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. 2022 Nov 17;42(2):104–113. doi: 10.12938/bmfh.2022-012

Deciphering microbial community dynamics along the fermentation course of soy sauce under different temperatures using metagenomic analysis

Nguyen Thanh Hai NGUYEN 1a, Ming Ban HUANG 1a, Fa Yong LIU 2a, Wei-Ling HUANG 1a, Huyen-Trang TRAN 3a, Tsai-Wen HSU 4a, Chao-Li HUANG 5,*, Tzen-Yuh CHIANG 1,5,*
PMCID: PMC10067331  PMID: 37016686

Abstract

Fermented soy sauce consists of microorganisms that exert beneficial effects. However, the microbial community dynamics during the fermentation course is poorly characterized. Soy sauce production is classified into the stages of mash fermentation with koji (S0), brine addition (S1), microbial transformation (S2), flavor creation (S3), and fermentation completion (S4). In this study, microbial succession was investigated across stages at different temperatures using metagenomics analyses. During mash fermentation, Aspergillus dominated the fungal microbiota in all stages, while the bacterial composition was dominated by Bacillus at room temperature and by a diverse composition of enriched lactic acid bacteria (LAB) at a controlled temperature. Compared with a stable fungal composition, bacterial dynamics were mostly attributable to fluctuations of LAB, which break down carbohydrates into lactic acid. After adding brine, increased levels of Enterococcus and decreased levels of Lactococcus from S1 to S4 may reflect differences in salinity tolerance. Staphylococcus, as a fermentation starter at S0, stayed predominant throughout fermentation and hydrolyzed soybean proteins. Meanwhile, Rhizopus and Penicillium may improve the flavor. The acidification of soy sauce was likely attributable to production of organic acids by Bacillus and LAB under room temperature and controlled temperature conditions, respectively. Metagenomic analysis revealed that microbial succession was associated with the fermentation efficiency and flavor enhancement. Controlled temperature nurture more LAB than uncontrolled temperatures and may ensure the production of lactic acid for the development of soy sauce flavor.

Keywords: Aspergillus, lactic acid bacteria (LAB), metagenomics, microbial succession, soy sauce

INTRODUCTION

Soy sauce, a popular seasoning for daily cooking, originated in ancient China (Zhou Dynasty). Traditional soy sauce is fermented through the growth of fungi and bacteria for more than six months. Specifically, soy sauce is traditionally made from fermented soybeans and salt hydrolyzed by proteolytic enzymes of the filamentous fungus Aspergillus oryzae or Aspergillus sojae [1]. Over the course of fermentation, soy sauce develops a complex microbial community of fungi and bacteria.

Soy sauce production consists of two major processes: koji fermentation and mash (moromi) fermentation. Koji fermentation, initiated by the inoculation of A. oryzae in steamed soybeans, is a critical step for producing high-quality soy sauce. Mash is a fermented mixture of koji, sea salt, and brine. Mash fermentation requires several months to complete. Therefore, soy sauce fermentation is a complex process in which bacteria and fungi undergo sequential changes at different time points, with dynamic changes in the dominant species. Microorganisms are critical for fermentation. By adding yeast and/or lactic acid bacteria (LAB), numerous food products can be produced via microbial activities. The metabolic differences in microbiota during fermentation yield various flavors of fermented foods. Most bacterial genera reacting in the koji at an early stage produce proteases and volatile fatty acids for soy sauce fermentation [2]. The most important function of the koji fungi in soy sauce fermentation is to provide hydrolytic enzymes, especially extracellular enzymes [3], as several studies have indicated that most hydrolysis of soy protein occurs during koji fermentation [4]. In mash fermentation, the mold is quickly destroyed, while its extracellular enzymes can continue to hydrolyze different substrates. Brine (20% salt) is added seven days later to inhibit the growth of undesirable microorganisms.

