Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Two polygenic mouse models of major depressive disorders identify TMEM161B as a potential biomarker of disease in humans

Neuropsychopharmacology (2024)Cite this article

Subjects

Abstract

Treatments are only partially effective in major depressive disorders (MDD) but no biomarker exists to predict symptom improvement in patients. Animal models are essential tools in the development of antidepressant medications, but while recent genetic studies have demonstrated the polygenic contribution to MDD, current models are limited to either mimic the effect of a single gene or environmental factor. We developed in the past a model of depressive-like behaviors in mice (H/Rouen), using selective breeding based on behavioral reaction after an acute mild stress in the tail suspension test. Here, we propose a new mouse model of depression (H-TST) generated from a more complex genetic background and based on the same selection process. We first demonstrated that H/Rouen and H-TST mice had similar phenotypes and were more sensitive to glutamate-related antidepressant medications than selective serotonin reuptake inhibitors. We then conducted an exome sequencing on the two mouse models and showed that they had damaging variants in 174 identical genes, which have also been associated with MDD in humans. Among these genes, we showed a higher expression level of Tmem161b in brain and blood of our two mouse models. Changes in TMEM161B expression level was also observed in blood of MDD patients when compared with controls, and after 8-week treatment with duloxetine, mainly in good responders to treatment. Altogether, our results introduce H/Rouen and H-TST as the two first polygenic animal models of MDD and demonstrate their ability to identify biomarkers of the disease and to develop rapid and effective antidepressant medications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: H-TST mice display depressive-like behaviors.
Fig. 2: Antidepressant treatments increase the mobility of H-TST males and females in the tail suspension test.
Fig. 3: Gria1 expression level in the prefrontal cortex (PFC) and the hippocampus of the TST and Rouen lines.
Fig. 4: H-TST and H/Rouen are polygenic models of depression with genetic variants in genes associated with major depressive disorder in human.
Fig. 5: LRFN5 and TMEM161B expression level in mouse lines and humans.

Similar content being viewed by others

References

  1. Kessler RC, Bromet EJ. The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34:119–38.

    Article  PubMed  PubMed Central  Google Scholar 

  2. World Health Organization. Depression and other common mental disorders: Global Health Estimates. World Health Organization: Geneva; 2017.

  3. Slattery DA, Cryan JF. The ups and downs of modelling mood disorders in rodents. ILAR J. 2014;55:297–309.

    Article  CAS  PubMed  Google Scholar 

  4. Malhi GS, Mann JJ. Depression. Lancet. 2018;392:2299–312.

    Article  PubMed  Google Scholar 

  5. Polderman TJ, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:702–9.

    Article  CAS  PubMed  Google Scholar 

  6. Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Major Depressive Disorder Working Group of the Psychiatric Gwas Consortium, Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry. 2013;18:497–511.

    Article  Google Scholar 

  8. Stroud CB, Davila J, Moyer A. The relationship between stress and depression in first onsets versus recurrences: a meta-analytic review. J Abnorm Psychol. 2008;117:206–13.

    Article  PubMed  Google Scholar 

  9. Nestler EJ, Hyman SE. Animal models of neuropsychiatric disorders. Nat Neurosci. 2010;13:1161–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. El Yacoubi M, Bouali S, Popa D, Naudon L, Leroux-Nicollet I, Hamon M, et al. Behavioral, neurochemical, and electrophysiological characterization of a genetic mouse model of depression. Proc Natl Acad Sci USA. 2003;100:6227–32.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  11. El Yacoubi M, Rappeneau V, Champion E, Malleret G, Vaugeois JM. The H/Rouen mouse model displays depression-like and anxiety-like behaviors. Behav Brain Res. 2013;256:43–50.

    Article  PubMed  Google Scholar 

  12. El Yacoubi M, Popa D, Martin B, Zimmer L, Hamon M, et al. Genetic association between helpless trait and depression-related phenotypes: evidence from crossbreeding studies with H/Rouen and NH/Rouen mice. Int J Neuropsychopharmacol. 2012;15:363–74.

