切换至 "中华医学电子期刊资源库"

中华肾病研究电子杂志 ›› 2020, Vol. 09 ›› Issue (05) : 232 -235. doi: 10.3877/cma.j.issn.2095-3216.2020.05.009

所属专题: 文献

综述

蛋白质组学及代谢组学在糖尿病肾病研究中的应用
丁潇楠1, 韩秋霞2, 张冬2, 朱晗玉2,()   
  1. 1. 100853 北京,解放军医学院;100853 北京,解放军总医院第一医学中心肾脏病科、解放军肾脏病研究所、肾脏疾病国家重点实验室、国家慢性肾病临床医学研究中心、肾脏疾病研究北京市重点实验室
    2. 100853 北京,解放军总医院第一医学中心肾脏病科、解放军肾脏病研究所、肾脏疾病国家重点实验室、国家慢性肾病临床医学研究中心、肾脏疾病研究北京市重点实验室
  • 收稿日期:2020-04-15 出版日期:2020-10-28
  • 通信作者: 朱晗玉
  • 基金资助:
    国家重点研发计划(2016YFC1305500); 国家自然科学基金项目(61971441,61671479)

Application of proteomics and metabolomics in the study of diabetic kidney disease

Xiaonan Ding1, Qiuxia Han2, Dong Zhang2, Hanyu Zhu2,()   

  1. 1. Medical School of Chinses PLA, Beijing 100853, China; Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing 100853, China
    2. Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases, Beijing 100853, China
  • Received:2020-04-15 Published:2020-10-28
  • Corresponding author: Hanyu Zhu
  • About author:
    Corresponding author: Zhu Hanyu, Email:
引用本文:

丁潇楠, 韩秋霞, 张冬, 朱晗玉. 蛋白质组学及代谢组学在糖尿病肾病研究中的应用[J]. 中华肾病研究电子杂志, 2020, 09(05): 232-235.

Xiaonan Ding, Qiuxia Han, Dong Zhang, Hanyu Zhu. Application of proteomics and metabolomics in the study of diabetic kidney disease[J]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2020, 09(05): 232-235.

糖尿病肾病是终末期肾病的最主要原因之一,已成为世界公共卫生的重大难题,解决临床诊疗方面的争议成为急需。本文综述了利用蛋白质组学及代谢组学技术在糖尿病肾病诊断及鉴别诊断、疾病进展、预后判断及疗效评估方面的研究进展。并对两种组学技术的异同进行阐述,在当代大数据及人工智能的背景下展望了利用蛋白质组学及代谢组学进行整合组学和精准医学研究。

Diabetic kidney disease is one of the most important causes of end-stage renal disease, and has become a major problem in public health in the world. It is a priority to resolve disputes in clinical diagnosis and treatment of the disease. This article reviewed the research progress in the diagnosis and differential diagnosis, disease progression, prognosis judgment, and efficacy evaluation of diabetic kidney disease, by means of proteomics and metabolomics. Besides, it also explained the similarities and differences between the two omics technologies, and prospected the use of proteomics and metabolomics for research of integrated omics and precision medicine in the context of contemporary big data and artificial intelligence.