Various approaches have been employed to study the soy sauce microbial population and its roles in flavor production [5,6,7]. Through culture-dependent and culture-independent methods, such as polymerase chain reaction (PCR)-denaturing gradient gel electrophoresis, the microbiota involved in different stages of soy sauce fermentation has been explored [8]. Most studies were conducted for a single stage fermentation, such as koji making or mash fermentation [9, 10], or compared the presence/absence of bacteria between stages [11]. Moreover, different fermentation temperatures tend to generate products with different flavors [12]. Nevertheless, no studies have compared the influence of environmental temperature on the microbiota associated with fermentation of soy sauce, except for previous research [13] that addressed the environmental seasonality affecting the bacterial community. In this study, we examined the microbial succession during the fermentation process, that is, from koji to mash, and illustrated fluctuations in microbial composition from one stage to another. We aimed to elucidate the microbial differences between the soy sauces fermented under different temperature conditions, identify dominant species at each stage of the fermentation process, and observe the changes in the bacterial and fungal communities during fermentation. Investigating the microbial community in soy sauce fermentation would help us to understand the dynamic changes associated with the quality of soy sauce. In this study, metagenomic analyses verified the importance of LAB, a group of beneficial microbes profoundly applied in soy sauce fermentation [5, 14].

MATERIALS AND METHODS

Fermentation experiments and sampling

Soy sauce was produced from black soybeans (Tainan No. 5, Taiwan). Black soybeans (15 kg) were steamed in a pressure cooker for an hour, and the beans were then put on a tray to detach water, cooled to 40°C, and mixed with A. oryzae mold (powder, 1 g/kg soybean, Wan Feng Sauce Farm, Yunlin, Taiwan) and fried high-gluten flour (1.5 kg). To ensure adequate growth of A. oryzae, moisture, temperature, and aeration were monitored. The koji-making process was controlled in a box at temperatures from 25°C to 35°C and humidity from 75% to 95%. Following spore formation after one week [15], the koji beans were mixed with 20% (w/w) sea salt and placed in a closed jar that had been sterilized. To test the effect of temperature stability, black soybean mash fermentation was conducted under different conditions, with one set-up at a controlled temperature of 24°C (CT) based on the average temperature in Tainan and another set-up at room temperature (RT). The CT experiment was conducted in a laboratory of National Cheng Kung University (Tainan, Taiwan), while the RT experiment followed factory production and was conducted indoors at a soy sauce factory in Douliu (Yunlin, Taiwan). Room temperature ranged from 32°C (September) to 26°C (March) during the daytime and from 25°C (September) to 17°C (March) during the nighttime (Supplementary Table 1).

Samples were taken from the center of each jar at five time points, day 0 (S0), day 7 (S1), day 30 (S2), day 90 (S3), and day 180 (S4), throughout the 6-month fermentation process (from September 2018 to March 2019). The jars were opened to take the samples during fermentation. At each time point, three replicates of equal amounts of mash sauce samples were collected. The mash samples (approximately 0.5 g) were finely squashed prior to DNA extraction. DNA was isolated using a Quick-DNA TM Fungal/Bacterial Miniprep Kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s instructions. The 16S rRNA gene of bacteria and internal transcribed spacer (ITS) of fungi were amplified using PCR with primer pairs for 16S (789–1053, 926–1392) and ITS (ITS1–ITS2, ITS3–ITS4; Supplementary Table 2).

Metagenomics sequencing and analysis

Bacterial 16S rRNA and fungal ITS amplicons were sequenced using a 300-bp paired-end MiSeq platform (Illumina, San Diego, CA, USA). Sequences with trimmed primer sequences were merged into haplotypes using the forward and reverse reads. Haplotypes were clustered into operational taxonomic units (OTUs) with 97% sequence identity by mapping to the Greengenes database (Aug. 2013 version) and UNITE database for 16S rRNA and ITS, respectively, using the “pick_closed_reference_otus.py” function implemented in QIIME package v.1.9.1 [16]. OTUs assigned to archaea, mitochondria, and chloroplasts were excluded from further analysis. To normalize the sequencing depth of the samples, we used multiple rarefactions on the OTU table at depths of 12,704 and 77,928 reads for 16S rRNA and ITS, respectively. The bacterial OTUs for RT and CT at S0, S1, S2, S3, and S4 were 378, 317, 555, 486, and 356 and 493, 585, 603, 638, and 657, respectively. Meanwhile, the fungal OTUs for RT and CT at S0, S1, S2, S3, and S4 were 26, 21, 28, 29, and 27 and 17, 27, 32, 41, and 28, respectively. A summary of the distribution of OTUs is presented in Supplementary Table 3.

