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Santa C, Rodrigues JE, Martinho A, Mendes VM, Madeira N, Coroa M, Santos V, Morais S, Bajouco M, Costa H, Anjo SI, Baldeiras I, Macedo A, Manadas B. Proteomic analysis of peripheral blood mononuclear cells in first episode psychosis - Protein and peptide-centered approaches to elucidate potential diagnostic biomarkers. J Proteomics 2024; 309:105296. [PMID: 39218299 DOI: 10.1016/j.jprot.2024.105296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 08/19/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
Diagnosing patients suffering from psychotic disorders is far from being achieved with molecular support, despite all the efforts to study these disorders from different perspectives. Characterizing the proteome of easily obtainable blood specimens, such as the peripheral blood mononuclear cells (PBMCs), has particular interest in biomarker discovery and generating pathophysiological knowledge. This approach has been explored in psychiatry, and while generating valuable information, it has not translated into meaningful biomarker discovery. In this project, we report the proof-of-concept of a methodology that aims to explore further information obtained with classical proteomics approaches that is easily overlooked. PBMC samples from first-episode psychosis and control subjects were subjected to a SWATH-MS approach, and the classical protein relative quantification was performed, where 389 proteins were found to be important to distinguish the two groups. Individual analysis of the quantified peptides was also performed, highlighting peptides of unchanged proteins that were significantly altered. With the combination of protein- and peptide-centered proteomics approaches, it is possible to highlight that information about proteoforms, namely regulation at the peptide level possibly due to post-translational modifications, is routinely overlooked and that its diagnostic potential should be further explored. SIGNIFICANCE: Our exploratory findings highlight the potential of MS-based proteomics strategies, combining protein- and peptide-centered approaches, to aid clinical decision-making in first-episode psychosis, helping to establish early biomarkers for schizophrenia and other psychotic disorders. Particularly, the less popular peptide-centered approach allows the identification/measurement of overlooked modulated peptides that may have potential biomarker characteristics. The application in parallel of protein- and peptide-centered strategies is transversal to research of other diseases, potentially allowing a more comprehensive characterization of the metabolic/pathophysiological alterations related to a specific disease.
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Affiliation(s)
- Catia Santa
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - João E Rodrigues
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Ana Martinho
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Vera M Mendes
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Nuno Madeira
- Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal; Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal; CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Portugal
| | - Manuel Coroa
- CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal; Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal; Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal
| | - Vítor Santos
- CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal; Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal; Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal
| | - Sofia Morais
- Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal; Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal; CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Portugal
| | - Miguel Bajouco
- Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal; Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal; CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Portugal
| | - Hélder Costa
- Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal
| | - Sandra I Anjo
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Inês Baldeiras
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal; Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal
| | - Antonio Macedo
- Faculty of Medicine of the University of Coimbra, University of Coimbra, Portugal; Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, Portugal; CIBIT - Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Portugal.
| | - Bruno Manadas
- CNC-Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal; III Institute for Interdisciplinary Research, University of Coimbra (IIIUC), Portugal.
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Smagin DA, Bezryadnov DV, Zavialova MG, Abramova AY, Pertsov SS, Kudryavtseva NN. Blood Plasma Markers in Depressed Mice under Chronic Social Defeat Stress. Biomedicines 2024; 12:1485. [PMID: 39062058 PMCID: PMC11275122 DOI: 10.3390/biomedicines12071485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/17/2024] [Accepted: 07/01/2024] [Indexed: 07/28/2024] Open
Abstract
It has previously been shown that, in mice, chronic social defeat stress in daily agonistic interactions leads to a depression-like state similar to that in depressive patients. With this model, it has become obvious that it is possible to study peripheral markers of the depression-like state in an experiment. This paper was aimed at searching for protein markers in the blood plasma of depressed mice in the chronic social conflict model, which allows for us to obtain male mice with repeated experiences of defeat. Proteomic analysis of blood plasma samples was conducted to identify proteins differentially expressed in this state. There were changes in the expression levels of the amyloid proteins SAA1, SAA4, and SAMP and apolipoproteins APOC3, APOD, and ADIPO in the blood plasma of depressed mice compared with controls (unstressed mice). Changes in the expression of serine protease inhibitors and/or proteins associated with lipid metabolism, inflammation, or immune function [ITIH4, SPA3, A1AT5, HTP (HP), CO9, and A2MG] were also found. Here, we showed that chronic social stress is accompanied by increased levels of amyloid proteins and apolipoproteins in blood plasma. A similarity was noted between the marker protein expression changes in the depressed mice and those in patients with Alzheimer's disease. These data indicate a psychopathogenic role of chronic social stress, which can form a predisposition to neurodegenerative and/or psychoemotional disorders.
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Affiliation(s)
- Dmitry A. Smagin
- Federal Research Center, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk 630090, Russia
| | - Dmitry V. Bezryadnov
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, P.K. Anokhin Research Institute of Normal Physiology, Moscow 125315, Russia; (D.V.B.); (S.S.P.)
| | | | - Anastasia Yu. Abramova
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, P.K. Anokhin Research Institute of Normal Physiology, Moscow 125315, Russia; (D.V.B.); (S.S.P.)
| | - Sergey S. Pertsov
- Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies, P.K. Anokhin Research Institute of Normal Physiology, Moscow 125315, Russia; (D.V.B.); (S.S.P.)
