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accession-icon GSE85649
Methylation and expression profiles of monocytes in monozygotic twins: role in Myasthenia Gravis
  • organism-icon Homo sapiens
  • sample-icon 25 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Methylome and transcriptome profiling in Myasthenia Gravis monozygotic twins.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE85452
Methylation and expression profiles of monocytes in monozygotic twins: role in Myasthenia Gravis [expression profiling]
  • organism-icon Homo sapiens
  • sample-icon 25 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

Myasthenia gravis (MG) is a relatively rare autoimmune neuromuscular disorder. Monozygotic twin studies indicate that discordance rate in MG is about 70-60%, suggesting that despite identical DNA unknown factors contribute to disease development. The aim of the current study was to identify novel disease-associated genes in purified monocytes, including both genes associated with predisposition or with disease course, using the unique model of MZ twins. Thus the transcriptome and methylome were compared between twins discordant and concordant for the diseases, as well as MG singletons, and healthy controls. Several transcripts associated with immune homeostasis and inflammation resolution were highlighted in the current study. High similarity between the healthy and the MG discordant twins found, suggest that genetic predisposition may have a stronger contribution then previously assumed. In addition, results suggest that numerous small changes in expression and DNA methylation might contribute to disease onset making it more difficult to pick up

Publication Title

Methylome and transcriptome profiling in Myasthenia Gravis monozygotic twins.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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