Psychiatric patient stratification using biosignatures based on cerebrospinal fluid protein expression clusters.

Psychiatric disorders are caused by perturbed molecular pathways that affect brain circuitries. The identification of specific biosignatures that are the result of altered pathway activities in major depression, bipolar disorder and schizophrenia can contribute to a better understanding of disease etiology and aid in the implementation of diagnostic assays. In the present study we identified disease-specific protein biosignatures in cerebrospinal fluid of depressed (n: 36), bipolar (n: 27) and schizophrenic (n: 35) patients using the Reverse Phase Protein Microarray technology. These biosignatures were able to stratify patient groups in an objective manner according to cerebrospinal fluid protein expression patterns. Correct classification rates were over 90%. At the same time several protein sets that play a role in neuronal growth, proliferation and differentiation (NEGR1, NPDC1), neurotransmission (SEZ6) and protection from oxidative damage (GPX3) were able to distinguish diseased from healthy individuals (n: 35) indicating a molecular signature overlap for the different psychiatric phenotypes. Our study is a first step toward implementing a psychiatric patient stratification system based on molecular biosignatures. Protein signatures may eventually be of use as specific and sensitive biomarkers in clinical trials not only for patient diagnostic and subgroup stratification but also to follow treatment response.

Cerebrospinal fluid biomarkers for major depression confirm relevance of associated pathophysiology.

Individual characteristics of pathophysiology and course of depressive episodes are at present not considered in diagnostics. There are no biological markers available that can assist in categorizing subtypes of depression and detecting molecular variances related to disease-causing mechanisms between depressed patients. Identification of such differences is important to create patient subgroups, which will benefit from medications that specifically target the pathophysiology underlying their clinical condition. To detect characteristic biological markers for major depression, we analyzed the cerebrospinal fluid (CSF) proteome of depressed vs control persons, using two-dimensional polyacrylamide gel electrophoresis and time-of-flight (TOF) mass spectrometry peptide profiling. Proteins of interest were identified by matrix-assisted laser desorption ionization TOF mass spectrometry (MALDI-TOF-MS). Validation of protein markers was performed by immunoblotting. We found 11 proteins and 144 peptide features that differed significantly between CSF from depressed patients and controls. In addition, we detected differences in the phosphorylation pattern of several CSF proteins. A subset of the differentially expressed proteins implicated in brain metabolism or central nervous system disease was validated by immunoblotting. The identified proteins are involved in neuroprotection and neuronal development, sleep regulation, and amyloid plaque deposition in the aging brain. This is one of the first hypothesis-free studies that identify characteristic protein expression differences in CSF of depressed patients. Proteomic approaches represent a powerful tool for the identification of disease markers for subgroups of patients with major depression.

Phenome-transcriptome correlation unravels anxiety and depression related pathways.

The identification of pathways pertinent to human diseases is critical for gaining a better understanding of their pathophysiology. Pathway knowledge in turn can provide disease marker information required for diagnosis, drug development and improved patient treatment. Psychiatric disorders including anxiety and depression are complex diseases and are caused by a combination of multiple genetic and environmental factors affecting certain brain circuits. Here we used a systems biology approach to identify molecular pathways that affect anxiety- and depression-like phenotypes. For this purpose we screened pathways for stable enrichment in a great number of publicly available transcriptome data from the Gene Expression Omnibus related to anxiety- and depression-like phenotypes. In case of anxiety our analysis implicate a dysregulation of carbohydrate metabolism, tight junction and the phosphatidylinositol signaling system, whereas for depression gap junction, gonadotropin-releasing hormone signaling and ubiquitin-mediated proteolysis pathways are affected. Furthermore, both anxiety and depression show a dysregulation of VEGF signaling, long term potentiation and the glycolysis pathway. Molecular entities that are part of the identified pathways can serve as biomarkers and potential therapeutic targets for diagnosis and treatment of depression and anxiety disorders.

Protein biomarkers in a mouse model of extremes in trait anxiety.

Brain proteome analysis of mice selectively bred for either high or low anxiety-related behavior revealed quantitative and qualitative protein expression differences. The enzyme glyoxalase-I was consistently expressed to a higher extent in low anxiety as compared with high anxiety mice in several brain areas. The same phenotype-dependent difference was also found in red blood cells with normal and cross-mated animals showing intermediate expression profiles of glyoxalase-I. Another protein that showed a different mobility during two-dimensional gel electrophoresis was identified as enolase phosphatase. The presence of both protein markers in red or white blood cells, respectively, creates the opportunity to screen for their expression in clinical blood specimens from patients suffering from anxiety.

The quest for brain disorder biomarkers.

The identification of disease markers in tissues and body fluids requires an extensive and thorough analysis of its protein constituents. In our efforts to identify biomarkers for affective and neurological disorders we are pursuing several different strategies. On one hand we are using animal models that represent defined phenotypes characteristic for the respective disorder in humans. In addition, we are analyzing human specimens from carefully phenotyped patient groups. Several fractions representing different protein classes from human cerebrospinal fluid obtained by lumbar puncture are used for this purpose. Our biomarker identification efforts range from classical proteomics approaches such as two dimensional gel electrophoresis and mass spectrometry to phage display screens with cerebrospinal fluid antibodies.