| | - Natalia N. Kudryavtseva
- Federal Research Center, Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk 630090, Russia
- Pavlov Institute of Physiology, Russian Academy of Sciences, Saint Petersburg 199034, Russia
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Zhao H, Liu Y, Zhang X, Liao Y, Zhang H, Han X, Guo L, Fan B, Wang W, Lu C. Identifying novel proteins for suicide attempt by integrating proteomes from brain and blood with genome-wide association data. Neuropsychopharmacology 2024; 49:1255-1265. [PMID: 38317018 PMCID: PMC11224332 DOI: 10.1038/s41386-024-01807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/28/2023] [Accepted: 01/16/2024] [Indexed: 02/07/2024]
Abstract
Genome-wide association studies (GWASs) have identified risk loci for suicide attempt (SA), but deciphering how they confer risk for SA remains largely unknown. This study aims to identify the key proteins and gain insights into SA pathogenesis. We integrated data from the brain proteome (N = 376) and blood proteome (N = 35,559) and combined it with the largest SA GWAS summary statistics to date (N = 518,612). A comprehensive set of methods was employed, including Mendelian randomization (MR), Steiger filtering, Bayesian colocalization, proteome‑wide association studies (PWAS), transcript-levels, cell-type specificity, correlation, and protein-protein interaction (PPI) network analysis. Validation was performed using other protein datasets and the SA dataset from FinnGen study. We identified ten proteins (GLRX5, GMPPB, B3GALTL, FUCA2, TTLL12, ADCK1, MMAA, HIBADH, ACP1, DOC2A) associated with SA in brain proteomics. GLRX5, GMPPB, and FUCA2 showed strong colocalization evidence and were supported by PWAS and transcript-level analysis, and were predominantly expressed in glutamatergic neuronal cells. In blood proteomics, one significant protein (PEAR1) and three near-significant proteins (NDE1, EVA1C, B4GALT2) were identified, but lacked colocalization evidence. Moreover, despite the limited correlation between the same protein in brain and blood, the PPI network analysis provided new insights into the interaction between brain and blood in SA. Furthermore, GLRX5 was associated with the GSTP1, the target of Clozapine. The comprehensive analysis provides strong evidence supporting a causal association between three genetically determined brain proteins (GLRX5, GMPPB, and FUCA2) with SA. These findings offer valuable insights into SA's underlying mechanisms and potential therapeutic approaches.
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Affiliation(s)
- Hao Zhao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| | - Yifeng Liu
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Xuening Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuhua Liao
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Huimin Zhang
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Xue Han
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Lan Guo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| | - Beifang Fan
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China.
| | - Wanxin Wang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China.
| | - Ciyong Lu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
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Ribeiro HC, Zandonadi FDS, Sussulini A. An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value. Expert Rev Proteomics 2023; 20:267-280. [PMID: 37830362 DOI: 10.1080/14789450.2023.2267756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023]
Abstract
INTRODUCTION Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics. AREAS COVERED In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value. EXPERT OPINION A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.
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Affiliation(s)
- Henrique Caracho Ribeiro
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Flávia da Silva Zandonadi
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Alessandra Sussulini
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
- Instituto Nacional de Ciência e Tecnologia de Bioanalítica (INCTBio), Institute of Chemistry, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
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Rhee SJ, Shin D, Shin D, Song Y, Joo EJ, Jung HY, Roh S, Lee SH, Kim H, Bang M, Lee KY, Lee J, Kim J, Kim Y, Kim Y, Ahn YM. Network analysis of plasma proteomes in affective disorders. Transl Psychiatry 2023; 13:195. [PMID: 37296094 DOI: 10.1038/s41398-023-02485-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/13/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.
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Affiliation(s)
- Sang Jin Rhee
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dongyoon Shin
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Daun Shin
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yoojin Song
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Republic of Korea
- Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Republic of Korea
| | - Hee Yeon Jung
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital and Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Hyeyoung Kim
- Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea
| | - Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Republic of Korea
- Department of Psychiatry, Nowon Eulji University Hospital, Seoul, Republic of Korea
| | - Jihyeon Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jaenyeon Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yeongshin Kim
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Youngsoo Kim
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam, Republic of Korea.
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
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Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics. Transl Psychiatry 2023; 13:44. [PMID: 36746927 PMCID: PMC9902608 DOI: 10.1038/s41398-023-02321-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/09/2023] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = -2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = -2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.
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Waszczuk MA, Kuan PF, Yang X, Miao J, Kotov R, Luft BJ. Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders. Transl Psychiatry 2023; 13:8. [PMID: 36631443 PMCID: PMC9834302 DOI: 10.1038/s41398-022-02302-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 11/28/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08-0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38-1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (β = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17-2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.
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Affiliation(s)
- Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Xiaohua Yang
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Jiaju Miao
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Benjamin J Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA.
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Serretti A. Clinical Utility of Fluid Biomarker in Depressive Disorder. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2022; 20:585-591. [PMID: 36263634 PMCID: PMC9606424 DOI: 10.9758/cpn.2022.20.4.585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 08/03/2022] [Indexed: 01/25/2023]
Abstract
Major depressive disorders are ranked as the single largest contributor to non-fatal health loss and biomarkers could largely improve our routine clinical activity by predicting disease course and guiding treatment. However there is still a dearth of valid biomarkers in the field of psychiatry. The initial assumption that a single biomarker can capture the myriad of complex processes proved to be naive. The purpose of this paper is to critically review the field and to illustrate the possible practical application for routine clinical care. Biomarkers derived from DNA analysis are the ones that have received the most attention. Other potential candidates include circulating transcription products, proteins, and inflammatory markers. DNA polygenic risk scores proved to be useful in other fields of medicine and preliminary results suggest that they could be useful both as risk and diagnostic biomarkers also in depression and for the choice of treatment. A number of other possible fluid biomarkers are currently under investigation for diagnosis, outcome prediction, staging, and stratification of interventions, however research is still needed before they can be used for routine clinical care. When available, clinicians may be able to receive a lab report with detailed information about disease risk, outcome prediction, and specific indications about preferred treatments.
