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

Antidepressant Mechanism Research of Acupuncture: Insights from a Genome-Wide Transcriptome Analysis of Frontal Cortex in Rats with Chronic Restraint Stress

Major depressive disorder (MDD) is a chronic disease that adversely affects mood and cognition. In this study, we randomly divided the rats into control group (C), model group (M), fluoxetine group (F), and acupuncture group (A), used open-field test to ascertain whether acupuncture affects chronic restraint stress (CRS) induced depression-like behaviors of rats, and explored the antidepressant mechanism of acupuncture at the molecular level of transcriptome in the frontal cortex of CRS rats by RNA-sequencing (RNA-seq). According to differentially expressed genes (DEG) analysis, we identified 134, 46, and 89 response genes differentially expressed in C versus M, F versus M, and A versus M, respectively. Through Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we identified the gene sets involved in extracellular space, inflammatory response, Toll-like receptor signaling pathway, chemokine signaling pathway, and TNF signaling pathway. In this study, RNA-seq technology was used to investigate the frontal cortex genome-wide transcriptomes in depression rats under CRS, which suggested that the antidepressant effect of acupuncture is effective and has a multitarget characteristic, which may be related to amino acid metabolism and inflammatory pathways, especially the Toll-like receptor signaling pathway, TNF signaling pathway, and NF-kappa B signaling pathway.

Short overview on metabolomic approach and redox changes in psychiatric disorders

Schizophrenia, depression and posttraumatic stress disorder (PTSD) are severe mental disorders and complicated diagnostic entities, due to their phenotypic, biological and genetic heterogeneity, unknown etiology, and poorly understood alterations in biological pathways and biological mechanisms. Disturbed homeostasis between overproduction of oxidant species, overcoming redox regulation and a lack of cellular antioxidant defenses, resulting in free radical-mediated pathology and subsequent neurotoxicity contributes to development of depression, schizophrenia and PTSD, their heterogeneous clinical presentation and resistance to treatment. Metabolomics is a discipline that combines different strategies with the aim to extract, detect, identify and quantify all metabolites that are present in a biological sample and might provide mechanistic insights into the etiology of various psychiatric disorders. Therefore, oxidative stress research combined with metabolomics might offer a novel approach in dissecting psychiatric disorders, since these data-driven but not necessarily hypothesis-driven methods might identify new targets, molecules and pathways responsible for development of schizophrenia, depression or PTSD. Findings from the oxidative research in psychiatry together with metabolomics data might facilitate development of specific and validated prognostic, therapeutic and clinical biomarkers. These methods might reveal bio-signatures of individual patients, leading to individualized treatment approach. In reviewing findings related to oxidative stress and metabolomics in selected psychiatric disorders, we have highlighted how these novel approaches might make a unique contribution to deeper understanding of psychopathological alterations underlying schizophrenia, depression and PTSD.

Metabolomics and lipidomics analyses by 1H nuclear magnetic resonance of schizophrenia patient serum reveal potential peripheral biomarkers for diagnosis

Using ¹H NMR-based metabolomics in association to chemometrics analysis, we analyzed here the metabolic differences between schizophrenia patients (SCZ) compared to healthy controls (HCs). HCs and SCZ patients underwent clinical interview using the Structured Clinical Interview for DSM Disorders (SCID). SCZ patients were further assessed by Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale, Global Assessment of Functioning Scale (GAF), and Clinical Global Impressions Scale (CGI). Using the principal component analysis (PCA) and supervised partial least-squares discriminate analysis (PLS-DA) in obtained NMR data, a clear group separation between HCs and SCZ patients was achieved. Interestingly, all metabolite compounds identified as exclusively present in the SCZ group, except for the gamma-aminobutyric acid (GABA), were never previously associated with mental disorders. Although the initial perception of an absence of obvious biological link among the different key molecules exclusively observed in each group, and no identification of any specific pathway yet, the present work represents an important contribution for the identification of potential biomarkers to inform diagnosis, as it was possible to completely separate the affected SCZ patients from HCs, with no outliers or exceptions. In addition, the data presented here reinforced the role of the modulation of glycolysis pathway and the loss of GABA interneuron/hyperglutamate hypothesis in SCZ.

