Despite intense scientific efforts, the neuropathology and pathophysiology of schizophrenia are poorly understood. Proteomic studies, by testing large numbers of proteins for associations with disease, may contribute to the understanding of the molecular mechanisms of schizophrenia. They may also indicate the types and locations of cells most likely to harbor pathological alterations. Investigations using proteomic approaches have already provided much information on quantitative and qualitative protein patterns in postmortem brain tissue, peripheral tissues and body fluids. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, SELDI-TOF, shotgun proteomics with label-based (ICAT), and label-free (MSE) quantification have been applied to the study of schizophrenia for the past 15 years. This review summarizes the results, mostly from brain but also from other tissues and bodily fluids, of proteomics studies in schizophrenia. Emphasis is given to proteomics platforms, varying sources of material, proposed candidate biomarkers emerging from comparative proteomics studies, and the specificity of the putative markers in terms of other mental illnesses. We also compare proteins altered in schizophrenia with reports of protein or mRNA sequences that are relatively enriched in specific cell types. While proteomic studies of schizophrenia find abnormalities in the expression of many proteins that are not cell type-specific, there appears to be a disproportionate representation of proteins whose synthesis and localization are highly enriched in one or more brain cell type compared with other types of brain cells. Two of the three proteins most commonly altered in schizophrenia are aldolase C and glial fibrillary acidic protein, astrocytic proteins with entirely different functions, but the studies are approximately evenly divided with regard to the direction of the differences and the concordance or discordance between the two proteins. Alterations of common myelin-associated proteins were also frequently observed, and in four studies that identified alterations in at least two, all differences were downwards in schizophrenia, consistent with earlier studies examining RNA or targeting myelin-associated proteins.
The Need for Proteomics Studies and Biomarkers for Schizophrenia
Genomic studies of schizophrenia, using genome wide association studies (GWAS), copy number variations (CNVs), microarrays, and next-generation sequencing (RNAseq) have linked schizophrenia with rare genetic variations (Sullivan et al., 2012). There is strong evidence that there are no known Mendelian variants identified for this disease. Instead, variations of many genes with confirmed involvement of rare structural variations and common variations with subtle effects are considered to be involved in the etiology of the disease (Owen et al., 2010). Genomics studies on schizophrenia have not answered the main questions on the pathophysiology of the disease, nor have they resulted in identification of diagnostic, prognostic, or therapeutic biomarkers. In addition, current research suggests that schizophrenia can arise from an interaction between neurodevelopmental processes and environmental effects (Albus, 2012).
Understanding schizophrenia as a complex disease therefore requires determination of not only gene expression and DNA variations, but also determination of the abundance and modifications of various proteins, and their distribution at gross anatomical, cellular, and subcellular levels. Proteomics aims to unravel biological processes based on qualitative and quantitative comparison of proteomes. It gives a different level of understanding than genomics for several reasons. First, the expression or function of proteins is modulated at many diverse points, from transcription of DNA to post-translational modifications (PTMs), and very little of this can be predicted from analysis of nucleic acids alone. Second, there is generally poor correlation between abundance of mRNA, transcribed from DNA, and abundance of protein translated from that mRNA. Third, many transcripts give rise to more than one protein, through alternative splicing or alternative PTMs such as phosphorylation, glycosylation, and acetylation, which profoundly affect their activities and lead to multiple protein products from the same gene.
Proteomic investigations have largely improved our understanding of schizophrenia, based on quantitative and qualitative identification of protein patterns in postmortem brain tissue, peripheral tissues, and body fluids (Martins-de-Souza et al., 2012b; Guest et al., 2013, 2014; Nascimento and Martins-de-Souza, 2015). This has enhanced our knowledge of complex protein networks and signal transduction pathways affected in this disease, as discussed in detail below. In addition, emerging proteomic platforms have facilitated the identification of biomarker candidates by simultaneous measurement of hundreds or thousands of molecules in non-hypothesis-driven comparative proteomics studies. This approach established the first blood-based test to aid in the diagnosis of schizophrenia (Schwarz et al., 2010). The test encompassed 51 biomarkers with an overall sensitivity and specificity of 83%. The clinical utility of this test has been studied in terms of specificity compared with other psychiatric disorders (Schwarz et al., 2012a), the ability to identify the disease prior to clinical manifestation, (Schwarz et al., 2012b) and the ability to define the complex schizophrenia syndrome on the basis of molecular profiles (Schwarz et al., 2014).
However, there is still an urgent need for biomarkers that will help to improve the diagnosis and stratification of patients and facilitate more effective treatments and care. The field of proteomics is rapidly developing, with improvements in mass spectrometry, peptide identification algorithms, and bioinformatics. Larger studies, standardized sample collection and processing, and highly sophisticated proteomics platforms promise new diagnostic, prognostic, and therapeutic biomarkers for schizophrenia.
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Front. Cell. Neurosci., 16 February 2016 | http://dx.doi.org/10.3389/fncel.2016.00018