Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

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.

A multidimensional systems biology analysis of cellular senescence in aging and disease

“…It is prudent to note that centrality measures in PPI networks must be interpreted with caution due to publication bias that can be an inherent part of the network [61,62]. The top network genes identified from the PPI network are likely to be heavily influenced by publication bias [63].…”

“…Network analyzer also calculates the fit of the distribution of the number of edges per node to the power law distribution. A significant fit to the power law indicates the presence of a scale-free structure in the network [61,107]. The analysis was applied to the PPI network, the RNA-seq Unweighted Co-expression network, and the Microarray Unweighted Co-expression network of cellular senescence (Additional file 2: Fig.…”

A Multidimensional Systems Biology Analysis of Cellular Senescence in Ageing and Disease

“…It is prudent to note that centrality measures in PPI networks must be interpreted with caution due to publication bias that can be an inherent part of the network (Safari-Alighiarloo et al, 2016;Sanz-Pamplona et al, 2012). The top network genes identified from the PPI network are likely to be heavily influenced by publication bias (Reguly et al, 2006).…”

“…A significant fit to the power law indicates the presence of a scale free structure in the network (Albert et al, 2000;Safari-Alighiarloo et al, 2016). The analysis was applied to the PPI network, the RNAseq Unweighted Co-expression network, and the Microarray Unweighted Co-expression network of cellular senescence.…”

Transcriptomic and Epigenetic Responses to Environmental Stress in Marine Bivalves with a Focus on Harmful Algal Blooms

“…Such subset usually comprises those genes that show a statistically significant change in expression (Tantai et al 2015, Safari-Alighiarloo et al 2016. Alternatively, the focus may be placed on specific protein families expected to play a major role in the stimulus or conditions under study (Jung et al 2013, Tang et al 2013.…”