Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis
Background The 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 inte...
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PeerJ Inc.
2016-12-01
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| Online Access: | https://peerj.com/articles/2775.pdf |
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| author | Nahid Safari-Alighiarloo Mostafa Rezaei-Tavirani Mohammad Taghizadeh Seyyed Mohammad Tabatabaei Saeed Namaki |
| author_facet | Nahid Safari-Alighiarloo Mostafa Rezaei-Tavirani Mohammad Taghizadeh Seyyed Mohammad Tabatabaei Saeed Namaki |
| author_sort | Nahid Safari-Alighiarloo |
| collection | DOAJ |
| description | Background The 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. Methods Gene 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. Results The 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. Discussion This 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. |
| first_indexed | 2024-03-09T06:55:00Z |
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| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:55:00Z |
| publishDate | 2016-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-46b657db76314925a358cc298cd371872023-12-03T10:04:21ZengPeerJ Inc.PeerJ2167-83592016-12-014e277510.7717/peerj.2775Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosisNahid Safari-Alighiarloo0Mostafa Rezaei-Tavirani1Mohammad Taghizadeh2Seyyed Mohammad Tabatabaei3Saeed Namaki4Proteomics Research Center, Department of Basic Science, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranProteomics Research Center, Department of Basic Science, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranBioinformatics Department, Institute of Biochemistry and Biophysics, Tehran University, Tehran, IranMedical Informatics Department, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranImmunology Department, Faculty of Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranBackground The 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. Methods Gene 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. Results The 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. Discussion This 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.https://peerj.com/articles/2775.pdfProtein–protein interaction network (PPIN)TranscriptomeTopologyModularityClique analysisMultiple sclerosis |
| spellingShingle | Nahid Safari-Alighiarloo Mostafa Rezaei-Tavirani Mohammad Taghizadeh Seyyed Mohammad Tabatabaei Saeed Namaki Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis PeerJ Protein–protein interaction network (PPIN) Transcriptome Topology Modularity Clique analysis Multiple sclerosis |
| title | Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis |
| title_full | Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis |
| title_fullStr | Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis |
| title_full_unstemmed | Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis |
| title_short | Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis |
| title_sort | network based analysis of differentially expressed genes in cerebrospinal fluid csf and blood reveals new candidate genes for multiple sclerosis |
| topic | Protein–protein interaction network (PPIN) Transcriptome Topology Modularity Clique analysis Multiple sclerosis |
| url | https://peerj.com/articles/2775.pdf |
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