Xiaoyun Guo1, Zezhi Li2, Chen Zhang2, Zhenghui Yi2, Haozhe Li2, Lan Cao2, Chengmei Yuan2, Wu Hong2, Zhiguo Wu2, Daihui Peng2, Jun Chen2, Weiping Xia2, Guoqing Zhao2, Fan Wang2, Shunying Yu3, Donghong Cui3, Yifeng Xu3, Chowdhury M I Golam4, Alicia K Smith5, Tong Wang6, Yiru Fang7. 1. Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Cellular and Molecular Physiology, Yale University, New Haven, CT 06511, United States; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States. 2. Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 3. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 4. Magnetic Resonance Research Center, Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States. 5. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, 101 Woodruff Circle, Suite 4000, Atlanta, GA 30322, United States. 6. Department of Cellular and Molecular Physiology, Yale University, New Haven, CT 06511, United States. 7. Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: yirufang@gmail.com.
Abstract
BACKGROUND: Subsyndromal symptomatic depression (SSD) is a common disease with significant social dysfunction. However, SSD is still not well understood and the pathophysiology of it remains unclear. METHODS: We classified 48 candidate genes for SSD according to our previous study into clusters and pathways using DAVID Bioinformatics Functional Annotation Tool. We further replicated the result by using real-time Quantitative PCR (qPCR) studies to examine the expression of identified genes (i.e., STAT5b, PKCB1, ABL1 and NRAS) in another group of Han Chinese patients with SSD (n = 50). We further validated the result by examining PRKCB1 expression collected from MDD patients (n = 20). To test whether a deficit in PRKCB1 expression leads to dysregulation in PRKCB1 dependent transcript networks, we tested mRNA expression levels for the remaining 44 genes out of 48 genes in SSD patients. Finally, the power of discovery was improved by incorporating information from Quantitative Trait (eQTL) analysis. RESULTS: The results showed that the PRCKB1 gene expression in peripheral blood mononuclear cells (PBMC) was 33.3% down-regulated in SSD patients (n = 48, t = 3.202, p = 0.002), and a more dramatic (n = 17, 49%) down-regulation in MDD patients than control (n = 49, t = 2.114, p = 0.001). We also identified 37 genes that displayed a strong correlation with PRKCB1 mRNA expression levels in SSD patients. The expression of PRKCB1 was regulated by multiple single nucleotide polymorphisms (SNPs) both at the transcript level and exon level. CONCLUSIONS: In conclusion, we first found a significant decrease of PRCKB1 mRNA expression in SSD, suggesting PRKCB1 might be the candidate gene and biomarker for SSD.
BACKGROUND:Subsyndromal symptomatic depression (SSD) is a common disease with significant social dysfunction. However, SSD is still not well understood and the pathophysiology of it remains unclear. METHODS: We classified 48 candidate genes for SSD according to our previous study into clusters and pathways using DAVID Bioinformatics Functional Annotation Tool. We further replicated the result by using real-time Quantitative PCR (qPCR) studies to examine the expression of identified genes (i.e., STAT5b, PKCB1, ABL1 and NRAS) in another group of Han Chinese patients with SSD (n = 50). We further validated the result by examining PRKCB1 expression collected from MDDpatients (n = 20). To test whether a deficit in PRKCB1 expression leads to dysregulation in PRKCB1 dependent transcript networks, we tested mRNA expression levels for the remaining 44 genes out of 48 genes in SSDpatients. Finally, the power of discovery was improved by incorporating information from Quantitative Trait (eQTL) analysis. RESULTS: The results showed that the PRCKB1 gene expression in peripheral blood mononuclear cells (PBMC) was 33.3% down-regulated in SSDpatients (n = 48, t = 3.202, p = 0.002), and a more dramatic (n = 17, 49%) down-regulation in MDDpatients than control (n = 49, t = 2.114, p = 0.001). We also identified 37 genes that displayed a strong correlation with PRKCB1 mRNA expression levels in SSDpatients. The expression of PRKCB1 was regulated by multiple single nucleotide polymorphisms (SNPs) both at the transcript level and exon level. CONCLUSIONS: In conclusion, we first found a significant decrease of PRCKB1 mRNA expression in SSD, suggesting PRKCB1 might be the candidate gene and biomarker for SSD.