Dataset Information

Metaproteomic analysis of microbial community in Zaopei for Chinese Baijiu production

ABSTRACT: Currently, Baijiu production is performed with different varieties of sorghum that vary considerably in price. Although studies have shown that sorghum varieties affect Baijiu quality, the specific effects of sorghum varieties on Baijiu fermentation have not been thoroughly studied. By using multiple-omics analyses, we compared the fermentation process and final quality of Baijiu brewed from four representative sorghum varieties and identified the involved microbial mechanisms. In addition to providing insight into Baijiu fermentation mechanisms, the results can guide the selection of suitable raw materials for production and future breeding of high quality varieties.

ORGANISM(S): Escherichia Coli

SUBMITTER: Maoke Liu

PROVIDER: PXD037349 | iProX | Thu Apr 20 00:00:00 BST 2023

REPOSITORIES: iProX

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Publications

Variation in microbiological heterogeneity in Chinese strong-flavor Baijiu fermentation for four representative varieties of sorghum.

Liu Mao-Ke MK Tang Yu-Ming YM Liu Cheng-Yuan CY Tian Xin-Hui XH Zhang Ji-Wei JW Fan Xiao-Li XL Jiang Kai-Feng KF Ni Xian-Lin XL Zhang Xin-Yu XY

International journal of food microbiology 20230414

The quality and composition of strong-flavor Baijiu (SFB), a type of Chinese liquor, depends on the variety of sorghum used in fermentation. However, comprehensive in situ studies measuring the effects sorghum varieties on the fermentation are lacking and the underlying microbial mechanisms remains poorly understood. We studied the in situ fermentation of SFB by using metagenomic, metaproteomic, and metabolomic techniques across four sorghum varieties. Sensory characteristics were best for SFB m ...[more]

PMID: 37084618

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Project description:Bio-augmentation could be a promising strategy to improve processes for treatment and resource recovery from wastewater. In this study, the Gram-positive bacterium Bacillus subtilis was co-cultured with the microbial communities present in wastewater samples with high concentrations of nitrate or ammonium. Glucose supplementation (1%) was used to boost biomass growth in all wastewater samples. In anaerobic conditions, the indigenous microbial community bio-augmented with B. subtilis was able to rapidly remove nitrate from wastewater. In these conditions, B. subtilis overexpressed nitrogen assimilatory and respiratory genes including NasD, NasE, NarG, NarH, and NarI, which arguably accounted for the observed boost in denitrification. Next, we attempted to use the the ammonium- and nitrate-enriched wastewater samples bio-augmented with B. subtilis in the cathodic compartment of bioelectrochemical systems (BES) operated in anaerobic condition. B. subtilis only had low relative abundance in the microbial community, but bio-augmentation promoted the growth of Clostridium butyricum and C. beijerinckii, which became the dominant species. Both bio-augmentation with B. subtilis and electrical current from the cathode in the BES promoted butyrate production during fermentation of glucose. A concentration of 3.4 g/L butyrate was reached with a combination of cathodic current and bio-augmentation in ammonium-enriched wastewater. With nitrate-enriched wastewater, the BES effectively removed nitrate reaching 3.2 mg/L after 48 h. In addition, 3.9 g/L butyrate was produced. We propose that bio-augmentation of wastewater with B. subtilis in combination with bioelectrochemical processes could both boost denitrification in nitrate-containing wastewater and enable commercial production of butyrate from carbohydrate- containing wastewater, e.g. dairy industry discharges. These results suggest that B. subtilis bio-augmentation in our BES promotes simultaneous wastewater treatment and butyrate production.

Dataset Information

Metaproteomic analysis of microbial community in Zaopei for Chinese Baijiu production

Publications

Variation in microbiological heterogeneity in Chinese strong-flavor Baijiu fermentation for four representative varieties of sorghum.

Similar Datasets

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