Abstract
Treatment response to antidepressants is limited and varies among patients with major depressive disorder (MDD). To discover genes and mechanisms related to the pathophysiology of MDD and antidepressant treatment response, we performed gene expression analyses using peripheral blood specimens from 38 MDD patients and 14 healthy individuals at baseline and at 6 weeks after the initiation of either selective serotonin reuptake inhibitor (SSRI) or mirtazapine treatment. The results were compared with results from public microarray data. Seven differentially expressed genes (DEGs) between MDD patients and controls were identified in our study and in the public microarray data: CD58, CXCL8, EGF, TARP, TNFSF4, ZNF583, and ZNF587. CXCL8 was among the top 10 downregulated genes in both studies. Eight genes related to SSRI responsiveness, including BTNL8, showed alterations in gene expression in MDD. The expression of the FCRL6 gene differed between SSRI responders and nonresponders and changed after SSRI treatment compared to baseline. In evaluating the response to mirtazapine, 21 DEGs were identified when comparing MDD patients and controls and responders and nonresponders. These findings suggest that the pathophysiology of MDD and treatment response to antidepressants are associated with a number of processes, including DNA damage and apoptosis, that can be induced by immune activation and inflammation.
Similar content being viewed by others
Introduction
Major depressive disorder (MDD) is a major burden in healthcare; worldwide, 12% of individuals suffer from MDD1,2. MDD is considered to develop as a consequence of environmental influences on genetic predispositions, but a definite pathogenesis of MDD remains obscure3,4. There have been several hypotheses for the pathogenesis of MDD, including alterations in neurotrophins, the neuroendocrine and neuroimmune systems, and molecules involved in brain neurotransmission, including monoamines and glutamate, as well as epigenetic mechanisms4,5,6,7,8,9. Other hypotheses include alterations in immune and inflammatory responses, oxidative stress, mitochondrial dysfunction, and disruption of DNA damage responses, and biomarkers related to these mechanisms have been proposed10,11,12,13,14,15,16,17. Although there are environmental factors known to be related to the development of MDD, such as stressful events in childhood, there is still no reliable biomarker that can explain the development of MDD or differences between MDD patients and healthy individuals.
For pharmacotherapy of MDD, many second-generation antidepressants are used, such as selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), atypical antidepressants, and serotonin modulators. SSRIs potentiate serotonin (5-HT) by inhibiting its neuronal uptake pump. Some SSRIs also have minor noradrenaline and dopamine reuptake inhibitory properties18. Mirtazapine has a dual mode of action, antagonizing the adrenergic α2-autoreceptors and α2-heteroreceptors as well as by blocking 5-HT2 and 5-HT3 receptors19. At least one-third of patients treated with second-generation antidepressants do not achieve response20,21. Although there is no evident difference in overall efficacy among second-generation antidepressants, individuals vary widely in their response to specific antidepressant treatments20,21,22. To predict responses to antidepressants and to choose the appropriate treatment for each individual, the discovery of biomarkers related to therapeutic efficacy is urgently required. Thus far, there have been few studies to identify biomarkers that could predict therapeutic response in MDD, and there is no absolute predictor to help guide the selection of antidepressants23,24,25,26,27,28,29,30,31.
RNA is the immediate expression product of genes and better reflects the current functional status of the biologic system than does DNA. Recently, a number of gene expression profiling studies aiming to discover genetic markers related to the pathogenesis of MDD and antidepressant treatment response using RNA from postmortem brain tissues and peripheral blood have been published32,33,34,35,36,37,81, were among the top 10 upregulated and downregulated genes in our study. Clinical research results on the relationships between these genes and MDD have not yet been reported, and the nature of alterations in immune and inflammatory responses has not been fully elucidated and might involve complex mechanisms10,58.