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Affiliation(s)
- Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy,Address for correspondence: Alessandro Serretti Department of Biomedical and Neuromotor Sciences, University of Bologna, Viale Carlo Pepoli 5, 40123 Bologna, Italy, E-mail: , ORCID: https://orcid.org/0000-0003-4363-3759
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Lebeau RH, Mendez-David I, Kucynski-Noyau L, Henry C, Attali D, Plaze M, Colle R, Corruble E, Gardier AM, Gaillard R, Guilloux JP, David DJ. Peripheral proteomic changes after electroconvulsive seizures in a rodent model of non-response to chronic fluoxetine. Front Pharmacol 2022; 13:993449. [DOI: 10.3389/fphar.2022.993449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 10/05/2022] [Indexed: 11/13/2022] Open
Abstract
Major depressive disorder (MDD) is the psychiatric disorder with the highest prevalence in the world. Pharmacological antidepressant treatment (AD), such as selective serotonin reuptake inhibitors [SSRI, i.e., fluoxetine (Flx)] is the first line of treatment for MDD. Despite its efficacy, lack of AD response occurs in numerous patients characterizing Difficult-to-treat Depression. ElectroConvulsive Therapy (ECT) is a highly effective treatment inducing rapid improvement in depressive symptoms and high remission rates of ∼50–63% in patients with pharmaco-resistant depression. Nevertheless, the need to develop reliable treatment response predictors to guide personalized AD strategies and supplement clinical observation is becoming a pressing clinical objective. Here, we propose to establish a proteomic peripheral biomarkers signature of ECT response in an anxio/depressive animal model of non-response to AD. Using an emotionality score based on the analysis complementary behavioral tests of anxiety/depression (Elevated Plus Maze, Novelty Suppressed Feeding, Splash Test), we showed that a 4-week corticosterone treatment (35 μg/ml, Cort model) in C57BL/6JRj male mice induced an anxiety/depressive-like behavior. A 28-day chronic fluoxetine treatment (Flx, 18 mg/kg/day) reduced corticosterone-induced increase in emotional behavior. A 50% decrease in emotionality score threshold before and after Flx, was used to separate Flx-responding mice (Flx-R, n = 18), or Flx non-responder mice (Flx-NR, n = 7). Then, Flx-NR mice received seven sessions of electroconvulsive seizure (ECS, equivalent to ECT in humans) and blood was collected before and after ECS treatment. Chronic ECS normalized the elevated emotionality observed in Flx-NR mice. Then, proteins were extracted from peripheral blood mononuclear cells (PBMCs) and isolated for proteomic analysis using a high-resolution MS Orbitrap. Data are available via ProteomeXchange with identifier PXD037392. The proteomic analysis revealed a signature of 33 peripheral proteins associated with response to ECS (7 down and 26 upregulated). These proteins were previously associated with mental disorders and involved in regulating pathways which participate to the depressive disorder etiology.
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Rodrigues JE, Martinho A, Santa C, Madeira N, Coroa M, Santos V, Martins MJ, Pato CN, Macedo A, Manadas B. Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia. Int J Mol Sci 2022; 23:ijms23094917. [PMID: 35563307 PMCID: PMC9105255 DOI: 10.3390/ijms23094917] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/24/2022] [Accepted: 04/26/2022] [Indexed: 01/27/2023] Open
Abstract
Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.
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Affiliation(s)
- João E. Rodrigues
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal; (J.E.R.); (A.M.); (C.S.); (M.J.M.)
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
| | - Ana Martinho
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal; (J.E.R.); (A.M.); (C.S.); (M.J.M.)
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
| | - Catia Santa
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal; (J.E.R.); (A.M.); (C.S.); (M.J.M.)
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
| | - Nuno Madeira
- Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal;
- Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, 3004-561 Coimbra, Portugal
- CIBIT—Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, 3000-548 Coimbra, Portugal
| | - Manuel Coroa
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
- Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal;
- Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, 3004-561 Coimbra, Portugal
| | - Vítor Santos
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
- Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal;
- Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, 3004-561 Coimbra, Portugal
| | - Maria J. Martins
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal; (J.E.R.); (A.M.); (C.S.); (M.J.M.)
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
- Medical Services, University of Coimbra, 3004-517 Coimbra, Portugal
| | - Carlos N. Pato
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA;
| | - Antonio Macedo
- Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal;
- Psychiatry Department, Centro Hospitalar e Universitário de Coimbra, 3004-561 Coimbra, Portugal
- CIBIT—Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, 3000-548 Coimbra, Portugal
- Correspondence: (A.M.); (B.M.)
| | - Bruno Manadas
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal; (J.E.R.); (A.M.); (C.S.); (M.J.M.)
- CIBB—Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-504 Coimbra, Portugal; (M.C.); (V.S.)
- III Institute for Interdisciplinary Research, University of Coimbra (IIIUC), 3030-789 Coimbra, Portugal
- Correspondence: (A.M.); (B.M.)
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11
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Zhang Y, Tian W, Han X, Yan G, Ma Y, Huo S, Shi Y, Dai S, Ni X, Li Z, Fan L, Zhang Q. Assessing the depression risk in the U.S. adults using nomogram. BMC Public Health 2022; 22:416. [PMID: 35232400 PMCID: PMC8889727 DOI: 10.1186/s12889-022-12798-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 02/15/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults. METHODS This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017-2012). In particular, NHANES (2007-2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011-2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms. RESULTS The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69-0.73) and 0.85 (95% CI, 0.83-0.86), and an externally validated C index of 0.71 (95% CI, 0.68-0.74) and 0.83 (95% CI, 0.81-0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10-17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions. CONCLUSIONS This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.
