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Melting the EIS

ABSTRACT: Melting the Eis: non-detection of kanamycin resistance markers by routine diagnostic tests and identification of new eis-promoter variants

PROVIDER: PRJEB41458 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR4896320_1.fastq.gz	Fastqsanger.gz
	ERR4896320_2.fastq.gz	Fastqsanger.gz
	ERR4896321_1.fastq.gz	Fastqsanger.gz
	ERR4896321_2.fastq.gz	Fastqsanger.gz
	ERR4896322_1.fastq.gz	Fastqsanger.gz

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Project description:Background: DNA methylation is an important component of epigenetic modifications that influences the transcriptional machinery and is aberrant in many human diseases. In particular, dysregulation of promoter methylation in cancer has already been shown to be associated with repression of tumor suppressors and activation of oncogenes. In addition, detection of altered promoter methylation status is suitable for the design of diagnostic or prognostic tests. In this study we present a new methodological approach for the robust identification of promoter methylation markers and the first genome-wide study for the detection of methylation markers in melanoma. Methods and Findings: Genome-wide promoter methylation and gene expression of ten early passage melanoma cell strains are compared to newborn and adult normal melanocytes. For the identification of markers we applied linear mixed effect models (LME) in combination with a series of filters based on the localization of promoter methylation relative to the transcription start site, overall promoter CpG content, and differential gene expression. The aim of this methodology is to identify markers whose promoter differential methylation is likely to be functionally related to differential expression. We identified 76 markers, 68 hyper- and 8-hypo-methylated in melanomas (LME P<0.05). Promoter methylation profiles and differential expression of five of these markers were successfully validated. In addition, promoter demethylation following Aza treatment consistently restored expression of markers hyper-methylated in melanoma. Conclusions: The proposed methodology allows the identification of robust markers and can be applied to other experimental scenarios where promoter methylation is evaluated. More importantly, the list of markers represents the first systematic effort in the identification of methylation markers in melanoma. Many of the identified markers were not previously known to be regulated by promoter methylation and/or associated with this or other types of cancer.

Project description:Background Published multi-gene classifiers suggested outcome prediction for patients with stage UICC II colon cancer based on different gene expression signatures. However, there is currently no translation of these classifiers for application in routine diagnostic. Therefore, we aimed at validating own and published gene expression signatures employing methods which enable RNA and protein detection in routine diagnostic specimens. Results Immunohistochemistry was applied to 68 stage UICC II colon cancers to determine the protein expression of five selected previously published classifier genes (CDH17, LAT, CA2, EMR3, and TNFRSF11A). Correlation of protein expression data with clinical outcome within a 5-year post-surgery course failed to separate patients with a disease-free follow-up [Group DF] and relapse [Group R]). In addition, RNA from macrodissected tumor samples from 53 of these 68 patients was profiled on Affymetrix GeneChips (HG-U133 Plus 2.0). Prognostic signatures were generated by Nearest Shrunken Centroids with cross-validation. Although gene expression profiling allowed the identification of differentially expressed genes between the groups DF and R, a stable classification and prognosis signature was not discernable in our data. Furthermore, the application of previously published gene signatures consisting of 22 and 19 genes, respectively, to our gene expression data set using ‘global tests’ and leave-one-out cross-validation was unable to predict clinical outcome (prediction rate 75.5% and 64.2%; n.s.). T-stage was the only independent prognostic factor for relapse in multivariate analysis with established clinical and pathological parameters including microsatellite status. Conclusions Our protein and gene expression analyses currently do not support application of molecular classifiers for prediction of clinical outcome in routine diagnostic as a basis for patient-orientated therapy in stage UICC II colon cancer. Further studies are needed to develop prognosis signatures applicable in patient care.

Project description:The diagnosis of Systemic Lupus Erythematosus (SLE) is challenging due to its heterogeneous clinical presentation and lack of robust biomarkers for laboratory testing to distinguish it from other autoimmune or infectious diseases. Current diagnostic criterion relies on a combination of clinical examination and laboratory tests, and it does not readily distinguish between those with active clinical disease from those that are clinically quiescent. Several groups have attempted to apply emerging high throughput profiling technologies to diagnose SLE. Despite showing promising diagnostic potential, many of them are expensive and technically challenging for routine clinical uses. Here we report a pilot study of applying a technically simple and highly customisable leukocyte capture antibody microarray to profile healthy (n=24) and SLE patients (n=60) of various disease activities. Our analysis reveals a set of surface antigen biomarkers that have significant association with SLE. In addition, we present a computational method to calculate a score from the entire microarray profile to obtain a score that correlate most reliably with SLE disease activity. Although the current version of our microarray alone does not match the discriminatory power of the standard laboratory tests (serum dsDNA, complements C3 and C4), the combination of the two yields a test with significantly increased discriminatory ability than what can be achieved individually. This work paves the way for a customised SLE-specific antibody microarray for accurate diagnosis and monitoring of SLE that can be readily translated to routine clinical use. Blood samples were collected from 60 patients of Systemic Lupus Erythematosus (SLE) of different disease activity (11 active, 16 semi-active, and 33 inactive), and compared it with blood samples from 24 healthy blood donors. For each subject, we extracted PBMCs and apply them to a leukocyte capture antibody microarray. The microarray was then scaned by a DotScan™ slide reader (Medsaic; Eveleigh, NSW, Australia). The number of immobilized cells was proportional to the light scattered at each antibody spot. Binding of PBMCs to each antibody dot was monitored by light scatter and averaged between the duplicate spots on each slide. We then performed statistical analysis to identify CD antigen markers that are associated with SLE, as well as determining the potential diagnostic ability of this microarray for SLE.

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Melting the EIS

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