Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

Journal of Psychiatric Research

Volumn 81, Issue , 2016, Pages 119-126

  Belzeaux, Raoul ^a,b,c   Lin, Chien Wei ^d   Ding, Ying ^d   Bergon, Aurélie ^e   Ibrahim, El Chérif ^b,c   Turecki, Gustavo ^a   Tseng, George ^d   Sibille, Etienne ^f  

^a MCGILL UNIVERSITY   (Canada)

^b FONDATION FONDAMENTAL   (France)

^c University of Mediterranée   (France)

^d UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

^e Aix Marseille university   (France)

^f UNIVERSITY OF TORONTO   (Canada)

Author keywords

Antidepressant; Gene network; Inflammation; MicroRNA; Mood disorder

Indexed keywords

BETA 2 ADRENERGIC RECEPTOR; CITALOPRAM; HISTONE; INTERLEUKIN 8; MAMMALIAN TARGET OF RAPAMYCIN; MICRORNA; MICRORNA 135; MICRORNA 16; UBIQUITIN; UNCLASSIFIED DRUG; ANTIDEPRESSANT AGENT; IL6 PROTEIN, HUMAN; INTERLEUKIN 6;

ADULT; ARTICLE; BLOOD SAMPLING; CLINICAL ARTICLE; COHORT ANALYSIS; CONTROLLED STUDY; CYTOKINE PRODUCTION; DISEASE PREDISPOSITION; EPIGENETICS; FEMALE; FOLLOW UP; GENE EXPRESSION; GENE EXPRESSION REGULATION; GENE ONTOLOGY; HISTONE MODIFICATION; HUMAN; INFLAMMATION; MAJOR DEPRESSION; MALE; MONOAMINERGIC SYSTEM; PRIORITY JOURNAL; REMISSION; RESPIRATORY CHAIN; TREATMENT DURATION; TREATMENT RESPONSE; DEPRESSIVE DISORDER, MAJOR; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETIC PREDISPOSITION; GENETICS; MIDDLE AGED; YOUNG ADULT;

ADULT; ANTIDEPRESSIVE AGENTS, SECOND-GENERATION; CITALOPRAM; COHORT STUDIES; DEPRESSIVE DISORDER, MAJOR; FEMALE; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENETIC PREDISPOSITION TO DISEASE; HUMANS; INTERLEUKIN-6; MALE; MICRORNAS; MIDDLE AGED; YOUNG ADULT;

EID: 84978484144     PISSN: 00223956     EISSN: 18791379     Source Type: Journal    
DOI: 10.1016/j.jpsychires.2016.07.009     Document Type: Article

References (47)

