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Li D, Pain O, Chiara F, Wong WLE, Lo CWH, Ripke S, Cattaneo A, Souery D, Dernovsek MZ, Henigsberg N, Hauser J, Lewis G, Mors O, Perroud N, Rietschel M, Uher R, Maier W, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Liu YL, Serretti A, Tsai SJ, Weinshilboum R, McIntosh AM, Lewis CM. Metabolic activity of CYP2C19 and CYP2D6 on antidepressant response from 13 clinical studies using genotype imputation: a meta-analysis. Transl Psychiatry 2024; 14:296. [PMID: 39025838 PMCID: PMC11258238 DOI: 10.1038/s41398-024-02981-1] [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: 06/27/2023] [Revised: 06/10/2024] [Accepted: 06/18/2024] [Indexed: 07/20/2024] Open
Abstract
Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. CYP2D6 structural variants cannot be imputed from genotype data, limiting the determination of metabolic phenotypes, and precluding testing for association with response. The association of CYP2C19 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR = 1.46, 95% CI [1.03, 2.06], p = 0.033, heterogeneity I2 = 0%, subgroup difference p = 0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.
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Affiliation(s)
- Danyang Li
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB, UK
- Cancer Centre, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, CN, China
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, GB, UK
| | - Fabbri Chiara
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Win Lee Edwin Wong
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB, UK
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chris Wai Hang Lo
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB, UK
| | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, DE, Germany
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Annamaria Cattaneo
- Biological Psychiatry Laboratory, IRCCS Fatebenefratelli, Brescia, Italy
- Department of Pharmacological and Biomedical Sciences, University of Milan, Milan, Italy
| | - Daniel Souery
- Laboratoire de Psychologie Medicale, Universitè Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Medicale, Brussels, Italy
| | - Mojca Z Dernovsek
- University Psychiatric Clinic, University of Ljubliana, Ljubljana, Slovenia
| | - Neven Henigsberg
- Department of Psychiatry, Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, HR, Croatia
| | - Joanna Hauser
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, GB, UK
| | - Ole Mors
- Psychosis Research Unit, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Nader Perroud
- Department of Psychiatry, Geneva University Hospitals, Geneva, CH, Switzerland
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, University of Heidelberg, Central Institute of Mental Health, Mannheim, Denmark
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Wolfgang Maier
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Denmark
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Denmark
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
- Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Joanna M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Guido Bondolfi
- Department of Psychiatry, Geneva University Hospitals, Geneva, CH, Switzerland
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Freiburg, Denmark
| | - Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | | | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | | | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB, UK.
- Department of Medical & Molecular Genetics, King's College London, London, GB, UK.
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You Z, Wang C, Lan X, Li W, Shang D, Zhang F, Ye Y, Liu H, Zhou Y, Ning Y. The contribution of polyamine pathway to determinations of diagnosis for treatment-resistant depression: A metabolomic analysis. Prog Neuropsychopharmacol Biol Psychiatry 2024; 128:110849. [PMID: 37659714 DOI: 10.1016/j.pnpbp.2023.110849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/26/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023]
Abstract
OBJECTIVES Approximately one-third of major depressive disorder (MDD) patients do not respond to standard antidepressants and develop treatment-resistant depression (TRD). We aimed to reveal metabolic differences and discover promising potential biomarkers in TRD. METHODS Our study recruited 108 participants including healthy controls (n = 40) and patients with TRD (n = 35) and first-episode drug-naive MDD (DN-MDD) (n = 33). Plasma samples were presented to ultra performance liquid chromatography-tandem mass spectrometry. Then, a machine-learning algorithm was conducted to facilitate the selection of potential biomarkers. RESULTS TRD appeared to be a distinct metabolic disorder from DN-MDD and healthy controls (HCs). Compared to HCs, 199 and 176 differentially expressed metabolites were identified in TRD and DN-MDD, respectively. Of all the metabolites that were identified, spermine, spermidine, and carnosine were considered the most promising biomarkers for diagnosing TRD and DN-MDD patients, with the resulting area under the ROC curve of 0.99, 0.99, and 0.93, respectively. Metabolic pathway analysis yielded compelling evidence of marked changes or imbalances in both polyamine metabolism and energy metabolism, which could potentially represent the primary altered pathways associated with MDD. Additionally, L-glutamine, Beta-alanine, and spermine were correlated with HAMD score. CONCLUSIONS A more disordered metabolism structure is found in TRD than in DN-MDD and HCs. Future investigations should prioritize the comprehensive analysis of potential roles played by these differential metabolites and disturbances in polyamine pathways in the pathophysiology of TRD and depression.
