Gao, Gao, Xu, and Xie: Microarray Analysis of the Major Depressive Disorder mRNA Profile Data

Abstract

Objective

Major depressive disorder (MDD) is a common mood disorder associated with several psychophysiological changes like disturbances of sleep, appetite, or sexual desire, and it affects the patients' life seriously. We aimed to explore a genetic method to investigate the mechanism of MDD.

Methods

The mRNA expression profile (GSE53987) of MDD was downloaded from Gene Expression Omnibus database, including 105 samples of three brain regions in post-mortem tissue suffered from MDD and unaffected controls. Differentially expressed genes (DEGs) in MDD were identified using the Limma package in R. Gene Ontology functions and Kyoto Enrichment of Genes and Genomes pathways of the selected DEGs were enriched using Database for Annotation, Visualization and Integrated Discovery. Protein-protein interactive network of DEGs was constructed using the Cytoscape software.

Results

Totally, 241 DEGs in MDD-hip group, 218 DEGs in MDD-pfc group, and 327 DEGs in MDD-str group were identified. Also, different kinds of biological processes of DEGs in each group were enriched. Besides, glycan biosynthesis of DEGs in MDD-str group, RIG-I-like receptor signaling and pyrimidine metabolism of DEGs in the MDD-hip group were enriched, respectively. Moreover, several DEGs like PTK2, TDG and CETN2 in MDD-str group, DCT, AR and GNRHR in MDD-pfc group, and AKT1 and IRAK1 in MDD-hip group were selected from PPI network.

Conclusion

Our data suggests that the brain striatum tissue may be greatly affected by MDD, and DEGs like PTK2, GALNT2 and GALNT2 in striatum, AR in prefrontal cortex and IRAK1 and IL12A in hippocampus may provide novel therapeutic basis for MDD treatment.

INTRODUCTION

Depression is a heterogeneous mental disorder that is characterized by mood depression, retardative thinking and bradypragia, and it does not remit when the external cause of sadness emotion dissipates.1 Major depressive disorder (MDD) is mainly accompanied with the bad mood, inappetence, despair and suicidal behavior, which brings a tremendous influence on both patients and society.23 MDD has an increasing trend year by year with the accessorial work and life pressure.4 The mechanism of depression is complicated while the medicine treatment on MDD patients is poor, therefore, there is an urgent need to explore several effective treatment methods on MDD.
Previous studies have demonstrated that variety factors participated in the MDD development, such as genetic factor, biological chemistry, psychology, society and environment.5 The development of MDD could promote some inflammatory cytokines and the level of pro-inflammatory cytokines such as interleukin (IL)-1β and tumor necrosis factor (TNF)-α was higher in MDD cells.6 It has been reported that hypothalamus-pituitary gland-thyroid (HPT) was associated with MDD.7 Also, Schutter proved that the stimulation of hypothalamus-pituitary gland-adrenal gland axis (HPA) were a symbol of MDD, and the diurnal blood pressure variation of HPA and adrenal glucocorticoids was abnormal in MDD patients.8 Besides, the level of adtevak 5-hydroxytryptamine (5-HT) and the serum total cholesterol (CHO) in MDD patients was significantly lower than in normal people.9 In Ronald's study, 5-HT plays a role in neural remodeling, and the decreased neuronal synapses in brain regions that regulate mood and cognition were related to MDD.10 In addition, some studies proved that phosphoinositide signal transduction pathway system was abnormal in MDD patients,11 and protein kinase C (PKC) was failed in regulating the 5-HT to reuptake the antidepressant moderator 5-HT2R.12
Despite many studies have found that MDD was associated with the nervous, endocrine and immune, it was till hard to clarify the mechanisms of MDD formation. Thus, studies at the genetic level would offer a deeper insight into the pathogenesis of MDD. Maura reported the number of hippocampal granule neuron was associated with MDD.13 Also, the increasing volume of dorsolateral prefrontal cortex could affect the antidepressant effects of sertraline.14 Additionally, sustained fronto-striatal connectivity was associated with the positive effect in MDD.15 Therefore, understanding the genetic basis of MDD will allow disease prediction and risk stratification.
Recently, Hideo et al.16 also used the mRNA GSE53987 microarrays to investigate the transcription immaturity of prefrontal cortex in patients with schizophrenia, and they found that transcriptomic immaturity of prefrontal cortex may be an endophenotype of schizophrenia. However, the study did not attempt to investigate the differentially expressed genes (DEGs) or pathways in three kinds brain tissues (hippocampus, prefrontal cortex and striatum) involved in the MDD. In this study, we used microarray analysis17 to screen the DEGs in MDD samples from three human brain tissues compared to the health-control samples based on the same profile data. Comprehensive bioinformatics analysis was used to enrich the significant functions and pathways of DEGs and to construct the protein-protein interactive (PPI) network to provide a deeper insight into the biological mechanisms of MDD. This approach was beneficial for predicting the hub genes and significant pathways that are most likely associated with MDD and identify the molecular mechanisms that could serve as novel therapeutic basis for MDD.

