Stress resilience is promoted by a Zfp189-driven transcriptional network in prefrontal cortex


Understanding the transcriptional changes that are engaged in stress resilience may reveal novel antidepressant targets. Here we use gene co-expression analysis of RNA-sequencing data from brains of resilient mice to identify a gene network that is unique to resilience. Zfp189, which encodes a previously unstudied zinc finger protein, is the highest-ranked key driver gene in the network, and overexpression of Zfp189 in prefrontal cortical neurons preferentially activates this network and promotes behavioral resilience. The transcription factor CREB is a predicted upstream regulator of this network and binds to the Zfp189 promoter. To probe CREB–Zfp189 interactions, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB or G9a (a repressive histone methyltransferase) to the Zfp189 promoter in prefrontal cortex neurons. Induction of Zfp189 with site-specific CREB is pro-resilient, whereas suppressing Zfp189 expression with G9a increases susceptibility. These findings reveal an essential role for Zfp189 and CREB–Zfp189 interactions in mediating a central transcriptional network of resilience.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Identification of the resilient-specific pink module and its pro-resilient top key driver Zfp189.
Fig. 2: Antidepressant-like effects of Zfp189 associate with pink module expression changes.
Fig. 3: CREB is an upstream regulator of the pink module.
Fig. 4: CREB KO in the PFC increases susceptibility but is rescued by Zfp189 overexpression.
Fig. 5: CRISPR-mediated, locus-specific modulation of Zfp189 with CREB or G9a bidirectionally controls resilient behavior.
Fig. 6: CRISPR-mediated induction of CREB–Zfp189 interactions activates the pink module.

Data availability

The RNA-seq data reported in the paper are deposited in GEO with the accession number GSE118317. Other data that support the findings of this study are available from the corresponding author upon request.

Code availability

Scripts and code utilized in the analysis of study data are available from the corresponding author upon request.


  1. 1.

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

  2. 2.

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

  3. 3.

    Bagot, R. C. et al. Circuit-wide transcriptional profiling reveals brain region-specific gene networks regulating depression susceptibility. Neuron 90, 969–983 (2016).

  4. 4.

    Maschietto, M. et al. Co-expression network of neural-differentiation genes shows specific pattern in schizophrenia. BMC Med. Genom. 8, 23 (2015).

  5. 5.

    Yue, Z. et al. Repositioning drugs by targeting network modules: a Parkinson’s disease case study. BMC Bioinformatics 18, 532 (2017).

  6. 6.

    Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).

  7. 7.

    Parikshak, N. N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

  8. 8.

    Labonte, B. et al. Sex-specific transcriptional signatures in human depression. Nat. Med. 23, 1102–1111 (2017).

  9. 9.

    Malki, K. et al. Identification of genes and gene pathways associated with major depressive disorder by integrative brain analysis of rat and human prefrontal cortex transcriptomes. Transl. Psychiatry 5, e519 (2015).

  10. 10.

    Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7, S7 (2006).

  11. 11.

    Zhang, B. & Zhu, J. Identification of key causal regulators in gene networks. Proc. World Congr. Eng. II, 5–8 (2013).

  12. 12.

    Covington, H. E. 3rd et al. Antidepressant effect of optogenetic stimulation of the medial prefrontal cortex. J. Neurosci. 30, 16082–16090 (2010).

  13. 13.

    Odeberg, J. et al. Cloning and characterization of ZNF189, a novel human Kruppel-like zinc finger gene localized to chromosome 9q22-q31. Genomics 50, 213–221 (1998).

  14. 14.

    Najafabadi, H. S. et al. C2H2 zinc finger proteins greatly expand the human regulatory lexicon. Nat. Biotechnol. 33, 555–562 (2015).

  15. 15.

    Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  16. 16.

    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).

  17. 17.

    Bleckmann, S. C. et al. Activating transcription factor 1 and CREB are important for cell survival during early mouse development. Mol. Cell. Biol. 22, 1919–1925 (2002).

  18. 18.

    Shaywitz, A. J. & Greenberg, M. E. CREB: a stimulus-induced transcription factor activated by a diverse array of extracellular signals. Annu. Rev. Biochem. 68, 821–861 (1999).

  19. 19.

    Xiao, X. et al. The cAMP responsive element-binding (CREB)-1 gene increases risk of major psychiatric disorders. Mol. Psychiatry 23, 1957–1967 (2018).

