Cortical GABA deficits that are consistently reported in schizophrenia may reflect an etiology of failed normal postnatal neurotransmitter maturation. Previous studies have found prefrontal cortical GABA(A) receptor alpha subunit alterations in schizophrenia, yet their relationship to normal developmental expression profiles in the human cortex has not been determined. The aim of this study was to quantify GABA(A) receptor alpha-subunit mRNA expression patterns in human dorsolateral prefrontal cortex (DLPFC) during normal postnatal development and in schizophrenia cases compared to controls. Transcript levels of GABA(A) receptor alpha subunits were measured using microarray and qPCR analysis of 60 normal individuals aged 6weeks to 49years and in 37 patients with schizophrenia/schizoaffective disorder and 37 matched controls. We detected robust opposing changes in cortical GABA(A) receptor alpha1 and alpha5 subunits during the first few years of postnatal development, with a 60% decrease in alpha5 mRNA expression and a doubling of alpha1 mRNA expression with increasing age. In our Australian schizophrenia cohort we detected decreased GAD67 mRNA expression (p=0.0012) and decreased alpha5 mRNA expression (p=0.038) in the DLPFC with no significant change of other alpha subunits. Our findings confirm that GABA deficits (reduced GAD67) are a consistent feature of schizophrenia postmortem brain studies. Our study does not confirm alterations in cortical alpha1 or alpha2 mRNA levels in the schizophrenic DLPFC, as seen in previous studies, but instead we report a novel down-regulation of alpha5 subunit mRNA suggesting that post-synaptic alterations of inhibitory receptors are an important feature of schizophrenia but may vary between cohorts.

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analysing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease specific biomarkers and potential novel therapeutic strategies.

Depression is a severe neuropsychiatric disorder affecting approximately 10% of the world population. Despite this, the molecular mechanisms underlying the disorder are still not understood. Novel technologies such as proteomic-based platforms are beginning to offer new insights into this devastating illness, beyond those provided by the standard targeted methodologies. Here, we will show the potential of proteome analyses as a tool to elucidate the pathophysiological mechanisms of depression as well as the discovery of potential diagnostic, therapeutic and disease course biomarkers.

Abstract BACKGROUND: Many Previous studies have attempted to gain insight into the underlying pathophysiology of schizophrenia by studying post-mortem brain tissues of schizophrenia patients. However, such analyses can be confounded by artefactual features of this approach such as lengthy agonal state and post-mortem interval times. As several aspects of schizophrenia are also manifested at the peripheral level in proliferating cell types, we have studied the disorder through systematic transcriptomic and proteomic analyses of skin fibroblasts biopsied from living patients. METHODOLOGY/PRINCIPAL FINDINGS: We performed comparative transcriptomic and proteomic profiling to characterise skin fibroblasts from schizophrenia patients compared to healthy controls. Transcriptomic profiling using cDNA array technology showed that pathways associated with cell cycle regulation and RNA processing were altered in the schizophrenia subjects (n=12) relative to controls (n=12). LC-MSE proteomic profiling led to identification of 16 proteins that showed significant differences in expression between schizophrenia (n=11) and control (n=11) subjects. Analysis in silico revealed that these proteins were also associated with proliferation and cell growth pathways. To validate these findings at the protein level, fibroblast protein extracts were analyzed by Western blotting which confirmed the differential expression of three key proteins associated with these pathways. At the functional level, we confirmed the decreased proliferation phenotype by showing that cultured fibroblasts from schizophrenia subjects (n=5) incorporated less 3H-thymidine into their nuclei compared to those from controls (n=6) by day 4 over an 8 day time course study. Similar abnormalities in cell cycle and growth pathways have been reported to occur in the central nervous system in schizophrenia. CONCLUSIONS/SIGNIFICANCE: These studies demonstrate that fibroblasts obtained from living schizophrenia subjects show alterations in cellular proliferation and growth pathways. Future studies aimed at characterizing such pathways in fibroblasts and other proliferating cell types from schizophrenia patients could elucidate the molecular mechanisms associated with the pathophysiology of schizophrenia and provide a useful model to support drug discovery efforts.

In order to exploit human blood as a source of protein disease biomarkers, robust analytical methods are needed to overcome the inherent molecular complexity of this bio-fluid. We present the coupling of label-free SAX chromatography and IMAC to a data-independent nanoLC-MS/MS (nanoLC-MS(E)) platform for analysis of blood plasma and serum proteins. The methods were evaluated using protein standards added at different concentrations to two groups of samples. The results demonstrate that both techniques enable accurate protein quantitation using low sample volumes and a minimal number of fractions. Combining both methods, 883 unique proteins were identified, of which 423 proteins showed high reproducibility. The two approaches resulted in identification of unique molecular signatures with an overlap of approximately 30%, thus providing complimentary information on sub-proteomes. These methods are potentially useful for systems biology, biomarker discovery, and investigation of phosphoproteins in blood.

BACKGROUND : In the diagnosis of complex diseases such as neurological pathologies, a wealth of clinical and molecular information is often available to help the interpretation. Yet, the pieces of information are usually considered in isolation and rarely integrated due to the lack of a sound statistical framework. This lack of integration results in the loss of valuable information about how disease associated factors act synergistically to cause the complex phenotype. RESULTS : Here, we investigated complex psychiatric diseases as networks. The networks were used to integrate data originating from different profiling platforms. The weighted links in these networks capture the association between the analyzed factors and allow the quantification of their relevance for the pathology. The heterogeneity of the patient population was analyzed by clustering and graph theoretical procedures. We provided an estimate of the heterogeneity of the population of schizophrenia and detected a subgroup of patients featuring remarkable abnormalities in a network of serum primary fatty acid amides. We compared the stability of this molecular network in an extended dataset between schizophrenia and affective disorder patients and found more stable structures in the latter. CONCLUSION : We quantified robust associations between analytes measured with different profiling platforms as networks. The methodology allows the quantitative evaluation of the complexity of the disease. The identified disease patterns can then be further investigated with regards to their diagnostic utility or help in the prediction of novel therapeutic targets. The applied framework is able to enhance the understanding of complex psychiatric diseases, and may give novel insights into drug development and personalized medicine approaches.