The genes involved in immune and inflammatory responses that were among the top 10 downregulated and upregulated genes related to responsiveness to SSRI treatment consisted of BTNL8, CLC, CTSW, FCRL6, GNAQ, HLA-DPB1, IGKC, KIR2DS1, NOD2, USP41, VNN1, and XCL1. The BTNL8, HSPH1, IGKC, KATNBL1, LYVE1, MIR15A, PTCH2, and SCARNA17 genes were also found to be differently expressed between MDD patients and controls. The BTNL8 gene, which plays an essential role in primary immune responses82, was among the top 10 downregulated and upregulated genes in both comparisons. In addition, the FCRL6 gene was more highly expressed in responders than in nonresponders and was downregulated relative to the control group after SSRI treatment only in responders. The FCRL6 gene encodes Fc receptor-like protein 6, which is involved in the interaction between cytotoxic lymphocytes and antigen-presenting cells (APCs) and is a member of the MHC class II receptor family83. Therefore, immune and inflammatory systems, including the BTNL8 and FCRL6 genes, might be involved in responsiveness to SSRIs as well as the underlying derangement in MDD. Previous transcriptomic studies have focused on the differences between MDD patients and healthy controls32,63,65,66,84, and there have been few clinical transcriptomic studies attempting to identify genetic biomarkers associated with antidepressant treatment response in MDD27,28,29,30,31. Our study findings on differences in gene expression according to treatment response and gene expression changes with antidepressant treatment have great implications for understanding and predicting treatment responses to antidepressants in MDD. In particular, our finding that the BTNL8 and FCRL6 genes have relatively large differences in expression in two comparisons (Table 3 and Supplementary Tables 2 and 3) has not been previously reported; accordingly, further validation studies will be required to confirm these findings.
Among the 281 genes that showed expression differences related to responsiveness to mirtazapine treatment, 21 were also differentially expressed between MDD patients and controls. In GO and pathway enrichment analyses of these 21 genes, GO terms involved in coagulation and DNA damage responses were identified. In the investigation of the 21 individual DEGs, the SLA2 and TOPORS genes, which might be involved in immune responses and apoptosis, were identified, as well as the CEP63 gene, which plays a role in the response to DNA damage. Disruption of DNA damage responses in MDD has previously been demonstrated in several studies11,12,13, including targeted gene expression analyses16,17. However, individual genes involved in DNA damage responses have not been previously identified in the context of MDD and antidepressant treatment. Our findings support a role for DNA damage as a result of immune activation in MDD, and the finding that the expression of these genes in responders to mirtazapine was closer to the expression levels of controls than to those of nonresponders implies that these genes and their mechanisms might be relevant to treatment resistance to mirtazapine.
In this study, we performed gene expression profiling and various comparison analyses. However, we should acknowledge the limitations of this study. We did not perform quantitative PCR, which has usually been employed in previous microarray studies, and we did not perform replicate analysis of each specimen. However, we validated our study results in other ways, including analysis of public microarray data, comparison with our cytokine study results55, and through replicates of comparisons that were performed in our study: between MDD patients and controls, between responders and nonresponders, and between baseline and 6 weeks after antidepressant treatment. Another limitation of our study involves the issues of study population and sample size. The lack of GO terms and pathways with statistically significant FDR-corrected p-values could be due to the limited sample size. Although our study population had a high proportion of older patients and females, MDD patients and healthy individuals did not show any clustering of baseline expression according to age and sex. The replication of our findings by future studies is needed to clarify and prove that the genes and mechanisms we identified are associated with MDD and responsiveness to antidepressant treatment. Our study has certain advantages in the discovery of genes related to antidepressant treatment response, which has rarely been a subject of transcriptomic studies, and in the classification of analyses according to type of antidepressant used. Most previous studies performed gene expression in MDD patients compared with healthy controls85. There have been few previous transcriptomic studies in MDD patients aiming to identify biomarkers related to antidepressant responsiveness27,28,29,30,31. Our study is the first to examine the treatment response to mirtazapine and to analyze changes in gene expression using genome-wide microarray techniques in responders compared to non-responders, and it is unique in that it was conducted in a non-Caucasian ethnic group39.