METHODS

Affymetrix microarray data and data preprocessing

The microarray and other forms of high flux data produced by the scientific community were archived and freely released from the Gene Expression Omnibus (GEO)18 database of National Center for Biotechnology Information (NCBI),19 which is the biggest completely public storage. We extracted the mRNA expression profiles (GSE53987) from the GEO database based on the GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 plus 2.0 Array. The study contains a total of 205 samples of human brain regions in post-mortem tissue suffered from schizophrenia, bipolar disorder or MDD and unaffected controls (n=19 from each group). We selected 105 samples in the post-mortem brain tissues matched with MDD, which including 18 health-hippocampus (control-hip) samples and 17 MDD-hip samples, 19 health-prefrontal cortex (control-pfc) samples and 17 MDD-pfc samples, and 18 health-striatum (control-str) samples and 16 MDD-str samples.
Probes that mapped with the gene names labeled in annotation platform were performed in log2 transformation. Quantile method20 was used to normalize the data from shewed normal distribution to the approximate normal distribution. The mRNA expression values were calculated based on the probe information.

Screening of DEGs in each group

The total 105 samples used in this study were separated into three groups based on the three kinds brain tissues, MDD-hip vs. control-hip, MDD-pfc vs. control-pfc and MDD-str vs. control-str. Limma package in R language21 was used to select the DEGs of the MDD-samples and control samples in each group. The p-value<0.01 was chosen as the cut-off criterion.

Gene ontology analysis and pathway enrichment analysis

Gene ontology (GO) analysis has become a commonly used approach for functional studies of large-scale genomic or transcription data.22 The Kyoto Enrichment of Genes and Genomes (KEGG) pathway database contains information of how molecules or genes are networked, which is complementary to most of the existing molecular biology databases containing the information of individual genes.23 Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources consist of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene or protein lists.24
We used the DAVID to enrich the functions and pathways of the DEGs in each group. The p-value<0.05 was chosen as the threshold.

PPI network construction

Varieties of cell physiological activities, and the reactions of cells to the external and internal environments were all connected by the protein interaction network.25 Therefore, a further investigation on the protein interaction is necessary for recognizing and understanding the biological phenomena.26 The PPI interactive network of DEGs in each group was constructed in the Search Tool for the Retrieval of Interacting Genes (STRING) database27 using the cytoscape software.28 The interactive pattern degree ≥0.4 was chosen as the threshold.

RESULTS

Screening of DEGs in each group

We obtained 34296 mRNA expression values (19745 gene expression values) after the profile data were normalized. Totally, 241 DEGs (138 up-regulated and 103 down-regulated) in MDD-hip group, 218 DEGs (143 up-regulated and 75 down-regulated) in MDD-pfc group, and 327 DEGs (236 up-regulated and 91 down-regulated) in MDD-str group were selected (Figure 1). The total DEGs in MDD-hip group were more than that in the other two kinds groups from the perspective of comparison within groups.

Function analysis of DEGs

To investigate the function changes of the screened DEGs in each group, we used DAVID to identify the significant GO categories in biological process. Thirty GO-BP terms in the MDD-str group that neuro development related and phosphorus metabolism associated biological processed were enriched, such as neuron differentiation, regulation of axonogenesis, negative regulation of cell development and regulation of neurogenesis (Figure 2A). Also, 7 GO terms in MDD-pfc group like positive regulation of macromolecule biosynthetic process, transcription and melanin metabolic process were enrich (Figure 2B). Besides, 15 GO terms like toll-like receptor signaling pathway, regulation of innate immune response and positive regulation of defense response in the MDD-hip group were enriched (Figure 2C).

