上海交通大学学报（医学版）

上海交通大学学报（医学版） >

2021 , Vol. 41 >Issue 10: 1277 - 1284

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.10.001

创新团队成果专栏

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 ,
  • 刘鑫奕 ,
  • 陆绍永 ,
  • 芦雪峰 ,
  • 张健
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  • 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
魏佳乐（1997—），男，硕士生；电子信箱：jialewei@sjtu.edu.cn。
芦雪峰，电子信箱：xuefenglu163@163.com。#为共同通信作者。
张 健，电子信箱：jian.zhang@sjtu.edu.cn

网络出版日期: 2021-09-23

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)

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

  • Jia-le WEI ,
  • Xin-yi LIU ,
  • Shao-yong LU ,
  • Xue-feng LU ,
  • Jian ZHANG
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  • 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
LU Xue-feng, Email: xuefenglu163@163.com. #Co-corresponding authors.
ZHANG Jian, Email: jian.zhang@sjtu.edu.cn

Online published: 2021-09-23

Supported by

National Natural Science Foundation of China(81925034);Innovative Research Team of High-Level Local Universities in Shanghai(SSMU-ZDCX20181202)

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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

本文引用格式

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 . DOI: 10.3969/j.issn.1674-8115.2021.10.001

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

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