Article(id=1198628667799732780, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198628666650493481, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2022-1234, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1668528000000, receivedDateStr=2022-11-16, revisedDate=1678032000000, revisedDateStr=2023-03-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1763704943847, onlineDateStr=2025-11-21, pubDate=1689091200000, pubDateStr=2023-07-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763704943847, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763704943847, creator=13701087609, updateTime=1763704943847, updator=13701087609, issue=Issue{id=1198628666650493481, tenantId=1146029695717560320, journalId=1189982191388893191, year='2023', volume='58', issue='7', pageStart='0', pageEnd='1980', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1763704943573, creator=13701087609, updateTime=1766137716668, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1208832456644490122, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198628666650493481, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1208832456644490123, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1198628666650493481, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1833, endPage=1841, ext={EN=ArticleExt(id=1198628668617622061, articleId=1198628667799732780, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Searching for potential biomarkers reflecting early injury and progressive of podocytes in nephropathy based on cellular metabolomics combined with ROC analysis, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

The pathogenesis of the nephrotic syndrome is complex and the pathological types are diverse, so the minor symptoms in its early phases are difficult to detect. Renal biopsy is the gold indicator for the diagnosis of renal pathology and progression, but poor patient compliance shows, and the optimal treatment time is often delayed. Therefore, the discovery of biomarkers for early diagnosis and disease progression monitoring is of great clinical significance. In this study, doxorubicin-injured podocyte models were used to simulate human kidney disease at different stages of progression. LC-MS-based metabolomic technology combined with statistical methods was used to screen and identify the potential biomarkers associated with early injury or progression of podocytes. The results of cell viability, apoptosis tests and podocyte structural protein analysis showed that the model was successfully constructed, and the degree of podocyte injury was significantly different between the two modeling methods. According to VIP > 1 and P < 0.05 based on the orthogonal partial least squares discriminant analysis (OPLS-DA) model, nine differential metabolites reflecting early podocyte injury and twelve differential metabolites reflecting the injury progression were screened, respectively. ROC analysis was adopted to focus on the potential biomarkers that can reflecting the early podocyte injury including L-tryptophan, guanosine triphosphate (GTP), 5′-thymidylic acid (dTMP) and thymidine, and the biomarkers reflecting the injury progression of podocytes composed of L-phenylalanine, L-tyrosine acid, uridine 5′-diphosphate (UDP) and guanosine 5′-diphosphate (GDP) AUC > 0.85. It indicated that these eight metabolites may have high sensitivity and diagnostic ability. This study provides a reference for the research on biomarkers of progressive diseases.

