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Karaglani M, Agorastos A, Panagopoulou M, Parlapani E, Athanasis P, Bitsios P, Tzitzikou K, Theodosiou T, Iliopoulos I, Bozikas VP, Chatzaki E. A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning. Transl Psychiatry 2024; 14:257. [PMID: 38886359 PMCID: PMC11183091 DOI: 10.1038/s41398-024-02946-4] [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: 08/06/2023] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/20/2024] Open
Abstract
Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.
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Affiliation(s)
- Makrina Karaglani
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece
| | - Agorastos Agorastos
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece
- II. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56430, Thessaloniki, Greece
| | - Maria Panagopoulou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece
| | - Eleni Parlapani
- Ι. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56429, Thessaloniki, Greece
| | - Panagiotis Athanasis
- II. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56430, Thessaloniki, Greece
| | - Panagiotis Bitsios
- Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, GR-71500, Heraklion, Greece
| | - Konstantina Tzitzikou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
| | - Theodosis Theodosiou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece
- ABCureD P.C, GR-68131, Alexandroupolis, Greece
| | - Ioannis Iliopoulos
- Division of Basic Sciences, School of Medicine, University of Crete, GR-71003, Heraklion, Greece
| | - Vasilios-Panteleimon Bozikas
- II. Department of Psychiatry, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, GR-56430, Thessaloniki, Greece
| | - Ekaterini Chatzaki
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, GR-68132, Alexandroupolis, Greece.
- Institute of Agri-food and Life Sciences, University Research & Innovation Center, H.M.U.R.I.C., Hellenic Mediterranean University, GR-71003, Crete, Greece.
- ABCureD P.C, GR-68131, Alexandroupolis, Greece.
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology, 70013, Heraklion, Greece.
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Chandrashekar PB, Alatkar S, Wang J, Hoffman GE, He C, Jin T, Khullar S, Bendl J, Fullard JF, Roussos P, Wang D. DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction. Genome Med 2023; 15:88. [PMID: 37904203 PMCID: PMC10617196 DOI: 10.1186/s13073-023-01248-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/16/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. METHOD To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. RESULTS We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). CONCLUSION We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.
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Affiliation(s)
- Pramod Bharadwaj Chandrashekar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Sayali Alatkar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Chenfeng He
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Department of Psychiatry and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 10962, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA.
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53076, USA.
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Yazarlou F, Tabibian M, Azarnezhad A, Sadeghi Rad H, Lipovich L, Sanati G, Mostafavi Abdolmaleky H, Alizadeh F. Evaluating Gene Expression and Methylation Profiles of TCF4, MBP, and EGR1 in Peripheral Blood of Drug-Free Patients with Schizophrenia: Correlations with Psychopathology, Intelligence, and Cognitive Impairment. J Mol Neurosci 2023; 73:738-750. [PMID: 37668894 DOI: 10.1007/s12031-023-02150-x] [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: 01/28/2023] [Accepted: 08/15/2023] [Indexed: 09/06/2023]
Abstract
Discovery and validation of new, reliable diagnostic and predictive biomarkers for schizophrenia (SCZ) are an ongoing effort. Here, we assessed the mRNA expression and DNA methylation of the TCF4, MBP, and EGR1 genes in the blood of patients with SCZ and evaluated their relationships to psychopathology and cognitive impairments. Quantitative real-time PCR and quantitative methylation-specific PCR methods were used to assess the expression level and promoter DNA methylation status of these genes in 70 drug-free SCZ patients and 72 healthy controls. The correlation of molecular changes with psychopathology and cognitive performance of participants was evaluated. We observed downregulation of TCF4 and upregulation of MBP mRNA levels in SCZ cases, relative to controls in our study. DNA methylation status at the promoter region of TCF4 demonstrated an altered pattern in SCZ as well. Additionally, TCF4 mRNA levels were inversely correlated with PANSS and Stroop total errors and positively correlated with WAIS total score and working memory, consistent with previous studies by our group. In contrast, MBP mRNA level was significantly positively correlated with PANSS and Stroop total errors and inversely correlated with WAIS total score and working memory. These epigenetic and expression signatures can help to assemble a peripheral biomarker-based diagnostic panel for SCZ.
