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. 2025 Feb 7;34(9):1981–1994. doi: 10.1007/s10068-025-01824-z

Bioturbation analysis of microbial community and organic acid metabolism in the enriched liquid of pit mud by Daqu, HuangShui, and ZaoPei

MingHong Bian 1,#, Enze Huang 1,2,#, Shangchao Xia 1,2, Chunyan He 1,2, Guangbin Ye 1,2,✉
PMCID: PMC11972265  PMID: 40196325

Abstract

In this study, the traditional fermentation starter (Daqu), a fermentation by-product (Huangshui) and fermentation grains (Zaopei) were combined with pit mud to provide the initial bacterial source, using a single pit mud bacterial inoculation source as the control group. Changes in metabolite accumulation and microbial community were assessed over six rounds of enrichment. Results showed that the addition of exogenous microorganisms (Daqu, Huangshui and Zaopei) better enhanced the quality of the enriched pit mud liquid compared to the use of multiple rounds of enrichment. The quality enhancement of the enriched pit mud liquid could be ranked in descending order as follows: Zaopei > Huangshui > Daqu. The quality of the enriched pit mud liquid was found to be highest in rounds 2–3 of the addition of Zaopei. These results provide theoretical guidance and technical support for the development of pit mud maintenance systems and techniques for the rapid aging of artificial pit mud.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-025-01824-z.

Keywords: Strong-flavor Baijiu, Pit mud, Exogenous microorganism, Enrichment culture, Microbial community succession

Introduction

Baijiu is a distilled spirit that is consumed widely across China, which according to the brewing process utilized, has aromas types that can be characterised as strong-, Maotai-, fen-, or rice-flavour (Wei et al., 2020). Among all, strong-flavour Baijiu (SFB) is one of the most popular types, occupying an important position in China's Baijiu market, with its typical characteristics formed through a unique production process. Ethyl caproate is the main flavour component of SFB, which in combination with appropriate amounts of ethyl acetate, ethyl lactate and ethyl butyrate, among other compounds, forms the unique characteristics of SFB such as a rich cellar aroma (referring to the unique cellar fermentation process used), with a sweet, mellow, clean and refreshing flavour. The precursors of ethyl caproate are mainly derived from the metabolism of caproic acid-producing bacteria (CAPB) in pit mud (PM) (Zhang et al., 2020c). Previous research has shown that the abundance of CAPB in old pit mud (OPM) is around two times higher than in new pit mud (NPM) (Zhang et al., 2020a). Therefore, OPM is more conducive to the metabolism of caproic acid and can help to improve the quality of SFB. However, the process of PM maturation from new to OPM is very slow (Liu et al., 2020), with some active eukaryotes and Anaeromyxobacter causing PM degradation during the aging process (Zhou et al., 2021). Subsequently, naturally matured, high-quality PM cannot be generated at a sufficient rate to meet the demand for SFB production.

The preparation of artificial pit mud (APM) provides a potential solution to the problem of growing demand for PM. Therefore, the rapid preparation of efficient APM is a research topic that has attracted considerable attention in recent years. The industrial procedure for the production of APM usually involves mixing high quality PM with exogenous microorganisms and nutrients, such as Daqu (DQ), Huangshui (HS) and Zaopei (ZP). DQ, HS, ZP and PM are key components of the SFB solid fermentation system, in which DQ provides crude enzymes as starters, ZP serves as the main body of the microbial fermentation and the liquor production substrate, HS is the main by-product, and PM provides microorganisms for metabolising typical flavour substances required for SFB production. DQ, PM and HS drive a complex process of microbial succession in the ZP fermentation system. Mu et al. (2022) prepared APM containing fortified DQ, showing that the addition of fortified DQ increased the abundance of archaea and Clostridium_sensu_stricto_12 in APM (Mu et al., 2022). Gao et al. (2020) showed that during the SFB fermentation process, the number of microbial populations shared between HS and PM gradually increased with increasing fermentation time (Gao et al., 2020). Other studies have shown that there is a synergy between microorganisms in ZP and PM during the synthesis and metabolism of caproic acid (Qian et al., 2021). With the use of solid medium conditions, microbial communication, metabolism and subsequent succession between DQ, PM and ZP are characteristically uneven, leading to slow mass transfer. These problems can be effectively solved by the use of liquid enrichment cultures, which can also effectively isolate difficult-to-cultivate microorganisms, especially functional microflora (Li et al., 2023). Previous studies have shown that the addition of fortified DQ can lead to an increase in the abundance of Methanogens, Caproiciproducens and Clostridium_sensu_stricto_12 in the enriched PM solution (Mao et al., 2023), which is similar to the conclusions reported in Mu et al. using the solid-state model. However, there is a lack of systematic research focusing on the effects of DQ, HS and ZP addition on metabolites and microbial flora in the enriched liquid prepared from PM. For example, in a previous study on the effects of fortified DQ on enriched PM liquid, only one round of fortified DQ enrichment was performed using PM. This is insufficient to illustrate the trend of succession in the PM microbial community structure under the influence of fortified DQ.