, correspAuthors=Xue-mei QIN, Zhan-xin YAO, Wei-na GAO, Ai-ping LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2023 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yun-feng ZHU, Ting CUI, Yi-rui ZHAO, Xue-mei QIN, Zhan-xin YAO, Wei-na GAO, Ai-ping LI), CN=ArticleExt(id=1198628669695558229, articleId=1198628667799732780, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于细胞代谢组学结合ROC寻找反映肾病足细胞早期损伤及进程的潜在生物标志物, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

肾病综合征发病机制复杂, 病理类型多样, 早期症状轻微不易发觉。肾穿刺活检作为肾病病理及进程诊断的金指标, 但患者接受程度低, 常常延误最佳治疗时间。因此, 发现可用于肾病早期诊断以及能够用于反映疾病进程的生物标志物对于临床具有重要意义。本研究以多柔比星损伤的足细胞模型模拟人类不同进展时期的肾病, 采用基于LC-MS的代谢组学技术结合统计学方法筛选并鉴定体外反映肾病足细胞早期损伤以及损伤进程的潜在生物标志物。细胞活力、凋亡测试以及足细胞结构蛋白分析结果显示, 模型复制成功, 且两种造模方式下足细胞损伤程度明显不同。依据正交偏最小二乘判别分析(orthogonal partial least squares discriminant analysis, OPLS-DA) 模型VIP > 1和P < 0.05分别筛选出9个反映肾病足细胞早期损伤以及12个反映损伤进程的差异代谢物。进一步经ROC分析聚焦可用于反映肾病足细胞早期损伤的潜在生物标志物为L-色氨酸、三磷酸鸟苷(guanosine triphosphate, GTP)、胸腺嘧啶脱氧核苷酸(5′-thymidylic acid, dTMP)、胸苷, 及用于监测足细胞损伤进程的潜在生物标志物为L-苯丙氨酸、L-酪氨酸、二磷酸尿苷(uridine 5′-diphosphate, UDP)、二磷酸鸟苷(guanosine 5′-diphosphate, GDP) AUC均大于0.85, 表明这8种代谢物可能具有较高的敏感性及诊断能力, 这一研究为进展性疾病相关生物标志物的研究提供参考。

, correspAuthors=秦雪梅, 姚站馨, 高蔚娜, 李爱平, authorNote=null, correspAuthorsNote=
*秦雪梅, E-mail: ;
姚站馨, E-mail: ;
高蔚娜, E-mail: ;
李爱平, E-mail:
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J Ren Nutr, 2023, 33: 316-325., articleTitle=Chronic kidney disease is associated with attenuated plasma metabolome response to oral glucose tolerance testing, refAbstract=null)], funds=[Fund(id=1198960101630509872, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, awardId=Zyczl2020001, language=CN, fundingSource=山西省卫生健康委员会“十大晋药”中药材质量标准规范制定和综合利用研究项目(Zyczl2020001), fundOrder=null, country=null), Fund(id=1198960101777310529, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, awardId=82204595, language=CN, fundingSource=国家自然科学青年基金(82204595), fundOrder=null, country=null), Fund(id=1198960101911528274, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, awardId=2019FP-03, language=CN, fundingSource=山西省深度贫困县科技精准扶贫专项(2019FP-03), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1198960093048963140, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, xref=null, ext=[AuthorCompanyExt(id=1198960093057351748, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, companyId=1198960093048963140, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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A: MTT assay detecting cell viability; B: Flow cytometry detecting cell apoptosis; C: The corresponding quantified histograms; D: Detection of podocin and nephrin protein expression levels in MPC5 cells using western blot; E: Podocine protein; F: Nephrin protein. <i>n</i> = 3, <span class="mag-xml-inline-formula">$\bar{x}$</span> ± <i>s</i>. <sup>##</sup><i>P</i> < 0.01, <sup>###</sup><i>P</i> < 0.001 <i>vs</i> C; <sup>**</sup><i>P</i> < 0.01, <sup>***</sup><i>P</i> < 0.001 <i>vs</i> M1. C: Control group; M1: Model 1 group; M2: Model 2 group , figureFileSmall=kDn+RSvVkbBmEN3mL62z/Q==, figureFileBig=G2dhFAIvf5zEI2UBC5JKhQ==, tableContent=null), ArticleFig(id=1198960100305109695, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=EN, label=null, caption=null, figureFileSmall=Yae6/dAivtLZ6c1RwTjHbw==, figureFileBig=2h4pz++P3dAuyFoIYQJutw==, tableContent=null), ArticleFig(id=1198960100456104655, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=CN, label=Figure 