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Analysis of microbial and metabolic diversity in Jiangshui from Northwest China

Abstract

Jiangshui is a traditional fermented food in Northwest China with high nutritional value and special pharmacological effects. To study the microbial community structure and differences in the main metabolic pathways and metabolites in naturally fermented Jiangshui, the microbial diversity and nontargeted metabolomics analyses were performed on 18 samples of Jiangshui from three different production areas, namely, Tianshui, Gansu, Guyuan, Ningxia, and Ankang, Shaanxi. First, the microbial diversity was analyzed using 16S rRNA high-throughput sequencing. Lactobacillus was the dominant bacterial genus in the three regions, and the relative abundance of the samples in each region ranged from 72.69% to 99.95%. The dominant fungal genus was Dipodascus, with a relative abundance > 40%. Second, untargeted metabolomics was used to analyze the differences among metabolites in Jiangshui samples. Significant differences were found in 761 different metabolites detected in Jiangshui from the three different regions. Based on ESI+, 78 significantly different metabolites were screened using variable importance projection (VIP) value > 1, fold change (FC) < 1, and P-value < 0.05. Finally, based on joint analysis, the microbiome and metabolite groups of Jiangshui samples from the three different regions were evenly and closely clustered, and the microorganisms and metabolites were highly correlated. The results provide theoretical basis and reference for Jiangshui in other regions.

Keywords:
Jiangshui; diversity microorganisms; metabolomics

1 Introduction

Fermented vegetables have become an indispensable part of the Chinese traditional fermented food diet. Due to differences in regions and customs, fermented vegetables vary considerably and attract consumers due to their uniqueness. Jiangshui is a distinctive traditional fermented vegetable widely distributed in Northwest China (Hou et al., 2013Hou, J. C., Jiang, C., & Long, Z. (2013). Nitrite level of pickled vegetables in Northeast China. Food Control, 29(1), 7-10. http://dx.doi.org/10.1016/j.foodcont.2012.05.067.
http://dx.doi.org/10.1016/j.foodcont.201...
; Lü et al., 2014Lü, X., Yi, L. H., Dang, J., Dang, Y., & Liu, B. F. (2014). Purification of novel bacteriocin produced by Lactobacillus coryniformis MXJ 32 for inhibiting bacterial foodborne pathogens including antibiotic-resistant microorganisms. Food Control, 46, 264-271. http://dx.doi.org/10.1016/j.foodcont.2014.05.028.
http://dx.doi.org/10.1016/j.foodcont.201...
). Jiangshui can improve taste, promote digestion, and prolong storage time. The lactic acid bacteria in Jiangshui can also degrade nitrite. The various raw ingredients are inexpensive, simple to process, and unique in flavor. Jiangshui tastes sour and mellow, is rich in nutrients, vitamin C2, microbial B2, organic acids (e.g., lactic acid, citric acid, malic acid), carotenoids, flavonoids, and sulfur amino acids, which are conducive to digestion. Jiangshui regulates qi, relieves thirst, boredom and lethargy, regulates viscera and urination, promotes digestion, and lowers cholesterol. Jiangshui can be directly used as a summer drink and as an ingredient for cooking and noodle soup (Cao et al., 2017Cao, J., Yang, J., Hou, Q., Xu, H., Zheng, Y., Zhang, H., & Zhang, L. (2017). Assessment of bacterial profiles in aged, home-made Sichuan paocai brine with varying titratable acidity by PacBio SMRT sequencing technology. Food Control, 78, 14-23. http://dx.doi.org/10.1016/j.foodcont.2017.02.006.
http://dx.doi.org/10.1016/j.foodcont.201...
; Li et al., 2022Li, P., Ju, N., Zhang, S., Wang, Y., & Luo, Y. (2022). Evaluation of microbial diversity of Jiangshui from the Ningxia Hui autonomous region in China. Food Biotechnology, 36(2), 173-190. http://dx.doi.org/10.1080/08905436.2022.2054818.
http://dx.doi.org/10.1080/08905436.2022....
). However, the microbial community structure of Jiangshui is complex (Hou et al., 2013Hou, J. C., Jiang, C., & Long, Z. (2013). Nitrite level of pickled vegetables in Northeast China. Food Control, 29(1), 7-10. http://dx.doi.org/10.1016/j.foodcont.2012.05.067.
http://dx.doi.org/10.1016/j.foodcont.201...
). Zhang et al. (2018)Zhang, Q., Wang, C., Li, B., Li, L., Lin, D., Chen, H., Liu, Y., Li, S., Qin, W., Liu, J., Liu, W., & Yang, W. (2018). Research progress in tofu processing: from raw materials to processing conditions. Critical Reviews in Food Science and Nutrition, 58(9), 1448-1467. http://dx.doi.org/10.1080/10408398.2016.1263823. PMid:27977295.
http://dx.doi.org/10.1080/10408398.2016....
found that bacterial diversity changed with different Jiangshui. Although microbial diversity has been reported in various types of fermented vegetables, such as kimchi in Korea, sauerkraut in Northeast China, and camuoi (fermented eggplant) in Vietnam (Nguyen et al., 2013Nguyen, D. T. L., Van Hoorde, K., Cnockaert, M., Brandt, E., Aerts, M., Thanh, L. B., & Vandamme, P. (2013). A description of the lactic acid bacteria microbiota associated with the production of traditional fermented vegetables in Vietnam. International Journal of Food Microbiology, 163(1), 19-27. http://dx.doi.org/10.1016/j.ijfoodmicro.2013.01.024. PMid:23500611.
http://dx.doi.org/10.1016/j.ijfoodmicro....
), research investigating microbial populations involved in the production of Jiangshui is limited. Due to the different raw ingredients, processes, production conditions, and geographical differences in the production of Jiangshui, the microbial community of Jiangshui may also change accordingly (Zhang et al., 2018Zhang, Q., Wang, C., Li, B., Li, L., Lin, D., Chen, H., Liu, Y., Li, S., Qin, W., Liu, J., Liu, W., & Yang, W. (2018). Research progress in tofu processing: from raw materials to processing conditions. Critical Reviews in Food Science and Nutrition, 58(9), 1448-1467. http://dx.doi.org/10.1080/10408398.2016.1263823. PMid:27977295.
http://dx.doi.org/10.1080/10408398.2016....
).