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Affiliation(s)
- Zerui You
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Chengyu Wang
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xiaofeng Lan
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Weicheng Li
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Dewei Shang
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Fan Zhang
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yanxiang Ye
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Haiyan Liu
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yanling Zhou
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
| | - Yuping Ning
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
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3
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Gao Z, Ding P, Xu R. IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder. Pharmacol Res 2024; 199:106960. [PMID: 37832859 DOI: 10.1016/j.phrs.2023.106960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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4
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Li D, Pain O, Fabbri C, Wong WLE, Lo CWH, Ripke S, Cattaneo A, Souery D, Dernovsek MZ, Henigsberg N, Hauser J, Lewis G, Mors O, Perroud N, Rietschel M, Uher R, Maier W, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Liu YL, Serretti A, Tsai SJ, Weinshilboum R, McIntosh AM, Lewis CM. Meta-analysis of CYP2C19 and CYP2D6 metabolic activity on antidepressant response from 13 clinical studies using genotype imputation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.26.23291890. [PMID: 37425775 PMCID: PMC10327261 DOI: 10.1101/2023.06.26.23291890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Cytochrome P450 enzymes including CYP2C19 and CYP2D6 are important for antidepressant metabolism and polymorphisms of these genes have been determined to predict metabolite levels. Nonetheless, more evidence is needed to understand the impact of genetic variations on antidepressant response. In this study, individual clinical and genetic data from 13 studies of European and East Asian ancestry populations were collected. The antidepressant response was clinically assessed as remission and percentage improvement. Imputed genotype was used to translate genetic polymorphisms to metabolic phenotypes (poor, intermediate, normal, and rapid+ultrarapid) of CYP2C19 and CYP2D6. The association of CYP2C19 and CYP2D6 metabolic phenotypes with treatment response was examined using normal metabolizers as the reference. Among 5843 depression patients, a higher remission rate was found in CYP2C19 poor metabolizers compared to normal metabolizers at nominal significance but did not survive after multiple testing correction (OR=1.46, 95% CI [1.03, 2.06], p=0.033, heterogeneity I2=0%, subgroup difference p=0.72). No metabolic phenotype was associated with percentage improvement from baseline. After stratifying by antidepressants primarily metabolized by CYP2C19 and CYP2D6, no association was found between metabolic phenotypes and antidepressant response. Metabolic phenotypes showed differences in frequency, but not effect, between European- and East Asian-ancestry studies. In conclusion, metabolic phenotypes imputed from genetic variants using genotype were not associated with antidepressant response. CYP2C19 poor metabolizers could potentially contribute to antidepressant efficacy with more evidence needed. CYP2D6 structural variants cannot be imputed from genotype data, limiting inference of pharmacogenetic effects. Sequencing and targeted pharmacogenetic testing, alongside information on side effects, antidepressant dosage, depression measures, and diverse ancestry studies, would more fully capture the influence of metabolic phenotypes.
