Hunting for molecules in schizophrenia through omics technologies

Schizophrenia is a complex mental disorder that affects 1% of the population worldwide with ~80% heritability rate. This mental disorder has dramatic impacts not only on the patients but also on the society as well. Unfortunately our knowledge about the molecular mechanisms underlying the disease is limited. To understand the pathological mechanisms that lead to disease phenotype we need to use genomics, transcriptomics, epigenomics, proteomics, metabolomics like approaches with newly developed technologies. These approaches will also help scientist to find out new diagnostic tools that can be used as biomarkers in a complex disease like schizophrenia or personalized therapy strategies. It is possible to map the molecular changings in disease and healthy state with the help of the OMICS based technologies. This review sheds light on these OMICS based approaches to hunt the biomarkers that can be used as diagnostic tools for schizophrenia and other mental disorders or to figure out the candidate molecules for new treatment options.

___

1. Acar C. Sozen MM, Gozukara H, et al. Lack of association between catechol-O methyltransferase and schizophrenia in a Turkish population. Turkish J Biochem 2015;40:205-9.

2. Birnbaum R, Weinberger DR. Genetic insights into the neurodevelopmental origins of schizophrenia. Nat Rev Neurosci. 2017;18:727-40.

3. Bragazzi NL. Rethinking psychiatry with OMICS science in the age of personalized P5 medicine: ready for psychiatome. Philosophy, Ethics, and Humanities in Medicine. 2013;8:4.

4. Sherrington R, Brynjolfsson, J, Petursson H, et al. Localization of a susceptibility locus for schizophrenia on chromosome

5. Nature. 1988;336:164-7.5. Wang Q, Chen R, Cheng F, et al. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data Nature Neurosci. 2019;22:691-9.

6. Kartalci S and Acar C. An association study of D-amino acid oxidase and D-amino acid oxidase activator polymorphisms and schizophrenia in patients from Turkey. Anadolu Psikiyatri Derg. 2016; 17:341-6.

7. Föcking M, Doyle B, Munawar N, et al. Epigenetic Factors in Schizophrenia: Mechanisms and Experimental Approaches. Mol Neuropsychiatry. 2019;5:6-12.

8. Huang K and Fan G. Epigenomics: An Overview. In: The OMICS Applications in Neuroscience. Oxford University Press, 2014 .p. 27-41.

9. Pickard BS. Schizophrenia biomarkers: translating the descriptive into the diagnostic. J Psychopharmacol. 2015;29:138-43.

10. Belgard TG and Geschwind DH, Transcriptomics. In: The OMICS Applications in Neuroscience. Oxford University Press, 2014 .p. 63-72.

11. Schizophrenia and miRNA https://www.ncbi.nlm.nih.gov/pmc/?term=miRNAs%20in%20schizophrenia

12. Trinidad JC. Proteomics. In: The OMICS Applications in Neuroscience. Oxford University Press. 2014 .p. 155-81.

13. Jaeger PA, Villeda SA, Berdnik D, et al. Focused Plasma Proteomics for the study of brain aging and neurodegeneration. In: The OMICS Applications in Neuroscience. Oxford University Press 2014;183-91.

14. Kartalci S, Karabulut A, Erbay LG, et al. Effects of Electroconvulsive Therapy on Some Inflammatory Factors in Patients with Treatment-Resistant Schizophrenia. J ECT. 2016;32:174-9.

15. Clish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud. 2015;1: a000588

16. Lindon JC, Holmes E, Nicholson JK. So, what’s the deal with metabonomics? Anal Chem. 2003;75:384A-391A.

17. Cao B, Jin M, Brietzke E, et al. Serum metabolic profiling using small molecular water-soluble metabolites in individuals with schizophrenia: A longitudinal study using a pre-post-treatment design. Psychiatry Clin Neurosci. 2019;73:100-8.

18. Cao B, Wang D, Brietzke E, et al. Characterizing amino-acid biosignatures amongst individuals with schizophrenia: a case-control study. Amino Acids. 2018; 50(8):1013-1023.

19. Yan X, Sun L, Zhao A, et al. Serum fatty acid patterns in patients with schizophrenia: a targeted metabonomics study. Transl Psychiatry. 2017;7:e1176.

20. Dougherty J. Cellomics: Characterization of Neural Subtypes by High-Throughput Methods and Trasngenic Mouse Models. In: The OMICS Applications in Neuroscience. Oxford University Press. 2014 .p. 195-219.
Medicine Science-Cover
  • ISSN: 2147-0634
  • Yayın Aralığı: 4
  • Başlangıç: 2012
  • Yayıncı: Effect Publishing Agency ( EPA )
Sayıdaki Diğer Makaleler

A new approach for green synthesis and characterization of Artemisia L. (Asteraceae) genotype extracts -Cu2+ nanocomplexes (nanoflower) and their effecitve antimicrobial activity

Ayse Baldemir KİLİC, Cevahir ALTINKAYNAK, Nilay ILDİZ, Nalan OZDEMİR, Vedat YİLMAZ, Ismail OCSOY

Changes in thiol/disulfide homeostasis in patients with chronic kidney disease

Ibrahim SOLAK, Seher MERCAN, Ibrahim GUNEY, Ozcan EREL, Salim NESELİOGLU, Cigdem Damla CETİNKAYA, Mehmet Ali ERYILMAZ

Antioxidant and anti-lipoxygenase activities of Cydonia oblonga

Mehmet BERKOZ

Ischemic stroke in young adults: Gender-based differences

Fatma Ebru ALGUL, Yuksel KAPLAN

A retrospecti̇ve evaluation of the epitelial lesions / neoplasms of the gallbladder in Uşak city and determination of the visual frequency

Sirin KUCUK, Cengiz KOCAK, Ersoy ERCİHAN, Asli Ucar UNCU, Mehmet GUNDOGAN

The knowledge and approaches of parents to tick bite and tick-borne disease

Mehmet Kayhan MUTLU, Sebahat GUCUK

Histopathological gastric mucosal changes in patients using proton pump inhibitors

Saadet ALAN, Ayse Nur AKATLİ

Functional evaluation of bilateral arthroereisis of subtalar joint and simultaneousgastrocnemius lengthening in symptomatic flexible flatfoot in children

Zafer ATBASİ

Barbaloin attenuates ischemia reperfusion-induced oxidative renal injury via antioxidant and anti-inflammatory effects

Ayhan TANYELİ, Mustafa Can GULER, Ersen ERASLAN, Fazile Nur Eki nci AKDEMİR, Omer TOPDAGİ, Elif Polat HOPCAN, Tuncer NACAR

Mitral annular calcification is associated with postexercise heart rate recovery

Vahit DEMİR, Yasar TURAN, Siho HİDAYET