    Article  CAS  PubMed  Google Scholar 

  13. Popa D, El Yacoubi M, Vaugeois JM, Hamon M, Adrien J. Homeostatic regulation of sleep in a genetic model of depression in the mouse: effects of muscarinic and 5-HT1A receptor activation. Neuropsychopharmacology. 2006;31:1637–46.

    Article  CAS  PubMed  Google Scholar 

  14. Belzeaux R, Gorgievski V, Fiori LM, Lopez JP, Grenier J, Lin R, et al. GPR56/ADGRG1 is associated with response to antidepressant treatment. Nat Commun. 2020;11:1635.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  15. Leday GGR, Vertes PE, Richardson S, Greene JR, Regan T, Khan S, et al. Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder. Biol Psychiatry. 2018;83:70–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wing JK, Babor T, Brugha T, Burke J, Cooper JE, Giel R, et al. SCAN. Schedules for Clinical Assessment in Neuropsychiatry. Arch Gen Psychiatry. 1990;47:589–93.

    Article  CAS  PubMed  Google Scholar 

  17. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

    Article  PubMed  PubMed Central  Google Scholar 

  18. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92.

    Article  CAS  PubMed  Google Scholar 

  20. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25:402–8.

    Article  CAS  PubMed  Google Scholar 

  21. Svenningsson P, Chergui K, Rachleff I, Flajolet M, Zhang X, El Yacoubi M, et al. Alterations in 5-HT1B receptor function by p11 in depression-like states. Science. 2006;311:77–80.

    Article  CAS  PubMed  ADS  Google Scholar 

  22. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D13.

    Article  CAS  PubMed  Google Scholar 

  23. Hashimoto K. Brain-derived neurotrophic factor as a biomarker for mood disorders: an historical overview and future directions. Psychiatry Clin Neurosci. 2010;64:341–57.

    Article  CAS  PubMed  Google Scholar 

  24. Duman RS, Monteggia LM. A neurotrophic model for stress-related mood disorders. Biol Psychiatry. 2006;59:1116–27.

    Article  CAS  PubMed  Google Scholar 

  25. Palucha-Poniewiera A, Podkowa K, Rafalo-Ulinska A. The group II mGlu receptor antagonist LY341495 induces a rapid antidepressant-like effect and enhances the effect of ketamine in the chronic unpredictable mild stress model of depression in C57BL/6J mice. Prog Neuropsychopharmacol Biol Psychiatry. 2021;109:110239.

    Article  CAS  PubMed  Google Scholar 

  26. Koike H, Iijima M, Chaki S. Involvement of AMPA receptor in both the rapid and sustained antidepressant-like effects of ketamine in animal models of depression. Behav Brain Res. 2011;224:107–11.

    Article  CAS  PubMed  Google Scholar 

  27. Koike H, Iijima M, Chaki S. Involvement of the mammalian target of rapamycin signaling in the antidepressant-like effect of group II metabotropic glutamate receptor antagonists. Neuropharmacology. 2011;61:1419–23.

    Article  CAS  PubMed  Google Scholar 

  28. Bittar TP, Labonte B. Functional Contribution of the Medial Prefrontal Circuitry in Major Depressive Disorder and Stress-Induced Depressive-Like Behaviors. Front Behav Neurosci. 2021;15:699592.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hare BD, Duman RS. Prefrontal cortex circuits in depression and anxiety: contribution of discrete neuronal populations and target regions. Mol Psychiatry. 2020;25:2742–58.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hu S, Li XJ, Law S, Shen CY, Yao GQ, Zhang XQ, et al. Prefrontal cortex alterations in major depressive disorder, generalized anxiety disorder and their comorbidity during a verbal fluency task assessed by multi-channel near-infrared spectroscopy. Psychiatry Res. 2021;306:114229.

    Article  PubMed  Google Scholar 

  31. Niciu MJ, Ionescu DF, Mathews DC, Richards EM, Zarate CA Jr. Second messenger/signal transduction pathways in major mood disorders: moving from membrane to mechanism of action, part I: major depressive disorder. CNS Spectr. 2013;18:231–41.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Coyle JT, Duman RS. Finding the intracellular signaling pathways affected by mood disorder treatments. Neuron. 2003;38:157–60.