[1]
GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the global burden of disease study 2017 [J]. Lancet, 2020, 395(10225): 709-733.
[2]
Doshi S, Friedman A. Diagnosis and management of type 2 diabetic kidney disease [J]. Clin J Am Soc Nephrol, 2017, 12(8): 1366-1373.
[3]
Ma S, Li Y, Ma C, et al. Challenges and advances in the fabrication of monolithic bioseparation materials and their applications in proteomics research [J]. Adv Mater, 2019, 31(50): e1902023.
[4]
Donnelly D, Rawlins C, DeHart C, et al. Best practices and benchmarks for intact protein analysis for top-down mass spectrometry [J]. Nat Methods, 2019, 16(7): 587-594.
[5]
Wishart DS, Feunang YD, Marcu A, et al. HMDB 4.0: the human metabolome database for 2018 [J]. Nucleic Acids Res, 2018, 46(D1): D608-D617.
[6]
Hirao Y, Saito S, Fujinaka H, et al. Proteome profiling of diabetic mellitus patient urine for discovery of biomarkers by comprehensive MS-based proteomics [J]. Proteomes, 2018, 6(1): 9.
[7]
Currie G, Delles C. Urinary proteomics for diagnosis and monitoring of diabetic nephropathy [J]. Curr Diab Rep, 2016, 16(11): 104.
[8]
Thippakorn C, Schaduangrat N, Nantasenamat C. Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy [J]. EXCLI J, 2018, 17: 312-330.
[9]
Glassock R. Con: kidney biopsy: an irreplaceable tool for patient management in nephrology [J]. Nephrol Dial Transplant, 2015, 30(4): 528-531.
[10]
BenAmeur R, Molina L, Bolvin C, et al. Proteomic approaches for discovering biomarkers of diabetic nephropathy [J]. Nephrol Dial Transplant, 2010, 25(9): 2866-2875.
[11]
Kim HJ, Cho EH, Yoo JH, et al. Proteome analysis of serum from type 2 diabetics with nephropathy [J]. J Proteome Res, 2007, 6(2): 735-743.
[12]
Rossing K, Mischak H, Dakna M, et al. Urinary proteomics in diabetes and CKD [J]. J Am Soc Nephrol, 2008, 19(7): 1283-1290.
[13]
Good D, Zürbig P, Argilés A, et al. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease [J]. Mol Cell Proteomics, 2010, 9(11): 2424-2437.
[14]
Siwy J, Schanstra J, Argiles A, et al. Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy [J]. Nephrol Dial Transplant, 2014, 29(8): 1563-1570.
[15]
Papale M, Paolo SD, Magistroni R, et al. Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy [J]. Diabetes Care, 2010, 33(11): 2409-2415.
[16]
Wei PZ, Fung WW, Jackkit CN, et al. Metabolomic changes of human proximal tubular cell line in high glucose environment [J]. Sci Rep, 2019, 9(1): 16617.
[17]
Lagies S, Pichler R, Bork T, et al. Impact of diabetic stress conditions on renal cell metabolome [J]. Cells, 2019, 8(10): 1141.
[18]
Tofte N, Vogelzangs N, Mook-Kanamori D, et al. Plasma metabolomics identifies markers of impaired renal function: a meta-analysis of 3,089 persons with type 2 diabetes [J]. J Clin Endocrinol Metab, 2020, 105(7): dgaa173.
[19]
Sharma K, Karl B, Mathew AV, et al. Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease [J]. J Am Soc Nephrol, 2013, 24(11): 1901-1912.
[20]
Liu JJ, Ghosh S, Kovalik JP, et al. Profiling of plasma metabolites suggests altered mitochondrial fuel usage and remodeling of sphingolipid metabolism in individuals with type 2 diabetes and kidney disease [J]. Kidney Int Rep, 2017, 2(3): 470-480.
[21]
Saulnier B, Feigerlova E, Halimi J, et al. Urinary lysophopholipids are increased in diabetic patients with nephropathy [J]. J Diabetes Complicat, 2017, 31(7): 1103-1108.
[22]