Therefore, in this study, DQ, HS and ZP were combined with PM and used during multiple rounds of enrichment. Their effects on the fermentation products and microbial communities of the PM enrichment solution were studied and compared to the effects of a single PM control subjected to the same enrichment conditions. In addition, the differences in microbial community interactions and metabolic function of different treatments were analysed to better understand the effects of DQ, HS and ZP addition on the microbiology of PM.

Materials and methods

Sample preparation

Samples including 25-year-old PM, DQ, HS and un-steamed ZP at the end of the fermentation period were collected from a winery in Mianyang City, Sichuan Province, China. After being transported to the laboratory using ice pack insulation, the samples were stored at 4 °C. Then, 10 g of PM, DQ, HS and ZP were transferred to a 100 mL glass bottle, mixed with 100 mL of sterile water and then subjected to ultrasonic extraction for 30 min (The conditions are: Temperature: 20℃, ultrasonic intensity: 0.12W/cm2, working frequency: 40 kHz). The filtered extracts were combined at different proportions, as described in Table 1, to form four separate treatments (J, JD, JH and JZ). The first round of enrichment was achieved by inoculating 10 mL of the extracts from each of the four treatments into 90 mL of deoxygenated RCM medium and incubated at 37 °C for 10 d. Subsequent rounds of enrichment were performed using 10 mL of the enrichment solution formed from the previous round of fermentation to inoculate into 90 mL of fresh deoxygenated RCM medium. Each sample was prepared in triplicate.

Table 1.

Sample processing and numbering

Process One Two Three Four Five Six
10 mL PM extracts (J) J1 J2 J3 J4 J5 J6
5 mL PM extracts + 5 mL DQ extracts (JD) JD1 JD2 JD3 JD4 JD5 JD6
5 mL PM extracts + 5 mL HS extracts (JH) JH1 JH2 JH3 JH4 JH5 JH6
5 mL PM extracts + 5 mL ZP extracts (JZ) JZ1 JZ2 JZ3 JZ4 JZ5 JZ6
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The RCM medium (per litre) consisted of: 5 g of yeast powder; 5 g of glucose; 10 g of peptone; 5 g of sodium chloride; 0.5 g of L-cysteine hydrochloride; 10 g of beef paste; 1 g of and soluble starch. The solution was diluted to 1 L using anaerobically distilled water, and its pH was adjusted to 6.5. The RCM medium was deoxygenated by nitrogen purging and then autoclaved at 121 °C for 20 min.

Determination of organic acid content

Volatile organic acid content test

Samples (2 mL) were collected at the end of fermentation under aseptic conditions and centrifuged using a high-speed centrifuge at 13,000 rpm for 5 min. The supernatant was collected and then filtered through a 0.22 μm organic phase microporous filter membrane. The filtered sample (990 μL) was subsequently transferred using a pipette into an injection bottle. After 10 μL of pre-configured internal standard n-pentyl acetate (21.9 mg/mL) was added, the solution was vigorously mixed. GC–MS was used to determine the content of volatile substances in the solution.

GC was performed using a DB-WAX capillary column (60 m × 250 μm × 0.25 μm), with an inlet temperature of 230 °C and a flame ionization detector (FID) temperature of 220 °C. Helium (He) (purity ≥ 99.9995%) was used as the carrier gas at a flow rate of 30 mL/min. The column flow rate was 1 mL/min, the H2 flow rate was 40 mL/min, the air flow rate was 400 mL/min and the tail blow flow rate was 30 mL/min. The inlet mode was set as non-split flow. The heating procedure was as follows: an initial temperature of 35 °C was maintained for 5 min, increased to 100 °C at 5 °C/min and held for 2 min, and then increased further to 230 °C at 15 °C/min and maintained for 5 min.

Mass spectrometry was performed using electron ionization (EI), at an electron energy of 70 eV, using a full scanning acquisition mode at a mass range of 20–550 amu. The ion source temperature was 230 °C, the quadrupole temperature was 150 °C and the interface temperature was 230 °C.

The mass spectra were compared with the National Institute of Standards and Technology (NIST) standard spectral library provided by Agilent Technology Co., Ltd. (Santa Clara, California, United States) and semi-quantitatively analysed using the internal standard method.

Lactic acid content test

The lactic acid content was determined by an external standard quantification method using HPLC. Samples were processed using the same methods used for preparing volatile organic acid samples. A representative standard curve used for lactic acid quantification can be found in the Supporting Information (SI), Fig S1, (y = 297291x + 8467.7, r2 = 0.99997).

