• 中国中文核心期刊
  • 中国科学引文数据库核心期刊
  • 中国科技核心期刊
  • 中国高校百佳科技期刊
高级检索

数据库中细菌与人源药物代谢酶的比较及肠道菌群对药物代谢影响的展望

皇甫逸凡, 冉雨叶, 封硕, 李菁

皇甫逸凡, 冉雨叶, 封硕, 李菁. 数据库中细菌与人源药物代谢酶的比较及肠道菌群对药物代谢影响的展望[J]. 中国药科大学学报, 2023, 54(1): 122-130. DOI: 10.11665/j.issn.1000-5048.20220705001
引用本文: 皇甫逸凡, 冉雨叶, 封硕, 李菁. 数据库中细菌与人源药物代谢酶的比较及肠道菌群对药物代谢影响的展望[J]. 中国药科大学学报, 2023, 54(1): 122-130. DOI: 10.11665/j.issn.1000-5048.20220705001
HUANGFU Yifan, RAN Yuye, FENG Shuo, LI Jing. Comparison of bacterial and human drug metabolizing enzymes in database and prospect of influence of intestinal bacteria on drug metabolism[J]. Journal of China Pharmaceutical University, 2023, 54(1): 122-130. DOI: 10.11665/j.issn.1000-5048.20220705001
Citation: HUANGFU Yifan, RAN Yuye, FENG Shuo, LI Jing. Comparison of bacterial and human drug metabolizing enzymes in database and prospect of influence of intestinal bacteria on drug metabolism[J]. Journal of China Pharmaceutical University, 2023, 54(1): 122-130. DOI: 10.11665/j.issn.1000-5048.20220705001

数据库中细菌与人源药物代谢酶的比较及肠道菌群对药物代谢影响的展望

基金项目: 国家自然科学基金资助项目(No.32170062);复杂基质样本生物分析湖南省重点实验室资助项目(No.2017TP1037);江苏省研究生科研与实践创新计划资助项目(No.3322200020)

Comparison of bacterial and human drug metabolizing enzymes in database and prospect of influence of intestinal bacteria on drug metabolism