    Article  CAS  PubMed  Google Scholar 

  33. Li N, Lee B, Liu RJ, Banasr M, Dwyer JM, Iwata M, et al. mTOR-dependent synapse formation underlies the rapid antidepressant effects of NMDA antagonists. Science. 2010;329:959–64.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  34. Kadriu B, Musazzi L, Henter ID, Graves M, Popoli M, Zarate CA Jr. Glutamatergic Neurotransmission: Pathway to Developing Novel Rapid-Acting Antidepressant Treatments. Int J Neuropsychopharmacol. 2019;22:119–35.

    Article  CAS  PubMed  Google Scholar 

  35. Howard DM, Adams MJ, Shirali M, Clarke TK, Marioni RE, Davies G, et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat Commun. 2018;9:1470.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  36. Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016;48:1031–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lie E, Li Y, Kim R, Kim E. SALM/Lrfn Family Synaptic Adhesion Molecules. Front Mol Neurosci. 2018;11:105.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Liu H. Synaptic organizers: synaptic adhesion-like molecules (SALMs). Curr Opin Struct Biol. 2019;54:59–67.

    Article  CAS  PubMed  Google Scholar 

  40. Nam J, Mah W, Kim E. The SALM/Lrfn family of leucine-rich repeat-containing cell adhesion molecules. Semin Cell Dev Biol. 2011;22:492–8.

    Article  CAS  PubMed  Google Scholar 

  41. Wang PY, Seabold GK, Wenthold RJ. Synaptic adhesion-like molecules (SALMs) promote neurite outgrowth. Mol Cell Neurosci. 2008;39:83–94.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Mah W, Ko J, Nam J, Han K, Chung WS, Kim E. Selected SALM (synaptic adhesion-like molecule) family proteins regulate synapse formation. J Neurosci. 2010;30:5559–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Cappuccio G, Attanasio S, Alagia M, Mutarelli M, Borzone R, Karali M, et al. Microdeletion of pseudogene chr14.232.a affects LRFN5 expression in cells of a patient with autism spectrum disorder. Eur J Hum Genet. 2019;27:1475–80.

    Article  PubMed  PubMed Central  Google Scholar 

  44. de Bruijn DR, van Dijk AH, Pfundt R, Hoischen A, Merkx GF, Gradek GA, et al. Severe Progressive Autism Associated with Two de novo Changes: A 2.6-Mb 2q31.1 Deletion and a Balanced t(14;21)(q21.1;p11.2) Translocation with Long-Range Epigenetic Silencing of LRFN5 Expression. Mol Syndromol. 2010;1:46–57.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Xu B, Woodroffe A, Rodriguez-Murillo L, Roos JL, van Rensburg EJ, Abecasis GR, et al. Elucidating the genetic architecture of familial schizophrenia using rare copy number variant and linkage scans. Proc Natl Acad Sci USA. 2009;106:16746–51.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  46. Nho K, Ramanan VK, Horgusluoglu E, Kim S, Inlow MH, Risacher SL, et al. Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults. J Alzheimers Dis. 2015;45:1197–206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Johnston KJA, Adams MJ, Nicholl BI, Ward J, Strawbridge RJ, McIntosh AM, et al. Identification of novel common variants associated with chronic pain using conditional false discovery rate analysis with major depressive disorder and assessment of pleiotropic effects of LRFN5. Transl Psychiatry. 2019;9:310.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Xu K, Zheng P, Zhao S, Wang J, Feng J, Ren Y, et al. LRFN5 and OLFM4 as novel potential biomarkers for major depressive disorder: a pilot study. Transl Psychiatry. 2023;13:188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Akula SK, Marciano JH, Lim Y, Exposito-Alonso D, Hylton NK, Hwang GH, et al. TMEM161B regulates cerebral cortical gyration, Sonic Hedgehog signaling, and ciliary structure in the developing central nervous system. Proc Natl Acad Sci USA. 2023;120:e2209964120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Muench C, Schwandt M, Jung J, Cortes CR, Momenan R, Lohoff FW. The major depressive disorder GWAS-supported variant rs10514299 in TMEM161B-MEF2C predicts putamen activation during reward processing in alcohol dependence. Transl Psychiatry. 2018;8:131.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Wilkinson MB, Xiao G, Kumar A, LaPlant Q, Renthal W, Sikder D, et al. Imipramine treatment and resiliency exhibit similar chromatin regulation in the mouse nucleus accumbens in depression models. J Neurosci. 2009;29:7820–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported the Agence National de la Recherche (ANR-12-SAMA-0012-01, project DEPSOM) and by the Fondation de France under references 00081240 and 00091264. This work also received financial support from the Institut National pour la Santé et la Recherche Médicale (Inserm) and the Centre National pour la Recherche Scientifique (CNRS). The Jamain’s team is affiliated to the Paris School of Neuroscience (ENP).