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Affiliation(s)
- Danyang Li
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB
| | - Oliver Pain
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, GB
| | - Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, IT
| | - Win Lee Edwin Wong
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, SG
| | - Chris Wai Hang Lo
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB
| | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, DE
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, US
| | - Annamaria Cattaneo
- Biological Psychiatry Laboratory, IRCCS Fatebenefratelli, Brescia, IT
- Department of Pharmacological and Biomedical Sciences, University of Milan, Milan, IT
| | - Daniel Souery
- Laboratoire de Psychologie Medicale, Universitè Libre de Bruxelles and Psy Pluriel, Centre Européen de Psychologie Medicale, Brussels, BE
| | - Mojca Z Dernovsek
- University Psychiatric Clinic, University of Ljubliana, Ljubljana, SI
| | - Neven Henigsberg
- Department of Psychiatry, Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, HR
| | - Joanna Hauser
- Psychiatric Genetic Unit,, Poznan University of Medical Sciences, Poznan, PL
| | - Glyn Lewis
- Division of Psychiatry, University College London, London, GB
| | - Ole Mors
- Psychosis Research Unit, Aarhus University Hospital - Psychiatry, Aarhus, DK
| | - Nader Perroud
- Department of Psychiatry, Geneva University Hospitals, Geneva, CH
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, University of Heidelberg, Central Institute of Mental Health, Mannheim, DE
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, CA
| | - Wolfgang Maier
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, DE
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, DE
- Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, AU
- Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, AU
| | - Joanna M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Guido Bondolfi
- Department of Psychiatry, Geneva University Hospitals, Geneva, CH
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Medical Center, University of Freiburg, Freiburg, DE
| | - Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka, JP
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, TW
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, IT
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, TW
- Division of Psychiatry, School of Medicine, National Yang-Ming University, Taipei, TW
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | | | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, GB
- Department of Medical & Molecular Genetics, King's College London, London, GB
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5
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Davyson E, Shen X, Gadd DA, Bernabeu E, Hillary RF, McCartney DL, Adams M, Marioni R, McIntosh AM. Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids. Biol Psychiatry 2023; 94:630-639. [PMID: 36764567 PMCID: PMC10804990 DOI: 10.1016/j.biopsych.2023.01.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 01/31/2023] [Accepted: 01/31/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Metabolic differences have been reported between individuals with and without major depressive disorder (MDD), but their consistency and causal relevance have been unclear. METHODS We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in the UK Biobank (n = 29,757). We then applied two-sample bidirectional Mendelian randomization and colocalization analysis to identify potentially causal relationships between each metabolite and MDD. RESULTS A total of 191 metabolites tested were significantly associated with MDD (false discovery rate-corrected p < .05), which decreased to 129 after adjustment for likely confounders. Lower abundance of omega-3 fatty acid measures and a higher omega-6 to omega-3 ratio showed potentially causal effects on liability to MDD. There was no evidence of a causal effect of MDD on metabolite levels. Furthermore, genetic signals associated with docosahexaenoic acid colocalized with loci associated with MDD within the fatty acid desaturase gene cluster. Post hoc Mendelian randomization of gene-transcript abundance within the fatty acid desaturase cluster demonstrated a potentially causal association with MDD. In contrast, colocalization analysis did not suggest a single causal variant for both transcript abundance and MDD liability, but rather the likely existence of two variants in linkage disequilibrium with one another. CONCLUSIONS Our findings suggest that decreased docosahexaenoic acid and increased omega-6 to omega-3 fatty acids ratio may be causally related to MDD. These findings provide further support for the causal involvement of fatty acids in MDD.
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Affiliation(s)
- Eleanor Davyson
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Danni A Gadd
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Elena Bernabeu
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Robert F Hillary
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Daniel L McCartney
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Riccardo Marioni
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom.
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6
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Grotzinger AD, Singh K, Miller-Fleming TW, Lam M, Mallard TT, Chen Y, Liu Z, Ge T, Smoller JW. Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing. JAMA Psychiatry 2023; 80:811-821. [PMID: 37314780 PMCID: PMC10267850 DOI: 10.1001/jamapsychiatry.2023.1808] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/05/2023] [Indexed: 06/15/2023]
Abstract
Importance Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design, Setting, and Participants This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023. Main Outcomes and Measures Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders. Conclusions and Relevance The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.
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Affiliation(s)
- Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tyne W. Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Max Lam
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York
- Research Division, Institute of Mental Health Singapore, Singapore
- Human Genetics, Genome Institute of Singapore, Singapore
| | - Travis T. Mallard
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Yu Chen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Zhaowen Liu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston
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7
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Liu M, Li Q, Liang Y. Pyroptosis-related genes prognostic model for predicting targeted therapy and immunotherapy response in soft tissue sarcoma. Front Pharmacol 2023; 14:1188473. [PMID: 37214439 PMCID: PMC10196039 DOI: 10.3389/fphar.2023.1188473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023] Open
Abstract
Several studies have highlighted the potential of pyroptosis as a target for cancer treatment. This article focuses on the specific roles and clinical implications of pyroptosis-related genes (PRGs) in soft tissue sarcoma (STS). By analyzing differentially expressed PRGs in STS compared to normal tissue, our study evaluates the interactions, biological functions, and prognostic values of PRGs in STS. Through LASSO COX regression analysis, a five-gene survival related-risk score (PLCG1, PYCARD, CASP8, NOD1, and NOD2) was created, which examined both in TCGA cohort and training cohort (GSE21050, GSE30929, and GSE63157). Furthermore, we developed a nomogram incorporating clinic factors and the risk scores of the PRGs, which showed decent accuracy of prediction as evidenced by calibration curves. Additionally, our study analyzed the Tumor Immune Dysfunction and Exclusion Algorithm (TIDE) and IMvigor 210 cohorts to investigate the immunotherapy response, and found that immunotherapy was more beneficial for patients with minimal risk of PRGs than those exhibiting greater risk. Finally, GDSC and CAMP databases were used to screen for effective chemotherapy or targeted drugs that are sensitive to the high-risk populations, including doxorubicin, imatinib, and sorafenib. In conclusion, this study provides a comprehensive analysis of the PRG landscape in STS and constructs a novel risk model to predict prognosis and different therapeutic responses of STS patients, which is helpful for achieving precision medicine.