Various genes and mechanisms involved in immune and inflammatory responses, including the CXCL8, BTNL8, and FCRL6 genes, which are commonly related to T lymphocytes, were identified in our study. However, as in most previous studies, we did not confirm whether these differences and changes are the causes or the results of a depressive episode. The mechanism underlying these differences and their relation to disease and treatment response remains unknown because our results are preliminary and have not been verified by functional analysis. MDD is a multifactorial disorder, and a previous study proposed that complex and multiple mechanisms, including the disruption of oxidative stress response, damage to DNA and mitochondria, and neuroprogression including neuronal apoptosis and lowered neuroplasticity, as concomitants and sequelae of the activation of immune and inflammatory systems play roles in the development of MDD11. Various genes involved in mechanisms other than those mentioned above also showed alterations in expression in our study. Our study results support the complexity of the development and treatment response of MDD. The complete interrelationships among these multiple mechanisms cannot be detected with the current statistical analyses; thus, the application and development of further bioinformatic analyses would be required to dissect these complex associations in MDD. In addition, it will be necessary to correlate these findings with other studies, including proteomic and metabolic results, to thoroughly investigate the roles of the identified genes and mechanisms in the development of MDD and antidepressant responses.
In summary, our study identified several interesting genes and mechanisms that might be associated with MDD and treatment responsiveness to antidepressants using RNA microarray analyses of peripheral blood specimens from MDD patients. In this respect, our study provides clinical evidence relevant to previous theories on the development of MDD and can serve as a foundation for future studies on antidepressant treatment response. Our study results support the proposition that the development of MDD and antidepressant responses are associated with a series of events that includes DNA damage and apoptosis stemming from immune and inflammatory activation in MDD. Our study is the first to analyze changes in gene expression using genome-wide microarray techniques in treatment responders compared to non-responders. Our findings contribute to the elucidation of the biological disturbance of MDD and might lead to early therapeutic intervention and personalized medicine for the treatment of MDD with antidepressants. Specifically, genes that are involved in immune and inflammatory responses and their sequelae, including BTNL8, CXCL8, and FCRL6, are candidates for prediction of antidepressant treatment response as well as for diagnosis of MDD.
References
Kessler, R. C. et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 62, 593–602 (2005).
Kessler, R. C. et al. Development of lifetime comorbidity in the World Health Organization world mental health surveys. Arch. Gen. Psychiatry 68, 90–100 (2011).
Kendler, K. S., Karkowski, L. M. & Prescott, C. A. Causal relationship between stressful life events and the onset of major depression. Am. J. Psychiatry 156, 837–841 (1999).
Krishnan, V. & Nestler, E. J. The molecular neurobiology of depression. Nature 455, 894–902 (2008).
Shyn, S. I. et al. Novel loci for major depression identified by genome-wide association study of Sequenced Treatment Alternatives to Relieve Depression and meta-analysis of three studies. Mol. Psychiatry 16, 202–215 (2011).
Sullivan, P. F. et al. Genome-wide association for major depressive disorder: a possible role for the presynaptic protein piccolo. Mol. Psychiatry 14, 359–375 (2009).
Schildkraut, J. J. The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am. J. Psychiatry 122, 509–522 (1965).
Kilpatrick, D. G. et al. The serotonin transporter genotype and social support and moderation of posttraumatic stress disorder and depression in hurricane-exposed adults. Am. J. Psychiatry 164, 1693–1699 (2007).
Heim, C. et al. Effect of childhood trauma on adult depression and neuroendocrine function: sex-specific moderation by CRH Receptor 1 Gene. Front. Behav. Neurosci. 3, 41 (2009).
Gardner, A. & Boles, R. G. Beyond the serotonin hypothesis: mitochondria, inflammation and neurodegeneration in major depression and affective spectrum disorders. Prog. Neuropsychopharmacol. Biol. Psychiatry 35, 730–743 (2011).
Leonard, B. & Maes, M. Mechanistic explanations how cell-mediated immune activation, inflammation and oxidative and nitrosative stress pathways and their sequels and concomitants play a role in the pathophysiology of unipolar depression. Neurosci. Biobehav. Rev. 36, 764–785 (2012).
Czarny, P. et al. Elevated level of DNA damage and impaired repair of oxidative DNA damage in patients with recurrent depressive disorder. Med. Sci. Monit. 21, 412–418 (2015).
Black, C. N., Bot, M., Scheffer, P. G., Cuijpers, P. & Penninx, B. W. Is depression associated with increased oxidative stress? A systematic review and meta-analysis. Psychoneuroendocrinology 51, 164–175 (2015).
Moylan, S. et al. Oxidative & nitrosative stress in depression: why so much stress? Neurosci. Biobehav. Rev. 45, 46–62 (2014).