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Affiliation(s)
- Yafeng Zhang
- Department of Health Management, School of Health Management, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China
| | - Wei Tian
- Department of Biostatistics, School of Public Health, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China
| | - Xinhao Han
- Department of Biostatistics, School of Public Health, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China
| | - Guangcan Yan
- Department of Biostatistics, School of Public Health, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China
| | - Yuanshuo Ma
- Department of Health Management, School of Health Management, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China
| | - Shan Huo
- Sichuan Kelun Pharmaceutical Co, No. 36 Baihua West Road, Chengdu, 610071, China
| | - Yu Shi
- Department of Health Management, School of Health Management, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China
| | - Shanshan Dai
- People's medical publishing house, No. 19 Panjiayuan South Road, Beijing, 100021, China
| | - Xin Ni
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56 Nanlishi Road, Beijing, 100045, China
| | - Zhe Li
- Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56 Nanlishi Road, Beijing, 100045, China
| | - Lihua Fan
- Department of Health Management, School of Health Management, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China.
| | - Qiuju Zhang
- Department of Biostatistics, School of Public Health, Harbin Medical University, No.157 Baojian Road, Harbin, 150081, China.
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12
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Alterations in blood proteins in the prodromal stage of bipolar II disorders. Sci Rep 2022; 12:3174. [PMID: 35210508 PMCID: PMC8873249 DOI: 10.1038/s41598-022-07160-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
Although early intervention may help prevent the progression of bipolar disorder, there are some controversies over early pharmacological intervention. In this study, we recruited 40 subjects in the prodromal stage of BD-II (BP), according to bipolar at-risk state criteria. We compared the expression of their plasma proteins with that of 48 BD-II and 75 healthy control (HC) to identify markers that could be detected in a high-risk state. The multiple reaction monitoring method was used to measure target peptide levels with high accuracy. A total of 26 significant peptides were identified through analysis of variance with multiple comparisons, of which 19 were differentially expressed in the BP group when compared to the BD-II and HC groups. Two proteins were overexpressed in the BP group; and were related to pro-inflammation and impaired neurotransmission. The other under-expressed peptides in the BP group were related to blood coagulation, immune reactions, lipid metabolism, and the synaptic plasticity. In this study, significant markers observed in the BP group have been reported in patients with psychiatric disorders. Overall, the results suggest that the pathophysiological changes included in BD-II had already occurred with BP, thus justifying early pharmacological treatment to prevent disease progression.
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13
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Göteson A, Isgren A, Sparding T, Holmén-Larsson J, Jakobsson J, Pålsson E, Landén M. A serum proteomic study of two case-control cohorts identifies novel biomarkers for bipolar disorder. Transl Psychiatry 2022; 12:55. [PMID: 35136035 PMCID: PMC8826439 DOI: 10.1038/s41398-022-01819-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/12/2021] [Accepted: 01/17/2022] [Indexed: 01/08/2023] Open
Abstract
We set out to identify novel protein associations with potential as clinically viable biomarkers for bipolar disorder. To this end, we used proximity extension assay to analyze 201 unique proteins in blood serum from two independent cohorts comprising patients with bipolar disorder and healthy controls (total n = 493). We identified 32 proteins significantly associated with bipolar disorder in both case-control cohorts after adjusting for relevant covariates. Twenty-two findings are novel to bipolar disorder, but 10 proteins have previously been associated with bipolar disorder: chitinase-3-like protein 1, C-C motif chemokine 3 (CCL3), CCL4, CCL20, CCL25, interleukin 10, growth/differentiation factor-15, matrilysin (MMP-7), pro-adrenomedullin, and TNF-R1. Next, we estimated the variance in serum protein concentrations explained by psychiatric drugs and found that some case-control associations may have been driven by psychiatric drugs. The highest variance explained was observed between lithium use and MMP-7, and in post-hoc analyses and found that the serum concentration of MMP-7 was positively associated with serum lithium concentration, duration of lithium therapy, and inversely associated with estimated glomerular filtration rate in an interaction with lithium. This is noteworthy given that MMP-7 has been suggested as a mediator of renal tubulointerstitial fibrosis, which is characteristic of lithium-induced nephropathy. Finally, we used machine learning to evaluate the classification performance of the studied biomarkers but the average performance in unseen data was fair to moderate (area under the receiver operating curve = 0.72). Taken together, our serum biomarker findings provide novel insight to the etiopathology of bipolar disorder, and we present a suggestive biomarker for lithium-induced nephropathy.
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Affiliation(s)
- Andreas Göteson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden.
| | - Anniella Isgren
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Timea Sparding
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Jessica Holmén-Larsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Joel Jakobsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Erik Pålsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Mikael Landén
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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14
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Zhang T, Guo L, Li R, Wang F, Yang WM, Yang JB, Cui ZQ, Zhou CH, Chen YH, Yu H, Peng ZW, Tan QR. Alterations of Plasma Lipids in Adult Women With Major Depressive Disorder and Bipolar Depression. Front Psychiatry 2022; 13:927817. [PMID: 35923457 PMCID: PMC9339614 DOI: 10.3389/fpsyt.2022.927817] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
Lipidomics has been established as a potential tool for the investigation of mental diseases. However, the composition analysis and the comparison of the peripheral lipids regarding adult women with major depressive depression (MDD) or bipolar depression (BPD) has been poorly addressed. In the present study, age-matched female individuals with MDD (n = 28), BPD (n = 22) and healthy controls (HC, n = 25) were enrolled. Clinical symptoms were assessed and the plasma samples were analyzed by comprehensive lipid profiling based on liquid chromatography-mass spectrometry (LC/MS). We found that the composition of lipids was remarkably changed in the patients with MDD and BPD when compared to HC or compared to each other. Moreover, we identified diagnostic potential biomarkers comprising 20 lipids that can distinguish MDD from HC (area under the curve, AUC = 0.897) and 8 lipids that can distinguish BPD from HC (AUC = 0.784), as well as 13 lipids were identified to distinguish MDD from BPD with moderate reliability (AUC = 0.860). This study provides further understanding of abnormal lipid metabolism in adult women with MDD and BPD and may develop lipid classifiers able to effectively discriminate MDD from BPD and HC.
