Abstract
BackgroundThe involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein–protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease.MethodsGene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression.ResultsThe networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways.DiscussionThis study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.
Highlights
Multiple sclerosis (MS) is a complex disease affecting the central nervous system (CNS) in which genetic, environmental and immunological factors are considered as its etiology (Ebers, 2008; Svejgaard, 2008)
The full lists of annotated differentially expressed probe sets are shown in Table S1 for the multiple sclerosis (MS) vs. control comparison in cerebrospinal fluid (CSF) cells, and in Table S2 for the relapse versus remission in peripheral blood mononuclear cells (PBMCs) cells
This study showed the necessity of network-based analysis to get more insights in MS pathogenesis at post-genomic era
Summary
Multiple sclerosis (MS) is a complex disease affecting the central nervous system (CNS) in which genetic, environmental and immunological factors are considered as its etiology (Ebers, 2008; Svejgaard, 2008). MS shows both autoimmune and neurodegenerative features, the pathophysiological processes which may occur both within and outside of the CNS remain obscure and don’t have an uniform distribution within the MS population (Brynedal et al, 2010) To study such complex diseases, which involved noticeably missing heritability (Goris & Liston, 2012; Manolio et al, 2009), it is more efficient to describe perturbed processes and dysregulated pathways rather to identify individual genes (Kim, Wuchty & Przytycka, 2011). Peripheral blood mononuclear cells (PBMCs) are being considered as an accessible and informative source of biological material in MS transcriptome studies (Achiron et al, 2004; Bomprezzi et al, 2003; Singh et al, 2007). An experimental validation of candidate genes can lead to identification of potential therapeutic targets
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