对口腔微生物组图谱的汇总分析确定了与牙周炎相关的强健特征。

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2024-10-24 DOI:10.1128/msystems.00930-24
Assem Soueidan, Katia Idiri, Camille Becchina, Pauline Esparbès, Arnaud Legrand, Quentin Le Bastard, Emmanuel Montassier
{"title":"对口腔微生物组图谱的汇总分析确定了与牙周炎相关的强健特征。","authors":"Assem Soueidan, Katia Idiri, Camille Becchina, Pauline Esparbès, Arnaud Legrand, Quentin Le Bastard, Emmanuel Montassier","doi":"10.1128/msystems.00930-24","DOIUrl":null,"url":null,"abstract":"<p><p>Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Obtaining these signatures would significantly enhance our understanding of the underlying pathophysiological processes of this condition and foster the development of improved therapeutic strategies, potentially personalized to individual patients. Here, we sequenced newly collected samples from 24 patients with periodontitis, and we collected available oral microbiome data from 24 samples in patients with periodontitis and from 214 samples in healthy individuals (<i>n</i> = 262). Data were harmonized, and we performed a pooled analysis of individual patient data. By metagenomic sequencing of the plaque microbiome, we found microbial signatures for periodontitis and defined a periodontitis-related complex, composed by the most discriminative bacteria. A simple two-factor decision tree, based on <i>Tannerella forsythia</i> and <i>Fretibacterium fastidiosum</i>, was associated with periodontitis with high accuracy (area under the curve: 0.94). Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.</p><p><strong>Importance: </strong>Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Here, using ultra-deep metagenomic sequencing and machine learning tools, we defined a simple two-factor decision tree, based on <i>Tannerella forsythia</i> and <i>Fretibacterium fastidiosum</i>, that was highly associated with periodontitis. Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.</p>","PeriodicalId":18819,"journal":{"name":"mSystems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pooled analysis of oral microbiome profiles defines robust signatures associated with periodontitis.\",\"authors\":\"Assem Soueidan, Katia Idiri, Camille Becchina, Pauline Esparbès, Arnaud Legrand, Quentin Le Bastard, Emmanuel Montassier\",\"doi\":\"10.1128/msystems.00930-24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Obtaining these signatures would significantly enhance our understanding of the underlying pathophysiological processes of this condition and foster the development of improved therapeutic strategies, potentially personalized to individual patients. Here, we sequenced newly collected samples from 24 patients with periodontitis, and we collected available oral microbiome data from 24 samples in patients with periodontitis and from 214 samples in healthy individuals (<i>n</i> = 262). Data were harmonized, and we performed a pooled analysis of individual patient data. By metagenomic sequencing of the plaque microbiome, we found microbial signatures for periodontitis and defined a periodontitis-related complex, composed by the most discriminative bacteria. A simple two-factor decision tree, based on <i>Tannerella forsythia</i> and <i>Fretibacterium fastidiosum</i>, was associated with periodontitis with high accuracy (area under the curve: 0.94). Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.</p><p><strong>Importance: </strong>Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Here, using ultra-deep metagenomic sequencing and machine learning tools, we defined a simple two-factor decision tree, based on <i>Tannerella forsythia</i> and <i>Fretibacterium fastidiosum</i>, that was highly associated with periodontitis. Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.</p>\",\"PeriodicalId\":18819,\"journal\":{\"name\":\"mSystems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"mSystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1128/msystems.00930-24\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"mSystems","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1128/msystems.00930-24","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

在使用 16S rRNA 基因测序分析的研究中,口腔微生物菌群失调与牙周炎有关。然而,这项技术还不足以将细菌种类持续分离到物种水平,而且可重复的口腔微生物组特征也很少。获得这些特征将大大提高我们对该病症潜在病理生理过程的了解,促进改善治疗策略的开发,并有可能针对患者个体进行个性化治疗。在此,我们对新收集的 24 名牙周炎患者样本进行了测序,并收集了 24 名牙周炎患者样本和 214 名健康人(n = 262)样本的口腔微生物组数据。我们对数据进行了统一,并对单个患者的数据进行了汇总分析。通过对牙菌斑微生物组进行元基因组测序,我们发现了牙周炎的微生物特征,并定义了由最具鉴别力的细菌组成的牙周炎相关复合菌群。基于连翘坦奈氏菌和快速变形弗氏菌的简单双因子决策树与牙周炎的关联准确率很高(曲线下面积:0.94)。总之,我们定义了与牙周炎病理生理学相关的强大口腔微生物组特征,有助于在护理牙周炎患者时确定有希望的微生物组治疗调节目标:在使用 16S rRNA 基因测序分析的研究中,口腔微生物菌群失调与牙周炎有关。然而,这种技术不足以将细菌种类持续分离到物种水平,而且可重复的口腔微生物组特征也很少。在这里,我们利用超深度元基因组测序和机器学习工具,基于连翘坦奈氏菌和快速变形弗氏菌,定义了一个与牙周炎高度相关的简单双因素决策树。总之,我们定义了与牙周炎病理生理学相关的强大口腔微生物组特征,有助于在护理牙周炎患者时确定有希望的微生物组治疗调节目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pooled analysis of oral microbiome profiles defines robust signatures associated with periodontitis.

Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Obtaining these signatures would significantly enhance our understanding of the underlying pathophysiological processes of this condition and foster the development of improved therapeutic strategies, potentially personalized to individual patients. Here, we sequenced newly collected samples from 24 patients with periodontitis, and we collected available oral microbiome data from 24 samples in patients with periodontitis and from 214 samples in healthy individuals (n = 262). Data were harmonized, and we performed a pooled analysis of individual patient data. By metagenomic sequencing of the plaque microbiome, we found microbial signatures for periodontitis and defined a periodontitis-related complex, composed by the most discriminative bacteria. A simple two-factor decision tree, based on Tannerella forsythia and Fretibacterium fastidiosum, was associated with periodontitis with high accuracy (area under the curve: 0.94). Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.

Importance: Oral microbial dysbiosis has been associated with periodontitis in studies using 16S rRNA gene sequencing analysis. However, this technology is not sufficient to consistently separate the bacterial species to species level, and reproducible oral microbiome signatures are scarce. Here, using ultra-deep metagenomic sequencing and machine learning tools, we defined a simple two-factor decision tree, based on Tannerella forsythia and Fretibacterium fastidiosum, that was highly associated with periodontitis. Altogether, we defined robust oral microbiome signatures relevant to the pathophysiology of periodontitis that can help define promising targets for microbiome therapeutic modulation when caring for patients with periodontitis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信