Epigenetic silencing of DSC3 is a common event in human breast cancer

INTRODUCTION: Desmocollin 3 (DSC3) is a member of the cadherin superfamily of calcium-dependent cell adhesion molecules and a principle component of desmosomes. Desmosomal proteins such as DSC3 are integral to the maintenance of tissue architecture and the loss of these components leads to a lack of adhesion and a gain of cellular mobility. DSC3 expression is down-regulated in breast cancer cell lines and primary breast tumors; however, the loss of DSC3 is not due to gene deletion or gross rearrangement of the gene. In this study, we examined the prevalence of epigenetic silencing of DSC3 gene expression in primary breast tumor specimens. METHODS: We used bisulfite genomic sequencing to analyze the methylation state of the DSC3 promoter region from 32 primary breast tumor specimens. We also used a quantitative real-time RT-PCR approach, and analyzed all breast tumor specimens for DSC3 expression. Finally, in addition to bisulfite sequencing and RT-PCR, we used an in vivo nuclease accessibility assay to determine the chromatin architecture of the CpG island region from DSC3-negative breast cancer cells lines. RESULTS: DSC3 expression was downregulated in 23 of 32 (72%) breast cancer specimens comprising: 22 invasive ductal carcinomas, 7 invasive lobular breast carcinomas, 2 invasive ductal carcinomas that metastasized to the lymph node, and a mucoid ductal carcinoma. Of the 23 specimens showing a loss of DSC3 expression, 13 (56%) were associated with cytosine hypermethylation of the promoter region. Furthermore, DSC3 expression is limited to cells of epithelial origin and its expression of mRNA and protein is lost in a high proportion of breast tumor cell lines (79%). Lastly, DNA hypermethylation of the DSC3 promoter is highly correlated with a closed chromatin structure. CONCLUSION: These results indicate that the loss of DSC3 expression is a common event in primary breast tumor specimens, and that DSC3 gene silencing in breast tumors is frequently linked to aberrant cytosine methylation and concomitant changes in chromatin structure

A DIGE study on the effects of salbutamol on the rat muscle proteome - an exemplar of best practice for data sharing in proteomics

BACKGROUND: Proteomic techniques allow researchers to perform detailed analyses of cellular states and many studies are published each year, which highlight large numbers of proteins quantified in different samples. However, currently few data sets make it into public databases with sufficient metadata to allow other groups to verify findings, perform data mining or integrate different data sets. The Proteomics Standards Initiative has released a series of "Minimum Information About a Proteomics Experiment" guideline documents (MIAPE modules) and accompanying data exchange formats. This article focuses on proteomic studies based on gel electrophoresis and demonstrates how the corresponding MIAPE modules can be fulfilled and data deposited in public databases, using a new experimental data set as an example. FINDINGS: We have performed a study of the effects of an anabolic agent (salbutamol) at two different time points on the protein complement of rat skeletal muscle cells, quantified by difference gel electrophoresis. In the DIGE study, a total of 31 non-redundant proteins were identified as being potentially modulated at 24 h post treatment and 110 non redundant proteins at 96 h post-treatment. Several categories of function have been highlighted as strongly enriched, providing candidate proteins for further study. We also use the study as an example of best practice for data deposition. CONCLUSIONS: We have deposited all data sets from this study in public databases for further analysis by the community. We also describe more generally how gel-based protein identification data sets can now be deposited in the PRoteomics IDEntifications database (PRIDE), using a new software tool, the PRIDESpotMapper, which we developed to work in conjunction with the PRIDE Converter application. We also demonstrate how the ProteoRed MIAPE generator tool can be used to create and share a complete and compliant set of MIAPE reports for this experiment and others

DNA methylation changes in ovarian cancer are cumulative with disease progression and identify tumor stage

<p>Abstract</p> <p>Background</p> <p>Hypermethylation of promoter CpG islands with associated loss of gene expression, and hypomethylation of CpG-rich repetitive elements that may destabilize the genome are common events in most, if not all, epithelial cancers.</p> <p>Methods</p> <p>The methylation of 6,502 CpG-rich sequences spanning the genome was analyzed in 137 ovarian samples (ten normal, 23 low malignant potential, 18 stage I, 16 stage II, 54 stage III, and 16 stage IV) ranging from normal tissue through to stage IV cancer using a sequence-validated human CpG island microarray. The microarray contained 5' promoter-associated CpG islands as well as CpG-rich satellite and Alu repetitive elements.</p> <p>Results</p> <p>Results showed a progressive de-evolution of normal CpG methylation patterns with disease progression; 659 CpG islands showed significant loss or gain of methylation. Satellite and Alu sequences were primarily associated with loss of methylation, while promoter CpG islands composed the majority of sequences with gains in methylation. Since the majority of ovarian tumors are late stage when diagnosed, we tested whether DNA methylation profiles could differentiate between normal and low malignant potential (LMP) compared to stage III ovarian samples. We developed a class predictor consisting of three CpG-rich sequences that was 100% sensitive and 89% specific when used to predict an independent set of normal and LMP samples versus stage III samples. Bisulfite sequencing confirmed the NKX-2-3 promoter CpG island was hypermethylated with disease progression. In addition, 5-aza-2'-deoxycytidine treatment of the ES2 and OVCAR ovarian cancer cell lines re-expressed NKX-2-3. Finally, we merged our CpG methylation results with previously published ovarian expression microarray data and identified correlated expression changes.</p> <p>Conclusion</p> <p>Our results show that changes in CpG methylation are cumulative with ovarian cancer progression in a sequence-type dependent manner, and that CpG island microarrays can rapidly discover novel genes affected by CpG methylation in clinical samples of ovarian cancer.</p

Bioinformatics and molecular modeling in glycobiology

The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed

Glycomics using mass spectrometry

Mass spectrometry plays an increasingly important role in structural glycomics. This review provides an overview on currently used mass spectrometric approaches such as the characterization of glycans, the analysis of glycopeptides obtained by proteolytic cleavage of proteins and the analysis of glycosphingolipids. The given examples are demonstrating the application of mass spectrometry to study glycosylation changes associated with congenital disorders of glycosylation, lysosomal storage diseases, autoimmune diseases and cancer