2, caption= PCA score plot (A and B), PLS-DA model verification plot (C and D) and S-plot (E and F) between the control group (C) and model 1 group (M1); A, C, E: Positive ion mode; B, D, F: Negative ion mode , figureFileSmall=Yae6/dAivtLZ6c1RwTjHbw==, figureFileBig=2h4pz++P3dAuyFoIYQJutw==, tableContent=null), ArticleFig(id=1198960100590322393, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=EN, label=null, caption=null, figureFileSmall=o95ND5UFdYKORvCQobBEKQ==, figureFileBig=LxiKdqfBsP3RCs6jrQtnjA==, tableContent=null), ArticleFig(id=1198960100791648998, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=CN, label=Figure 3, caption= PCA score plot (A and B), PLS-DA model verification plot (C and D) and S-plot (E and F) between the control group (C) and model 2 group (M2); A, C, E: Positive ion mode; B, D, F: Negative ion mode , figureFileSmall=o95ND5UFdYKORvCQobBEKQ==, figureFileBig=LxiKdqfBsP3RCs6jrQtnjA==, tableContent=null), ArticleFig(id=1198960100930061045, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=EN, label=null, caption=null, figureFileSmall=wE83w/RDho3VAQDZSukhAw==, figureFileBig=UjUkB/+RRIA+f7ei1cYTIg==, tableContent=null), ArticleFig(id=1198960101089444610, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=CN, label=Figure 4, caption= Multivariate statistical analysis of differential metabolites. A: Venn diagram of differential metabolites between different groups; B: ROC analysis of potential biomarkers reflecting injury progression; C: Metabolic pathway analysis of potential biomarkers reflecting injury progression; D: ROC analysis of potential biomarkers reflecting podocyte early injury; E: Metabolic pathway analysis of potential biomarkers reflecting podocyte early injury. CM1: The control group and model 1 group; CM2: The control group and model 2 group; M1M2: The model 1 group and model 2 group , figureFileSmall=wE83w/RDho3VAQDZSukhAw==, figureFileBig=UjUkB/+RRIA+f7ei1cYTIg==, tableContent=null), ArticleFig(id=1198960101286576910, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No. Metabolite Formula m/z RT/min Ion Trend
M1 vs C M2 vs C M1 vs M2
1 N-Acetyl-DL-serine C5H9NO4 146.044 7 2.11 M-H *
2 L-Glutamic acid C5H9NO4 146.045 2 1.45 M-H *** **
3 L-Phenylalanine C9H11NO2 164.070 7 7.43 M-H **
4 Uridine 5′-diphosphate C9H14N2O12P2 402.991 3 2.14 M-H ** **
5 Adenosine-5′-diphosphate C10H15N5O10P2 426.019 6 2.94 M+H
6 Guanosine 5′-diphosphate C10H15N5O11P2 442.013 6 2.10 M-H
7 Adenosine triphosphate C10H16N5O13P3 505.983 7 3.24 M+H
8 Uridine-5-diphospho-α-D-galactose C15H24N2O17P2 565.044 4 2.07 M-H
9 Uridine 5′-monophosphate C9H9NO3 178.049 6 9.94 M+H *
10 L-Tyrosine C9H11NO3 180.065 4 5.06 M-H ** **
11 S-Adenosylhomocysteine C14H20N6O5S 383.110 5 6.88 M-H
12 Adenosine monophosphate C10H14N5O7P 346.052 0 2.70 M+H
13 L-Lactic acid C3H6O3 89.025 4 2.53 M-H - -
14 L-Tryptophan C11H12N2O2 203.080 7 8.53 M-H * - -
15 Uridine 5′-monophosphate C9H13N2O9P 323.026 1 2.16 M-H - -
16 Succinic acid C4H6O4 117.019 0 4.26 M-H * - -
17 Ureidosuccinic acid C5H8N2O5 175.033 5 2.00 M-H ** - -
18 Thymidine C10H14N2O5 241.080 7 7.42 M-H *** - -
19 5′-Thymidylic acid C10H15N2O8P 321.045 9 5.88 M-H *** - -
20 Inosinic acid C10H13N4O8P 347.035 7 2.83 M-H - -
