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β-catenin mediates stress resilience through Dicer1/microRNA regulation

Nature volume 516, pages 51–55 (2014)Cite this article

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Abstract

β-catenin is a multi-functional protein that has an important role in the mature central nervous system; its dysfunction has been implicated in several neuropsychiatric disorders, including depression. Here we show that in mice β-catenin mediates pro-resilient and anxiolytic effects in the nucleus accumbens, a key brain reward region, an effect mediated by D2-type medium spiny neurons. Using genome-wide β-catenin enrichment mapping, we identify Dicer1—important in small RNA (for example, microRNA) biogenesis—as a β-catenin target gene that mediates resilience. Small RNA profiling after excising β-catenin from nucleus accumbens in the context of chronic stress reveals β-catenin-dependent microRNA regulation associated with resilience. Together, these findings establish β-catenin as a critical regulator in the development of behavioural resilience, activating a network that includes Dicer1 and downstream microRNAs. We thus present a foundation for the development of novel therapeutic targets to promote stress resilience.

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Figure 1: β-catenin in NAc mediates pro-resilient, antidepressant, and anxiolytic responses.
Figure 2: Regulation of β-catenin signalling in human depression and mouse CSDS.
Figure 3: β-catenin ChIP-seq in NAc 48 h post CSDS.
Figure 4: Dicer1 bridges β-catenin and miRNA regulation in CSDS.

Accession codes

Primary accessions

Gene Expression Omnibus

Data deposits

All sequencing data have been deposited into the Gene Expression Omnibus with accession numbers GSE61294 and GSE61295.

References

  1. Madsen, T. M., Newton, S. S., Eaton, M. E., Russell, D. S. & Duman, R. S. Chronic electroconvulsive seizure up-regulates β-catenin expression in rat hippocampus: role in adult neurogenesis. Biol. Psychiatry 54, 1006–1014 (2003)

    CAS  PubMed  Google Scholar 

  2. Beaulieu, J.-M. et al. Lithium antagonizes dopamine-dependent behaviors mediated by an AKT/glycogen synthase kinase 3 signaling cascade. Proc. Natl Acad. Sci. USA 101, 5099–5104 (2004)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gould, T. D. et al. Beta-catenin overexpression in the mouse brain phenocopies lithium-sensitive behaviors. Neuropsychopharmacology 32, 2173–2183 (2007)

    CAS  PubMed  Google Scholar 

  4. Li, X. & Jope, R. S. Is glycogen synthase kinase-3 a central modulator in mood regulation? Neuropsychopharmacology 35, 2143–2154 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Brennand, K. J. et al. Modelling schizophrenia using human induced pluripotent stem cells. Nature 473, 221–225 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Behrens, J., von Kries, J., Kühl, M. & Bruhn, L. Functional interaction of β-catenin with the transcription factor LEF-1. Nature 382, 638–642 (1996)

    ADS  CAS  PubMed  Google Scholar 

  7. Molenaar, M. et al. XTcf-3 transcription factor mediates β-catenin-induced axis formation in Xenopus embryos. Cell 86, 391–399 (1996)

    CAS  PubMed  Google Scholar 

  8. van de Wetering, M. et al. Armadillo coactivates transcription driven by the product of the Drosophila segment polarity gene dTCF. Cell 88, 789–799 (1997)

    CAS  PubMed  Google Scholar 

  9. Wilkinson, M. B. et al. A novel role of the WNT-dishevelled-GSK3β signaling cascade in the mouse nucleus accumbens in a social defeat model of depression. J. Neurosci. 31, 9084–9092 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Sadot, E. et al. Regulation of S33/S37 phosphorylated β-catenin in normal and transformed cells. J. Cell Sci. 115, 2771–2780 (2002)

    CAS  PubMed  Google Scholar 

  11. Berton, O. et al. Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 311, 864–868 (2006)

    ADS  CAS  PubMed  Google Scholar 

  12. Krishnan, V. et al. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 131, 391–404 (2007)

    CAS  PubMed  Google Scholar 

  13. Kolligs, F. T., Hu, G., Dang, C. V. & Fearon, E. R. Neoplastic transformation of RK3E by mutant β-catenin requires deregulation of Tcf/Lef transcription but not activation of c-myc expression. Mol. Cell. Biol. 19, 5696–5706 (1999)

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang, Z. et al. β-catenin promotes survival of renal epithelial cells by inhibiting Bax. J. Am. Soc. Nephrol. 20, 1919–1928 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Rada, P. et al. Glutamate release in the nucleus accumbens is involved in behavioral depression during the Porsolt swim test. Neuroscience 119, 557–565 (2003)