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Affiliation(s)
- Mengmeng Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Quan Li
- Department of Burn and Plastic Surgery, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China
| | - Yao Liang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
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8
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Zimmerman M, Mackin DM. Reliability and validity of the difficult to treat depression questionnaire (DTDQ). Psychiatry Res 2023; 324:115225. [PMID: 37116322 DOI: 10.1016/j.psychres.2023.115225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/16/2023] [Accepted: 04/23/2023] [Indexed: 04/30/2023]
Abstract
It has recently been recommended that treatment resistant depression be reconceptualized and renamed as difficult to treat depression (DTD). A consensus statement by an expert panel identified multiple variables associated with DTD and emphasized the importance of conducting a comprehensive evaluation of patients to identify predictors of inadequate treatment response. For practical reasons, it would be desirable to develop a self-report scale that can be incorporated into clinical practice that identifies patient, clinical, and treatment risk factors for DTD. Nine hundred twenty depressed patients completed the Difficult to Treat Depression Questionnaire (DTDQ). A subset of patients completed the scale a second time and completed the Remission from Depression Questionnaire at admission and discharge from a partial hospital program. The DTDQ demonstrated excellent internal consistency and test-retest reliability. Both the total DTDQ and the number of prior failed medication trials, the metric primarily relied upon to classify treatment resistant depression, predicted outcome. However, the DTDQ continued to be significantly associated with outcome after controlling for the number of failed trials, whereas the number of failed trials did not predict outcome after controlling for DTDQ scores. The DTDQ is a reliable and valid measure of the recently discussed concept of DTD.
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Affiliation(s)
- Mark Zimmerman
- Department of Psychiatry and Human Behavior, Brown Medical School, Rhode Island Hospital, Providence, RI, United States.
| | - Daniel M Mackin
- Department of Psychiatry and Human Behavior, Brown Medical School, Rhode Island Hospital, Providence, RI, United States
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9
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Hicks EM, Seah C, Cote A, Marchese S, Brennand KJ, Nestler EJ, Girgenti MJ, Huckins LM. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Transl Psychiatry 2023; 13:129. [PMID: 37076454 PMCID: PMC10115809 DOI: 10.1038/s41398-023-02412-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 04/21/2023] Open
Abstract
Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.
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Affiliation(s)
- Emily M Hicks
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Carina Seah
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Alanna Cote
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Kristen J Brennand
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Departments of Psychiatry and of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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10
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Markov DD, Dolotov OV, Grivennikov IA. The Melanocortin System: A Promising Target for the Development of New Antidepressant Drugs. Int J Mol Sci 2023; 24:ijms24076664. [PMID: 37047638 PMCID: PMC10094937 DOI: 10.3390/ijms24076664] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Major depression is one of the most prevalent mental disorders, causing significant human suffering and socioeconomic loss. Since conventional antidepressants are not sufficiently effective, there is an urgent need to develop new antidepressant medications. Despite marked advances in the neurobiology of depression, the etiology and pathophysiology of this disease remain poorly understood. Classical and newer hypotheses of depression suggest that an imbalance of brain monoamines, dysregulation of the hypothalamic-pituitary-adrenal axis (HPAA) and immune system, or impaired hippocampal neurogenesis and neurotrophic factors pathways are cause of depression. It is assumed that conventional antidepressants improve these closely related disturbances. The purpose of this review was to discuss the possibility of affecting these disturbances by targeting the melanocortin system, which includes adrenocorticotropic hormone-activated receptors and their peptide ligands (melanocortins). The melanocortin system is involved in the regulation of various processes in the brain and periphery. Melanocortins, including peripherally administered non-corticotropic agonists, regulate HPAA activity, exhibit anti-inflammatory effects, stimulate the levels of neurotrophic factors, and enhance hippocampal neurogenesis and neurotransmission. Therefore, endogenous melanocortins and their analogs are able to complexly affect the functioning of those body’s systems that are closely related to depression and the effects of antidepressants, thereby demonstrating a promising antidepressant potential.