To evaluate the alpha diversity of each sample, OTU richness (Chao1) and Shannon diversity indices were calculated using the diversity function in the vegan package of R version 4.0.0 [17]. Nonmetric multidimensional scaling (NMDS) analyses applying the Bray-Curtis distance were used to plot the similarity of bacterial communities based on OTU composition. The shared proportions of OTUs among the stages of fermentation were visualized using the Venn Diagrams software (http://bioinformatics.psb.ugent.be/webtools/Venn/). Statistical significance for samples under the different temperature conditions was verified using the Mann–Whitney U test. A p-value ≤0.05 was considered statistically significant.

RESULTS

Taxonomic assignments of the soy sauce microbiome

In total, 1,734 OTUs were identified (1,641 bacteria and 93 fungi) from 10,503,229 raw reads of the mash samples during soy sauce fermentation (Supplementary Table 3). Rarefaction analysis showed that all samples almost reached saturation (Supplementary Fig. 1). The number of bacterial OTUs under room and controlled temperatures varied across stages. Likewise, fungal OTUs changed over the course of fermentation (Supplementary Table 3).

Dynamics in bacterial and fungal communities during soy sauce fermentation

Of the bacterial microbiota, phyla Firmicutes and Proteobacteria were persistent over the whole process in both experiment set-ups, with Firmicutes comprising 87% of the bacterial composition at RT. Under the CT conditions, Firmicutes accounted for 50–66% of the bacterial microbiota from S0 to S3, and Proteobacteria dominated S4 with a relative abundance of 54% (Fig. 1a).

Fig. 1.

Fig. 1.

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Relative abundances of bacteria in the RT and CT samples from S0 to S4. (a) Two major phyla of the bacterial microbiota in the two experiment sets. (b) Relative abundances of the top 10 bacterial genera in the RT and CT samples from S0 to S4, namely RT0–RT4 and CT0–CT4. Each sample is grouped according to the time point of fermentation. Top 10 rankings with respect to the relative abundances of genera in each sample.

RT: room temperature; CT: controlled temperature.

The top 20 abundant OTUs identified in the RT samples were mostly members of Firmicutes, including five belonging to Bacillus and six belonging to Staphylococcus (Table 1). The top 20 OTUs in the CT samples were dominated by Enterococcus, Staphylococcus, Erwinia, and Lactococcus (Table 2). In the RT samples, Bacillus predominated over other genera in all stages, with relative abundances ranging from 61% to 85% (74% on average), while it was rare in the CT samples, with frequencies ranging from 0.1% to 0.3% across all stages (Fig. 1b), revealing a sharp difference between the two experimental set-ups. Of the CT samples, Enterococcus (Firmicutes) was predominant during mash fermentation (28% on average), followed by Enterobacteriaceae (25%), Staphylococcus (15%), Erwinia (14%), and Lactococcus (9%). It was noted that LAB were predominant across all stages (31–47%) but remained at low abundances (0.1–0.2%) under the RT conditions (Fig. 2). Despite the sharp contrast in abundance, LAB peaked at the S2 stage under both conditions, showing similar trends in their changes. Among the seven genera of LAB detected here, Lactobacillus and Vagococcus exclusively occurred under the CT conditions, while Enterococcus, Tetragenococcus, Lactococcus, Streptococcus, and Aerococcus were found under both conditions. Of the LAB, Enterococcus was the most abundant under both conditions (on average, 0.06% in the RT samples and 28% in the CT samples). Lactococcus was second under the CT conditions (9%) and third under the RT conditions (0.01%). Streptococcus was third under the CT conditions (0.07%) and second under the RT conditions (0.02%). Accordingly, the predominant LAB under both conditions were similar, and the total abundance of LAB increased drastically in the CT experiment. In addition, Staphylococcus was very abundant under both conditions, with relative abundances of up to 23% in the RT samples (S1 stage) and up to 21% in the CT samples (S3 stage). In the final stage, its abundance dropped to 2% in the RT samples and 11% in the CT samples. In contrast, Erwinia was relatively stable during mash fermentation under both conditions (3–7% for RT; 10–16% for CT).

Table 1. Top 20 bacterial OTUs of samples of RT ranked by relative abundance and assigned by Greengenes database.