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Affiliation(s)
- Ting Zhang
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Lin Guo
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Rui Li
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Fei Wang
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Wen-Mao Yang
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Jia-Bin Yang
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Zhi-Quan Cui
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
| | - Cui-Hong Zhou
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Yi-Huan Chen
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Huan Yu
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Zheng-Wu Peng
- Department of Psychiatry, Chang'an Hospital, Xi'an, China.,Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, China
| | - Qing-Rong Tan
- Department of Psychiatry, Chang'an Hospital, Xi'an, China
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15
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Chen Y, Wang W, Fu X, Sun Y, Lv S, Liu L, Zhou P, Zhang K, Meng J, Zhang H, Zhang S. Investigation of the antidepressant mechanism of combined Radix Bupleuri and Radix Paeoniae Alba treatment using proteomics analysis of liver tissue. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1179:122858. [PMID: 34329891 DOI: 10.1016/j.jchromb.2021.122858] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/19/2021] [Accepted: 07/06/2021] [Indexed: 11/24/2022]
Abstract
Depression is a chronic, common mental illness characterized by depressed mood, anxiety, insomnia, cognitive impairment, and even suicidal tendency. In traditional Chinese medicine theory, the cause of depression is deemed to be "stagnation of liver qi". So relieving "stagnation of liver qi" is effective for depression. The combination of Radix Bupleuri and Radix Paeoniae Alba, which is used to soothe the liver and relieve depression, has antidepressant effects, but the mechanisms of the effects are still unclear. In this study, a rat model of chronic unpredictable mild stress was established as a model of depression, and proteomics analysis was used to explore the potential mechanisms of this combination in alleviating depression. Biological information analysis was performed on the selected differential proteins, and the enriched pathways mainly included the Jak-STAT signaling pathway, valine, leucine, and isoleucine degradation, and oxidative phosphorylation. The expression of key proteins included metallothionein-1, cyclin-dependent kinase, ubiquitin carboxyl-terminal hydrolase-1, and Cryab was further verified by western blotting, and the results which were consistent with the proteomics results, confirmed the reliability of the proteomic analysis. The antidepressant mechanism of combined Radix Bupleuri and Radix Paeoniae Alba treatment may be related to the oxidative stress response, neuroplasticity, the immune response, and neuroprotection.
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Affiliation(s)
- Yanyan Chen
- The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Wenran Wang
- The First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Xin Fu
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Yonghui Sun
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Shaowa Lv
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Lei Liu
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Peng Zhou
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Ke Zhang
- Shenyang Pharmaceutical University, Shenyang 110000, China
| | - Jiannan Meng
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China
| | - Hongcai Zhang
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China.
| | - Shuxiang Zhang
- Heilongjiang University of Chinese Medicine, Heping Road 24, Harbin 150040, China.
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16
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Wang Y, Liu X, Liu D, Cheng M, Zhao N, He M, Zhang X. Plasma nontargeted peptidomics discovers potential biomarkers for major depressive disorder. Proteomics Clin Appl 2021; 15:e2000058. [PMID: 34329527 DOI: 10.1002/prca.202000058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 07/22/2021] [Accepted: 07/28/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE There are great demands for identifying biomarkers of major depressive disorder (MDD), a common mental illness with a prevalence of approximately 6%. Finding potential biomarkers to aid MDD diagnosis is in high demand. EXPERIMENTAL DESIGN In this study, a combination of pretreatment methods named salt-out assisted liquid-liquid extraction (SALLE) and nontargeted peptidomics based on nano-LC-Orbitrap/MS was primarily employed to discover the candidate peptide markers from the plasma of 238 subjects. RESULTS Many peptides were enriched and identified from the plasma, 42 of which showed significant differences between MDD patients and controls by univariate statistical analysis. A binary logistic regression (BLR) model combined four peptide markers (P1, P9, P17, P29) was established, yielding an overall prediction accuracy of 91.7% and 82.2% in the discovery and validation sets, respectively. CONCLUSIONS AND CLINICAL RELEVANCE In conclusion, the excellent performance of the BLR model in both discovery and validation sets demonstrates the robustness of the four peptide markers panel. It is very valuable for quantification of the absolute content of four peptides and further verification.
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Affiliation(s)
- Yi Wang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China.,University of Chinese Academy of Sciences, Beijing, PR China
| | - Xinxin Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China
| | - Dan Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China
| | - Mengchun Cheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China
| | - Nan Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China
| | - Meixi He
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China.,University of Chinese Academy of Sciences, Beijing, PR China
| | - Xiaozhe Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian, China
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17
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Shin D, Rhee SJ, Lee J, Yeo I, Do M, Joo EJ, Jung HY, Roh S, Lee SH, Kim H, Bang M, Lee KY, Kwon JS, Ha K, Ahn YM, Kim Y. Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry. J Proteome Res 2021; 20:3188-3203. [PMID: 33960196 DOI: 10.1021/acs.jproteome.1c00058] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.
