The cardiac myosin light chain-2 (MLC-2) gene promoter contains several positive and negative cis-acting sequences that are involved in the regulation of its expression. We describe here the properties of two activato...
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The cardiac myosin light chain-2 (MLC-2) gene promoter contains several positive and negative cis-acting sequences that are involved in the regulation of its expression. We describe here the properties of two activator sequences, elements A and P, and their DNA-binding factors (ABFs). Element A (CCAAAAGTGG), located at -61, has homology with the evolutionarily conserved sequence CC(A/T)6GG, present in the genes of many contractile proteins. Element P (TAACCTTGAAAGC), located 114 bp upstream of element A, is conserved in both chicken and rat cardiac MLC-2 gene promoters. Deletion mutagenesis demonstrated that these two elements are involved in the positive regulation of MLC-2 gene transcription. At least two sequence-specific element A-binding proteins, ABF-1 and ABF-2, were identified by gel shift analysis of the fractionated cardiac nuclear proteins. ABF-1 binds to element A with strict dependence on the internal element A sequence AAAAGT. In contrast, ABF-2 exhibits a relaxed sequence requirement, as it recognizes the consensus CArG and CCAAT box sequences as well. ABF-2 also recognizes the distal element P despite the fact that the sequences of elements A and P are divergent. DNase I footprinting, methylation interference, and gel shift analyses demonstrated unequivocally that the element A-DNA affinity-purified protein ABF-2 binds to element P with sequence specificity. Since both elements A and P play a positive regulatory role in MLC-2 gene transcription and bind to a single protein (ABF-2), it would appear that ABF-2 is a key transcription factor with the ability to recognize divergent sequence elements involved in a common regulatory pathway during myogenesis.
Error-rate evaluation of Space-Time codes using Union bounds sometimes requires very heavy computational loads and so is impractical to use. In this paper, a common function shared by different Union bounds is derived...
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ISBN:
(纸本)9781424435098
Error-rate evaluation of Space-Time codes using Union bounds sometimes requires very heavy computational loads and so is impractical to use. In this paper, a common function shared by different Union bounds is derived and used to develop a modified Union bound (MUB) for error-rate evaluation. Results of numerical evaluations and Monte-Carlo simulation on two 2x2 rotation-based S-T codes show that the MUB provides a good compromise between the required computational load and the accuracy for error-rate evaluation.
Background: Several different microarray platforms are available for measuring gene expression. There are disagreements within the microarray scientific community for intra-and inter-platform consistency of these plat...
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Background: Several different microarray platforms are available for measuring gene expression. There are disagreements within the microarray scientific community for intra-and inter-platform consistency of these platforms. Both high and low consistencies were demonstrated across different platforms in terms of genes with significantly differential expression. Array studies for gene expression are used to explore biological causes and effects. Therefore, consistency should eventually be evaluated in a biological setting to reveal the functional differences between the examined samples, not just a list of differentially expressed genes (DEG). In this study, we investigated whether different platforms had a high consistency from the biologically functional perspective. Results: DEG data without filtering the different probes in microarrays from different platforms generated from kidney samples of rats treated with the kidney carcinogen, aristolochic acid, in five test sites using microarrays from Affymetrix, Applied Biosystems, Agilent, and GE health platforms (two sites using Affymetrix for intra-platform comparison) were input into the Ingenuity Pathway Analysis (IPA) system for functional analysis. The functions of the DEG lists determined by IPA were compared across the four different platforms and two test sites for Affymetrix platform. Analysis results showed that there is a very high level of consistency between the two test sites using the same platform or among different platforms. The top functions determined by the different platforms were very similar and reflected carcinogenicity and toxicity of aristolochic acid in the rat kidney. Conclusion: Our results demonstrate that highly consistent biological information can be generated from different microarray platforms.
Background: Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively stud...
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Background: Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures. We investigated the breast cancer signature genes to address our hypothesis that the genes of different signature may share common functions, as well as to use these previously reported signature genes to build better prognostic models. Methods: A total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency and the gene overlap in these signatures. Then the gene functions of each signature gene list were analysed and compared by the KEGG pathways and gene ontology (GO) terms. A classifier built using the common genes was tested using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene mean expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034). Results: Among a total of 2239 genes collected from the 33 breast cancer signatures, only 238 genes overlapped in at least two signatures;while from a total of 1979 function terms enriched in the 33 signature gene lists, 429 terms were common in at least two signatures. Most of the common function terms were involved in cell cycle processes. While there is almost no common overlapping genes between signatures developed for ER-positive (e.g. 21- gene signature) and those developed for ER-negative (e.g. basal signatures) tumours, they have common function terms such as cell death, regulation of cell proliferation. We used the 62 genes that were common in at least three signatures as a classifier and subtyped 1141 METABRIC cases including 144 normal samples into nine subgroups. These subgroups
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