Author information

Author notes

  1. Laure Bougarel

    Present address: NETRIS Pharma, Lyon, France

  2. These authors contributed equally: Jean-Marie Vaugeois, Stéphane Jamain.

Authors and Affiliations

  1. Univ Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, F-94010, Créteil, France

    Malika El Yacoubi, Claire Altersitz, Violaine Latapie, Elari Rizkallah, Laure Bougarel & Stéphane Jamain

  2. SLEEP Team, CNRS UMR5292; INSERM U1028; Lyon Neuroscience Research; Center, Lyon, F-69372, France

    Sébastien Arthaud

  3. University of Lyon 1, Lyon, France

    Sébastien Arthaud

  4. Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden

    Marcela Pereira & Per Svenningsson

  5. ProfileXpert, SFR Santé Lyon-Est, UCBL UMS 3453 CNRS, US7 INSERM, Lyon, France

    Anne Wierinckx & Joël Lachuer

  6. UMR 1253, iBrain, Université de Tours, CHRU de Tours, INSERM, Tours, France

    Wissam El-Hage

  7. Centre Expert Dépression Résistante, Fondation FondaMental, Tours, France

    Wissam El-Hage

  8. Pôle Universitaire de Psychiatrie, CHU de Montpellier, Montpellier, France

    Raoul Belzeaux

  9. Fondation FondaMental, Créteil, F-94000, France

    Raoul Belzeaux & Stéphane Jamain

  10. McGill Group for Suicide Studies, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada

    Gustavo Turecki

  11. Univ Rennes, Inserm, LTSI (Laboratoire de Traitement du Signal et de l’Image), UMR-1099, F-35000, Rennes, France

    Benoît Martin

  12. Univ Rouen Normandie, Université Caen Normandie, Normandie Univ, ABTE UR 4651, F-76000, Rouen, France

    Jean-Marie Vaugeois

Authors
  1. Malika El Yacoubi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Claire Altersitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Violaine Latapie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elari Rizkallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sébastien Arthaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laure Bougarel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marcela Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anne Wierinckx
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wissam El-Hage
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Raoul Belzeaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gustavo Turecki
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Per Svenningsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Benoît Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Joël Lachuer
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jean-Marie Vaugeois
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Stéphane Jamain
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MEY, JMV and SJ designed the study. MEY and JMV generated mouse lines by selective breeding with the help of BM. MEY and LB conducted mouse behavioral analyses and pharmacological studies, with the help of SA for sleep exploration. CA, VL, ER, AW and JL generated and analyzed real-time PCR. PS and MP performed and quantified in situ hybridization. AW and JL generated exome data and conducted genetic analyses with SJ. WEH and SJ conducted human genetic analyses. RB and GT generated transcriptome data after duloxetine treatment and analyzed them with SJ. SJ, MEY, JMV and JL wrote the article.

Corresponding author

Correspondence to Stéphane Jamain.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Yacoubi, M., Altersitz, C., Latapie, V. et al. Two polygenic mouse models of major depressive disorders identify TMEM161B as a potential biomarker of disease in humans. Neuropsychopharmacol. (2024). https://doi.org/10.1038/s41386-024-01811-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41386-024-01811-8

Search

Advanced search

Quick links