功能基因筛选和鉴定策略的研究进展及其优缺点

doi:10.16352/j.issn.1001-6325.2022.06.010

基础医学与临床 ›› 2022, Vol. 42 ›› Issue (6): 969-974.doi: 10.16352/j.issn.1001-6325.2022.06.010

功能基因筛选和鉴定策略的研究进展及其优缺点

刘旭¹, 常德^1*, 王俊锋²

1.中国人民解放军总医院第三医学中心,北京 100069;
2.中国人民解放军总医院第二医学中心,北京 100853

收稿日期:2020-09-07 修回日期:2022-04-11 出版日期:2022-06-05 发布日期:2022-06-02
通讯作者: * changde5501@foxmail.com
基金资助:
国家十四五重点研发计划青年科学家项目(2021YFC2302300);解放军总医院青年科技专项(QNF19074);北京市科技新星计划交叉学科合作课题(Z191100001119021);后勤科研重点项目(BWS17J030)

Advantages, disadvantages and research progress on strategies for screening and identifying functional genes

LIU Xu¹, CHANG De^1*, WANG Jun-feng²

1. the Third Medical Center,Chinese People's Liberation Army General Hospital, Beijing 100069;
2. the Second Medical Center,Chinese People's Liberation Army General Hospital, Beijing 100853, China

Received:2020-09-07 Revised:2022-04-11 Online:2022-06-05 Published:2022-06-02
Contact: * changde5501@foxmail.com

摘要/Abstract

摘要： 筛选和鉴定功能基因是研究各种生物学表型发生分子机制的关键步骤,也是人类后基因组时代主要研究内容之一。传统遗传学、全基因组关联分析、表达谱差异、基因组测序、生物信息学等基于正向遗传学的筛选方法和cDNA文库、RNA干扰、CRISPR/Cas9技术、插入突变、模式生物等基于反向遗传学的筛选策略已被应用于筛选和鉴定功能基因,各种技术方法均具备各自的优势和缺陷,但可相互结合使用,发挥各自优点。

关键词: 功能基因, 正向遗传, 反向遗传, 生物信息学, 模式生物

Abstract: The screening and identification of functional genes is a key step to deeply understand various biological phenotypes. Forward genetics including traditional genetics, genome-wide association analysis, the comparison of gene expression profiles and bioinformatics, and reverse genetics including cDNA library, RNA interference, CRISPR-Cas9 technology, insertion mutation as well as model organisms have been applied to screening functional genes. Different methods have their own advantages and disadvantages and technology combination to screen and to identify functional genes is a good choice of strategy.

Key words: functional genes, forward genetics, reverse genetics, bioinformatics, model organism

中图分类号:

刘旭, 常德, 王俊锋. 功能基因筛选和鉴定策略的研究进展及其优缺点[J]. 基础医学与临床, 2022, 42(6): 969-974.

LIU Xu, CHANG De, WANG Jun-feng. Advantages, disadvantages and research progress on strategies for screening and identifying functional genes[J]. Basic & Clinical Medicine, 2022, 42(6): 969-974.

参考文献

[1] Nagy LG, Merényi Z, Hegedüs B, et al. Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing[J]. Nucleic Acids Res, 2020, 48: 2209-2219.
[2] 方福德.有关“组”(-ome)和“组学”(-omics)的几个名词[J].基础医学与临床,2011,31(4):封2.
[3] Funato H, Miyoshi C, Fujiyama T, et al. Forward-genetics analysis of sleep in randomly mutagenized mice[J]. Nature, 2016, 539: 378-383.
[4] Nogales A, Martínez-Sobrido L. Reverse genetics approaches for the development of influenza vaccines[J]. Int J Mol Sci, 2017, 18: 20. doi: 10.3390/ijms18010020.
[5] Gao B, Guo J, She C, et al. Mutations in IHH, encoding Indian hedgehog, cause brachydactyly type A-1[J]. Nat Genet, 2001, 28: 386-388.
[6] Buniello A, MacArthur JAL, Cerezo M, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019[J]. Nucleic Acids Res, 2019, 47: D1005-D1012.
[7] Luo C, Wang F, Ren X, et al. Identification of a molecular signaling gene-gene regulatory network between GWAS susceptibility genes ADTRP and MIA3/TANGO1 for coronary artery disease[J]. Biochim Biophys Acta Mol Basis Dis, 2017, 1863: 1640-1653.
[8] Speakman JR, Loos RJF, O'Rahilly S, et al. GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity[J]. Int J Obes (Lond), 2018, 42: 1524-1531.
[9] Bovijn J, Jackson L, Censin J, et al. GWAS identifies risk locus for erectile dysfunction and implicates hypothalamic neurobiology and diabetes in etiology[J]. Am J Hum Genet, 2019, 104: 157-163.
[10] Slotta-Huspenina J, Drecoll E, Feith M, et al. MicroRNA expression profiling for the prediction of resistance to neoadjuvant radiochemotherapy in squamous cell carcinoma of the esophagus[J]. J Transl Med, 2018, 16: 1-9.
[11] Ruggles KV, Krug K, Wang X, et al. Methods, tools and current perspectives in proteogenomics[J]. Mol Cell Proteomics, 2017, 16: 959-981.
[12] Liu X, Wu J, Zhang D, et al. Identification of potential key genes associated with the pathogenesis and prognosis of gastric cancer based on integrated bioinformatics analysis[J]. Front Genet, 2018, 9: 265. doi: 10.3389/fgene.2018.00265.
[13] Wong FS, Karttunen J, Dumont C, et al. Identification of an MHC class I-restricted autoantigen in type 1 diabetes by screening an organ-specific cDNA library[J]. Nat Med, 1999;5:1026-1031.
[14] Mamta B, Rajam MV. RNAi technology: a new platform for crop pest control[J]. Physiol Mol Biol Plants, 2017, 23: 487-501.
[15] Mohr SE, Smith JA, Shamu CE, et al. RNAi screening comes of age: improved techniques and complementary approaches[J]. Nat Rev Mol Cell Biol, 2014, 15: 591-600.
[16] Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR-Cas9[J]. Nat Rev Genet, 2015, 16: 299-311.
[17] Wade M. High-throughput silencing using the CRISPR-Cas9 system: a review of the benefits and challenges[J]. J Biomol Screen, 2015, 20: 1027-1039.
[18] Weber J, de la Rosa J, Grove CS, et al. PiggyBac transposon tools for recessive screening identify B-cell lymphoma drivers in mice[J]. Nat Commun, 2019, 10: 1-16.
[19] Nielsen J. Yeast systems biology: model organism and cell factory[J]. Biotechnol J, 2019,14:e1800421. doi: 10.1002/biot.201800421.
[20] Beckmann PJ, Largaespada DA. Transposon insertion mutagenesis in mice for modeling human cancers: critical insights gained and new opportunities[J]. Int J Mol Sci, 2020,21:1172. doi: 10.3390/ijms21031172.
[21] Ranzani M, Annunziato S, Adams DJ, et al. Cancer gene discovery: exploiting insertional mutagenesis[J]. Mol Cancer Res, 2013, 11: 1141-1158.
[22] Zwaal RR, Broeks A, van Meurs J, et al. Target-selected gene inactivation in Caenorhabditis elegans by using a frozen transposon insertion mutant bank[J]. Proc Natl Acad Sci U S A, 1993, 90: 7431-7435.
[23] Thibault ST, Singer MA, Miyazaki WY, et al. A complementary transposon tool kit for Drosophila melanogaster using P and piggyBac[J]. Nat Genet, 2004, 36: 283-287.
[24] Rad R, Rad L, Wang W, et al. PiggyBac transposon mutagenesis: a tool for cancer gene discovery in mice[J]. Science, 2010, 330: 1104-1107.
[25] Weber J, de la Rosa J, Grove CS, et al. PiggyBac transposon tools for recessive screening identify B-cell lymphoma drivers in mice[J]. Nat commun, 2019, 10: 1-16.
[26] 由磊. 全基因组筛选胰腺癌耐药相关基因[D].北京协和医学院,2010.
[27] 常德. 全基因组筛选和鉴定ARPC1B基因在胰腺癌转移中的作用和机理[D].北京协和医学院,2011.

