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Background: Melanoma is an aggressive tumor with increasing incidence. To develop accurate prognostic markers and targeted therapies, changes leading to malignant transformation of melanocytes need to be understood. In the Xiphophorus melanoma model system, a mutated version of the EGF receptor Xmrk (Xiphophorus melanoma receptor kinase) triggers melanomagenesis. Cellular events downstream of Xmrk, such as the activation of Akt, Ras, B-Raf or Stat5, were also shown to play a role in human melanomagenesis. This makes the elucidation of Xmrk downstream targets a useful method for identifying processes involved in melanoma formation. Methods: Here, we analyzed Xmrk-induced gene expression using a microarray approach. Several highly expressed genes were confirmed by realtime PCR, and pathways responsible for their induction were revealed using small molecule inhibitors. The expression of these genes was also monitored in human melanoma cell lines, and the target gene FOSL1 was knocked down by siRNA. Proliferation and migration of siRNA-treated melanoma cell lines were then investigated. Results: Genes with the strongest upregulation after receptor activation were FOS-like antigen 1 (Fosl1), early growth response 1 (Egr1), osteopontin (Opn), insulin-like growth factor binding protein 3 (Igfbp3), dual-specificity phosphatase 4 (Dusp4), and tumor-associated antigen L6 (Taal6). Interestingly, most genes were blocked in presence of a SRC kinase inhibitor. Importantly, we found that FOSL1, OPN, IGFBP3, DUSP4, and TAAL6 also exhibited increased expression levels in human melanoma cell lines compared to human melanocytes. Knockdown of FOSL1 in human melanoma cell lines reduced their proliferation and migration. Conclusion: Altogether, the data show that the receptor tyrosine kinase Xmrk is a useful tool in the identification of target genes that are commonly expressed in Xmrk-transgenic melanocytes and melanoma cell lines. The identified molecules constitute new possible molecular players in melanoma development. Specifically, a role of FOSL1 in melanomagenic processes is demonstrated. These data are the basis for future detailed analyses of the investigated target genes.
Applying microarray‐based techniques to study gene expression patterns: a bio‐computational approach
(2010)
The regulation and maintenance of iron homeostasis is critical to human health. As a constituent of hemoglobin, iron is essential for oxygen transport and significant iron deficiency leads to anemia. Eukaryotic cells require iron for survival and proliferation. Iron is part of hemoproteins, iron-sulfur (Fe-S) proteins, and other proteins with functional groups that require iron as a cofactor. At the cellular level, iron uptake, utilization, storage, and export are regulated at different molecular levels (transcriptional, mRNA stability, translational, and posttranslational). Iron regulatory proteins (IRPs) 1 and 2 post-transcriptionally control mammalian iron homeostasis by binding to iron-responsive elements (IREs), conserved RNA stem-loop structures located in the 5’- or 3‘- untranslated regions of genes involved in iron metabolism (e.g. FTH1, FTL, and TFRC). To identify novel IRE-containing mRNAs, we integrated biochemical, biocomputational, and microarray-based experimental approaches. Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Methods In this project response to the iron treatment was examined under different conditions using bioinformatical methods. This would improve our understanding of an iron regulatory network. For these purposes we used microarray gene expression data. To identify novel IRE-containing mRNAs biochemical, biocomputational, and microarray-based experimental approaches were integrated. IRP/IRE messenger ribonucleoproteins were immunoselected and their mRNA composition was analysed using an IronChip microarray enriched for genes predicted computationally to contain IRE-like motifs. Analysis of IronChip microarray data requires specialized tool which can use all advantages of a customized microarray platform. Novel decision-tree based algorithm was implemented using Perl in IronChip Evaluation Package (ICEP). Results IRE-like motifs were identified from genomic nucleic acid databases by an algorithm combining primary nucleic acid sequence and RNA structural criteria. Depending on the choice of constraining criteria, such computational screens tend to generate a large number of false positives. To refine the search and reduce the number of false positive hits, additional constraints were introduced. The refined screen yielded 15 IRE-like motifs. A second approach made use of a reported list of 230 IRE-like sequences obtained from screening UTR databases. We selected 6 out of these 230 entries based on the ability of the lower IRE stem to form at least 6 out of 7 bp. Corresponding ESTs were spotted onto the human or mouse versions of the IronChip and the results were analysed using ICEP. Our data show that the immunoselection/microarray strategy is a feasible approach for screening bioinformatically predicted IRE genes and the detection of novel IRE-containing mRNAs. In addition, we identified a novel IRE-containing gene CDC14A (Sanchez M, et al. 2006). The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip, but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls (Vainshtein Y, et al., 2010).
