Stefan Reckow

Proteome analysis of the thalamus and cerebrospinal fluid reveals glycolysis dysfunction and potential biomarkers candidates for schizophrenia

Schizophrenia (SCZ) is the result of DNA alterations and environmental factors, which together lead to differential protein expression and ultimately to the development of the illness. The diagnosis is based on clinical symptoms, and the molecular background of SCZ is not completely understood. The thalamus, whose dysfunction has been associated with SCZ based in diverse lines of evidences, plays for instance a pivotal role in the central nervous system as a relay center by re-distributing auditory and visual stimuli from diverse brain regions to the cerebral cortex. We analyzed the proteome of postmortem mediodorsal thalamus (MDT) samples from 11 SCZ patients and 8 non-SCZ individuals by using quantitative shotgun-mass spectrometry and two-dimensional gel electrophoresis. Our analyses identified 551 proteins, 50 of which showed significant differential expression. The main pathways affected by the differentially expressed proteins include energy metabolism, oligodendrocyte metabolism, and cytoskeleton assembly. The potential protein biomarkers candidates myelin basic protein and myelin oligodendrocyte protein were validated by Western blot in the MDT samples and also in cerebrospinal fluid from a separate set of samples of 17 first-episode SCZ patients and 10 healthy controls. The differential expression of μ-crystallin, protein kinase C-gamma, and glial fibrillary acidic protein were confirmed in MDT. Because we found several glycolysis enzymes to be differentially expressed, we measured the levels of pyruvate and NADPH and found them to be altered in MDT. The protein changes described here corroborate the importance of myelin/oligodendrocyte and energy metabolism in SCZ and highlight new potential biomarkers candidates that may contribute to the understanding of the pathogenesis of this complex disease.

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.

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.

Proteome Scale Turnover Analysis in Live Animals Using Stable Isotope Metabolic Labeling

At present most quantitative proteomics investigations are focused on the analysis of protein expression differences between two or more sample specimens. With each analysis a static snapshot of a cellular state is captured with regard to protein expression. However, any information on protein turnover cannot be obtained using classic methodologies. Protein turnover, the result of protein synthesis and degradation, represents a dynamic process, which is of equal importance to understanding physiological processes. Methods employing isotopic tracers have been developed to measure protein turnover. However, applying these methods to live animals is often complicated by the fact that an assessment of precursor pool relative isotope abundance is required. Also, data analysis becomes difficult in case of low label incorporation, which results in a complex convolution of labeled and unlabeled peptide mass spectrometry signals. Here we present a protein turnover analysis method that circumvents this problem using a (15)N-labeled diet as an isotopic tracer. Mice were fed with the labeled diet for limited time periods and the resulting partially labeled proteins digested and subjected to tandem mass spectrometry. For the interpretation of the mass spectrometry data, we have developed the ProTurnyzer software that allows the determination of protein fractional synthesis rates without the need of precursor relative isotope abundance information. We present results validating ProTurnyzer with Escherichia coli protein data and apply the method to mouse brain and plasma proteomes for automated turnover studies.

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.

A MS data search method for improved 15N‐labeled protein identification

Quantitative proteomics using stable isotope labeling strategies combined with MS is an important tool for biomarker discovery. Methods involving stable isotope metabolic labeling result in optimal quantitative accuracy, since they allow the immediate combination of two or more samples. Unfortunately, stable isotope incorporation rates in metabolic labeling experiments using mammalian organisms usually do not reach 100%. As a consequence, protein identifications in 15N database searches have poor success rates. We report on a strategy that significantly improves the number of 15N‐labeled protein identifications and results in a more comprehensive and accurate relative peptide quantification workflow.

Shotgun mass spectrometry analysis of the human thalamus proteome

The thalamus plays pivotal roles in the central nervous system as relay center for organizing information, such as auditory and visual senses from diverse brain regions and their re-distribution to the cerebral cortex. Brain diseases including schizophrenia, Parkinsons disease, epilepsy, and bipolar disorder have been associated with the thalamus. We performed a shotgun proteome analysis of iTRAQ-labeled tryptic peptides of human mediodorsal thalamus protein extracts coming from two healthy male and two healthy female subjects. The shotgun workflow consisted of IEF fractionation, RP LC and MALDI-TOF/TOF mass spectrometric analysis. We were able to identify 542 proteins that are involved in different biological processes and from diverse cellular localizations. A considerable fraction of these proteins had not been identified by traditional proteomics methods such as 2-DE. The thalamus proteome contributes to the knowledge of the human brain proteome and future applications in basic and clinical research.

The 15N isotope effect in Escherichia coli: A neutron can make the difference

Several techniques based on stable isotope labeling are used for quantitative MS. These include stable isotope metabolic labeling methods for cells in culture as well as live organisms with the assumption that the stable isotope has no effect on the proteome. Here, we investigate the 15N isotope effect on Escherichia coli cultures that were grown in either unlabeled (14N) or 15N‐labeled media by LC‐ESI‐MS/MS‐based relative protein quantification. Consistent protein expression level differences and altered growth rates were observed between 14N and 15N‐labeled cultures. Furthermore, targeted metabolite analyses revealed altered metabolite levels between 14N and 15N‐labeled bacteria. Our data demonstrate for the first time that the introduction of the 15N isotope affects protein and metabolite levels in E. coli and underline the importance of implementing controls for unbiased protein quantification using stable isotope labeling techniques.

Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest.

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an experts manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into “Open MIMS,” an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php.

The 15N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model

Stable isotope labeling techniques hold great potential for accurate quantitative proteomics comparisons by MS. To investigate the effect of stable isotopes in vivo, we metabolically labeled high anxiety‐related behavior (HAB) mice with the heavy nitrogen isotope 15N. 15N‐labeled HAB mice exhibited behavioral alterations compared to unlabeled (14N) HAB mice in their depression‐like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the 15N isotope effect on the brain proteome by comparing protein expression levels between 15N‐labeled and 14N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in 15N‐labeled HAB mice, including major metabolic pathways such as the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. Here, we discuss the affected pathways with regard to their relevance for the behavioral phenotype and critically assess the utility of exploiting the 15N isotope effect for correlating phenotypic and molecular alterations.