Zhu C, Liang QL, Hu P, et al. Phospholipidomic identification of potential plasma biomarkers associated with type 2 diabetes mellitus and diabetic nephropathy [J]. Talanta, 2011, 85(4): 1711-1720.
[23]
American Diabetes Association. Microvascular complications and foot care: standards of medical care in diabetes-2020 [J]. Diabetes Care, 2020, 43(Suppl 1): S135-S151.
[24]
Cañadas-Garre M, Anderson K, McGoldrick J, et al. Proteomic and metabolomic approaches in the search for biomarkers in chronic kidney disease [J]. J Proteomics, 2019, 193: 93-122.
[25]
Tofte N, Lindhardt M, Adamova K, et al. Early detection of diabetic kidney disease by urinary proteomics and subsequent intervention with spironolactone to delay progression (PRIORITY): a prospective observational study and embedded randomised placebo-controlled trial [J]. Lancet Diabetes Endocrinol, 2020, 8(4): 301-312.
[26]
Siwy J, Klein T, Rosler M, et al. Urinary proteomics as a tool to identify kidney responders to dipeptidyl peptidase-4 inhibition: a hypothesis-generating analysis from the MARLINA-T2D trial [J]. Proteomics Clin Appl, 2019, 13(2): e1800144.
[27]
Liu J, Liu S, Wong M, et al. Urinary haptoglobin predicts rapid renal function decline in asians with type 2 diabetes and early kidney disease [J]. J Clin Endocrinol Metab, 2016, 101(10): 3794-3802.
[28]
Vander K, Tempels F, Ismail N, et al. Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study) [J]. Metabolomics, 2012, 8(1): 109-119.
[29]
Xu L, Hu C, Liu Y, et al. Development of a sensitive and quantitative method for the identification of two major furan fatty acids in human plasma [J]. J Lipid Res, 2020, 61(4): 560-569.
[30]
Du Y, Xu B, Deng X, et al. Predictive metabolic signatures for the occurrence and development of diabetic nephropathy and the intervention of Ginkgo biloba leaves extract based on gas or liquid chromatography with mass spectrometry [J]. J Pharm Biomed Anal, 2019, 166: 30-39.
[31]
Karczewski K, Snyder M. Integrative omics for health and disease [J]. Nat Rev Genet, 2018, 19(5): 299-310.
[1] 韩春颖, 王婷婷, 李艳艳, 朴金霞. 子宫内膜癌患者淋巴管间隙浸润预测因素研究现状[J]. 中华妇幼临床医学杂志(电子版), 2023, 19(04): 403-409.
[2] 王璐, 王宇, 曾俊, 陈伟, 江华. 机器学习与多组学结合推动精准营养的研究进展[J]. 中华损伤与修复杂志(电子版), 2022, 17(06): 540-544.
[3] 程莉, 章晓良. 血尿酸和胱抑素C与糖尿病视网膜病变患者合并糖尿病肾病的关系及影响因素[J]. 中华肾病研究电子杂志, 2023, 12(04): 194-199.
[4] 吴震宇, 胡亚芬, 董晓芬, 马远方. 血清CTGF、TGF-β1、MMP2水平对糖尿病肾病肾间质纤维化的预测分析[J]. 中华肾病研究电子杂志, 2022, 11(06): 332-337.
[5] 雷建东, 吴林军, 季沙, 蒋志敏. 糖尿病肾病维持性血液透析患者低血糖预测模型及评分量表的建立[J]. 中华肾病研究电子杂志, 2022, 11(06): 311-317.
[6] 徐新丽, 于小勇. 表观遗传——中医药治疗糖尿病肾病新视角[J]. 中华肾病研究电子杂志, 2022, 11(05): 276-280.
[7] 贾英民, 张术姣, 耿运玲, 曹梓静, 王耀献, 吕仁和, 刘玉宁, 刘伟敬. 蝉花菌丝联合海昆肾喜胶囊对糖尿病肾小管上皮细胞自噬-溶酶体通路的影响[J]. 中华肾病研究电子杂志, 2022, 11(04): 212-218.
[8] 潘娟, 乔晞. 环状核糖核酸:糖尿病肾病治疗新靶点[J]. 中华肾病研究电子杂志, 2022, 11(01): 44-47.
[9] 王宁, 吴慢莉, 杨东霞. 代谢组学生物标志物:子宫内膜异位症诊疗的新靶点[J]. 中华临床医师杂志(电子版), 2022, 16(12): 1280-1283.
[10] 朱艺平, 陈一平, 赵艳英, 陆玮玮, 牙侯军, 苏复霞. 二十味沉香丸调控糖尿病肾病大鼠肠道菌群益生菌构成的机制研究[J]. 中华临床医师杂志(电子版), 2022, 16(06): 572-578.
[11] 冉启玉, 杜鹏宇, 孔蕾, 孙冰. 神经酰胺与糖尿病及其并发症关系研究进展[J]. 中华诊断学电子杂志, 2022, 10(03): 158-162.
[12] 刘倩影, 刘雪彦, 周佩如, 胡申玲, 叶倩呈, 黄洁微. 糖尿病肾病患者血液透析期间低血糖管理的证据总结[J]. 中华肥胖与代谢病电子杂志, 2023, 09(01): 22-27.
[13] 王慧卿, 李银玉, 张继敏, 黄正丽, 孙喜明, 薛少青, 焦爱富, 赵慧媛, 尉杰忠. 血清adipsin及皮下脂肪面积与早期糖尿病肾病的相关性分析[J]. 中华肥胖与代谢病电子杂志, 2022, 08(04): 256-262.
[14] 沈地, 权莉, 梁存禹, 孟齐, 艾比拜·玉素甫. 葡萄糖目标范围内时间与2型糖尿病患者尿微量白蛋白水平的相关性研究[J]. 中华肥胖与代谢病电子杂志, 2022, 08(04): 249-255.
[15] 何圣清, 袁唯唯, 孟莞瑞, 符青松, 郑晓斌, 武红梅. 达格列净联合二甲双胍治疗对早期2型糖尿病肾病患者肾小管功能和血清Klotho的影响[J]. 中华肥胖与代谢病电子杂志, 2022, 08(04): 236-242.
阅读次数
全文


摘要