In the present study, the Jiangshui from Guyuan, Ningxia, Tianshui, Gansu, and Ankang, Shaanxi was used to analyze its microbial diversity using 16S rRNA high-throughput sequencing technology, explore differences in microbial classification, composition and abundance, and use untargeted metabolomics technology to determine the relationship between the related metabolic differential substances and metabolic pathways in the microorganisms. This study complements previous research and provides a reference for other regions and future research on the composition and content of Jiangshui metabolites.

2 Materials and methods

The raw materials used in this experiment included six samples of fresh commercial Jiangshui (designated NX, SX, and GS, respectively) from Guyuan, Ningxia, Ankang, Shaanxi, and Tianshui, Gansu. The clear, transparent, and foam-free Jiangshui was placed into a sterile centrifuge tube and stored at low temperature before transporting to the laboratory. CTAB genomic DNA extraction kit, HPLC grade methanol, and acetonitrile were purchased from Thermo Fisher Scientific, formic acid from CNW, 2-propanol from Merck, 2-chloro-l-phenylalanine from Adamas-beta, AxypreDNA gel recovery kit from Axygen Co., Ltd., PCR amplification reagents from Bao Bioengineering (Dalian) Co., Ltd., and methanol from Tianjin Damao chemical reagent factory. In addition, PCR instrument (ABI GeneAmp®9700), QuantiFluor™ St blue fluorescence quantitative system (Promega), UHPLC system (Vanquish Horizon system), and Q-Exactive HF-X mass spectrometer were used for analyses.

2.1 16S rRNA high-throughput sequencing

DNA extraction of environmental samples and PCR amplification

The genomic DNA was extracted and detected using 1% agarose gel electrophoresis. The extracted Jiangshui DNA was diluted to 1 ng/μL as a template for PCR amplification. Bacteria amplified the V3-V4 region of 16S rDNA as the target DNA sequence, and fungi amplified the ITS1 region of the endogenic 18S rDNA-5.8S rDNA transcription interval as the target DNA sequence. Each sample had three replicates. After mixing the PCR products from the same sample, 2% agarose gel electrophoresis was performed to detect the purity and concentration of the PCR products. The AxyPrepDNA gel recovery kit was used to cut the gel to recover the PCR products.

Quantification and homogenization of PCR products

Based on the preliminary quantitative electrophoresis results, QuantiFluor™ St blue fluorescence quantitative system (Promega) was used for the detection and quantification of PCR products. Based on the sequencing volume requirements of each sample, the corresponding proportion was mixed and sent to Shanghai Majorbio Bio-pharm Technology Co., Ltd.

