NPR-C activation by in vivo treatment with C-ANP may be mediated through its ability to inhibit the enhanced activation of c-Src and resultant inhibition of the overexpression of cell cycle proteins.

Epidermal growth factor

Several growth factor receptors are expressed in VSMC and their activation has been shown to induce cell proliferation [ 40 , 41 ]. Taken together, it may be suggested that the inhibition of enhanced activation of growth factor receptors contribute to the antiproliferative effect of C-ANP through the attenuation of overexpression of cell cycle proteins. In conclusion, we demonstrate the implication of enhanced oxidative stress, c-Src and growth factor receptor activation in the overexpression of cell cycle proteins in VSMC from SHR.

From these studies, it can be suggested that C-ANP could be used as a therapeutic agent in the treatment of vascular complications associated with hypertension, atherosclerosis and restenosis. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Data Availability: All relevant data are within the paper.

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Materials and methods 2. Download: PPT. Fig 1. Fig 2. Fig 3. Fig 4. Fig 5. The role of recombinant hematopoietic growth factors". American Journal of Physiology. Heart and Circulatory Physiology. The Cochrane Database of Systematic Reviews. Growth factors. Nerve Hepatocyte. Cell signaling : cytokines. Wound healing. Angiogenesis Intussusception Vasculogenesis. Growth factor receptor modulators. Kinase inhibitors: Agerafenib. See here instead. Categories : Growth factors Immune system.

Altogether, these results indicate that the automatic analysis provided by FADA yields results consistent with the known biochemistry of yeast. Samples were classified into two major branches: samples from cultured cells, and samples from tissues, which in turn could be further bifurcated into two well-supported branches, one corresponding to samples enriched for carcinomatous cells and one for non-neoplastic prostate cells Figure 2. The first-level grouping into cultured vs. Within the cultured cells subgroup, samples were generally clustered according to cell type, with haematopoietic cell lines forming well-clustered groups and epithelial and fibroblastic prostate-derived cells clustering together with endothelial cells.

A separate cluster was formed by the androgen-sensitive epithelial cell line LNCaP, the prostate cancer cell lines included in the study. The genes most significantly contributing to each sample cluster were analyzed for their participation in the pathways contained both in GenMAPP [ 32 ], and GO Tables 2 and 3. Since pathway categorization is a difficult problem, as partition of the global interaction network in "parts" inevitably introduces artefacts, we also proceeded to a detailed, gene-by-gene inspection of the most discriminative genes based on inspection of literature data.

Dendrogram for the Welsh dataset [14]. The dashed line indicates the thresholding used to define the clusters. Cluster 1 corresponds to the LNCaP cluster. It is placed in a branch distinctly separated from the rest of the cultured prostatic cells. LNCaP cells were originally derived from metastatic prostate cells, presumably of epithelial origin [ 33 ] and respond to androgens through its cognate receptor [ 34 ].

Other sets of genes likely relevant to the LNCaP cluster, but not highlighted in the pathway mapping protocol, are those for proteins in steroid metabolism and signalling, such UDP glycosyltransferases B15 Table 2 of the Supporting Information. Cluster 2 includes mesenchimal, epithelial, and endothelial cells. This cluster shows a bias for genes and pathways involved in ubiquitin and proteasome-dependent protein degradation, cell cycle regulation, inflammatory responses and cell-matrix interaction.

Cluster 3 hematopoietic cells showed a significant bias in genes and pathways involved in cell cycle regulation and RNA processing.

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The selected genes included known markers of differentiation of B cell, T cell or myelomonocytic lineages. Examples are genes for immunoglobulin, histocompatibility antigens, haematopoietic-specific cytokines and their receptors, and regulatory proteins known to play significant roles in such lineages in processes such as signal transduction or cytoskeletal dynamics. Expression levels for the 20 most relevant genes selected in each cluster for the Welsh dataset. Gene descriptions can be found in Table 2 of the Supporting Information.

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Vascular endothelial growth factor receptormediated mitogenesis is negatively regulated by vascular endothelial growth factor receptor-1 in tumor epithelial cells. After washing, the cells were counterstained with the nuclei staining dye propidium iodide PI and were observed under a fluorescence microscope. Effects Unrelated to Growth. Oxidative stress and cardiovascular disease. Our data suggest that multiple peptide growth factors may have an important role in tumor progression and desmoplastic reaction accompanying these tumors.

See Figure 4. Regarding Cluster 4 prostate tumor tissue , GenMAPP mapping finds significant overexpression of enzymes related to fatty acid metabolism Table 2. Other genes and KEGG pathways with a significantly biased association with cluster 4 are those for ribosomal function and fatty acid synthesis Table 2 of the Supporting Information. The upregulation of these two functions in prostate cancer has been noted previously [ 14 , 35 ].

