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Background: Claudin-6 (CLDN6), a member of CLDN family and a key component of tight junction, has been reported to function as a tumor suppressor in breast cancer. However, whether CLDN6 plays any role in breast cancer chemoresistance remains unclear. In this study, we investigated the role of CLDN6 in the acquisition of chemoresistance in breast cancer cells., Methods: We manipulated the expression of CLDN6 in MCF-7 and MCF-7/MDR cells with lv-CLDN6 and CLDN6-shRNA and investigated whether CLDN6 manipulation lead to different susceptibilities to several chemotherapeutic agents in these cells. The cytotoxicity of adriamycin (ADM), 5-fluorouracil (5-FU), and cisplatin (DDP) was tested by cck-8 assay. Cell death was determined by DAPI nuclear staining. The enzyme activity of glutanthione S-transferase-p1 (GSTP1) was detected by a GST activity kit. Then lv-GSTP1 and GSTP1-shRNA plasmids were constructed to investigate the potential of GSTP1 in regulating chemoresistance of breast cancer. The TP53-shRNA was adopted to explore the regulation mechanism of GSTP1. Finally, immunohistochemistry was used to explore the relationship between CLDN6 and GSTP1 expression in breast cancer tissues., Results: Silencing CLDN6 increased the cytotoxicity of ADM, 5-FU, and DDP in MCF-7/MDR cells. Whereas overexpression of CLDN6 in MCF-7, the parental cell line of MCF-7/MDR expressing low level of CLDN6, increased the resistance to the above drugs. GSTP1 was upregulated in CLDN6-overexpressed MCF-7 cells. RNAi -mediated silencing of CLDN6 downregulated both GSTP1 expression and GST enzyme activity in MCF-7/MDR cells. Overexpresssion of GSTP1 in CLDN6 silenced MCF-7/MDR cells restored chemoresistance, whereas silencing GSTP1 reduced the chemoresistance due to ectopic overexpressed of CLDN6 in MCF-7 cells. These observations were also repeated in TNBC cells Hs578t. We further confirmed that CLDN6 interacted with p53 and promoted translocation of p53 from nucleus to cytoplasm, and both the expression and enzyme activity of GSTP1 were regulated by p53. Clinicopathologic analysis revealed that GSTP1 expression was positively associated with CLDN6 in human breast cancer samples., Conclusion: High expression of CLDN6 confers chemoresistance on breast cancer which is mediated by GSTP1, the activity of which is regulated by p53. Our findings provide a new insight into mechanisms and strategies to overcome chemoresistance in breast cancer.

Background: Claudin-6 (CLDN6), a member of claudin transmembrane protein family, has recently been reported to be undetectable or at low levels in human breast cancer cell lines and tissues and plays a role in suppression of migration and invasion in breast cancer cells. In addition, it is reported that CLDN6 expression is regulated by DNA methylation in various human cancers and cell lines. However, it is unclear how DNA methylation regulates CLDN6 expression. Here we show the mechanism by which DNA methylation regulates CLDN6 expression in human breast cancer cell line MCF-7., Methods: RT-PCR, Western blot and immunofluorescent staining were utilized to investigate CLDN6 expression in breast cancer tissues and MCF-7 cells. Methylation-Specific PCR (MSP) was applied to determine DNA methylation status in CLDN6 gene promoter region. Wound-healing assay and invasion assay were utilized to test mobility of MCF-7 cells treated with 5-aza-dC (DNA methyltransferase inhibitor). MeCP2 binding, H3Ac and H4Ac in CLDN6 promoter region were analyzed by ChIP assay. Nuclease accessibility assay was performed for analysis of the chromatin conformation of CLDN6 gene. To study the role of CLDN6 in malignant progression, we used RNAi to knockdown CLDN6 expression in MCF-7 cells treated with 5-aza-dC, and examined the mobility of MCF-7 cells by wound-healing assay and invasion assay., Results: 5-aza-dC and TSA (histone deacetylase inhibitor) application induced CLDN6 expression in MCF-7 cells respectively and synergistically. 5-aza-dC treatment induced CLDN6 demethylation, inhibited MeCP2 binding to CLDN6 promoter and increased H3Ac and H4Ac in the promoter. In addition, TSA increased H4Ac, not H3Ac in the promoter. The chromatin structure of CLDN6 gene became looser than the control group after treating with 5-aza-dC in MCF-7 cells. 5-aza-dC up-regulated CLDN6 expression and suppressed migration and invasion in MCF-7 cells, whereas CLDN6 silence restored tumor malignance in MCF-7 cells., Conclusions: DNA methylation down-regulates CLDN6 expression through MeCP2 binding to the CLDN6 promoter, deacetylating H3 and H4, and altering chromatin structure, consequently promoting migratory and invasive phenotype in MCF-7 cells.

Background: Modern pyrosequencing techniques make it possible to study complex bacterial populations, such as 16S rRNA, directly from environmental or clinical samples without the need for laboratory purification. Alignment of sequences across the resultant large data sets (100,000+ sequences) is of particular interest for the purpose of identifying potential gene clusters and families, but such analysis represents a daunting computational task. The aim of this work is the development of an efficient pipeline for the clustering of large sequence read sets., Methods: Pairwise alignment techniques are used here to calculate genetic distances between sequence pairs. These methods are pleasingly parallel and have been shown to more accurately reflect accurate genetic distances in highly variable regions of rRNA genes than do traditional multiple sequence alignment (MSA) approaches. By utilizing Needleman-Wunsch (NW) pairwise alignment in conjunction with novel implementations of interpolative multidimensional scaling (MDS), we have developed an effective method for visualizing massive biosequence data sets and quickly identifying potential gene clusters., Results: This study demonstrates the use of interpolative MDS to obtain clustering results that are qualitatively similar to those obtained through full MDS, but with substantial cost savings. In particular, the wall clock time required to cluster a set of 100,000 sequences has been reduced from seven hours to less than one hour through the use of interpolative MDS., Conclusions: Although work remains to be done in selecting the optimal training set size for interpolative MDS, substantial computational cost savings will allow us to cluster much larger sequence sets in the future.

Background: Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister., Results: Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications., Conclusions: The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications., Methods: We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.