Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Whole blood RNA-seq data of SPMS vs RRMS patients from paper titled \"Relapsing remitting and secondary progressive multiple sclerosis are characterised by distinct metabolomic and transcriptomic signatures that could aid patient management strategies\\

ABSTRACT: We combined analysis of serum metabolomics and whole blood transcriptomics in patients with RRMS and SPMS to build an integrated network describing these different phases of MS and to help improve our understanding of the potential mechanisms driving disease progression. Transcriptomic analysis of whole blood was performed to assess differential gene expression between five patients with RRMS and eight patients with SPMS.

INSTRUMENT(S): NextSeq 500

ORGANISM(S): Homo sapiens

SUBMITTER: Alexandra Oppong

PROVIDER: E-MTAB-13378 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:One of our new major finding among the genes that contributes to MS susceptibility is ICSBP1. The so called disease modifying therapies like interferon-beta (IFN-β), possibly acting on the peripheral T-cells, reduce the disease activity and the clinical progression, with a MRI-detectable effect in preventing lesion burden and cerebral atrophy development in RR-MS. It suggests a critical role of peripheral blood mononuclear cells (PBMCs) immune response and modulation in developing inflammation in the brain. We tested the hypothesis that the genetic effect of the susceptible allele ICSBP1 can impact the gene expression profile of molecules belonging to the interferon pathway. We therefore interrogated the PBMC for changes in gene expression profile. We correlate those changes with the minor allele frequency for ICSBP1, performing independent quantitative trait analysis for each treatment category. Expression Quantitative Trait Loci Association with a p value < 0.05 have been used in follow up analysis. The regression coefficient of the Quantitative trait association represents the degree of correlation between the gene expression for each interrogated target gene and the minor allele frequency of the SNP for our gene of interest. This coefficient has been used as input in the subsequent Gene Set Enrichment Analysis performed in a pre-ranked approach. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype might affect genes constituting a signaling pathway or genes with a common regulation. Therefore, GSEA-SNP can facilitate the identification of pathways or of underlying biological mechanisms. We used microarrays to capture gene expression profile of untreated subjects and of subjects under disease modyfing treatment (Interferon Beta and Glatiramer Acetate), in order to correlate gene expression and genotype data and in order to identify sets of genes specifically regulated in the different treatment categories.

Dataset Information

Whole blood RNA-seq data of SPMS vs RRMS patients from paper titled \"Relapsing remitting and secondary progressive multiple sclerosis are characterised by distinct metabolomic and transcriptomic signatures that could aid patient management strategies\\

Similar Datasets

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