Construction and Analysis of Human Diseases and Metabolites Network

Kai Mi; Yanan Jiang; Jiaxin Chen; Dongxu Lv; Zhipeng Qian; Hui Sun; Desi Shang

doi:10.3389/fbioe.2020.00398

Construction and Analysis of Human Diseases and Metabolites Network

Front Bioeng Biotechnol. 2020 Apr 30:8:398. doi: 10.3389/fbioe.2020.00398. eCollection 2020.

Authors

Kai Mi¹, Yanan Jiang^{1

2

3}, Jiaxin Chen⁴, Dongxu Lv¹, Zhipeng Qian¹, Hui Sun⁵, Desi Shang¹

Affiliations

¹ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
² Department of Pharmacology (State-Province Key Laboratories of Biomedicine - Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, China.
³ Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China.
⁴ School of Medical Informatics, Harbin Medical University, Daqing, China.
⁵ Pharmaceutical Experiment Teaching Center, College of Pharmacy, Harbin Medical University, Harbin, China.

Abstract

The relationship between aberrant metabolism and the initiation and progression of diseases has gained considerable attention in recent years. To gain insights into the global relationship between diseases and metabolites, here we constructed a human diseases-metabolites network (HDMN). Through analyses based on network biology, the metabolites associated with the same disorder tend to participate in the same metabolic pathway or cascade. In addition, the shortest distance between disease-related metabolites was shorter than that of all metabolites in the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic network. Both disease and metabolite nodes in the HDMN displayed slight clustering phenomenon, resulting in functional modules. Furthermore, a significant positive correlation was observed between the degree of metabolites and the proportion of disease-related metabolites in the KEGG metabolic network. We also found that the average degree of disease metabolites is larger than that of all metabolites. Depicting a comprehensive characteristic of HDMN could provide great insights into understanding the global relationship between disease and metabolites.

Keywords: correlation; disease; functional modules; metabolite; network random.