Consolidation of metabolomic, proteomic, and GWAS data in connective model of schizophrenia

被引:8
|
作者
Kopylov, Arthur T. [1 ]
Stepanov, Alexander A. [1 ]
Butkova, Tatiana V. [1 ]
Malsagova, Kristina A. [1 ]
Zakharova, Natalia V. [2 ]
Kostyuk, Georgy P. [2 ]
Elmuratov, Artem U. [1 ,3 ]
Kaysheva, Anna L. [1 ]
机构
[1] Inst Biomed Chem, Dept Prote Res, Biobank Grp, 10 Pogodinskaya Str,Bld 8, Moscow 119121, Russia
[2] Alexeev NA 1St Clin Mental Hlth, 2 Zagorodnoe Rd, Moscow 115119, Russia
[3] Ctr Med Genet Genotek, 17-1 Nastavnichesky Lane, Moscow 105120, Russia
关键词
DHEA; BLOOD; MECHANISMS; BIOMARKERS; DOPAMINE; BRAIN; NEUROINFLAMMATION; IDENTIFICATION; TESTOSTERONE; DYSFUNCTION;
D O I
10.1038/s41598-023-29117-7
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Despite of multiple systematic studies of schizophrenia based on proteomics, metabolomics, and genome-wide significant loci, reconstruction of underlying mechanism is still a challenging task. Combination of the advanced data for quantitative proteomics, metabolomics, and genome-wide association study (GWAS) can enhance the current fundamental knowledge about molecular pathogenesis of schizophrenia. In this study, we utilized quantitative proteomic and metabolomic assay, and high throughput genotyping for the GWAS study. We identified 20 differently expressed proteins that were validated on an independent cohort of patients with schizophrenia, including ALS, A1AG1, PEDF, VTDB, CERU, APOB, APOH, FASN, GPX3, etc. and almost half of them are new for schizophrenia. The metabolomic survey revealed 18 group-specific compounds, most of which were the part of transformation of tyrosine and steroids with the prevalence to androgens (androsterone sulfate, thyroliberin, thyroxine, dihydrotestosterone, androstenedione, cholesterol sulfate, metanephrine, dopaquinone, etc.). The GWAS assay mostly failed to reveal significantly associated loci therefore 52 loci with the smoothened p < 10(-5) were fractionally integrated into proteome-metabolome data. We integrated three omics layers and powered them by the quantitative analysis to propose a map of molecular events associated with schizophrenia psychopathology. The resulting interplay between different molecular layers emphasizes a strict implication of lipids transport, oxidative stress, imbalance in steroidogenesis and associated impartments of thyroid hormones as key interconnected nodes essential for understanding of how the regulation of distinct metabolic axis is achieved and what happens in the conditioned proteome and metabolome to produce a schizophrenia-specific pattern.
引用
收藏
页数:20
相关论文
共 50 条
  • [41] Use of the polygenic model to predict risk of keratoconus using GWAS data
    Li, Xiaohui
    Bykhovskaya, Yelena
    Haritunians, Talin
    Siscovick, David
    Aldave, Anthony
    Szczotka-Flynn, Loretta
    Iyengar, Sudha
    Rotter, Jerome
    Taylor, Kent
    Rabinowitz, Yaron
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2013, 54 (15)
  • [42] Integration of Proteomic and Metabolomic Data Reveals the Lipid Metabolism Disorder in the Liver of Rats Exposed to Simulated Microgravity
    Ru, Mengyao
    He, Jun
    Bai, Yungang
    Zhang, Kun
    Shi, Qianqian
    Gao, Fang
    Wang, Yunying
    Li, Baoli
    Shen, Lan
    BIOMOLECULES, 2024, 14 (06)
  • [43] Intrauterine Growth Restriction Programs the Hypothalamus of Adult Male Rats: Integrated Analysis of Proteomic and Metabolomic Data
    Pedroso, Amanda P.
    Souza, Adriana P.
    Dornellas, Ana P. S.
    Oyama, Lila M.
    Nascimento, Claudia M. O.
    Santos, Gianni M. S.
    Rosa, Jose C.
    Bertolla, Ricardo P.
    Kla-Witter, Jelena
    Christians, Uwe
    Tashima, Alexandre K.
    Ribeire, Eliane B.
    JOURNAL OF PROTEOME RESEARCH, 2017, 16 (04) : 1515 - 1525
  • [44] BIOENGINEERING SILICON QUANTUM DOT THERANOSTICS USING A NETWORK ANALYSIS OF METABOLOMIC AND PROTEOMIC DATA IN CARDIAC ISCHAEMIA
    Gladding, Patrick
    Erogbogbo, Folarin
    Swihart, Mark
    Smart, Katie
    Stewart, Ralph
    Zeng, Irene
    Jullig, Mia
    Bakeev, Katherine
    Hu, Raphael
    Schliebs, Stefan
    Gopalan, Banu
    El-Jack, Seif
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2012, 59 (13) : E453 - E453
  • [45] Biomarkers and pathways in autism spectrum disorder: An individual meta-analysis based on proteomic and metabolomic data
    Xie, Kun
    Sun, Yi
    Li, Xue
    Yang, Shuo
    Wang, Menghe
    Zhang, Yi
    Wang, Qi
    Wu, Kunpeng
    Kong, Di
    Guo, Tingting
    Luo, Xiangyang
    Chen, Wen
    EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE, 2024,
  • [46] Pathway- and network- based analysis of GWAS data revealed susceptibility gene sets to schizophrenia
    Jia, Peilin
    Zhao, Zhongming
    BMC BIOINFORMATICS, 2010, 11
  • [47] Bioengineering Silicon Quantum Dot Theranostics using a Network Analysis of Metabolomic and Proteomic Data in Cardiac Ischemia
    Erogbogbo, Folarin
    May, Jasmine
    Swihart, Mark
    Prasad, Paras N.
    Smart, Katie
    El Jack, Seif
    Korcyk, Dariusz
    Webster, Mark
    Stewart, Ralph
    Zeng, Irene
    Jullig, Mia
    Bakeev, Katherine
    Jamieson, Michelle
    Kasabov, Nikolas
    Gopalan, Banu
    Liang, Linda
    Hu, Raphael
    Schliebs, Stefan
    Villas-Boas, Silas
    Gladding, Patrick
    THERANOSTICS, 2013, 3 (09): : 719 - 728
  • [48] Integration of transcriptomic, proteomic and metabolomic data to reveal the biological mechanisms of AAI injury in renal epithelial cells
    Li, Yu
    Xu, Houxi
    Cai, Danhong
    Zhu, Sirui
    Liu, Xiaoli
    Zhao, Ye
    Zhang, Zhaofeng
    Bian, Yaoyao
    Xue, Mei
    Zhang, Liang
    TOXICOLOGY IN VITRO, 2021, 70
  • [49] Information enhanced model selection for Gaussian graphical model with application to metabolomic data
    Zhou, Jie
    Hoen, Anne G.
    Mcritchie, Susan
    Pathmasiri, Wimal
    Viles, Weston D.
    Nguyen, Quang P.
    Madan, Juliette C.
    Dade, Erika
    Karagas, Margaret R.
    Gui, Jiang
    BIOSTATISTICS, 2021, : 926 - 948
  • [50] Pathway- and network-based analysis of GWAS data revealed susceptibility gene sets to schizophrenia
    Peilin Jia
    Zhongming Zhao
    BMC Bioinformatics, 11