This SuperSeries is composed of the SubSeries listed below.
No associated publication
Sex, Age, Specimen part, Subject
View SamplesDNA methylation has an impact on regulation of gene expression, however the relation between the two is complex. By performing an integrative analysis of the methylome and transcriptome of the four main circulating immune cell-subsets from healthy females, namely B cells, monocytes, CD4 and CD8 T cells, the relation between the two was characterized. In addition, in light of the known sex bias in the prevalence of several immune-mediated diseases, the female datasets were compared with similar public available male datasets. Immune subset-specific differentially methylated regions (DMRs) were found to be highly similar between males and females; however numerous sex-specific DMRs shared by the four leukocytes subsets were identified, most located on autosomal chromosomes. This provides a list of highly interesting candidate genes to be studied in diseases with sexual dimorphism like autoimmunity. Immune cell-specific DMRs were mainly located in the gene body and intergenic region, distant from CpG islands but overlapping with enhancer elements, thus indicating the importance of distal regulatory elements in leukocyte subsets. In contrast; sex-specific DMRs were over-represented in CpG islands, suggesting some difference in regulation between sex and immune-cell specificity. Both positive and negative correlations between cell-specific expression and methylation were observed, with negative correlation being more frequent. Our acquired immune cell- and sex- specific methylome and transcriptome profiles provide novel insight on their complex regulatory interactions, and may particularly contribute to research of immune-mediated diseases.
No associated publication
Sex, Age, Specimen part, Subject
View SamplesExploring the expression profile of ovarian clear cell carcinoma cancer cell subpopulations- derived tumors grown within a murine and a human cellular tissues.
Niche-dependent gene expression profile of intratumoral heterogeneous ovarian cancer stem cell populations.
Specimen part
View SamplesBiological networks are inherently modular, yet little is known about how modules are assembled to enable coordinated and complex functions. We used RNAi and time-series, whole-genome microarray analyses to systematically perturb and characterize components of a C. elegans lineage-specific transcriptional regulatory network. These data are supported by select reporter gene analyses and comprehensive yeast-one-hybrid and promoter sequence analyses. Based on these results we define and characterize two modules composed of muscle- and epidermal-specifying transcription factors that function together within a single cell lineage to robustly specify multiple cell types. The expression of these two modules, although positively regulated by a common factor, is reliably segregated among daughter cells. Our analyses indicate that these modules repress each other, and we propose that this cross-inhibition coupled with their relative time of induction function to enhance the initial asymmetry in their expression patterns, thus leading to the observed invariant gene expression patterns and cell lineage. The coupling of asynchronous and topologically distinct modules may be a general principle of module assembly that functions to potentiate genetic switches.
Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression.
No sample metadata fields
View SamplesAbout 40% IBD patients treated with anti-TNF antibodies do not respond to therapy. Baseline biomarkers of response are therefore of interest. By combining computational deconvolution of gene expression and meta-analysis approaches we identified cellular biomarkers in tissue (validated in 2 cohorts by IHC of biopsies), and investigated associated gene biomarkers in blood. This dataset provides data from the validation cohort III (blood).
Cell-centred meta-analysis reveals baseline predictors of anti-TNFα non-response in biopsy and blood of patients with IBD.
Disease, Disease stage, Treatment, Subject, Time
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