21 Guanosine triphosphate C10H16N5O14P3 523.996 5 1.84 M+H * - -
22 Pyroglutamic acid C5H7NO3 128.035 1 3.57 M-H - -
23 Citric acid C6H8O7 191.018 1 3.18 M-H - -
24 Cytidine triphosphate C9H16N3O14P3 481.974 7 1.44 M-H - -
25 1-Pyrroline-5-carboxylic acid C5H7NO2 112.039 8 2.05 M-H - * -
26 Flavin mononucleotide C17H21N4O9P 455.092 9 9.08 M-H - -
27 Ketoleucine C6H10O3 129.055 1 9.15 M-H - -
28 Flavin adenine dinucleotide C27H33N9O15P2 784.144 2 8.32 M+H - -
29 Spermidine C7H19N3 146.165 0 1.21 M+H - -
30 Oleamide C18H35NO 282.279 7 21.23 M+H - * -
), ArticleFig(id=1198960101387240218, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1198628667799732780, language=CN, label=Table 1, caption=

Summary of biomarkers in the C group vs M1 group or vs M2 group. *P < 0.05, **P < 0.01, ***P < 0.001

, figureFileSmall=null, figureFileBig=null, tableContent=
No. Metabolite Formula m/z RT/min Ion Trend
M1 vs C M2 vs C M1 vs M2
1 N-Acetyl-DL-serine C5H9NO4 146.044 7 2.11 M-H *
2 L-Glutamic acid C5H9NO4 146.045 2 1.45 M-H *** **
3 L-Phenylalanine C9H11NO2 164.070 7 7.43 M-H **
4 Uridine 5′-diphosphate C9H14N2O12P2 402.991 3 2.14 M-H ** **
5 Adenosine-5′-diphosphate C10H15N5O10P2 426.019 6 2.94 M+H
6 Guanosine 5′-diphosphate C10H15N5O11P2 442.013 6 2.10 M-H
7 Adenosine triphosphate C10H16N5O13P3 505.983 7 3.24 M+H
8 Uridine-5-diphospho-α-D-galactose C15H24N2O17P2 565.044 4 2.07 M-H
9 Uridine 5′-monophosphate C9H9NO3 178.049 6 9.94 M+H *
10 L-Tyrosine C9H11NO3 180.065 4 5.06 M-H ** **
11 S-Adenosylhomocysteine C14H20N6O5S 383.110 5 6.88 M-H
12 Adenosine monophosphate C10H14N5O7P 346.052 0 2.70 M+H
13 L-Lactic acid C3H6O3 89.025 4 2.53 M-H - -
14 L-Tryptophan C11H12N2O2 203.080 7 8.53 M-H * - -
15 Uridine 5′-monophosphate C9H13N2O9P 323.026 1 2.16 M-H - -
16 Succinic acid C4H6O4 117.019 0 4.26 M-H * - -
17 Ureidosuccinic acid C5H8N2O5 175.033 5 2.00 M-H ** - -
18 Thymidine C10H14N2O5 241.080 7 7.42 M-H *** - -
19 5′-Thymidylic acid C10H15N2O8P 321.045 9 5.88 M-H *** - -
20 Inosinic acid C10H13N4O8P 347.035 7 2.83 M-H - -
21 Guanosine triphosphate C10H16N5O14P3 523.996 5 1.84 M+H * - -
22 Pyroglutamic acid C5H7NO3 128.035 1 3.57 M-H - -
23 Citric acid C6H8O7 191.018 1 3.18 M-H - -
24 Cytidine triphosphate C9H16N3O14P3 481.974 7 1.44 M-H - -
25 1-Pyrroline-5-carboxylic acid C5H7NO2 112.039 8 2.05 M-H - * -
26 Flavin mononucleotide C17H21N4O9P 455.092 9 9.08 M-H - -
27 Ketoleucine C6H10O3 129.055 1 9.15 M-H - -
28 Flavin adenine dinucleotide C27H33N9O15P2 784.144 2 8.32 M+H - -
29 Spermidine C7H19N3 146.165 0 1.21 M+H - -
30 Oleamide C18H35NO 282.279 7 21.23 M+H - * -
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基于细胞代谢组学结合ROC寻找反映肾病足细胞早期损伤及进程的潜在生物标志物
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朱云峰 1, 3 , 崔婷 1 , 赵怡蕊 2 , 秦雪梅 1, * , 姚站馨 3, * , 高蔚娜 3, * , 李爱平 1, *
药学学报 | 研究论文 2023,58(7): 1833-1841
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药学学报 | 研究论文 2023, 58(7): 1833-1841
基于细胞代谢组学结合ROC寻找反映肾病足细胞早期损伤及进程的潜在生物标志物
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朱云峰1, 3, 崔婷1, 赵怡蕊2, 秦雪梅1, * , 姚站馨3, * , 高蔚娜3, * , 李爱平1, *
作者信息
  • 1.山西大学中医药现代研究中心, 山西 太原 030006
  • 2.山西省中西医结合医院, 山西 太原 030006
  • 3.军事科学院军事医学研究院环境医学与作业医学研究所, 天津 300050

通讯作者:

*秦雪梅, E-mail: ;
姚站馨, E-mail: ;
高蔚娜, E-mail: ;
李爱平, E-mail:
Searching for potential biomarkers reflecting early injury and progressive of podocytes in nephropathy based on cellular metabolomics combined with ROC analysis