Funds: This study was supported by the National Natural Science Foundation of China (No.32170062); the Project of Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples (No.2017TP1037) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.3322200020)
  • 摘要: 聚焦药物代谢相关的各种数据库和文献,对细菌来源与人源药物代谢酶的相关信息进行整理与分析,比较数据库中细菌与人源药物代谢酶收录信息的异同。结果发现细菌来源药物代谢酶比人源药物代谢酶要多很多(9 703 vs 964),但是细菌来源药物代谢酶的数量相较于BRENDA数据库中细菌酶的总体数量却少很多(9 703 vs 20 835 235)。这说明细菌对药物代谢的影响可能被大大地低估,需要进行深入的系统性研究。本文总结了目前研究肠道菌群影响药物代谢的进展及不足,提出研究思路,即通过人工智能对肠道细菌来源蛋白是否具有药物代谢的能力进行预测,用基因编辑与体内外实验等方法进行生物学功能的验证,并建立注释功能完善的肠道菌群与药物代谢相关数据库,为如何深入挖掘肠道菌群对药物代谢影响的研究提供坚实的理论基础。
    Abstract: This study focused on various databases and literatures related to drug metabolism, collated and analyzed the information related to bacterial and human drug metabolic enzymes, and compared the similarities and differences between the information included in the database of bacterial and human drug metabolic enzymes. Results found more bacterial drug metabolic enzymes than human drug metabolic enzymes (9 703 vs 964), but much less than the total number of bacterial enzymes in BRENDA database (9 703 vs 20 835 235), indicating that the influence of bacteria on drug metabolism could have been greatly underestimated, and that further systematic research is needed.We summarized the progress and shortcomings of the current research on the influence of intestinal flora on drug metabolism, and proposed a research idea, that is, to predict through artificial intelligence whether intestinal bacterial proteins have the ability to metabolize drugs, to verify their biological functions by in vitro/in vivo experiments and gene editing, and to establish a database of drug metabolic enzymes from intestinal bacteria with complete annotation functions, in an attempt to provide a solid theoretical basis for further exploration of the effects of intestinal flora on drug metabolism.
  • [1] . Gastroenterology,2021,160(2):524-537.
    [2] Witkowski M,Weeks TL,Hazen SL. Gut microbiota and cardiovascular disease[J]. Circ Res,2020,127(4):553-570.
    [3] Sepich-Poore GD,Zitvogel L,Straussman R,et al. The microbiome and human cancer[J]. Science,2021,371(6536):eabc4552.
    [4] van den Elsen LWJ,Garssen J,Burcelin R,et al. Shaping the gut microbiota by breastfeeding:the gateway to allergy prevention[J]?Front Pediatr,2019,7:47.
    [5] Generoso JS,Giridharan VV,Lee J,et al. The role of the microbiota-gut-brain axis in neuropsychiatric disorders[J]. Braz J Psychiatry,2021,43(3):293-305.
    [6] Agus A,Clément K,Sokol H. Gut microbiota-derived metabolites as central regulators in metabolic disorders[J]. Gut,2021,70(6):1174-1182.
    [7] Sharpton SR,Schnabl B,Knight R,et al. Current concepts,opportunities,and challenges of gut microbiome-based personalized medicine in nonalcoholic fatty liver disease[J]. Cell Metab,2021,33(1):21-32.
    [8] Vich Vila A,Collij V,Sanna S,et al. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota[J]. Nat Commun,2020,11(1):362.
    [9] Tong XL,Xu J,Lian FM,et al. Structural alteration of gut microbiota during the amelioration of human type 2 diabetes with hyperlipidemia by metformin and a traditional Chinese herbal formula:a multicenter,randomized,open label clinical trial[J]. mBio,2018,9(3):e02392-e02317.
    [10] Qin JJ,Li RQ,Raes J,et al. A human gut microbial gene catalogue established by metagenomic sequencing[J]. Nature,2010,464(7285):59-65.
    [11] Spanogiannopoulos P,Bess EN,Carmody RN,et al. The microbial pharmacists within us:a metagenomic view of xenobiotic metabolism[J]. Nat Rev Microbiol,2016,14(5):273-287.
    [12] Guthrie L,Gupta S,Daily J,et al. Human microbiome signatures of differential colorectal cancer drug metabolism[J]. NPJ Biofilms Microbiomes,2017,3:27.
    [13] van Kessel SP,Frye AK,El-Gendy AO,et al. Gut bacterial tyrosine decarboxylases restrict levels of levodopa in the treatment of Parkinson''s disease[J]. Nat Commun,2019,10(1):310.
    [14] Maini Rekdal V,Bess EN,Bisanz JE,et al. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism[J]. Science,2019,364(6445):eaau6323.
    [15] Maier LS,Pruteanu M,Kuhn M,et al. Extensive impact of non-antibiotic drugs on human gut bacteria[J]. Nature,2018,555(7698):623-628.
    [16] Zimmermann M,Zimmermann-Kogadeeva M,Wegmann R,et al. Mapping human microbiome drug metabolism by gut bacteria and their genes[J]. Nature,2019,570(7762):462-467.
    [17] Weersma RK,Zhernakova A,Fu JY. Interaction between drugs and the gut microbiome[J]. Gut,2020,69(8):1510-1519.
    [18] Clarke G,Sandhu KV,Griffin BT,et al. Gut reactions:breaking down xenobiotic-microbiome interactions[J]. Pharmacol Rev,2019,71(2):198-224.
