Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context
Summary: Background: Major depressive disorder (MDD) is a leading cause of disability, with a twofold increase in prevalence in women compared to men. Over the last few years, identifying molecular biomarkers of MDD has proven challenging, reflecting interactions among multiple environmental and ge...
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Elsevier
2025-03-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396425000131 |
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| author | Amazigh Mokhtari El Chérif Ibrahim Arnaud Gloaguen Claire-Cécile Barrot David Cohen Margot Derouin Hortense Vachon Guillaume Charbonnier Béatrice Loriod Charles Decraene Ipek Yalcin Cynthia Marie-Claire Bruno Etain Raoul Belzeaux Andrée Delahaye-Duriez Pierre-Eric Lutz |
| author_facet | Amazigh Mokhtari El Chérif Ibrahim Arnaud Gloaguen Claire-Cécile Barrot David Cohen Margot Derouin Hortense Vachon Guillaume Charbonnier Béatrice Loriod Charles Decraene Ipek Yalcin Cynthia Marie-Claire Bruno Etain Raoul Belzeaux Andrée Delahaye-Duriez Pierre-Eric Lutz |
| author_sort | Amazigh Mokhtari |
| collection | DOAJ |
| description | Summary: Background: Major depressive disorder (MDD) is a leading cause of disability, with a twofold increase in prevalence in women compared to men. Over the last few years, identifying molecular biomarkers of MDD has proven challenging, reflecting interactions among multiple environmental and genetic factors. Recently, epigenetic processes have been proposed as mediators of such interactions, with the potential for biomarker development. Methods: We characterised gene expression and two mechanisms of epigenomic regulation, DNA methylation (DNAm) and microRNAs (miRNAs), in blood samples from a cohort of individuals with MDD and healthy controls (n = 169). Case-control comparisons were conducted for each omic layer. We also defined gene coexpression networks, followed by step-by-step annotations across omic layers. Third, we implemented an advanced multiomic integration strategy, with covariate correction and feature selection embedded in a cross-validation procedure. Performance of MDD prediction was systematically compared across 6 methods for dimensionality reduction, and for every combination of 1, 2 or 3 types of molecular data. Feature stability was further assessed by bootstrapping. Findings: Results showed that molecular and coexpression changes associated with MDD were highly sex-specific and that the performance of MDD prediction was greater when the female and male cohorts were analysed separately, rather than combined. Importantly, they also demonstrated that performance progressively increased with the number of molecular datasets considered. Interpretation: Informational gain from multiomic integration had already been documented in other medical fields. Our results pave the way toward similar advances in molecular psychiatry, and have practical implications for developing clinically useful MDD biomarkers. Funding: This work was supported by the Centre National de la Recherche Scientifique (contract UPR3212), the University of Strasbourg, the Université Sorbonne Paris Nord, the Université Paris Cité, the Fondation de France (FdF N° Engt:00081244 and 00148126; ECI, IY, RB, PEL), the French National Research Agency (ANR-18-CE37-0002, BE, CMC, ADD, PEL, ECI; ANR-18-CE17-0009, ADD; ANR-19-CE37-0010, PEL; ANR-21-RHUS-009, ADD, BE, CMC, CCB; ANR-22-PESN-0013, ADD), the Fondation pour la Recherche sur le Cerveau (FRC 2019, PEL), Fondation de France (2018, BE, CMC, ADD) and American Foundation for Suicide Prevention (AFSP YIG-1-102-19; PEL). |
| format | Article |
| id | doaj-art-51042a4b21eb4ba4bda6efc5e488590f |
| institution | Kabale University |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | EBioMedicine |
| spelling | doaj-art-51042a4b21eb4ba4bda6efc5e488590f2025-02-07T04:47:46ZengElsevierEBioMedicine2352-39642025-03-01113105569Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in contextAmazigh Mokhtari0El Chérif Ibrahim1Arnaud Gloaguen2Claire-Cécile Barrot3David Cohen4Margot Derouin5Hortense Vachon6Guillaume Charbonnier7Béatrice Loriod8Charles Decraene9Ipek Yalcin10Cynthia Marie-Claire11Bruno Etain12Raoul Belzeaux13Andrée Delahaye-Duriez14Pierre-Eric Lutz15Université Paris Cité, Inserm, NeuroDiderot, UMR-1141, 75019, Paris, FranceAix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, 13005, Marseille, FranceCentre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, 91000, Evry, FranceUniversité Paris Cité, Inserm, NeuroDiderot, UMR-1141, 75019, Paris, FranceUniversité Paris Cité, Inserm, NeuroDiderot, UMR-1141, 75019, Paris, FranceUniversité Paris Cité, Inserm, NeuroDiderot, UMR-1141, 75019, Paris, FranceAix-Marseille Université, INSERM, TAGC, 13009, Marseille, FranceAix-Marseille Université, INSERM, TAGC, 13009, Marseille, FranceAix-Marseille Université, INSERM, TAGC, 13009, Marseille, FranceCentre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, F-67000, Strasbourg, FranceCentre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, F-67000, Strasbourg, France; Department of Psychiatry and Neuroscience, Université