Blood-based gene expression signatures of medication-free outpatients with major depressive disorder: Integrative genome-wide and candidate gene analyses

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Hori, Hiroaki ^a,b   Sasayama, Daimei ^a   Teraishi, Toshiya ^a   Yamamoto, Noriko ^a   Nakamura, Seiji ^c   Ota, Miho ^a   Hattori, Kotaro ^a   Kim, Yoshiharu ^b   Higuchi, Teruhiko ^b   Kunugi, Hiroshi ^a  

^a NATIONAL INSTITUTE OF NEUROSCIENCE   (Japan)

^b NATIONAL INSTITUTE OF MENTAL HEALTH   (Japan)

^c DNA CHIP RESEARCH INC   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; TRANSCRIPTOME;

BIOLOGY; BLOOD; CASE CONTROL STUDY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETIC ASSOCIATION STUDY; GENETIC PREDISPOSITION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN; MAJOR DEPRESSION; METABOLISM; OUTPATIENT; PROCEDURES; PROTEIN ANALYSIS; PROTEIN PROTEIN INTERACTION;

BIOMARKERS; CASE-CONTROL STUDIES; COMPUTATIONAL BIOLOGY; DEPRESSIVE DISORDER, MAJOR; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; OUTPATIENTS; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; TRANSCRIPTOME;

EID: 84953215951     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep18776     Document Type: Article

References (60)

1. Reis-Filho, J. S., Pusztai, L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 378, 1812-1823 (2011).
2. Mesko, B., Poliska, S., Nagy, L. Gene expression profiles in peripheral blood for the diagnosis of autoimmune diseases. Trends Mol. Med. 17, 223-233 (2011).
3. Scherzer, C. R. et al. Molecular markers of early Parkinson's disease based on gene expression in blood. Proc. Natl. Acad. Sci. USA 104, 955-960 (2007).
4. Gibson, G. The environmental contribution to gene expression profiles. Nat. Rev. Genet. 9, 575-581 (2008).
5. Segman, R. H. et al. Peripheral blood mononuclear cell gene expression profiles identify emergent post-traumatic stress disorder among trauma survivors. Mol. Psychiatry 10, 500-513 (2005).
6. Kumarasinghe, N., Tooney, P. A., Schall, U. Finding the needle in the haystack: a review of microarray gene expression research into schizophrenia. Aust. N. Z. J. Psychiatry 46, 598-610 (2012).
7. Munkholm, K., Vinberg, M., Berk, M., Kessing, L. V. State-related alterations of gene expression in bipolar disorder: a systematic review. Bipolar Disord. 14, 684-696 (2012).
8. Hepgul, N., Cattaneo, A., Zunszain, P. A., Pariante, C. M. Depression pathogenesis and treatment: what can we learn from blood mRNA expression BMC Med. 11, 28 (2013).
9. Mehta, D., Menke, A., Binder, E. B. Gene expression studies in major depression. Curr. Psychiatry Rep. 12, 135-144 (2010).
10. Liew, C. C., Ma, J., Tang, H. C., Zheng, R., Dempsey, A. A. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J. Lab. Clin. Med. 147, 126-132 (2006).
11. Rollins, B., Martin, M. V., Morgan, L., Vawter, M. P. Analysis of whole genome biomarker expression in blood and brain. Am. J. Med. Genet. B Neuropsychiatr. Genet. 153B, 919-936 (2010).
12. Iwamoto, K., Kato, T. Gene expression profiling in schizophrenia and related mental disorders. Neuroscientist 12, 349-361 (2006).
13. Segman, R. H. et al. Blood mononuclear cell gene expression signature of postpartum depression. Mol. Psychiatry 15, 93-100 (2010).
14. Belzeaux, R. et al. Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode. Transl. Psychiatry 2, e185 (2012).
15. 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).
16. 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).
17. 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).
18. 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).
19. Jansen, R. et al. Gene expression in major depressive disorder. Mol. Psychiatry (2015), doi: 10.1038/mp.2015.94. [Epub ahead of print].
20. 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).
21. 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).
22. Flint, J., Kendler, K. S. The genetics of major depression. Neuron 81, 484-503 (2014).
23. Barabási, A. L., Gulbahce, N., Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
24. Schadt, E. E. Molecular networks as sensors and drivers of common human diseases. Nature 461, 218-223 (2009).
25. Ramanan, V. K., Shen, L., Moore, J. H., Saykin, A. J. Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet. 28, 323-332 (2012).
26. Gormanns, P. et al. Phenome-transcriptome correlation unravels anxiety and depression related pathways. J. Psychiatr. Res. 45, 973-979 (2011).
27. Kao, C. F., Fang, Y. S., Zhao, Z., Kuo, P. H. Prioritization and evaluation of depression candidate genes by combining multidimensional data resources. PLoS One 6, e18696 (2011).
28. Pertea, M., Salzberg, S. L. Between a chicken and a grape: estimating the number of human genes. Genome Biol. 11, 206 (2010).
29. Green, E. K. et al. The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol. Psychiatry 15, 1016-1022 (2010).
30. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371-1379 (2013).
31. Sun, D. S. et al. Identification, molecular characterization, and chromosomal localization of the cDNA encoding a novel leucine zipper motif-containing protein. Genomics 36, 54-62 (1996).
32. Davidson, J. D. et al. Identification and characterization of an ataxin-1-interacting protein: A1Up, a ubiquitin-like nuclear protein. Hum. Mol. Genet. 9, 2305-2312 (2000).
33. Schnaar, R. L., Gerardy-Schahn, R., Hildebrandt, H. Sialic acids in the brain: gangliosides and polysialic acid in nervous system development, stability, disease, and regeneration. Physiol. Rev. 94, 461-518 (2014).
34. Malki, K. et al. The endogenous and reactive depression subtypes revisited: integrative animal and human studies implicate multiple distinct molecular mechanisms underlying major depressive disorder. BMC Med. 12, 73 (2014).
35. Hunter, A. M. et al. A genome-wide association study of a sustained pattern of antidepressant response. J. Psychiatr. Res. 47, 1157-1165 (2013).
36. Galtrey, C. M., Fawcett, J. W. The role of chondroitin sulfate proteoglycans in regeneration and plasticity in the central nervous system. Brain Res. Rev. 54, 1-18 (2007).
37. Gelernter, J. et al. Genome-wide association study of nicotine dependence in American populations: identification of novel risk loci in both African-Americans and European-Americans. Biol. Psychiatry 77, 493-503 (2015).
38. Sato, T. et al. Chondroitin sulfate N-acetylgalactosaminyltransferase 1 is necessary for normal endochondral ossification and aggrecan metabolism. J. Biol. Chem. 286, 5803-5812 (2011).
39. Gao, L., Gao, Y., Xu, E., Xie, J. Microarray Analysis of the Major Depressive Disorder mRNA Profile Data. Psychiatry Investig. 12, 388-396 (2015).
40. Riggs, D. L. et al. The Hsp90-binding peptidylprolyl isomerase FKBP52 potentiates glucocorticoid signaling in vivo. EMBO J. 22, 1158-1167 (2003).
41. Tatro, E. T., Everall, I. P., Kaul, M., Achim, C. L. Modulation of glucocorticoid receptor nuclear translocation in neurons by immunophilins FKBP51 and FKBP52: implications for major depressive disorder. Brain Res. 1286, 1-12 (2009).
42. Kimmel, R. J. et al. Cosegregation of bipolar disorder and autosomal-dominant medullary cystic kidney disease in a large family. Am. J. Psychiatry 162, 1972-1974 (2005).
43. Gaiteri, C., Sibille, E. Differentially expressed genes in major depression reside on the periphery of resilient gene coexpression networks. Front. Neurosci. 5, 95 (2011).
44. 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).
45. Wojcik, S. M., Brose, N. Regulation of membrane fusion in synaptic excitation-secretion coupling: speed and accuracy matter. Neuron 55, 11-24 (2007).
46. Glynn, D. et al. Clorgyline-mediated reversal of neurological deficits in a Complexin 2 knockout mouse. Hum. Mol. Genet. 19, 3402-3412 (2010).
47. Li, X. H. et al. Gene expression profile of the hippocampus of rats subjected to chronic immobilization stress. PLoS One 8, e57621 (2013).
48. Andrus, B. M. et al. Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models. Mol. Psychiatry 17, 49-61 (2012).
49. Su, Z. et al. An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol. 15, 523 (2014).
50. Wang, C. et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32, 926-932 (2014).
51. Le-Niculescu, H. et al. Identifying blood biomarkers for mood disorders using convergent functional genomics. Mol. Psychiatry 14, 156-174 (2009).
52. Rung, J., Brazma, A. Reuse of public genome-wide gene expression data. Nat. Rev. Genet. 14, 89-99 (2013).
53. First, M. B., Spitzer, R. L., Gibbon, M., Williams, J. User's Guide for the Structured Clinical Interview for DSM-IV Axis I Disorders. (New York State Psychiatric Institute, Biometrics Research Department, 1995).
54. Sheehan, D. V. et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59 (Suppl 20), 22-57 (1998).
55. Hamilton, M. Development of a rating scale for primary depressive illness. Br. J. Soc. Clin. Psychol. 6, 278-296 (1967).
56. Menchetti, M. et al. Moderators of remission with interpersonal counselling or drug treatment in primary care patients with depression: randomised controlled trial. Br. J. Psychiatry 204, 144-150 (2014).
57. Shi, L. M. et al. The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151-1161 (2006).
58. Yeung, K. Y., Medvedovic, M., Bumgarner, R. E. Clustering gene-expression data with repeated measurements. Genome Biol. 4, R34 (2003).
59. 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 (2008).
60. Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-815 (2013).