High performance liquid chromatography conditions were as follows: column, C18 column (250 mm × 4.6 mm, with 5 μm internal diameter); and column temperature, 30 °C. The mobile phase consisted of 2% methanol-0.12% aqueous phosphoric acid, and its flow rate was 0.8 mL/min. Detection was performed at a wavelength of 214 nm, and the injection volume was 10 μL.

DNA extraction

Bacterial genomic DNA was extracted from samples using a Bacterial Genomic DNA Rapid Extraction Kit (Aidlab Biotechnologies Co., Ltd, China). The quality of the samples was evaluated by agarose gel electrophoresis and spectrophotometry (Nanodrop, Thermo Fisher Scientific, USA), and then used for the subsequent construction of 16S rRNA gene amplicon libraries.

Construction and sequencing of 16S rRNA gene amplicon library

Library construction and amplicon sequencing were conducted by Shanghai Majorbio Biotechnology Co. Ltd. (Shanghai, China). Bacteria and archaea were amplified using the universal primers ArBa515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), which are specific to the V4 region of 16S rRNA genes (Joshi et al., 2021). Amplicon libraries were constructed and sequenced using the Illumina MiSeq sequencing platform. The resulting sequences were filtered using fastp (https://github.com/OpenGene/fastp, v. 0.19.6) and merged using FLASH (http://www.cbcb.umd.edu/software/flash, v. 1.2.11). After quality control splicing, the optimised sequences were subjected to noise reduction using the DADA2 plugin available in Qiime2. Among the sequences of amplicon sequence variants (ASVs) obtained after DADA2 noise reduction, chloroplast and mitochondrial sequences were removed. Species annotation of ASVs was performed based on the Sliva 16S rRNA gene database (v. 138) using the Naive bayes classifier in Qiime2. The data were deposited in the National Microbiology Data Centre (NMDC) with accession number NMDC40056978 (https://nmdc.cn/resource/genomics/sra/detail/NMDC40056978).

Statistical analysis

Organic acid data were plotted and statistically analysed using Origin software (2021). Microbiological data analysis was performed using the Meji BioCloud platform (https://cloud.majorbio.com) as follows: Alpha diversity index was calculated using Mothur (Wang et al., 2007) software (http://www.mothur.org/wiki/Calculators); Principal Coordinate Analysis (PCoA) based on the bray_curtis distance algorithm was used to determine the similarity in microbial community structures among samples; Correlations between organic acids and microorganisms were analyzed using Spearman’s correlation analysis. Visual network analysis of the correlations between microorganisms was performed using Cytoscape software (v. 3.6.1). Finally, functional information for microbial communities was predicted using PICRUSt2 (Mu et al., 2022).

Results and discussion

Analysis of organic acids

To study the effects of DQ, HS, and ZP on the organic acid content of PM liquid during the continuous enrichment process, liquid samples were collected during six rounds of enrichment (N = 24 in total) and analysed by GC–MS and HPLC (Fig. 1).

Fig. 1.

Fig. 1

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Trend plots of different treatments of caproic acid (A), butyric acid (B), lactic acid (C), acetic acid (D), and caproic acid/lactic acid (E) with increasing rounds

Overall, the yields of four main acids in all enriched liquid samples could be ranked as follows: lactic acid > butyric acid > acetic acid > caproic acid. The concentrations of acetic acid, butyric acid, caproic acid and lactic acid ranged from 1.26–3.34 g/L, 2.07–4.39 g/L, 0.07–1.45 g/L and 3.35–7.15 g/L, respectively. The caproic acid content in the four treatment groups (J, JD, JH and JZ) initially increased and then decreased with increasing enrichment rounds, reaching the maximum value of 0.79 g/L, 0.73 g/L, 1.13 g/L and 1.45 g/L, respectively, in the third round of enrichment. Throughout the enrichment process, except for the JD3 sample, JD, JH and JZ groups had higher caproic acid yield compared to group J. Unlike caproic acid, the butyric acid content increased initially and then gradually became stabilised. The butyric acid content of JD, JH and JZ groups was lower than that of group J (except for JZ1 and JZ3). A decreasing trend in lactate content was observed, with groups JD, JH and JZ having a lower lactate content compared to group J (except JZ5) after the second round of enrichment. The acetic acid content fluctuated between 2.07 and 4.39 g/L with ongoing rounds of enrichment, with group JD consistently having a lower acetic acid content compared to group J.

