Michaela D. Filiou

Proteomics and Metabolomics Analysis of a Trait Anxiety Mouse Model Reveals Divergent Mitochondrial Pathways

BACKGROUND Although anxiety disorders are the most prevalent psychiatric disorders, no molecular biomarkers exist for their premorbid diagnosis, accurate patient subcategorization, or treatment efficacy prediction. To unravel the neurobiological underpinnings and identify candidate biomarkers and affected pathways for anxiety disorders, we interrogated the mouse model of high anxiety-related behavior (HAB), normal anxiety-related behavior (NAB), and low anxiety-related behavior (LAB) employing a quantitative proteomics and metabolomics discovery approach. METHODS We compared the cingulate cortex synaptosome proteomes of HAB and LAB mice by in vivo (15)N metabolic labeling and mass spectrometry and quantified the cingulate cortex metabolomes of HAB/NAB/LAB mice. The combined data sets were used to identify divergent protein and metabolite networks by in silico pathway analysis. Selected differentially expressed proteins and affected pathways were validated with immunochemical and enzymatic assays. RESULTS Altered levels of up to 300 proteins and metabolites were found between HAB and LAB mice. Our data reveal alterations in energy metabolism, mitochondrial import and transport, oxidative stress, and neurotransmission, implicating a previously nonhighlighted role of mitochondria in modulating anxiety-related behavior. CONCLUSIONS Our results offer insights toward a molecular network of anxiety pathophysiology with a focus on mitochondrial contribution and provide the basis for pinpointing affected pathways in anxiety-related behavior.

N-acetyl cysteine treatment rescues cognitive deficits induced by mitochondrial dysfunction in G72/G30 transgenic mice.

Genetic studies have implicated the evolutionary novel, anthropoid primate-specific gene locus G72/G30 in psychiatric diseases. This gene encodes the protein LG72 that has been discussed to function as a putative activator of the peroxisomal enzyme D-amino-acid-oxidase (DAO) and as a mitochondrial protein. We recently generated ‘humanized’ bacterial artificial chromosome transgenic mice (G72Tg) expressing G72 transcripts in cells throughout the brain. These mice exhibit several behavioral phenotypes related to psychiatric diseases. Here we show that G72Tg mice have a reduced activity of mitochondrial complex I, with a concomitantly increased production of reactive oxygen species. Affected neurons display deficits in short-term plasticity and an impaired capability to sustain synaptic activity. These deficits lead to an impairment in spatial memory, which can be rescued by pharmacological treatment with the glutathione precursor N-acetyl cysteine. Our results implicate LG72-induced mitochondrial and synaptic defects as a possible pathomechanism of psychiatric disorders.

Quantitative proteomics for investigating psychiatric disorders

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 15N 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.

Proteomic and Metabolomic Profiling of a Trait Anxiety Mouse Model Implicate Affected Pathways

Depression and anxiety disorders affect a great number of people worldwide. Whereas singular factors have been associated with the pathogenesis of psychiatric disorders, growing evidence emphasizes the significance of dysfunctional neural circuits and signaling pathways. Hence, a systems biology approach is required to get a better understanding of psychiatric phenotypes such as depression and anxiety. Furthermore, the availability of biomarkers for these disorders is critical for improved diagnosis and monitoring treatment response. In the present study, a mouse model presenting with robust high versus low anxiety phenotypes was subjected to thorough molecular biomarker and pathway discovery analyses. Reference animals were metabolically labeled with the stable 15N isotope allowing an accurate comparison of protein expression levels between the high anxiety-related behavior versus low anxiety-related behavior mouse lines using quantitative mass spectrometry. Plasma metabolomic analyses identified a number of small molecule biomarkers characteristic for the anxiety phenotype with particular focus on myo-inositol and glutamate as well as the intermediates involved in the tricarboxylic acid cycle. In silico analyses suggested pathways and subnetworks as relevant for the anxiety phenotype. Our data demonstrate that the high anxiety-related behavior and low anxiety-related behavior mouse model is a valuable tool for anxiety disorder drug discovery efforts.

To label or not to label: Applications of quantitative proteomics in neuroscience research

Proteomics has provided researchers with a sophisticated toolbox of labeling‐based and label‐free quantitative methods. These are now being applied in neuroscience research where they have already contributed to the elucidation of fundamental mechanisms and the discovery of candidate biomarkers. In this review, we evaluate and compare labeling‐based and label‐free quantitative proteomic techniques for applications in neuroscience research. We discuss the considerations required for the analysis of brain and central nervous system specimens, the experimental design of quantitative proteomic workflows as well as the feasibility, advantages, and disadvantages of the available techniques for neuroscience‐oriented questions. Furthermore, we assess the use of labeled standards as internal controls for comparative studies in humans and review applications of labeling‐based and label‐free mass spectrometry approaches in relevant model organisms and human subjects. Providing a comprehensive guide of feasible and meaningful quantitative proteomic methodologies for neuroscience research is crucial not only for overcoming current limitations but also for gaining useful insights into brain function and translating proteomics from bench to bedside.