    [19] Klünemann M,Andrejev S,Blasche S,et al. Bioaccumulation of therapeutic drugs by human gut bacteria[J]. Nature,2021,597(7877):533-538.
    [20] Uechi K,Tada T,Sawachi Y,et al. A carbapenem-resistant clinical isolate of Aeromonas hydrophila in Japan harbouring an acquired gene encoding GES-24 β-lactamase[J]. J Med Microbiol,2018,67(11):1535-1537.
    [21] Dingsdag SA,Hunter N. Metronidazole:an update on metabolism,structure-cytotoxicity and resistance mechanisms[J]. J Antimicrob Chemother,2018,73(2):265-279.
    [22] Boddu RS,Perumal O,Divakar K. Microbial nitroreductases:a versatile tool for biomedical and environmental applications[J]. Biotechnol Appl Biochem,2021,68(6):1518-1530.
    [23] Day MA,Jarrom D,Christofferson AJ,et al. The structures of E. coli NfsA bound to the antibiotic nitrofurantoin; to 1,4-benzoquinone and to FMN[J]. Biochem J,2021,478(13):2601-2617.
    [24] Mao BY,Li DY,Zhao JX,et al. In vitro fermentation of lactulose by human gut bacteria[J]. J Agric Food Chem,2014,62(45):10970-10977.
    [25] Wang PP,Jia YF,Wu RR,et al. Human gut bacterial β-glucuronidase inhibition:an emerging approach to manage medication therapy[J]. Biochem Pharmacol,2021,190:114566.
    [26] Kumar K,Dhoke GV,Sharma AK,et al. Mechanistic elucidation of amphetamine metabolism by tyramine oxidase from human gut microbiota using molecular dynamics simulations[J]. J Cell Biochem,2019,120(7):11206-11215.
    [27] Haiser HJ,Seim KL,Balskus EP,et al. Mechanistic insight into digoxin inactivation by Eggerthella lenta augments our understanding of its pharmacokinetics[J]. Gut Microbes,2014,5(2):233-238.
    [28] Gomes AC,Hoffmann C,Mota JF. The human gut microbiota:metabolism and perspective in obesity[J]. Gut Microbes,2018,9(4):308-325.
    [29] Wang QW,Ma BB,Fushinobu S,et al. Regio- and stereoselective hydroxylation of testosterone by a novel cytochrome P450 154C2 from Streptomyces avermitilis[J]. Biochem Biophys Res Commun,2020,522(2):355-361.
    [30] Rabelo-Fernandez RJ,Santiago-Morales K,Morales-Vale L,et al. The metagenome of Caracolus marginella gut microbiome using culture independent approaches and shotgun sequencing[J]. Data Brief,2018,16:501-505.
    [31] Crouwel F,Buiter HJC,de Boer NK. Gut microbiota-driven drug metabolism in inflammatory bowel disease[J]. J Crohns Colitis,2020,15(2):307-315.
    [32] Yan A,Culp E,Perry J,et al. Transformation of the anticancer drug doxorubicin in the human gut microbiome[J]. ACS Infect Dis,2018,4(1):68-76.
    [33] Yang GY,Ge SF,Singh R,et al. Glucuronidation:driving factors and their impact on glucuronide disposition[J]. Drug MeTable Rev,2017,49(2):105-138.
    [34] Lee SH,Choi N,Sung JH. Pharmacokinetic and pharmacodynamic insights from microfluidic intestine-on-a-chip models[J]. Expert Opin Drug MeTable Toxicol,2019,15(12):1005-1019.
    [35] Sun PN,Zhou XL,Farnworth SL,et al. Modeling human liver biology using stem cell-derived hepatocytes[J]. Int J Mol Sci,2013,14(11):22011-22021.
    [36] Lee SY,Kim D,Lee SH,et al. Microtechnology-based in vitro models:Mimicking liver function and pathophysiology[J]. APL Bioeng,2021,5(4):041505.
    [37] Chen SL,Li Z,Zhang SY,et al. Emerging biotechnology applications in natural product and synthetic pharmaceutical analyses[J]. Acta Pharm Sin B,2022,12(11):4075-4097.
    [38] Xie RP,Li JH,Wang JW,et al. DeepVF:a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy[J]. Brief Bioinform,2021,22(3):bbaa125.
    [39] Zhang Z,Zhang Q,Wang T,et al. Assessment of global health risk of antibiotic resistance genes[J]. Nat Commun,2022,13(1):1553.
    [40] Kataria R,Khatkar A. Molecular docking,synthesis,kinetics study,structure-activity relationship and ADMET analysis of morin analogous as Helicobacter pylori urease inhibitors[J]. BMC Chem,2019,13(1):45.
    [41] Jumper J,Evans R,Pritzel A,et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature,2021,596(7873):583-589.
    [42] McCoubrey LE,Thomaidou S,Elbadawi M,et al. Machine learning predicts drug metabolism and bioaccumulation by intestinal microbiota[J]. Pharmaceutics,2021,13(12):2001.
    [43] McCoubrey LE,Elbadawi M,Orlu M,et al. Machine learning uncovers adverse drug effects on intestinal bacteria[J]. Pharmaceutics,2021,13(7):1026.
    [44] Zimmermann M,Zimmermann-Kogadeeva M,Wegmann R,et al. Separating host and microbiome contributions to drug pharmacokinetics and toxicity[J]. Science,2019,363(6427):eaat9931.
  • 期刊类型引用(0)

    其他类型引用(2)

计量
  • 文章访问数:  571
  • HTML全文浏览量:  11
  • PDF下载量:  370
  • 被引次数: 2
出版历程
  • 收稿日期:  2022-07-04
  • 修回日期:  2023-01-31
  • 刊出日期:  2023-02-24

目录

    /

    返回文章
    返回
    x 关闭 永久关闭