Differential urinary metabolites related with the severity of major depressive disorder

Major depressive disorder (MDD) is a common mental disorder that affects a person's general health. However, there is still no objective laboratory test for diagnosing MDD. Here, an integrated analysis of data from our previous studies was performed to identify the differential metabolites in the urine of moderate and severe MDD patients. A dual platform approach (NMR spectroscopy and GC-MS) was used. Consequently, 14 and 22 differential metabolites responsible for separating moderate and severe MDD patients, respectively, from their respective healthy controls (HCs) were identified. Meanwhile, the moderate MDD-specific panel (N-Methylnicotinamide, Acetone, Choline, Citrate, vanillic acid and azelaic acid) and severe MDD-specific panel (indoxyl sulphate, Taurine, Citrate, 3-hydroxyphenylacetic acid, palmitic acid and Lactate) could discriminate moderate and severe MDD patients, respectively, from their respective HCs with high accuracy. Moreover, the differential metabolites in severe MDD were significantly involved in three metabolic pathways and some biofunctions. These results showed that there were divergent urinary metabolic phenotypes in moderate and severe MDD patients, and the identified potential urinary biomarkers might be useful for future developing objective diagnostic tests for MDD diagnosis. Our results could also be helpful for researchers to study the pathogenesis of MDD.

Psychiatric disorders biochemical pathways unraveled by human brain proteomics

Approximately 25 % of the world population is affected by a mental disorder at some point in their life. Yet, only in the mid-twentieth century a biological cause has been proposed for these diseases. Since then, several studies have been conducted toward a better comprehension of those disorders, and although a strong genetic influence was revealed, the role of these genes in disease mechanism is still unclear. This led most recent studies to focus on the molecular basis of mental disorders. One line of investigation that has risen in the post-genomic era is proteomics, due to its power of revealing proteins and biochemical pathways associated with biological systems. Therefore, this review compiled and analyzed data of differentially expressed proteins, which were found in postmortem brain studies of the three most prevalent psychiatric diseases: schizophrenia, bipolar disorder and major depressive disorders. Overviewing both the proteomic methods used in postmortem brain studies, the most consistent metabolic pathways found altered in these diseases. We have unraveled those disorders share about 21 % of proteins affected, and though most are related to energy metabolism pathways deregulation, the main differences found are 14-3-3-mediated signaling in schizophrenia, mitochondrial dysfunction in bipolar disorder and oxidative phosphorylation in depression.

Protein biomarkers of mood disorders

Psychiatric evaluation presents a significant challenge because it conceptually integrates the input from multiple psychopathological approaches. Recent technological advances in the study of protein structure, function, and interactions have provided a breakthrough in the diagnosis and treatment of mood disorders (MD), and have identified novel biomarkers to be used as indicators of normal and disease states or response to drug treatment. The investigation of biomarkers for psychiatric disorders, such as enzymes (catechol-O-methyl transferase and monoamine oxidases) or neurotransmitters (dopamine, serotonin, norepinephrine) and their receptors, particularly their involvement in neuroendocrine activity, brain structure, and function, and response to psychotropic drugs, should facilitate the diagnosis of MD. In clinical settings, prognostic biomarkers may be revealed by analyzing serum, saliva, and/or the cerebrospinal fluid, which should promote timely diagnosis and personalized treatment. The mechanisms underlying the activity of most currently used drugs are based on the functional regulation of proteins, including receptors, enzymes, and metabolic factors. In this study, we analyzed recent advances in the identification of biomarkers for MD, which could be used for the timely diagnosis, treatment stratification, and prediction of clinical outcomes.

Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Plasma lipidomics reveals potential lipid markers of major depressive disorder

Major depressive disorder (MDD) is a grave debilitating mental disease with a high incidence and severely impairs quality of life. Therefore, its physiopathological basis study and diagnostic biomarker discovery are extremely valuable. In this study, a non-targeted lipidomics strategy using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was performed to reveal differential lipids between MDD (n = 60) and healthy controls (HCs, n = 60). Validation of changed lipid species was performed in an independent batch including 75 MDD and 52 HC using the same lipidomic method. Pronouncedly changed lipid species in MDD were discovered, which mainly were lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), 1-O-alkyl-2-acyl-PE (PE O), 1-O-alkyl-2-acyl-PC (PC O), sphingomyelin (SM), diacylglycerol (DG), and triacylglycerol (TG). Among these lipid species, LPC, LPE, PC, PE, PI, TG, etc. remarkably increased in MDD and showed pronounced positive relationships with depression severity, while 1-O-alkyl-2-acyl-PE and SM with odd summed carbon number significantly decreased in MDD and demonstrated negative relationships with depression severity. A combinational lipid panel including LPE 20:4, PC 34:1, PI 40:4, SM 39:1, 2, and TG 44:2 was defined as potential diagnostic biomarker with a good sensitivity and specificity for distinguishing MDD from HCs. Our study brings insights into lipid metabolism disorder in MDD and provides a specific potential biomarker for MDD diagnose.