1. Abelaira, H.M., Reus, G.Z., Neotti, M.V., Quevedo, J., The role of mTOR in depression and antidepressant responses. Life Sci. 101 (2014), 10–14.
2. Barca-Mayo, O., De Pietri Tonelli, D., Convergent microRNA actions coordinate neocortical development. Cell Mol. Life Sci. 71 (2014), 2975–2995.
3. Baudry, A., Mouillet-Richard, S., Launay, J.M., Kellermann, O., New views on antidepressant action. Curr. Opin. Neurobiol. 21 (2011), 858–865.
4. Belzeaux, R., Bergon, A., Jeanjean, V., Loriod, B., Formisano-Treziny, C., Verrier, L., et al. Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode. Transl. Psychiatry, 2, 2012, e185.
5. Biernacka, J.M., Sangkuhl, K., Jenkins, G., Whaley, R.M., Barman, P., Batzler, A., et al. The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl. Psychiatry, 5, 2015, e553.
6. Carvalho, L.A., Bergink, V., Sumaski, L., Wijkhuijs, J., Hoogendijk, W.J., Birkenhager, T.K., 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, 2014, e344.
7. Cattaneo, A., Gennarelli, M., Uher, R., Breen, G., Farmer, A., Aitchison, K.J., et al. Candidate genes expression profile associated with antidepressants response in the GENDEP study: differentiating between baseline ’predictors’ and longitudinal ’targets’. Neuropsychopharmacol. Off. Publ. Am. Coll. Neuropsychopharmacol. 38 (2013), 377–385.
8. Chang, L.C., Jamain, S., Lin, C.W., Rujescu, D., Tseng, G.C., Sibille, E., A conserved BDNF, glutamate- and GABA-enriched gene module related to human depression identified by coexpression meta-analysis and DNA variant genome-wide association studies. PloS One, 9, 2014, e90980.
9. Chen, C., Cheng, L., Grennan, K., Pibiri, F., Zhang, C., Badner, J.A., et al. Two gene co-expression modules differentiate psychotics and controls. Mol. Psychiatry 18 (2013), 1308–1314.
10. Chen, J., Bardes, E.E., Aronow, B.J., Jegga, A.G., ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37 (2009), W305–W311.
11. Dahl, J., Ormstad, H., Aass, H.C., Malt, U.F., Bendz, L.T., Sandvik, L., et al. The plasma levels of various cytokines are increased during ongoing depression and are reduced to normal levels after recovery. Psychoneuroendocrinology 45 (2014), 77–86.
12. Dias, C., Feng, J., Sun, H., Shao, N.Y., Mazei-Robison, M.S., Damez-Werno, D., et al. beta-catenin mediates stress resilience through Dicer1/microRNA regulation. Nature 516 (2014), 51–55.
13. Ding, Y., Chang, L.C., Wang, X., Guilloux, J.P., Parrish, J., Oh, H., et al. Molecular and genetic characterization of depression: overlap with other psychiatric disorders and aging. Mol. Neuropsychiatry 1 (2015), 1–12.
14. Dwivedi, Y., Pathogenetic and therapeutic applications of microRNAs in major depressive disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 64 (2016), 341–348.
15. Dwivedi, Y., Roy, B., Lugli, G., Rizavi, H., Zhang, H., Smalheiser, N.R., Chronic corticosterone-mediated dysregulation of microRNA network in prefrontal cortex of rats: relevance to depression pathophysiology. Transl. Psychiatry, 5, 2015, e682.
16. Gaiteri, C., Ding, Y., French, B., Tseng, G.C., Sibille, E., Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13 (2014), 13–24.
17. Gaiteri, C., Guilloux, J.P., Lewis, D.A., Sibille, E., Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression. PloS One, 5, 2010, e9970.
18. Gaiteri, C., Sibille, E., Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks. Front. Neurosci., 5, 2011, 95.
19. Guilloux, J.P., Bassi, S., Ding, Y., Walsh, C., Turecki, G., Tseng, G., et al. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression. Neuropsychopharmacol. Off. Publ. Am. Coll. Neuropsychopharmacol. 40 (2015), 701–710.
20. Hall, K.T., Loscalzo, J., Kaptchuk, T.J., Genetics and the placebo effect: the placebome. Trends Mol. Med. 21 (2015), 285–294.
21. Hochberg, Y., Benjamini, Y., More powerful procedures for multiple significance testing. Stat. Med. 9 (1990), 811–818.
22. Hodgson, K., Tansey, K.E., Powell, T.R., Coppola, G., Uher, R., Zvezdana Dernovsek, M., et al. Transcriptomics and the mechanisms of antidepressant efficacy. Eur. Neuropsychopharmacol. J. Eur. Coll. Neuropsychopharmacol. 26 (2016), 105–112.
23. Issler, O., Haramati, S., Paul, E.D., Maeno, H., Navon, I., Zwang, R., et al. MicroRNA 135 is essential for chronic stress resiliency, antidepressant efficacy, and intact serotonergic activity. Neuron 83 (2014), 344–360.
