Format

Send to

Choose Destination
Cells. 2018 Nov 2;7(11). pii: E194. doi: 10.3390/cells7110194.

Inferring Novel Autophagy Regulators Based on Transcription Factors and Non-Coding RNAs Coordinated Regulatory Network.

Author information

1
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. bioccwsy@163.com.
2
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. wangwencan1314@163.com.
3
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. mqq1992hmu@163.com.
4
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. zhoushunheng@163.com.
5
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. liuhaizhou2015@126.com.
6
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. 18345550297@163.com.
7
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. biomathzx@163.com.
8
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. liuhui870320@163.com.
9
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China. hrbmucxw@163.com.
10
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. weijiang@nuaa.edu.cn.

Abstract

Autophagy is a complex cellular digestion process involving multiple regulators. Compared to post-translational autophagy regulators, limited information is now available about transcriptional and post-transcriptional regulators such as transcription factors (TFs) and non-coding RNAs (ncRNAs). In this study, we proposed a computational method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated regulatory (TNCR) network. First, we constructed a comprehensive TNCR network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we gathered the known autophagy-associated factors, including TFs, miRNAs and lncRNAs, from public data resources. Then, the random walk with restart (RWR) algorithm was conducted on the TNCR network by using the known autophagy-associated factors as seeds and novel autophagy regulators were finally prioritized. Leave-one-out cross-validation (LOOCV) produced an area under the curve (AUC) of 0.889. In addition, functional analysis of the top 100 ranked regulators, including 55 TFs, 26 miRNAs and 19 lncRNAs, demonstrated that these regulators were significantly enriched in cell death related functions and had significant semantic similarity with autophagy-related Gene Ontology (GO) terms. Finally, extensive literature surveys demonstrated the credibility of the predicted autophagy regulators. In total, we presented a computational method to infer credible autophagy regulators of transcriptional factors and non-coding RNAs, which would improve the understanding of processes of autophagy and cell death and provide potential pharmacological targets to autophagy-related diseases.

KEYWORDS:

RWR algorithm; autophagy regulator; non-coding RNA; regulatory network; transcriptional factor

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI) Icon for PubMed Central
Loading ...
Support Center