Article(id=1241023930715140512, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241023927812682133, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202409313, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1726761600000, receivedDateStr=2024-09-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773812762191, onlineDateStr=2026-03-18, pubDate=1739116800000, pubDateStr=2025-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773812762191, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773812762191, creator=13701087609, updateTime=1773812762191, updator=13701087609, issue=Issue{id=1241023927812682133, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773812761500, creator=13701087609, updateTime=1773812858867, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241024336258200259, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241023927812682133, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241024336258200260, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241023927812682133, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=526, endPage=532, ext={EN=ArticleExt(id=1241023930996158888, articleId=1241023930715140512, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Optimization of VNTR loci screening and its application in MLVA typing of mycobacterium tuberculosis in Guizhou province, columnId=1228016572065837304, journalTitle=Modern Preventive Medicine, columnName=Experimental Technology and Applications, runingTitle=null, highlight=null, articleAbstract=
Objective

To conduct genetic typing of Mycobacterium tuberculosis (MTB) in Guizhou Province using the MLVA technique based on 24 VNTR loci, to identify VNTR typing loci suitable for Guizhou MTB strains, and to establish a high-resolution, user-friendly MLVA typing model for MTB. This model will provide technical means for tracing the sources, transmission, and prevention and control of tuberculosis in Guizhou Province.

Methods

A total of 124 MTB strains were collected from Guizhou Province. PCR amplification was performed using 24 internationally recognized VNTR loci for MTB, followed by analysis of the amplification products using capillary electrophoresis. The number of repeats at each locus was counted, and allele polymorphism and the discriminatory index for each locus were calculated. Clustering analysis of the tested strains was conducted using BIONUMERICS 8.0 to explore the typing characteristics of MTB in Guizhou Province based on 24-VNTR, 15-VNTR, 12-VNTR, and 8-VNTR.

Results

The allele polymorphism and discriminatory index of the 24 VNTR loci indicated that seven loci, including QUB-11b, had high discriminatory power, eleven loci, including MIRU-10, had moderate power, and six loci, including MIRU-20, had low power. Furthermore, clustering analysis revealed that the 24-VNTR typing exhibited the highest discriminatory ability at 95.16% (118/124), approaching the level of single strain identification, followed by 15-VNTR at 91.13% (113/124), 12-VNTR at 90.32% (112/124), and 8-VNTR at 83.06% (103/124). All four VNTR typing models successfully categorized the 124 MTB strains into four clusters.

Conclusion

This study identified six high-resolution loci, including QUB-11b, as the preferred VNTR loci for MLVA typing of MTB strains in Guizhou Province, with other loci ranked according to their discriminatory power as supplementary typing methods. The 24-VNTR, 15-VNTR, and 12-VNTR methods demonstrated high typing capabilities, providing technical support for molecular tracing of tuberculosis in Guizhou Province.

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目的

应用基于24个VNTR位点的多基因座可变数量串联重复序列分析(MLVA)技术对贵州省结核分枝杆菌进行基因分型研究,筛选出适合贵州结核分枝杆菌菌株的VNTR分型基因座,建立分辨力高、操作简便的结核分枝杆菌MLVA分型模型,为贵州省结核病传染源的追踪溯源、传播及防控提供技术手段。

方法

收集来自贵州省的124株结核分枝杆菌菌株,采用结核分枝杆菌国际通用的24个VNTR基因座进行PCR扩增,应用毛细管电泳法对扩增产物进行分析,统计各基因座的重复数,计算等位基因多态性及各基因座分辨指数,使用BIONUMERICS 8.0对受试菌株进行聚类分析,探索贵州省结核分枝杆菌的24-VNTR、15-VNTR、12-VNTR、8-VNTR分型特征。

结果

24个VNTR基因座等位基因多态性及分辨指数显示:分辨力高的基因座基因有QUB-11b等7个基因座,分辨力中等的有MIRU-10等11个基因座,分辨力低的有MIRU-20等6个基因座;此外,聚类分析显示:24-VNTR分型能力最强,分辨力高达95.16%(118/124),接近单菌株鉴定水平,依次15-VNTR分辨力为91.13%(113/124)、12-VNTR分辨力为90.32%(112/124)、8-VNTR分辨力为83.06%(103/124)。以上4种VNTR分型模型均能将124株结核分枝杆菌菌株分为4个种群。

结论

本研究评估筛选出了以QUB-11b等6个高分辨力基因座为贵州省结核分枝杆菌菌株MLVA分型首选,其他基因座依据其分辨力排序作为补充的分型方法。其中24-VNTR、15-VNTR及12-VNTR具有较高分型能力,可为贵州省结核病分子溯源提供技术支持。