Greengenes OTU ID Phylum Class Order Family Genus Nearest valid taxon
1050364 Firmicutes Bacilli Bacillales Bacillaceae Bacillus
820837 Firmicutes Bacilli Bacillales Bacillaceae Bacillus
1111874 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
1099674 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus S. succinus
825033 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Erwinia E. dispersa
529219 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus S. sciuri
895390 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus
851811 Firmicutes Bacilli Bacillales Listeriaceae Listeria L. grayi
811219 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
1098655 Firmicutes Bacilli Bacillales Bacillaceae Bacillus
816420 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Delftia
752584 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
1081348 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus
434127 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus
811492 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
1097955 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus S. aureus
823118 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
286880 Firmicutes Bacilli Bacillales Bacillaceae Bacillus
950872 Firmicutes Bacilli Bacillales Paenibacillaceae Brevibacillus
574051 Firmicutes Bacilli Bacillales Bacillaceae Bacillus
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OTUs: operational taxonomic units; RT: room temperature.

Table 2. Top 20 bacterial OTUs of samples of CT ranked by relative abundance and assigned by Greengenes database.

Greengenes OTU ID Phylum Class Order Family Genus Nearest valid taxon
1111582 Firmicutes Bacilli Lactobacillales Enterococcaceae Enterococcus
1111874 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
825033 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Erwinia E. dispersa
1099674 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus S. succinus
529219 Firmicutes Bacilli Bacillales Staphylococcaceae Staphylococcus S. sciuri
294254 Firmicutes Bacilli Lactobacillales Streptococcaceae Lactococcus
823118 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
656889 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae Erwinia
303204 Firmicutes Bacilli Lactobacillales Streptococcaceae Lactococcus L. garvieae
811219 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
752584 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
698795 Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceae Acinetobacter
928829 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas
810399 Firmicutes Bacilli Lactobacillales Enterococcaceae Enterococcus
593781 Firmicutes Bacilli Lactobacillales Enterococcaceae Enterococcus E.haemoperoxidus
811492 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
1109844 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
829851 Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae Pseudomonas
797229 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
4352745 Proteobacteria Gammaproteobacteria Enterobacteriales Enterobacteriaceae
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OTUs: operational taxonomic units; CT: controlled temperature.

Fig. 2.

Fig. 2.

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Changes in the relative abundance of lactic acid bacteria (LAB) in soy sauce mash fermentation. The bars indicate the average abundance for each genus. Different time points are distinguished by colors. The LAB genera were ranked by average relative abundance. (a) LAB in the RT samples. (b) Major LAB in the CT samples. (c) Minor LAB in the CT samples.

RT: room temperature; CT: controlled temperature.

Venn diagrams were used to visualize the numbers of persistent and unique OTUs under the different conditions. Sixty-three OTUs were shared among the microbial communities at different time points under the RT conditions (Fig. 3a). The numbers of unique OTUs varied from 6 to 38 across the stages in the RT samples. In the CT samples, 154 persistent OTUs were detected, with the number of unique OTUs varying across stages (Fig. 3b). The persistent OTUs accounted for more than 99% of the bacteria involved in RT and CT mash fermentation. In the RT samples, the most persistent OTUs were identified as Bacillus (75%) followed by Staphylococcus (12%). In the CT samples, Enterococcus was the most abundant (28%), followed by unidentified Enterobacteriaceae (25%), Staphylococcus (15%), and Erwinia (14%; Supplementary Fig. 2).

Fig. 3.

Fig. 3.

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Venn diagram of the number of bacterial OTUs shared among the S0 to S4 stages. Different colors represent samples exclusively from single stages in the RT (a) and CT (b) samples. Persistent and unique OTUs were revealed.

OTUs: operational taxonomic units; RT: room temperature; CT: controlled temperature.

In the fungal microbiota, Aspergillus was dominant in both experimental set-ups (Fig. 4), with relative abundances ranging from 95–99% and 94–99% in the RT and CT samples, respectively. In the RT samples, Aspergillus, Penicillium, and Rhizopus persisted across stages (Supplementary Fig. 3). In the CT samples, the abundance of Penicillium ranged from 1% to 5% during fermentation. The abundance of Rhizopus ranged from 0.01% to 2%. Debaryomyces was found in the CT samples from S0 (3%) to S3 (0.1%), whereas it was absent from the RT samples (Fig. 4). In total, nine and 13 persistent fungal OTUs were detected in the RT and CT samples, respectively (Supplementary Fig. 4).

Fig. 4.

Fig. 4.

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Relative abundances of the top 5 genera of fungi in the RT and CT samples. Each sample is grouped according to the time point of fermentation.

RT: room temperature; CT: controlled temperature.