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Affiliation(s)
- Dmitrii D. Markov
- National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia
| | - Oleg V. Dolotov
- National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia
- Faculty of Biology, Lomonosov Moscow State University, Leninskie Gory, 119234 Moscow, Russia
| | - Igor A. Grivennikov
- National Research Center “Kurchatov Institute”, Kurchatov Sq. 2, 123182 Moscow, Russia
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11
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Brain function changes reveal rapid antidepressant effects of nitrous oxide for treatment-resistant depression:Evidence from task-state EEG. Psychiatry Res 2023; 322:115072. [PMID: 36791487 DOI: 10.1016/j.psychres.2023.115072] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 01/28/2023]
Abstract
Nitrous oxide has rapid antidepressant effects in patients with treatment-resistant depression (TRD), but its underlying mechanisms of therapeutic actions are not well understood. Moreover, most of the current studies lack objective biological indicators to evaluate the changes of nitrous oxide-induced brain function for TRD. Therefore, this study assessed the effect of nitrous oxide on brain function for TRD based on event-related potential (ERP) components and functional connectivity networks (FCNs) methods. In this randomized, longitudinal, placebo-controlled trial, all TRD participants were divided into two groups to receive either a 1-hour inhalation of nitrous oxide or a placebo treatment, and they took part in the same task-state electroencephalogram (EEG) experiment before and after treatment. The experimental results showed that nitrous oxide improved depressive symptoms better than placebo in terms of 17-Hamilton Depression Rating Scale score (HAMD-17). Statistical analysis based on ERP components showed that nitrous oxide-induced significant differences in amplitude and latency of N1, P1, N2, P2. In addition, increased brain functional connectivity was found after nitrous oxide treatment. And the change of network metrics has a significant correlation with decreased depressive symptoms. These findings may suggest that nitrous oxide improves depression symptoms for TRD by modifying brain function.
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12
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In Silico Analysis of Ferroptosis-Related Genes and Its Implication in Drug Prediction against Fluorosis. Int J Mol Sci 2023; 24:ijms24044221. [PMID: 36835629 PMCID: PMC9961266 DOI: 10.3390/ijms24044221] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/22/2023] Open
Abstract
Fluorosis is a serious global public health problem. Interestingly, so far, there is no specific drug treatment for the treatment of fluorosis. In this paper, the potential mechanisms of 35 ferroptosis-related genes in U87 glial cells exposed to fluoride were explored by bioinformatics methods. Significantly, these genes are involved in oxidative stress, ferroptosis, and decanoate CoA ligase activity. Ten pivotal genes were found by the Maximal Clique Centrality (MCC) algorithm. Furthermore, according to the Connectivity Map (CMap) and the Comparative Toxicogenomics Database (CTD), 10 possible drugs for fluorosis were predicted and screened, and a drug target ferroptosis-related gene network was constructed. Molecular docking was used to study the interaction between small molecule compounds and target proteins. Molecular dynamics (MD) simulation results show that the structure of the Celestrol-HMOX1 composite is stable and the docking effect is the best. In general, Celastrol and LDN-193189 may target ferroptosis-related genes to alleviate the symptoms of fluorosis, which may be effective candidate drugs for the treatment of fluorosis.
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13
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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14
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Amasi-Hartoonian N, Pariante CM, Cattaneo A, Sforzini L. Understanding treatment-resistant depression using "omics" techniques: A systematic review. J Affect Disord 2022; 318:423-455. [PMID: 36103934 DOI: 10.1016/j.jad.2022.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/26/2022] [Accepted: 09/07/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND Treatment-resistant depression (TRD) results in huge healthcare costs and poor patient clinical outcomes. Most studies have adopted a "candidate mechanism" approach to investigate TRD pathogenesis, however this is made more challenging due to the complex and heterogeneous nature of this condition. High-throughput "omics" technologies can provide a more holistic view and further insight into the underlying mechanisms involved in TRD development, expanding knowledge beyond already-identified mechanisms. This systematic review assessed the information from studies that examined TRD using hypothesis-free omics techniques. METHODS PubMed, MEDLINE, Embase, APA PsycInfo, Scopus and Web of Science databases were searched on July 2022. 37 human studies met the eligibility criteria, totalling 17,518 TRD patients, 571,402 healthy controls and 62,279 non-TRD depressed patients (including antidepressant responders and untreated MDD patients). RESULTS Significant findings were reported that implicate the role in TRD of various molecules, including polymorphisms, genes, mRNAs and microRNAs. The pathways most commonly reported by the identified studies were involved in immune system and inflammation, neuroplasticity, calcium signalling and neurotransmitters. LIMITATIONS Small sample sizes, variability in defining TRD, and heterogeneity in study design and methodology. CONCLUSIONS These findings provide insight into TRD pathophysiology, proposing future research directions for novel drug targets and potential biomarkers for clinical staging and response to antidepressants (citalopram/escitalopram in particular) and electroconvulsive therapy (ECT). Further validation is warranted in large prospective studies using standardised TRD criteria. A multi-omics and systems biology strategy with a collaborative effort will likely deliver robust findings for translation into the clinic.
