Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis

Abstract

Differential gene expression analyses to investigate multiple sclerosis (MS) molecular pathogenesis cannot detect genes harboring genetic and/or epigenetic modifications that change the gene functions without affecting their expression. Differential co-expression network approaches may capture changes in functional interactions resulting from these alterations. We re-analyzed 595 mRNA arrays from publicly available datasets by studying changes in gene co-expression networks in MS and in response to interferon (IFN)-β treatment. Interestingly, MS networks show a reduced connectivity relative to the healthy condition, and the treatment activates the transcription of genes and increases their connectivity in MS patients. Importantly, the analysis of changes in gene connectivity in MS patients provides new evidence of association for genes already implicated in MS by single-nucleotide polymorphism studies and that do not show differential expression. This is the case of amiloride-sensitive cation channel 1 neuronal (ACCN1) that shows a reduced number of interacting partners in MS networks, and it is known for its role in synaptic transmission and central nervous system (CNS) development. Furthermore, our study confirms a deregulation of the vitamin D system: among the transcription factors that potentially regulate the deregulated genes, we find TCF3 and SP1 that are both involved in vitamin D3-induced p27Kip1 expression. Unveiling differential network properties allows us to gain systems-level insights into disease mechanisms and may suggest putative targets for the treatment.

DOI: 10.3390/ijms17060936

Extracted Key Phrases

9 Figures and Tables

Cite this paper

@inproceedings{Creanza2016MetaAnalysisOD, title={Meta-Analysis of Differential Connectivity in Gene Co-Expression Networks in Multiple Sclerosis}, author={Teresa Maria Creanza and Maria Liguori and Sabino Liuni and Nicoletta Nuzziello and Nicola Ancona}, booktitle={International journal of molecular sciences}, year={2016} }