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Affiliation(s)
- Fatemeh Yazarlou
- Department of Basic Medical Sciences, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, United Arab Emirates
| | - Mobina Tabibian
- Department of Cellular and Molecular Biology, Faculty of Life Sciences and Biotechnologies, Shahid Beheshti University, Tehran, Iran
| | - Asaad Azarnezhad
- Liver and Digestive Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Habib Sadeghi Rad
- Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Leonard Lipovich
- Shenzhen Huayuan Biological Science Research Institute, Shenzhen Huayuan Biotechnology Co. Ltd., 601 Building C1, Guangming Science Park, Fenghuang Street, 518000, Shenzhen, Guangdong, People's Republic of China
- Center for Molecular Medicine and Genetics, Wayne State University, 540 E. Canfield St., Detroit, MI, 48201, USA
| | - Golshid Sanati
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | | | - Fatemeh Alizadeh
- Department of Genomic Psychiatry and Behavioral Genomics (DGPBG), Roozbeh Hospital, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
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Yao Y, Jin C, Liao Y, Huang X, Wei Z, Zhang Y, Li D, Su H, Han W, Qin D. Schizophrenia-Like Behaviors Arising from Dysregulated Proline Metabolism Are Associated with Altered Neuronal Morphology and Function in Mice with Hippocampal PRODH Deficiency. Aging Dis 2023:AD.2023.0902. [PMID: 37815900 DOI: 10.14336/ad.2023.0902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/02/2023] [Indexed: 10/12/2023] Open
Abstract
Despite decades of research being conducted to understand what physiological deficits in the brain are an underlying basis of psychiatric diseases like schizophrenia, it has remained difficult to establish a direct causal relationship between neuronal dysfunction and specific behavioral phenotypes. Moreover, it remains unclear how metabolic processes, including amino acid metabolism, affect neuronal function and consequently modulate animal behaviors. PRODH, which catalyzes the first step of proline degradation, has been reported as a susceptibility gene for schizophrenia. It has consistently been shown that PRODH knockout mice exhibit schizophrenia-like behaviors. However, whether the loss of PRODH directly impacts neuronal function or whether such neuronal deficits are linked to schizophrenia-like behaviors has not yet been examined. Herein, we first ascertained that dysregulated proline metabolism in humans is associated with schizophrenia. We then found that PRODH was highly expressed in the oreins layer of the mouse dorsal hippocampus. By using AAV- mediated shRNA, we depleted PRODH expression in the mouse dorsal hippocampus and subsequently observed hyperactivity and impairments in the social behaviors, learning, and memory of these mice. Furthermore, the loss of PRODH led to altered neuronal morphology and function both in vivo and in vitro. Our study demonstrates that schizophrenia-like behaviors may arise from dysregulated proline metabolism due to the loss of PRODH and are associated with altered neuronal morphology and function in mice.
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Affiliation(s)
- Yuxiao Yao
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Chenchen Jin
- Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Yilie Liao
- Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Xiang Huang
- Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Ziying Wei
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Yahong Zhang
- Guangzhou Laboratory, Guangzhou, Guangdong, China
| | - Dongwei Li
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Huanxing Su
- Institute of Chinese Medical Sciences, University of Macau, Macau, China
| | - Weiping Han
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Dajiang Qin
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
- Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
- Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences; Hong Kong SAR, China
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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Lokmer A, Alladi CG, Troudet R, Bacq-Daian D, Boland-Auge A, Latapie V, Deleuze JF, RajKumar RP, Shewade DG, Bélivier F, Marie-Claire C, Jamain S. Risperidone response in patients with schizophrenia drives DNA methylation changes in immune and neuronal systems. Epigenomics 2023; 15:21-38. [PMID: 36919681 DOI: 10.2217/epi-2023-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023] Open
Abstract
Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material & methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 weeks into risperidone therapy. Results: Several CpGs exhibiting response-specific temporal dynamics were identified in otherwise temporally stable methylomes and noticeable global response-related differences were observed between good and bad responders. These were associated with genes involved in immunity, neurotransmission and neuronal development. Polymorphisms in many of these genes were previously linked with schizophrenia etiology and antipsychotic response. Conclusion: Antipsychotic response seems to be shaped by both stable and medication-induced methylation differences.