During the natural maturation process of PM from 10 to 30 years, the content of organic acids regularly changes. Among all organic acids, the content of acetic acid and butyric acid fluctuates within 3.0–4.0 g/kg and 2.0–3.0 g/kg, respectively, and the content of caproic acid increases from 0.5–0.7 to 1–3 g/kg, and the content of lactic acid decreases from 10–20 to 5–10 g/kg (Shoubao et al., 2023a; Wu et al., 2022). During the maturation of APM, the caproic acid content shows an increasing trend from 1 to 3 g/kg, whereas the lactic acid content shows a decreasing trend from 7 to 3 g/kg (Chen et al., 2021). Similar to the above studies, in this study, the content of caproic acid in JD, JH, and JZ increased and the content of lactic acid decreased compared to J in the same round. This indicates that the addition of DQ, HS, and ZP can improve the quality of PM-enriched solutions. Previous studies have shown that the ratio of ethyl caproic acid and ethyl lactate can vary in response to the quality of SFB, with the optimal ratio of ethyl caproic acid and ethyl lactate ranging from 1.25 to 2 (Li et al., 2019). Moreover, ethyl caproic acid and ethyl lactate are formed by the esterification reaction between caproic acid and lactic acid metabolised by microorganisms in the cellar and ethanol metabolised by other microorganisms; thus, the ratio between ethyl caproic acid and ethyl lactate is directly related to the ratio between caproic acid and lactic acid in the PM (CLR) (Gao et al., 2021; Qian et al., 2021). Throughout the enrichment process presented in this study, the CLR of all treatments increased and then decreased with increasing fermentation rounds. The maximum CLR was mainly observed during rounds 2–4 (Fig. 1E). JD, JH, and JZ had higher CLR compared to J. The highest CLR (maximum CLR = 0.23) was observed in JZ, and the CLR was stable during rounds 2–4. This was followed by JH, which had a maximum CLR of 0.22, reached in earlier rounds compared to JD (maximum CLR = 0.21). Studies have shown that CLR in the enriched solution prepared with cellar mud aged 50 and 100 years was 0.15 and 0.41 (Mao et al., 2023), respectively. In this study, JD, JH, and JZ each had a maximum CLR higher than 0.15. However, it has also been shown that the CLR of PM rises from 0.03 to 0.77 during this natural maturation process (Tao et al., 2014). The CLR of APM also increased from 0.14 to 0.82 during maturation (Chen et al., 2021). In comparison, the solid PM had a higher CLR than the PM-enriched liquid, and this may be due to the difference between the solid model and the liquid model.

Taken together, using CLR as the criterion, the addition of DQ, HS and ZP was found to improve the quality of PM-enriched liquid, and the quality enhancement could be ranked as follows: JZ > JH > JD.

Bacterial community diversity

Twenty-four enriched samples were sequenced. After removing low-quality sequences, a total of 1,627,627 sequences were obtained with an average length of 252.88 bases. Each sample contained 57,493–78,253 sequences. The dilution curves were close to the saturation point (SI, Fig. S2), indicating that the sequencing depth of the amplicon libraries was able to adequately reflect the composition of bacterial communities in the samples.

The Shannon diversity index (Fig. 2A) showed that there were no significant differences in diversity between groups for each treatment. Each treatment group had the lowest level of biodiversity in the first round of enrichment, and large fluctuations in biodiversity index values were not observed between rounds 2–6.

Fig. 2.

Fig. 2

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A Changes in Shannon index of microbial community with increasing enrichment rounds in different treatments; B Principal Coordinate Analysis (PCoA) of microbial community among different treatments

PCoA analysis (Fig. 2B) showed that 24 samples could be divided into three regions, with one cluster containing samples from all six enrichment rounds of group J, one cluster containing all six enrichment rounds of group JD and one cluster containing all six enrichment rounds of both groups JH and JZ. The addition of exogenous microorganisms could significantly impede the succession of microbial enrichment communities in PM. Comparing all samples, the effects of HS and ZP on the enriched PM liquid were similar, which may be due to the fact that both HS and ZP were sampled at the end of the PM cellar fermentation process. The direct contact between the middle and lower ZP and HS layers in the late stage of fermentation may also allow the migration of microorganisms. In addition, the distance between enrichment round 1 samples from the same treatments (J1, JD1, JZ1) and the samples from other rounds were further away from one another, indicating that the microbial flora composition of these three treatments in round 1 was more drastically different compared to that of samples in the following rounds of enrichment (this finding was further confirmed by the results in Fig. 3B). This may be due to the fact that microorganisms in these three treatment groups were still in the domestication stage in round 1, while the microbial community structure was more stabilised in the remaining rounds (Li et al., 2023). In contrast, the differences between JH treatment samples from round 1 and rounds 2–6 were not significant. In conclusion, the addition of exogenous microorganisms (from DQ, HS and ZP) had a greater impact on the microbial community in the enriched PM liquid compared to the number of enrichment rounds.

Fig. 3.

Fig. 3

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A Bacterial community composition at genus level for each sample; B Abundance of marker microorganisms at the genus level for each sample

Microbial community composition

At the phylum level (SI, Fig. S3), Firmicutes was the dominant phylum in all samples with a relative abundance of 97.33%-99.99%, except for sample J1 (which contained 36.21% Firmicutes and 63.78% Proteobacteria).

