基于生物医学大数据寻找不孕症关键致病基因及通路

doi:10.3969/j.issn.1674-8115.2021.10.001

上海交通大学学报(医学版) ›› 2021, Vol. 41 ›› Issue (10): 1277-1284.doi: 10.3969/j.issn.1674-8115.2021.10.001

• 创新团队成果专栏 • 下一篇

基于生物医学大数据寻找不孕症关键致病基因及通路

魏佳乐¹^,²()(), 刘鑫奕¹^,², 陆绍永¹^,², 芦雪峰³(), 张健¹^,²()

^1.上海交通大学基础医学院药物化学与生物信息学中心，上海 200025
^2.上海交通大学医学院细胞分化与凋亡教育部重点实验室，上海 200025
^3.上海交通大学医学院附属第九人民医院辅助生殖科，上海 200011

出版日期:2021-10-28 发布日期:2021-09-23
通讯作者: 芦雪峰,张健 E-mail:jialewei@sjtu.edu.cn;xuefenglu163@163.com;jian.zhang@sjtu.edu.cn
作者简介:魏佳乐（1997—），男，硕士生；电子信箱：jialewei@sjtu.edu.cn。
基金资助:
国家自然科学基金(81925034);上海交通大学医学院高水平地方高校创新团队(SSMU-ZDCX20181202)

Research of key causative genes and associated pathway in infertility based on big data of biological medicine

Jia-le WEI¹^,²()(), Xin-yi LIU¹^,², Shao-yong LU¹^,², Xue-feng LU³(), Jian ZHANG¹^,²()

^1.Medicinal Bioinformatics Center, Shanghai Jiao Tong University College of Basic Medical Sciences, Shanghai 200025, China
^2.Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
^3.Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China

Online:2021-10-28 Published:2021-09-23
Contact: Xue-feng LU,Jian ZHANG E-mail:jialewei@sjtu.edu.cn;xuefenglu163@163.com;jian.zhang@sjtu.edu.cn
Supported by:
National Natural Science Foundation of China(81925034);Innovative Research Team of High-Level Local Universities in Shanghai(SSMU-ZDCX20181202)

摘要/Abstract

摘要：

目的·通过整合不孕症遗传因素大数据，挖掘突变规律及相关致病基因。方法·根据全球蛋白资源数据库（Universal Protein Resource，Uniprot）蛋白类别对搜集的人类不孕症致病基因进行分类，统计分析临床病例中每类基因的突变频数。针对功能异常的氧化还原酶这一重要致病因素，进行功能和通路分析，观察通路的相关情况。于小鼠基因数据库（Mouse Genome Informatics，MGI）获取人类类固醇激素生成通路的小鼠同源基因及致病信息，寻找规律并结合基因互作分析评估其中的潜在基因。结果·不孕症致病基因里氧化还原酶类基因突变最常见，可能最易发生突变；其聚集于类固醇激素生成通路，发现潜在相关突变基因，并发现此通路醛酮还原酶家族1成员C3（aldo-keto reductase family 1 member C3，AKR1C3）是其中最可能的人类不孕症的潜在致病基因。结论·主要产生类固醇激素的氧化还原酶类致病基因在不孕症中突变最常见，可能包括AKR1C3。

关键词: 不孕症, 大数据, 氧化还原酶, 类固醇激素通路, AKR1C3

Abstract:

Objective·To mine regular patterns of mutation and associated causative genes through integrating big data from the perspective of genetic factors in infertility.

Methods·A classification of causative genes collected in human infertility according to the protein category in the Universal Protein Resource (Uniprot) and statistics of mutation frequency in each kind of genes in clinical cases were made. The function and pathway of oxidoreductase with abnormal function, an important pathogenic factor, were analyzed, and related information was investigated. The mouse ortholog genes and pathogenic information of human steroid hormone biosynthesis were acquired from Mouse Genome Informatics (MGI) in order to evaluate potential genes after finding out regularity and analysis of gene interaction.

