NDLI: MicroRNA and gene expression profiling of response to lithium treatment for bipolar I disorder

Content Provider	IEEE Xplore Digital Library
Author	Weng, Julia Tzu-Ya Chi, Yu-Xiang Wu, Lawrence Shih-Hsin Lee, Chau-Shoun Cheng, Andrew T. A.
Copyright Year	2015
Description	Author affiliation: Department of Psychiatry, Mackay Memorial Hospital, New Taipei City, Taiwan (Lee, Chau-Shoun) \|\| Department of Computer Science & Engineering, Innovation Center for Big Data & Digital Convergence, Yuan Ze University, Chung-Li, Taiwan (Weng, Julia Tzu-Ya) \|\| Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan (Cheng, Andrew T. A.) \|\| Department of Computer Science & Engineering, Yuan Ze University, Chung-Li, Taiwan (Chi, Yu-Xiang) \|\| Institute of Medical Sciences, Tzu Chi University, Hualien Taiwan (Wu, Lawrence Shih-Hsin)
Abstract	Bipolar is a debilitating mood disorder characterized by mixed episodes of depression and mania. It is classified into bipolar I and II according to the occurrences and frequencies of depressive and manic episodes. As the sixth leading cause of disability in the world, bipolar disorder exerts a huge amount of burden on the patient's family and the society as a whole. Lithium is often the prescribed medication; however, not all patients are responsive to it. In fact, some may even develop adverse drug reactions to the drug. In an attempt to understand the mechanisms underlying the response to lithium among bipolar I patients, we profiled the gene and microRNA expression differences between the good and poor responders in peripheral blood. We identified a number of differentially expressed genes and microRNAs, determined their functions using gene ontology and pathway annotation tools, and assessed the potential interactions between these two types of molecules via target prediction algorithms, association analyses, and existing literature evidence. We found several microRNA-gene interaction biomarkers that differ between the poor and good lithium responders. With future validations, these biomarkers may provide insights into the mechanism underlying lithium response.
Starting Page	446
Ending Page	452
File Size	1139412
Page Count	7
File Format	PDF
e-ISBN	9781509000227
DOI	10.1109/BMEI.2015.7401546
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Mood bipolar I disorder RNA Biomarkers Lithium microRNA Gene expression Blood gene expression lithium response
Content Type	Text
Resource Type	Article

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