Yun-feng ZHU1, 3, Ting CUI1, Yi-rui ZHAO2, Xue-mei QIN1, * , Zhan-xin YAO3, * , Wei-na GAO3, * , Ai-ping LI1, *
Affiliations
  • 1. Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006, China
  • 2. Shanxi Provincial Integrated Traditional Chinese and Western Medicine Hospital, Taiyuan 030006, China
  • 3. Institute of Environmental and Operation Medicine, Tianjin 300050, China
出版时间: 2023-07-12 doi: 10.16438/j.0513-4870.2022-1234
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肾病综合征发病机制复杂, 病理类型多样, 早期症状轻微不易发觉。肾穿刺活检作为肾病病理及进程诊断的金指标, 但患者接受程度低, 常常延误最佳治疗时间。因此, 发现可用于肾病早期诊断以及能够用于反映疾病进程的生物标志物对于临床具有重要意义。本研究以多柔比星损伤的足细胞模型模拟人类不同进展时期的肾病, 采用基于LC-MS的代谢组学技术结合统计学方法筛选并鉴定体外反映肾病足细胞早期损伤以及损伤进程的潜在生物标志物。细胞活力、凋亡测试以及足细胞结构蛋白分析结果显示, 模型复制成功, 且两种造模方式下足细胞损伤程度明显不同。依据正交偏最小二乘判别分析(orthogonal partial least squares discriminant analysis, OPLS-DA) 模型VIP > 1和P < 0.05分别筛选出9个反映肾病足细胞早期损伤以及12个反映损伤进程的差异代谢物。进一步经ROC分析聚焦可用于反映肾病足细胞早期损伤的潜在生物标志物为L-色氨酸、三磷酸鸟苷(guanosine triphosphate, GTP)、胸腺嘧啶脱氧核苷酸(5′-thymidylic acid, dTMP)、胸苷, 及用于监测足细胞损伤进程的潜在生物标志物为L-苯丙氨酸、L-酪氨酸、二磷酸尿苷(uridine 5′-diphosphate, UDP)、二磷酸鸟苷(guanosine 5′-diphosphate, GDP) AUC均大于0.85, 表明这8种代谢物可能具有较高的敏感性及诊断能力, 这一研究为进展性疾病相关生物标志物的研究提供参考。

LC-MS  /  代谢组学  /  足细胞  /  ROC  /  潜在生物标志物

The pathogenesis of the nephrotic syndrome is complex and the pathological types are diverse, so the minor symptoms in its early phases are difficult to detect. Renal biopsy is the gold indicator for the diagnosis of renal pathology and progression, but poor patient compliance shows, and the optimal treatment time is often delayed. Therefore, the discovery of biomarkers for early diagnosis and disease progression monitoring is of great clinical significance. In this study, doxorubicin-injured podocyte models were used to simulate human kidney disease at different stages of progression. LC-MS-based metabolomic technology combined with statistical methods was used to screen and identify the potential biomarkers associated with early injury or progression of podocytes. The results of cell viability, apoptosis tests and podocyte structural protein analysis showed that the model was successfully constructed, and the degree of podocyte injury was significantly different between the two modeling methods. According to VIP > 1 and P < 0.05 based on the orthogonal partial least squares discriminant analysis (OPLS-DA) model, nine differential metabolites reflecting early podocyte injury and twelve differential metabolites reflecting the injury progression were screened, respectively. ROC analysis was adopted to focus on the potential biomarkers that can reflecting the early podocyte injury including L-tryptophan, guanosine triphosphate (GTP), 5′-thymidylic acid (dTMP) and thymidine, and the biomarkers reflecting the injury progression of podocytes composed of L-phenylalanine, L-tyrosine acid, uridine 5′-diphosphate (UDP) and guanosine 5′-diphosphate (GDP) AUC > 0.85. It indicated that these eight metabolites may have high sensitivity and diagnostic ability. This study provides a reference for the research on biomarkers of progressive diseases.

LC-MS  /  metabolomics  /  podocyte  /  ROC  /  potential biomarker
朱云峰, 崔婷, 赵怡蕊, 秦雪梅, 姚站馨, 高蔚娜, 李爱平. 基于细胞代谢组学结合ROC寻找反映肾病足细胞早期损伤及进程的潜在生物标志物. 药学学报, 2023 , 58 (7) : 1833 -1841 . DOI: 10.16438/j.0513-4870.2022-1234
