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A new bioinformatic insight into the associated proteins in psychiatric disorders

Overview of attention for article published in SpringerPlus, November 2016
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Title
A new bioinformatic insight into the associated proteins in psychiatric disorders
Published in
SpringerPlus, November 2016
DOI 10.1186/s40064-016-3655-6
Pubmed ID
Authors

Wenlong Zhao, Wenjing Yang, Shuanglin Zheng, Qiong Hu, Ping Qiu, Xinghua Huang, Xiaoqian Hong, Fenghua Lan

Abstract

Psychiatric diseases severely affect the quality of patients' lives and bring huge economic pressure to their families. Also, the great phenotypic variability among these patients makes it difficult to investigate the pathogenesis. Nowadays, bioinformatics is hopeful to be used as an effective tool for the diagnosis of psychiatric disorders, which can identify sensitive biomarkers and explore associated signaling pathways. In this study, we performed an integrated bioinformatic analysis on 1945 mental-associated proteins including 91 secreted proteins and 593 membrane proteins, which were screened from the Universal Protein Resource (Uniport) database. Then the function and pathway enrichment analyses, ontological classification, and constructed PPI network were executed. Our present study revealed that the majority of mental proteins were closely related to metabolic processes and cellular processes. We also identified some significant molecular biomarkers in the progression of mental disorders, such as HRAS, ALS2, SLC6A1, SLC39A12, SIL1, IDUA, NEPH2 and XPO1. Furthermore, it was found that hub proteins, such as COMT, POMC, NPS and BDNF, might be the potential targets for mental disorders therapy. Finally, we demonstrated that psychiatric disorders may share the same signaling pathways with cancers, involving ESR1, BCL2 and MAPK3. Our data are expected to contribute to explaining the possible mechanisms of psychiatric diseases and providing a useful reference for the diagnosis and therapy of them.

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Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 18%
Student > Ph. D. Student 6 18%
Student > Master 4 12%
Student > Postgraduate 4 12%
Researcher 3 9%
Other 4 12%
Unknown 6 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 18%
Agricultural and Biological Sciences 5 15%
Psychology 5 15%
Medicine and Dentistry 4 12%
Neuroscience 4 12%
Other 2 6%
Unknown 7 21%