Exploring transcription factors-microRNAs co-regulation networks in schizophrenia

Yong Xu, Weihua Yue, Yin Yao Shugart, Sheng Li, Lei Cai, Qiang Li, Zaohuo Cheng, Guoqiang Wang, Zhenhe Zhou, Chunhui Jin, Jianmin Yuan, Lin Tian, Jun Wang, Kai Zhang, Kerang Zhang, Sha Liu, Yuqing Song, Fuquan Zhang

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1037-1045
Number of pages9
JournalSchizophrenia Bulletin
Volume42
Issue number4
DOIs
StatePublished - 2016
Externally publishedYes

Fingerprint

MicroRNAs
Schizophrenia
Transcription Factors
Genes
Messenger RNA
Gene Regulatory Networks
Regulator Genes
Real-Time Polymerase Chain Reaction
Genome
Databases
Datasets
Therapeutics
Pharmaceutical Preparations

Keywords

  • EGRl
  • MicroRNA
  • MiR-124-3p
  • Schizophrenia
  • Transcriptional factor

ASJC Scopus subject areas

  • Psychiatry and Mental health

Cite this

Xu, Y., Yue, W., Shugart, Y. Y., Li, S., Cai, L., Li, Q., ... Zhang, F. (2016). Exploring transcription factors-microRNAs co-regulation networks in schizophrenia. Schizophrenia Bulletin, 42(4), 1037-1045. https://doi.org/10.1093/schbul/sbv170

Exploring transcription factors-microRNAs co-regulation networks in schizophrenia. / Xu, Yong; Yue, Weihua; Shugart, Yin Yao; Li, Sheng; Cai, Lei; Li, Qiang; Cheng, Zaohuo; Wang, Guoqiang; Zhou, Zhenhe; Jin, Chunhui; Yuan, Jianmin; Tian, Lin; Wang, Jun; Zhang, Kai; Zhang, Kerang; Liu, Sha; Song, Yuqing; Zhang, Fuquan.

In: Schizophrenia Bulletin, Vol. 42, No. 4, 2016, p. 1037-1045.

Research output: Contribution to journalArticle

Xu, Y, Yue, W, Shugart, YY, Li, S, Cai, L, Li, Q, Cheng, Z, Wang, G, Zhou, Z, Jin, C, Yuan, J, Tian, L, Wang, J, Zhang, K, Zhang, K, Liu, S, Song, Y & Zhang, F 2016, 'Exploring transcription factors-microRNAs co-regulation networks in schizophrenia', Schizophrenia Bulletin, vol. 42, no. 4, pp. 1037-1045. https://doi.org/10.1093/schbul/sbv170
Xu, Yong ; Yue, Weihua ; Shugart, Yin Yao ; Li, Sheng ; Cai, Lei ; Li, Qiang ; Cheng, Zaohuo ; Wang, Guoqiang ; Zhou, Zhenhe ; Jin, Chunhui ; Yuan, Jianmin ; Tian, Lin ; Wang, Jun ; Zhang, Kai ; Zhang, Kerang ; Liu, Sha ; Song, Yuqing ; Zhang, Fuquan. / Exploring transcription factors-microRNAs co-regulation networks in schizophrenia. In: Schizophrenia Bulletin. 2016 ; Vol. 42, No. 4. pp. 1037-1045.
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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.",
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T1 - Exploring transcription factors-microRNAs co-regulation networks in schizophrenia

AU - Xu, Yong

AU - Yue, Weihua

AU - Shugart, Yin Yao

AU - Li, Sheng

AU - Cai, Lei

AU - Li, Qiang

AU - Cheng, Zaohuo

AU - Wang, Guoqiang

AU - Zhou, Zhenhe

AU - Jin, Chunhui

AU - Yuan, Jianmin

AU - Tian, Lin

AU - Wang, Jun

AU - Zhang, Kai

AU - Zhang, Kerang

AU - Liu, Sha

AU - Song, Yuqing

AU - Zhang, Fuquan

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - EGRl

KW - MicroRNA

KW - MiR-124-3p

KW - Schizophrenia

KW - Transcriptional factor

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DO - 10.1093/schbul/sbv170

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JO - Schizophrenia Bulletin

JF - Schizophrenia Bulletin

SN - 0586-7614

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