Investigation of multiple sclerosis-related pathways through the integration of genomic and proteomic data

Everest E., Ulgen E., UYGUNOĞLU U., TÜTÜNCÜ M., SAİP S., SEZERMAN O. U., ...More

PEERJ, vol.9, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.7717/peerj.11922
  • Journal Name: PEERJ
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: Bioinformatics, Genomics, Proteomics, Multiple sclerosis, CENTRAL-NERVOUS-SYSTEM, WHITE-MATTER, CANCER-RISK, WEB SERVER, DISEASE, SNP, JUNCTIONS, PROTEIN, LESIONS
  • Istanbul Technical University Affiliated: Yes


Background. Multiple sclerosis (MS) has a complex pathophysiology, variable clinical presentation, and unpredictable prognosis; understanding the underlying mechanisms requires combinatorial approaches that warrant the integration of diverse molecular omics data. Methods. Here, we combined genomic and proteomic data of the same individuals among a Turkish MS patient group to search for biologically important networks. We previously identified differentially-expressed proteins by cerebrospinal fluid proteome analysis of 179 MS patients and 42 non-MS controls. Among this study group, 11 unrelated MS patients and 60 independent, healthy controls were subjected to whole-genome SNP genotyping, and genome-wide associations were assessed. Pathway enrichment analyses of MS-associated SNPs and differentially-expressed proteins were conducted using the functional enrichment tool, PANOGA. Results. Nine shared pathways were detected between the genomic and proteomic datasets after merging and clustering the enriched pathways. Complement and coagulation cascade was the most significantly associated pathway (hsa04610, P = 6.96x10-30). Other pathways involved in neurological or immunological mechanisms included adherens junctions (hsa04520, P = 6.64 x 10-25), pathogenic Escherichia coli infection (hsa05130, P = 9.03 x 10-14), prion diseases (hsa05020, P = 5.13 x 10-13). Conclusion. We conclude that integrating multiple datasets of the same patients helps reducing false negative and positive results of genome-wide SNP associations and highlights the most prominent cellular players among the complex pathophysiological mechanisms.