This is confirmed by a survey of the list of selected genes, where one can find a number of proteins involved in steroid signalling, including the coactivators GRIP1 and NRIP1, and genes that have been described as transcriptional targets of these pathways [ 36 ], such as the secreted proteases KLK2 and KLK3, and protein IQGAP, involved in cytoskeletal dynamics [ 37 ], or the enzymes fatty acid CoA-ligase or androgen-regulated short chain dehydrogenase Table 2 of the Supporting Information. A second group of genes significantly contributing to this cluster are those for cell surface polypeptide growth factor receptors, associated signalling molecules and regulators, and known transcriptional targets for these pathways.

Finally, the highest ranking genes for samples from normal prostate tissue Cluster 5 correspond, according to GO, to proteins involved in the control of cytoskeletal architecture and dynamics in muscle cells Table 2. GenMAPP finds a significant overrepresentation of muscle-associated functions. The implication is that, in these experiments, normal prostate tissue samples possibly are strongly enriched for muscle cells.

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This strong overrepresentation of genes corresponding to a smooth muscle phenotype suggests that the non-neoplastic tissues used correspond to areas of prostate hyperplasia or adenoma derived from the transition zone, in which smooth muscle cells are often major contributors [ 38 ]. In practical terms, this suggests that these experiments may be used with caution in the comparison of tumor epithelial cells with corresponding normal epithelial counterparts. In recent years, several transcriptional profiling studies have been performed in prostate cancer, aimed at the identification of novel tumor markers [ 14 , 39 — 41 ] or prognostic signatures [ 42 — 44 ].

So far, only one study has systematically searched for overrepresented biochemical pathways in a meta-analysis of four previously published prostate cancer transcriptional profiling studies [ 45 ]. This study used KEGG as reference pathway database, which is biased towards metabolic pathways [ 46 ]. Our study, however, focuses on GenMapp and GO terms, and therefore on the identification of signalling pathways.

In order to validate the pathways found to be overrepresented in prostate tumor samples, we used real-time RT-PCR. Hepsin was found to be overexpressed in most tumor samples, and validated by immunohistochemical analysis [ 14 ]. This gene has been shown to be overexpressed in prostate cancer by several other groups. KLK3 PSA is the marker par excellence of prostate epithelial activity and cellular bulk, and detection of its serum protein levels is the best available marker for monitoring prostate cancer [ 47 ].

HER3 is a receptor for the paracrine growth factor neregulin-1, and a transmembrane protein that tethers the ligand to its dimerization partners, the receptor tyrosine kinases HER2 and HER4 [ 48 ], and known to play important roles in the development and progression of the malignant phenotype in breast cancer [ 49 ].

The abnormal expression and activity of HER2 has been studied extensively in the context of prostate cancer [ 50 ], being found overexpressed in advanced tumors, either metastatic or homone-independent, but infrequently in primary, organ-confined tumors. More controversial is the information available on the role of HER3, with reports of its overexpression in prostate cancer together with HER2, HER4, or both [ 51 , 52 ], but also of its overexpression only in metastatic tumors, in particular of a truncated form corresponding to the extracellular domains of HER3 [ 53 ].

Furthermore, several transcriptional profiling analyses have found overexpression of this gene in prostate cancer. IQGAP2 is a calmodulin-binding protein that participates in cell signalling and modulation of cytoskeletal dynamics [ 37 ], and its activity has been reported to be positively [ 54 ] and negatively associated with neoplastic phenotype.

Levels of desmin transcripts were determined as an index of the contribution of stromal cells, suggesting that the overexpression of the analyzed genes are detected in tumor samples even in the presence of substantial stromal contamination Figure 5. Of particular interest is the observed upregulation of HER3 in prostate tumor tissues relative to normal tissues.

Recent experimental evidence further highlights the importance of HER3 in conferring a malignant phenotype and a hormone-refractory state to prostate epithelial cells [ 61 ]. Thus, whenever HER3 is expressed it is reasonable to expect co-expression of at least one other member of the HER family. Therefore, we determined by real-time RT-PCR the relative expression in our prostate tissue samples of the genes for all four members of the HER family of receptor tyrosine kinases.

Our results show that HER4 is expressed at increased levels in 10 of 14 prostate tumor samples Fig. The expression values for each gene, previously normalized with respect to the S14r expression level in each sample, are shown as ratios of the normalized values in prostate cancer vs. Quantitation of desmin expression levels was used to assess the degree of contribution of stromal components in the samples analyzed. Values equal to or above fold are shown as B Heatmap representation of the same data color scale as shown below.

FGF1 peptide | Fibroblast Growth Factor, Acidic Peptide-NP_

C Real-time PCR analysis for HER3 transcript levels of laser microdissected tumor and normal samples, compared with relative transcript levels in enriched non-microdissected tissues from the same cases. D Immunohistochemical analysis of HER3 on paraffin-embedded prostate tissue sections arranged in tissue microarrays see Methods. As mentioned in the Methods section, both tumor and normal tissues were carefully chosen to have similar representation of epithelial compartment. However, to further ensure that the observed expression of HER3 was not due to a dilution effect of normal epithelial cells by stroma, we performed real-time PCR analysis of laser microdissected samples.