功能基因筛选和鉴定策略的研究进展及其优缺点

Advantages, disadvantages and research progress on strategies for screening and identifying functional genes

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	孙旭, 李顺顺, 王殿恒, 王珍凤. CD44差异表达在胶质母细胞瘤中的生物信息学分析及细胞实验验证[J]. 基础医学与临床, 2024, 44(6): 800-808.
[2]	刘潇, 王曌慧, 魏心怡, 周玥, 赵丽, 王玥, 李俊发. 糖尿病合并TREM2突变相关认知功能障碍的生物信息学分析[J]. 基础医学与临床, 2024, 44(5): 630-636.
[3]	鄢雯, 李囡, 古同男, 孔祥照. 从GEO数据库筛选结肠癌差异关键基因及验证[J]. 基础医学与临床, 2023, 43(11): 1685-1692.
[4]	白静, 刘娜, 丁力, 雷丽, 倪瑞, 闫少春, 邵国. 放射治疗后宫颈癌组织内差异表达基因鉴定[J]. 基础医学与临床, 2022, 42(2): 215-220.
[5]	孔杰, 高瑛瑛, 王雪琴. 基于miRNA芯片对系统性红斑狼疮的生物信息学分析[J]. 基础医学与临床, 2021, 41(6): 865-870.
[6]	姚涵, 徐文彬, 王冬来. 抗肿瘤药物FTY720对p53依赖的基因转录调控[J]. 基础医学与临床, 2021, 41(3): 311-317.
[7]	闫晓俊, 徐文彬, 王冬来. 羧基末端乙酰化修饰调控抑癌蛋白p53转录特异性[J]. 基础医学与临床, 2021, 41(11): 1577-1582.
[8]	邢书娟, 董明清, 杨力侠, 罗颖. 两种肺动脉高压模型小鼠的基因表达谱分析[J]. 基础医学与临床, 2020, 40(11): 1462-1467.
[9]	胡雪停, 吴晓凤, 徐祥. 长链非编码RNA lnc-DC的生物信息学分析与验证[J]. 基础医学与临床, 2020, 40(10): 1328-1334.
[10]	宋奇, 程安源, 余铃, 徐立. GPRC5A促进人骨肉瘤细胞系143B迁移和侵袭[J]. 基础医学与临床, 2020, 40(10): 1335-1341.
[11]	王波刘思雨罗红. 重组人烯醇酶-α的原核表达及生物信息学分析[J]. 基础医学与临床, 2019, 39(9): 1300-1304.
[12]	谢丽华孙圣华卢俊娟林桦. COPD动态miRNA表达谱的构建及生物信息学分析[J]. 基础医学与临床, 2016, 36(6): 739-746.
[13]	钱建升李宇窦建卫. miR-26a对人肝癌细胞系SMMC-7721增殖和迁移的影响[J]. 基础医学与临床, 2016, 36(10): 1335-1340.
[14]	乔子君马文丽郑文岭. 肥胖患者性别差异的基因表达谱数据分析[J]. 基础医学与临床, 2015, 35(6): 723-728.
[15]	庞希宁李彩虹施萍王竟刘晓玉张涛张殿宝赵峰. EGF对hAMSCs基因表达谱影响及生物信息学分析[J]. 基础医学与临床, 2013, 33(11): 1391-1397.