Yun-feng ZHU, Ting CUI, Yi-rui ZHAO, Xue-mei QIN, Zhan-xin YAO, Wei-na GAO, Ai-ping LI. Searching for potential biomarkers reflecting early injury and progressive of podocytes in nephropathy based on cellular metabolomics combined with ROC analysis[J]. Acta Pharmaceutica Sinica, 2023 , 58 (7) : 1833 -1841 . DOI: 10.16438/j.0513-4870.2022-1234
肾病综合征是由微小病变肾小球肾炎、局灶性肾小球硬化、肾小球急性感染等[1]多种病因导致的肾小球基底膜受损通透性增加的临床综合征[2], 以水肿、低白蛋白血症、高脂血症和大量蛋白尿为临床特征[3]。然而以上检测指标对于肾病早期诊断困难, 进一步的肾穿刺活检患者依从性差, 常常致使病情进一步恶化。因此, 寻找可用于肾病早期诊断以及反映疾病进展性病变的生物标志物对于疾病早期诊断和延缓肾脏疾病至关重要。
目前肾病代谢标志物的研究主要集中在探究肾病发病的分子机制, 指导临床用药以及疾病诊断和预后。目前临床主要以血清肌酐和尿素氮等评价肾脏功能, 然而不断积累的证据表明, 这些生物标志物在早期检测肾脏疾病方面并不理想[4]。近年来, 发现多种与肾病相关的生物标志物, 如犬尿氨酸、对称二甲基精氨酸、壬二酸等生物标志物可用于准确鉴别糖尿病肾病患者[5]。甘氨酸、柠檬酸盐和丙酮酸等生物标志物能够区分临床肾癌患者发病的不同时期[6], 对预测疾病的发生和发展具有重要意义。由于肾病发病机制以及病理类型的复杂性, 单一的生物标志物难以精准判别, 可能需要一组生物标志物联合进行诊断。
足细胞是肾小囊脏层的上皮细胞, 是一种终末分化的细胞, 覆盖于肾小球毛细血管外表面与肾小球基底膜和肾小球内皮细胞一起形成肾小球滤过屏障[7], 足细胞是其中关键组成部分。滤过膜的受损往往是由于足细胞足突融合, 或者足细胞与基底膜黏附减弱从而脱落凋亡所导致[8]。肾病综合征蛋白尿的发病机制与肾小球中足细胞上皮细胞结构和功能有关[9], 足细胞的损伤可导致蛋白尿产生, 进而导致肾小球滤过率发生改变[10], 最终诱导慢性肾病的发生与发展[11]。足细胞损伤是肾病综合征的典型特征, 在许多肾脏疾病的发生和进展中起关键作用, 同时也是其病程中发现最早的异常特征之一[12], 在一定程度能反映肾脏损伤[13, 14], 因此减少足细胞损伤是维持肾功能的重要研究方向。多柔比星是一种含醌的蒽环类抗生素, 在肾脏代谢还原为半醌自由基后与氧气反应产生活性氧, 可导致肾小球上皮细胞脂质过氧化, 破坏滤过膜的结构和功能, 并导致进行性和不可逆蛋白尿等症状[15]。多柔比星诱导的肾病模型是一种可高度重复的经典肾病综合征模型[16], 能够较为理想地模拟人类肾脏疾病。
代谢组学作为一项成熟的组学技术, 主要用于定性定量的评估代谢物组在疾病治疗、诊断和预后的能力, 具有无创、高敏感性、高特异性等[17]优势, 已成功应用于肝癌、阿尔茨海默症、哮喘等[18-20]疾病的生物标志物发现。本研究使用永生化小鼠肾足细胞, 通过多柔比星诱导模拟不同肾病进程的损伤模型, 基于LC-MS代谢组学技术结合统计学方法对肾病足细胞损伤潜在生物标志物进行初筛, 经ROC曲线进一步聚焦用于早期诊断和反映疾病进程的潜在生物标志物, 在代谢水平上探究疾病的病理机制, 以期为临床早期诊断以及延缓肾病进展提供一定的研究基础。
仪器   Agilent 1290 Infinity Ⅱ超高效液相色谱系统(美国Agilent公司); 5600+ Q-TOF型高分辨质谱仪(美国AB SCIEX公司); Sartorius BSA124S分析天平(德国Sartorius公司); Spectra Max 190酶标仪(美国Molecular Devices公司); SC-3610低速离心机(安徽中科中佳科学仪器有限公司); Neofuge 1600R台式高速离心机; HF90二氧化碳细胞培养箱(中国力康生物医疗科技控股有限公司); Cyto Flex流式细胞仪(中国贝克曼库尔特商贸有限公司); Mini-PROTEAN Tera电泳仪(美国Bio-Rad公司)。
药品与试剂   盐酸多柔比星(北京索莱宝公司, 纯度≥ 98%)、噻唑蓝(MTT)、ECL显影液、SDS-PAGE变性丙烯酰胺凝胶快速制备试剂盒(碧云天生物科技有限公司); RPMI 1640培养液(赛默飞世尔科技公司); 胎牛血清(FBS) (浙江天杭生物科技股份有限公司); 磷酸盐缓冲液(PBS); 青链霉素混合液; 胰蛋白酶-EDTA消化液; BCA蛋白测定试剂盒(北京索莱宝公司); Hoechst/PI双染细胞凋亡检测试剂盒(南京建成生物工程研究所有限公司); Podocin、Nephrin抗体(武汉三鹰生物技术有限公司); 二甲基亚砜、HRP-山羊兔抗免疫球蛋白(生工生物工程股份有限公司)。
细胞株   永生化小鼠肾小球足细胞(MPC-5) 购于广州吉妮欧生物科技有限公司。
细胞培养   MPC5细胞用RPMI 1640培养基(含10%胎牛血清, 100 u·mL-1青霉素及100 u·mL-1链霉素)培养, 置于37 ℃, 5% CO2细胞培养箱中。当细胞浓度达到90%时进行传代, 每2~3天传代1次, 取对数生长期细胞用于实验。
多柔比星诱导体外足细胞损伤模型的建立   取对数生长期的MPC5细胞, 以1×105个/孔的密度接种于96孔板, 待细胞贴壁后对细胞进行处理, 正常对照组无特殊处理, 置于培养箱中孵育24 h, 模型1组(M1) 加入0.07 μg·mL-1多柔比星处理12 h后, 再次加入0.03 μg·mL-1多柔比星培养12 h, 模拟肾病足细胞早期损伤。模型2组(M2) 加入0.1 μg·mL-1多柔比星培养24 h, 模拟肾病足细胞晚期损伤, 每组设置6个复孔。
MTT比色法检测MPC5细胞活力   药物干预结束后, 每孔加入5 g·L-1 MTT 10 μL, 继续孵育4 h。反应结束后, 弃去上清液, 每孔加入100 μL的DMSO, 振荡10 min完全反应后, 使用酶标仪检测490 nm处的吸光度, GraphPadPrism v 8.0.1软件计算细胞增殖抑制率。
流式细胞术检测细胞凋亡   取对数生长期的MPC5细胞接种于96孔板中, 待细胞贴壁后, 按前述多柔比星肾病体外细胞模型方法进行分组和处置。于37 ℃培养24 h后, 加入PBS清洗细胞三次, 胰酶消化, 重悬, 以3 000 r·min-1离心5 min, 弃上清液, 加入5 mL碘化丙啶染液避光放置10 min后混匀, 以200目尼龙网过滤细胞至流式细胞管中, 使用流式细胞仪检测各组细胞的凋亡情况。
Western blot法检测蛋白表达   取对数生长期的MPC5细胞, 接种于10 mm培养皿, 给予多柔比星处理24 h后提取蛋白。用预冷的PBS洗涤细胞后, 细胞刮刀刮下细胞收集于2 mL离心管中, 3 000 r·min-1离心5 min, 倾去上清, 加入细胞裂解液150 μL, 冰浴裂解30 min后离心(4 ℃、12 000 r·min-1) 15 min, 收集上清。用BCA试剂盒测定蛋白浓度, 调整蛋白浓度后加入1×loading buffer置于金属中加热变性(95 ℃, 10 min)。取变性蛋白经SDS-PAGE电泳(80 V), 湿转转膜(300 mA, 90 min), 5%脱脂牛奶封闭2 h, 一抗(1∶1 000) 4 ℃孵育过夜, 后TBST (Tris buffered saline Tween) 缓冲液洗涤3次, 室温孵育二抗(1∶10 000) 1 h, TBST缓冲液洗涤3次。最后用ECL剂盒发光显色, 并用Image Studio Ver软件对目的条带进行半定量分析。