  20. 20.

    Juhasz, G. et al. The CREB1–BDNF–NTRK2 pathway in depression: multiple gene–cognition–environment interactions. Biol. Psychiatry 69, 762–771 (2011).

  21. 21.

    Carlezon, W. A. J., Duman, R. S. & Nestler, E. J. The many faces of CREB. Trends Neurosci. 28, 436–445 (2005).

  22. 22.

    Covington, H. E. et al. A role for repressive histone methylation in cocaine-induced vulnerability to stress. Neuron 71, 656–670 (2011).

  23. 23.

    Wilkinson, M. B. et al. Imipramine treatment and resiliency exhibit similar chromatin regulation in the mouse nucleus accumbens in depression models. J. Neurosci. 29, 7820–7832 (2009).

  24. 24.

    Chen, A. C., Shirayama, Y., Shin, K. H., Neve, R. L. & Duman, R. S. Expression of the cAMP response element binding protein (CREB) in hippocampus produces an antidepressant effect. Biol. Psychiatry 49, 753–762 (2001).

  25. 25.

    Green, T. A. et al. Induction of activating transcription factors (ATFs) ATF2, ATF3, and ATF4 in the nucleus accumbens and their regulation of emotional behavior. J. Neurosci. 28, 2025–2032 (2008).

  26. 26.

    Hodes, G. E. et al. Sex differences in nucleus accumbens transcriptome profiles associated with susceptibility versus resilience to subchronic variable stress. J. Neurosci. 35, 16362–16376 (2015).

  27. 27.

    LaPlant, Q. et al. Role of nuclear factor kappaB in ovarian hormone-mediated stress hypersensitivity in female mice. Biol. Psychiatry 65, 874–880 (2009).

  28. 28.

    Gray, A. L., Hyde, T. M., Deep-Soboslay, A., Kleinman, J. E. & Sodhi, M. S. Sex differences in glutamate receptor gene expression in major depression and suicide. Mol. Psychiatry 20, 1139 (2015).

  29. 29.

    Lorsch, Z. S. et al. Estrogen receptor α drives pro-resilient transcription in mouse models of depression. Nat. Commun. 9, 1116 (2018).

  30. 30.

    Vialou, V. et al. DeltaFosB in brain reward circuits mediates resilience to stress and antidepressant responses. Nat. Neurosci. 13, 745–752 (2010).

  31. 31.

    Dias, C. et al. β-catenin mediates stress resilience through Dicer1/microRNA regulation. Nature 516, 51–55 (2014).

  32. 32.

    Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

  33. 33.

    Menard, C. et al. Social stress induces neurovascular pathology promoting depression. Nat. Neurosci. 20, 1752–1760 (2017).

  34. 34.

    Heller, E. A. et al. Locus-specific epigenetic remodeling controls addiction- and depression-related behaviors. Nat. Neurosci. 17, 1720–1727 (2014).

  35. 35.

    Hamilton, P. J. et al. Cell-type-specific epigenetic editing at the fosb gene controls susceptibility to social defeat stress. Neuropsychopharmacology 43, 272–284 (2018).

  36. 36.

    Heller, E. A. et al. Targeted epigenetic remodeling of the Cdk5 gene in nucleus accumbens regulates cocaine- and stress-evoked behavior. J. Neurosci. 36, 4690–4697 (2016).

  37. 37.

    Liu, X. S. et al. Rescue of fragile X syndrome neurons by DNA methylation editing of the FMR1 gene. Cell 172, 979–992.e6 (2018).

  38. 38.

    Liu, X. S. et al. Editing DNA methylation in the mammalian genome. Cell 167, 233–247.e17 (2016).

  39. 39.

    Neve, R. L., Neve, K. A., Nestler, E. J. & Carlezon, W. A. J. Use of herpes virus amplicon vectors to study brain disorders. Biotechniques 39, 381–391 (2005).

  40. 40.

    Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

  41. 41.

    Hamilton, P. J., Lim, C. J., Nestler, E. J. & Heller, E. A. Viral expression of epigenome editing tools in rodent brain using stereotaxic surgery techniques. Methods Mol. Biol. 1767, 205–214 (2018).

  42. 42.

    Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

  43. 43.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

  44. 44.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  45. 45.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  46. 46.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  47. 47.

