Shibo Ban, Wei Cheng, Xi Wang, Jiao Niu, Qun Wu, Yan Xu

Predicting the final metabolic profile based on the succession-related microbiota during spontaneous fermentation of the starter for Chinese liquor making

  • Computer Science Applications
  • Genetics
  • Molecular Biology
  • Modeling and Simulation
  • Ecology, Evolution, Behavior and Systematics
  • Biochemistry
  • Physiology
  • Microbiology
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ABSTRACT Microbial inoculation is an effective way to improve the quality of fermented foods via affecting the microbiota structure. However, it is unclear how the inoculation regulates the microbiota structure, and it is still difficult to directionally control the microbiota function via the inoculation. In this work, using the spontaneous fermentation of the starter ( Daqu ) for Chinese liquor fermentation as a case, we inoculated different microbiota groups at different time points in Daqu fermentation, and analyzed the effect of the inoculation on the final metabolic profile of Daqu . The inoculated microbiota and inoculated time points both significantly affected the final metabolites via regulating the microbial succession ( P < 0.001), and multiple inoculations can promote deterministic assembly. Twenty-seven genera were identified to be related to microbial succession, and drove the variation of 121 metabolites. We then constructed an elastic network model to predict the profile of these 121 metabolites based on the abundances of 27 succession-related genera in Daqu fermentation. Procrustes analysis showed that the model could accurately predict the metabolic abundances (average Spearman correlation coefficients >0.3). This work revealed the effect of inoculation on the microbiota succession and the metabolic profile. The established predicted model of metabolic profile would be beneficial for directionally improving the food quality. IMPORTANCE This work revealed the importance of microbial succession to microbiota structure and metabolites. Multi-inoculations would promote deterministic assembly. It would facilitate the regulation of microbiota structure and metabolic profile. In addition, we established a model to predict final metabolites based on microbial genera related to microbial succession. This model was beneficial for optimizing the inoculation of the microbiota. This work would be helpful for controlling the spontaneous food fermentation and directionally improving the food quality.

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