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Affiliation(s)
| | - Sang Jin Rhee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | | | | | | | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Hee Yeon Jung
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital, Seoul 04763, Republic of Korea.,Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Hyeyoung Kim
- Department of Psychiatry, Inha University Hospital, Incheon 22332, Republic of Korea
| | - Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Kyooseob Ha
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
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18
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Wei J, Zhao L, Du Y, Tian Y, Ni P, Ni R, Wang Y, Ma X, Hu X, Li T. A plasma metabolomics study suggests alteration of multiple metabolic pathways in patients with bipolar disorder. Psychiatry Res 2021; 299:113880. [PMID: 33770709 DOI: 10.1016/j.psychres.2021.113880] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/14/2021] [Indexed: 02/05/2023]
Abstract
Previous omics studies have greatly contributed to our knowledge of bipolar disorder. Metabolomics is a relatively new field of omics science that can provide complementary insight into data obtained from genomics, transcriptomics or proteomics analyses. In this study, we aimed to identify metabolic pathways associated with bipolar disorder. We performed a liquid chromatography-mass spectrometry-based study to identify plasma metabolic profiles in patients with bipolar disorder (N = 91) and healthy controls (N = 92). Multivariate features selection by sparse partial least square-discriminant analysis combined with metabolite set enrichment analysis were used to identify metabolites and biological pathways that discriminate patients with bipolar disorder from healthy controls. The results showed that eighty metabolites in the plasma were identified to discriminate patients with bipolar disorder from healthy controls, and nine metabolic pathways, i.e., (1) glycine and serine metabolism, (2) glutamate metabolism, (3) arginine and proline metabolism, (4) tyrosine metabolism, (5) catecholamine biosynthesis, (6) purine metabolism, (7) amino sugar metabolism, (8) ammonia recycling, and (9) carnitine synthesis, were identified to be altered in bipolar disorder compared to healthy controls. We conclude that the 80 metabolites and nine metabolic pathways identified might serve as biomarkers to distinguish bipolar disorder patients from healthy controls.
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Affiliation(s)
- Jinxue Wei
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yue Du
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yang Tian
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Peiyan Ni
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Rongjun Ni
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yingcheng Wang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xun Hu
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China; Huaxi Biobank, West China Hospital of Sichuan University, Chengdu, China
| | - Tao Li
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China.
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19
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Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders. Comput Struct Biotechnol J 2020; 18:2535-2546. [PMID: 33033576 PMCID: PMC7522539 DOI: 10.1016/j.csbj.2020.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/06/2020] [Accepted: 09/07/2020] [Indexed: 12/30/2022] Open
Abstract
Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.
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20
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Olmert T, Cooper JD, Han SYS, Barton-Owen G, Farrag L, Bell E, Friend LV, Ozcan S, Rustogi N, Preece RL, Eljasz P, Tomasik J, Cowell D, Bahn S. A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study. JMIR Res Protoc 2020; 9:e18453. [PMID: 32773373 PMCID: PMC7445599 DOI: 10.2196/18453] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/24/2020] [Accepted: 04/26/2020] [Indexed: 12/12/2022] Open
Abstract
Background Mood disorders affect hundreds of millions of people worldwide, imposing a substantial medical and economic burden. Existing diagnostic methods for mood disorders often result in a delay until accurate diagnosis, exacerbating the challenges of these disorders. Advances in digital tools for psychiatry and understanding the biological basis of mood disorders offer the potential for novel diagnostic methods that facilitate early and accurate diagnosis of patients. Objective The Delta Trial was launched to develop an algorithm-based diagnostic aid combining symptom data and proteomic biomarkers to reduce the misdiagnosis of bipolar disorder (BD) as a major depressive disorder (MDD) and achieve more accurate and earlier MDD diagnosis. Methods Participants for this ethically approved trial were recruited through the internet, mainly through Facebook advertising. Participants were then screened for eligibility, consented to participate, and completed an adaptive digital questionnaire that was designed and created for the trial on a purpose-built digital platform. A subset of these participants was selected to provide dried blood spot (DBS) samples and undertake a World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI). Inclusion and exclusion criteria were chosen to maximize the safety of a trial population that was both relevant to the trial objectives and generalizable. To provide statistical power and validation sets for the primary and secondary objectives, 840 participants were required to complete the digital questionnaire, submit DBS samples, and undertake a CIDI. Results The Delta Trial is now complete. More than 3200 participants completed the digital questionnaire, 924 of whom also submitted DBS samples and a CIDI, whereas a total of 1780 participants completed a 6-month follow-up questionnaire and 1542 completed a 12-month follow-up questionnaire. The analysis of the trial data is now underway. Conclusions If a diagnostic aid is able to improve the diagnosis of BD and MDD, it may enable earlier treatment for patients with mood disorders. International Registered Report Identifier (IRRID) DERR1-10.2196/18453
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Affiliation(s)
- Tony Olmert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Jason D Cooper
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Sung Yeon Sarah Han
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | | | | | | | | | - Sureyya Ozcan
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Nitin Rustogi
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Rhian L Preece
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Pawel Eljasz
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Jakub Tomasik
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | | | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
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21
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Rhee SJ, Han D, Lee Y, Kim H, Lee J, Lee K, Shin H, Kim H, Lee TY, Kim M, Kim SH, Ahn YM, Kwon JS, Ha K. Comparison of serum protein profiles between major depressive disorder and bipolar disorder. BMC Psychiatry 2020; 20:145. [PMID: 32245436 PMCID: PMC7118970 DOI: 10.1186/s12888-020-02540-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 03/10/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Major depressive disorder and bipolar disorder are prevalent and debilitating psychiatric disorders that are difficult to distinguish, as their diagnosis is based on behavioural observations and subjective symptoms. Quantitative protein profile analysis might help to objectively distinguish between these disorders and increase our understanding of their pathophysiology. Thus, this study was conducted to compare the peripheral protein profiles between the two disorders. METHODS Serum samples were collected from 18 subjects with major depressive disorder and 15 subjects with bipolar disorder. After depleting abundant proteins, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and label-free quantification were performed. Data-dependent acquisition data were statistically analysed from the samples of 15 subjects with major depressive disorder and 10 subjects with bipolar disorder who were psychotropic drug-free. Two-sided t-tests were performed for pairwise comparisons of proteomes to detect differentially-expressed proteins (DEPs). Ingenuity Pathway Analysis of canonical pathways, disease and functions, and protein networks based on these DEPs was further conducted. RESULTS Fourteen DEPs were significant between subjects with major depressive disorder and those with bipolar disorder. Ras-related protein Rab-7a (t = 5.975, p = 4.3 × 10- 6) and Rho-associated protein kinase 2 (t = 4.782, p = 8.0 × 10- 5) were significantly overexpressed in subjects with major depressive disorder and Exportin-7 (t = -4.520, p = 1.5 × 10- 4) was significantly overexpressed in subjects with bipolar disorder after considering multiple comparisons. Bioinformatics analysis showed that cellular functions and inflammation/immune pathways were significantly different. CONCLUSIONS Ras-related protein Rab-7a, Rho-associated protein kinase 2, and Exportin-7 were identified as potential peripheral protein candidates to distinguish major depressive disorder and bipolar disorder. Further large sample studies with longitudinal designs and validation processes are warranted.