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Affiliation(s)
- Ana Lokmer
- Univ Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, F-94000, France.,Fondation FondaMental, Créteil, F-94000, France
| | - Charanraj Goud Alladi
- Université de Paris, INSERM UMRS 1144, Optimisation Thérapeutique en Neuropsychopharmacologie (OTeN), Paris, F-75006, France
| | - Réjane Troudet
- Univ Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, F-94000, France.,Fondation FondaMental, Créteil, F-94000, France
| | - Delphine Bacq-Daian
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, F-91057, France
| | - Anne Boland-Auge
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, F-91057, France
| | - Violaine Latapie
- Univ Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, F-94000, France.,Fondation FondaMental, Créteil, F-94000, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), Evry, F-91057, France
| | - Ravi Philip RajKumar
- Department of Pharmacology, Jawaharlal Institute of Postgraduate Medical Education & Research, Puducherry, 605006, India
| | - Deepak Gopal Shewade
- Department of Psychiatry, Jawaharlal Institute of Postgraduate Medical Education & Research, Puducherry, 605006, India.,Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, F-91000, France
| | - Frank Bélivier
- Fondation FondaMental, Créteil, F-94000, France.,Université de Paris, INSERM UMRS 1144, Optimisation Thérapeutique en Neuropsychopharmacologie (OTeN), Paris, F-75006, France.,Hôpitaux Lariboisière-Fernand Widal, GHU APHP Nord, Département de Psychiatrie et de Médecine Addicto-logique, Paris, F-75010, France
| | - Cynthia Marie-Claire
- Université de Paris, INSERM UMRS 1144, Optimisation Thérapeutique en Neuropsychopharmacologie (OTeN), Paris, F-75006, France
| | - Stéphane Jamain
- Univ Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, F-94000, France.,Fondation FondaMental, Créteil, F-94000, France
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Bharadhwaj VS, Mubeen S, Sargsyan A, Jose GM, Geissler S, Hofmann-Apitius M, Domingo-Fernández D, Kodamullil AT. Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities. Prog Neuropsychopharmacol Biol Psychiatry 2023; 122:110688. [PMID: 36462601 DOI: 10.1016/j.pnpbp.2022.110688] [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: 04/11/2022] [Revised: 11/04/2022] [Accepted: 11/27/2022] [Indexed: 12/04/2022]
Abstract
Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.
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Affiliation(s)
- Vinay Srinivas Bharadhwaj
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany.