Pathway analysis of DEGs

To gain further insights into the pathways of DEGs in each group, we used DAVID to identify the significant pathways. Only 3 pathways, 1 pathway "glycan biosynthesis" associated with the up-regulated DEGs like GALNT6 (polypeptide N-acetylgalactosaminyltransferase 6), GALNT2 and GALNT12 in the MDD-str group and 2 pathways "RIG-I-like receptor signaling pathway" and "pyrimidine metabolism" in the MDD-hip group were enriched (Table 1). In addition, 4 up-regulated like IFNE (interferon, epsilon), IL12A (interleukin 12A), NLRX1 (NLR family member X1) and TFNK were enriched in the RIG-I-like receptor signaling pathway while the other 4 down-regulated DEGs such as POLR3G [polymerase (RNA) III (DNA directed) polypeptide G], NT5C1A (5'-nucleotidase, cytosolic IA), DHODH (dihydroorotate dehydrogenase) and POLR2B [polymerase (RNA) II (DNA directed) polypeptide B] were enriched in the pyrimidine metabolism pathway.

PPI network construction

The PPI network of DEGs in MDD-hip, MDD-pfc and MDD-str group were annotated by calculating their interactive degrees with STRING database, respectively. The MDD-str PPI network were shown in Figure 3, and DEGs with the top 5 node degrees were PTK2 (protein tyrosine kinase 2), ANAPC5 (anaphase promoting complex subunit 5), TDG (thymine DNA glycosylase), CETN2 (centrin, EF-hand protein 2), SYNJ2 (synaptojanin 2). Also, the MDD-pfc PPI network were shown in Figure 4, and DCT (dopachrome tautomerase), GNRHR (gonadotropin-releasing hormone receptor), AR (androgen receptor), SVIL (supervilin), ABCC2 (ATP-binding cassette, sub-family C) were the genes with the top 5 node degrees. In addition, AKT1 (v-akt murine thymoma vrial oncogene homolog 1), CHEK1 (checkpoint kinase 1), MCM8 (minichromosome maintenance 8), EGF (epidermal growth factor), IRAK1 (interleukin-1 receptor-associated kinase 1) were the genes with the top 5 node degrees in MDD-hip PPI network (Figure 5).