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黄俊飞,E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点,除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=qwqhXORFpkTUxWOykk87lg==, magXml=HHXPngwNFILG6uGRoSUElw==, pdfUrl=null, pdf=fiVvTeIApTDjY222OoT3ZQ==, pdfFileSize=1137134, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=HfLG+OYFLY2mffPtH9MrYw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=nejXgty5JB1M2TxbPoAlfw==, mapNumber=null, authorCompany=null, fund=null, authors=

陈依江(1973—),女,本科,副主任医师,研究方向:病原微生物研究

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注:黑色小箭头所指的为成簇的相同基因型菌株。

, figureFileSmall=ZGNlmRd0ytaqsMwu6Pbrcw==, figureFileBig=Ua4ib0qhwpwtJY2hOWL2pw==, tableContent=null), ArticleFig(id=1241023940701778762, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023930715140512, language=EN, label=Table 1, caption=

Table 1 The basic information of 24 VNTR loci for MTB

, figureFileSmall=null, figureFileBig=null, tableContent=
基因座PCR引物序列及荧光标记位置 (5’ to 3’)重复单元长度H37Rv重复单元数别名
MIRU-04GCGCGAGAGCCCGAACTGC(FAM)773ETR-D
GCGCAGCAGAAACGCCAGC
MIRU-26TAGGTCTACCGTCGAAATCTGTGAC513
CATAGGCGACCAGGCGAATAG(VIC)
MIRU-40GGGTTGCTGGATGACAACGTGT(NED)541
GGGTGATCTCGGCGAAATCAGATA
MIRU-10GTTCTTGACCAACTGCAGTCGTCC533
GCCACCTTGGTGATCAGCTACCT(FAM)
MIRU-16TCGGTGATCGGGTCCAGTCCAAGTA532
CCCGTCGTGCAGCCCTGGTAC(VIC)
MIRU-31ACTGATTGGCTTCATACGGCTTTA533ETR-E
GTGCCGACGTGGTCTTGAT(NED)
VNTR-42CTTGGCCGGCATCAAGCGCATTATT512Mtub04
GGCAGCAGAGCCCGGGATTCTTC(FAM)
VNTR-43CGAGAGTGGCAGTGGCGGTTATCT(VIC)584ETR-C
AATGACTTGAACGCGCAAATTGTGA
ETR-AAAATCGGTCCCATCACCTTCTTAT(NED)753
CGAAGCCTGGGGTGCCCGCGATTT
VNTR-47CTTGAAGCCCCGGTCTCATCTGT(FAM)582Mtub30
ACTTGAACCCCCACGCCCATTAGTA
VNTR-52CGGTGGAGGCGATGAACGTCTTC(VIC)585Mtub39
TAGAGCGGCACGGGGGAAAGCTTAG
VNTR-53TGACCACGGATTGCTCTAGT592QUB-4156c
GCCGGCGTCCATGTT(NED)
QUB-11bCGTAAGGGGGATGCGGGAAATAGG695
CGAAGTGAATGGTGGCAT(FAM)
VNTR-1955AGATCCCAGTTGTCGTCGTC(VIC)572Mtub21
CAACATCGCCTGGTTCTGTA
QUB-26AACGCTCAGCTGTCGGAT(NED)1115
CGGCCGTGCCGGCCAGGTCCTTCCCGAT
MIRU-02TGGACTTGCAGCAATGGACCAACT522
TACTCGGACGCCGGCTCAAAAT(FAM)
MIRU-23CTGTCGATGGCCGCAACAAAACG(VIC)536
AGCTCAACGGGTTCGCCCTTTTGTC
MIRU-39CGCATCGACAAACTGGAGCCAAAC532
CGGAAACGTCTACGCCCCACACAT(NED)
MIRU-20TCGGAGAGATGCCCTTCGAGTTAG(FAM)772
GGAGACCGCGACCAGGTACTTGTA
MIRU-24CGACCAAGATGTGCAGGAATACAT541
GGGCGAGTTGAGCTCACAGAA(VIC)
MIRU-27TCGAAAGCCTCTGCGTGCCAGTAA533QUB-5
GCGATGTGAGCGTGCCACTCAA(NED)
VNTR-46GCCAGCCGCCGTGCATAAACCT(FAM)574Mtub29
AGCCACCCGGTGTGCCTTGTATGAC
VNTR-48ATGGCCACCCGATACCGCTTCAGT(VIC)573ETR-B
CGACGGGCCATCTTGGATCAGCTAC
VNTR-49GGTGCGCACCTGCTCCAGATAA(NED)543Mtub34
GGCTCTCATTGCTGGAGGGTTGTAC
), ArticleFig(id=1241023940831802196, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023930715140512, language=CN, label=表1, caption=

