Proteomics is not only a biomarker discovery tool

Martins-de-Souza, Daniel

doi:10.1002/prca.200900096

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…Despite all the knowledge regarding treatment, the mechanisms of antidepressant drugs still require further elucidation. Proteome analysis could be helpful for the better understanding of antidepressant treatments, especially because proteome analyses can reveal the proteins involved in such mechanisms as well as the identification of potential biomarkers [ 50 ].…”

Section: Proteomics In Depression Researchmentioning

confidence: 99%

The role of proteomics in depression research

Martins-de-Souza

Harris

Guest

et al. 2009

Eur Arch Psychiatry Clin Neurosci

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Depression is a severe neuropsychiatric disorder affecting approximately 10% of the world population. Despite this, the molecular mechanisms underlying the disorder are still not understood. Novel technologies such as proteomic-based platforms are beginning to offer new insights into this devastating illness, beyond those provided by the standard targeted methodologies. Here, we will show the potential of proteome analyses as a tool to elucidate the pathophysiological mechanisms of depression as well as the discovery of potential diagnostic, therapeutic and disease course biomarkers.Depression, unipolar depression, clinical depression or major depressive disorder (MDD) is a brain disorder which affects around 10% of world population [76]. It is characterized by low mood, low self-esteem, inappropriate guilt, thoughts of death and suicide, diminished concentration, loss of interest or pleasure in normally enjoyable activities, and disturbance of sleep and appetite. In the United States, approximately 3% of depressed patients commit suicide and around 60% of people who commit suicide have MDD or another mood disorder (NIH). MDD is a leading cause of global disability and a risk factor for non-compliance with medical treatment [11].MDD appears to be a multifactorial disease arising from genetic or metabolic predisposition in conjunction with environmental factors such as stressful life events and other stressors. Even though there is no anatomical hallmark, the risk of MDD may be due to significant variations in responsiveness of neural circuits and can be affected by other factors such as addiction or alterations in the dopaminergic system and the molecular mechanisms of memory [5].Neuropathological studies have shown volume deficits, decreased neuronal size and glial pathology as well as decreased expression of synaptic markers, particularly in the anterior cingulate, orbitofrontal and dorsolateral prefrontal cortices [26]. Hippocampal volume reductions have been demonstrated by magnetic resonance imaging (MRI) [53] and may be linked to the effects of stress, hypercortisolaemia and hypothalamic-pituitary-adrenal (HPA) axis dysfunction, although evidence of cellular correlates for such effects is lacking [26]. Hippocampal neurogenesis has also been proposed as an underlying factor in the biology of MDD, mainly based on animal model studies and the effects of antidepressant medication [13]. For the last three decades, the main line of investigation into the neurobiology of MDD has been focused on changes in the monoamine neurotransmitters serotonin and norepinephrine, based on the known mechanisms of action of drugs with antidepressant activity. However, the relationship between low neurotransmitter levels and symptomatology is not simple and may be dependent on genetic background [1].D. Martins-de-Souza and L. W. Harris contributed equally to this work.

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Section: Proteomics In Depression Researchmentioning

confidence: 99%

The role of proteomics in depression research

Martins-de-Souza

Harris

Guest

et al. 2009

Eur Arch Psychiatry Clin Neurosci

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“…Data reviewed here must be combined with information obtained from preclinical models. 25,37,[80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95] These have the advantage of showing fewer confounding factors than human samples. Their limited biomechanical range must be noted, since not all features of a complex human disease such as MDD can be considered.…”

Section: Discussionmentioning

confidence: 99%

“…Even though these are processes previously shown as involved in MDD by other fields of study, 84 this in silico interactome study has pinpointed the role players in the dysregulation of these pathways, which is an important example of the information that omics technologies are able to provide. 85…”

Section: Protein Interactome In Patients With Depressionmentioning

confidence: 99%

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Martins‐de‐Souza

2014

Dialogues in Clinical Neuroscience

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Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types-prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

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“…neurotransmitters, neurotransmitter receptors and transcription factors) is crucial for unraveling molecular pathophysiology. In this regard, the holistic nature and increased sensitivity of proteomic technologies makes them a valuable tool for studying psychiatric disorders 4.…”

Section: The Need For Biomarkers and Molecular Understanding Of Psymentioning

confidence: 99%

Quantitative proteomics for investigating psychiatric disorders

Filiou

Turck

Martins-de-Souza

2010

Proteomics Clinical Apps

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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 (15)N 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.

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Proteomics is not only a biomarker discovery tool

Cited by 7 publications

References 24 publications

The role of proteomics in depression research

The role of proteomics in depression research

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Quantitative proteomics for investigating psychiatric disorders

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