NMDS analysis indicated that the S0, S1, and S2 bacterial communities in the CT samples were clustered together, separating them from those at S3 and S4 (Fig. 5a). In contrast, the S0, S1, and S2 fungal communities clustered together, separating S3 and S4 (Fig. 5b). Principal component analysis (PCA) of the CT samples revealed that bacterial communities varied during the fermentation process (Supplementary Fig. 5). Principle component 1 (PC1) contributed 44.4% of the total variance and was negatively correlated with the samples at S2. Principle component 2 (PC2) accounted for 23.4% of the total variance, was positively correlated with samples at S3 and S4, and was negatively correlated with samples at S0 and S1. The NMDS and PCA results indicated stepwise changes of microbiota as the fermentation of soy sauce progressed.

Fig. 5.

Fig. 5.

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NMDS plots for microbial communities of the CT soy sauce fermentation samples. (a) NMDS plot of bacterial communities. (b) NMDS plot of fungal communities. Colors represent samples taken at different time points: black for S0, red for S1, green for S2, yellow for S3, and blue for S4.

CT: controlled temperature; NMDS: nonmetric multidimensional scaling.

In the RT samples, Chao1 species richness increased from S0 to S2 and decreased from S2 to S4. In the CT samples, species richness increased from S0 to S1, decreased from S1 to S3, and increased again at S4. Specifically, S2 (214.4) showed the highest richness in the RT samples, and S4 (332.9) showed the highest richness in the CT samples (Table 3). The Shannon diversity indices of the RT samples were in the range of 1.42–1.95 (1.66 in average), and those of the CT samples were in the range of 2.69–3.01 (2.83 in average). A significant difference was observed between the two set-ups (p<0.001, Levene’s test). Moreover, the S3 stage displayed the largest difference between the two experimental sets (Supplementary Fig. 6).

Table 3. Alpha diversity of bacterial communities in samples of RT and CT.

Diversity index Stages RT CT
Chao1 S0 158.78 ± 12.11 314.43 ± 24.13
S1 164.22 ± 13.44 318.89 ± 49.79
S2 214.44 ± 34.61 305.85 ± 63.04
S3 214.18 ± 47.88 277.95 ± 87.49
S4 163.88 ± 47.31 332.88 ± 78.40
Shannon-Wiener Index S0 1.95 ± 0.02 3.01 ± 0.06
(H’) S1 1.85 ± 0.10 2.92 ± 0.12
S2 1.64 ± 0.06 2.76 ± 0.06
S3 1.42 ± 0.04 2.75 ± 0.03
S4 1.45 ± 0.60 2.69 ± 0.39
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Mean values ± SD. RT: room temperature; CT: controlled temperature.

DISCUSSION

Brewed soy sauce consists of various microorganisms that determine the sauce quality and exert beneficial health effects [10]. Agreeing with previous research [18], the phyla Firmicutes and Proteobacteria, which are known to be critical in determining fermentation quality, were prevalent in both experimental set-ups (Fig. 1a).

In the RT experiment, which mimicked industrial production, both Aspergillus and Bacillus were predominantly stable throughout the fermentation process (Figs. 1b and 4, Supplementary Figs. 4 and 5), contrasting with the microbiota of fermented brine (in Malaysia), which was dominated by Candida and Weissella, followed by Bacillus and Lactobacillus [9]. Aside from the previously detected Staphylococcus and Bacillus [2], several bacterial genera, including Lactococcus, Erwinia, Acinetobacter, Pseudomonas, Klebsiella, and Streptococcus, involved in soy sauce fermentation were observed for the first time in this study (Fig. 1b, Tables 1 and 2). The soaking step prior to the steaming of soybeans was the probable source of Streptococcus, which has been found in soybean soaking in Indonesian tempe [19]. Staphylococcus was possibly derived from the salts added during mash fermentation [20]. However, as it was detected before the addition of brine, the salts can be simply ruled out as the origin of the Staphylococcus. Furthermore, Staphylococcus and Klebsiella are thought to have derived from the hands of workers during production [21]. Pseudomonas, some members of which are able to survive or thrive in various environments [22], was likely incorporated into the mash fermentation environment from the surrounding environment.

