Multi-omics analysis identifies mitochondrial pathways associated with anxiety-related behavior

English version

Autoři: Zuzanna Misiewicz ^aff001; Stella Iurato ^aff002; Natalia Kulesskaya ^aff001; Laura Salminen ^aff001; Luis Rodrigues ^aff002; Giuseppina Maccarrone ^aff002; Jade Martins ^aff002; Darina Czamara ^aff002; Mikaela A. Laine ^aff001; Ewa Sokolowska ^aff001; Kalevi Trontti ^aff001; Christiane Rewerts ^aff002; Bozidar Novak ^aff002; Naama Volk ^aff004; Dong Ik Park ^aff002; Eija Jokitalo ^aff005; Lars Paulin ^aff006; Petri Auvinen ^aff006; Vootele Voikar ^aff007; Alon Chen ^aff004; Angelika Erhardt ^aff002; Christoph W. Turck ^aff002; Iiris Hovatta ^aff001
Působiště autorů: Molecular and Integrative Biosciences Research Program, University of Helsinki, Helsinki, Finland ^aff001; Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany ^aff002; Department of Psychology and Logopedics, Medicum, University of Helsinki, Helsinki, Finland ^aff003; Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel ^aff004; Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, Helsinki, Finland ^aff005; Institute of Biotechnology, University of Helsinki, Helsinki, Finland ^aff006; Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland ^aff007; Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany ^aff008
Vyšlo v časopise: Multi-omics analysis identifies mitochondrial pathways associated with anxiety-related behavior. PLoS Genet 15(9): e32767. doi:10.1371/journal.pgen.1008358
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008358

Souhrn

Stressful life events are major environmental risk factors for anxiety disorders, although not all individuals exposed to stress develop clinical anxiety. The molecular mechanisms underlying the influence of environmental effects on anxiety are largely unknown. To identify biological pathways mediating stress-related anxiety and resilience to it, we used the chronic social defeat stress (CSDS) paradigm in male mice of two inbred strains, C57BL/6NCrl (B6) and DBA/2NCrl (D2), that differ in their susceptibility to stress. Using a multi-omics approach, we identified differential mRNA, miRNA and protein expression changes in the bed nucleus of the stria terminalis (BNST) and blood cells after chronic stress. Integrative gene set enrichment analysis revealed enrichment of mitochondrial-related genes in the BNST and blood of stressed mice. To translate these results to human anxiety, we investigated blood gene expression changes associated with exposure-induced panic attacks. Remarkably, we found reduced expression of mitochondrial-related genes in D2 stress-susceptible mice and in exposure-induced panic attacks in humans, but increased expression of these genes in B6 stress-susceptible mice. Moreover, stress-susceptible vs. stress-resilient B6 mice displayed more mitochondrial cross-sections in the post-synaptic compartment after CSDS. Our findings demonstrate mitochondrial-related alterations in gene expression as an evolutionarily conserved response in stress-related behaviors and validate the use of cross-species approaches in investigating the biological mechanisms underlying anxiety disorders.

Klíčová slova:

Blood cells – Gene expression – Mice – MicroRNAs – Mitochondria – Psychological stress – Transcriptome analysis – Panic disorder

Zdroje

1. Pirkola S, Isometsa E, Aro H, Kestila L, Hamalainen J, Veijola J, et al. Childhood adversities as risk factors for adult mental disorders: results from the Health 2000 study. Soc Psychiatry Psychiatr Epidemiol. 2005;40(10):769–77. doi: 10.1007/s00127-005-0950-x 16205853

2. Nestler EJ, Hyman SE. Animal models of neuropsychiatric disorders. Nat Neurosci. 2010;13(10):1161–9. doi: 10.1038/nn.2647 20877280

3. Laine MA, Sokolowska E, Dudek M, Callan SA, Hyytia P, Hovatta I. Brain activation induced by chronic psychosocial stress in mice. Sci Rep. 2017;7(1):15061. doi: 10.1038/s41598-017-15422-5 29118417