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Affiliation(s)
- Nare Amasi-Hartoonian
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Department of Psychological Medicine, London, UK.
| | - Carmine Maria Pariante
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Department of Psychological Medicine, London, UK; National Institute for Health and Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, UK
| | - Annamaria Cattaneo
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy; Laboratory of Biological Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Luca Sforzini
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, Department of Psychological Medicine, London, UK
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15
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Are mGluR2/3 Inhibitors Potential Compounds for Novel Antidepressants? Cell Mol Neurobiol 2022:10.1007/s10571-022-01310-8. [DOI: 10.1007/s10571-022-01310-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 11/18/2022] [Indexed: 11/30/2022]
Abstract
AbstractDepression is the most common mental illness characterized by anhedonia, avolition and loss of appetite and motivation. The majority of conventional antidepressants are monoaminergic system selective inhibitors, yet the efficacies are not sufficient. Up to 30% of depressed patients are resistant to treatment with available antidepressants, underscoring the urgent need for development of novel therapeutics to meet clinical needs. Recent years, compounds acting on the glutamate system have attracted wide attention because of their strong, rapid and sustained antidepressant effects. Among them, selective inhibitors of metabotropic glutamate receptors 2 and 3 (mGluR2/3) have shown robust antidepressant benefits with fewer side-effects in both preclinical and clinical studies. Thus, we here attempt to summarize the antidepressant effects and underlying mechanisms of these inhibitors revealed in recent years as well as analyze the potential value of mGluR2/3 selective inhibitors in the treatment of depression.
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16
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Sun J, Guo C, Ma Y, Du Z, Wang Z, Luo Y, Chen L, Gao D, Li X, Xu K, Hong Y, Yu X, Xiao X, Fang J, Liu Y. A comparative study of amplitude of low-frequence fluctuation of resting-state fMRI between the younger and older treatment-resistant depression in adults. Front Neurosci 2022; 16:949698. [PMID: 36090288 PMCID: PMC9462398 DOI: 10.3389/fnins.2022.949698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
Background Treatment-resistant depression (TRD) may have different physiopathological neuromechanism in different age groups. This study used the amplitude of low frequency fluctuations (ALFF) to initially compare abnormalities in local functional brain activity in younger and older patients with TRD. Materials and methods A total of 21 older TRD patients, 19 younger TRD, 19 older healthy controls (HCs), and 19 younger HCs underwent resting-state functional MRI scans, and the images were analyzed using the ALFF and further analyzed for correlation between abnormal brain regions and clinical symptoms in TRD patients of different age groups. Results Compared with the older TRD, the younger TRD group had increased ALFF in the left middle frontal gyrus and decreased ALFF in the left caudate nucleus. Compared with the matched HC group, ALFF was increased in the right middle temporal gyrus and left pallidum in the older TRD group, whereas no significant differences were found in the younger TRD group. In addition, ALFF values in the left middle frontal gyrus in the younger TRD group and in the right middle temporal gyrus in the older TRD were both positively correlated with the 17-item Hamilton Rating Scale for Depression score. Conclusion Different neuropathological mechanisms may exist in TRD patients of different ages, especially in the left middle frontal gyrus and left caudate nucleus. This study is beneficial in providing potential key targets for the clinical management of TRD patients of different ages.
