Quantitative proteomics for investigating psychiatric disorders

Filiou, Michaela D.; Turck, Christoph W.; Martins-de-Souza, Daniel

doi:10.1002/prca.201000060

Cited by 61 publications

(43 citation statements)

References 131 publications

(152 reference statements)

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“…Metabolomics analyses of urine samples have also been performed to study depression [10,11] and effects of antidepressant treatment in rat models [12]. For further proteomics and metabolomics studies in animal models and human cohorts, the reader is referred to several review papers [3,13,14,15,16]. …”

Section: Proteomics and Metabolomics In Psychiatric Disorders: Currenmentioning

confidence: 99%

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Turck

Filiou

2015

Complex Psychiatry

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Understanding the molecular causes and finding appropriate therapies for psychiatric disorders are challenging tasks for research; -omics technologies are used to elucidate the molecular mechanisms underlying brain dysfunction in a hypothesis-free manner. In this review, we will focus on mass spectrometry-based proteomics and metabolomics and address how these approaches have contributed to our understanding of psychiatric disorders. Specifically, we will discuss what we have learned from mass spectrometry-based proteomics and metabolomics studies in rodent models and human cohorts, outline current limitations and discuss the potential of these methods for future applications in psychiatry.

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Section: Proteomics and Metabolomics In Psychiatric Disorders: Currenmentioning

confidence: 99%

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Turck

Filiou

2015

Complex Psychiatry

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“…These peptides may be labeled using stable isotopes such as isotope-coded affinity tags, iTRAQ (isobaric tags for relative and absolute quantitation), isotope-coded protein labeling (ICPL) and 4-trimethylammoniumbutyryl, a deuterium-based label [15] that helps in posterior quantification. In vivo studies may also use stable isotope-labeling such as SILAC (stable isotope labeling by amino acids in cell culture) and 15 N metabolic labeling, although they remain high-cost experiments [7,16,17]. Another approach is label-free analysis, in which quantification is done directly by comparing the MS measurement of different samples once you consider that the chromatographic peak area of a given peptide corresponds to its concentration [7,17].…”

Section: Proteomicsmentioning

confidence: 99%

“…In vivo studies may also use stable isotope-labeling such as SILAC (stable isotope labeling by amino acids in cell culture) and 15 N metabolic labeling, although they remain high-cost experiments [7,16,17]. Another approach is label-free analysis, in which quantification is done directly by comparing the MS measurement of different samples once you consider that the chromatographic peak area of a given peptide corresponds to its concentration [7,17]. The reproducibility of label-free analysis was already evaluated in blood serum samples [18].…”

Section: Proteomicsmentioning

confidence: 99%

“…In agreement with these data, another proteomic study using a selected reaction monitoring assay indicates alterations in energy metabolism including glycolytic enzymes. The most consistent data include aldolase C (ALDOC), γ-enolase (ENO2), hexokinase (HK1), phosphoglycerate mutase 1 (PGAM1), aconitate hydratase (ACO2) and triosephosphate isomerase (TPI1) [17,28]. HK is a rate-limiting enzyme of glycolysis, and among the isozymes, HK1 is the only one that is predominantly bound to mitochondria.…”

Section: Glycolysis Pathway and Oxidative Stressmentioning

confidence: 99%

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Proteomic Characterization of the Brain and Cerebrospinal Fluid of Schizophrenia Patients