The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays
(2010)
Background: Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Results: The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls. Conclusions: ICEP is a stand-alone Windows application to obtain optimal data quality from custom-designed microarrays and is freely available here (see “Additional Files” section) and at: http://www.alice-dsl.net/evgeniy. vainshtein/ICEP/
In our analysis I was interested in the gene duplications, with focus on in-paralogs. In-paralogs are gene duplicates which arose after species split. Here I analysed the in-paralogs quantitatively, as well as qualitatively. For quantitative analysis genomes of 21 species were taken. Most of them have vastly different lifestyles with maximum evolutionary distance between them 1100 million years. Species included mammals, fish, insects and worm, plus some other chordates. All the species were pairwised analysed by the Inparanoid software, and in-paralogs matrix were built representing number of in-paralogs in all vs. all manner. Based on the in-paralogs matrix I tried to reconstruct the evolutionary tree using in-paralog numbers as evolutionary distance. If all 21 species were used the resulting tree was very far from real one: a lot of species were misplaced. However if the number was reduced to 12, all of the species were placed correctly with only difference being wrong insect and fish clusters switched. Then to in-paralogs matrix the neighbour-net algorithm was applied. The resulting "net" tree showed the species with fast or slow duplications rates compared to the others. We could identify species with very high or very low duplications frequencies and it correlates with known occurrences of the whole genome duplications. As the next step I built the graphs for every single species showing the correlation between their in-paralogs number and evolutionary distance. As we have 21 species, graph for every species is built using 20 points. Coordinates of the points are set using the evolutionary distance to that particular species and in-paralogs number. In mammals with increasing the distance from speciation the in-paralogs number also increased, however not in linear fashion. In fish and insects the graph close to zero is just the same in mammals' case. However, after reaching the evolutionary distances more than 800 million years the number of inparalogs is beginning to decrease. We also made a simulation of gene duplications for all 21 species and all the splits according to the fossil and molecular clock data from literature. In our simulation duplication frequency was minimal closer to the past and maximum in the near-present time. Resulting curves had the same shape the experimental data ones. In case of fish and insect for simulation the duplication rate coefficient even had to be set negative in order to repeat experimental curve shape. To the duplication rate coefficient in our simulation contribute 2 criteria: gene duplications and gene losses. As gene duplication is stochastical process it should always be a constant. So the changing in the coefficient should be solely explained by the increasing gene loss of old genes. The processes are explained by the evolution model with high gene duplication and loss ratio. The drop in number of in-paralogs is probably due to the BLAST algorithm. It is observed in comparing highly divergent species and BLAST cannot find the orthologs so precisely anymore. In the second part of my work I concentrated more on the specific function of inparalogs. Because such analysis is time-consuming it could be done on the limited number species. Here I used three insects: Drosophila melanogaster (fruit y), Anopheles gambiae (mosquito) and Apis mellifera (honeybee). After Inparnoid analyses and I listed the cluster of orthologs. Functional analyses of all listed genes were done using GO annotations and also KEGG PATHWAY database. We found, that the gene duplication pattern is unique for each species and that this uniqueness is rejected through the differences in functional classes of duplicated genes. The preferences for some classes reject the evolutionary trends of the last 350 million years and allow assumptions on the role of those genes duplications in the lifestyle of species. Furthermore, the observed gene duplications allowed me to find connections between genomic changes and their phenotypic manifestations. For example I found duplications within carbohydrate metabolism rejecting feed pattern adaptation, within photo- and olfactory-receptors indicating sensing adaptation and within troponin indicating adaptations in the development. Despite these species specific differences, found high correlations between the independently duplicated genes between the species. This might hint for a "pool" of genes preferentially duplicated. Taken together, the observed duplication patterns reject the adaptational process and provide us another link to the field of genomic zoology.
Aim of this thesis was to study the contribution of the hosts immune system during tumor regression. A wild-type rejection model was studied in which tumor regression is mediated through an adaptive, T cell host response (Research article 1). Additionally, the relationship between VACV infection and cancer rejection was assessed by applying organism-specific microarray platforms to infected and non-infected xenografts. It could be shown that tumor rejection in this nude mouse model was orchestrated solely by the hosts innate immune system without help of the adaptive immunity. In a third study the inflammatory baseline status of 75 human cancer cell lines was tested in vitro which was correlated with the susceptibility to VACV and Adenovirus 5 (Ad5) replication of the respective cell line (Manuscript for Research article 3). Although xenografts by themselves lack the ability to signal danger and do not provide sufficient proinflammatory signals to induce acute inflammation, the presence of viral replication in the oncolytic xenograft model provides the "tissue-specific trigger" that activates the immune response and in concordance with the hypothesis, the ICR is activated when chronic inflammation is switched into an acute one. Thus, in conditions in which a switch from a chronic to an acute inflammatory process can be induced by other factors like the immune-stimulation induced by the presence of a virus in the target tissue, adaptive immune responses may not be necessary and immune-mediated rejection can occur without the assistance of T or B cells. However, in the regression study using neu expressing MMC in absence of a stimulus such as a virus and infected cancer cells thereafter, adaptive immunity is needed to provoke the switch into an acute inflammation and initiate tissue rejection. Taken together, this work is supportive of the hypothesis that the mechanisms prompting TSD differ among immune pathologies but the effect phase converges and central molecules can be detected over and over every time TSD occurs. It could be shown that in presence of a trigger such as infection with VACV and functional danger signaling pathways of the infected tumor cells, innate immunity is sufficient to orchestrate rejection of manifested tumors.
This article is about a measurement analysis based approach to help software practitioners in managing the additional level complexities and variabilities in software product line applications. The architecture of the proposed approach i.e. ZAC is designed and implemented to perform preprocessesed source code analysis, calculate traditional and product line metrics and visualize results in two and three dimensional diagrams. Experiments using real time data sets are performed which concluded with the results that the ZAC can be very helpful for the software practitioners in understanding the overall structure and complexity of product line applications. Moreover the obtained results prove strong positive correlation between calculated traditional and product line measures.