Yong Xu1, Weihua Yue2, Yin Yao Shugart3, Sheng Li4, Lei Cai4, Qiang Li5, Zaohuo Cheng6, Guoqiang Wang6, Zhenhe Zhou6, Chunhui Jin6, Jianmin Yuan6, Lin Tian6, Jun Wang6, Kai Zhang6, Kerang Zhang1, Sha Liu1, Yuqing Song7, Fuquan Zhang8. 1. Department of Psychiatry, First Clinical Medical College/First Hospital of Shanxi Medical University, Taiyuan, China; 2. Department of Psychiatry, Institute of Mental Health, Sixth Hospital, Peking University, Beijing, China; Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders (Peking University), Beijing, China; 3. Unit on Statistical Genomics, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD; 4. Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China; 5. Shanghai Key Laboratory of Birth Defect, Children's Hospital of Fudan University, Shanghai, China; 6. Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China. 7. Department of Psychiatry, Institute of Mental Health, Sixth Hospital, Peking University, Beijing, China; 8. Department of Psychiatry, Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China zhangfq@njmu.edu.cn.
Abstract
BACKGROUND: Transcriptional factors (TFs) and microRNAs (miRNAs) have been recognized as 2 classes of principal gene regulators that may be responsible for genome coexpression changes observed in schizophrenia (SZ). METHODS: This study aims to (1) identify differentially coexpressed genes (DCGs) in 3 mRNA expression microarray datasets; (2) explore potential interactions among the DCGs, and differentially expressed miRNAs identified in our dataset composed of early-onset SZ patients and healthy controls; (3) validate expression levels of some key transcripts; and (4) explore the druggability of DCGs using the curated database. RESULTS: We detected a differential coexpression network associated with SZ and found that 9 out of the 12 regulators were replicated in either of the 2 other datasets. Leveraging the differentially expressed miRNAs identified in our previous dataset, we constructed a miRNA-TF-gene network relevant to SZ, including an EGR1-miR-124-3p-SKIL feed-forward loop. Our real-time quantitative PCR analysis indicated the overexpression of miR-124-3p, the under expression of SKIL and EGR1 in the blood of SZ patients compared with controls, and the direction of change of miR-124-3p and SKIL mRNA levels in SZ cases were reversed after a 12-week treatment cycle. Our druggability analysis revealed that many of these genes have the potential to be drug targets. CONCLUSIONS: Together, our results suggest that coexpression network abnormalities driven by combinatorial and interactive action from TFs and miRNAs may contribute to the development of SZ and be relevant to the clinical treatment of the disease.
BACKGROUND: Transcriptional factors (TFs) and microRNAs (miRNAs) have been recognized as 2 classes of principal gene regulators that may be responsible for genome coexpression changes observed in schizophrenia (SZ). METHODS: This study aims to (1) identify differentially coexpressed genes (DCGs) in 3 mRNA expression microarray datasets; (2) explore potential interactions among the DCGs, and differentially expressed miRNAs identified in our dataset composed of early-onset SZ patients and healthy controls; (3) validate expression levels of some key transcripts; and (4) explore the druggability of DCGs using the curated database. RESULTS: We detected a differential coexpression network associated with SZ and found that 9 out of the 12 regulators were replicated in either of the 2 other datasets. Leveraging the differentially expressed miRNAs identified in our previous dataset, we constructed a miRNA-TF-gene network relevant to SZ, including an EGR1-miR-124-3p-SKIL feed-forward loop. Our real-time quantitative PCR analysis indicated the overexpression of miR-124-3p, the under expression of SKIL and EGR1 in the blood of SZ patients compared with controls, and the direction of change of miR-124-3p and SKIL mRNA levels in SZ cases were reversed after a 12-week treatment cycle. Our druggability analysis revealed that many of these genes have the potential to be drug targets. CONCLUSIONS: Together, our results suggest that coexpression network abnormalities driven by combinatorial and interactive action from TFs and miRNAs may contribute to the development of SZ and be relevant to the clinical treatment of the disease.
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