SOX5 is involved in balanced MITF regulation in human melanoma cells

Background: Melanoma is a cancer with rising incidence and new therapeutics are needed. For this, it is necessary to understand the molecular mechanisms of melanoma development and progression. Melanoma differs from other cancers by its ability to produce the pigment melanin via melanogenesis; this biosynthesis is essentially regulated by microphthalmia-associated transcription factor (MITF). MITF regulates various processes such as cell cycling and differentiation. MITF shows an ambivalent role, since high levels inhibit cell proliferation and low levels promote invasion. Hence, well-balanced MITF homeostasis is important for the progression and spread of melanoma. Therefore, it is difficult to use MITF itself for targeted therapy, but elucidating its complex regulation may lead to a promising melanoma-cell specific therapy. Method: We systematically analyzed the regulation of MITF with a novel established transcription factor based gene regulatory network model. Starting from comparative transcriptomics analysis using data from cells originating from nine different tumors and a melanoma cell dataset, we predicted the transcriptional regulators of MITF employing ChIP binding information from a comprehensive set of databases. The most striking regulators were experimentally validated by functional assays and an MITF-promoter reporter assay. Finally, we analyzed the impact of the expression of the identified regulators on clinically relevant parameters of melanoma, i.e. the thickness of primary tumors and patient overall survival. Results: Our model predictions identified SOX10 and SOX5 as regulators of MITF. We experimentally confirmed the role of the already well-known regulator SOX10. Additionally, we found that SOX5 knockdown led to MITF up-regulation in melanoma cells, while double knockdown with SOX10 showed a rescue effect; both effects were validated by reporter assays. Regarding clinical samples, SOX5 expression was distinctively up-regulated in metastatic compared to primary melanoma. In contrast, survival analysis of melanoma patients with predominantly metastatic disease revealed that low SOX5 levels were associated with a poor prognosis. Conclusion: MITF regulation by SOX5 has been shown only in murine cells, but not yet in human melanoma cells. SOX5 has a strong inhibitory effect on MITF expression and seems to have a decisive clinical impact on melanoma during tumor progression

Analyzing the regulation of metabolic pathways in human breast cancer

<p>Abstract</p> <p>Background</p> <p>Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.</p> <p>Methods</p> <p>For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.</p> <p>Results</p> <p>Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.</p> <p>Conclusion</p> <p>We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.</p

The Transcription Factor SOX18 Regulates the Expression of Matrix Metalloproteinase 7 and Guidance Molecules in Human Endothelial Cells

Mutations in the transcription factor SOX18 are responsible for specific cardiovascular defects in humans and mice. In order to gain insight into the molecular basis of its action, we identified target genes of SOX18 and analyzed one, MMP7, in detail.SOX18 was expressed in HUVEC using a recombinant adenoviral vector and the altered gene expression profile was analyzed using microarrays. Expression of several regulated candidate SOX18 target genes was verified by real-time PCR. Knock-down of SOX18 using RNA interference was then used to confirm the effect of the transcription factor on selected genes that included the guidance molecules ephrin B2 and semaphorin 3G. One gene, MMP7, was chosen for further analysis, including detailed promoter studies using reporter gene assays, electrophoretic mobility shift analysis and chromatin-immunoprecipitation, revealing that it responds directly to SOX18. Immunohistochemical analysis demonstrated the co-expression of SOX18 and MMP7 in blood vessels of human skin.The identification of MMP7 as a direct SOX18 target gene as well as other potential candidates including guidance molecules provides a molecular basis for the proposed function of this transcription factor in the regulation of vessel formation

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

Reported happiness, fast and slow

The paper uses paradata on response time, cognitive effort and questionnaire order from a large Dutch internet panel survey to study the association between reporting process and reported happiness. We find that slower responses and higher self-stated cognitive effort are associated with lower reported happiness, potentially, because they proxy for momentary mood. Moreover, in multivariate happiness equations, these factors moderate the estimated effect of income on happiness, while no interaction effects are found for other socio-economic determinants of happiness. Our findings have implications for the interpretation of relative marginal effects in economic happiness equations

Data on subgroup specific baseline characteristics and serum sphingosine-1-phosphate concentrations in the Study of Health in Pomerania

In this data article, we provide subgroup specific baseline characteristics and serum sphingosine-1-phosphate (S1P) concentrations for healthy individuals within the Study of Health in Pomerania (SHIP)-TREND cohort. After exclusion of subjects with cardiovascular disease, diabetes mellitus, hypertension, metabolic syndrome, elevated liver enzymes and/or chronic kidney disease stadium III or IV, four subgroups were defined according to different limits for body mass index (BMI), alterations in blood lipid levels and smoking status. Tables show respective clinical and laboratory parameters stratified by gender. Serum S1P concentrations are also stratified by age groups. The data presented herein is related to the research article entitled “Reference intervals for serum sphingosine-1-phosphate in the population-based Study of Health in Pomerania” (E. Moritz, D. Wegner, S. Groß, M. Bahls, M. Dörr, S.B. Felix, T. Ittermann, S. Oswald, M. Nauck, N. Friedrich, R.H. Böger, G. Daum, E. Schwedhelm, B.H. Rauch, Clin Chim Acta. 468 (2017) 25–31) [1]

Characterizing Protein Interactions Employing a Genome-Wide siRNA Cellular Phenotyping Screen

<div><p>Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted <i>de novo</i> unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.</p></div