结核分枝杆菌24个VNTR基因座基本信息

, figureFileSmall=null, figureFileBig=null, tableContent=
基因座PCR引物序列及荧光标记位置 (5’ to 3’)重复单元长度H37Rv重复单元数别名
MIRU-04GCGCGAGAGCCCGAACTGC(FAM)773ETR-D
GCGCAGCAGAAACGCCAGC
MIRU-26TAGGTCTACCGTCGAAATCTGTGAC513
CATAGGCGACCAGGCGAATAG(VIC)
MIRU-40GGGTTGCTGGATGACAACGTGT(NED)541
GGGTGATCTCGGCGAAATCAGATA
MIRU-10GTTCTTGACCAACTGCAGTCGTCC533
GCCACCTTGGTGATCAGCTACCT(FAM)
MIRU-16TCGGTGATCGGGTCCAGTCCAAGTA532
CCCGTCGTGCAGCCCTGGTAC(VIC)
MIRU-31ACTGATTGGCTTCATACGGCTTTA533ETR-E
GTGCCGACGTGGTCTTGAT(NED)
VNTR-42CTTGGCCGGCATCAAGCGCATTATT512Mtub04
GGCAGCAGAGCCCGGGATTCTTC(FAM)
VNTR-43CGAGAGTGGCAGTGGCGGTTATCT(VIC)584ETR-C
AATGACTTGAACGCGCAAATTGTGA
ETR-AAAATCGGTCCCATCACCTTCTTAT(NED)753
CGAAGCCTGGGGTGCCCGCGATTT
VNTR-47CTTGAAGCCCCGGTCTCATCTGT(FAM)582Mtub30
ACTTGAACCCCCACGCCCATTAGTA
VNTR-52CGGTGGAGGCGATGAACGTCTTC(VIC)585Mtub39
TAGAGCGGCACGGGGGAAAGCTTAG
VNTR-53TGACCACGGATTGCTCTAGT592QUB-4156c
GCCGGCGTCCATGTT(NED)
QUB-11bCGTAAGGGGGATGCGGGAAATAGG695
CGAAGTGAATGGTGGCAT(FAM)
VNTR-1955AGATCCCAGTTGTCGTCGTC(VIC)572Mtub21
CAACATCGCCTGGTTCTGTA
QUB-26AACGCTCAGCTGTCGGAT(NED)1115
CGGCCGTGCCGGCCAGGTCCTTCCCGAT
MIRU-02TGGACTTGCAGCAATGGACCAACT522
TACTCGGACGCCGGCTCAAAAT(FAM)
MIRU-23CTGTCGATGGCCGCAACAAAACG(VIC)536
AGCTCAACGGGTTCGCCCTTTTGTC
MIRU-39CGCATCGACAAACTGGAGCCAAAC532
CGGAAACGTCTACGCCCCACACAT(NED)
MIRU-20TCGGAGAGATGCCCTTCGAGTTAG(FAM)772
GGAGACCGCGACCAGGTACTTGTA
MIRU-24CGACCAAGATGTGCAGGAATACAT541
GGGCGAGTTGAGCTCACAGAA(VIC)
MIRU-27TCGAAAGCCTCTGCGTGCCAGTAA533QUB-5
GCGATGTGAGCGTGCCACTCAA(NED)
VNTR-46GCCAGCCGCCGTGCATAAACCT(FAM)574Mtub29
AGCCACCCGGTGTGCCTTGTATGAC
VNTR-48ATGGCCACCCGATACCGCTTCAGT(VIC)573ETR-B
CGACGGGCCATCTTGGATCAGCTAC
VNTR-49GGTGCGCACCTGCTCCAGATAA(NED)543Mtub34
GGCTCTCATTGCTGGAGGGTTGTAC
), ArticleFig(id=1241023940974408544, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023930715140512, language=EN, label=Table 2, caption=

The repeat numbers and allelic diversity of 24 VNTR loci for 124 MTB strains

, figureFileSmall=null, figureFileBig=null, tableContent=
序号VNTR
基因座
拷贝数h HDGI
12345678910111213None
1QUB-11b111324202025101000000.8430.851
2VNTR-1955022245302212000000.7440.752
3VNTR-530100000003649170210.7390.745
4MIRU-260010010431630534210.7300.739
5MIRU-310000000001051104940.6580.667
6ETR-A100184358200000020.6370.647
7VNTR-4200000000053617660.6090.617
8MIRU-100000000026152900.5730.581
9MIRU-4000000015367210000.5600.568
10VNTR-5200002708105200000.5240.532
11MIRU-390000000003705010.5140.522
12VNTR-470000591640000000.5040.511
13VNTR-430000388600000000.4200.427
14MIRU-1600000000002492350.4070.415
15VNTR-480000031930000000.3700.378
16MIRU-040019881511000000.3510.359
17MIRU-270000000022397200.3480.356
18QUB-2601021388000000020.3090.317
19MIRU-2000000511410000040.1460.154
20VNTR-460000003111433000.1460.154
21MIRU-230001021183000000.0860.094
22VNTR-490000032118100000.0860.094
23MIRU-240000001213000000.0390.048
24MIRU-020000000112300000.0080.016
), ArticleFig(id=1241023941117014894, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241023930715140512, language=CN, label=表2, caption=

124株结核分枝杆菌24个VNTR基因座重复单位及多态性分布

, figureFileSmall=null, figureFileBig=null, tableContent=
序号VNTR
基因座
拷贝数h HDGI
12345678910111213None
1QUB-11b111324202025101000000.8430.851
2VNTR-1955022245302212000000.7440.752
3VNTR-530100000003649170210.7390.745
4MIRU-260010010431630534210.7300.739
5MIRU-310000000001051104940.6580.667
6ETR-A100184358200000020.6370.647
7VNTR-4200000000053617660.6090.617
8MIRU-100000000026152900.5730.581
9MIRU-4000000015367210000.5600.568
10VNTR-5200002708105200000.5240.532
11MIRU-390000000003705010.5140.522
12VNTR-470000591640000000.5040.511
13VNTR-430000388600000000.4200.427
14MIRU-1600000000002492350.4070.415
15VNTR-480000031930000000.3700.378
16MIRU-040019881511000000.3510.359
17MIRU-270000000022397200.3480.356
18QUB-2601021388000000020.3090.317
19MIRU-2000000511410000040.1460.154
20VNTR-460000003111433000.1460.154
21MIRU-230001021183000000.0860.094
22VNTR-490000032118100000.0860.094
23MIRU-240000001213000000.0390.048
24MIRU-020000000112300000.0080.016
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贵州省结核分枝杆菌VNTR基因座筛选优化及其在MLVA分型中的应用
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陈依江 1, 2 , 童毅 1, 2 , 杨幸贵 1, 2 , 郑雯琳 1, 2 , 白贵欢 3 , 莫祖甄 3 , 韦小瑜 1, 2, 3 , 李世军 1, 2, 3 , 黄俊飞 1, 2, 3
现代预防医学 | 实验技术及其应用 2025,52(3): 526-532
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现代预防医学 | 实验技术及其应用 2025, 52(3): 526-532
贵州省结核分枝杆菌VNTR基因座筛选优化及其在MLVA分型中的应用
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陈依江1, 2, 童毅1, 2, 杨幸贵1, 2, 郑雯琳1, 2, 白贵欢3, 莫祖甄3, 韦小瑜1, 2, 3, 李世军1, 2, 3, 黄俊飞1, 2, 3
作者信息
  • 1.贵州省疾病预防控制中心,贵州 贵阳 550004
  • 2.贵州省微生物组与传染性疾病防控重点实验室,贵州 贵阳 550004
  • 3.贵州医科大学公共卫生与健康学院,贵州 贵阳 550004
  • 陈依江(1973—),女,本科,副主任医师,研究方向:病原微生物研究

通讯作者:

黄俊飞,E-mail:
Optimization of VNTR loci screening and its application in MLVA typing of mycobacterium tuberculosis in Guizhou province
Yi-jiang CHEN1, 2, Yi TONG1, 2, Xing-gui YANG1, 2, Wen-lin ZHENG1, 2, Gui-huan BAI3, Zu-zhen MO3, Xiao-yu WEI1, 2, 3, Shi-jun LI1, 2, 3, Jun-fei HUANG1, 2, 3
Affiliations
  • Guizhou Provincial Center for Disease Control and Prevention, Guiyang, Guizhou 550004, China
出版时间: 2025-02-10 doi: 10.20043/j.cnki.MPM.202409313
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目的

应用基于24个VNTR位点的多基因座可变数量串联重复序列分析(MLVA)技术对贵州省结核分枝杆菌进行基因分型研究,筛选出适合贵州结核分枝杆菌菌株的VNTR分型基因座,建立分辨力高、操作简便的结核分枝杆菌MLVA分型模型,为贵州省结核病传染源的追踪溯源、传播及防控提供技术手段。

方法

收集来自贵州省的124株结核分枝杆菌菌株,采用结核分枝杆菌国际通用的24个VNTR基因座进行PCR扩增,应用毛细管电泳法对扩增产物进行分析,统计各基因座的重复数,计算等位基因多态性及各基因座分辨指数,使用BIONUMERICS 8.0对受试菌株进行聚类分析,探索贵州省结核分枝杆菌的24-VNTR、15-VNTR、12-VNTR、8-VNTR分型特征。

结果

24个VNTR基因座等位基因多态性及分辨指数显示:分辨力高的基因座基因有QUB-11b等7个基因座,分辨力中等的有MIRU-10等11个基因座,分辨力低的有MIRU-20等6个基因座;此外,聚类分析显示:24-VNTR分型能力最强,分辨力高达95.16%(118/124),接近单菌株鉴定水平,依次15-VNTR分辨力为91.13%(113/124)、12-VNTR分辨力为90.32%(112/124)、8-VNTR分辨力为83.06%(103/124)。以上4种VNTR分型模型均能将124株结核分枝杆菌菌株分为4个种群。

结论

本研究评估筛选出了以QUB-11b等6个高分辨力基因座为贵州省结核分枝杆菌菌株MLVA分型首选,其他基因座依据其分辨力排序作为补充的分型方法。其中24-VNTR、15-VNTR及12-VNTR具有较高分型能力,可为贵州省结核病分子溯源提供技术支持。

结核分枝杆菌  /  MLVA分型  /  VNTR基因座  /  贵州省
Objective

To conduct genetic typing of Mycobacterium tuberculosis (MTB) in Guizhou Province using the MLVA technique based on 24 VNTR loci, to identify VNTR typing loci suitable for Guizhou MTB strains, and to establish a high-resolution, user-friendly MLVA typing model for MTB. This model will provide technical means for tracing the sources, transmission, and prevention and control of tuberculosis in Guizhou Province.

Methods

A total of 124 MTB strains were collected from Guizhou Province. PCR amplification was performed using 24 internationally recognized VNTR loci for MTB, followed by analysis of the amplification products using capillary electrophoresis. The number of repeats at each locus was counted, and allele polymorphism and the discriminatory index for each locus were calculated. Clustering analysis of the tested strains was conducted using BIONUMERICS 8.0 to explore the typing characteristics of MTB in Guizhou Province based on 24-VNTR, 15-VNTR, 12-VNTR, and 8-VNTR.

Results

The allele polymorphism and discriminatory index of the 24 VNTR loci indicated that seven loci, including QUB-11b, had high discriminatory power, eleven loci, including MIRU-10, had moderate power, and six loci, including MIRU-20, had low power. Furthermore, clustering analysis revealed that the 24-VNTR typing exhibited the highest discriminatory ability at 95.16% (118/124), approaching the level of single strain identification, followed by 15-VNTR at 91.13% (113/124), 12-VNTR at 90.32% (112/124), and 8-VNTR at 83.06% (103/124). All four VNTR typing models successfully categorized the 124 MTB strains into four clusters.

Conclusion

This study identified six high-resolution loci, including QUB-11b, as the preferred VNTR loci for MLVA typing of MTB strains in Guizhou Province, with other loci ranked according to their discriminatory power as supplementary typing methods. The 24-VNTR, 15-VNTR, and 12-VNTR methods demonstrated high typing capabilities, providing technical support for molecular tracing of tuberculosis in Guizhou Province.

