Laboratory of Neuroproteomics, Dept. of Biochemistry, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil.
Proteomics has emerged in the post-genomic era as an alternative strategy to provide an integrated biochemical view of biological systems, which may lead to the establishment of diseases as well as to reveal potential biomarker candidates.
We have compared the proteomes of 5 post-mortem brain regions from schizophrenia patients to controls as well as cerebrospinal fluid using a wide range of proteomic techniques – from the classical combination 2-DE/MS to shotgun-MS and stable isotope-labeling. We have also established an SRM assay for multiplex analysis of glycolysis enzymes which we found affected in schizophrenia aiming to characterize preclinical models. Additionally, we compared dorsolateral prefrontal cortex proteomes from depression patients to controls using shotgun-label-free proteomics and SRM. The most frequent alterations found in schizophrenia were related to energy metabolism, cell structure, synaptic function, and myelinization, which are thought to belong to the core of the pathobiology. Metabolites such as pyruvate and NADPH were also validated in such studies. The multivariate analyses of SRM data applied to schizophrenia preclinical model generated a bi-dimensional chart that can distinguish the models from the controls. While studying depression brains, we identified mostly differences associated with energy metabolism and synaptic function. Interestingly, we also found differential proteome profiles in depression patients with and without psychosis, which showed a marked overlap to changes seen in the brain proteome of schizophrenia patients. This is particularly interesting considering that psychosis is one of the main features of schizophrenia. Regarding biomarkers, we have analyzed plasma and mononuclear cells from schizophrenia and depression patients prior and after 6 weeks of antidepressant treatment, revealing proteins that can point to a successful response, considering almost half of the patients do not respond properly to the treatment.
Proteomic findings presented here not only support and are supported by other data from different fields of expertise, but also reveal new insights about the pathobiology of psychiatric disorders. Proteomic data have provided integrated pictures of the biochemical systems involved in these disorders, revealing common and distinct aspects of schizophrenia and depression. Yet, potential biomarkers may be implementable to clinical settings. Strategies to prognosis, more effective diagnosis as well as more efficient treatments arise from hypotheses created from a non-hypothesis driven approach as used here.