Metabolomic Analysis in Brain Research: Opportunities and Challenges

Metabolism being a fundamental part of molecular physiology, elucidating the structure and regulation of metabolic pathways is crucial for obtaining a comprehensive perspective of cellular function and understanding the underlying mechanisms of its dysfunction(s). Therefore, quantifying an accurate metabolic network activity map under various physiological conditions is among the major objectives of systems biology in the context of many biological applications. Especially for CNS, metabolic network activity analysis can substantially enhance our knowledge about the complex structure of the mammalian brain and the mechanisms of neurological disorders, leading to the design of effective therapeutic treatments. Metabolomics has emerged as the high-throughput quantitative analysis of the concentration profile of small molecular weight metabolites, which act as reactants and products in metabolic reactions and as regulatory molecules of proteins participating in many biological processes. Thus, the metabolic profile provides a metabolic activity fingerprint, through the simultaneous analysis of tens to hundreds of molecules of pathophysiological and pharmacological interest. The application of metabolomics is at its standardization phase in general, and the challenges for paving a standardized procedure are even more pronounced in brain studies. In this review, we support the value of metabolomics in brain research. Moreover, we demonstrate the challenges of designing and setting up a reliable brain metabolomic study, which, among other parameters, has to take into consideration the sex differentiation and the complexity of brain physiology manifested in its regional variation. We finally propose ways to overcome these challenges and design a study that produces reproducible and consistent results.

Proteomics strategies for bipolar disorder evaluation: From sample preparation to validation

Bipolar disorder (BD) is a complex and costly psychiatric disorder, which affects one hundred million people worldwide. Due to its heterogeneity, correct BD diagnosis is still a challenge. In order to overcome this issue, different bioanalytical strategies have been proposed in the literature recently. Among these strategies, proteomic approaches have arisen as some of the most promising in the area. Thus, recent applications suggest protein profiles to further refine the proteome of BD as well as the discovery of novel protein biomarkers to facilitate diagnostics. In this review, the state-of-art of proteomic research in BD is summarized. Furthermore, important aspects of proteomics for understanding of BD, such as sample type and size, sampling, sample preparation, gel-based and gel-free proteomics, proteomic quantitative and protein validation are overviewed.

Proteomics Research in Schizophrenia

Despite intense scientific efforts, the neuropathology and pathophysiology of schizophrenia are poorly understood. Proteomic studies, by testing large numbers of proteins for associations with disease, may contribute to the understanding of the molecular mechanisms of schizophrenia. They may also indicate the types and locations of cells most likely to harbor pathological alterations. Investigations using proteomic approaches have already provided much information on quantitative and qualitative protein patterns in postmortem brain tissue, peripheral tissues and body fluids. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, SELDI-TOF, shotgun proteomics with label-based (ICAT), and label-free (MS^E) quantification have been applied to the study of schizophrenia for the past 15 years. This review summarizes the results, mostly from brain but also from other tissues and bodily fluids, of proteomics studies in schizophrenia. Emphasis is given to proteomics platforms, varying sources of material, proposed candidate biomarkers emerging from comparative proteomics studies, and the specificity of the putative markers in terms of other mental illnesses. We also compare proteins altered in schizophrenia with reports of protein or mRNA sequences that are relatively enriched in specific cell types. While proteomic studies of schizophrenia find abnormalities in the expression of many proteins that are not cell type-specific, there appears to be a disproportionate representation of proteins whose synthesis and localization are highly enriched in one or more brain cell type compared with other types of brain cells. Two of the three proteins most commonly altered in schizophrenia are aldolase C and glial fibrillary acidic protein, astrocytic proteins with entirely different functions, but the studies are approximately evenly divided with regard to the direction of the differences and the concordance or discordance between the two proteins. Alterations of common myelin-associated proteins were also frequently observed, and in four studies that identified alterations in at least two, all differences were downwards in schizophrenia, consistent with earlier studies examining RNA or targeting myelin-associated proteins.

Intermediate phenotypes and biomarkers of treatment outcome in major depressive disorder

Major depressive disorder (MDD) is a pleomorphic illness originating from gene x environment interactions. Patients with differing symptom phenotypes receive the same diagnosis and similar treatment recommendations without regard to genomics, brain structure or function, or other physiologic or psychosocial factors. Using this present approach, only one third of patients enter remission with the first medication prescribed, and patients may take longer than 1 year to enter remission with repeated trials. Research to improve treatment effectiveness recently has focused on identification of intermediate phenotypes (IPs) that could parse the heterogeneous population of patients with MDD into subgroups with more homogeneous responses to treatment. Such IPs could be used to develop biomarkers that could be applied clinically to match patients with the treatment that would be most likely to lead to remission. Putative biomarkers include genetic polymorphisms, RNA and protein expression (transcriptome and proteome), neurotransmitter levels (metabolome), additional measures of signaling cascades, oscillatory synchrony, neuronal circuits and neural pathways (connectome), along with other possible physiologic measures. All of these measures represent components of a continuum that extends from proximity to the genome to proximity to the clinical phenotype of depression, and there are many levels along this continuum at which useful IPs may be defined. Because of the highly integrative nature of brain systems and the complex neurobiology of depression, the most useful biomarkers are likely to be those with intermediate proximity both to the genome and the clinical phenotype of MDD. Translation of findings across the spectrum from genotype to phenotype promises to better characterize the complex disruptions in signaling and neuroplasticity that accompany MDD, and ultimately to lead to greater understanding of the causes of depressive illness.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.