Mycobacterium tuberculosis  /  MLVA typing  /  VNTR loci  /  Guizhou Province
陈依江, 童毅, 杨幸贵, 郑雯琳, 白贵欢, 莫祖甄, 韦小瑜, 李世军, 黄俊飞. 贵州省结核分枝杆菌VNTR基因座筛选优化及其在MLVA分型中的应用. 现代预防医学, 2025 , 52 (3) : 526 -532 . DOI: 10.20043/j.cnki.MPM.202409313
Yi-jiang CHEN, Yi TONG, Xing-gui YANG, Wen-lin ZHENG, Gui-huan BAI, Zu-zhen MO, Xiao-yu WEI, Shi-jun LI, Jun-fei HUANG. Optimization of VNTR loci screening and its application in MLVA typing of mycobacterium tuberculosis in Guizhou province[J]. Modern Preventive Medicine, 2025 , 52 (3) : 526 -532 . DOI: 10.20043/j.cnki.MPM.202409313
结核病是由结核分枝杆菌(Mycobacterium tuberculosis)引起的一种重大传染病,可复发和再发,是重大的公共卫生疾病,2022年全球范围内新发病例约1 060万例(133/10万)[1]。中国仍是世界上30个结核病高负担国家之一,2022年结核病患者数量居世界第3位,约74.8万(52/10万),占全球7.1%[1]。贵州省是结核病疫情高发省份,根据结核病网报系统查询2022年新发结核病患者约2.9万例,发病率占全省总人数76.2/10万,远高于全国平均水平,依然是严重影响贵州公共卫生的主要疾病。
结核分枝杆菌基因分型是结核病防治的重要监测手段,尤其在结核病传染源的溯源以及流行趋势判断发挥着至关作用[2]。常用的分型方法包括插入IS6110限制性片段长度多态性(IS6110-RFLP)、间隔区寡核苷酸分型(spoligotyping)、长片段多态性分析(LSP)、多基因座可变数量串联重复序列分析(MLVA)、多基因座序列分型(MLST)和单核苷酸多态性(SNP)分析等[2-6]。其中MLVA是目前最常用的一种分型技术,该技术通过对结核分枝杆菌标准株(H37Rv)基因组分析获得可变数目串联重复序列相对应的片段(大小在52~77bp之间),并对重复单位相应基因座进行扩增确定扩增产物的大小,计算重复数[7]。该分型技术具有操作简便、重复性好、分辨力高等优点[8-9]。本研究采用国际通用的24个VNTR基因座对贵州省地区的结核分枝杆菌进行基因分型研究,并通过分析各基因座的基因分型能力,筛选出适合贵州省结核分枝杆菌MLVA分型基因座,为贵州省结核病传染源的追踪溯源、传播和遗传学等研究提供技术支持。
结核分枝杆菌124株,收集于2021年贵州省地区;结核分枝杆菌标准菌株H37Rv(ATCC27294)由国家疾控提供;以上菌株均保存于贵州省疾病预防控制中心。
自动核酸提取仪(NP968-C)及配套的细菌基因组DNA提取试剂盒(天隆科技,中国西安),罗氏培养基(珠海贝索),移液器(美国Thermo),核酸片段分析仪(ABI 3730-XL,美国),PCR扩增仪(LifeTouch,杭州博日科技股份有限公司,中国),生物安全柜(NU-440-600E,美国)。
应用分枝杆菌含药罗氏培养基PNB(对硝基苯甲酸,500 μg/ml)、TCH(噻吩-2羧酸肼,5 μg/ml)及中性罗氏培养基对收集菌株培养3~4周进行鉴定。
鉴定后的菌株,在生物安全柜中挑取1~2环菌株,转移至盛有0.5ml 1×TE缓冲液的EP管中,置于金属浴上85℃灭活30 min,然后12 000 r/min(离心半径7.5 cm)离心2 min,弃去上清液,按照试剂盒步骤提取核酸,-20℃保存备用。
参照国际通用的24个VNTR基因座[10],引物序列及荧光标记见表1,引物由北京天一辉远生物技术有限公司合成。PCR体系:2×Taq Mix 25 μl,上下游引物各2.5 μl,核酸模板5 μl,纯水15 μl。PCR扩增条件:95℃预变性15 min;95℃ 1 min,59℃ 1 min,72℃ 1.5 min,30个循环;72℃延伸10min。将24个VNTR基因座分成8组每组3个基因座,并用荧光进行标记,同组可在同一反应体系中扩增也可单个基因座扩增,PCR产物应用基于毛细管电泳的核酸片段分析仪(ABI 3730-XL)进行检测,记录基因座结果,并计算重复数。
本研究分别用等位基因多态性(Allelic diversity)及各基因座分辨指数(hunter-gaston discriminatory Index, HGDI)来分析应用的24个VNTR基因座[8-911],公式如下:等位基因多态性(其中xi表示第i个基因的频率,n为总菌株数),h值越大表示多态性越好,h>0.60表示多态程度性较好,0.30 ≤h≤0.60表示多态性程度中等,h<0.30表示多态性程度较差;各基因座分辨指数(N表示总菌株数,S为分型方法划分的总类型数,nj为第j个基因的菌株数),指数值越高其分辨能力越强。