4. Laine MA, Trontti K, Misiewicz Z, Sokolowska E, Kulesskaya N, Heikkinen A, et al. Genetic Control of Myelin Plasticity after Chronic Psychosocial Stress. eNEURO. 2018;5(4): ENEURO.0166-18.2018. doi: 10.1523/ENEURO.0166-18.2018 30073192

5. Avgustinovich DF, Kovalenko IL, Kudryavtseva NN. A model of anxious depression: persistence of behavioral pathology. Neurosci Behav Physiol. 2005;35(9):917–24. doi: 10.1007/s11055-005-0146-6 16270173

6. Krishnan V, Han MH, Graham DL, Berton O, Renthal W, Russo SJ, et al. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell. 2007;131(2):391–404. doi: 10.1016/j.cell.2007.09.018 17956738

7. Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jonsson B, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655–79. doi: 10.1016/j.euroneuro.2011.07.018 21896369

8. Craske MG, Stein MB, Eley TC, Milad MR, Holmes A, Rapee RM, et al. Anxiety disorders. Nat Rev Dis Primers. 2017;3:17024. doi: 10.1038/nrdp.2017.24 28470168

9. Ising M, Hohne N, Siebertz A, Parchmann AM, Erhardt A, Keck M. Stress response regulation in panic disorder. Curr Pharm Des. 2012;18(35):5675–84. doi: 10.2174/138161212803530880 22632473

10. Faravelli C, Lo Sauro C, Godini L, Lelli L, Benni L, Pietrini F, et al. Childhood stressful events, HPA axis and anxiety disorders. World J Psychiatry. 2012;2(1):13–25. doi: 10.5498/wjp.v2.i1.13 24175164

11. Klauke B, Deckert J, Reif A, Pauli P, Domschke K. Life events in panic disorder-an update on "candidate stressors". Depress Anxiety. 2010;27(8):716–30. doi: 10.1002/da.20667 20112245

12. Faravelli C, Lo Sauro C, Lelli L, Pietrini F, Lazzeretti L, Godini L, et al. The role of life events and HPA axis in anxiety disorders: a review. Curr Pharm Des. 2012;18(35):5663–74. doi: 10.2174/138161212803530907 22632471

13. Provencal N, Binder EB. The effects of early life stress on the epigenome: From the womb to adulthood and even before. Exp Neurol. 2015;268:10–20. doi: 10.1016/j.expneurol.2014.09.001 25218020

14. Sokolowska E, Hovatta I. Anxiety genetics—findings from cross-species genome-wide approaches. Biol Mood Anxiety Disord. 2013;3(1):9. doi: 10.1186/2045-5380-3-9 23659354

15. Floriou-Servou A, von Ziegler L, Stalder L, Sturman O, Privitera M, Rassi A, et al. Distinct Proteomic, Transcriptomic, and Epigenetic Stress Responses in Dorsal and Ventral Hippocampus. Biol Psychiatry. 2018;84(7):531–41. doi: 10.1016/j.biopsych.2018.02.003 29605177

16. Bagot RC, Parise EM, Pena CJ, Zhang HX, Maze I, Chaudhury D, et al. Ventral hippocampal afferents to the nucleus accumbens regulate susceptibility to depression. Nat Commun. 2015;6:7062. doi: 10.1038/ncomms8062 25952660

17. Lebow MA, Chen A. Overshadowed by the amygdala: the bed nucleus of the stria terminalis emerges as key to psychiatric disorders. Mol Psychiatry. 2016;21(4):450–63. doi: 10.1038/mp.2016.1 26878891

18. Gungor NZ, Pare D. Functional Heterogeneity in the Bed Nucleus of the Stria Terminalis. J Neurosci. 2016;36(31):8038–49. doi: 10.1523/JNEUROSCI.0856-16.2016 27488624

19. Hovatta I, Tennant RS, Helton R, Marr RA, Singer O, Redwine JM, et al. Glyoxalase 1 and glutathione reductase 1 regulate anxiety in mice. Nature. 2005;438(7068):662–6. doi: 10.1038/nature04250 16244648