Laval, Québec, QC, G1V 0A6, CanadaUniversité Paris Cité, INSERM UMR-S 1144, Optimisation thérapeutique en neuropsychopharmacologie, OTeN, F-75006, Paris, FranceUniversité Paris Cité, INSERM UMR-S 1144, Optimisation thérapeutique en neuropsychopharmacologie, OTeN, F-75006, Paris, France; Assistance Publique des Hôpitaux de Paris, GHU Lariboisière-Saint Louis-Fernand Widal, DMU Neurosciences, Département de psychiatrie et de Médecine Addictologique, F-75010, Paris, FranceAix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, 13005, Marseille, France; Département de psychiatrie, CHU de Montpellier, Montpellier, FranceUniversité Paris Cité, Inserm, NeuroDiderot, UMR-1141, 75019, Paris, France; Unité fonctionnelle de médecine génomique et génétique clinique, Hôpital Jean Verdier, Assistance Publique des Hôpitaux de Paris, F-93140, Bondy, France; Université Sorbonne Paris Nord, F-93000, Bobigny, France; Corresponding author. Neurodiderot, Inserm U1141, Hôpital Robert Debré, 48 boulevard Sérurier, 75019, Paris, France.Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR 3212, F-67000, Strasbourg, France; Douglas Mental Health University Institute, McGill University, QC, H4H 1R3, Montréal, Canada; Corresponding author. INCI UPR 3212, 8 allée du général Rouvillois, 67000, Strasbourg, France.Summary: Background: Major depressive disorder (MDD) is a leading cause of disability, with a twofold increase in prevalence in women compared to men. Over the last few years, identifying molecular biomarkers of MDD has proven challenging, reflecting interactions among multiple environmental and genetic factors. Recently, epigenetic processes have been proposed as mediators of such interactions, with the potential for biomarker development. Methods: We characterised gene expression and two mechanisms of epigenomic regulation, DNA methylation (DNAm) and microRNAs (miRNAs), in blood samples from a cohort of individuals with MDD and healthy controls (n = 169). Case-control comparisons were conducted for each omic layer. We also defined gene coexpression networks, followed by step-by-step annotations across omic layers. Third, we implemented an advanced multiomic integration strategy, with covariate correction and feature selection embedded in a cross-validation procedure. Performance of MDD prediction was systematically compared across 6 methods for dimensionality reduction, and for every combination of 1, 2 or 3 types of molecular data. Feature stability was further assessed by bootstrapping. Findings: Results showed that molecular and coexpression changes associated with MDD were highly sex-specific and that the performance of MDD prediction was greater when the female and male cohorts were analysed separately, rather than combined. Importantly, they also demonstrated that performance progressively increased with the number of molecular datasets considered. Interpretation: Informational gain from multiomic integration had already been documented in other medical fields. Our results pave the way toward similar advances in molecular psychiatry, and have practical implications for developing clinically useful MDD biomarkers. Funding: This work was supported by the Centre National de la Recherche Scientifique (contract UPR3212), the University of Strasbourg, the Université Sorbonne Paris Nord, the Université Paris Cité, the Fondation de France (FdF N° Engt:00081244 and 00148126; ECI, IY, RB, PEL), the French National Research Agency (ANR-18-CE37-0002, BE, CMC, ADD, PEL, ECI; ANR-18-CE17-0009, ADD; ANR-19-CE37-0010, PEL; ANR-21-RHUS-009, ADD, BE, CMC, CCB; ANR-22-PESN-0013, ADD), the Fondation pour la Recherche sur le Cerveau (FRC 2019, PEL), Fondation de France (2018, BE, CMC, ADD) and American Foundation for Suicide Prevention (AFSP YIG-1-102-19; PEL).http://www.sciencedirect.com/science/article/pii/S2352396425000131DepressionTranscriptomicmicroRNADNA methylationSex differencesMultiomic integration |
| spellingShingle | Amazigh Mokhtari El Chérif Ibrahim Arnaud Gloaguen Claire-Cécile Barrot David Cohen Margot Derouin Hortense Vachon Guillaume Charbonnier Béatrice Loriod Charles Decraene Ipek Yalcin Cynthia Marie-Claire Bruno Etain Raoul Belzeaux Andrée Delahaye-Duriez Pierre-Eric Lutz Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context EBioMedicine Depression Transcriptomic microRNA DNA methylation Sex differences Multiomic integration |
| title | Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context |
| title_full | Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context |
| title_fullStr | Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context |
| title_full_unstemmed | Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context |
| title_short | Using multiomic integration to improve blood biomarkers of major depressive disorder: a case-control studyResearch in context |
| title_sort | using multiomic integration to improve blood biomarkers of major depressive disorder a case control studyresearch in context |
| topic | Depression Transcriptomic microRNA DNA methylation Sex differences Multiomic integration |
| url | http://www.sciencedirect.com/science/article/pii/S2352396425000131 |
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