In the CT experiment, LAB played critical roles beginning at the initiation of fermentation (40% at S0) and stayed predominant throughout fermentation (Fig. 2). In contrast to the low abundance of LAB under the RT conditions (<1%), the CT conditions greatly enriched LAB to 31–47% and increased their influences on the development of soy sauce flavor. Besides, all of the LAB that occurred under the RT conditions were detected under the CT conditions with greater relative abundances, suggesting that maintaining a constant temperature (24°C) universally enhanced the LAB involved in soy sauce fermentation. Among the LAB detected in this study, Lactococcus, Streptococcus, and Lactobacillus showed the highest abundances at the S0 stage and decreased after brine addition, indicating that their roles were more influential during koji fermentation. Enterococcus, Aerococcus, Vagococcus were highest at the S2 stage and stayed dominant until the S4 stage. Tetragenococcus gradually increased in the late stage of the fermentation, indicating its effects on the maturation of soy sauce.

The addition of brine, specifically in S1, represents another stage of fermentation in which the microbial community was transformed. High salt concentrations tend to inhibit the growth of contaminating bacteria during fermentation [23], leading to dynamic microbial changes, which ensure better flavor and stability of the final soy sauce product [9, 24]. For instance, in the CT experiment, the relative abundance of LAB, mostly Enterococcus and Lactococcus, after the addition of brine increased from stage S1 (35%) to S2 (47%). Specifically, the relative abundance of Lactococcus decreased as the fermentation process progressed (16% at S0 → 3% at S4), suggesting its role was largely affected by the high salinity. In contrast, the Enterococcus involved here may have had better salt tolerance than Lactococcus, as its abundance increased after adding brine to the mash (22% to 35%). Enterococcus faecium has been found to have the ability to survive in a high-salinity environment (30%) [8]. This result also implied that the role played by Enterococcus was more dominant in the latter stages of soy sauce fermentation.

The acidification during soy sauce fermentation is attributable to the accumulation of organic acids, particularly lactic acid. LAB are the major producers of the lactic acid in soy sauce, while other bacteria generate other types of organic acids [24]. For instance, Bacillus was found to mainly generate acetic acid, malic acid, and propionic acid during the fermentation of Daqu, and the accumulation of lactic acid was relatively lower [25]. As the abundance of Bacillus was much greater than that of LAB in the RT soy sauce, acidification was not likely to be attributable to the accumulation of lactic acid. Under the CT conditions, the boosted amount of LAB probably enhanced the accumulation of lactic acid, as lactic acid comprises more than 90% of the organic acids produced by LAB [26, 27]. Although we did not determine the level of lactic acid, the elevated abundance LAB under the CT conditions may enrich the lactic acid content (Fig. 2).

The acidification effect of LAB via the production of organic acids is well documented in fermentation [5, 9, 15]. LAB are responsible for breaking down carbohydrates into lactic acid and simple sugars, causing acidification of mash [28]. Brine is thereby acidified by halophilic LAB, which limits the growth of harmful microorganisms during fermentation [29]. In general, LAB are complex autotrophs that require sugars, vitamins, and amino acids. Although it fluctuates, the substantial growth of LAB indicates that cooked soybeans contain sufficient nutrients for the rapid growth of these bacteria. LAB-acidifying ingredients lead to tangled lactic acid flavors, frequently act on proteolytic and lipolytic activity, and produce aromatic compounds, contributing to the flavor of the final product [29]. The LAB in soy sauce fermentation help produce 2,5-dimethyl-4-hydroxy-3(2H)-furanone, which is an important aroma synthesized during mash fermentation [24].

Temperature is known to determine the microbial composition during fermentation [30]. In the present study, the diverse temperatures in the RT and CT experiments differentiated the microbiota during fermentation. In the CT experiment, the microbiota diversity was higher than that in the RT experiment (20.3–30.6°C), and 252 bacterial OTUs were observed exclusively under its conditions, indicating that a stable environment may have nurtured diverse species. The bacteria unique to the CT experiment span a diverse phylogeny across 14 orders, including Enterobacteriales, Lactobacillales, Bacillales, and Pseudomonadales. Furthermore, dominance of LAB occurred exclusively in the CT experiment (Fig. 1b). Apparently, LAB contain genera and species that are highly sensitive to temperature [31], whereas Bacillus can tolerate temperature fluctuations better [32]. Given their high sensitivity to temperature, the heat resistance of LAB is complex and involves proteins playing various roles in cell physiology. In addition, the timing of initiation of the stress response varies greatly depending of the species/strains [33]. Adapting to a narrow temperature range is one of the characteristics of LAB [34]. Based on the above, when the temperature shifted from daytime to nighttime, the growth of LAB was constrained.

Interestingly, previous research [11] detected seven genera missing from koji and 12 genera newly appearing in mash using metagenomic analysis. Such a “gain and loss” scenario was also present in our study, with 25 and 29 genera exclusive to the koji stage in the RT and CT experiments, respectively. One genus was exclusive to the mash stage in the CT experiment, and there were no genera exclusive to the mash stage in the RT experiment. The higher number of taxa in koji may be attributable to the black soybeans, which provide much more organic material for fermentation than the defatted regular soybeans used in the previous study [11]. Nevertheless, many bacteria disappeared when shifting to the mash stage. In contrast, almost all bacteria in the mash stage were already present in koji. The sharp difference between our study and that of the previous research [11] may be simply due to a lower threshold for filtering taxa of a certain abundance in our study (0.002% in our study vs. 1.0% in the previous study), especially given the approximate sequencing depths.

Key microbes in the fermentation of soy sauce

The fungal species of Aspergillus, Penicillium and Rhizopus are often used for fermented food due to the enzymes they secrete [35]. Rhizopus has been detected in bean sauce mash and has functions involved in flavor improvement [36]. In the present study, Rhizopus and Penicillium were persistent in both experiment sets, indicating their critical roles in mash fermentation. Although some studies have detected Zygosaccharomyces, a common salt-tolerant yeast, in mash fermentation, we found Debaryomyces as the only yeast participating in soy sauce fermentation. Many production methods for soy sauce ensure the participation of Zygosaccharomyces by artificial inoculation for flavor development [37]. In the study of Harada et al. [5], both Zygosaccharomyces and Tetragenococcus were intentionally inoculated in the mash fermentation of soy sauce. In the present study, we aimed to mimic the traditional method used in Taiwan, and therefore, no microorganisms were artificially added during fermentation of the soy sauce, except for the Aspergillus used in koji making. In addition, Sulaiman et al. [9] also did not detect Zygosaccharomyces in Chinese soy sauce and suggested that the absence of the yeast was related to the low ethanol amount in Chinese soy sauce. Therefore, the absence of Zygosaccharomyces in our observations was not surprising. The aroma developed by yeast may also be attributable to the halotolerant yeast Debaryomyces, and its function has been confirmed in a Korean soy sauce [38].

It was noted that the CT conditions could largely increase the involvement of LAB in soy sauce fermentation (Fig. 1). LAB might first be present after wheat flour and molds are added during koji preparation. Among LAB, Lactococcus and Enterococcus are homofermentative bacteria with lactic acid as the final product via the Embden-Meyerhof-Parnas pathway [39]. Enterococcus displayed a sharp change with temperature [40], and it was also dominant in other fermented soybean products, in which it was used to prevent food spoilage and inhibit the growth of pathogenic bacteria by producing enterocin [38]. Meanwhile, a previous study found that the enzyme activity of Lactococcus affected the aroma of fermented foods by producing ester and phenolic compounds [12]. Lactococcus piscium can tolerate a maximum NaCl concentration of 23 g/L [41]. Lactococcus was identified as an abundant genus during soy sauce fermentation with a salt content ranging from 19 to 20 g/100 mL [42]. In the present study, Lactococcus was detected throughout fermentation in the CT experiment, implying that the growth of functional microorganisms may suppress the activities of harmful bacteria [12].

In contrast to the CT experiment, which was dominated by LAB, Bacillus was the most dominant in the RT experiment (Fig. 1). LAB and Bacillus have been found in various fermented foods with effective enzymes for the hydrolysis of soybean nutrients and for food spoilage [43]. Bacillus is often found in the early stages of soy sauce fermentation [44]. It is known that Bacillus can grow in a wide temperature range, from 41–65°C [32], and can produce spores that adapt to various stresses [45]. In our study, Bacillus increased from S0 to S3 in the RT experiment, while it decreased at S4; however, it remained dominant across all stages. In the CT experiment, Bacillus was detected in all samples with very low frequencies and with the opposite trend as compared with that in the RT experiment (Supplementary Fig. 7). Bacillus is a common bacterium in traditional sunbathing mash and can transform a sauce’s aroma and flavor [44]. It has been widely used for fermented foods. Some Bacillus strains have been shown to hydrolyze proteins in soybean into active peptides by proteases and peptidases in fish sauce [46]. Bacillus subtilis has been found to have proteolytic and amylolytic activities that hydrolyze soybean proteins, starch, and fat in fermented soy-dawadawa, an African condiment [47]. Likewise, B. subtilis is often used as a starter to control the fermentation quality of natto, a popular Japanese food. Moreover, most Bacillus species do not grow well in conditions with more than a 15% salt content [48], as observed in our experiments.