20. Pleil KE, Lopez A, McCall N, Jijon AM, Bravo JP, Kash TL. Chronic stress alters neuropeptide Y signaling in the bed nucleus of the stria terminalis in DBA/2J but not C57BL/6J mice. Neuropharmacology. 2012;62(4):1777–86. doi: 10.1016/j.neuropharm.2011.12.002 22182779

21. Anyan J, Amir S. Too Depressed to Swim or Too Afraid to Stop? A Reinterpretation of the Forced Swim Test as a Measure of Anxiety-Like Behavior. Neuropsychopharmacology. 2018;43(5):931–3. doi: 10.1038/npp.2017.260 29210364

22. Commons KG, Cholanians AB, Babb JA, Ehlinger DG. The Rodent Forced Swim Test Measures Stress-Coping Strategy, Not Depression-like Behavior. ACS Chem Neurosci. 2017;8(5):955–60. doi: 10.1021/acschemneuro.7b00042 28287253

23. Volk N, Paul ED, Haramati S, Eitan C, Fields BK, Zwang R, et al. MicroRNA-19b associates with Ago2 in the amygdala following chronic stress and regulates the adrenergic receptor beta 1. J Neurosci. 2014;34(45):15070–82. doi: 10.1523/JNEUROSCI.0855-14.2014 25378171

24. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3. doi: 10.2202/1544-6115.1027 16646809

25. Volk N, Pape JC, Engel M, Zannas AS, Cattane N, Cattaneo A, et al. Amygdalar MicroRNA-15a Is Essential for Coping with Chronic Stress. Cell Rep. 2016;17(7):1882–91. doi: 10.1016/j.celrep.2016.10.038 27829158

26. Leung AK, Sharp PA. MicroRNA functions in stress responses. Mol Cell. 2010;40(2):205–15. doi: 10.1016/j.molcel.2010.09.027 20965416

27. Qiagen Inc. Ingenuity Pathway Analysis 2018 [cited 25 January 2019] [Internet]. Available from: https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/.

28. Urbanska M, Gozdz A, Swiech LJ, Jaworski J. Mammalian target of rapamycin complex 1 (mTORC1) and 2 (mTORC2) control the dendritic arbor morphology of hippocampal neurons. J Biol Chem. 2012;287(36):30240–56. doi: 10.1074/jbc.M112.374405 22810227

29. Skalecka A, Liszewska E, Bilinski R, Gkogkas C, Khoutorsky A, Malik AR, et al. mTOR kinase is needed for the development and stabilization of dendritic arbors in newly born olfactory bulb neurons. Dev Neurobiol. 2016;76(12):1308–27. doi: 10.1002/dneu.22392 27008592

30. Siuta MA, Robertson SD, Kocalis H, Saunders C, Gresch PJ, Khatri V, et al. Dysregulation of the norepinephrine transporter sustains cortical hypodopaminergia and schizophrenia-like behaviors in neuronal rictor null mice. PLoS Biol. 2010;8(6):e1000393. doi: 10.1371/journal.pbio.1000393 20543991

31. Ruiz-Perez MV, Henley AB, Arsenian-Henriksson M. The MYCN Protein in Health and Disease. Genes (Basel). 2017;8(4).

32. Jacobs EG, Holsen LM, Lancaster K, Makris N, Whitfield-Gabrieli S, Remington A, et al. 17beta-estradiol differentially regulates stress circuitry activity in healthy and depressed women. Neuropsychopharmacology. 2015;40(3):566–76. doi: 10.1038/npp.2014.203 25113601

33. Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2018;46(D1):D239–D45. doi: 10.1093/nar/gkx1141 29156006

34. Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37:D105–10. doi: 10.1093/nar/gkn851 18996891

35. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4.