Results·It's commonest for oxidoreductase genes to be mutated in infertility-causative genes. The genes that mutated most easily seemed possibly to focus on the steroid hormone biosynthesis pathway, and, furthermore, potential and related mutation genes were found. Aldo-keto reductase family 1 member C3 (AKR1C3), an oxidoreductase, was considered as a candidate gene bringing about human infertility most probably after analyzing this pathway.

Conclusion·It's commonest for oxidoreductase-causative genes, which are mainly responsible for generating steroid hormone, to be mutated in infertility, and these genes maybe include AKR1C3.

Key words: infertility, big data, oxidoreductase, steroid hormone biosynthesis, AKR1C3

中图分类号:

R711.6

魏佳乐, 刘鑫奕, 陆绍永, 芦雪峰, 张健. 基于生物医学大数据寻找不孕症关键致病基因及通路[J]. 上海交通大学学报(医学版), 2021, 41(10): 1277-1284.

Jia-le WEI, Xin-yi LIU, Shao-yong LU, Xue-feng LU, Jian ZHANG. Research of key causative genes and associated pathway in infertility based on big data of biological medicine[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2021, 41(10): 1277-1284.

图/表 4

图1 人类不孕症基因的分类及突变频数统计Note：A. Classification of 307 human causative genes in infertility (with or without PDB). B. Mutation frequency of causative genes of types (all or with PDB). GPCR—G protein-coupled receptor.

Fig 1 Classification and statistics of mutation frequency for infertility-causative genes in human

图2 氧化还原酶类基因的KEGG通路富集分析及与内分泌系统的关系Note：A. KEGG pathway enrichment analysis of oxidoreductase genes (top 15 pathways). The Y-axis represents the name of the pathway. The color of the dot represents the size of the Q value. B. KEGG pathway annotation of oxidoreductase genes. C. The most significant cluster in 34 endocrine system-related causative genes in infertility.

Fig 2 Oxidoreductase genes of KEGG pathway enrichment analysis and its correlation with endocrine system

图3 已知不孕症基因在类固醇激素生成通路及互作分析中的规律Note：A. Cartoon of steroid hormone biosynthesis (The known causative genes in infertility are colored pink, the most probable candidate gene is colored red, and unmapped genes are colored green). B. Interaction analysis of known causative genes and candidate genes in steroid hormone biosynthesis pathway. Prefuse Force Directed Layout is used in layout style (Genes are colored as Fig A).

Fig 3 Regularity of known infertility genes in steroid hormone biosynthesis and interaction analysis

表1 类固醇激素生成通路中人类与小鼠同源基因致病情况

Tab 1 Pathogenicity for human and mouse orthology genes in the pathway of steroid hormone biosynthesis

Homo sapiens	Mus musculus	Pathogenicity
CYP11A1	Cyp11a1	H
CYP17A1	Cyp17a1	H
STS
SULT2B1	Sult2b1
CYP21A2	Cyp21a1	H
HSD3B2	Hsd3b2	H
SRD5A1	Srd5a1	M
SRD5A2	Srd5a2	H
SRD5A3	Srd5a3
AKR1C3	Akr1c18	M
CYP1B1	Cyp1b1
CYP11B1/CYP11B2	Cyp11b1/Cyp11b2	H （CYP11B1）
AKR1D1	Akr1d1
AKR1C4	Akr1c6
HSD11B1	Hsd11b1
HSD11B2	Hsd11b2
CYP7B1	Cyp7b1	M
SULT1E1	Sult1e1	M
HSD17B1	Hsd17b1	M
COMT	Comt	M
HSD17B3	Hsd17b3	H
DHRS11	Dhrs11
HSD17B2	Hsd17b2
HSD17B6	Hsd17b6
HSD17B7	Hsd17b7
HSD17B8	H2-Ke6
HSD17B12	Hsd17b12
CYP1A1	Cyp1a1
CYP1A2	Cyp1a2
CYP3A5	Cyp3a11
CYP3A7	CYP3a13
CYP2E1	Cyp2e1
CYP3A4
CYP19A1	Cyp19a1	H
CYP7A1	Cyp7a1
UGT	Slc35a2

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基于生物医学大数据寻找不孕症关键致病基因及通路

Research of key causative genes and associated pathway in infertility based on big data of biological medicine

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