Plasma-metabolite-biomarkers for the therapeutic response in depressed patients by the traditional Chinese medicine formula Xiaoyaosan: A (1)H NMR-based metabolomics approach

BACKGROUND

Depression is one of the most prevalent and serious mental disorders. Xiaoyaosan, a well-known Chinese prescription, has been widely used for the treatment of depression in China. Both clinical studies and animal experiments indicate that Xiaoyaosan has an obvious antidepressant activity. Additionally, a large number of candidate biomarkers have emerged that can be used for early disease detection and for monitoring ongoing treatment response to therapy because of their correlations with the characteristics of the disease. However, there have been few reports on biomarkers that measure the treatment response to the clinical use of Xiaoyaosan using a metabolomics approach. The current study is aimed at discovering biomarkers and biochemical pathways to facilitate the diagnosis of depression and the efficient evaluation of Xiaoyaosan using plasma metabolomics profiles based on (1)H NMR.

METHODS

Sixteen depressed patients diagnosed by standard methods (HAMD and CGI-SI) and sixteen healthy volunteers were recruited. (1)H NMR-based metabolomics techniques and multivariate statistical methods were used to analyze the plasma metabolites of the depressed patients before and after treatment and to compare them with healthy controls.

RESULTS

The plasma levels of trimethylamine oxide, glutamine and lactate in depressed patients increased significantly (p≤0.05) compared with healthy controls, whereas the levels of phenylalanine, valine, alanine, glycine, leucine, citrate, choline, lipids and glucose decreased significantly (p≤0.05). Additionally, alanine, choline, trimethylamine oxide, glutamine, lactate and glucose were returned to normal levels after Xiaoyaosan treatment. These statistically significant perturbations are involved in energy metabolism, amino acid metabolism and gut microbiota metabolism.

LIMITATIONS

Additional experimentation with gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) is required to confirm our findings.

CONCLUSIONS

Application of these biomarkers in clinical practice may help to optimize the diagnosis of depression and to evaluate the efficacy of Xiaoyaosan. Metabolomics is promising as a biomarker discovery tool.

Investigation on the antidepressant effect of sea buckthorn seed oil through the GC-MS-based metabolomics approach coupled with multivariate analysis

Depression is one of the prevalent and serious mental disorders and the number of depressed patients has been on the rise globally during the recent decades. Sea buckthorn seed oil from traditional Chinese medicine (TCM) is edible and has been widely used for treatment of different diseases for a long time. However, there are few published reports on the antidepressant effect of sea buckthorn seed oil. With the objective of finding potential biomarkers of the therapeutic response of sea buckthorn seed oil in chronic unpredictable mild stress (CUMS) rats, urine metabolomics based on gas chromatography-mass spectrometry (GC-MS) coupled with multivariate analysis was applied. In this study, we discovered a higher level of pimelic acid as well as palmitic acid and a lower level of suberic acid, citrate, phthalic acid, cinnamic acid and Sumiki's acid in urine of rats exposed to CUMS procedures after sea buckthorn seed oil was administered. These changes of metabolites are involved in energy metabolism, fatty acid metabolism and other metabolic pathways as well as in the synthesis of neurotransmitters and it is helpful to facilitate the efficacy evaluation and mechanism elucidating the effect of sea buckthorn seed oil for depression management.

Salivary proteomics and biomarkers in neurology and psychiatry

Biomarkers are greatly needed in the fields of neurology and psychiatry, to provide objective and earlier diagnoses of CNS conditions. Proteomics and other omics MS-based technologies are tools currently being utilized in much recent CNS research. Saliva is an interesting alternative biomaterial for the proteomic study of CNS disorders, with several advantages. Collection is noninvasive and saliva has many proteins. It is easier to collect than blood and can be collected by professionals without formal medical training. For psychiatric and neurological patients, supplying a saliva sample is less anxiety-provoking than providing a blood sample, and is less embarrassing than producing a urine specimen. The use of saliva as a biomaterial has been researched for the diagnosis of and greater understanding of several CNS conditions, including neurodegenerative diseases, autism, and depression. Salivary biomarkers could be used to rule out nonpsychiatric conditions that are often mistaken for psychiatric/neurological conditions, such as fibromyalgia, and potentially to assess cognitive ability in individuals with compromised brain function. As MS and omics technology advances, the sensitivity and utility of assessing CNS conditions using distal human biomaterials such as saliva is becoming increasingly possible.