代谢组学分析   LC-MS样本制备: 将相同数量的MPC5细胞置于直径10 cm的培养皿中, 每组设置8个重复, 当细胞长至约90%, 按前述多柔比星肾病体外细胞模型方法进行分组和处理, 于37 ℃培养24 h后, 用预冷的生理盐水洗涤细胞3次, 用细胞刮刀刮下培养皿中细胞, 于4 ℃、1 200 r·min-1离心10 min, 收集各组细胞沉淀, 冷冻干燥后放于-80 ℃冰箱保存, 用于进一步代谢组学分析。将细胞从-80 ℃冰箱取出, 在4 ℃反复冻融5次后, 加入1 mL 75%甲醇, 于冰上超声破碎15 min, 4 ℃, 13 000 r·min-1离心20 min, 收集上清液, 余下沉淀加入1 mL 75%甲醇, 超声破碎15 min, 合并两次上清后冻干。进样前, 将冻干的细胞提取物复溶于60 μL的0.1%甲酸水溶液, 涡旋1 min。在4 ℃, 13 000 r·min-1离心20 min后, 收集上清液于液相小瓶中。
QC样本的制备   在上述每个样品中吸取10 μL的细胞代谢物提取液进行混合, 涡旋30 s, 作为质控样本评估仪器稳定性。
色谱条件   色谱柱:ACQUITY UPLC HSS T3 (100 mm × 2.1 mm, 1.7 μm),流动相为A (水,含0.1%甲酸)-B (乙腈), 流动相梯度为0~2 min, 1% B; 2~5 min, 1%~12% B; 5~8 min, 12%~20% B; 8~14 min, 20%~40% B; 14~18 min, 40%~99% B; 18~19 min, 99% B; 19~20 min, 99%~1% B; 20~23 min, 1% B。体积流量为0.2 mL·min-1, 进样量为5 μL, 柱温为40 ℃。
质谱条件   使用电喷雾离子源(ESI), 喷雾电压大小为正极3.0 kV, 负极3.5 kV。毛细管温度为320 ℃, 加热器温度为300 ℃, 鞘气为241.5 kPa, 辅助气为34.5 kPa, 采集范围为m/z 50~1 500。
数据处理与分析   将采集到的原始LC-MS/MS数据文件导入OSI-SMMS软件(中国大连达硕信息技术有限公司) 和SCIEX OS (美国AB SCIEX公司), 设置参数, 获取匹配和对齐的峰值数据, 将峰面积进行归一化后的数据导入SIMCA-P软件(version 13.0, Umetrics, Umea, Sweden) 进行主成分分析(principal component analysis, PCA)、偏最小二乘判别分析(partial least squares discriminant analysis, PLS-DA) 和OPLS-DA。结合OPLS-DA的S-plot图中VIP > 1以及独立样本t检验筛选差异变量(以P < 0.05为筛选标准)。使用HMDB数据库并结合二级碎片离子对筛选出的差异变量进行指认。通过Metaboanalyst (https://www.metaboanalyst.ca/) 网站进行通路富集分析, 采用统计学软件SPSS 26.0 (美国IBM公司) 对差异代谢物进行ROC分析。
细胞活力结果图 1A显示, 与C组相比, 不同多柔比星诱导足细胞损伤模型组细胞活力明显降低, 且具有显著差异性, 其中M2组细胞损伤较M1组更为明显。
用流式细胞仪检测正常组、损伤早期模型组和损伤晚期模型组足细胞凋亡情况。LR代表早期凋亡细胞, UR代表晚期凋亡细胞, 凋亡率是早期凋亡率与晚期凋亡率之和。结果如图 1BC所示, 多柔比星诱导早期肾脏模型细胞凋亡率为15.30%, 多柔比星诱导晚期肾脏模型细胞凋亡率为21.89%。模型组与正常组相比, 凋亡率明显增加, 且M2组细胞凋亡率明显高于M1细胞凋亡率。
Western blot结果图 1D显示, 与C组相比, 足细胞结构蛋白nephrin和podocin[21]表达下调, 且M2组关键蛋白表达下调更明显, 表明多柔比星造模后足细胞受损, 提示造模成功。
多元统计分析PCA散点图(图 2AB) 显示, C组与M1组有明显的分离趋势, 相应PLS-DA随机排列检验(n = 200, R2Y = 0.991, Q2 = 0.923; R2Y = 0.956, Q2 = 0.865) 如图 2CD所示, 右侧原始解释变量(R2) 和预测变量(Q2) 均高于左侧排列值, 表明所建立的模型成立。有监督OPLS-DA模式适用于寻找组间差异, 依据S-plot (图 2EF)、VIP > 1和t检验(P < 0.05) 共筛选出21个差异代谢物。
对C组与M2组进行PCA (图 3AB) 分析, 结果显示二者明显分开。PLS-DA随机排列检验(n = 200, R2Y = 0.991, Q2 = 0.965; R2Y = 0.995, Q2 = 0.887) 如图 3CD所示, 右侧原始解释变量和预测变量均高于左侧排列值, 表明所建立的模型成立。根据OPLS-DA相应的S-plot (图 3EF)、VIP > 1和t检验(P < 0.05) 共筛选出21个差异代谢物。差异代谢物鉴定信息详见表 1
将C与M1组以及C与M2组中鉴定的共有差异代谢物定义为反映足细胞损伤进程的潜在生物标志物, 如图 4A所示, 共12个经定性鉴定分别为L-谷氨酸、L-苯丙氨酸、UDP、二磷酸腺苷(adenosine-5′-diphosphate, ADP)、GDP、三磷酸腺苷(adenosine triphosphate, ATP)、单磷酸腺苷(adenosine monophosphate, AMP)、UDP-半乳糖、L-酪氨酸、S-腺苷高半胱氨酸、N-乙酰丝氨酸和马尿酸。M1组与M2组中的差异代谢物与反映足细胞损伤进程的潜在生物标志物有9个相同, 这些代谢物一定程度上能监测足细胞损伤进程, 反映肾脏损伤程度。因此进一步对其进行ROC分析, 如图 4B所示, 4个差异代谢物L-苯丙氨酸、UDP、GDP和L-酪氨酸的AUC > 0.85, 与足细胞损伤进程相关性强, 可能有较好的判别能力。采用Metaboanalyst对上述4个差异代谢物进行代谢通路分析(图 4C), 依据impact > 0.01确定苯丙氨酸, 酪氨酸和色氨酸生物合成通路、苯丙氨酸代谢通路、嘌呤代谢通路、嘧啶代谢通路和酪氨酸代谢通路为反映足细胞损伤进程的关键代谢通路。
将C与M1组中除去12个反映足细胞损伤进程的潜在标志物剩余的差异代谢物定义为反映足细胞早期损伤的潜在生物标志物, 共9个分别为L-乳酸、L-色氨酸、琥珀酸、胸苷、dTMP、GTP、单磷酸尿苷(uridine 5′-monophosphate, UMP)、肌苷酸(inosinic acid, IMP)、脲基琥珀酸。对早期潜在生物标志物进行ROC分析, 如图 4D显示, L-色氨酸、GTP、dTMP和胸苷的AUC > 0.85, 表明具有较强诊断能力。对上述潜在生物标志物进行通路分析, 依据impact > 0.01确定嘧啶代谢、叶酸的生物合成以及色氨酸代谢通路为潜在反映足细胞早期损伤的关键代谢通路, 有望用于肾病早期诊断。
肾病综合征的发病机制复杂, 有研究表明, 其与足细胞损伤密切相关, 足细胞中代谢物可能是治疗肾病综合征的新靶点[22]。在临床中, 尿蛋白常作为肾病诊断的重要指标, 虽能一定程度上反映疾病的严重程度, 但不能对肾病病理类型进行准确判断。肾穿刺活检术作为临床肾病病理诊断的金指标, 但术后不良反应多, 患者接受程度低, 且难以作为肾病早期诊断标准以及用于疾病动态监测。因此, 亟需一种动态、准确、无创的诊断方法。代谢组学由于其固有的敏感性, 可以检测到受外界刺激后生物途径细微的变化, 从而深入了解各种生理和病理过程, 已广泛用作临床和生物医药研究中新型的整体诊断工具。