DISCUSSION

MDD, which is a common mood disorder associated with several psychophysiological changes like disturbances of sleep, appetite, or sexual desire, and it affects the patients' life seriously, but the treatment on MDD was poor.3 Therefore, it is an urgent necessity to investigate the mechanism of MDD and to develop an effective preventative strategy. In the present study, the mRNA expression profile GSE53987 from GEO database was used to analyze the possible functions of DEGs in three kinds of brain tissues between MDD and healthy samples. As a result, we screened 241 DEGs in MDD-hip group, 218 DEGs in MDD-pfc group and 327 DEGs in MDD-str group. Also, different kinds of biological processes of DEGs in each group were enriched. Additionally, glycan biosynthesis pathway of DEGs in MDD-str group while RIG-I-like receptor signaling pathway and pyrimidine metabolism of DEGs in the MDD-hip group were enriched, respectively. In addition, several DEGs such as PTK2, TDG and CETN2 in MDD-str group, DCT, AR and GNRHR in MDD-pfc group and AKT1 and IRAK1 in MDD-hip group were selected from their PPI network.
Previous studies prove that MDD are associated with several different tissues of human brain regions. The small hippocampal volumes is correlated with the complex MDD in clinical and biological perspectives.29 Positive emotion induced by the fronto-striatal circuitry tracks might benefit the MDD treatment. 15 Also, decreased medial prefrontal cortex was detected in the MDD patients compared to the healthy controls.30 Our work showed that the total DEG numbers in MDD-str group was more than that in the other two groups, indicating that there may be more DEGs related to the MDD in striatum tissue. Thus, we speculated that the striatum tissue may be closely associated with MDD compared with the hippocampal tissue and prefrontal cortex tissue in human brain region.
AR, encoded by the AR gene, is a sterols receptor of the nuclear receptor superfamily and functions as a steroid-hormone activated transcription factor.31 It has been discussed that testosterone is the main form of AR.32 Studies have demonstrated that testosterone level declined in the male patients with MDD compared to the normal persons.3334 Besides, the mood disorder in postpartum and postmenopausal women was associated with the serum testosterone.35 Both the serum cortisol and testosterone levels in MDD women were different than that in men with MDD.36 Also, AR is produced in the brain region and is related to some neuropsychiatric disorders.37 In this study, the AR gene in the prefrontal cortex brain region of the postmortem tissue was up-regulated, implying that it might be crucial for the MDD development and could act as a biomarker for the MDD detection.
IRAK1 (encoded by IRAK1 gene) is a key inherent immune signal regulating molecule of the IRAK kinase family, regulating the expression of inflammatory factors.38 Lots of reactions could be regulated by IRAK1 via the signal pathways that are regulated by the toll-like receptor and IRAK1 receptor.39 Also, IRAK1 could decrease the TNF-α expression to prevent the body tissue form damaging.40 TNF-α demonstrates an important role in neuro-inflammation that leads to the anxiety symptom in MDD patients.4142 Neuro-inflammation played an important role in major depression because anxiety was a typical symptoms of MDD.43 In this work IRAK1 gene was down-regulated in the MDD-hip, suggesting that it might be play a crucial role in MDD development in the hippocampus tissue of brain region.
Meanwhile, our findings showed that IL12A was up-regulated in MDD-hip via the RIG-I-like receptor signaling pathway, suggesting IL12A may be important to MDD. Kim et al. reported the plasma level of IL12A was higher in patients with schizophrenia.44 Dysregulation of RLR signaling program may lead to the development of autoimmune diseases.45 IL12 was a key factor in regulating the immune system.46 Therefore, we speculated that IL12A may be a index for MDD progression regulated by the RIG-I-like receptor signaling pathway in brain hippocampus tissue.
Furthermore, PTK2 (encoded by PTK2 gene), is a member of the focal adhesion kinase (FAK) subfamily of protein tyrosine kinases,47 and has the ability to combine the cytoplasmic domain structure of the type I/II cytokines receptors, then to transfer the cytokine signals via the phosphorylation receptor subunits.48 Otherwise, the role of PTK2 in MDD development has not been fully discussed. It has been proven tyrosine kinase A could be used as the neurochemical marker for the major depression in postmortem brains.49 Therefore, tyrosine receptor kinase B might be associated with the depression treatment.50 Our data showed that PTK2 gene was up-regulated in the striatum brain region with MDD, we speculated that PTK2 gene might act as a novel biomarker for the MDD diagnosis.
On the other hand, GALNT2, together with its paralog GALNT12, are two members of the GalNAc-transferases family that have distinct activities and initiation of O-glycosylation in cells.51 Role of GALNT2 in MDD has not been fully discussed. However, Marucci et al.52 suggested that GALNT2 was a mediator of intermediate glucose metabolism. Besides, previous study reveals that the glucose metabolism in regional brain was lower in patients suffered with MDD.53 Our data displayed that the GALNT2 and GALNT2 were up-regulated in the MDD-str group, implying that GALNT2 and GALNT12 might play roles in preventing the MDD progression via the glucose metabolism pathway.
In conclusion, our present data suggests that the striatum brain region in postmortem tissue might be more affected by the MDD compared with the hippocampus and prefrontal cortex brain regions. AR and PTK2 are up-regulated while IRAK1, IL12A, GALNT12 and GALNT2 are down-regulated in MDD, indicating that they may have different roles in MDD development, and they might provide novel therapeutic basis for MDD treatment.

Acknowledgments

This study was supported by the Seoul Child and Adolescent Mental Health Center Grant (07-2005-013-02).