At the genus level (Fig. 3A), the microbial community in group J consisted mainly of Caproiciproducens, unclassified_f__Oscillospiraceae and Clostridium_sensu_stricto_1. The microbial community in group JD was mainly composed of Caproiciproducens, Clostridium_sensu_stricto_12, Acidiluribacter and Enterococcus. The microbial communities in groups JH and JZ consisted mainly of Caproiciproducens, Clostridium_sensu_stricto_12, Clostridium_sensu_stricto_1 and Acidiluribacter. The abundance of Caproiciproducens, Clostridium_sensu_stricto_12 and Acidiluribacter in groups JD, JH and JZ were higher compared to those in group J, and the highest abundance was observed in samples JH1 (65.78%), JD3 (48.47%) and JH6 (27.74%). In addition, the abundance of Clostridium_sensu_stricto_1 in groups JH and JZ increased compared to group J, while the abundance of unclassified_f__Oscillospiraceae decreased to less than 0.5%.

Previous studies have shown that Caproiciproducens and Clostridium_sensu_stricto_12 are the main CAPB, and the increasing abundance of Caproiciproducens and Clostridium_sensu_stricto_12 improves the quality of SFB (Flaiz et al., 2020; Liu et al., 2017). In this study, the treatments that most effectively improved the abundance of Caproiciproducens and Clostridium_sensu_stricto_12 were JD and JH, respectively. However, it remained unclear which treatment most effectively improved the quality of the PM enriched liquid.

The relative abundance of Clostridium was found to increase from 8.53 ± 6.07% to 26.41 ± 0.84% (Liu et al., 2020) during the formation and maturation of APM. The results of differential analysis of microbial community composition in PM with different cellar ages (5, 20 and 50 years) showed that the abundance of Caproiciproducens gradually increased with increasing cellar age, accounting for 1.26%, 7.56% and 26.75% (He et al., 2024) of the microbial community in PM aged 5, 20 and 50 years, respectively. Wu et al. (2022) also reported that the highest abundance of Caproiciproducens and Clostridium was observed in mature PM (Wu et al., 2022). Therefore, in the present study, the total abundance of Caproiciproducens and Clostridium (TACAC) was used as a criterion for assessing the CAPB flora system in enriched PM liquid, as shown in Fig. 3B.

According to TACAC, groups J, JD, JH and JZ were divided into two categories, and groups J and JD were distinctly different from groups JH and JZ. No significant differences were observed in TACAC (around 60%) in group JD compared to group J. Additionally, group JD had a higher relative abundance of Caproiciproducens and Clostridium_sensu_stricto_12; however, its TACAC appeared less stable as the number of enrichment rounds increased. The TACAC in groups JH and JZ were significantly improved compared to group J. It reduced to below 70% only in rounds 5 and 6 of JH enrichment and round 1 of JZ enrichment, while remaining above 70% in all other rounds. The TACAC in group JH gradually decreased with increasing enrichment rounds, while that in group JZ tended to increase in accordance with the number of enrichment rounds, until eventually becoming stable. This suggests that the TACAC in group JZ is more stable despite the increasing number of enrichment rounds.

In summary, when the TACAC was used as a quality assessment standard for PM-enriched liquid, the quality of cellar-enriched liquid could be improved by the addition of DQ, HS and ZP. The addition of ZP had the greatest effect, followed by HS; and DQ had the weakest effect. These results are consistent with those obtained when caproic acid content and CLR were used as assessment criteria.

Correlation between microbial communities and organic acids

Potential relationships between all samples and the four major organic acids (acetic, lactic, butyric and caproic acid) were analysed using Canonical Correlation Analysis (CCA) (Fig. 4A). The results showed that acetic, butyric and lactic acids were mainly positively correlated with group J, while caproic acid was mainly positively correlated with groups JD, JH and JZ (with the exception of JD1 and JZ1). This suggests that the addition of DQ, HS and ZP contributes to caproic acid production during PM enrichment.

Fig. 4.

Fig. 4

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Relationship between microbial communities and organic acids. A CCA analysis of samples with organic acids; B Spearman analysis of microbes with organic acids