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Affiliation(s)
- Jifei Sun
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Chunlei Guo
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yue Ma
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zhongming Du
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Zhi Wang
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yi Luo
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Limei Chen
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Deqiang Gao
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaojiao Li
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ke Xu
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yang Hong
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xue Yu
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing, China
| | - Xue Xiao
- Beijing First Hospital of Integrated Chinese and Western Medicine, Beijing, China
| | - Jiliang Fang
- Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Jiliang Fang,
| | - Yong Liu
- Affiliated Hospital of Traditional Chinese Medicine, Southwest Medical University, Luzhou, China
- Yong Liu,
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17
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García-Marín LM, Rabinowitz JA, Ceja Z, Alcauter S, Medina-Rivera A, Rentería ME. The pharmacogenomics of selective serotonin reuptake inhibitors. Pharmacogenomics 2022; 23:597-607. [PMID: 35673953 DOI: 10.2217/pgs-2022-0037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.
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Affiliation(s)
- Luis M García-Marín
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.,Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Zuriel Ceja
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Miguel E Rentería
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
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18
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Taliaz D, Serretti A. Investigation of Psychoactive Medications: Challenges and a Practical and Scalable New Path. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2022; 22:CNSNDDT-EPUB-124837. [PMID: 35762546 DOI: 10.2174/1871527321666220628103843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/17/2022] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
In the last two decades the validity of clinical trials in psychiatry has been subject to discussion. The most accepted clinical study method in the medical area, randomized controlled trial (RCT), faces significant problems when applied to the psychiatric world. One of the causes for this scenario is the strict participant inclusion and exclusion criteria that may not represent the real world. The inconsistency of the different endpoint parameters that are used in the field is another cause. We think that psychiatric RCTs' challenges, together with the underlying complexity of psychiatry, lead to a problematic clinical practice. Today, psychoactive drugs are routinely tested not in an official clinical trial setting. Off-label psychoactive drugs are commonly prescribed, and other substances, such as herbal remedies, are also regularly consumed. Learning from those real-life experiments can teach us useful lessons. Real-world data (RWD) includes information about heterogeneous patient populations, and it can be measured with standardized parameters. Collecting RWD can also address the need for systematically documenting and sharing case reports' outcomes. We suggest using digital tools to capture objective and continuous behavioral data from patients passively. New conclusions will be constantly drawn, possibly allowing more personalized treatment outcomes. The relevant next-generation decision support tools are already available.
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Affiliation(s)
- Dekel Taliaz
- 2 Prof. Yehezkel Kaufmann Street, Textile Center (15th Floor), Tel Aviv 6801294, Israel
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Italy
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19
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Fabbri C. Genetics in psychiatry: Methods, clinical applications and future perspectives. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e6. [PMID: 38868637 PMCID: PMC11114394 DOI: 10.1002/pcn5.6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/18/2022] [Accepted: 03/02/2022] [Indexed: 06/14/2024]
Abstract
Psychiatric disorders and related traits have a demonstrated genetic component, with heritability estimated by twin studies generally between 80% and 40%. Their pathogenesis is complex and multi-determined: environmental factors interact with a polygenic architecture, making difficult the development of models able to stratify patients or predict mental health outcomes. Despite this difficult challenge, relevant progress has been made in the field of psychiatric genetics in recent years. This review aims to present the main current methods in psychiatric genetics, their output, limitations, clinical applications, and possible future developments. Genome-wide association studies (GWASs) performed in increasingly large samples have led to the identification of replicated genetic loci associated with the risk of major psychiatric disorders, including schizophrenia and mood disorders. Statistical and biological approaches have been developed to improve our understanding of the etiopathogenetic mechanisms behind genome-wide significant associations, as well as for estimating the cumulative effect of risk variants at the individual level and the genetic overlap between different disorders, as pleiotropy is the rule rather than the exception. Clinical applications are available in the pharmacogenetics field. The main issues that remain to be addressed include improving ethnic diversity in genetic studies and the optimization of statistical power through methodological improvements, such as the definition of dimensional phenotypes with specific biological correlates and the integration of different types of omics data.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and Neuromotor SciencesUniversity of BolognaBolognaItaly
- Institute of Psychiatry, Psychology & NeuroscienceKing's College LondonLondonUK
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20
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Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood. Mol Psychiatry 2022; 27:2849-2857. [PMID: 35296807 DOI: 10.1038/s41380-022-01507-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 12/15/2022]
Abstract
Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.