    CAS  PubMed  Google Scholar 

  16. Abe, K. & Takeichi, M. NMDA-receptor activation induces calpain-mediated β-catenin cleavages for triggering gene expression. Neuron 53, 387–397 (2007)

    CAS  PubMed  Google Scholar 

  17. Tye, K. M. et al. Amygdala circuitry mediating reversible and bidirectional control of anxiety. Nature 471, 358–362 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Britt, J. P. et al. Synaptic and behavioral profile of multiple glutamatergic inputs to the nucleus accumbens. Neuron 76, 790–803 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Feng, J. et al. Chronic cocaine-regulated epigenomic changes in mouse nucleus accumbens. Genome Biol. 15, R65 (2014)

    PubMed  PubMed Central  Google Scholar 

  20. Shen, L. et al. diffReps: detecting differential chromatin modification sites from ChIP-seq data with biological replicates. PLoS ONE 8, e65598 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hödar, C. et al. Genome-wide identification of new Wnt/β-catenin target genes in the human genome using CART method. BMC Genomics 11, 348 (2010)

    PubMed  PubMed Central  Google Scholar 

  22. Wexler, E. M. et al. Genome-wide analysis of a Wnt1-regulated transcriptional network implicates neurodegenerative pathways. Sci. Signal. 4, ra65 (2011)

    PubMed  Google Scholar 

  23. Bernstein, E., Caudy, A. A., Hammond, S. M. & Hannon, G. J. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature 409, 363–366 (2001)

    ADS  CAS  PubMed  Google Scholar 

  24. Cuellar, T. L. et al. Dicer loss in striatal neurons produces behavioral and neuroanatomical phenotypes in the absence of neurodegeneration. Proc. Natl Acad. Sci. USA 105, 5614–5619 (2008)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lee, E. J. et al. Identification of piRNAs in the central nervous system. RNA 17, 1090–1099 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Rajasethupathy, P. et al. A role for neuronal piRNAs in the epigenetic control of memory-related synaptic plasticity. Cell 149, 693–707 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Graybiel, A. M. The basal ganglia. Curr. Biol. 10, R509–R511 (2000)

    CAS  PubMed  Google Scholar 

  28. Gerfen, C. R. The neostriatal mosaic: multiple levels of compartmental organization in the basal ganglia. Annu. Rev. Neurosci. 15, 285–320 (1992)

    CAS  PubMed  Google Scholar 

  29. Kravitz, A. V., Tye, L. D. & Kreitzer, A. C. Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nature Neurosci. 15, 816–818 (2012)

    CAS  PubMed  Google Scholar 

  30. Lobo, M. K. & Nestler, E. J. The striatal balancing act in drug addiction: distinct roles of direct and indirect pathway medium spiny neurons. Front. Neuroanat. 5, 41 (2011)

    PubMed  PubMed Central  Google Scholar 

  31. Hikida, T., Kimura, K., Wada, N., Funabiki, K. & Nakanishi, S. Distinct roles of synaptic transmission in direct and indirect striatal pathways to reward and aversive behavior. Neuron 66, 896–907 (2010)

    CAS  PubMed  Google Scholar 

  32. Darvas, M. & Palmiter, R. Contributions of striatal dopamine signaling to the modulation of cognitive flexibility. Biol. Psychiatry 69, 704–707 (2011)

    CAS  PubMed  Google Scholar 

  33. Yawata, S., Yamaguchi, T., Danjo, T., Hikida, T. & Nakanishi, S. Pathway-specific control of reward learning and its flexibility via selective dopamine receptors in the nucleus accumbens. Proc. Natl Acad. Sci. USA 109, 12764–12769 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Southwick, S. M. & Charney, D. S. The science of resilience: implications for the prevention and treatment of depression. Science 338, 79–82 (2012)

    ADS  CAS  PubMed  Google Scholar 

  35. Veronese, A. et al. Mutated β-catenin evades a microRNA-dependent regulatory loop. Proc. Natl Acad. Sci. USA 108, 4840–4845 (2011)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kosik, K. S. The neuronal microRNA system. Nature Rev. Neurosci. 7, 911–920 (2006)

    CAS  Google Scholar 

  37. Im, H.-I. & Kenny, P. J. MicroRNAs in neuronal function and dysfunction. Trends Neurosci. 35, 325–334 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Issler, O. et al. MicroRNA 135 is essential for chronic stress resiliency, antidepressant efficacy, and intact serotonergic activity. Neuron 83, 344–360 (2014)

    CAS  PubMed  Google Scholar 

  39. Robison, A. J. et al. Fluoxetine epigenetically alters the CaMKIIα promoter in nucleus accumbens to regulate ΔFosB binding and antidepressant effects. Neuropsychopharmacology 39, 1178–1186 (2014)

    CAS  PubMed  Google Scholar 

  40. Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nature Protocols 3, 1101–1108 (2008)

