The molecular portrait of in vitro growth by meta-analysis of gene-expression profiles

BACKGROUND: Cell lines as model systems of tumors and tissues are essential in molecular biology, although they only approximate the properties of in vivo cells in tissues. Cell lines have been selected under in vitro conditions for a long period of time, affecting many specific cellular pathways and processes. RESULTS: To identify the transcriptional changes caused by long term in vitro selection, we performed a gene-expression meta-analysis and compared 60 tumor cell lines (of nine tissue origins) to 135 human tissue and 176 tumor tissue samples. Using significance analysis of microarrays we demonstrated that cell lines showed statistically significant differential expression of approximately 30% of the approximately 7,000 genes investigated compared to the tissues. Most of the differences were associated with the higher proliferation rate and the disrupted tissue organization in vitro. Thus, genes involved in cell-cycle progression, macromolecule processing and turnover, and energy metabolism were upregulated in cell lines, whereas cell adhesion molecules and membrane signaling proteins were downregulated. CONCLUSION: Detailed molecular understanding of how cells adapt to the in vitro environment is important, as it will both increase our understanding of tissue organization and result in a refined molecular portrait of proliferation. It will further indicate when to use immortalized cell lines, or when it is necessary to instead use three-dimensional cultures, primary cell cultures or tissue biopsies

Administration of Insurance Rate Regulatory Laws

microRNAs (miRNAs) are key posttranscriptional regulators of gene expression. In the present study, regulation of tumor-suppressor gene D-glucuronyl C5-epimerase (GLCE) by miRNA-218 was investigated. Significant downregulation of miRNA-218 expression was shown in primary breast tumors. Exogenous miRNA-218/anti-miRNA-218 did not affect GLCE mRNA but regulated GLCE protein level in MCF7 breast carcinoma cells in vitro. Comparative analysis showed a positive correlation between miRNA-218 and GLCE mRNA, and negative correlation between miRNA-218 and GLCE protein levels in breast tissues and primary tumors in vivo, supporting a direct involvement of miRNA-218 in posttranscriptional regulation of GLCE in human breast tissue. A common scheme for the regulation of GLCE expression in normal and tumor breast tissues is suggested.Funding Agencies|Russian Foundation for Basic Research|11-04-90400-Ukr_f_a|Ukranian State Foundation of Fundamental Research|F40/146-2011F46/457-2011|Swedish Institute|2011/00888|UICC International Cancer Technology Transfer Fellowship|ICRETT-09-069|FEBS Short-term Fellowship||Karolinska Institute||Swedish Cancer Society||Swedish Research Council||</p

The Effect of Antiretroviral Combination Treatment on Epstein-Barr Virus (EBV) Genome Load in HIV-Infected Patients

We evaluated the effect of combination anti-retroviral treatment (cART) on the host control of EBV infection in moderately immunosuppressed HIV-1 patients. Twenty HIV-1 infected individuals were followed for five years with repeated measurements of EBV DNA load in peripheral blood lymphocytes in relation to HIV-RNA titers and CD4+ cell counts. Individuals with optimal response, i.e. durable non-detectable HIV-RNA, showed a decline of EBV load to the level of healthy controls. Individuals with non-optimal HIV-1 control did not restore their EBV control. Long-lasting suppression of HIV-replication after early initiation of cART is a prerequisite for re-establishing the immune control of EBV

DNA-dependent conversion of Oct-1 and Oct-2 into transcriptional repressors by Groucho/TLE

POU domain proteins contain a bipartite DNA-binding element that can confer allosteric control of coactivator recruitment. Dimerization of Oct-1 and Oct-2 on palindromic response elements results in the conformational dependent inclusion or exclusion of the transcriptional coactivator OBF-1. In this paper, we demonstrate that Oct-1 and Oct-2 can function as transcriptional repressors by recruiting and physically interacting with members of the Grg/TLE family of corepressors. In accordance with a model of DNA induced cofactor assembly, and analogous to the recruitment of the OBF-1 coactivator, the different Grg/TLE members can discriminate between both Oct-1 and Oct-2, and the monomeric or dimeric nature of the POU/DNA complex

Cancer Core Europe: A translational research infrastructure for a European mission on cancer.