Spearman's correlation heat map (Fig. 4B) showed that acetic acid was significantly positively correlated mainly with the genera Oscillibacter and Blautia. Butyric acid was significantly positively correlated mainly with Acidiluribacter, Clostridium_sensu_stricto_18, Oscillibacter, Blautia and unclassified_f__Ruminococcaceae. Caproic acid was significantly positively correlated with Caproiciproducens, Clostridium_sensu_stricto_12, Clostridium and Ethanoligenens. Lactic acid was significantly positively correlated mainly with Ethanoligenens, Clostridium_sensu_stricto_18, Oscillibacter and Blautia. Previous studies have shown that Oscillibacter (Gophna et al., 2017) and Blautia (Liu et al., 2021) mainly metabolise acetic and butyric acids, while Caproiciproducens (Flaiz et al., 2020) and Clostridium_sensu_stricto_12 (Liu et al., 2017) are the main caproic acid-metabolizing bacteria. Ethanoligenens (Li et al., 2021) are representative ethanol-based fermenting bacteria capable of utilising organic matter to produce organic acids, ethanol and H2. In addition, Acidiluribacter (Fan et al., 2023), a novel genera recently isolated from PM, belongs to the family Tissierellaceae, although its ability to metabolise butyric acid remains unknown. Studies have shown that the optimum pH for microorganisms in isolated Caproiciproducens is 6.5–7.0 (Gu et al., 2021) and that for microorganisms in Clostridium_sensu_stricto_12 is 5.8–6.8 (Shoubao et al., 2023b). During liquid enrichment, Oscillibacter, Blautia, and Lactobacillus produce acetic acid and lactic acid, causing a rapid drop in the pH of the liquid medium, thereby inhibiting caproic acid production by Caproiciproducens and Clostridium_sensu_stricto_12. By contrast, in solid PM, acids produced by other microorganisms need to pass through the solid medium before reaching Caproiciproducens and Clostridium_sensu_stricto_12. A considerable loss of acid during this delivery process attenuates the inhibition of Caproiciproducens and Clostridium_sensu_stricto_12. This also explains why the solid PM (discussed above) has a higher CLR than the PM-enriched liquid.

Microbial interaction

Ethyl caproate has been considered a key flavour compound in SFB. Caproic acid not only is the precursor of ethyl caproate, but also has its own unique flavour and aroma attributes (Wei et al., 2020). Our results showed that the caproic acid content increased after the addition of DQ, HS and ZP to PM. To date, Caproiciproducens (Flaiz et al., 2020), Clostridium, Caproicibacterium (Wang et al., 2022a), Enterococcu (Luo et al., 2022) and Rummeliibacillus (Liu et al., 2022) are considered the main CAPB. Caproiciproducens and Clostridium_sensu_stricto_12 were found to be the two highest abundance CAPB in the present study, and the abundance of both can be used as indicators of PM maturity (Chai et al., 2021). Caproiciproducens is a recently discovered carbon chain-extending bacterium that uses lactic acid as an electron donor (Contreras-Dávila et al., 2020). Clostridium_sensu_stricto_12 can utilise ethanol (as an electron donor for carbon chain extension reactions) and lactic acid, while also promoting the formation of various volatile compounds (Liu et al., 2017). The abundances of Caproiciproducens and Clostridium_sensu_stricto_12 were found to enhance following the addition of DQ, HS and ZP, as shown in Fig. 3C. To understand the reasons for the elevated abundance of Caproiciproducens and Clostridium_sensu_stricto_12, the symbiotic relationships between bacteria were explored during the enrichment process based on their significant correlation (r > 0.6, P < 0.05). A total of 41 nodes and 102 edges were observed, as shown in Fig. 5. Caproiciproducens was mainly positively correlated with Oxobacter, unclassified_f__Ruminococcaceae, Pseudomonas, Clostridium_sensu_stricto_1, Clostridium_sensu_stricto_11 and Clostridium_sensu_stricto_18, whereas Clostridium_sensu_stricto_12 was mainly positively correlated with Lactobacillus, Weissella, Lactococcus, and Ethanoligenens. Weissella (Deng et al., 2021) was found to be the dominant microorganism in high-temperature DQ that can metabolise organic acids such as acetic acid, lactic acid and caproic acid (Wang et al., 2022b). The dominant microorganism in HS was Oxobacter, which is a type of organism that can metabolise acetic acid and degrade aromatic compounds to produce butyric acid (Bengelsdorf et al., 2015). Clostridium (Clostridium_sensu_stricto_1, Clostridium_sensu_stricto_11, Clostridium_sensu_stricto_18) and Lactobacillus were the dominant genera in ZP and HS (Ma et al., 2022; Zhou et al., 2023). These Clostridium strains can metabolise butyric acid (Van den Abbeele et al., 2013), while Lactobacillus mainly produces lactic acid. Ethanoligenens are related to ethanol oxidation (Agler et al., 2012). These microorganisms were found to be positively associated with CAPB and have a common feature of producing precursors required for caproic acid metabolism, such as acetic acid, ethanol, lactic acid and butyric acid. Thus, the addition of DQ, HS and ZP altered the bacterial zonation network within PM and increased the abundance of Caproiciproducens and Clostridium_sensu_stricto_12. Both HS and ZP were able to provide Clostridium and Lactobacillus, which may explain the similarity in microbial community structure and high Clostridium abundance in JH and JZ. In addition, we found that although both Caproiciproducens and Clostridium_sensu_stricto_12 act as members of the core caproic acid-producing microorganisms in SFB, Clostridium_sensu_stricto_12 was positively correlated with Lactobacillus, whereas Caproiciproducens was negatively correlated with Lactobacillus. According to the literature, Caproiciproducens can metabolise medium and long-chain fatty acids (e.g. caproic acid) to inhibit the growth of Lactobacillus (Andersen et al., 2017). Additionally, while the abundance of Caproiciproducens gradually increases with increasing PM age, the abundance of Lactobacillus gradually decreases (Zhang et al., 2020b). This suggests the negative correlation between Caproiciproducens and Lactobacillus. Although Clostridium_sensu_stricto_12 can also metabolise caproic acid, it is more effective at metabolising short-chain fatty acids (e.g., acetic acid, propionic acid, etc.) to favor the growth of lactobacillus than Caproiciproducens (Yang et al., 2021). In addition, lactic acid produced by Lactobacillus can also be utilised by Clostridium_sensu_stricto_12 (Liu et al., 2017). Furthermore, Clostridium_sensu_stricto_12 has more tolerance to acidic environments compared to Caproiciproducens (Gu et al., 2021; Shoubao et al., 2023b), which may explain why it is positively correlated with Lactobacillus.