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Affiliation(s)
- Sang Jin Rhee
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dohyun Han
- grid.412484.f0000 0001 0302 820XProteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yunna Lee
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeyoung Kim
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea ,grid.411605.70000 0004 0648 0025Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea
| | - Junhee Lee
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Kangeun Lee
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyunsuk Shin
- grid.412484.f0000 0001 0302 820XProteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeyoon Kim
- grid.412484.f0000 0001 0302 820XProteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tae Young Lee
- grid.412484.f0000 0001 0302 820XInstitute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea ,grid.412591.a0000 0004 0442 9883Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
| | - Minah Kim
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Se Hyun Kim
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yong Min Ahn
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea ,grid.412484.f0000 0001 0302 820XInstitute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Jun Soo Kwon
- grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea ,grid.412484.f0000 0001 0302 820XInstitute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Kyooseob Ha
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea. .,Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea. .,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
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22
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Wu Y, Wei Z, Li Y, Wei C, Li Y, Cheng P, Xu H, Li Z, Guo R, Qi X, Jia J, Jia Y, Wang W, Gao X. Perturbation of Ephrin Receptor Signaling and Glutamatergic Transmission in the Hypothalamus in Depression Using Proteomics Integrated With Metabolomics. Front Neurosci 2019; 13:1359. [PMID: 31920518 PMCID: PMC6928102 DOI: 10.3389/fnins.2019.01359] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 12/02/2019] [Indexed: 12/16/2022] Open
Abstract
Hypothalamic dysfunction is a key pathological factor in inflammation-associated depression. In the present study, isobaric tags for relative-absolute quantitation (iTRAQ) combined with mass spectrometry and gas chromatography-mass spectrometry (GC-MS) were employed to detect the proteomes and metabolomes in the hypothalamus of the lipopolysaccharide (LPS)-induced depression mouse, respectively. A total of 187 proteins and 27 metabolites were differentially expressed compared with the control group. Following the integration of bi-omics data, pertinent pathways and molecular interaction networks were further identified. The results indicated altered molecules were clustered into Ephrin receptor signaling, glutamatergic transmission, and inflammation-related signaling included the LXR/RXR activation, FXR/RXR activation, and acute phase response signaling. First discovered in the hypothalamus, Ephrin receptor signaling regulates N-methyl-D-aspartate receptor (NMDAR)-predominant glutamatergic transmission, and further acted on AKT signaling that contributed to changes in hypothalamic neuroplasticity. Ephrin type-B receptor 2 (EPHB2), a transmembrane receptor protein in Ephrin receptor signaling, was significantly elevated and interacted with the accumulated NMDAR subunit GluN2A in the hypothalamus. Additionally, molecules involved in synaptic plasticity regulation, such as hypothalamic postsynaptic density protein-95 (PSD-95), p-AKT and brain-derived neurotrophic factor (BDNF), were significantly altered in the LPS-induced depressed group. It might be an underlying pathogenesis that the EPHB2-GluN2A-AKT cascade regulates synaptic plasticity in depression. EPHB2 can be a potential therapeutic target in the correction of glutamatergic transmission dysfunction. In summary, our findings point to the previously undiscovered molecular underpinnings of the pathophysiology in the hypothalamus of inflammation-associated depression and offer potential targets to develop antidepressants.
