Rapid specific detection of oral bacteria using Cas13-based SHERLOCK.

IF 3.7 2区 医学 Q2 MICROBIOLOGY Journal of Oral Microbiology Pub Date : 2023-05-11 eCollection Date: 2023-01-01 DOI:10.1080/20002297.2023.2207336
Jett Liu, Camden Carmichael, Hatice Hasturk, Wenyuan Shi, Batbileg Bor
{"title":"Rapid specific detection of oral bacteria using Cas13-based SHERLOCK.","authors":"Jett Liu, Camden Carmichael, Hatice Hasturk, Wenyuan Shi, Batbileg Bor","doi":"10.1080/20002297.2023.2207336","DOIUrl":null,"url":null,"abstract":"<p><p>Decades of ongoing research has established that oral microbial communities play a role in oral diseases such as periodontitis and caries. Yet the detection of oral bacteria and the profiling of oral polymicrobial communities currently rely on methods that are costly, slow, and technically complex, such as qPCR or next-generation sequencing. For the widescale screening of oral microorganisms suitable for point-of-care settings, there exists the need for a low-cost, rapid detection technique. Here, we tailored the novel CRISPR-Cas-based assay SHERLOCK for the species-specific detection of oral bacteria. We developed a computational pipeline capable of generating constructs suitable for SHERLOCK and experimentally validated the detection of seven oral bacteria. We achieved detection within the single-molecule range that remained specific in the presence of off-target DNA found within saliva. Further, we adapted the assay for detecting target sequences directly from unprocessed saliva samples. The results of our detection, when tested on 30 healthy human saliva samples, fully aligned with 16S rRNA sequencing. Looking forward, this method of detecting oral bacteria is highly scalable and can be easily optimized for implementation at point-of-care settings.</p>","PeriodicalId":16598,"journal":{"name":"Journal of Oral Microbiology","volume":"15 1","pages":"2207336"},"PeriodicalIF":3.7000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177689/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Oral Microbiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/20002297.2023.2207336","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Decades of ongoing research has established that oral microbial communities play a role in oral diseases such as periodontitis and caries. Yet the detection of oral bacteria and the profiling of oral polymicrobial communities currently rely on methods that are costly, slow, and technically complex, such as qPCR or next-generation sequencing. For the widescale screening of oral microorganisms suitable for point-of-care settings, there exists the need for a low-cost, rapid detection technique. Here, we tailored the novel CRISPR-Cas-based assay SHERLOCK for the species-specific detection of oral bacteria. We developed a computational pipeline capable of generating constructs suitable for SHERLOCK and experimentally validated the detection of seven oral bacteria. We achieved detection within the single-molecule range that remained specific in the presence of off-target DNA found within saliva. Further, we adapted the assay for detecting target sequences directly from unprocessed saliva samples. The results of our detection, when tested on 30 healthy human saliva samples, fully aligned with 16S rRNA sequencing. Looking forward, this method of detecting oral bacteria is highly scalable and can be easily optimized for implementation at point-of-care settings.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用基于 Cas13 的 SHERLOCK 快速特异性检测口腔细菌。
数十年的研究证实,口腔微生物群落在牙周炎和龋齿等口腔疾病中起着重要作用。然而,口腔细菌的检测和口腔多微生物群落的分析目前依赖于成本高、速度慢、技术复杂的方法,如 qPCR 或新一代测序。为了大规模筛查适用于护理点环境的口腔微生物,需要一种低成本、快速的检测技术。在这里,我们为口腔细菌的物种特异性检测定制了基于 CRISPR-Cas 的新型检测方法 SHERLOCK。我们开发了一个能够生成适用于 SHERLOCK 的构建体的计算管道,并对七种口腔细菌的检测进行了实验验证。我们实现了单分子范围内的检测,并且在唾液中存在脱靶 DNA 的情况下仍具有特异性。此外,我们还调整了检测方法,以便直接从未经处理的唾液样本中检测目标序列。在对 30 份健康人类唾液样本进行检测时,我们的检测结果与 16S rRNA 测序结果完全一致。展望未来,这种检测口腔细菌的方法具有很强的可扩展性,可以很容易地进行优化,以便在护理点环境中实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
8.00
自引率
4.40%
发文量
52
审稿时长
12 weeks
期刊介绍: As the first Open Access journal in its field, the Journal of Oral Microbiology aims to be an influential source of knowledge on the aetiological agents behind oral infectious diseases. The journal is an international forum for original research on all aspects of ''oral health''. Articles which seek to understand ''oral health'' through exploration of the pathogenesis, virulence, host-parasite interactions, and immunology of oral infections are of particular interest. However, the journal also welcomes work that addresses the global agenda of oral infectious diseases and articles that present new strategies for treatment and prevention or improvements to existing strategies. Topics: ''oral health'', microbiome, genomics, host-pathogen interactions, oral infections, aetiologic agents, pathogenesis, molecular microbiology systemic diseases, ecology/environmental microbiology, treatment, diagnostics, epidemiology, basic oral microbiology, and taxonomy/systematics. Article types: original articles, notes, review articles, mini-reviews and commentaries
期刊最新文献
Oral administration of Porphyromonas gingivalis to mice with diet-induced obesity impairs cognitive function associated with microglial activation in the brain. RE: colonization of bacterial and viral respiratory pathogens among healthcare workers in China during COVID-19 pandemic. In vitro plaque formation model to unravel biofilm formation dynamics on implant abutment surfaces. Evaluation of a Burkholderia ambifaria strain from plants as a novel promising probiotic in dental caries management. Methanobrevibacter oralis: a comprehensive 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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1