Jorgensen, A. et al. Systemic oxidatively generated DNA/RNA damage in clinical depression: associations to symptom severity and response to electroconvulsive therapy. J. Affect Disord. 149, 355–362 (2013).
Ota, K. T. et al. REDD1 is essential for stress-induced synaptic loss and depressive behavior. Nat. Med. 20, 531–535 (2014).
Teyssier, J. R., Chauvet-Gelinier, J. C., Ragot, S. & Bonin, B. Up-regulation of leucocytes genes implicated in telomere dysfunction and cellular senescence correlates with depression and anxiety severity scores. PLoS. One. 7, e49677 (2012).
Vaswani, M., Linda, F. K. & Ramesh, S. Role of selective serotonin reuptake inhibitors in psychiatric disorders: a comprehensive review. Prog. Neuropsychopharmacol. Biol. Psychiatry 27, 85–102 (2003).
Anttila, S. A. & Leinonen, E. V. A review of the pharmacological and clinical profile of mirtazapine. Cns. Drug. Rev. 7, 249–264 (2001).
Gartlehner, G. et al. Second-Generation Antidepressants in the Pharmacologic Treatment of Adult Depression: An Update of the 2007 Comparative Effectiveness Review: Major Depressive Disorder: Detailed Analysis. (Agency for Healthcare Research and Quality (US), MD, USA, 2011).
Gartlehner, G. et al. Comparative benefits and harms of second-generation antidepressants for treating major depressive disorder: an updated meta-analysis. Ann. Intern. Med. 155, 772–785 (2011).
Simon, G. E. & Perlis, R. H. Personalized medicine for depression: can we match patients with treatments? Am. J. Psychiatry 167, 1445–1455 (2010).
Zhu, H. et al. Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder—possible role for methoxyindole pathway. PLoS. One. 8, e68283 (2013).
McGrath, C. L. et al. Toward a neuroimaging treatment selection biomarker for major depressive disorder. JAMA Psychiatry 70, 821–829 (2013).
Janicak, P. G., Davis, J. M., Chan, C., Altman, E. & Hedeker, D. Failure of urinary MHPG levels to predict treatment response in patients with unipolar depression. Am. J. Psychiatry 143, 1398–1402 (1986).
Cobbin, D. M., Requin-Blow, B., Williams, L. R. & Williams, W. O. Urinary MHPG levels and tricyclic antidepressant drug selection. A preliminary communication on improved drug selection in clinical practice. Arch. Gen. Psychiatry 36, 1111–1115 (1979).
Belzeaux, R. et al. Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode. Transl. Psychiatry 2, e185 (2012).
Mamdani, F. et al. Gene expression biomarkers of response to citalopram treatment in major depressive disorder. Transl. Psychiatry 1, e13 (2011).
Guilloux, J. P. et al. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression. Neuropsychopharmacology 40, 701–710 (2015).
Hennings, J. M. et al. RNA expression profiling in depressed patients suggests retinoid-related orphan receptor alpha as a biomarker for antidepressant response. Transl. Psychiatry 5, e538 (2015).
Redei, E. E. et al. Blood transcriptomic biomarkers in adult primary care patients with major depressive disorder undergoing cognitive behavioral therapy. Transl. Psychiatry 4, e442 (2014).
Shelton, R. C. et al. Altered expression of genes involved in inflammation and apoptosis in frontal cortex in major depression. Mol. Psychiatry 16, 751–762 (2011).
Kang, H. J. et al. Gene expression profiling in postmortem prefrontal cortex of major depressive disorder. J. Neurosci. 27, 13329–13340 (2007).
Tochigi, M. et al. Gene expression profiling of major depression and suicide in the prefrontal cortex of postmortem brains. Neurosci. Res. 60, 184–191 (2008).
Yi, Z. et al. Blood-based gene expression profiles models for classification of subsyndromal symptomatic depression and major depressive disorder. PLoS. ONe. 7, e31283 (2012).
Liu, Z. et al. Microarray profiling and co-expression network analysis of circulating lncRNAs and mRNAs associated with major depressive disorder. PLoS. ONE. 9, e93388 (2014).
Zhurov, V. et al. Molecular pathway reconstruction and analysis of disturbed gene expression in depressed individuals who died by suicide. PLoS. ONE. 7, e47581 (2012).
Gao, L., Gao, Y., Xu, E. & **e, J. Microarray Analysis of the Major Depressive Disorder mRNA Profile Data. Psychiatry Investig. 12, 388–396 (2015).
Lin, E. & Tsai, S. J. Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy. Prog. Neuropsychopharmacol. Biol. Psychiatry 64, 334–340 (2016).
Spijker, S. et al. Stimulated gene expression profiles as a blood marker of major depressive disorder. Biol. Psychiatry 68, 179–186 (2010).
Menke, A. et al. Dexamethasone stimulated gene expression in peripheral blood is a sensitive marker for glucocorticoid receptor resistance in depressed patients. Neuropsychopharmacology 37, 1455–1464 (2012).
Kim, D. K. et al. Serotonin transporter gene polymorphism and antidepressant response. Neuroreport 11, 215–219 (2000).
First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W. User’s Guide for the Structured Clinical Interview for DSM-IV Axis I Disorders SCID-I: Clinician Version. (American Psychiatric Press, Washington, DC, USA, 1997).
Hamilton, M. A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62 (1960).
Sacco, K. & Grech, G. Actionable pharmacogenetic markers for prediction and prognosis in breast cancer. EPMA J. 6, 15 (2015).
Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).
Murtagh, F. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J. Classif. 31, 274–295 (2014).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Huang da, W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).
Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).
Milacic, M. et al. Annotating cancer variants and anti-cancer therapeutics in reactome. Cancers (Basel) 4, (1180–1211 (2012).
Cerami, E. G. et al. Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Myung, W. et al. Serum cytokine levels in major depressive disorder and its role in antidepressant response. Psychiatry Investig. 13, 644–651 (2016).
Dowlati, Y. et al. A meta-analysis of cytokines in major depression. Biol. Psychiatry 67, 446–457 (2010).
Kern, S. et al. Higher CSF interleukin-6 and CSF interleukin-8 in current depression in older women. Results from a population-based sample. Brain Behav. Immun. 41, 55–58 (2014).
Lehto, S. M. et al. Serum chemokine levels in major depressive disorder. Psychoneuroendocrinology 35, 226–232 (2010).
Song, Y., Zhou, D., Guan, Z. & Wang, X. Disturbance of serum interleukin-2 and interleukin-8 levels in posttraumatic and non-posttraumatic stress disorder earthquake survivors in northern China. Neuroimmunomodulation 14, 248–254 (2007).
Powell, T. R. et al. Putative transcriptomic biomarkers in the inflammatory cytokine pathway differentiate major depressive disorder patients from control subjects and bipolar disorder patients. PLoS. ONE. 9, e91076 (2014).
Kaizuka, Y., Douglass, A. D., Vardhana, S., Dustin, M. L. & Vale, R. D. The coreceptor CD2 uses plasma membrane microdomains to transduce signals in T cells. J. Cell. Biol. 185, 521–534 (2009).
Hecker, M., Fitzner, B., Blaschke, J., Blaschke, P. & Zettl, U. K. Susceptibility variants in the CD58 gene locus point to a role of microRNA-548ac in the pathogenesis of multiple sclerosis. Mutat. Res Rev. Mutat. Res 763, 161–167 (2015).
Garbett, K. A. et al. Coordinated messenger RNA/microRNA changes in fibroblasts of patients with major depression. Biol. Psychiatry 77, 256–265 (2015).
Carvalho, L. A. et al. Inflammatory activation is associated with a reduced glucocorticoid receptor alpha/beta expression ratio in monocytes of inpatients with melancholic major depressive disorder. Transl. Psychiatry 4, e344 (2014).
Jansen, R. et al. Gene expression in major depressive disorder. Mol. Psychiatry 21, 339–347 (2016).
Mostafavi, S. et al. Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing. Mol. Psychiatry 19, 1267–1274 (2014).
Jansen, R. et al. Gene expression in major depressive disorder. Mol. Psychiatry 21, 444 (2016).
Maes, M. et al. Immune disturbances during major depression: upregulated expression of interleukin-2 receptors. Neuropsychobiology 24, 115–120 (1990).
Maes, M., Smith, R. & Scharpe, S. The monocyte-T-lymphocyte hypothesis of major depression. Psychoneuroendocrinology 20, 111–116 (1995).
Maes, M. et al. The inflammatory & neurodegenerative (I&ND) hypothesis of depression: leads for future research and new drug developments in depression. Metab. Brain. Dis. 24, 27–53 (2009).
Muller, N. et al. The cyclooxygenase-2 inhibitor celecoxib has therapeutic effects in major depression: results of a double-blind, randomized, placebo controlled, add-on pilot study to reboxetine. Mol. Psychiatry 11, 680–684 (2006).
Nery, F. G. et al. Celecoxib as an adjunct in the treatment of depressive or mixed episodes of bipolar disorder: a double-blind, randomized, placebo-controlled study. Hum. Psychopharmacol. 23, 87–94 (2008).
Hannestad, J., DellaGioia, N. & Bloch, M. The effect of antidepressant medication treatment on serum levels of inflammatory cytokines: a meta-analysis. Neuropsychopharmacology 36, 2452–2459 (2011).
Abbasi, S. H., Hosseini, F., Modabbernia, A., Ashrafi, M. & Akhondzadeh, S. Effect of celecoxib add-on treatment on symptoms and serum IL-6 concentrations in patients with major depressive disorder: randomized double-blind placebo-controlled study. J. Affect Disord. 141, 308–314 (2012).
Lledo, P. M., Alonso, M. & Grubb, M. S. Adult neurogenesis and functional plasticity in neuronal circuits. Nat. Rev. Neurosci. 7, 179–193 (2006).
Schmidt, H. D. & Duman, R. S. The role of neurotrophic factors in adult hippocampal neurogenesis, antidepressant treatments and animal models of depressive-like behavior. Behav. Pharmacol. 18, 391–418 (2007).
O’Keeffe, G. C. et al. Dopamine-induced proliferation of adult neural precursor cells in the mammalian subventricular zone is mediated through EGF. Proc. Natl Acad. Sci. USA 106, 8754–8759 (2009).
Tian, W. et al. A study of the functional significance of epidermal growth factor in major depressive disorder. Psychiatr. Genet. 22, 161–167 (2012).
Sen, S., Duman, R. & Sanacora, G. Serum brain-derived neurotrophic factor, depression, and antidepressant medications: meta-analyses and implications. Biol. Psychiatry 64, 527–532 (2008).
Evans, S. J. et al. Dysregulation of the fibroblast growth factor system in major depression. Proc. Natl Acad. Sci. USA 101, 15506–15511 (2004).
Tsapakis, E. M. et al. Effects of antidepressant drug exposure on gene expression in the develo** cerebral cortex. Synapse 68, 209–220 (2014).
Chapoval, A. I. et al. BTNL8, a butyrophilin-like molecule that costimulates the primary immune response. Mol. Immunol. 56, 819–828 (2013).
Schreeder, D. M. et al. Cutting edge: FcR-like 6 is an MHC class II receptor. J. Immunol. 185, 23–27 (2010).
Sibille, E. et al. A molecular signature of depression in the amygdala. Am. J. Psychiatry 166, 1011–1024 (2009).
Forero, D. A., Guio-Vega, G. P. & Gonzalez-Giraldo, Y. A comprehensive regional analysis of genome-wide expression profiles for major depressive disorder. J. Affect Disord. 218, 86–92 (2017).
Acknowledgements
Bernard J. Carroll, MB, BS, Ph.D., advised on several issues of analysis and interpretation, and edited the manuscript. This study was supported by grants from the Korea Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (A110339, HI14C2071), and the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (NRF-2017R1A2A1A17069653; DK Kim).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Woo, H.I., Lim, SW., Myung, W. et al. Differentially expressed genes related to major depressive disorder and antidepressant response: genome-wide gene expression analysis. Exp Mol Med 50, 1–11 (2018). https://doi.org/10.1038/s12276-018-0123-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s12276-018-0123-0
- Springer Nature Limited
This article is cited by
-
Epigenome-wide analysis identifies methylome profiles linked to obsessive-compulsive disorder, disease severity, and treatment response
Molecular Psychiatry (2023)
-
Downregulation of peripheral PTGS2/COX-2 in response to valproate treatment in patients with epilepsy
Scientific Reports (2020)
-
A Novel Animal Model for Studying Depression Featuring the Induction of the Unfolded Protein Response in Hippocampus
Molecular Neurobiology (2019)