Antidepressant treatment effects on hippocampal protein turnover: Molecular and spatial insights from mass spectrometry.

A major challenge in managing depression is that antidepressant drugs take a long time to exert their therapeutic effects. For the development of faster-acting treatments, it is crucial to get an improved understanding of the molecular mechanisms underlying antidepressant mode of action. Here, we used a novel mass spectrometry-based workflow to investigate how antidepressant treatment affects brain protein turnover at single-cell and subcellular resolution. We combined stable isotope metabolic labeling, quantitative Tandem Mass Spectrometry (qTMS) and Multi-isotope Imaging Mass Spectrometry (MIMS) to simultaneously quantify and image protein synthesis and turnover in hippocampi of mice treated with the antidepressant paroxetine. We identified changes in turnover of individual hippocampal proteins that reveal altered metabolism-mitochondrial processes and report on subregion-specific turnover patterns upon paroxetine treatment. This workflow can be used to investigate brain protein turnover changes in vivo upon pharmacological interventions at a resolution and precision that has not been possible with other methods to date. Our results reveal acute paroxetine effects on brain protein turnover and shed light on antidepressant mode of action.

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 (15)N 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.

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 (15) N isotope effect on Escherichia coli cultures that were grown in either unlabeled ((14) N) or (15) N-labeled media by LC-ESI-MS/MS-based relative protein quantification. Consistent protein expression level differences and altered growth rates were observed between (14) N and (15) N-labeled cultures. Furthermore, targeted metabolite analyses revealed altered metabolite levels between (14) N and (15) N-labeled bacteria. Our data demonstrate for the first time that the introduction of the (15) N 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.

The (15)N 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 (15)N. (15)N-labeled HAB mice exhibited behavioral alterations compared to unlabeled ((14)N) HAB mice in their depression-like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the (15)N isotope effect on the brain proteome by comparing protein expression levels between (15)N-labeled and (14)N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in (15)N-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 (15)N isotope effect for correlating phenotypic and molecular alterations.

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 (15)N database searches have poor success rates. We report on a strategy that significantly improves the number of (15)N-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, Parkinson's 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.

Analysis of individual protein turnover in live animals on a proteome-wide scale.

Classical quantitative proteomics studies focus on the relative or absolute concentration of proteins at a given time. In contrast, the investigation of protein turnover reveals the dynamics leading to these states. Analyzing the balance between synthesis and degradation of individual proteins provides insights into the regulation of protein concentration and helps understanding underlying biological processes. Comparing the half-lives of proteins allows detecting functional relationships and common regulation mechanisms. Moreover, comparing turnover of individual brain and plasma proteins between control- and treatment-groups indicates turnover changes induced by the treatment.Here, we describe a procedure for determining turnover information of individual proteins in mice on a proteome-wide scale based on partial (15)N metabolic labeling. We will outline the complete experimental workflow starting from (15)N labeling the animals over sample preparation and mass spectrometric measurement up to the analysis of the data.

¹5N metabolic labeling: evidence for a stable isotope effect on plasma protein levels and peptide chromatographic retention times.

Many quantitative proteomics methods rely on protein and peptide labeling with stable isotopes. We have recently found that the introduction of ¹5N into organisms via in vivo metabolic labeling affects protein expression levels as well as metabolic pathways and behavioral phenotypes. Here, we present further evidence for a stable isotope effect based on the plasma proteome analysis of ¹5N-labeled mice. We compared plasma proteomes of ¹5N-labeled and unlabeled (¹4N) mice by quantitative MS. We found a number of protein level differences, some of which were verified immunochemically. In addition, we observed divergent chromatographic retention time and peak full width at half maximum (FWHM) between ¹5N-labeled and ¹4N tryptic peptides. Our data point toward a systemic effect of the introduction of heavy isotopes in vivo.

Proteomic and metabolomic profiling reveals time-dependent changes in hippocampal metabolism upon paroxetine treatment and biomarker candidates.

Most of the commonly used antidepressants block monoamine reuptake transporters to enhance serotonergic or noradrenergic neurotransmission. Effects besides or downstream of monoamine reuptake inhibition are poorly understood and yet presumably important for the drugs' mode of action. In the present study we aimed at identifying hippocampal cellular pathway alterations in DBA/2 mice using paroxetine as a representative Selective Serotonin Reuptake Inhibitor (SSRI). Furthermore we identified biomarker candidates for the assessment of antidepressant treatment effects in plasma. Hippocampal protein levels were compared between chronic paroxetine- and vehicle-treated animals using in vivo(15)N metabolic labeling combined with mass spectrometry. We also studied the time course of metabolite level changes in hippocampus and plasma using a targeted polar metabolomics profiling platform. In silico pathway analyses revealed profound alterations related to hippocampal energy metabolism. Glycolytic metabolite levels acutely increased while Krebs cycle metabolite levels decreased upon chronic treatment. Changes in energy metabolism were influenced by altered glycogen metabolism rather than by altered glycolytic or Krebs cycle enzyme levels. Increased energy levels were reflected by an increased ATP/ADP ratio and by increased ratios of high-to-low energy purines and pyrimidines. In the course of our analyses we also identified myo-inositol as a biomarker candidate for the assessment of antidepressant treatment effects in the periphery. This study defines the cellular response to paroxetine treatment at the proteome and metabolome levels in the hippocampus of DBA/2 mice and suggests novel SSRI modes of action that warrant consideration in antidepressant development efforts.

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 expert's 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.

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.

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.

Stable isotope metabolic labeling with a novel N-enriched bacteria diet for improved proteomic analyses of mouse models for psychopathologies.

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed (15)N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from (15)N-labeled vs. (14)N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope (15)N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed (15)N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry.