Synergistic Effect in Core Microbiota Associated with Sulfur Metabolism in Spontaneous Chinese Liquor Fermentation

Abstract

Microbial sulfur metabolism plays crucial roles in various food and alcoholic beverage fermentations. 3-(Methylthio)-1-propanol and dimethyl disulfide are important sulfur compounds in fermented foods and alcoholic beverages. Here, we studied the dynamics of these two compounds during spontaneous Chinese liquor fermentation. The two compounds reached the maximum concentration at day 10 and the maximum production rate at day 3. Metatranscriptomic analysis at days 3 and 10 revealed a total of 354 metabolically active microorganisms. Saccharomyces and Lactobacillus were identified as core microbiota critical for sulfur compound production based on both the transcript abundances of the principal genes and the distribution frequencies of 31 enzymes involved in sulfur metabolism. Saccharomyces transcribed genes encoding 23 enzymes related to the generation of 3-(methylthio)-1-propanol and dimethyl disulfide, and Lactobacillus was active in the methyl cycle, which recycles methionine, the precursor of the two sulfur compounds. Furthermore, the sulfur metabolism-related characteristics of two representative species were studied in coculture during a simulated fermentation. Saccharomyces cerevisiae JZ109 produced 158.4 μg/liter 3-(methylthio)-1-propanol and 58.5 μg/liter dimethyl disulfide in monoculture, whereas Lactobacillus buchneri JZ-JN-2017 could not produce these two compounds in monoculture. Their coculture significantly enhanced the generation of 3-(methylthio)-1-propanol (350.0 μg/liter) and dimethyl disulfide (123.8 μg/liter). In addition, coculture significantly enhanced the gene transcriptions (fold change, 1.5 to ∼55.0) that convert methionine to these two compounds in S. cerevisiae and in the methyl cycle of L. buchneri This study reveals a novel synergistic effect between members of the core microbiota in the production of sulfur compounds via methionine recycling in spontaneous Chinese liquor fermentation.IMPORTANCE Sulfur compounds play a crucial role in the aroma quality of various fermented foods and alcoholic beverages. However, it is unclear how these compounds are produced by microbes during their spontaneous fermentations. Here, we identified the core microbiota (Saccharomyces and Lactobacillus) associated with sulfur metabolism by determining both transcript abundance and distribution frequency of each genus in spontaneous Chinese liquor fermentation. This study provides a system-level analysis of sulfur metabolism by the metatranscriptomic analysis and culture-dependent methods. It sheds new light on how the metabolic behavior of the microbiota contributes to the liquor aroma quality. Furthermore, this work reveals a novel synergistic effect between Saccharomyces and Lactobacillus in the production of sulfur compounds, in which Lactobacillus regenerates the precursor methionine for sulfur compound production by Saccharomyces Our findings can contribute to the enhancement of aroma characteristics in Chinese liquor and open new avenues for improving various food and alcoholic beverage fermentation processes.

Keywords: Chinese liquor; core microbiota; metatranscriptomic; microbial interaction; sulfur metabolism; synergistic effect.

FIG 1

Dynamics profile of 3-(methylthio)-1-propanol (a) and dimethyl disulfide (b) in liquor fermentation. Data are shown as the mean ± standard deviation (SD) (n = 4).

FIG 2

Dynamics of microbial population and community makeup based on transcript abundance. (a) Microbial population of bacteria, yeasts, and molds during the fermentation process. Data are shown as the mean ± SD (n = 4). (b) Relative transcript abundances of major genera at day 3 and day 10 (two biological replicates, 3-1 and 3-2 for day 3, and 10-1 and 10-2 for day 10). (c) Transcript abundances of Saccharomyces and Lactobacillus at days 3 and 10 (two biological replicates as in panel b).

FIG 3

Analysis of transcript abundances and distribution frequencies of core microbiota at days 3 and 10 (two biological replicates, 3-1 and 3-2 for day 3, and 10-1 and 10-2 for day 10).

FIG 4

Metabolic pattern of 3-(methylthio)-1-propanol and dimethyl disulfide at days 3 and 10. (a) Schematic depicting the metabolic profile of the microbiota. The red solid lines represent reactions carried out by Lactobacillus, black solid lines represent reactions carried out by Saccharomyces, green solid lines represent reactions by other genera, and the black dashed lines represent chemical reactions. Different colored areas show different modules. A, S, and E represent sulfur sources from aspartate, serine, and sulfite, respectively. M, cysteine to methionine; C, methyl cycle; L, methionine catabolism. (b) Heatmap showing the transcription level. The boxes on the left side of heatmap show the microbiota, and boxes on the right side represent corresponding enzymes. A complete list of the transcription levels of genes encoding enzymes can also be found in Table S3, Data Set S1, and Fig. S4. CoA, coenzyme A; PLP, pyridoxal phosphate.

FIG 5

Microbial population and production of 3-(methylthio)-1-propanol and dimethyl disulfide in mono- and coculture in sorghum medium. (a to c) Populations of S. cerevisiae and L. buchneri in monoculture and coculture (a), 3-(methylthio)-1-propanol (b), and dimethyl disulfide (c). Data are shown as the mean ± SD (n = 3).

FIG 6

Model of S. cerevisiae-L. buchneri interactions based on RT-qPCR and 3-(methylthio)-1-propanol and dimethyl disulfide analyses. (a) Model of S. cerevisiae-L. buchneri interactions. The red solid lines represent upregulated reactions in coculture relative to monoculture. The black dashed lines represent the generation of 3-(methylthio)-1-propanol and dimethyl disulfide. M1 to M3 represent the pathway from cysteine to methionine, C1 to C5 represent the methyl cycle pathway, and L1 to L4 represent methionine catabolism. (b and c) Relative gene expression ratios in S. cerevisiae (b) and in L. buchneri (c). The red lines indicate >2-fold difference. Data are shown as the mean ± SD (n = 3).