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany; Fraunhofer Center for Machine Learning, Germany
| | - Astghik Sargsyan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany
| | - Geena Mariya Jose
- Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala 683503, India
| | | | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Fraunhofer Center for Machine Learning, Germany; Enveda Biosciences, Boulder, CO, 80301, USA
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany; Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala 683503, India
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8
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The Role of D-Serine and D-Aspartate in the Pathogenesis and Therapy of Treatment-Resistant Schizophrenia. Nutrients 2022; 14:nu14235142. [PMID: 36501171 PMCID: PMC9736950 DOI: 10.3390/nu14235142] [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: 10/19/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Schizophrenia (Sch) is a severe and widespread mental disorder. Antipsychotics (APs) of the first and new generations as the first-line treatment of Sch are not effective in about a third of cases and are also unable to treat negative symptoms and cognitive deficits of schizophrenics. This explains the search for new therapeutic strategies for a disease-modifying therapy for treatment-resistant Sch (TRS). Biological compounds are of great interest to researchers and clinicians, among which D-Serine (D-Ser) and D-Aspartate (D-Asp) are among the promising ones. The Sch glutamate theory suggests that neurotransmission dysfunction caused by glutamate N-methyl-D-aspartate receptors (NMDARs) may represent a primary deficiency in this mental disorder and play an important role in the development of TRS. D-Ser and D-Asp are direct NMDAR agonists and may be involved in modulating the functional activity of dopaminergic neurons. This narrative review demonstrates both the biological role of D-Ser and D-Asp in the normal functioning of the central nervous system (CNS) and in the pathogenesis of Sch and TRS. Particular attention is paid to D-Ser and D-Asp as promising components of a nutritive disease-modifying therapy for TRS.
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Ferguson LB, Mayfield RD, Messing RO. RNA biomarkers for alcohol use disorder. Front Mol Neurosci 2022; 15:1032362. [PMID: 36407766 PMCID: PMC9673015 DOI: 10.3389/fnmol.2022.1032362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.
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Affiliation(s)
- Laura B. Ferguson
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States,Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, United States,Department of Neuroscience, University of Texas at Austin, Austin, TX, United States,*Correspondence: Laura B. Ferguson,
| | - R. Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States,Department of Neuroscience, University of Texas at Austin, Austin, TX, United States
| | - Robert O. Messing
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States,Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, United States,Department of Neuroscience, University of Texas at Austin, Austin, TX, United States
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Supino D, Minute L, Mariancini A, Riva F, Magrini E, Garlanda C. Negative Regulation of the IL-1 System by IL-1R2 and IL-1R8: Relevance in Pathophysiology and Disease. Front Immunol 2022; 13:804641. [PMID: 35211118 PMCID: PMC8861086 DOI: 10.3389/fimmu.2022.804641] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/03/2022] [Indexed: 12/11/2022] Open
Abstract
Interleukin-1 (IL-1) is a primary cytokine of innate immunity and inflammation. IL-1 belongs to a complex family including ligands with agonist activity, receptor antagonists, and an anti-inflammatory cytokine. The receptors for these ligands, the IL-1 Receptor (IL-1R) family, include signaling receptor complexes, decoy receptors, and negative regulators. Agonists and regulatory molecules co-evolved, suggesting the evolutionary relevance of a tight control of inflammatory responses, which ensures a balance between amplification of innate immunity and uncontrolled inflammation. IL-1 family members interact with innate immunity cells promoting innate immunity, as well as with innate and adaptive lymphoid cells, contributing to their differentiation and functional polarization and plasticity. Here we will review the properties of two key regulatory receptors of the IL-1 system, IL-1R2, the first decoy receptor identified, and IL-1R8, a pleiotropic regulator of different IL-1 family members and co-receptor for IL-37, the anti-inflammatory member of the IL-1 family. Their complex impact in pathology, ranging from infections and inflammatory responses, to cancer and neurologic disorders, as well as clinical implications and potential therapeutic exploitation will be presented.
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Affiliation(s)
- Domenico Supino
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Luna Minute
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy.,Department of Biomedical Science, Humanitas University, Pieve Emanuele, Italy
| | - Andrea Mariancini
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy.,Department of Biomedical Science, Humanitas University, Pieve Emanuele, Italy
| | - Federica Riva
- Department of Veterinary Medicine, University of Milan, Milan, Italy
| | - Elena Magrini
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Cecilia Garlanda
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, Italy.,Department of Biomedical Science, Humanitas University, Pieve Emanuele, Italy
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The 5-HT6R agonist E-6837 and the antagonist SB-271046 reverse the psychotic-like behaviors induced by ketamine. Behav Pharmacol 2022; 33:249-254. [DOI: 10.1097/fbp.0000000000000669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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