For this, we selected four samples that had shown overexpression of HER3 in the enriched tumor samples described above, and two that had levels that did not differ significantly from non-tumor containing normal matched tissues. Of the four samples in which the enriched tumor tissue had shown increased levels of HER3 transcript, three microdissected samples overexpressed HER3 Fig. In two of the microdissected samples, HER3 transcript levels were equal in normal and tumor microdissected epithelia, and this also corresponded to samples in which HER3 levels did not differ significantly between enriched tumor and normal prostate tissues Fig.

This analysis showed that overexpression of HER3 in prostate tumor tissues is not due to simple enrichment of epithelial cells in comparison with non-tumor tissues. To further confirm the cell type expressing HER3 in prostate tissues, immunohistochemical analysis with a monoclonal antibody to HER3 was performed on 16 prostate samples, arranged in duplicate 1-mm diameter cores in tissue microarrays, in which both tumor and normal glands were present.

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This two-volume treatise, the collected effort of more than 50 authors, represents the first comprehensive survey of the chemistry and biology of the set of. In this report 30 midgut carcinoids and endocrine pancreatic tumors were examined for the expression of peptide growth factors and their receptors, both by.

HER3 protein was found clearly overexpressed in tumor epithelia in 13 of the 16 cases In all cases, normal epithelia showed weak reactivities for HER3 Fig. In summary, our transcriptome re-analysis, validated by real-time RT-PCR of non-microdissected and microdissected samples and by immunohistochemical analysis, significantly reinforces previous immunohistochemical studies that reported high levels of expression of HER3 and HER4 in primary prostate cancer [ 51 , 52 ]. We have shown that the method presented here for the analysis of expression microarray data permits the classification of samples into meaningful categories and, simultaneously, to identify a subset of genes and their assignment to pathways most significantly contributing to the corresponding phenotypes, while allowing for a given gene to participate as significant in more than one cluster of samples.

The analysis of the yeast dataset validates the approach. Our results are consistent with biochemical pathways known to be activated in the different stress conditions analyzed, and the clustering of samples reflects the underlying similarity of the biochemical responses. In the application to the prostate cancer dataset, we have found that two pathways, one modulated by androgen receptor and a second one by signals that originate from cell surface growth factor receptors, are prominently active in the organ-confined, non-metastatic prostate cancer samples analyzed.

The latter pathway has been reported to be spuriously active in at least a subset of prostate tumors that have progressed to invasive and hormone-independent states [ 62 ]. Our results suggest that such altered activation may already be present in primary tumors. Although a prevailing model for prostate tumor progression is that acquisition of the capacity for metastatic and hormone independent growth proceeds through selection of rare populations of cells concealed among primary tumor cells, there is also evidence that a transcriptional program for metastasis may already be present in the bulk of primary tumors at the time of diagnosis [ 63 , 64 ].

Our analysis would be more consistent with the latter model.

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Finally, we have unveiled and validated several markers highlighted by the analysis of the prostate cancer dataset. Overexpression of HER2 and consequent increased signalling have been associated with advanced prostate cancer, development of hormone independent state and poor prognosis [ 65 , 66 ], but is infrequently observed in primary tumors [ 67 , 68 ].

On the other hand, our results suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than receptor complexes involving HER2, could play important roles in the biology of these tumors. It originally consists of spotted array measurements of genes in experimental conditions that include temperature shocks, hyper and hypoosmotic shocks, exposure to various agents such as peroxide, menadione, diamide, dithiothreitol, amino acid starvation, nitrogen source depletion and progression into stationary phase.

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Log-ratios were preprocessed following several steps: first data from genes with missing values were filtered out, and their missing values estimated with LSimpute [ 69 ] using the 'Adaptive' method. Next, ratios were computed from the log-ratios and quantile-normalized experiment-wise using the normalizeQuantile function from the R package [ 70 ], so that all experiments had the same average sample distribution. Finally, ratios were log transformed again. The prostate cancer dataset chosen is described in [ 14 ]. It was originally obtained by hybridizations on Affymetrix U95A oligonuleotide arrays with probes for a total of 55 samples.

Intensity values were preprocessed following several steps: first intensity data were thresholded, with intensities below 10 fixed at 10 and values above fixed at The thresholded values were log-transformed and then centered by the median of all experiments. Finally, genes were subjected to z-transformation per gene basis. Q-mode Factor Analysis FA [ 9 ] seeks to find an underlying orthogonal factor model of an original X -matrix nxm where n are the number of samples and m the number of mRNA levels measured of the form:.

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L is the loadings matrix of size nxk , where k is the number of factors, and F the scores matrix of size kxm , while E is the residual matrix , which contains both the specific variance of the individual genes and the errors in the model see Figure 1.