Fig. 5.

Fig. 5

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Relationships between microbial communities. Note Firmicutes are traced in red, and Protebacteria are traced in blue. yellow fills represent from JZ, green fills from J, purple fills from JD, and orange fills from JH. red lines represent positive correlations, and blue lines represent negative correlations. The thickness of the line represents the strength of the correlation

Metabolic pathway analysis

Caproic acid is metabolised mainly through the RBO pathway (Wang et al., 2021) (Fig. 6A). The factors responsible for the increase in caproic acid content in the PM enrichment solution after the addition of DQ, HS and ZP were investigated. The difference in caproic acid content between JD, JH, JZ and J was significant after 2, 4 and 5 rounds of enrichment, according to the analysis of variance and multiple comparisons (SI, Table S1). Combined with Fig. 1A, the second round of enrichment resulted in the greatest difference in caproic acid content among the three rounds. Therefore, round 2 was further analysed to understand the differences in caproic acid metabolism gene expression between treatments.

Fig. 6.

Fig. 6

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A Metabolic pathway of caproic acid synthesis; B Expression of abundance of key enzyme genes for caproic acid synthesis in the second round by different treatments

The change in the gene expression of key enzymes involved in caproic acid synthesis was predicted using 16S rRNA gene sequencing results and the PICRUSt2 tool (Fig. 6B). The results showed that 1–3–9 glucokinase (EC:2.7.1.2), phosphofructokinase-1 (EC:2.7.1.11) and pyruvate kinase (EC:2.7.1.40) were key enzymes in the catabolism of glucose into pyruvate, and the expression of genes encoding these three key enzymes was up-regulated in the JD, JH and JZ groups compared to group J. Among all, the expression of EC:2.7.1.2 was elevated by 2.18, 2.23 and 2.18 folds in samples JD2, JH2, and JZ2, respectively, suggesting that the ability of the colony to utilize glucose was improved. L-type lactate dehydrogenase (EC:1.1.1.27) and D-type lactate dehydrogenase (EC:1.1.1.28) can catalyse the reversible reaction between pyruvate and lactate. The total expression of lactate dehydrogenase (L-type + D-type) genes was elevated by 3.57, 2.17 and 3.29 folds after the addition of DQ, HS and ZP, respectively. It is worth noting that the expression of EC:1.1.1.27 in sample J2 was only 1.29-fold higher than that of EC:1.1.1.28, while this ratio was elevated to 39.85, 22.46 and 10.70 folds in samples JD2, JH2 and JZ2, respectively. These findings show that when lactic acid is accumulated to a certain amount, the ability of the microbial community to utilise it is elevated, becoming more biased towards utilising L-type of lactic acid in samples JD2, JH2 and JZ2. Ethanol dehydrogenase (EC:1.1.1.1) is a key enzyme in ethanol metabolism and the expression of EC:1.1.1.1 was elevated by 2.11, 1.89 and 2.08 folds after the addition of DQ, HS and ZP, respectively. Previous studies have shown that the gene encoding ethanol dehydrogenase (EC:1.1.1.1) is mainly concentrated in Clostridium, which is consistent with the increase in Clostridium abundance after the addition of DQ, HS and ZP observed in this study. The expression of acetaldehyde dehydrogenase gene (EC:1.2.1.10) was up-regulated by 1.88, 2.99 and 3.24 folds after the addition of DQ, HS and ZP, respectively. Among all, the ability to produce caproic acid from ethanol was strongest in samples with the addition of ZP. Previous studies (Mao et al., 2023) have shown that the co-enrichment of DQ and PM increased caproic acid production by inhibiting the expression of phosphate butyryltransferase gene (EC:2.3.1.19), reducing butyric acid production and thus allowing more carbon to be allocated to hexanoyl coenzyme A synthesis. In contrast to the reduction in gene expression of EC:2.3.1.19 following the addition of DQ in this study, the expression of EC:2.3.1.19 gene was increased following the addition of HS and ZP, which is the opposite of the reduction in butyric acid yield. However, the expression of gene encoding enzyme catalysing the conversion of butyryl coenzyme A to 3-oxohexanoyl coenzyme A (EC:6.4.1.2) was increased by 2.16 and 2.69 folds in samples containing HS and ZP, respectively. It may be presumed that the increased expression of EC:6.4.1.2 leads to an increase in the partitioning of the carbon chain lengthening towards caproic acid, thereby decreasing the partitioning of butyryl coenzyme A shift towards butyric acid. The expression of enzymes EC:1.1.1.27, EC:1.2.1.10, EC:6.4.1.2 and EC:2.8.3.9 were highest in the JZ group, and this is consistent with the JZ group, which had the highest caproic acid content during the second round of enrichment. In conclusion, the addition of DQ, HS and ZP not only up-regulated the expression of genes encoding key enzymes responsible for the utilisation of substrates such as glucose, lactic acid and ethanol, but also enhanced the expression of genes encoding key enzymes required for carbon chain extension to caproic acid, which increased the caproic acid yield.

Overall, the addition of DQ, HS and ZP could effectively alter the microbial community in the PM enrichment liquid, and the combined HS and ZP had a higher effect on PM microbiota compared to DQ. The addition of exogenous microorganisms (DQ, HS and ZP) and the use of many rounds of enrichment (2–4 rounds) helped to significantly improve the quality of the enriched PM. Furthermore, the addition of exogenous microorganisms had a stronger influence compared to the use of extended rounds of enrichment. The improvement in quality of the enriched PM due to exogenous microorganisms could be ranked in descending order as follows: ZP > HS > DQ. Moreover, the highest quality of enriched PM was observed in the 2nd and 3rd rounds of enrichment with the addition of ZP. The addition of DQ, HS and ZP improved the quality of the PM enrichment solution as they provided microorganisms that could metabolise caproic acid precursors (Oxobacter, Pseudomonas, Clostridium_sensu_stricto_1, Clostridium_sensu_stricto_18, Clostridium_sensu_stricto_11, Lactobacillus, Weissella, Ethanoligenens) and up-regulated the expression of genes encoding enzymes that extend the carbon chain to generate caproic acid (such as EC:2.7.1.2, EC:1.1.1.27, EC:1.1.1.1, EC:6.4.1.2). These results provide theoretical and practical guidance for accelerating the evolution of functional microbiota in PM and for supporting the development and application of functional bacteria through microbial enhancement technologies to improve the quality of SFB.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 6605 kb) (6.5MB, docx)

Acknowledgements

This work was supported by the Sichuan University of Science & Engineering Talent Introduction Project (2018RCL27), the 2022 Luzhou Laojiao Postgraduate Innovation Fund Project (LJCX2022-5), and the 2023 Luzhou Laojiao Postgraduate Innovation Fund Project (LJCX2023-2).

Author contributions

Conceptualization:[Guangbin Ye] [Enze Huang]; methodology:[Enze Huang] [Shangchao Xia]; software:[Enze Huang]; validation:[Enze Huang] [Minghong Bian]; formal analysis:[Enze Huang]; investigation:[Enze Huang] [Shangchao Xia]; resources:[Enze Huang] [Minghong Bian] [Guangbin Ye]; data curatio [Enze Huang] [Shangchao Xia]; writing—original draft preparation[Enze Huang]; writing—review and editing: [Enze Huang] [Guangbin Ye] [Minghong Bian]; visualization[Enze Huang]; supervision: [Guangbin Ye] [Minghong Bian]; project administration: [Guangbin Ye] [Minghong Bian]; funding acquisition: [Guangbin Ye]; All authors have read and agreed to the publishedversion of the manuscript.

Funding

Funding was provided by Sichuan University of Science & Engineering Talent Introduction Project (2018RCL27), 2022 Luzhou Laojiao Postgraduate Innovation Fund Project (LJCX2022-5), 2023 Luzhou Laojiao Postgraduate Innovation Fund Project (LJCX2023-2).

Declarations

Competing interest

The authors declare that they no conflicts of interest.

Data availability

Datasets generated and analyzed in this study are available from the corresponding author upon reasonable request.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

MingHong Bian and Enze Huang have contributed to the work equally and should be regarded as co-first authors.

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Associated Data

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Supplementary Materials

Supplementary file1 (DOCX 6605 kb) (6.5MB, docx)

Data Availability Statement

Datasets generated and analyzed in this study are available from the corresponding author upon reasonable request.

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