    Friedman, A. K. et al. Enhancing depression mechanisms in midbrain dopamine neurons achieves homeostatic resilience. Science 344, 313–319 (2014).

  48. 48.

    Wang, M., Zhao, Y. & Zhang, B. Efficient test and visualization of multi-set intersections. Sci. Rep. 5, 16923 (2015).

  49. 49.

    Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).

  50. 50.

    Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

Download references


This work was supported by National Institutes of Health grants F30MH110073 (Z.S.L.), T32GM007280 (Z.S.L.), T32MH096678 (Z.S.L.), K99DA045795 (P.J.H), U01AG046170 (B.Z.), R01AG057907 (B.Z.), RF1AG057440 (B.Z.), P50MH096890 and R01MH051399 (E.J.N.), and by the Hope for Depression Research Foundation.

Author information

Conceptualization: Z.S.L., P.J.H., R.C.B. and E.J.N. Methodology: Z.S.L., P.J.H., I.O.T., H.G.K., S.E.M., I.M. and E.J.N. Software: Z.S.L., A.R., A.M., X.Z., Y.-H.E.L., B.Z. and L.S. Formal analysis: Z.S.L., P.J.H., A.R., X.Z., Y.-H.E.L., B.Z. and L.S. Investigation: Z.S.L., P.J.H., E.M.P., M.S., W.J.W., A.E.L., P.M., O.I., L.F.P., S.T.P., B.L., A.C. and A.E.S. Resources: A.M., R.L.N. and G.T. Writing (original draft): Z.S.L. and P.J.H. Writing (reviewing and editing): Z.S.L., P.J.H., R.C.B. and E.J.N. Visualization: Z.S.L., P.J.H., M.S. and X.Z. Supervision: I.M., Y.D., B.Z., L.S., R.C.B. and E.J.N. Project administration and research funding: E.J.N.

Correspondence to Eric J. Nestler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Neuroscience thanks Cornelius Gross and the other, anonymous, reviewers for their contribution to the peer review of this work.

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

Integrated supplementary information

Supplementary Figure 1 Gene module biological validity and disease-relevance.

a) Resilient module enrichment for known and predicted protein-protein interactions (PPIs). Presence of color indicates statistical significance (FDR q < 0.05) with intensity of color scaled according to -log10(p-value). b-c) Module preservation analysis in RNA-seq data from post-mortem brain from (b) human controls and (c) MDD patients. Dotted line represents significance threshold (Bonferroni corrected p < 0.05). d) Resilient module preservation in human controls and MDD. Presence of color indicates statistical significance (Bonferroni p < 0.05) with intensity of color scaled according to -log10(p-value). e) Change in resilient module preservation between human controls and MDD. Modules showing more preservation in controls (difference between control and MDD -log 10 p-value > 1) are shown in blue. Modules showing more preservation in MDD (difference between control and MDD -log 10 p-value < -1) are shown in red. Modules not significantly (Bonferroni corrected p < 0.05) preserved or preserved similarly (difference in |-log 10 (p-value)| < 1) are not assigned a color.

Supplementary Figure 2 Pink module resilient characteristics and Zfp189 behavior.

a) Pink module genes overlaid with genes upregulated (yellow) and downregulated (blue) in the differentially expressed gene (DEG) comparison resilient vs. control in PFC (p < 0.05, FC > 1.3). b) Connections between pink module key driver genes. c) Correspondence between differential expression for the top 10 key driver genes and DEG enrichment for the pink module across phenotypes and brain areas. Presence of color indicates statistical significance (p q < 0.05) with intensity of color scaled according to -log10(p-value).

Supplementary Figure 3 Neuronal enrichment of Zfp189 and HSV infection.

a) RNAscope highlighting robust Zfp189 mRNA (red) expression in mouse PFC neurons (green, NeuN mRNA). Scale bar is 5 μm. Roughly 80% of Zfp189+ cells are NeuN+. Repeated with similar results in five animals. B) IHC showing colocalization of HSV transgene expression (GFP) and neurons (NeuN) in the mouse PFC. 20x magnification; Scale bar is 50 μm. Repeated with similar results in four animals.