36. Jin HM, Shrestha Muna S, Bagalkot TR, Cui Y, Yadav BK, Chung YC. The effects of social defeat on behavior and dopaminergic markers in mice. Neuroscience. 2015;288:167–77. doi: 10.1016/j.neuroscience.2014.12.043 25575945

37. Kovalenko IL, Smagin DA, Galyamina AG, Orlov YL, Kudryavtseva NN. [Changes in the Expression of Dopaminergic Genes in Brain Structures of Male Mice Exposed to Chronic Social Defeat Stress: An RNA-seq Study]. Mol Biol (Mosk). 2016;50(1):184–7.

38. Scaini G, Fries GR, Valvassori SS, Zeni CP, Zunta-Soares G, Berk M, et al. Perturbations in the apoptotic pathway and mitochondrial network dynamics in peripheral blood mononuclear cells from bipolar disorder patients. Transl Psychiatry. 2017;7(5):e1111. doi: 10.1038/tp.2017.83 28463235

39. Hroudova J, Fisar Z. Connectivity between mitochondrial functions and psychiatric disorders. Psychiatry Clin Neurosci. 2011;65(2):130–41. doi: 10.1111/j.1440-1819.2010.02178.x 21414088

40. Kunii Y, Hyde TM, Ye T, Li C, Kolachana B, Dickinson D, et al. Revisiting DARPP-32 in postmortem human brain: changes in schizophrenia and bipolar disorder and genetic associations with t-DARPP-32 expression. Mol Psychiatry. 2014;19(2):192–9. doi: 10.1038/mp.2012.174 23295814

41. Clay HB, Sillivan S, Konradi C. Mitochondrial dysfunction and pathology in bipolar disorder and schizophrenia. Int J Dev Neurosci. 2011;29(3):311–24. doi: 10.1016/j.ijdevneu.2010.08.007 20833242

42. Youle RJ, Karbowski M. Mitochondrial fission in apoptosis. Nat Rev Mol Cell Biol. 2005;6(8):657–63. doi: 10.1038/nrm1697 16025099

43. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. doi: 10.1073/pnas.0506580102 16199517

44. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73. doi: 10.1038/ng1180 12808457

45. Muinos-Gimeno M, Espinosa-Parrilla Y, Guidi M, Kagerbauer B, Sipila T, Maron E, et al. Human microRNAs miR-22, miR-138-2, miR-148a, and miR-488 are associated with panic disorder and regulate several anxiety candidate genes and related pathways. Biol Psychiatry. 2011;69(6):526–33. doi: 10.1016/j.biopsych.2010.10.010 21168126

46. He K, Guo C, He L, Shi Y. MiRNAs of peripheral blood as the biomarker of schizophrenia. Hereditas. 2018;155:9. doi: 10.1186/s41065-017-0044-2 28860957

47. Filiou MD, Zhang Y, Teplytska L, Reckow S, Gormanns P, Maccarrone G, et al. Proteomics and metabolomics analysis of a trait anxiety mouse model reveals divergent mitochondrial pathways. Biol Psychiatry. 2011;70(11):1074–82. doi: 10.1016/j.biopsych.2011.06.009 21791337

48. Hollis F, van der Kooij MA, Zanoletti O, Lozano L, Canto C, Sandi C. Mitochondrial function in the brain links anxiety with social subordination. Proc Natl Acad Sci U S A. 2015;112(50):15486–91. doi: 10.1073/pnas.1512653112 26621716

49. Hunter RG, Seligsohn M, Rubin TG, Griffiths BB, Ozdemir Y, Pfaff DW, et al. Stress and corticosteroids regulate rat hippocampal mitochondrial DNA gene expression via the glucocorticoid receptor. Proc Natl Acad Sci U S A. 2016;113(32):9099–104. doi: 10.1073/pnas.1602185113 27457949

50. Heinzeller T. Impact of psychosocial stress on pineal structure of male gerbils (Meriones unguiculatus, cricetidae). J Pineal Res. 1985;2(2):145–59. 3831304

51. Magarinos AM, Verdugo JM, McEwen BS. Chronic stress alters synaptic terminal structure in hippocampus. Proc Natl Acad Sci U S A. 1997;94(25):14002–8. doi: 10.1073/pnas.94.25.14002 9391142

52. Eisner V, Picard M, Hajnoczky G. Mitochondrial dynamics in adaptive and maladaptive cellular stress responses. Nat Cell Biol. 2018;20(7):755–65. doi: 10.1038/s41556-018-0133-0 29950571

53. Lebeau J, Saunders JM, Moraes VWR, Madhavan A, Madrazo N, Anthony MC, et al. The PERK Arm of the Unfolded Protein Response Regulates Mitochondrial Morphology during Acute Endoplasmic Reticulum Stress. Cell Rep. 2018;22(11):2827–36. doi: 10.1016/j.celrep.2018.02.055 29539413

54. Picard M, McEwen BS. Psychological Stress and Mitochondria: A Systematic Review. Psychosom Med. 2018;80(2):141–53. doi: 10.1097/PSY.0000000000000545 29389736

55. Picard M, McEwen BS. Psychological Stress and Mitochondria: A Conceptual Framework. Psychosom Med. 2018;80(2):126–40. doi: 10.1097/PSY.0000000000000544 29389735

56. Picard M, McEwen BS, Epel ES, Sandi C. An energetic view of stress: Focus on mitochondria. Front Neuroendocrinol. 2018.

57. Lai CY, Yu SL, Hsieh MH, Chen CH, Chen HY, Wen CC, et al. MicroRNA expression aberration as potential peripheral blood biomarkers for schizophrenia. PLoS One. 2011;6(6):e21635. doi: 10.1371/journal.pone.0021635 21738743

58. Sun N, Lei L, Wang Y, Yang C, Liu Z, Li X, et al. Preliminary comparison of plasma notch-associated microRNA-34b and -34c levels in drug naive, first episode depressed patients and healthy controls. J Affect Disord. 2016;194:109–14. doi: 10.1016/j.jad.2016.01.017 26807671

59. Bavamian S, Mellios N, Lalonde J, Fass DM, Wang J, Sheridan SD, et al. Dysregulation of miR-34a links neuronal development to genetic risk factors for bipolar disorder. Mol Psychiatry. 2015;20(5):573–84. doi: 10.1038/mp.2014.176 25623948

60. Haramati S, Navon I, Issler O, Ezra-Nevo G, Gil S, Zwang R, et al. MicroRNA as repressors of stress-induced anxiety: the case of amygdalar miR-34. J Neurosci. 2011;31(40):14191–203. doi: 10.1523/JNEUROSCI.1673-11.2011 21976504

61. Li C, Liu Y, Liu D, Jiang H, Pan F. Dynamic Alterations of miR-34c Expression in the Hypothalamus of Male Rats after Early Adolescent Traumatic Stress. Neural Plast. 2016;2016:5249893. doi: 10.1155/2016/5249893 26925271

62. Femminella GD, Ferrara N, Rengo G. The emerging role of microRNAs in Alzheimer's disease. Front Physiol. 2015;6:40. doi: 10.3389/fphys.2015.00040 25729367

63. Schipper HM, Maes OC, Chertkow HM, Wang E. MicroRNA expression in Alzheimer blood mononuclear cells. Gene Regul Syst Bio. 2007;1:263–74. 19936094

64. Migliore L, Fontana I, Trippi F, Colognato R, Coppede F, Tognoni G, et al. Oxidative DNA damage in peripheral leukocytes of mild cognitive impairment and AD patients. Neurobiol Aging. 2005;26(5):567–73. doi: 10.1016/j.neurobiolaging.2004.07.016 15708428

65. Bhatnagar S, Chertkow H, Schipper HM, Yuan Z, Shetty V, Jenkins S, et al. Increased microRNA-34c abundance in Alzheimer's disease circulating blood plasma. Front Mol Neurosci. 2014;7:2. doi: 10.3389/fnmol.2014.00002 24550773

66. Kiezun A, Artzi S, Modai S, Volk N, Isakov O, Shomron N. miRviewer: a multispecies microRNA homologous viewer. BMC Res Notes. 2012;5:92. doi: 10.1186/1756-0500-5-92 22330228

67. Fernandez E, Schiappa R, Girault JA, Le Novere N. DARPP-32 is a robust integrator of dopamine and glutamate signals. PLoS Comput Biol. 2006;2(12):e176. doi: 10.1371/journal.pcbi.0020176 17194217

68. Mozhui K, Karlsson RM, Kash TL, Ihne J, Norcross M, Patel S, et al. Strain differences in stress responsivity are associated with divergent amygdala gene expression and glutamate-mediated neuronal excitability. J Neurosci. 2010;30(15):5357–67. doi: 10.1523/JNEUROSCI.5017-09.2010 20392957

69. Malki K, Mineur YS, Tosto MG, Campbell J, Karia P, Jumabhoy I, et al. Pervasive and opposing effects of Unpredictable Chronic Mild Stress (UCMS) on hippocampal gene expression in BALB/cJ and C57BL/6J mouse strains. BMC Genomics. 2015;16:262. doi: 10.1186/s12864-015-1431-6 25879669

70. Pothion S, Bizot JC, Trovero F, Belzung C. Strain differences in sucrose preference and in the consequences of unpredictable chronic mild stress. Behav Brain Res. 2004;155(1):135–46. doi: 10.1016/j.bbr.2004.04.008 15325787

71. Anisman H, Matheson K. Stress, depression, and anhedonia: caveats concerning animal models. Neurosci Biobehav Rev. 2005;29(4–5):525–46. doi: 10.1016/j.neubiorev.2005.03.007 15925696

72. Mineur YS, Belzung C, Crusio WE. Effects of unpredictable chronic mild stress on anxiety and depression-like behavior in mice. Behav Brain Res. 2006;175(1):43–50. doi: 10.1016/j.bbr.2006.07.029 17023061

73. Razzoli M, Domenici E, Carboni L, Rantamaki T, Lindholm J, Castren E, et al. A role for BDNF/TrkB signaling in behavioral and physiological consequences of social defeat stress. Genes Brain Behav. 2011;10(4):424–33. doi: 10.1111/j.1601-183X.2011.00681.x 21272243

74. Savignac HM, Dinan TG, Cryan JF. Resistance to early-life stress in mice: effects of genetic background and stress duration. Front Behav Neurosci. 2011;5:13. doi: 10.3389/fnbeh.2011.00013 21519375

75. Arai I, Tsuyuki Y, Shiomoto H, Satoh M, Otomo S. Decreased body temperature dependent appearance of behavioral despair in the forced swimming test in mice. Pharmacol Res. 2000;42(2):171–6. doi: 10.1006/phrs.2000.0672 10887048

76. Petit-Demouliere B, Chenu F, Bourin M. Forced swimming test in mice: a review of antidepressant activity. Psychopharmacology (Berl). 2005;177(3):245–55.

77. Kulesskaya N, Karpova NN, Ma L, Tian L, Voikar V. Mixed housing with DBA/2 mice induces stress in C57BL/6 mice: implications for interventions based on social enrichment. Front Behav Neurosci. 2014;8:257. doi: 10.3389/fnbeh.2014.00257 25147512

78. Sullivan PF, Fan C, Perou CM. Evaluating the comparability of gene expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(3):261–8. doi: 10.1002/ajmg.b.30272 16526044

79. Russo SJ, Murrough JW, Han MH, Charney DS, Nestler EJ. Neurobiology of resilience. Nat Neurosci. 2012;15(11):1475–84. doi: 10.1038/nn.3234 23064380

80. Golden SA, Covington HE 3rd, Berton O, Russo SJ. A standardized protocol for repeated social defeat stress in mice. Nat Protoc. 2011;6(8):1183–91. doi: 10.1038/nprot.2011.361 21799487

81. Lorsch ZS, Loh YE, Purushothaman I, Walker DM, Parise EM, Salery M, et al. Estrogen receptor alpha drives pro-resilient transcription in mouse models of depression. Nat Commun. 2018;9(1):1116. doi: 10.1038/s41467-018-03567-4 29549264