    CAS  PubMed  Google Scholar 

  41. Lobo, M. K. et al. Cell type-specific loss of BDNF signaling mimics optogenetic control of cocaine reward. Science 330, 385–390 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  42. Koo, J. W. et al. BDNF is a negative modulator of morphine action. Science 338, 124–128 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chaudhury, D. et al. Rapid regulation of depression-related behaviours by control of midbrain dopamine neurons. Nature 493, 532–536 (2013)

    ADS  CAS  PubMed  Google Scholar 

  44. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014)

    PubMed  PubMed Central  Google Scholar 

  46. Hackenberg, M., Rodríguez-Ezpeleta, N. & Aransay, A. M. MiRanalyzer: An update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res. 39, (Suppl. 2)W132–W138 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Griffiths-Jones, S. miRBase: the microRNA sequence database. Methods Mol. Biol. 342, 129–138 (2006)

    CAS  PubMed  Google Scholar 

  48. Sai Lakshmi, S. & Agrawal, S. piRNABank: a web resource on classified and clustered Piwi-interacting RNAs. Nucleic Acids Res. 36, (Suppl. 1)W173–W177 (2008)

    Google Scholar 

  49. Kozomara, A. & Griffiths-Jones, S. MiRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014)

    CAS  PubMed  Google Scholar 

  50. Burge, S. W. et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41, D226–D232 (2013)

    CAS  PubMed  Google Scholar 

  51. Goodstadt, L. Ruffus: A lightweight python library for computational pipelines. Bioinformatics 26, 2778–2779 (2010)

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank O. Jabado and M. Mahajan for support and S. Borkan for providing β-catenin constructs. This work was supported by grants from the National Institute of Mental Health and the Hope for Depression Research Foundation (HDRF).

Author information

Author notes

  1. Michelle S. Mazei-Robison, Pamela Kennedy, Vincent Vialou & Deveroux Ferguson

    Present address: Present addresses: Department of Physiology, Michigan State University, East Lansing, Michigan 48824, USA (M.S.M.-R.); Department of Psychology, UCLA College of Life Sciences, Los Angeles, California 90095, USA (P.K.); Institut National de la Santé et de la Recherche Médicale (INSERM) U1130; CNRS UMR8246; UPMC UM18, Neuroscience Paris Seine, 75005 Paris, France (V.V.); Department of Basic Medical Sciences, The University of Arizona College of Medicine-Phoenix, Arizona 85004, USA (D.F.).,

  2. Caroline Dias and Jian Feng: These authors contributed equally to this work.

Authors and Affiliations

  1. Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, 10029, New York, USA

    Caroline Dias, Jian Feng, Haosheng Sun, Ning yi Shao, Michelle S. Mazei-Robison, Diane Damez-Werno, Kimberly Scobie, Rosemary Bagot, Benoit LaBonté, Efrain Ribeiro, XiaoChuan Liu, Pamela Kennedy, Vincent Vialou, Deveroux Ferguson, Catherine Peña, Erin S. Calipari, Ja Wook Koo, Ezekiell Mouzon, Li Shen & Eric J. Nestler

  2. Department of Psychiatry, University of Texas Southwestern, Dallas, 75390, Texas, USA

    Subroto Ghose & Carol Tamminga

  3. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Rachael Neve

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Contributions

C.D. and J.F. conceived the project, designed research, conducted experiments, interpreted the results, and wrote the manuscript; H.S., M.S.M.-R., D.D.-W., K.S., R.B., B.L., E.R., P.K., V.V., D.F., C.P., E.C., J.K. and E.M. conducted experiments; S.G., C.T. provided reagents and tools; R.N. conducted experiments and provided reagents; N.S., X.L. performed bioinformatic analysis; L.S. performed and supervised bioinformatic analysis; E.J.N. conceived the project, designed and supervised research, interpreted the results, and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Li Shen or Eric J. Nestler.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Validation of HSV-β-catenin.

a, β-catenin mRNA levels following HSV-β-catenin versus HSV-GFP injection into NAc (*P < 0.05, two-tailed t-test, n = 3 per group). b, Top panel, subcellular fractionation of NAc lysates from HSV-GFP or HSV-β-catenin injected mice. Middle panel, representative western blots of data shown in panel a. CYT, cytosolic fraction; NUC, nuclear fraction (−chromatin); CHR, chromatin fraction. Bottom panel, IHC of nuclear β-catenin 5 days post-injection with HSV-β-catenin versus HSV-GFP (***P < 0.001, two-tailed t-test, n = 3 per group). c, β-catenin IP on virus-injected NAc. IP results are representative of 5 replications. All other data shown are representative of at least two experiments. Data are presented as mean and s.e.m.

