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.
Keywords: community structure; dormant microorganisms; gene function prediction; high-throughput sequencing; microbial distribution; new method.