Profiling of mouse synaptosome proteome and phosphoproteome by IEF

Synapses play important roles in neurotransmission and neuroplasticity. For an in‐depth analysis of the synaptic proteome and phosphoproteome, synaptosomal proteins from whole mouse brain were analyzed by IEF and MS resulting in the largest synaptosome proteome described to date, with 2980 unique proteins identified with two or more peptides. At the same time, 118 synaptosomal phosphoproteins were identified, eight of which are reported for the first time as phosphorylated. Expression of selected proteins in synaptosomes was investigated by Western blot. We demonstrate that IEF is a powerful method to interrogate complex samples such as brain tissue both at the proteome and the phosphoproteome level without the need of additional enrichment for phosphoproteins. The detailed synaptoproteome data set reported here will help to elucidate the molecular complexity of the synapse and contribute to our understanding of synaptic systems biology in health and disease.

‘Neuroinflammation’ differs categorically from inflammation: transcriptomes of Alzheimer's disease, Parkinson's disease, schizophrenia and inflammatory diseases compared

Abstract‘Neuroinflammation’ has become a widely applied term in the basic and clinical neurosciences but there is no generally accepted neuropathological tissue correlate. Inflammation, which is characterized by the presence of perivascular infiltrates of cells of the adaptive immune system, is indeed seen in the central nervous system (CNS) under certain conditions. Authors who refer to microglial activation as neuroinflammation confuse this issue because autoimmune neuroinflammation serves as a synonym for multiple sclerosis, the prototypical inflammatory disease of the CNS. We have asked the question whether a data-driven, unbiased in silico approach may help to clarify the nomenclatorial confusion. Specifically, we have examined whether unsupervised analysis of microarray data obtained from human cerebral cortex of Alzheimers, Parkinsons and schizophrenia patients would reveal a degree of relatedness between these diseases and recognized inflammatory conditions including multiple sclerosis. Our results using two different data analysis methods provide strong evidence against this hypothesis demonstrating that very different sets of genes are involved. Consequently, the designations inflammation and neuroinflammation are not interchangeable. They represent different categories not only at the histophenotypic but also at the transcriptomic level. Therefore, non-autoimmune neuroinflammation remains a term in need of definition.

QuantiSpec - Quantitative mass spectrometry data analysis of 15N-metabolically labeled proteins

For relative protein quantitation by mass spectrometry we metabolically labeled E. coli bacteria with (15)N-enriched diets. Proteins extracted from (15)N-labeled and unlabeled E. coli bacteria were mixed, separated by two-dimensional gel electrophoresis and enzymatically digested. The resulting tryptic peptides were analyzed by MALDI mass spectrometry. For the relative protein quantitation we developed fully automated software, QuantiSpec (Quantitative Mass Spectrometry Analysis Software), which uses data from MALDI TOF mass spectrometry and the Mascot database search engine. QuantiSpec detects natural as well as partially or fully labeled peptide isotope distributions. For each identified peptide the (15)N incorporation rate is determined by comparing the experimental to a set of theoretical isotope patterns based on the peptide sequence. Relative quantitation is accomplished by calculating the signal intensity ratios for each (14)N/(15)N peptide pair.

Lipidomics reveals dysfunctional glycosynapses in schizophrenia and the G72/G30 transgenic mouse

BACKGROUND Abnormal structural/functional connectivity has been proposed to underlie the pathophysiology of schizophrenia. However, the biochemical basis of abnormal connectivity remains undefined. METHODS We undertook a shotgun lipidomic analysis of over 700 lipids across 26 lipid subclasses in the frontal cortex of schizophrenia subjects and hippocampus of G72/G30 transgenic mice. RESULTS We demonstrate that glycosphingolipids and choline plasmalogens, structural lipid pools in myelin, are significantly elevated in the frontal cortex obtained from patients suffering from schizophrenia and the hippocampus of G72/G30 transgenic mice. CONCLUSIONS Our data suggest that structural lipid alterations in oligodendrocyte glycosynapses are responsible for dysconnectivity in schizophrenia and that increased expression of G72 protein may play a role in the development of abnormal glycosynapses.

Myelination and oxidative stress alterations in the cerebellum of the G72/G30 transgenic schizophrenia mouse model

G72/G30 is a primate-specific locus that has been repeatedly implicated as a risk factor in genetic studies of schizophrenia. The function of the longest G72 splice variant (LG72 protein) encoded by this locus is not fully understood. To investigate the role of the LG72 protein in vivo, we have generated transgenic (G72Tg) mice carrying the G72/G30 locus that exhibit schizophrenia-like symptoms. We investigated protein expression alterations in the cerebella of G72Tg compared to wild type (WT) mice using a proteomics approach based on in vivo(15)N metabolic labeling and quantitative mass spectrometry (MS). Our data revealed expression level differences of proteins involved in myelin-related processes, oxidative stress and mitochondrial function. Furthermore, in silico pathway analyses suggested common regulators and targets for the observed protein alterations. Our work sheds light on the functional role of the LG72 protein and pinpoints molecular correlates of schizophrenia-like behavior.