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Affiliation(s)
- Yu Wu
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China.,Gansu Provincial Biobank and Bioinformation Engineering Research Center, Lanzhou, China
| | - Zhenhong Wei
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China.,Gansu Provincial Biobank and Bioinformation Engineering Research Center, Lanzhou, China
| | - Yonghong Li
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China.,Gansu Provincial Biobank and Bioinformation Engineering Research Center, Lanzhou, China
| | - Chaojun Wei
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China.,Gansu Provincial Biobank and Bioinformation Engineering Research Center, Lanzhou, China
| | - Yuanting Li
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Pengfei Cheng
- Department of Neurology, The First Affiliated Hospital of Jiamusi University, Jiamusi, China
| | - Hui Xu
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Zhenhao Li
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Rui Guo
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Xiaoming Qi
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Jing Jia
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Yanjuan Jia
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Wanxia Wang
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China
| | - Xiaoling Gao
- The Institute of Clinical Research and Translational Medicine, Gansu Provincial Hospital, Lanzhou, China.,NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, China.,Gansu Provincial Biobank and Bioinformation Engineering Research Center, Lanzhou, China
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23
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Smirnova L, Seregin A, Boksha I, Dmitrieva E, Simutkin G, Kornetova E, Savushkina O, Letova A, Bokhan N, Ivanova S, Zgoda V. The difference in serum proteomes in schizophrenia and bipolar disorder. BMC Genomics 2019; 20:535. [PMID: 31291891 PMCID: PMC6620192 DOI: 10.1186/s12864-019-5848-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Purpose of study is revealing significant differences in serum proteomes in schizophrenia and bipolar disorder (BD). RESULTS Quantitative mass-spectrometry based proteomic analysis was used to quantify proteins in the blood serum samples after the depletion of six major blood proteins. Comparison of proteome profiles of different groups revealed 27 proteins being specific for schizophrenia, and 18 - for BD. Protein set in schizophrenia was mostly associated with immune response, cell communication, cell growth and maintenance, protein metabolism and regulation of nucleic acid metabolism. Protein set in BD was mostly associated with immune response, regulating transport processes across cell membrane and cell communication, development of neurons and oligodendrocytes and cell growth. Concentrations of ankyrin repeat domain-containing protein 12 (ANKRD12) and cadherin 5 in serum samples were determined by ELISA. Significant difference between three groups was revealed in ANKRD12 concentration (p = 0.02), with maximum elevation of ANKRD12 concentration (median level) in schizophrenia followed by BD. Cadherin 5 concentration differed significantly (p = 0.035) between schizophrenic patients with prevailing positive symptoms (4.78 [2.71, 7.12] ng/ml) and those with prevailing negative symptoms (1.86 [0.001, 4.11] ng/ml). CONCLUSIONS Our results are presumably useful for discovering the new pathways involved in endogenous psychotic disorders.
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Affiliation(s)
- Liudmila Smirnova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
| | - Alexander Seregin
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
| | | | - Elena Dmitrieva
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
- Siberian State Medical University, Tomsk, Russia
| | - German Simutkin
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
| | - Elena Kornetova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
- Siberian State Medical University, Tomsk, Russia
| | | | | | - Nikolay Bokhan
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
| | - Svetlana Ivanova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Tomsk, Russia
- Siberian State Medical University, Tomsk, Russia
| | - Victor Zgoda
- Institute of Biomedical Chemistry, Moscow, Russia
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24
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Stratification and prediction of remission in first-episode psychosis patients: the OPTiMiSE cohort study. Transl Psychiatry 2019; 9:20. [PMID: 30655509 PMCID: PMC6336802 DOI: 10.1038/s41398-018-0366-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 12/10/2018] [Indexed: 11/09/2022] Open
Abstract
Early response to first-line antipsychotic treatments is strongly associated with positive long-term symptomatic and functional outcome in psychosis. Unfortunately, attempts to identify reliable predictors of treatment response in first-episode psychosis (FEP) patients have not yet been successful. One reason for this could be that FEP patients are highly heterogeneous in terms of symptom expression and underlying disease biological mechanisms, thereby impeding the identification of one-size-fits-all predictors of treatment response. We have used a clustering approach to stratify 325 FEP patients into four clinical subtypes, termed C1A, C1B, C2A and C2B, based on their symptoms assessed using the Positive and Negative Syndrome Scale (PANSS) scale. Compared to C1B, C2A and C2B patients, those from the C1A subtype exhibited the most severe symptoms and were the most at risk of being non-remitters when treated with the second-generation antipsychotic drug amisulpride. Before treatment, C1A patients exhibited higher serum levels of several pro-inflammatory cytokines and inflammation-associated biomarkers therefore validating our stratification approach on external biological measures. Most importantly, in the C1A subtype, but not others, lower serum levels of interleukin (IL)-15, higher serum levels of C-X-C motif chemokine 12 (CXCL12), previous exposure to cytomegalovirus (CMV), use of recreational drugs and being younger were all associated with higher odds of being non-remitters 4 weeks after treatment. The predictive value of this model was good (mean area under the curve (AUC) = 0.73 ± 0.10), and its specificity and sensitivity were 45 ± 0.09% and 83 ± 0.03%, respectively. Further validation and replication of these results in clinical trials would pave the way for the development of a blood-based assisted clinical decision support system in psychosis.
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25
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Silva-Costa LC, Carlson PT, Guest PC, de Almeida V, Martins-de-Souza D. Proteomic Markers for Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1118:191-206. [DOI: 10.1007/978-3-030-05542-4_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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26
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Kraut A, Louwagie M, Bruley C, Masselon C, Couté Y, Brun V, Hesse AM. Protein Biomarker Discovery in Non-depleted Serum by Spectral Library-Based Data-Independent Acquisition Mass Spectrometry. Methods Mol Biol 2019; 1959:129-150. [PMID: 30852820 DOI: 10.1007/978-1-4939-9164-8_9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In discovery proteomics experiments, tandem mass spectrometry and data-dependent acquisition (DDA) are classically used to identify and quantify peptides and proteins through database searching. This strategy suffers from known limitations such as under-sampling and lack of reproducibility of precursor ion selection in complex proteomics samples, leading to somewhat inconsistent analytical results across large datasets. Data-independent acquisition (DIA) based on fragmentation of all the precursors detected in predetermined isolation windows can potentially overcome this limitation. DIA promises reproducible peptide and protein quantification with deeper proteome coverage and fewer missing values than DDA strategies. This approach is particularly attractive in the field of clinical biomarker discovery, where large numbers of samples must be analyzed. Here, we describe a DIA workflow for non-depleted serum analysis including a straightforward approach through which to construct a dedicated spectral library, and indications on how to optimize chromatographic and mass spectrometry analytical methods to produce high-quality DIA data and results.
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Affiliation(s)
- Alexandra Kraut
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | | | | | | | - Yohann Couté
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France
| | - Anne-Marie Hesse
- Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France.
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