依据VNTR基因座的多态性(h)及分辨指数(HGDI)值的大小排序,分别由高到低选择不同数量的基因座构建24-VNTR基因座、15-VNTR基因座、12-VNTR基因座和8-VNTR基因座的分析模型,见表1。应用BIONUMERICS 8.0软件对菌株的VNTR基因座采用非加权组平均法(unweighted pair-group method with arithmetic means, UPGMA)进行聚类分析,同时进行最小间距图分析,并对比模型间的差异。数据采用SPSS 19.0软件进行Pearson χ2检验分析,检验水准α=0.05。
124株结核分枝杆菌各基因座的重复数为1~13不等(参考菌株H37Rv各基因座的重复数为1~5)。如表2所示,h及HDGI最高的为QUB-11b(h=0.843,HDGI=0.851),最低的为MIRU-02(h=0.008,HDGI=0.016)。其中有7个基因座分辨指数HGDI>0.60,分别为QUB-11b、VNTR-1955、VNTR-53、MIRU-26、 MIRU-31、 ETR-A、VNTR-42,可选择为贵州省结核分枝杆菌MLVA分型首选的VNTR基因座;有11个分辨力中等的基因座,可按照分辨率指数值的大小选择性的组成不同VNTR基因座数量的分型模型;有6个分辨力较低的基因座,可作为补充基因座以提高贵州省结核分枝杆菌MLVA分型的分型能力。此外,部分菌株某些基因座出现缺失,在VNTR-53基因座上缺失较多,达16.94%(21/124),MIRU-26、 MIRU-31、 ETR-A、 MIRU-16、QUB-26、MIRU-20基因座中有少数缺失,在0.81%~4.03%范围。
24-VNTR、15-VNTR、12-VNTR及8-VNTR基因座模型聚类分析如图1,构建的15-VNTR、12-VNTR、8-VNTR均能同24-VNTR将124株结核分枝杆菌聚类成4个基因群;聚类分析结果显示24-VNTR分型能力最强,分辨力高达95.16%(118/124),接近鉴定个体菌株水平;其次15-VNTR分辨力为91.13%(113/124),12-VNTR分辨力为90.32%(112/124),8-VNTR分辨力为83.06%(103/124);15-VNTR、12-VNTR分析模型与24-VNTR分析模型相比分辨能力无统计学意义(P>0.05),8-VNTR模型与24-VNTR分析模型相比分辨能力有统计学意义(P<0.05)。
通过对各基因群所含的菌株进行分析发现:8-I群与12-I群、8-II群与12-II群、8-III群与12-III群、8群-IV与12-IV群所含的菌株完全一致;12-I群比15-I群多1株菌(编号为BJ-22),其余一致,12-II群与15-II群、12-III群与15-III群所含的菌株一致,12群-IV比15-IV群少1株菌(编号为BJ-22),其余一致;15-I群比24-III群少1株菌(编号为QDN-17),其余一致,15-II群与24-IV群、15-III群与24-II群所含的菌株一致,15-IV群比24-I群多1株菌(编号为QDN-17),其余一致。见图2
24-VNTR将124株结核分枝杆菌分成118个基因型,其中在24-I群形成2个簇,在24-IV群形成1个簇;15-VNTR将124株结核分枝杆菌分成113个基因型,其中在15-I群形成1个簇,在15-II群形成1个簇,在15-IV群形成3个簇;12-VNTR将124株结核分枝杆菌分成112个基因型,其中在12-I群形成2个簇,在12-II群形成1个簇,在12-IV群形成3个簇;8-VNTR将124株结核分枝杆菌分成103个基因型,其中在8-I群形成4个簇,在8-II群形成1个簇,在8-III群形成1个簇,在8-IV群形成4个簇。见图13
结核分枝杆菌的基因分型有助于结核病的控制,可阐明结核病患者之间可能的流行病学联系、判断是否暴发疫情、实验室交叉污染的检测,以及区分复发病例的外源性再感染和内源性再感染[11]。结核分枝杆菌的MLVA分型是基于分枝杆菌散置的重复单元(mycobacterial interspersed repetitive units,MIRUs)中可变数量串联重复序列(variable-number tandem repeat, VNTR)的分析[11-13]。不同国家和地区选用的VNTR基因座会有不同[8-914-18],常用的15-VNTR基因座的分型模型已经在中国许多地区得到了应用和评估[8-9, 14-18],本研究主要通过对贵州省地区结核分枝杆菌24个VNTR基因座进行分析,优化贵州省结核分枝杆菌MLVA分型的VNTR基因座选择。
在基于VNTR基因座的MLVA分型中随着选择的基因座个数增多,其实验操作程序也增多,为了简便、快速、低成本的应用MLVA技术对结核分枝杆菌进行分型,大多数的研究报道多采用15个VNTR基因座的分型模型直接进行研究。陈旭等[9]研究人员虽然也采用了基于15个VNTR基因座对贵州省的结核分枝杆菌菌株进行MLVA分析,但该研究并未通过多基因座的筛选,不足够反映贵州省结核分枝杆菌菌株基因座的特征;然而本研究通过多基因座等位基因多态性(h)和基因座分辨指数(HDGI)分析,筛选分辨力更高的基因座组成不同的分析模型,更具优势。国内只有少数研究通过对多VNTR基因座筛选、优化最后形成具有地区特征的MLVA分型模型[19-21],其中陈海霞、李卫民教授等人使用2007年全国耐药基线调查的结核分枝杆菌,通过对菌株15个VNTR基因座的筛选,为各省列出12-VNTR、10-VNTR、8-VNTR、5-VNTR的分析模型,其中包括了贵州省,并以贵州的两个耐药检测点(七星关区和西秀区)的菌株为代表[21]。本研究与该研究相比较,使用了2021年的菌株进行模型的建立,更接近于当前流行菌株的特征,此外本研究使用的菌株来源于贵州省大部分地区(涵盖贵州5个市州),更能代表贵州结核分枝杆菌的特征。在贵州不同VNTR基因座分析中,李卫民教授等人共计筛选出6个高分辨力基因座,本研究筛选出7个高分辨基因座,其中有5个基因座与该研究一致,但分辨力排名有所变化,且有1个基因座变为了中等分辨力;进一步对比发现中等分辨力基因座的排名也有所变化,其原因可能是当前菌株遗传特征漂变的结果。本研究在此前研究的基础上,所使用的菌株来源及采集时间更能反映贵州省当前结核分枝杆菌的遗传特征,并对更多基因座进行分辨能力的筛选,并通过分辨力的排序旨在构建更合理的MLVA分析模型。
贵州省是结核病的高发省份,也是结核病疫情高风险地区,持续的分子遗传特征分析也是结核病监测的重要手段,筛选适合贵州省VNTR基因座的MLVA分型对贵州省结核病疫情防控、传染源的追踪溯源、传播和遗传学研究至关重要。本研究通过国际通用的24个VNTR基因座在贵州结核分枝杆菌的应用研究,初步阐明了适合当前贵州MLVA分型的VNTR基因座,为贵州省结核病相关研究提供数据支持。此外,本研究为了提高15-VNTR、12-VNTR、8-VNTR分型模型的分辨力,应用BIONUMERICS 8.0软件对15-VNTR+1、12-VNTR+1、8-VNTR+1及8-VNTR+2分型模型进行了测试,其分辨力分别为91.94%(114/124)、91.13%(113/124)、87.10%(108/124)和88.71%(110/124)。通过与构建的15-VNTR(91.13%)、12-VNTR(90.32%)、8-VNTR(83.06%)相比,虽然8-VNTR+1(87.10%)及8-VNTR+2(88.71%)分型模型分辨能力明显提高了,但结合我省的实际情况,综合分析成本、分辨力等因素我们推荐使用12-VNTR(90.32%)或者12-VNTR+1(91.13%)模型进行结核分枝杆菌分型工作开展,该模型与15-VNTR(91.13%)模型的分型能力相近或者相等(P>0.05),比24-VNTR(95.16%)分型模型节约了一半左右的成本,且分辨能力无统计学差异(P>0.05)。本研究还结合了毛细管电泳分析及荧光标记引物法,很大程度的简化了结核分枝杆菌MLVA分型的操作步骤,缩短了实验时间。
  • 贵州省科技计划项目(黔科合基础—ZK[2024]一般616)
  • 贵州省微生物组与传染性疾病防控重点实验室项目(ZDSYS[2023]004)
  • 贵州省卫生健康委科学技术基金项目(gzwkj2021-431; gzwkj2021-433; gzwjkj2017-1-086)
  • 贵州省疾病预防控制中心科学(青年)技术基金项目(2020-E1-3青)
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doi: 10.20043/j.cnki.MPM.202409313
  • 接收时间:2024-09-20
  • 首发时间:2026-03-18
  • 出版时间:2025-02-10
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  • 收稿日期:2024-09-20
基金
贵州省科技计划项目(黔科合基础—ZK[2024]一般616)
贵州省微生物组与传染性疾病防控重点实验室项目(ZDSYS[2023]004)
贵州省卫生健康委科学技术基金项目(gzwkj2021-431; gzwkj2021-433; gzwjkj2017-1-086)
贵州省疾病预防控制中心科学(青年)技术基金项目(2020-E1-3青)
作者信息
    1.贵州省疾病预防控制中心,贵州 贵阳 550004
    2.贵州省微生物组与传染性疾病防控重点实验室,贵州 贵阳 550004
    3.贵州医科大学公共卫生与健康学院,贵州 贵阳 550004

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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