Miseq sequencing

Sequencing analysis was performed using the Miseq high-throughput sequencing platform. The original sequence data obtained were uploaded to the sequence reading Archive (SRA) of NCBI (accession number PRJNA844900).

2.2 Ultra-high Performance Liquid Chromatography (UHPLC) tandem Fourier-transform Mass Spectrometry (MS)

Sample handling

A total of 200 μL sample was placed into a 1.5 mL centrifuge tube and 800 μL extraction solution (methanol:acetonitrile = 1:1 (V:V)) containing 0.02 mg/mL internal standard (l-2-chlorophenylalanine) was added. The sample was vortexed for 30 s followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). The sample was left at -20 °C for 30 min then centrifuged at 13,000 g, 4 °C for 15 min. The supernatant was removed, dried with nitrogen, and 100 μL complex solution (acetonitrile:water = 1:1) was added for redissolution. The sample was vortexed for 30 s followed by low-temperature ultrasonic extraction for 5 min (5 °C, 40 kHz). Next, the sample was centrifuged for 10 min under the same centrifugation conditions and the supernatant transferred to the injection vial with internal cannula for analysis. In addition, 20 mL supernatant from each sample was mixed and used as the quality control (QC) sample. The QC sample was prepared by mixing the extracts of all samples in the same volume. The volume of each QC sample was the same as the sample volume. A QC sample was inserted every 5-15 analyses to investigate the stability of the entire detection process.

LC conditions

ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm; Waters Corporation, Milford, USA) and UHPLC system combined with Q precision mass spectrometer were used for analysis. The mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), mobile phase B was 5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid), and the injection volume was 2 µL. The column temperature was 40 °C (Wang et al., 2022Wang, H., Wu, Y., Xiang, H., Sun-Waterhouse, D., Zhao, Y., Chen, S., Li, L., & Wang, Y. (2022). UHPLC-Q-Exactive Orbitrap MS/MS-based untargeted lipidomics reveals molecular mechanisms and metabolic pathways of lipid changes during golden pomfret (Trachinotus ovatus) fermentation. Food Chemistry, 396, 133676. http://dx.doi.org/10.1016/j.foodchem.2022.133676. PMid:35868287.
http://dx.doi.org/10.1016/j.foodchem.202...
; Wang et al., 2019Wang, Y., Li, C., Li, L., Yang, X., Chen, S., Wu, Y., Zhao, Y., Wang, J., Wei, Y., & Yang, D. (2019). Application of UHPLC-Q/TOF-MS-based metabolomics in the evaluation of metabolites and taste quality of Chinese fish sauce (Yu-lu) during fermentation. Food Chemistry, 296, 132-141. http://dx.doi.org/10.1016/j.foodchem.2019.05.043. PMid:31202297.
http://dx.doi.org/10.1016/j.foodchem.201...
). The following elution gradient was used: 0-3.5 min, 0% B; 3.5-5 min, 24.5-65% B; 5-7.4 min, 65-100% B; 5-7.4 min, 65-100% B; 7.4-7.6 min, 100-51.5% B; 7.6-10 min, 0% B. The primary and secondary mass spectrometry data were collected using the UHPLC-Q-Exactive HF-X mass spectrometer of Thermo Fisher Scientific.

MS conditions

The samples were ionized by spray and the MS signals collected in the positive and negative ion scanning modes. The conditions of the ESI+ source were as follows: sheath gas flow, 50 Arb; auxiliary gas flow, 13 Arb; capillary temperature, 325 °C; full scan resolution, 60,000; MS2 resolution, 7,500; collision energy, 20/40/60 eV; ionization voltage, 3.5 kV (positive) and -3.5 kV (negative).

2.3 Bioinformatics analysis

16S rRNA high-throughput sequencing

Paired-end (PE) reads obtained from Miseq sequencing were first spliced based on the overlap relationship, and simultaneously, the sequence quality was controlled and filtered. After the samples were identified, OTU clustering and taxonomic analysis were performed. Then, diversity index analysis and sequencing depth detection were performed based on OTU and taxonomic information. Subsequently, statistical analysis of community structure was performed at various taxonomic levels and the community composition of multiple samples analyzed.