Supplementary Figure 4 HSV-Zfp189 expression and behavior.

a) Injection of HSV-Zfp189 in PFC increases Zfp189 mRNA relative to HSV-GFP (t=2.718, p = 0.024, n = 10). b) Exposure to CSDS reduces OFT exploration in HSV-GFP mice (χ2(3) = 10.903, p = 0.012, n = 8-14, Mann Whitney post hoc p = 0.031), but not HSV-Zfp189 mice. However, unstressed mice show baseline differences (n = 8,11,10,14). c) Defeat reduces locomotion regardless of whether Zfp189 is overexpressed (F1,41 = 30.26, p < 0.001, n = 9,12,10,14). d) Null effects of Zfp189 overexpression in FST (χ2(3) = 0.564, p = 0.905, n = 9,11,10,14). e-g) Null effects in (h) OFT (t = 0.049, p = 0.962), (i) locomotor behavior (t = 0.077, p = 0.450), and (j) FST (t = 0.142, p = 0.888) for previously susceptible mice injected with Zfp189 in PFC (n = 12,13). *p < 0.05, **p < 0.01, ***p < 0.001. Bar graphs show mean ± SEM.

Supplementary Figure 5 Zfp189 overexpression upregulates the pink module in the absence of stress.

a) DEGs (p < 0.05, FC > log2|0.2|) in PFC in Zfp189 overexpressing unstressed controls. b) Module-wide enrichment for HSV-Zfp189 overexpression in PFC in unstressed mice. c-d) Gene Ontology Biological Process (GOBP) pathways affected Zfp189 overexpression in PFC in (c) previously susceptible mice and (d) unstressed controls (DEG threshold = p < 0.05, FC > log2|0.2|). *FDR q < 0.05.

Supplementary Figure 6 Colocalization of AAV and HSV infection.

Cells infected by an HSV vector (expressing mCherry) overlap 100% with cells infected by a previously injected AAV vector (expressing GFP) in the mouse PFC. Scale bar is 50 μm. Repeated with similar results in three animals.

Supplementary Figure 7 CREB-Zfp189 effects in splash test and FST in females.

a) Effect of CREB knockout (KO) and Zfp189 overexpression in splash test (interaction F1,40 = 0.067, p = 0.797, n = 10,11,11,12). b) Effect of CREB KO and Zfp189 overexpression in FST (interaction F1,35 = 0.121, p = 0.730, n = 10,11,10,8).

Supplementary Figure 8 CRISPR-mediated overexpression of Zfp189 in N2A cells.

a) Targeting dCas9 with no functional domain to the Zfp189 promoter does not affect Zfp189 expression (U = 4.0, p = 0.343, n = 4). b) Targeting dCas9 fused to phosphomimetic CREBS133D increases Zfp189 (U = 1.0, p = 0.009, n = 5,6). c) Localizing dCas9 fused to a phospho-null CREBS133A does not affect Zfp189 expression (t=0.9868, p = 0.362, n = 4).

Supplementary Figure 9 Transcriptional effects of CRISPR CREB-Zfp189 interactions in PFC.

a-b) GOBP pathways affected by dCas9-CREBS133D combined with Zfp189-sgRNA in PFC in (a) mice exposed to social defeat and (b) unstressed controls. (DEG threshold = p < 0.05, FC > log2|0.2|, Asterisk = FDR q < 0.05) c) Overlap for PFC DEGs (p < 0.05, log2FC > |0.2|) resulting from Zfp189-targeted dCas9-CREBS133D compared to NT-dCas9-CREBS133D in unstressed controls. d) Pink module genes differentially expressed in unstressed controls. e) On and off-target CRISPR gene regulation in RNA-seq datasets. Expression of the targeted Zfp189 gene is significantly up-regulated (p < 0.05, FC > log2|0.2|) in both defeated and un-stressed mice. The other 49 genes analyzed were those in closest proximity to other genomic regions with closest homology to the Zfp189-sgRNA used. No genomic region contains fewer than 3 mismatches with this sgRNA. Only one of these predicted off-target sites is affected by dCas9-CREBS133D plus Zfp189-sgRNA, but this regulation was only seen in defeated mice, suggesting that this is not an off-target effect of the CRISPR tools, but rather an effect of the defeat per se.

Supplementary information

Supplementary Figures 1–9 and Supplementary Tables 4–6.

Reporting Summary

Supplementary Table 1

Pink module genes and key drivers.

Supplementary Table 2

Human cohort demographics.

Supplementary Table 3

Upstream motifs of module genes.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading