Skip to main content

Advertisement

Log in

Hypothalamic Proteomic Analysis Reveals Dysregulation of Glutamate Balance and Energy Metabolism in a Mouse Model of Chronic Mild Stress-Induced Depression

  • Original Paper
  • Published:
Neurochemical Research Aims and scope Submit manuscript

Abstract

Hypothalamus–pituitary–adrenal (HPA) axis hyperactivity is observed in many patients suffering from depression. However, the mechanism underlying the dysfunction of the HPA axis is not well understood. Moreover, dysfunction of the hypothalamus, the key brain region of the HPA axis, has not been well-explored. The aim of our study was to examine possible alterations in hypothalamus protein expression in a model of depression using proteomic analysis. In order to achieve this aim, mice were exposed to chronic unpredictable mild stress (CUMS), as the paradigm results in hyperactivity of the HPA axis. Differential protein expression between the hypothalamic proteomes of CUMS and control mice was then assessed through two-dimensional electrophoresis followed by matrix-assisted laser desorption ionization-time of flight-tandem mass spectrometry. Thirty-seven proteins with a threshold of a 1.5-fold change and a p value ≤0.05 were identified as being differentially expressed between CUMS and control mice, and were quantified for bioinformatics analysis. Glycometabolism, citrate cycle (TCA cycle) and oxidation respiratory chain were found to have changed significantly. Glial fibrillary acidic protein and glutamine synthetase were further validated by Western Blot. Our results demonstrated that CUMS mice exhibited a dramatic protein change both in glutamate metabolism and energy mobilization, which may shed some light on the role of the hypothalamus in the pathology of stress-induced depression.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

2-DE:

Two-dimensional electrophoresis

ACTH:

Adrenocorticotropic hormone

CON:

Control

CRF:

Corticotrophin-releasing factor

CUMS:

Chronic unpredictable mild stress

DAVID:

Database for annotation, visualization and integrated discovery

FST:

Forced swimming test

GAPDH:

Glyceraldehyde-3-phosphate dehydrogenase

GFAP:

Glial fibrillary acidic protein

GO:

Gene ontology

GR:

Glucocorticoid receptor

GS:

Glutamine synthetase

HPA:

Hypothalamus–pituitary–adrenal

IEF:

Isoelectric focusing

KEGG:

Kyoto Encyclopedia of Genes and Genomes

MALDI-TOF–MS/MS:

Matrix-assisted laser desorption ionization-time of flight–tandem mass spectrometry

MW:

Molecular weight

OD:

Optical density

OFT:

Open field test

pI:

Isoelectric point

SPSS:

Statistical Package of Social Science

SPT:

Sucrose preference test

TCA:

Tricarboxylic acid

UniProt:

Universal protein resource

MDD:

Major depressive disorder

References

  1. Mathers CD, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 3(11):e442. doi:10.1371/journal.pmed.0030442

    Article  PubMed  PubMed Central  Google Scholar 

  2. Robins E, Murphy GE, Wilkinson RH Jr, Gassner S, Kayes J (1959) Some clinical considerations in the prevention of suicide based on a study of 134 successful suicides. Am J Public Health Nation’s Health 49(7):888–899

    Article  CAS  Google Scholar 

  3. Chopra K, Kumar B, Kuhad A (2011) Pathobiological targets of depression. Expert Opin Ther Targets 15(4):379–400. doi:10.1517/14728222.2011.553603

    Article  PubMed  Google Scholar 

  4. Sapolsky RM (1996) Why stress is bad for your brain. Science (New York, NY) 273(5276):749–750

    Article  CAS  Google Scholar 

  5. Krishnan V, Nestler EJ (2008) The molecular neurobiology of depression. Nature 455(7215):894–902. doi:10.1038/nature07455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Herman JP, Flak J, Jankord R (2008) Chronic stress plasticity in the hypothalamic paraventricular nucleus. Prog Brain Res 170:353–364. doi:10.1016/s0079-6123(08)00429-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Brown ES, Rush AJ, McEwen BS (1999) Hippocampal remodeling and damage by corticosteroids: implications for mood disorders. Neuropsychopharmacology 21(4):474–484. doi:10.1016/s0893-133x(99)00054-8

    Article  CAS  PubMed  Google Scholar 

  8. Jankord R, Herman JP (2008) Limbic regulation of hypothalamo-pituitary-adrenocortical function during acute and chronic stress. Ann N Y Acad Sci 1148:64–73. doi:10.1196/annals.1410.012

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zhu LJ, Liu MY, Li H, Liu X, Chen C, Han Z, Wu HY, Jing X, Zhou HH, Suh H, Zhu DY, Zhou QG (2014) The different roles of glucocorticoids in the hippocampus and hypothalamus in chronic stress-induced HPA axis hyperactivity. PLoS One 9(5):e97689. doi:10.1371/journal.pone.0097689

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liu Y, Yang N, Hao W, Zhao Q, Ying T, Liu S, Li Q, Liang Y, Wang T, Dong Y, Ji C, Zuo P (2011) Dynamic proteomic analysis of protein expression profiles in whole brain of Balb/C mice subjected to unpredictable chronic mild stress: implications for depressive disorders and future therapies. Neurochem Int 58(8):904–913. doi:10.1016/j.neuint.2011.02.019

    Article  CAS  PubMed  Google Scholar 

  11. Lehmann ML, Mustafa T, Eiden AM, Herkenham M, Eiden LE (2013) PACAP-deficient mice show attenuated corticosterone secretion and fail to develop depressive behavior during chronic social defeat stress. Psychoneuroendocrinology 38(5):702–715. doi:10.1016/j.psyneuen.2012.09.006

    Article  CAS  PubMed  Google Scholar 

  12. Gold PW, Chrousos GP (1985) Clinical studies with corticotropin releasing factor: implications for the diagnosis and pathophysiology of depression, Cushing’s disease, and adrenal insufficiency. Psychoneuroendocrinology 10(4):401–419

    Article  CAS  PubMed  Google Scholar 

  13. Murphy BE (1991) Steroids and depression. J Steroid Biochem Mol Biol 38(5):537–559

    Article  CAS  PubMed  Google Scholar 

  14. DeBattista C, Belanoff J (2006) The use of mifepristone in the treatment of neuropsychiatric disorders. Trends Endocrinol Metab TEM 17(3):117–121. doi:10.1016/j.tem.2006.02.006

    Article  CAS  PubMed  Google Scholar 

  15. de Kloet ER, Sibug RM, Helmerhorst FM, Schmidt MV (2005) Stress, genes and the mechanism of programming the brain for later life. Neurosci Biobehav Rev 29(2):271–281. doi:10.1016/j.neubiorev.2004.10.008

    Article  PubMed  Google Scholar 

  16. Martins-de-Souza D, Guest PC, Harris LW, Vanattou-Saifoudine N, Webster MJ, Rahmoune H, Bahn S (2012) Identification of proteomic signatures associated with depression and psychotic depression in post-mortem brains from major depression patients. Transl Psychiatry 2:e87. doi:10.1038/tp.2012.13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mu J, Xie P, Yang ZS, Yang DL, Lv FJ, Luo TY, Li Y (2007) Neurogenesis and major depression: implications from proteomic analyses of hippocampal proteins in a rat depression model. Neurosci Lett 416(3):252–256. doi:10.1016/j.neulet.2007.01.067

    Article  CAS  PubMed  Google Scholar 

  18. Yang Y, Yang D, Tang G, Zhou C, Cheng K, Zhou J, Wu B, Peng Y, Liu C, Zhan Y, Chen J, Chen G, Xie P (2013) Proteomics reveals energy and glutathione metabolic dysregulation in the prefrontal cortex of a rat model of depression. Neuroscience 247:191–200. doi:10.1016/j.neuroscience.2013.05.031

    Article  CAS  PubMed  Google Scholar 

  19. Hu Y, Zhou J, Fang L, Liu H, Zhan Q, Luo D, Zhou C, Chen J, Li Q, Xie P (2013) Hippocampal synaptic dysregulation of exo/endocytosis-associated proteins induced in a chronic mild-stressed rat model. Neuroscience 230:1–12. doi:10.1016/j.neuroscience.2012.08.026

    Article  CAS  PubMed  Google Scholar 

  20. Shao WH, Chen JJ, Fan SH, Lei Y, Xu HB, Zhou J, Cheng PF, Yang YT, Rao CL, Wu B, Liu HP, Xie P (2015) Combined metabolomics and proteomics analysis of major depression in an animal model: perturbed energy metabolism in the chronic mild stressed rat cerebellum. OMICS 19(7):383–392. doi:10.1089/omi.2014.0164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yang D, Li Q, Fang L, Cheng K, Zhang R, Zheng P, Zhan Q, Qi Z, Zhong S, Xie P (2011) Reduced neurogenesis and pre-synaptic dysfunction in the olfactory bulb of a rat model of depression. Neuroscience 192:609–618. doi:10.1016/j.neuroscience.2011.06.043

    Article  CAS  PubMed  Google Scholar 

  22. Xu HB, Zhang RF, Luo D, Zhou Y, Wang Y, Fang L, Li WJ, Mu J, Zhang L, Zhang Y, Xie P (2012) Comparative proteomic analysis of plasma from major depressive patients: identification of proteins associated with lipid metabolism and immunoregulation. Int J Neuropsychopharmacol 15(10):1413–1425. doi:10.1017/s1461145712000302

    Article  CAS  PubMed  Google Scholar 

  23. Yang Y, Mu J, Chen G, Zhan Y, Zhong J, Wei Y, Cheng K, Qin B, You H, Xie P (2015) iTRAQ-based quantitative proteomic analysis of cerebrospinal fluid reveals NELL2 as a potential diagnostic biomarker of tuberculous meningitis. Int J Mol Med 35(5):1323–1332. doi:10.3892/ijmm.2015.2131

    CAS  PubMed  Google Scholar 

  24. Zheng P, Gao HC, Li Q, Shao WH, Zhang ML, Cheng K, de Yang Y, Fan SH, Chen L, Fang L, Xie P (2012) Plasma metabonomics as a novel diagnostic approach for major depressive disorder. J Proteome Res 11(3):1741–1748. doi:10.1021/pr2010082

    Article  CAS  PubMed  Google Scholar 

  25. Willner P (2005) Chronic mild stress (CMS) revisited: consistency and behavioural–neurobiological concordance in the effects of CMS. Neuropsychobiology 52(2):90–110. doi:10.1159/000087097

    Article  CAS  PubMed  Google Scholar 

  26. Baxter LR Jr, Schwartz JM, Phelps ME, Mazziotta JC, Guze BH, Selin CE, Gerner RH, Sumida RM (1989) Reduction of prefrontal cortex glucose metabolism common to three types of depression. Arch Gen Psychiatry 46(3):243–250

    Article  CAS  PubMed  Google Scholar 

  27. Capra JC, Cunha MP, Machado DG, Zomkowski AD, Mendes BG, Santos AR, Pizzolatti MG, Rodrigues AL (2010) Antidepressant-like effect of scopoletin, a coumarin isolated from Polygala sabulosa (Polygalaceae) in mice: evidence for the involvement of monoaminergic systems. Eur J Pharmacol 643(2–3):232–238. doi:10.1016/j.ejphar.2010.06.043

    Article  CAS  PubMed  Google Scholar 

  28. Porsolt RD, Bertin A, Jalfre M (1977) Behavioral despair in mice: a primary screening test for antidepressants. Arch Int Pharmacodyn Ther 229(2):327–336

    CAS  PubMed  Google Scholar 

  29. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3

    Article  PubMed  Google Scholar 

  30. Merico D, Isserlin R, Stueker O, Emili A, Bader GD (2010) Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5(11):e13984. doi:10.1371/journal.pone.0013984

    Article  PubMed  PubMed Central  Google Scholar 

  31. Razafimanjato H, Garmy N, Guo XJ, Varini K, Di Scala C, Di Pasquale E, Taieb N, Maresca M (2010) The food-associated fungal neurotoxin ochratoxin A inhibits the absorption of glutamate by astrocytes through a decrease in cell surface expression of the excitatory amino-acid transporters GLAST and GLT-1. Neurotoxicology 31(5):475–484. doi:10.1016/j.neuro.2010.06.003

    Article  CAS  PubMed  Google Scholar 

  32. Liu W, Xu Z, Yang T, Deng Y, Xu B, Feng S (2015) Tea polyphenols protect against methylmercury-induced cell injury in rat primary cultured astrocytes, involvement of oxidative stress and glutamate uptake/metabolism disorders. Mol Neurobiol. doi:10.1007/s12035-015-9161-y

    Google Scholar 

  33. Zelena D, Mergl Z, Makara GB (2005) (2005) Glutamate agonists activate the hypothalamic–pituitary–adrenal axis through hypothalamic paraventricular nucleus but not through vasopressinerg neurons. Brain Res 1031(2):185–193. doi:10.1016/j.brainres.2004.10.034

    Article  CAS  PubMed  Google Scholar 

  34. Sandi C (2011) Glucocorticoids act on glutamatergic pathways to affect memory processes. Trends Neurosci 34(4):165–176. doi:10.1016/j.tins.2011.01.006

    Article  CAS  PubMed  Google Scholar 

  35. Jun C, Choi Y, Lim SM, Bae S, Hong YS, Kim JE, Lyoo IK (2014) Disturbance of the glutamatergic system in mood disorders. Exp Neurobiol 23(1):28–35. doi:10.5607/en.2014.23.1.28

    Article  PubMed  PubMed Central  Google Scholar 

  36. Machado-Vieira R, Manji HK, Zarate CA (2009) The role of the tripartite glutamatergic synapse in the pathophysiology and therapeutics of mood disorders. Neurosci Rev J Bringing Neurobiol Neurol Psychiatry 15(5):525–539. doi:10.1177/1073858409336093

    CAS  Google Scholar 

  37. Gamaro GD, Streck EL, Matte C, Prediger ME, Wyse AT, Dalmaz C (2003) Reduction of hippocampal Na+, K+-ATPase activity in rats subjected to an experimental model of depression. Neurochem Res 28(9):1339–1344

    Article  CAS  PubMed  Google Scholar 

  38. Gardner A, Johansson A, Wibom R, Nennesmo I, von Dobeln U, Hagenfeldt L, Hallstrom T (2003) Alterations of mitochondrial function and correlations with personality traits in selected major depressive disorder patients. J Affect Disord 76(1–3):55–68

    Article  CAS  PubMed  Google Scholar 

  39. Videbech P (2000) PET measurements of brain glucose metabolism and blood flow in major depressive disorder: a critical review. Acta Psychiatr Scand 101(1):11–20

    Article  CAS  PubMed  Google Scholar 

  40. Kennedy SH, Evans KR, Kruger S, Mayberg HS, Meyer JH, McCann S, Arifuzzman AI, Houle S, Vaccarino FJ (2001) Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. Am J Psychiatry 158(6):899–905

    Article  CAS  PubMed  Google Scholar 

  41. Harris RB (2015) Chronic and acute effects of stress on energy balance: are there appropriate animal models? Am J Physiol Regul Integr Comp Physiol 308(4):R250–R265. doi:10.1152/ajpregu.00361.2014

    Article  CAS  PubMed  Google Scholar 

  42. Rutters F, Nieuwenhuizen AG, Lemmens SG, Born JM, Westerterp-Plantenga MS (2009) Hyperactivity of the HPA axis is related to dietary restraint in normal weight women. Physiol Behav 96(2):315–319. doi:10.1016/j.physbeh.2008.10.015

    Article  CAS  PubMed  Google Scholar 

  43. Weinstein SE, Shide DJ, Rolls BJ (1997) Changes in food intake in response to stress in men and women: psychological factors. Appetite 28(1):7–18. doi:10.1006/appe.1996.0056

    Article  CAS  PubMed  Google Scholar 

  44. Harris RB, Mitchell TD, Simpson J, Redmann SM Jr, Youngblood BD, Ryan DH (2002) Weight loss in rats exposed to repeated acute restraint stress is independent of energy or leptin status. Am J Physiol Regul Integr Comp Physiol 282(1):R77–R88

    CAS  PubMed  Google Scholar 

  45. Rybkin II, Zhou Y, Volaufova J, Smagin GN, Ryan DH, Harris RB (1997) Effect of restraint stress on food intake and body weight is determined by time of day. Am J Physiol 273(5 Pt 2):R1612–R1622

    CAS  PubMed  Google Scholar 

  46. Fujita C, Ichikawa F, Teratani T, Murakami G, Okada T, Shinohara M, Kawato S, Ohta Y (2009) Direct effects of corticosterone on ATP production by mitochondria from immortalized hypothalamic GT1-7 neurons. J Steroid Biochem Mol Biol 117(1–3):50–55. doi:10.1016/j.jsbmb.2009.07.002

    Article  CAS  PubMed  Google Scholar 

  47. Datson NA, Speksnijder N, Mayer JL, Steenbergen PJ, Korobko O, Goeman J, de Kloet ER, Joels M, Lucassen PJ (2012) The transcriptional response to chronic stress and glucocorticoid receptor blockade in the hippocampal dentate gyrus. Hippocampus 22(2):359–371. doi:10.1002/hipo.20905

    Article  CAS  PubMed  Google Scholar 

  48. Nieuwenhuizen AG, Rutters F (2008) The hypothalamic–pituitary–adrenal-axis in the regulation of energy balance. Physiol Behav 94(2):169–177. doi:10.1016/j.physbeh.2007.12.011

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We thank Dr. ChenruiHou (Shanghai Institute for Biological Sciences, Chinese Academy of Sciences) for his assistance with the mass spectrometric analysis. We would like to thank Editage (http://www.editage.cn/) for English language editing and proofreading this manuscript. This work was supported by grants from the National Basic Research Program of China (“973 program”, Grant No. 2009CB918300). The Health Bureau medical scientific research projects of Chongqing (Grant No. 20142022).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peng Xie.

Additional information

Chenglong Rao, Haiyang Shi, Chanjuan Zhou and Dan Zhu have contributed equally to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplemental Fig. 1

Time schedule of procedures used in the present study. After 7 days of acclimatization, mice were trained to adapt 1 % (w/v) sucrose solution. Then a baseline of sucrose preference was gotten for group dividing, CUMS procedures were performed for 28 days. The sucrose preference test (SPT) and body weights were conducted weekly throughout the CUMS period. Open field test (OFT) and forced swimming test (FST) were executed respectively on day 29 and day 30. Tissue separated at the end of the schedule (TIFF 106 kb)

Supplemental Fig. 2

The sketch map of the molecular dysregulations supporting stress-induced depressive-like behaviors. Dysregulation of glutamatergic synapses may result in the accumulation of glutamate in the synaptic cleft, exciting the HPA axis (TIFF 353 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, C., Shi, H., Zhou, C. et al. Hypothalamic Proteomic Analysis Reveals Dysregulation of Glutamate Balance and Energy Metabolism in a Mouse Model of Chronic Mild Stress-Induced Depression. Neurochem Res 41, 2443–2456 (2016). https://doi.org/10.1007/s11064-016-1957-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11064-016-1957-2

Keywords

Navigation