24. Kessler, R.C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K.R., et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA 289 (2003), 3095–3105.
25. Langfelder, P., Horvath, S., Eigengene networks for studying the relationships between co-expression modules. BMC Syst. Biol., 1, 2007, 54.
26. Langfelder, P., Horvath, S.W.G.C.N.A., An R package for weighted correlation network analysis. BMC Bioinforma., 9, 2008, 559.
27. Langfelder, P., Luo, R., Oldham, M.C., Horvath, S., Is my network module preserved and reproducible?. PLoS Comput. Biol., 7, 2011, e1001057.
28. Launay, J.M., Mouillet-Richard, S., Baudry, A., Pietri, M., Kellermann, O., Raphe-mediated signals control the hippocampal response to SRI antidepressants via miR-16. Transl. Psychiatry, 1, 2011, e56.
29. Libbrecht, M.W., Noble, W.S., Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16 (2015), 321–332.
30. Lin, E., Tsai, S.J., Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy. Prog. Neuropsychopharmacol. Biol. Psychiatry 64 (2016), 334–340.
31. Lopez, J.P., Lim, R., Cruceanu, C., Crapper, L., Fasano, C., Labonte, B., et al. miR-1202 is a primate-specific and brain-enriched microRNA involved in major depression and antidepressant treatment. Nat. Med. 20 (2014), 764–768.
32. Mamdani, F., Berlim, M.T., Beaulieu, M.M., Labbe, A., Merette, C., Turecki, G., Gene expression biomarkers of response to citalopram treatment in major depressive disorder. Transl. Psychiatry, 1, 2011, e13.
33. Menke, A., Gene expression: biomarker of antidepressant therapy?. Int. Rev. Psychiatry 25 (2013), 579–591.
34. Rush, A.J., Trivedi, M.H., Wisniewski, S.R., Nierenberg, A.A., Stewart, J.W., Warden, D., et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163 (2006), 1905–1917.
35. Strawbridge, R., Arnone, D., Danese, A., Papadopoulos, A., Herane Vives, A., Cleare, A.J., Inflammation and clinical response to treatment in depression: a meta-analysis. Eur. Neuropsychopharmacol. J. Eur. Coll. Neuropsychopharmacol. 25 (2015), 1532–1543.
36. Szewczyk, B., Pochwat, B., Rafalo, A., Palucha-Poniewiera, A., Domin, H., Nowak, G., Activation of mTOR dependent signaling pathway is a necessary mechanism of antidepressant-like activity of zinc. Neuropharmacology 99 (2015), 517–526.
37. Tansey, K.E., Guipponi, M., Hu, X., Domenici, E., Lewis, G., Malafosse, A., et al. Contribution of common genetic variants to antidepressant response. Biol. Psychiatry 73 (2013), 679–682.
38. Thase, M.E., Using biomarkers to predict treatment response in major depressive disorder: evidence from past and present studies. Dialogues Clin. Neurosci. 16 (2014), 539–544.
39. Torkamani, A., Dean, B., Schork, N.J., Thomas, E.A., Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res. 20 (2010), 403–412.
40. Tusher, V.G., Tibshirani, R., Chu, G., Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 98 (2001), 5116–5121.
41. Uher, R., Tansey, K.E., Dew, T., Maier, W., Mors, O., Hauser, J., et al. An inflammatory biomarker as a differential predictor of outcome of depression treatment with escitalopram and nortriptyline. Am. J. Psychiatry 171 (2014), 1278–1286.
42. Vetere, G., Barbato, C., Pezzola, S., Frisone, P., Aceti, M., Ciotti, M., et al. Selective inhibition of miR-92 in hippocampal neurons alters contextual fear memory. Hippocampus 24 (2014), 1458–1465.
43. Voineagu, I., Wang, X., Johnston, P., Lowe, J.K., Tian, Y., Horvath, S., et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474 (2011), 380–384.
44. Xu, Y., Yue, W., Yao Shugart, Y., Li, S., Cai, L., Li, Q., et al. Exploring transcription factors-microRNAs Co-regulation networks in schizophrenia. Schizophr. Bull. 42 (2016), 1037–1045.
45. Yu, H.C., Wu, J., Zhang, H.X., Zhang, G.L., Sui, J., Tong, W.W., et al. Alterations of miR-132 are novel diagnostic biomarkers in peripheral blood of schizophrenia patients. Prog. Neuropsychopharmacol. Biol. Psychiatry 63 (2015), 23–29.
46. Zhang, B., Horvath, S., A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol., 4, 2005 Article17.
47. Zhu, X.L., Ai, Z.H., Wang, J., Xu, Y.L., Teng, Y.C., Weighted gene co-expression network analysis in identification of endometrial cancer prognosis markers. Asian Pac. J. Cancer Prev. 13 (2012), 4607–4611.