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封闭系统中复苏的扩增子序列变体监测可识别出更多休眠微生物。

Revived Amplicon Sequence Variants Monitoring in Closed Systems Identifies More Dormant Microorganisms.

作者信息

Lu Ya-Xian, Deng Wei, Qi Fu-Liang, Yang Xiao-Yan, Xiao Wen

机构信息

Institute of Eastern-Himalaya Biodiversity Research, Dali University, Dali 671003, China.

Collaborative Innovation Center for Biodiversity and Conservation in the Three Parallel Rivers Region of China, Dali 671003, China.

出版信息

Microorganisms. 2023 Mar 15;11(3):757. doi: 10.3390/microorganisms11030757.

Abstract

The large number of dormant microorganisms present in the environment is an important component of microbial diversity, and neglecting dormant microorganisms would be disruptive to all research under the science of microbial diversity. However, current methods can only predict the dormancy potential of microorganisms in a sample and are not yet able to monitor dormant microorganisms directly and efficiently. Based on this, this study proposes a new method for the identification of dormant microorganisms based on high-throughput sequencing technology: Revived Amplicon sequence variants (ASV) Monitoring (RAM). Pao cai (Chinese fermented vegetables) soup was used to construct a closed experimental system, and sequenced samples were collected at 26 timepoints over a 60-day period. RAM was used to identify dormant microorganisms in the samples. The results were then compared with the results of the currently used gene function prediction (GFP), and it was found that RAM was able to identify more dormant microorganisms. In 60 days, GFP monitored 5045 ASVs and 270 genera, while RAM monitored 27,415 ASVs and 616 genera, and the RAM results were fully inclusive of the GFP results. Meanwhile, the consistency of GFP and RAM was also found in the results. The dormant microorganisms monitored by both showed a four-stage distribution pattern over a 60-day period, with significant differences in the community structure between the stages. Therefore, RAM monitoring of dormant microorganisms is effective and feasible. It is worth noting that the results of GFP and RAM can complement and refer to each other. In the future, the results obtained from RAM can be used as a database to extend and improve the monitoring of dormant microorganisms by GFP, and the two can be combined with each other to build a dormant microorganism detection system.

摘要

环境中存在的大量休眠微生物是微生物多样性的重要组成部分,忽视休眠微生物会对微生物多样性科学下的所有研究造成干扰。然而,目前的方法只能预测样本中微生物的休眠潜力,尚无法直接且高效地监测休眠微生物。基于此,本研究提出一种基于高通量测序技术的休眠微生物鉴定新方法:复苏扩增子序列变体(ASV)监测(RAM)。以泡菜汤构建封闭实验系统,在60天内的26个时间点采集测序样本,用RAM鉴定样本中的休眠微生物。然后将结果与目前使用的基因功能预测(GFP)结果进行比较,发现RAM能够鉴定出更多的休眠微生物。在60天内,GFP监测到5045个ASV和270个属,而RAM监测到27415个ASV和616个属,RAM的结果完全包含了GFP的结果。同时,在结果中也发现了GFP与RAM的一致性。两者监测到的休眠微生物在60天内呈现四阶段分布模式,各阶段群落结构存在显著差异。因此,RAM对休眠微生物的监测是有效可行的。值得注意的是,GFP和RAM的结果可以相互补充和参考。未来,可将RAM获得的结果作为数据库,扩展和完善GFP对休眠微生物的监测,二者相互结合构建休眠微生物检测系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc52/10055844/b9072291d223/microorganisms-11-00757-g001.jpg

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