82. Iglewicz B, Hoaglin DC. How to detect and handle outliers. Milwaukee, Wis.: ASQC Quality Press; 1993. pp. 16

83. Lebow M, Neufeld-Cohen A, Kuperman Y, Tsoory M, Gil S, Chen A. Susceptibility to PTSD-like behavior is mediated by corticotropin-releasing factor receptor type 2 levels in the bed nucleus of the stria terminalis. J Neurosci. 2012;32(20):6906–16. doi: 10.1523/JNEUROSCI.4012-11.2012 22593059

84. Winn ME, Zapala MA, Hovatta I, Risbrough VB, Lillie E, Schork NJ. The effects of globin on microarray-based gene expression analysis of mouse blood. Mamm Genome. 2010;21(5–6):268–75. doi: 10.1007/s00335-010-9261-y 20473674

85. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886

86. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. doi: 10.1093/bioinformatics/btu638 25260700

87. Friedlander MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol. 2008;26(4):407–15. doi: 10.1038/nbt1394 18392026

88. 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(3):R25. doi: 10.1186/gb-2009-10-3-r25 19261174

89. Phipson B, Lee S, Majewski IJ, Alexander WS, Smyth GK. Robust Hyperparameter Estimation Protects against Hypervariable Genes and Improves Power to Detect Differential Expression. Ann Appl Stat. 2016;10(2):946–63. doi: 10.1214/16-AOAS920 28367255

90. Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. doi: 10.1186/gb-2014-15-2-r29 24485249

91. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27. doi: 10.1093/biostatistics/kxj037 16632515

92. Karpievitch Y, Stanley J, Taverner T, Huang J, Adkins JN, Ansong C, et al. A statistical framework for protein quantitation in bottom-up MS-based proteomics. Bioinformatics. 2009;25(16):2028–34. doi: 10.1093/bioinformatics/btp362 19535538

93. Taverner T, Karpievitch YV, Polpitiya AD, Brown JN, Dabney AR, Anderson GA, et al. DanteR: an extensible R-based tool for quantitative analysis of -omics data. Bioinformatics. 2012;28(18):2404–6. doi: 10.1093/bioinformatics/bts449 22815360

94. Goeminne LJ, Gevaert K, Clement L. Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics. Mol Cell Proteomics. 2016;15(2):657–68. doi: 10.1074/mcp.M115.055897 26566788

95. Efstathiou G, Antonakis AN, Pavlopoulos GA, Theodosiou T, Divanach P, Trudgian DC, et al. ProteoSign: an end-user online differential proteomics statistical analysis platform. Nucleic Acids Res. 2017;45(W1):W300–W6. doi: 10.1093/nar/gkx444 28520987

96. Iurato S, Carrillo-Roa T, Arloth J, Czamara D, Diener-Holzl L, Lange J, et al. "DNA Methylation signatures in panic disorder". Transl Psychiatry. 2017;7(12):1287. doi: 10.1038/s41398-017-0026-1 29249830

97. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005;21(16):3439–40. doi: 10.1093/bioinformatics/bti525 16082012

98. Plaisier SB, Taschereau R, Wong JA, Graeber TG. Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Res. 2010;38(17):e169. doi: 10.1093/nar/gkq636 20660011

99. Alexa A, Rahnenfuhrer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22(13):1600–7. doi: 10.1093/bioinformatics/btl140 16606683

100. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19(9):1639–45. doi: 10.1101/gr.092759.109 19541911

101. Belevich I, Joensuu M, Kumar D, Vihinen H, Jokitalo E. Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets. PLoS Biol. 2016;14(1):e1002340. doi: 10.1371/journal.pbio.1002340 26727152

102. Hanley JA, Negassa A, Edwardes MD, Forrester JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol. 2003;157(4):364–75. doi: 10.1093/aje/kwf215 12578807

103. Allen Institute for Brain Science. Allen Mouse Brain Atlas 2004 [cited 25 January 2019] [Internet]. Available from: http://mouse.brain-map.org/.

104. Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A, Bernard A, et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature. 2007;445(7124):168–76. doi: 10.1038/nature05453 17151600