Literature DB >> 21280236

Quantitative proteomics for investigating psychiatric disorders.

Michaela D Filiou1, Christoph W Turck, Daniel Martins-de-Souza.   

Abstract

The underlying pathophysiology of psychiatric disorders remains elusive. The use of quantitative proteomics to investigate disease-specific protein signatures holds great promise to improve the understanding of psychiatric disorders and identify relevant biomarkers. In this review, we discuss quantitative proteomic approaches for elucidating molecular mechanisms of psychiatric disorders, i.e. anxiety, schizophrenia, bipolar disorder and depression, by studying specimens from animal models and patients. We present gel-based, label-free and stable isotope-labeling methodologies and evaluate their strengths and limitations in the context of psychiatric research, with a focus on (15)N metabolic labeling of live animals due to its increased accuracy and potential for future applications. We also review biomarker candidate validation methods and present quantitative proteomic studies from the literature that aim to disentangle the molecular pathobiology of psychiatric disorders and identify candidate biomarkers. Finally, we explore the applicability of implementing proteomic methods as a routine diagnostic tool in the clinical laboratory.
Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Year:  2010        PMID: 21280236     DOI: 10.1002/prca.201000060

Source DB:  PubMed          Journal:  Proteomics Clin Appl        ISSN: 1862-8346            Impact factor:   3.494


  15 in total

Review 1.  Approaches for targeted proteomics and its potential applications in neuroscience.

Authors:  Sumit Sethi; Dipti Chourasia; Ishwar S Parhar
Journal:  J Biosci       Date:  2015-09       Impact factor: 1.826

Review 2.  What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Authors:  Christoph W Turck; Michaela D Filiou
Journal:  Mol Neuropsychiatry       Date:  2015-05-12

3.  Improved detection of quantitative differences using a combination of spectral counting and MS/MS total ion current.

Authors:  Dana M Freund; Jessica E Prenni
Journal:  J Proteome Res       Date:  2013-03-12       Impact factor: 4.466

Review 4.  Psychiatric disorders biochemical pathways unraveled by human brain proteomics.

Authors:  Verônica M Saia-Cereda; Juliana S Cassoli; Daniel Martins-de-Souza; Juliana M Nascimento
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2016-07-04       Impact factor: 5.270

Review 5.  Biomarkers in development of psychotropic drugs.

Authors:  K Wiedemann
Journal:  Dialogues Clin Neurosci       Date:  2011       Impact factor: 5.986

Review 6.  Potential biomarkers in psychiatry: focus on the cholesterol system.

Authors:  Alisa G Woods; Izabela Sokolowska; Regina Taurines; Manfred Gerlach; Edward Dudley; Johannes Thome; Costel C Darie
Journal:  J Cell Mol Med       Date:  2012-06       Impact factor: 5.310

7.  Time-dependent metabolomic profiling of Ketamine drug action reveals hippocampal pathway alterations and biomarker candidates.

Authors:  K Weckmann; C Labermaier; J M Asara; M B Müller; C W Turck
Journal:  Transl Psychiatry       Date:  2014-11-11       Impact factor: 6.222

Review 8.  Mass spectrometry for the detection of potential psychiatric biomarkers.

Authors:  Armand G Ngounou Wetie; Izabela Sokolowska; Kelly Wormwood; Katherine Beglinger; Tanja Maria Michel; Johannes Thome; Costel C Darie; Alisa G Woods
Journal:  J Mol Psychiatry       Date:  2013-06-05

9.  Identification of proteomic signatures associated with depression and psychotic depression in post-mortem brains from major depression patients.

Authors:  D Martins-de-Souza; P C Guest; L W Harris; N Vanattou-Saifoudine; M J Webster; H Rahmoune; S Bahn
Journal:  Transl Psychiatry       Date:  2012-03-13       Impact factor: 6.222

Review 10.  Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder.

Authors:  Daniel Martins-de-Souza
Journal:  Dialogues Clin Neurosci       Date:  2014-03       Impact factor: 5.986

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