References

1. Beck AT, Alford BA. Depression: Causes and Treatment. Pennsylvania: University of Pennsylvania Press; 2009.

2. Hasin DS, Goodwin RD, Stinson FS, Grant BF. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch Gen Psychiatry 2005;62:1097–1106. PMID: 16203955.
crossref pmid
3. Belmaker R, Agam G. Major depressive disorder. N Engl J Med 2008;358:55–68. PMID: 18172175.
crossref pmid
4. Heim C, Binder EB. Current research trends in early life stress and depression: Review of human studies on sensitive periods, gene-environment interactions, and epigenetics. Exp Neurol 2012;233:102–111. PMID: 22101006.
crossref pmid
5. Kupfer DJ, Frank E, Phillips ML. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet 2012;379:1045–1055. PMID: 22189047.
crossref pmid
6. Rethorst CD, Toups MS, Greer TL, Nakonezny PA, Carmody TJ, Grannemann BD, et al. Pro-inflammatory cytokines as predictors of antidepressant effects of exercise in major depressive disorder. Mol Psychiatry 2013;18:1119–1124. PMID: 22925832.
crossref pmid
7. Fortunato RS, Ferreira AC, Hecht F, Dupuy C, Carvalho DP. Sexual dimorphism and thyroid dysfunction: a matter of oxidative stress? J Endocrinol 2014;221:R31–R40. PMID: 24578296.
crossref pmid
8. Schutter DJ. The cerebello-hypothalamic-pituitary-adrenal axis dysregulation hypothesis in depressive disorder. Med Hypotheses 2012;79:779–783. PMID: 22999737.
crossref pmid
9. Vargas HO, Nunes SOV, Barbosa DS, Vargas MM, Cestari A, Dodd S, et al. Castelli risk indexes 1 and 2 are higher in major depression but other characteristics of the metabolic syndrome are not specific to mood disorders. Life Sci 2014;102:65–71. PMID: 24607777.
crossref pmid
10. Duman RS, Aghajanian GK. Synaptic dysfunction in depression: potential therapeutic targets. Science 2012;338:68–72. PMID: 23042884.
crossref pmid pmc
11. Lo Vasco VR, Polonia P. Molecular cytogenetic interphase analysis of Phosphoinositide-specific Phospholipase C β1 gene in paraffin-embedded brain samples of major depression patients. J Affect Disord 2012;136:177–180. PMID: 21880371.
crossref pmid
12. Wu X, Kushwaha N, Banerjee P, Albert PR, Penington NJ. Role of protein kinase C in agonist-induced desensitization of 5-HT 1A receptor coupling to calcium channels in F11 cells. Eur J Pharmacol 2013;706:84–91. PMID: 23510743.
crossref pmid pmc
13. Boldrini M, Santiago AN, Hen R, Dwork AJ, Rosoklija GB, Tamir H, et al. Hippocampal granule neuron number and dentate gyrus volume in antidepressant-treated and untreated major depression. Neuropsychopharmacology 2013;38:1068–1077. PMID: 23303074.
crossref pmid pmc
14. Smith R, Chen K, Baxter L, Fort C, Lane RD. Antidepressant effects of sertraline associated with volume increases in dorsolateral prefrontal cortex. J Affect Disord 2013;146:414–419. PMID: 23017544.
crossref pmid
15. Heller AS, Johnstone T, Light SN, Peterson MJ, Kolden GG, Kalin NH, et al. Relationships between changes in sustained fronto-striatal connectivity and positive affect in major depression resulting from antidepressant treatment. Am J Psychiatry 2013;170:197–206. PMID: 23223803.
crossref pmid pmc
16. Hagihara H, Ohira K, Takao K, Miyakawa T. Transcriptomic evidence for immaturity of the prefrontal cortex in patients with schizophrenia. Mol Brain 2014;7:41PMID: 24886351.
crossref pmid pmc
17. Liu D, Sartor MA, Nader GA, Pistilli EE, Tanton L, Lilly C, et al. Microarray analysis reveals novel features of the muscle aging process in men and women. J Gerontol A Biol Sci Med Sci 2013;68:1035–1044. PMID: 23418191.
crossref pmid pmc
18. Barrett T, Edgar R. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol 2006;411:352–369. PMID: 16939800.
crossref pmid pmc
19. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res 2007;35:D760–D765. PMID: 17099226.
crossref pmid
20. Bolstad BM, Irizarry RA, Åstrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003;19:185–193. PMID: 12538238.
crossref pmid
21. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:Article3PMID: 16646809.
crossref pmid
22. Hulsegge I, Kommadath A, Smits MA. Globaltest and GOEAST: Two Different Approaches for Gene Ontology Analysis. BMC Proceedings: BioMed Central Ltd; 2009.

23. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27–30. PMID: 10592173.
crossref pmid pmc
24. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44–57. PMID: 19131956.
crossref pmid
25. Fan S, Hurd TW, Liu CJ, Straight SW, Weimbs T, Hurd EA, et al. Polarity proteins control ciliogenesis via kinesin motor interactions. Curr Biol 2004;14:1451–1461. PMID: 15324661.
crossref pmid
26. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y. A protein interaction map of Drosophila melanogaster. Science 2003;302:1727–1736. PMID: 14605208.
crossref pmid
27. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9. 1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013;41:D808–D815. PMID: 23203871.
crossref pmid
28. Kohl M, Wiese S, Warscheid B. Cytoscape: Software for Visualization and Analysis of Biological Networks. Data Mining in Proteomics. Springer; 2011.

29. MacQueen G, Frodl T. The hippocampus in major depression: evidence for the convergence of the bench and bedside in psychiatric research? Mol Psychiatry 2011;16:252–264. PMID: 20661246.
crossref pmid
30. Lemogne C, Delaveau P, Freton M, Guionnet S, Fossati P. Medial prefrontal cortex and the self in major depression. J Affect Disord 2012;136:e1–e11. PMID: 21185083.
crossref pmid
31. Wang Q, Li W, Liu XS, Carroll JS, Jänne OA, Keeton EK, et al. A hierarchical network of transcription factors governs androgen receptor-dependent prostate cancer growth. Mol Cell 2007;27:380–392. PMID: 17679089.
crossref pmid pmc
32. Zitzmann M, Nieschlag E. Androgen receptor gene CAG repeat length and body mass index modulate the safety of long-term intramuscular testosterone undecanoate therapy in hypogonadal men. J Clin Endocrinol Metab 2007;92:3844–3853. PMID: 17635942.
crossref pmid
33. Margolese HC. The male menopause and mood: testosterone decline and depression in the aging male--is there a link? J Geriatr Psychiatry Neurol 2000;13:93–101. PMID: 10912731.
crossref pmid
34. Kheirkhah F, Hosseini SR, Hosseini SF, Ghasemi N, Bijani A, G Cumming R. Relationship between testosterone levels and depressive symptoms in older men in Amirkola, Iran. Caspian J Intern Med 2014;5:65–70. PMID: 24778779.
pmid pmc
35. Basson R. Testosterone therapy for reduced libido in women. Ther Adv Endocrinol Metab 2010;1:155–164. PMID: 23148160.
crossref pmid pmc
36. Matsuzaka H, Maeshima H, Kida S, Kurita H, Shimano T, Nakano Y, et al. Gender differences in serum testosterone and cortisol in patients with major depressive disorder compared with controls. Int J Psychiatry Med 2013;46:203–221. PMID: 24552043.
crossref pmid
37. Hajszan T, MacLusky NJ, Leranth C. Role of androgens and the androgen receptor in remodeling of spine synapses in limbic brain areas. Horm Behav 2008;53:638–646. PMID: 18262185.
crossref pmid
38. Gosu V, Basith S, Durai P, Choi S. Molecular evolution and structural features of IRAK family members. PloS One 2012;7:e49771PMID: 23166766.
crossref pmid pmc
39. Saitoh T, Satoh T, Yamamoto N, Uematsu S, Takeuchi O, Kawai T, et al. Antiviral protein Viperin promotes Toll-like receptor 7-and Toll-like receptor 9-mediated type I interferon production in plasmacytoid dendritic cells. Immunity 2011;34:352–363. PMID: 21435586.
crossref pmid
40. Xiong Y, Qiu F, Piao W, Song C, Wahl LM, Medvedev AE. Endotoxin tolerance impairs IL-1 receptor-associated kinase (IRAK) 4 and TGF-β-activated kinase 1 activation, K63-linked polyubiquitination and assembly of IRAK1, TNF receptor-associated factor 6, and IκB kinase γ and increases A20 expression. J Biol Chem 2011;286:7905–7916. PMID: 21220427.
crossref pmid pmc
41. Haji N, Mandolesi G, Gentile A, Sacchetti L, Fresegna D, Rossi S. TNF-α-mediated anxiety in a mouse model of multiple sclerosis. Exp Neurol 2012;237:296–303. PMID: 22836148.
crossref pmid
42. Kasper S, Hajak G, Wulff K, Hoogendijk WJ, Luis Montejo A, Smeraldi E, et al. Efficacy of the novel antidepressant agomelatine on the circadian rest-activity cycle and depressive and anxiety symptoms in patients with major depressive disorder: a randomized, double-blind comparison with sertraline. J Clin Psychiatry 2010;71:109–120. PMID: 20193645.
crossref pmid
43. Dobos N, Korf J, Luiten PG, Eisel UL. Neuroinflammation in Alzheimer's disease and major depression. Biol Psychiatry 2010;67:503–504. PMID: 20185031.
crossref pmid
44. Kim YK, Suh I, Kim H, Han C, Lim C, Choi S, et al. The plasma levels of interleukin-12 in schizophrenia, major depression, and bipolar mania: effects of psychotropic drugs. Mol Psychiatry 2002;7:1107–1114. PMID: 12476326.
crossref pmid
45. Loo YM, Gale M Jr. Immune signaling by RIG-I-like receptors. Immunity 2011;34:680–692. PMID: 21616437.
crossref pmid pmc
46. Lamont AG, Adorini L. IL-12: a key cytokine in immune regulation. Immunol Today 1996;17:214–217. PMID: 8991382.
crossref pmid
47. Kim M, Park HL, Park HW, Ro SH, Nam SG, Reed JM, et al. Drosophila Fip200 is an essential regulator of autophagy that attenuates both growth and aging. Autophagy 2013;9:1201–1213. PMID: 23819996.
crossref pmid pmc
48. Robinson DR, Wu YM, Lin SF. The protein tyrosine kinase family of the human genome. Oncogene 2000;19:5548–5557. PMID: 11114734.
crossref pmid
49. Torrey EF, Barci BM, Webster MJ, Bartko JJ, Meador-Woodruff JH, Knable MB. Neurochemical markers for schizophrenia, bipolar disorder, and major depression in postmortem brains. Biol Psychiatry 2005;57:252–260. PMID: 15691526.
crossref pmid
50. Bayer TA, Schramm M, Feldmann N, Knable MB, Falkai P. Antidepressant drug exposure is associated with mRNA levels of tyrosine receptor kinase B in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2000;24:881–888. PMID: 11041531.
crossref pmid
51. Bennett EP, Mandel U, Clausen H, Gerken TA, Fritz TA, Tabak LA. Control of mucin-type O-glycosylation: a classification of the polypeptide GalNAc-transferase gene family. Glycobiology 2012;22:736–756. PMID: 22183981.
crossref pmid
52. Marucci A, di Mauro L, Menzaghi C, Prudente S, Mangiacotti D, Fini G, et al. GALNT2 expression is reduced in patients with Type 2 diabetes: possible role of hyperglycemia. PLoS one 2013;8:e70159PMID: 23894607.
crossref pmid pmc
53. Kennedy SH, Evans KR, Krüger S, Mayberg HS, Meyer JH, McCann S, et al. Changes in regional brain glucose metabolism measured with positron emission tomography after paroxetine treatment of major depression. Am J Psychiatry 2001;158:899–905. PMID: 11384897.
crossref pmid
Figure 1

