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A prolific neuronal progenitor cell population in the anterior portion of the neonatal rat forebrain subventricular zone, the SVZa, is specialized for the production of olfactory bulb interneurons. At all ages, SVZa-derived cells traverse a tangential migratory pathway, the rostral migratory stream (RMS), while en route to the olfactory bulb. Unlike other neuronal progenitor cells of the forebrain, migrating progeny of SVZa progenitors express neuronal-specific proteins and continue to divide into adulthood. Recent studies indicate that in the adult, migrating SVZa-derived cells are ensheathed by astrocytes, although the function of these astrocytes has not been determined. To explore the possible role(s) of astrocytes in the rat SVZa and RMS, we examined the expression of astroglial-specific genes in the postnatal SVZa and RM$ using RT-PCR, in situ hybridization, and immunohistochemistry during (Postnatal Days 1-10) and after the period of peak olfactory bulb interneuron generation. We also examined the expression of neuronal-specific genes throughout the rostral-caudal extent of the postnatal subventricular zone to determine if differential cell type-specific gene expression could distinguish the neurogenic SVZa as a region distinct from the remainder of the SVZ. We found little to no astrocyte-specific gene expression in the P0-P7 SVZa, although the neuron-specific isoforms of tubulin (T(alpha)1 and (beta)-III tubulin) were expressed abundantly in the SVZa and RMS. In contrast, astrocyte-specific genes were strongly expressed in the SVZ posterior to the SVZa. GFAP expressions begins to appear in some restricted areas of the rostral migratory stream after the first postnatal week. These data suggest that astroglia are not involved in the generation or migration of most olfactory bulb interneurons. Moreover, the scarcity of glial markers in the neonatal SVZa indicates that the forebrain subventricular zone includes a distinct neurogenic anterior region containing predominantly committed neuronal progenitor cells.

Abstract Background With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories. Results TAMEE (Tissue Array Management and Evaluation Environment) is a web-based database application for the management and analysis of data resulting from the production and application of TMAs. It facilitates storage of production and experimental parameters, of images generated throughout the TMA workflow, and of results from core evaluation. Database content consistency is achieved using structured classifications of parameters. This allows the extraction of high quality results for subsequent biologically-relevant data analyses. Tissue cores in the images of stained tissue sections are automatically located and extracted and can be evaluated using a set of predefined analysis algorithms. Additional evaluation algorithms can be easily integrated into the application via a plug-in interface. Downstream analysis of results is facilitated via a flexible query generator. Conclusion We have developed an integrated system tailored to the specific needs of research projects using high density TMAs. It covers the complete workflow of TMA production, experimental use and subsequent analysis. The system is freely available for academic and non-profit institutions from http://genome.tugraz.at/Software/TAMEE.

Abstract Background The adenine nucleotide translocator 1 (Ant1) is an inner mitochondrial membrane protein involved with energy mobilization during oxidative phosphorylation. We recently showed that rodent Ant1 is upregulated by transforming growth factor-beta (TGF-β) in reactive astrocytes following CNS injury. In the present study, we describe the molecular mechanisms by which TGF-β1 regulates Ant1 gene expression in cultured primary rodent astrocytes. Results Transcription reporter analysis verified that TGF-β1 regulates transcription of the mouse Ant1 gene, but not the gene encoding the closely related Ant2 isoform. A 69 basepair TGF-β1 responsive element of the Ant1 promoter was also identified. Electrophoretic mobility shift assays demonstrated that astrocyte nuclear proteins bind to this response element and TGF-β1 treatment recruits additional nuclear protein binding to this element. Antibody supershift and promoter deletion analyses demonstrated that Sp1 consensus binding sites in the RE are important for TGF-β1 regulation of Ant1 in astrocytes. Additionally, we demonstrate that Smad 2, 3 and 4 transcription factors are expressed in injured cerebral cortex and in primary astrocyte cultures. TGF-β1 activated Smad transcription factors also contribute to Ant1 regulation since transcription reporter assays in the presence of dominant negative (DN)-Smads 3 and 4 significantly reduced induction of Ant1 by TGF-β1. Conclusion The specific regulation of Ant1 by TGF-β1 in astrocytes involves a cooperative interaction of both Smad and Sp1 binding elements located immediately upstream of the transcriptional start site. The first report of expression of Smads 2, 3 and 4 in astrocytes provided here is consistent with a regulation of Ant1 gene expression by these transcription factors in reactive astrocytes. Given the similarity in TGF-β1 regulation of Ant1 with other genes that are thought to promote neuronal survival, this interaction may represent a general mechanism that underlies the neuroprotective effects of TGF-β1.

