Metabolic framework of spontaneous and synthetic sourdough metacommunities to reveal microbial players responsible for resilience and performance

Abstract

Background In nature, microbial communities undergo changes in composition that threaten their resiliency. Here, we interrogated sourdough, a natural cereal-fermenting metacommunity, as a dynamic ecosystem in which players are subjected to continuous environmental and spatiotemporal stimuli. Results The inspection of spontaneous sourdough metagenomes and transcriptomes revealed dominant, subdominant and satellite players that are engaged in different functional pathways. The highest microbial richness was associated with the highest number of gene copies per pathway. Based on meta-omics data collected from 8 spontaneous sourdoughs and their identified microbiota, we de novo reconstructed a synthetic microbial community SDG. We also reconstructed SMC-SD43 from scratch using the microbial composition of its spontaneous sourdough equivalent for comparison. The KEGG number of dominant players in the SDG was not affected by depletion of a single player, whereas the subdominant and satellite species fluctuated, revealing unique contributions. Compared to SMC-SD43, SDG exhibited broader transcriptome redundancy. The invariant volatilome profile of SDG after in situ long-term back slopping revealed its stability. In contrast, SMC-SD43 lost many taxon members. Dominant, subdominant and satellite players together ensured gene and transcript redundancy. Conclusions Our study demonstrates how, by starting from spontaneous sourdoughs and reconstructing these communities synthetically, it was possible to unravel the metabolic contributions of individual players. For resilience and good performance, the sourdough metacommunity must include dominant, subdominant and satellite players, which together ensure gene and transcript redundancy. Overall, our study changes the paradigm and introduces theoretical foundations for directing food fermentations.

Exploration of microbial profile of traditional starters and its influence on aroma profile and quality of Chinese steamed bread

“…Different sub-predominant fungi were detected in traditional starters, whose relative abundance was greater than 1% but less than the dominant ones; this difference might lead to differences in the quality of steamed bread. 34,35 SJ-ST, HD-ST and LF-ST had more sub-predominant fungus than others. Lactobacillus was the predominant bacterium in HD-ST, CZ-ST (Cangzhou), LF-ST, CD-ST, ZJ-ST (Zhangjiakou), QH-ST (Qinhuangdao), XT-ST and HS-ST, with relative abundance higher than 39.86%; the dominant Lactobacillus (53.64%), Bacillus (31.17%), Clostridium sensustricto 1 (33.30%) and Enterobacter (37.13%) were found in XT-ST, SJ-ST, BD-ST and TS-ST, respectively; TS-ST and SJ-ST had more sub-predominant bacteria than the others.…”

“…Saccharomyces was predominant in HS‐ST, CZ‐ST, QH‐ST, ZJ‐ST, XT‐ST and HD‐ST with a relative abundance greater than 38.58%, while a dominated unclassified Saccharomycetales (53.57%), Aspergillus (99.38%), unclassified Dipodascaceae (52.11%), Penicillium (75.20%), and Kazachstania (83.86%) were found in SJ‐ST, BD‐ST (Baoding), LF‐ST, TS‐ST and CD‐ST, respectively. Different sub‐predominant fungi were detected in traditional starters, whose relative abundance was greater than 1% but less than the dominant ones; this difference might lead to differences in the quality of steamed bread 34,35 . SJ‐ST, HD‐ST and LF‐ST had more sub‐predominant fungus than others.…”

“…32 Stepwise canonical discriminant analysis was used to discriminate the geographical origins of steamed breads fermented with different traditional starters according to their aroma compounds. 28,29 Sensory evaluation of steamed bread The cooled steamed breads were placed on separate white porcelain plates, coded with three randomized letters, before presenting to 20 trained sensory panelists (10 male and 10 female, aged [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] in individual sensory rooms at 25 ± 1 °C with natural daylight. They were evaluated according to the criteria in Supporting Information Table S1.…”

Non‐gene‐editing microbiome engineering of spontaneous food fermentation microbiota—Limitation control, design control, and integration

“…Compared to soil and gut microbiota, the range of SFFMs created by traditional NgeME is relatively broad, including cheese rind microbiota (Wolfe et al., 2014), sourdough microbiota (Calabrese et al., 2022), and daqu microbiota (Xia et al., 2023). The common raw materials include milk, meat, cereals, and vegetables (Table 1).…”

“…This design strategy has also been used extensively in the field of fermented food research, which greatly enhances our ability to control microbiota and can be verified reproducibly at different scales (Cheng et al., 2022; Gao et al., 2022). However, most current applications of designed SMCs are limited to the bench (Calabrese et al., 2022; Kastman et al., 2016; Wolfe & Dutton, 2015) due to the targeting of a single function or unpredictable in situ activity during fermentation (Scarborough et al., 2018), resulting in a gap between design control at the bench and limitation control in actual production.…”