The screening DEGs in each group. hip: hippocampus, MDD: major depressive disorder, pfc: prefrontal cortex, str: striatum, DEG: defferentially expressed gene.

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Figure 2

The GO-BP terms of DEGs in each group. A: GO-BP terms of DEGs in MDD-str group. B: GO-BP terms of DEGs in MDD-pfc group. C: GO-BP terms of DEGs in MDD-hip group. GO-BP: gene ontology-biological process, DEG: differentially expressed gene, MDD: major depressive disorder.

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Figure 3

PPI network of DEGs in MDD-str group. Oval node stands for the up-regulated DEGs while rectangular node stands for the down-regulated DEGs. PPI: protein-protein interaction, DEG: differentially expressed gene, MDD: major depressive disorder.

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Figure 4

PPI network of DEGs in MDD-pfc group. Oval node stands for the up-regulated DEGs while rectangular node stands for the down-regulated DEGs. PPI: protein-protein interaction, DEG: differentially expressed gene, MDD: major depressive disorder.

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Figure 5

PPI network of DEGs in MDD-hip group. Oval node stands for the up-regulated DEGs while rectangular node stands for the down-regulated DEGs. PPI: protein-protein interaction, DEG: differentially expressed gene, MDD: major depressive disorder.

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Table 1

The KEGG pathway enrichment analysis of DEGs in each group

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MDD: major depressive disorder, hip: hippocampus, str: striatum, KEGG: Kyoto Enrichment of Genes and Genomes, DEG: differentially expressed gene