Staphylococcus has been detected in soybean mash samples with a salt concentration of more than 20% [49]. In our study, analysis of the bacterial community showed that Staphylococcus increased from S0 to S3 during fermentation in the CT experiment. The relative abundance of Staphylococcus decreased at the S2 stage in the RT experiment and in the S4 samples in the CT experiment (Supplementary Fig. 7). It has been shown that Staphylococcus plays an important role in protein solubilization and contributes to flavor development [50]. Therefore, Staphylococcus likely participated from the start of fermentation by hydrolyzing proteins, and it maintained a high abundance throughout the fermentation course in both experiments (Fig. 1b).

Erwinia was detected in all soybean mash samples [51]. We also observed this genus in all samples from both experimental set-ups, though with much a higher abundance in the CT experiment (Supplementary Fig. 7). The growth of Erwinia during vegetable fermentation might help sugar degradation and participate in alcoholic fermentation [52]. Erwinia was also found to be a dominant genus in da-jiang, a salty fermented soybean paste, suggesting its tolerance of high-salt conditions [53].

Pseudomonas was also detected in the CT experiment (Fig. 1b). Some chemical compounds generated during early fermentation may help create suitable environments for Pseudomonas [54], which produces glutaminase for the synthesis of glutamic acid in brewed soy sauce [55]. In our study, Pseudomonas appeared at S3 in the RT experiment, whereas it was detected from S0 to S4 with an increasing trend in the CT experiment, revealing differential fermentation processes between the temperature sets. Acinetobacter, Streptococcus, Citrobacter, Pragia, Serratia, and Curtobacterium were found in both experimental set-ups. These genera have different effects on the fermentation process; for example, Acinetobacter is a harmful microbe for soy sauce fermentation [15].

Interestingly, Tetragenococcus is recognized as one of the major bacteria in soy sauce fermentation [49]. However, its abundance was less than 1% in S3 samples (Supplementary Fig. 4). We found Tetragenococcus in the mash samples of both types. Previous studies found that Tetragenococcus adapted to high salt concentrations (10–18% NaCl) during mash fermentation and have an immunomodulatory effect in soy mash samples [6, 56]. It was also found that the dynamics of Tetragenococcus halophilus might be affected by pH values during mash fermentation [6]. Tetragenococcus could be inoculated as starter to ensure the production of lactic acid in soy sauce. In the present study, we did not add Tetragenococcus at the beginning of mash fermentation, so it was only detected at a later stage, likely due to the involvement of natural flora, and may not have played a dominant role in the production of lactic acid. Since lactic acid could be generated by other microorganisms, such as Bacillus spp. under the RT conditions, as well as Lactococcus and Enterococcus under the CT conditions, these bacteria may replace the role of Tetragenococcus, as observed in other studies [5, 6, 49, 56], in the production of lactic acid, especially when they are not artificially added.

Soy sauce fermentation is a successional process involving the dynamics of microbes that are associated with the quality of soy sauce. Controlled temperature nurture more microbes than uncontrolled temperatures and may better ensure the production of soy sauce. In our study, Enterococcus dominated the bacterial microbiota in samples under the controlled temperature, whereas Bacillus was dominant at room temperature. In contrast to the fluctuations in bacterial composition, the fungal community was stable, with Aspergillus as the predominant taxon under all conditions and stages. This study contributes to the knowledge of microorganisms in the fermentation process, which is valuable in controlling optimal quality.

DATA AVAILABILITY

The sequencing data have been submitted to the NCBI Sequence Read Archive under BioProject PRJNA719692 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA719692) with accession numbers SAMN18614620–SAMN18614639.

CONFLICT OF INTEREST

The authors declare no conflicts of interests.

Supplementary Material

Supplement Files
bmfh-42-104-s001.pdf (615.4KB, pdf)

Acknowledgments

The authors would like to thank Mr. Guo-Bin Wu for helpful advice on experimental procedures.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement Files
bmfh-42-104-s001.pdf (615.4KB, pdf)

Data Availability Statement

The sequencing data have been submitted to the NCBI Sequence Read Archive under BioProject PRJNA719692 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA719692) with accession numbers SAMN18614620–SAMN18614639.

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