Cancer Core Europe is a European legal alliance consisting of seven leading cancer centres - most of them Comprehensive Cancer Centres (CCCs) - with a single portal system to engage in various research projects with partners. Cancer Core Europe was established to create a sustainable, high-level, shared research infrastructure platform hosting research collaborations and task forces (data sharing, clinical trials, genomics, immunotherapy, imaging, education and training, and legal and ethical issues), with a controlled expansion agenda. Translational cancer research covers the cancer research continuum from basic to preclinical to early clinical, late clinical, and outcomes research. Basic-preclinical research serves as the 'engine' for early clinical research by bridging the early translational research gap and is the primary and current focus of the consortium as exemplified by the launching of the Basket of Baskets trial, Europe's largest precision cancer medicine trial. Inspired by the creation of Cancer Core Europe, the prevention community established Cancer Prevention Europe, a consortium of ten cancer prevention centres aimed at supporting the complete prevention research continuum. Presently, Cancer Core Europe and Cancer Prevention Europe are integrating therapeutics and prevention strategies to address in partnership the widening cancer problem. By providing innovative approaches for cancer research, links to healthcare systems, development of quality-assured multidisciplinary cancer care, and assessment of long-term outcomes, the virtual infrastructure will serve as a hub to connect and interact with other centres across Europe and beyond. Together, Cancer Core Europe and Cancer Prevention Europe are prepared to function as a central engine to tackle, in collaboration with various partners, a potential 'mission on cancer' addressing the cancer burden

Support systems to guide clinical decision-making in precision oncology: The Cancer Core Europe Molecular Tumor Board Portal.

To the editor: the optimal management of cancer patients is increasingly dependent on individualized treatments guided by tumor sequencing data. As comprehensive genomic tests become routine in many disease settings and academic centers promote omics-guided clinical trial recruitment, accurate and scalable data interpretation represents a major challenge. The meticulous task of matching tumor alterations with approved or experimental therapies relies heavily on the expertise of each center

Uncovering cis Regulatory Codes Using Synthetic Promoter Shuffling

Revealing the spectrum of combinatorial regulation of transcription at individual promoters is essential for understanding the complex structure of biological networks. However, the computations represented by the integration of various molecular signals at complex promoters are difficult to decipher in the absence of simple cis regulatory codes. Here we synthetically shuffle the regulatory architecture — operator sequences binding activators and repressors — of a canonical bacterial promoter. The resulting library of complex promoters allows for rapid exploration of promoter encoded logic regulation. Among all possible logic functions, NOR and ANDN promoter encoded logics predominate. A simple transcriptional cis regulatory code determines both logics, establishing a straightforward map between promoter structure and logic phenotype. The regulatory code is determined solely by the type of transcriptional regulation combinations: two repressors generate a NOR: NOT (a OR b) whereas a repressor and an activator generate an ANDN: a AND NOT b. Three-input versions of both logics, having an additional repressor as an input, are also present in the library. The resulting complex promoters cover a wide dynamic range of transcriptional strengths. Synthetic promoter shuffling represents a fast and efficient method for exploring the spectrum of complex regulatory functions that can be encoded by complex promoters. From an engineering point of view, synthetic promoter shuffling enables the experimental testing of the functional properties of complex promoters that cannot necessarily be inferred ab initio from the known properties of the individual genetic components. Synthetic promoter shuffling may provide a useful experimental tool for studying naturally occurring promoter shuffling

CD44s and CD44v6 Expression in Head and Neck Epithelia

Background: CD44 splice variants are long-known as being associated with cell transformation. Recently, the standard form of CD44 (CD44s) was shown to be part of the signature of cancer stem cells (CSCs) in colon, breast, and in head and neck squamous cell carcinomas (HNSCC). This is somewhat in contradiction to previous reports on the expression of CD44s in HNSCC. The aim of the present study was to clarify the actual pattern of CD44 expression in head and neck epithelia. Methods: Expression of CD44s and CD44v6 was analysed by immunohistochemistry with specific antibodies in primary head and neck tissues. Scoring of all specimens followed a two-parameters system, which implemented percentages of positive cells and staining intensities from − to +++ (score = %×intensity; resulting max. score 300). In addition, cell surface expression of CD44s and CD44v6 was assessed in lymphocytes and HNSCC. Results: In normal epithelia CD44s and CD44v6 were expressed in 60–95% and 50–80% of cells and yielded mean scores with a standard error of a mean (SEM) of 249.5±14.5 and 198±11.13, respectively. In oral leukoplakia and in moderately differentiated carcinomas CD44s and CD44v6 levels were slightly increased (278.9±7.16 and 242±11.7; 291.8±5.88 and 287.3±6.88). Carcinomas in situ displayed unchanged levels of both proteins whereas poorly differentiated carcinomas consistently expressed diminished CD44s and CD44v6 levels. Lymphocytes and HNSCC lines strongly expressed CD44s but not CD44v6. Conclusion: CD44s and CD44v6 expression does not distinguish normal from benign or malignant epithelia of the head and neck. CD44s and CD44v6 were abundantly present in the great majority of cells in head and neck tissues, including carcinomas. Hence, the value of CD44s as a marker for the definition of a small subset of cells (i.e. less than 10%) representing head and neck cancer stem cells may need revision

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

BACKGROUND: A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. METHODOLOGY/PRINCIPAL FINDINGS: To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. CONCLUSIONS/SIGNIFICANCE: ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple