Jung-Won Shin1, Jung-Hun Lee2, Hyeyoon Kim2, Da-Hye Lee3, Kwang-Hyun Baek3, Jun-Sang Sunwoo4, Jung-Ick Byun5, Tae-Joon Kim6, Jin-Sun Jun7, Dohyun Han8, Ki-Young Jung9. 1. Department of Neurology, CHA University, Bundang CHA Medical Center, Republic of Korea. 2. Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Republic of Korea. 3. Department of Biomedical Science, CHA University, Republic of Korea. 4. Department of Neurosurgery, Seoul National University Hospital, Republic of Korea. 5. Department of Neurology, Kyung Hee University Hospital at Gangdong, Republic of Korea. 6. Department of Neurology, Ajou University School of Medicine, Republic of Korea. 7. Department of Neurology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Republic of Korea. 8. Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Republic of Korea. Electronic address: hdh03@snu.ac.kr. 9. Seoul National University College of Medicine, Department of Neurology, Seoul National University Hospital, Republic of Korea. Electronic address: jungky@snu.ac.kr.
Abstract
OBJECTIVE/ BACKGROUND: We performed bioinformatic analysis of proteomic data to identify the biomarkers of restless legs syndrome (RLS) and provide insights into the putative pathomechanisms, including iron deficiency, inflammation, and hypoxic pathways. PATIENTS/ METHODS: Patients with drug-naïve idiopathic RLS were recruited at a university hospital from June 2017 to February 2018. Serum samples from patients with RLS (n = 7) and healthy sex- and age-matched controls (n = 6) were evaluated by proteomic analysis. For differentially expressed proteins (DEPs) in patients with RLS, compared to those in controls, the expression profiles and protein-protein interaction (PPI) network were characterized between dysregulated proteins and extracted proteins involved in iron deficiency, hypoxia, and inflammation responses using the String database (http://string-DB.org). The PPI network was visualized by Cytoscape ver. 3. 7. 1. Statistical analyses of the validation Western blot assays were performed using a Student's t-test. RESULTS: Interactome network analysis revealed a relationship among the eight proteins, their associated genes, and 150, 47, and 11 proteins related to iron deficiency, inflammation, and hypoxic pathways, respectively. All DEPs were well associated with inflammation, and complement 3, complement C4A, alpha-2 HS glycoprotein, and alpha-2 macroglobulin precursor were found to be in hub positions of networks involved in PPIs including iron deficiency, hypoxia pathway, and inflammation. C3 and C4A were verified using western blotting. CONCLUSIONS: We identified key molecules that represent the selected cellular pathways as protein biomarkers by PPI network analysis. Changes in inflammation can mediate or affect the pathomechanism of RLS and can thus act as systemic biomarkers.
OBJECTIVE/ BACKGROUND: We performed bioinformatic analysis of proteomic data to identify the biomarkers of restless legs syndrome (RLS) and provide insights into the putative pathomechanisms, including iron deficiency, inflammation, and hypoxic pathways. PATIENTS/ METHODS:Patients with drug-naïve idiopathic RLS were recruited at a university hospital from June 2017 to February 2018. Serum samples from patients with RLS (n = 7) and healthy sex- and age-matched controls (n = 6) were evaluated by proteomic analysis. For differentially expressed proteins (DEPs) in patients with RLS, compared to those in controls, the expression profiles and protein-protein interaction (PPI) network were characterized between dysregulated proteins and extracted proteins involved in iron deficiency, hypoxia, and inflammation responses using the String database (http://string-DB.org). The PPI network was visualized by Cytoscape ver. 3. 7. 1. Statistical analyses of the validation Western blot assays were performed using a Student's t-test. RESULTS: Interactome network analysis revealed a relationship among the eight proteins, their associated genes, and 150, 47, and 11 proteins related to iron deficiency, inflammation, and hypoxic pathways, respectively. All DEPs were well associated with inflammation, and complement 3, complement C4A, alpha-2 HS glycoprotein, and alpha-2 macroglobulin precursor were found to be in hub positions of networks involved in PPIs including iron deficiency, hypoxia pathway, and inflammation. C3 and C4A were verified using western blotting. CONCLUSIONS: We identified key molecules that represent the selected cellular pathways as protein biomarkers by PPI network analysis. Changes in inflammation can mediate or affect the pathomechanism of RLS and can thus act as systemic biomarkers.
Authors: Leonard B Weinstock; Jill B Brook; Arthur S Walters; Ashleigh Goris; Lawrence B Afrin; Gerhard J Molderings Journal: J Clin Sleep Med Date: 2022-05-01 Impact factor: 4.324
Authors: Joseph Dowsett; Maria Didriksen; Jakob Hjorth von Stemann; Margit Hørup Larsen; Lise Wegner Thørner; Erik Sørensen; Christian Erikstrup; Ole Birger Pedersen; Morten Bagge Hansen; Jesper Eugen-Olsen; Karina Banasik; Sisse Rye Ostrowski Journal: Sci Rep Date: 2022-01-31 Impact factor: 4.379