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21
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Psychopharmacology: past, present and future. Int Clin Psychopharmacol 2022; 37:82-83. [PMID: 35258034 DOI: 10.1097/yic.0000000000000402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The origin of modern psychopharmacology dates to the 50s, with the discovery of imipramine and chlorpromazine. At present, we can choose among over 100 different compounds, which are effective in many psychiatric disturbances but are far from perfect in terms of efficacy and tolerability. The main limitation of available treatments is their lack in specificity, both in terms of pharmacologic targets and regional brain specificity. Several new compounds with innovative mechanisms of action have been recently approved; however, pharmacologic treatments targeted for specific tissues are still not available. Recent imaging and genetic findings suggest that we may be close to discovering the regional pathophysiologic mechanisms of psychiatric disorders. Targeted treatment to specific proteins or even genes may be possible using monoclonal antibodies, RNA silencing, gene editing or drug repurposing. We may be therefore close to a shift of paradigm in the treatment of psychiatric disorders, with innovative and targeted treatments.
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22
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A rapid and sensitive LC-MS/MS method for determination of the active component K6 in serum of patients with depression. J Pharm Biomed Anal 2022; 213:114691. [DOI: 10.1016/j.jpba.2022.114691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/07/2022] [Accepted: 02/24/2022] [Indexed: 11/21/2022]
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23
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Identification of TMEM106B amyloid fibrils provides an updated view of TMEM106B biology in health and disease. Acta Neuropathol 2022; 144:807-819. [PMID: 36056242 PMCID: PMC9547799 DOI: 10.1007/s00401-022-02486-5] [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: 06/10/2022] [Revised: 08/15/2022] [Accepted: 08/18/2022] [Indexed: 01/26/2023]
Abstract
Since the initial identification of TMEM106B as a risk factor for frontotemporal lobar degeneration (FTLD), multiple genetic studies have found TMEM106B variants to modulate disease risk in a variety of brain disorders and healthy aging. Neurodegenerative disorders are typically characterized by inclusions of misfolded proteins and since lysosomes are an important site for cellular debris clearance, lysosomal dysfunction has been closely linked to neurodegeneration. Consequently, many causal mutations or genetic risk variants implicated in neurodegenerative diseases encode proteins involved in endosomal-lysosomal function. As an integral lysosomal transmembrane protein, TMEM106B regulates several aspects of lysosomal function and multiple studies have shown that proper TMEM106B protein levels are crucial for maintaining lysosomal health. Yet, the precise function of TMEM106B at the lysosomal membrane is undetermined and it remains unclear how TMEM106B modulates disease risk. Unexpectedly, several independent groups recently showed that the C-terminal domain (AA120-254) of TMEM106B forms amyloid fibrils in the brain of patients with a diverse set of neurodegenerative conditions. The recognition that TMEM106B can form amyloid fibrils and is present across neurodegenerative diseases sheds new light on TMEM106B as a central player in neurodegeneration and brain health, but also raises important new questions. In this review, we summarize current knowledge and place a decade's worth of TMEM106B research into an exciting new perspective.
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24
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Fabbri C. An interview with Dr Chiara Fabbri: pharmacogenomics and drug repurposing for treatment-resistant depression. Pharmacogenomics 2021; 22:1107-1109. [PMID: 34761695 DOI: 10.2217/pgs-2021-0134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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25
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Pardiñas AF, Owen MJ, Walters JTR. Pharmacogenomics: A road ahead for precision medicine in psychiatry. Neuron 2021; 109:3914-3929. [PMID: 34619094 DOI: 10.1016/j.neuron.2021.09.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/05/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022]
Abstract
Psychiatric genomics is providing insights into the nature of psychiatric conditions that in time should identify new drug targets and improve patient care. Less attention has been paid to psychiatric pharmacogenomics research, despite its potential to deliver more rapid change in clinical practice and patient outcomes. The pharmacogenomics of treatment response encapsulates both pharmacokinetic ("what the body does to a drug") and pharmacodynamic ("what the drug does to the body") effects. Despite early optimism and substantial research in both these areas, they have to date made little impact on clinical management in psychiatry. A number of bottlenecks have hampered progress, including a lack of large-scale replication studies, inconsistencies in defining valid treatment outcomes across experiments, a failure to routinely incorporate adverse drug reactions and serum metabolite monitoring in study designs, and inadequate investment in the longitudinal data collections required to demonstrate clinical utility. Nonetheless, advances in genomics and health informatics present distinct opportunities for psychiatric pharmacogenomics to enter a new and productive phase of research discovery and translation.
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Affiliation(s)
- Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK.
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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