Extended Data Figure 2 Other β-catenin manipulations.

a, Schematic of Cre-dependent HSV-lox-stop (LS1L)-β-catenin cassette. b, Validation of β-catenin knockdown in the NAc of floxed β-catenin mice (***P < 0.001, two-tailed t-test, n = 4 GFP, n = 5 Cre). c, Failure of dominant negative β-catenin to rescue social interaction as compared to GFP after previous excision of β-catenin from NAc in floxed β-catenin mice undergoing defeat (P > 0.05, two-tailed t-test, n = 7 per group). Data are presented as mean and s.e.m. All data shown are representative of at least two experiments.

Extended Data Figure 3 No effect of β-catenin deletion on baseline behaviours.

a, Social interaction (SI) in control, non-stressed animals (P > 0.05, two-tailed t-test, n = 5 per group). b, Total distance travelled in arena (P > 0.05, two-tailed t-test, n = 5 per group). c, Average velocity (P > 0.05, two-tailed t-test, n = 5 per group). Data are presented as mean and s.e.m. All data shown are representative of at least two experiments.

Extended Data Figure 4 Regulation of β-catenin signalling in human depression and after CSDS in mice.

a, Axin2 expression is suppressed in both medicated and unmedicated depressed patients, both groups of which were clinically depressed at their time of death (P < 0.01 one-way ANOVA, post-hoc test P > 0.05 between depressed unmedicated and medicated groups, *P < 0.01 for either depressed group versus control, n = 6 control, n = 5 unmedicated depressed, medicated depressed). b, Phospho-Ser 675 β-catenin and total β-catenin levels from mouse control, susceptible, and resilient NAc 48 h post CSDS (phospho-Ser 675: *P < 0.05, one-way ANOVA, post-hoc test susceptible versus resilient, n = 5 for control, susceptible, n = 8 for resilient). Data are presented as mean and s.e.m. Human data are from one experiment. All other data shown are representative of two experiments.

Extended Data Figure 5 Repeated optogenetic burst stimulation of VTA cell bodies has no effect on canonical β-catenin signalling in NAc.

Experiment was performed as in Fig. 2 with the exception of the optic fibre, which was placed above VTA for cell body stimulation (P > 0.05, two-tailed t-test, n = 8 per group). Data are presented as mean and s.e.m. Data are from one experiment.

Extended Data Figure 6 Genome-wide enrichment of H3K4me3 and H4K16ac binding in NAc at TSSs.

NGS plot was used to visualize binding patterns.

Extended Data Figure 7 Genome-wide pattern of H3K4me3 binding to genic regions in NAc under control, susceptible (defeat), and resilient mice.

Note the lack of difference across the three conditions. Data are from one experiment.

Extended Data Figure 8 Genome-wide pattern of H4K16ac binding to genic regions in NAc under control, susceptible (defeat), and resilient mice.

Note the lack of difference across the three conditions. Shading represents standard error. Data are from one experiment.

Extended Data Figure 9 Ingenuity pathway analysis (IPA) identifies a network of genes that show upregulated β-catenin binding at promoter regions in the NAc of resilient versus susceptible mice.

Nodes represent differentially regulated genes, with green meaning up in resilient versus susceptible and red meaning down in resilient versus susceptible. The blue arrows indicate that the direction of regulation is consistent with IPA prediction of an upregulated β-catenin network in resilience; for example, a blue arrow means that a target gene that would be expected to be upregulated by β-catenin is in fact upregulated in this list. In contrast, yellow arrows indicate that the regulation observed is inconsistent with expectations, while grey arrows indicate lack of pre-existing data to formulate expectations of β-catenin action. Left panel shows mostly expected regulation of the β-catenin network (that is, upregulation) in resilience; right panel shows non-specific changes occurring in a randomly chosen signal transducer and activator of transcription-4 (STAT4) network.

Extended Data Figure 10 Validation of local Dicer1 knockdown.

Note significant knockdown of Dicer expression in NAc after intra-NAc delivery of viral-Cre to floxed Dicer mice (*P < 0.05, two-tailed t-test, n = 7 GFP, n = 6 Cre). Data are presented as mean and s.e.m. and are representative of two experiments.

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-9 and Supplementary Notes. (PDF 608 kb)

Supplementary Data 1

β-catenin ChIP-seq dataset: contains peak lists from each of three conditions and three pair-wise differential peak lists. (XLSX 14694 kb)

Supplementary Data 2

H3K4me3 and H4K16 ChIP-seq datasets: contains differential peak lists as compared to control. (XLSX 555 kb)

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Dias, C., Feng, J., Sun, H. et al. β-catenin mediates stress resilience through Dicer1/microRNA regulation. Nature 516, 51–55 (2014). https://doi.org/10.1038/nature13976

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