Differential co-expression and regulation analyses reveal different mechanisms underlying major depressive disorder and subsyndromal symptomatic depression

作者：Xu, Fan; Yang, Jing; Chen, Jin; Wu, Qingyuan; Gong, Wei; Zhang, Jianguo; Shao, Weihua; Mu, Jun; Yang, Deyu; Yang, Yongtao; Li, Zhiwei^*; Xie, Peng

来源：BMC Bioinformatics, 2015, 16(1): 112.

DOI：10.1186/s12859-015-0543-y

• Major depressive disorder
• MDD
• Subsyndromal symptomatic depression
• SSD
• Differential co-expression analysis
• DCEA
• Differential regulation analysis
• DRA
• Antidepressant
• Venlafaxine

Background: Recent depression research has revealed a growing awareness of how to best classify depression into depressive subtypes. Appropriately subtyping depression can lead to identification of subtypes that are more responsive to current pharmacological treatment and aid in separating out depressed patients in which current antidepressants are not particularly effective. Differential co-expression analysis (DCEA) and differential regulation analysis (DRA) were applied to compare the transcriptomic profiles of peripheral blood lymphocytes from patients with two depressive subtypes: major depressive disorder (MDD) and subsyndromal symptomatic depression (SSD). @@@ Results: Six differentially regulated genes (DRGs) (FOSL1, SRF, JUN, TFAP4, SOX9, and HLF) and 16 transcription factor-to-target differentially co-expressed gene links or pairs (TF2target DCLs) appear to be the key differential factors in MDD; in contrast, one DRG (PATZ1) and eight TF2target DCLs appear to be the key differential factors in SSD. There was no overlap between the MDD target genes and SSD target genes. Venlafaxine (Efexor (TM), Effexor (TM)) appears to have a significant effect on the gene expression profile of MDD patients but no significant effect on the gene expression profile of SSD patients. @@@ Conclusion: DCEA and DRA revealed no apparent similarities between the differential regulatory processes underlying MDD and SSD. This bioinformatic analysis may provide novel insights that can support future antidepressant R&D efforts.

• 出版日期2015-4-3
• 单位华东理工大学; 重庆医科大学