在反映肾病足细胞早期损伤的潜在生物标志物研究中, 色氨酸作为人体必需氨基酸之一, 在人体20种氨基酸中含量最低[23], 但其衍生代谢物与多种病理生理学状态有关。色氨酸主要通过犬尿氨酸途径(kynurenine pathway, KP)、5-羟色胺途径和肠道微生物群吲哚途径代谢。其中尿毒素硫酸吲哚在吲哚代谢途径产生, 游离的硫酸吲哚与白蛋白结合无法被透析去除并逐渐在肾脏中积累[24]被认为是肾脏疾病进展的原因。另外, 色氨酸代谢通路中代谢物或代谢酶的改变可激活芳香烃受体(aryl hydrocarbon receptor, AhR), 进而激活氧化应激和炎症, 从而导致进行性肾小球和肾小管细胞损伤, 从而引起肾小球硬化和肾间质纤维化并加重疾病进程[25]。本研究中色氨酸代谢(impact = 0.143) 涉及色氨酸, 发现M1中足细胞色氨酸水平上升, 提示多柔比星造模后色氨酸代谢通路紊乱, 改变的代谢物或酶可能进一步加重肾脏足细胞进展性损伤。在慢性肾病的长期临床研究中认为吲哚胺2, 3-双加氧酶介导的色氨酸代谢的上调可能是慢性肾病综合征的早期特征[26], 课题组前期基于动物肾病模型发现, 肾病不同时期均涉及色氨酸水平异常变化[27]。在本研究中因未检测到其下游3条途径中其他代谢产物的含量, 不足以确证其具体的损伤机制。
叶酸是水溶性维生素B的复合体之一, 在嘌呤与嘧啶的生物合成、氨基酸代谢以及线粒体中蛋白质合成中起着关键作用[28]。叶酸的缺乏可能导致氧化应激的增加、内皮一氧化氮合酶的降低、内皮功能发生障碍, 而这可能会加速慢性肾病的发展[29]。有研究表明, 补充叶酸可以显著降低慢性肾病的进展风险[30], 低剂量的叶酸可以增加肾病蛋白表达, 肾病蛋白是一种重要的荚膜细胞裂隙膜分子, 在维持肾小球滤过屏障方面起着关键作用[31], 其可减轻肾小球损伤, 使肾小球结构和功能明显改善[32]。另外在慢性肾病患者血清中发现叶酸生物合成发生明显改变, 但未发现GTP水平明显变化[33]。在本研究中叶酸生物合成(impact = 0.091) 涉及GTP, 其是叶酸合成的前体物质, 其含量的增加, 有助于促进叶酸的生物合成, 可能对足细胞损伤起到一定的保护作用。
嘧啶代谢(impact = 0.149) 是基本的代谢途径, 嘧啶核苷酸是DNA和RNA的前体[34], 是合成磷脂、糖脂、糖蛋白的原料和交感神经系统中细胞表面受体的信号分子[35], 而高浓度的胸苷容易导致DNA发生损伤, 使肾脏疾病进一步恶化[36]。肾癌动物模型研究发现, 胸苷的水平变化与其发生和发展密切相关[37]。本研究反映足细胞早期损伤的潜在生物标志物参与的嘧啶代谢, 涉及dTMP和胸苷, M1组中胸苷水平的升高, 表明嘧啶生物代谢途径发生改变。肾损伤模型大鼠研究中发现, UDP水平升高提示嘧啶代谢紊乱, 这与作者前期进展性生物标志物的筛选结果一致[38], 表明嘧啶代谢紊乱可能伴随肾病足细胞损伤进程。
在反映足细胞损伤进程的潜在生物标志物研究中, 苯丙氨酸, 酪氨酸和色氨酸生物合成通路、苯丙氨酸代谢通路、嘌呤代谢通路、嘧啶代谢通路和酪氨酸代谢通路被确定为与足细胞损伤进展密切相关的代谢通路。通路主要涉及苯丙氨酸、酪氨酸、UDP和GDP四种代谢物。苯丙氨酸羟基化转变为酪氨酸是苯丙氨酸分解代谢第一个不可逆过程, 同时也是酪氨酸主要的来源。肾脏和肝脏是合成酪氨酸的关键器官, 肾脏合成的酪氨酸主要用于体循环, 因此肾脏是酪氨酸产生的主要器官[39]。在肾脏中, 肾素仅在足细胞中表达, 酪氨酸是多巴胺和肾上腺素的前体, 酪氨酸磷酸化参与信号通路的转导, 研究指出, 肾素酪氨酸磷酸化的损失促进了肾脏疾病的发展[40], 缺乏肾素酪氨酸磷酸化的足细胞不能参与细胞骨架机制, 因此不能在肾脏损伤后得到修复[41]。在慢性肾衰竭中, 酪氨酸水平降低[42], 苯丙氨酸转化为酪氨酸存在障碍[43]。在透析肾病患者中发现苯丙氨酸, 酪氨酸和色氨酸生物合成通路及苯丙氨酸代谢通路与肾病进程紧密联系[44]。本研究中发现, 相对于对照组, 足细胞损伤早期酪氨酸水平增加, 可能有助于维持足细胞结构与功能, 而晚期酪氨酸水平相对早期有所下降, 提示随着损伤程度的增加, 足细胞结构和功能可能发生不可逆改变。
嘌呤是生物体内一种普遍存在的供能物质与信号分子, 经过一系列化学变化终产物生成尿酸, 然而无论是高尿酸排泄或低尿酸排泄均对肾脏产生一定影响[45], 表明肾病的损伤与嘌呤的代谢紊乱有关。本研究中反映足细胞损伤进程的潜在生物标志物GDP下调, 提示嘌呤代谢发生异常, 进而可能导致能量传递障碍和信号传导受阻, 使损伤进一步加重, 这与临床肾病的研究结果相似[46]
综上, 本文通过建立足细胞损伤模型, 结合LC-MS细胞代谢组学方法, 分析找到反映肾病足细胞早期损伤的潜在生物标志物4个, 主要涉及3条代谢通路分别为嘧啶代谢、叶酸的生物合成及色氨酸代谢; 反映损伤进程的潜在生物标志物4个, 主要涉及苯丙氨酸, 酪氨酸和色氨酸生物合成、苯丙氨酸代谢通路、嘌呤代谢通路、嘧啶代谢通路和酪氨酸代谢通路。该研究为肾病早期诊断及监测疾病进程潜在标志物的筛选提供了思路和参考, 然潜在生物标志物的应用仍需动物实验以及临床进一步验证, 以明确其在肾病进程诊断中的临床价值。
作者贡献: 朱云峰是本文的主要撰写者, 搜集资料并分析, 撰写草稿; 崔婷、赵怡蕊是实验的参与者; 李爱平、秦雪梅负责本文思路的提出; 李爱平、高蔚娜、姚站馨指导并修改论文。所有作者阅读并认可终稿。
利益冲突: 所有作者均声明不存在利益冲突。
  • 山西省卫生健康委员会“十大晋药”中药材质量标准规范制定和综合利用研究项目(Zyczl2020001)
  • 国家自然科学青年基金(82204595)
  • 山西省深度贫困县科技精准扶贫专项(2019FP-03)
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2023年第58卷第7期
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doi: 10.16438/j.0513-4870.2022-1234
  • 接收时间:2022-11-16
  • 首发时间:2025-11-21
  • 出版时间:2023-07-12
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  • 收稿日期:2022-11-16
  • 修回日期:2023-03-06
基金
山西省卫生健康委员会“十大晋药”中药材质量标准规范制定和综合利用研究项目(Zyczl2020001)
国家自然科学青年基金(82204595)
山西省深度贫困县科技精准扶贫专项(2019FP-03)
作者信息
    1.山西大学中医药现代研究中心, 山西 太原 030006
    2.山西省中西医结合医院, 山西 太原 030006
    3.军事科学院军事医学研究院环境医学与作业医学研究所, 天津 300050

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*秦雪梅, E-mail: ;
姚站馨, E-mail: ;
高蔚娜, E-mail: ;
李爱平, E-mail:
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2种不同金属材料的力学参数

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genus
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Percentage of
total species (%)

Genus
种数
Number of
species
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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