Café-Mendes

Gattaz

Schmitt

et al. 2014

Advances in Biological Psychiatry

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As a multifactorial mental disorder, schizophrenia presents a complex combination of genetic, neurodevelopmental and environmental components. These lead to difficulties in comprehending the molecular basis of schizophrenia as well as in identifying biomarkers. These issues can be tackled by proteomics, which has been used increasingly along with genetics and other '-omics' approaches. In the present chapter, we explore some advances in proteomic studies involving brain tissue and cerebrospinal fluid collected from schizophrenic patients. We demonstrate that proteomic findings have been confirmed by other approaches, and added new information about the role of synaptic connectivity, oxidative stress, glucose metabolism and cytoskeletal alterations as core features of the disease pathophysiology. Integrative systems analysis including proteomics is a valid strategy for understanding the molecular basis of schizophrenia and may indicate the way to future clinical applications. SchizophreniaSchizophrenia is a severe mental disorder with a lifetime prevalence of 0.3-0.7% and presenting a heterogeneous range of symptoms [1]. It is a multifactorial disorder composed of a number of etiological factors of small effect that may be triggered by environmental components [2]. The causes of schizophrenia are strongly determined by genetics, but also by other factors such as epistatic (interaction between two or more genes controlling a single phenotype) and environmental ones (epigenetics). This is supported by the fact that 90% of individuals who develop schizophrenia have no history of schizophrenic parents or relatives. This conclusion does not exclude the heritable factor of schizophrenia, which is high (up to 80%), but reinforces the fact that environmental factors influence the onset/development of the disease [3]. Although interactions occur and are responsible for the establishment of the Martins-de-Souza D (ed): Proteomics and Metabolomics in Psychiatry.

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“…A number of quantitative proteomics approaches are available and applicable to schizophrenia research 1 . Among them, in vivo 15 N metabolic labeling holds great potential for studies in animal models as well as in patient cohorts.…”

Section: N Metabolic Labelingmentioning

confidence: 99%

O potencial da rotulação metabólica de 15N para a pesquisa de esquizofrenia

Filiou¹

2012

Arch. Clin. Psychiatry (São Paulo)

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Psychiatric research is in need of non-hypothesis driven approaches to unravel the neurobiological underpinnings and identify molecular biomarkers for psychiatric disorders. Proteomics methodologies constitute a state-of-the-art toolbox for biomarker discovery in psychiatric research. Here we present the principle of in vivo 15 N metabolic labeling for quantitative proteomics experiments and applications of this method in animal models of psychiatric phenotypes, with a particular focus on schizophrenia. Additionally we explore the potential of 15 N metabolic labeling in different experimental set-ups as well as methodological considerations of 15 N metabolic labeling-based quantification studies.Filiou MD / Rev Psiq Clín. 2013;40(1):51-2 Keywords: Schizophrenia, quantitative proteomics, 15 N metabolic labeling, biomarker, G72. ResumoPesquisas em psiquiatria ainda necessitam de estudos não dirigidos por hipóteses para revelar fundamentos neurobiológicos e biomarcadores moleculares para distúrbios psiquiátricos. Metodologias proteômicas disponibilizam uma série de ferramentas para esses fins. Apresentamos o princípio de rotulação metabólica utilizando 15 N para proteômica quantitativa e suas aplicações em modelos animais de fenótipos psiquiátricos com um foco particular em esquizofrenia. Exploramos o potencial de rotulação metabólica por 15 N em diferentes tipos de experimentos, bem como suas considerações metodológicas. The need for quantitative proteomics in schizophrenia researchIn the last years, the development of holistic approaches such as genomics, transcriptomics, proteomics and metabolomics has given rise to quantitative, non-hypothesis driven research applications. In this regard, populations of genes, proteins or metabolites of two states (e.g. disease vs. control) can be compared to identify expression level differences relevant to the observed phenotypic alterations. As the proteome of an organism can reflect phenotypic changes at the molecular level, proteomics constitutes a valuable tool to investigate the underlying mechanisms involved in psychiatric disorders.Research in schizophrenia suffers from a lack of molecular correlates for the observed behavioral alterations and disease symptoms. Molecular biomarkers for schizophrenia can aid the assessment of predisposition risk, the accurate subcategorization of patients, the monitoring of disease progression and the discovery of novel therapeutic targets. To this end, quantitative proteomics has the potential to provide sensitive molecular biomarker information and therefore offer valuable insights for schizophrenia prognosis, diagnosis and treatment. N metabolic labelingA number of quantitative proteomics approaches are available and applicable to schizophrenia research 1 . Among them, in vivo 15 N metabolic labeling holds great potential for studies in animal models as well as in patient cohorts. The principle of 15 N metabolic labeling is based on the introduction of the stable nitrogen isotope 15 N in an organism through either a 15 N-...

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Quantitative proteomics for investigating psychiatric disorders

Cited by 61 publications

References 131 publications

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Proteomic Characterization of the Brain and Cerebrospinal Fluid of Schizophrenia Patients

O potencial da rotulação metabólica de 15N para a pesquisa de esquizofrenia

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