Analysis of the major patterns of B cell gene expression changes in response to short-term stimulation with 33 single ligands.
ABSTRACT We examined the major patterns of changes in gene expression in mouse splenic B cells in response to stimulation with 33 single ligands for 0.5, 1, 2, and 4 h. We found that ligands known to directly induce or costimulate proliferation, namely, anti-IgM (anti-Ig), anti-CD40 (CD40L), LPS, and, to a lesser extent, IL-4 and CpG-oligodeoxynucleotide (CpG), induced significant expression changes in a large number of genes. The remaining 28 single ligands produced changes in relatively few genes, even though they elicited measurable elevations in intracellular Ca(2+) and cAMP concentration and/or protein phosphorylation, including cytokines, chemokines, and other ligands that interact with G protein-coupled receptors. A detailed comparison of gene expression responses to anti-Ig, CD40L, LPS, IL-4, and CpG indicates that while many genes had similar temporal patterns of change in expression in response to these ligands, subsets of genes showed unique expression patterns in response to IL-4, anti-Ig, and CD40L.
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ABSTRACT: Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.PLoS ONE 04/2014; 9(4):e91315. DOI:10.1371/journal.pone.0091315 · 3.53 Impact Factor
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ABSTRACT: Abstract Krüppel-like factor 4 (KLF4) is expressed in a variety of tissues with diverse physiological functions and activities. KLF4 can also function as a tumor suppressor or an oncogene depending on the cellular context. Its role in hematological malignancies is controversial. This study examined the expression levels of KLF4 by immunohistochemistry on 73 pediatric No-Hodgkin lymphomas in a tissue microarray and also on several B-NHL cell lines. Elevated levels of KLF4 expression were detected in 66% of lymphoma cases and were more frequent in the Burkitt lymphoma (p=0.05) subtype. There was a significant predictive power for outcome with low KLF4 expression, predicting a favorable overall survival compared to high levels. Multivariate analyses confirmed the association of KLF4 expression with unfavorable overall survival (p<0.005). These findings were consistent with analyses in existing NHL microarray datasets. The present findings revealed that KLF4 is overexpressed in Burkitt pediatric lymphoma and is a potential biomarker for inferior overall survival.Leukemia & lymphoma 09/2013; DOI:10.3109/10428194.2013.848437 · 2.61 Impact Factor
The Journal of Immunology 02/2006; 176(5). DOI:10.4049/jimmunol.176.5.2711 · 5.36 Impact Factor