Background: With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories. Results: TAMEE (Tissue Array Management and Evaluation Environment) is a web-based database application for the management and analysis of data resulting from the production and application of TMAs. It facilitates storage of production and experimental parameters, of images generated throughout the TMA workflow, and of results from core evaluation. Database content consistency is achieved using structured classifications of parameters. This allows the extraction of high quality results for subsequent biologically-relevant data analyses. Tissue cores in the images of stained tissue sections are automatically located and extracted and can be evaluated using a set of predefined analysis algorithms. Additional evaluation algorithms can be easily integrated into the application via a plug-in interface. Downstream analysis of results is facilitated via a flexible query generator. Conclusion: We have developed an integrated system tailored to the specific needs of research projects using high density TMAs. It covers the complete workflow of TMA production, experimental use and subsequent analysis. The system is freely available for academic and nonprofit institutions from http://genome.tugraz.at/Software/TAMEE. [ABSTRACT FROM AUTHOR]

Background: The adenine nucleotide translocator 1 (Ant1) is an inner mitochondrial membrane protein involved with energy mobilization during oxidative phosphorylation. We recently showed that rodent Ant1 is upregulated by transforming growth factor-beta (TGF-β) in reactive astrocytes following CNS injury. In the present study, we describe the molecular mechanisms by which TGF- β1 regulates Ant1 gene expression in cultured primary rodent astrocytes. Results: Transcription reporter analysis verified that TGF-β1 regulates transcription of the mouse Ant1 gene, but not the gene encoding the closely related Ant2 isoform. A 69 basepair TGF-β1 responsive element of the Ant1 promoter was also identified. Electrophoretic mobility shift assays demonstrated that astrocyte nuclear proteins bind to this response element and TGF-β1 treatment recruits additional nuclear protein binding to this element. Antibody supershift and promoter deletion analyses demonstrated that Sp1 consensus binding sites in the RE are important for TGF-β1 regulation of Ant1 in astrocytes. Additionally, we demonstrate that Smad 2, 3 and 4 transcription factors are expressed in injured cerebral cortex and in primary astrocyte cultures. TGF-β1 activated Smad transcription factors also contribute to Ant1 regulation since transcription reporter assays in the presence of dominant negative (DN)-Smads 3 and 4 significantly reduced induction of Ant1 by TGF-β1. Conclusion: The specific regulation of Ant1 by TGF-β1 in astrocytes involves a cooperative interaction of both Smad and Sp1 binding elements located immediately upstream of the transcriptional start site. The first report of expression of Smads 2, 3 and 4 in astrocytes provided here is consistent with a regulation of Ant1 gene expression by these transcription factors in reactive astrocytes. Given the similarity in TGF-β1 regulation of Ant1 with other genes that are thought to promote neuronal survival, this interaction may represent a general mechanism that underlies the neuroprotective effects of TGF-β1. [ABSTRACT FROM AUTHOR]

Overview We have developed the Proteome Discovery Pipeline, a stand-alone bioinformatics platform used for LC/MS data analysis and biomarker discovery. Data is processed in a series of self-contained analytical steps using modules that are controlled by a graphical user interface. The user interface was developed in Visual C++ 6.0 and provides a multi-threaded, tabbed user interface with each tab representing a step in the analysis process. Modules included are spectrum deconvolution, alignment, normalization, significance tests and pattern recognition. Modules consist of applications developed in C++ and the R scripting language, which are called as external processes from the GUI using inputted parameters. Molecular correlation analysis can be viewed interactively using SysNet. Figure 1 shows the architecture of the Proteome Discovery Pipeline. Spectrum deconvolution XMass [1] uses chemical noise filtering, charge state fitting and de-isotoping for improved analysis of complex peptide samples. Overlapping peptide signals in mass spectra were deconvoluted by correlation with modeled peptide isotopic peak profiles. Isotopic peak profiles for peptides were generated in silico from a protein database to produce reference model distributions. Peak alignment XAlign [2] is a two-step alignment algorithm. The first step is to detect significant peaks that are common to all samples. In the second step, all samples are aligned to the median sample using refined m/z and retention time variation values, where pattern recognition is applied as needed. Normalization Several normalization methods have been developed for proteomics, including auto-scaling, reference sample, log linear model, trimmed constant mean, and average intensity. Statistical significance tests Several different test methods (two-tailed t-test, one-way ANOVA, Kolmogorov-Smirnov test, the Mann-Whitney test) can be used to identify data elements that make large contributions to the protein profile of a sample or that distinguish groups of samples from others. Pattern recognition We have implemented principal component analysis (PCA), linear discriminate analysis (LDA), canonical discriminate analysis (CDA), and clustering objects on subset of attributes (COSA) [3] as clustering methods. Molecular correlation The software package, SysNet [4], is used to provide a dynamic visualization environment for molecular correlation of 'omics data. SysNet visualizes the 'omics expression data as a two-dimensional network. It features a circular layout, where molecular species are represented as nodes and all nodes are located on circles. The intermolecular correlations are represented as links, or edges, between nodes. [ABSTRACT FROM AUTHOR]

Hundreds of tissue samples may be assembled in a tissue microarray format for simultaneous immunostaining assessment of protein expression profiling. A DNA microarray two-color laser scanner was used for automated analysis of tissue microarray indirect immunofluorescence. On sections from both a human lung adenocarcinoma and a squamous cell carcinoma tissue microarray, fluorescence intensity for two epidermal growth factor receptors (EGFR and c-erbB2) correlates with diagnostic pathologic assessment, indicating that immunohistochemistry quantitation can be achieved. Importantly, double-label indirect immunofluorescence detection with the cDNA scanner demonstrates that one reference antigen can normalize tumor marker immunosignal for the cellular content of tissue microarray tissue cores. Therefore, DNA microarray scanners and associated image analysis software provide general and efficient analysis of tissue microarray immunostaining, including estimation of specific protein expression levels.