Untargeted metabolism

The raw data were imported into the metabolomics processing software ProgenesisQI (Waters Corporation) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. A data matrix containing information such as retention time, mass-to-charge ratio, and peak intensity was obtained. Then the software was used to identify the characteristic peak search library, match the MS and MS/MS information with the metabolic database, set the MS mass error to < 10 ppm, and identify the metabolites based on the secondary MS matching score. The main databases used were the mainstream public databases and self-built databases (Li et al., 2021Li, S., Deng, B., Tian, S., Guo, M., Liu, H., & Zhao, X. (2021). Metabolic and transcriptomic analyses reveal different metabolite biosynthesis profiles between leaf buds and mature leaves in Ziziphus jujuba mill. Food Chemistry, 347, 129005. http://dx.doi.org/10.1016/j.foodchem.2021.129005. PMid:33482487.
http://dx.doi.org/10.1016/j.foodchem.202...
). Subsequently, the metabolites were subjected to multivariate statistical analyses, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), cluster analysis, and heat map analysis. Variable importance projection (VIP), fold change (FC), and P-values were used to screen the substances with significant differences between samples from different regions, and KEGG metabolic pathway enrichment analysis was performed on the metabolites with significant differences.

3 Results

3.1 16S rRNA high-throughput sequencing

Sample OTU analysis results

Based on high-throughput sequencing analysis of Jiangshui samples collected from the three different regions, the effective sequences of bacterial communities in Jiangshui samples from Tianshui, Gansu, Guyuan, Ningxia, and Ankang, Shaanxi were 75,908, 62,704, and 83,439, respectively. OTU cluster analysis was performed on the obtained sequences and a total of 55 OTUs were obtained. As shown in Figure 1A, all OTUs were divided into 3 phyla, 4 classes, 5 orders, 5 families, and 6 genera at different classification levels. The effective sequences of fungal communities in Tianshui, Gansu, Guyuan, Ningxia, and Ankang, Shaanxi were 112,081, 76,443, and 85,099, respectively. As shown in Figure 1B, 347 OTUs were obtained by clustering all sequences, which were divided into 4 phyla, 14 classes, 32 orders, 68 families, and 100 genera at different classification levels.

Figure 1
Bacterial 16S rRNA (A) in Jiangshui from three different regions at OTU (operational taxonomic units) level Venn diagram of fungal ITS (B) and column diagram of the total number of species in each group.

Analysis results of microbial community structure

The proportion of each species in the bacterial community of Jiangshui samples from the three regions at the genus classification level is shown in Figure 2A. At the genus classification level, the bacterial community mainly belonged to Lactobacillus and Acetobacter. Lactobacillus was determined the core bacterial group of the bacterial community in the Jiangshui fermentation process and plays an important role in the formation of its sour taste (Chen et al., 2016aChen, P., Wu, Z., Zhao, Y., Wei, Y., Xu, R., Yan, L., & Li, H. (2016a). Cultivation-independent comprehensive investigations on bacterial communities in serofluid dish, a traditional Chinese fermented food. Genomics Data, 7, 127-128. http://dx.doi.org/10.1016/j.gdata.2015.12.019. PMid:26981386.
http://dx.doi.org/10.1016/j.gdata.2015.1...
). As shown in Figure 2B, the genus Dipodascus was the dominant population with the highest abundance in the Jiangshui samples from various regions; however, the abundance of different genera in each sample significantly varied. In general, the fungal communities in the Jiangshui samples from the three regions showed significant differences in the composition and abundance of dominant flora.

Figure 2
Column chart of bacterial community (A) and fungal community (B) structure in Jiangshui from three different areas. The ordinate is the sample name, the abscissa the proportion of the species in the sample, the columns of different colors represent different species, and the length of the columns represents the proportion of the species.

Rank curve and cluster curve analysis results

Rarefaction and Rank abundance clustering were used to analyze species abundance and evenness. The changes in bacterial communities in the Jiangshui samples from the different regions are shown in Figure 3A. The grade curve shows the bacterial species richness in most of the Jiangshui samples was from 10-40 at the OTU level. The Jiangshui samples from Tianshui, Gansu had the widest distribution range and the highest species diversity, and the dominant bacterial groups in the Jiangshui samples from Ankang, Shaanxi had a high proportion and low diversity. As shown in Figure 3B, the species richness of the fungal community was mostly from 20-100. Most of the Jiangshui samples from Ankang, Shaanxi, Guyuan Ningxia, and a small portion of the Jiangshui samples from Tianshui, Gansu showed an overall steady downward trend, indicating the species diversity was high. The curve for most of the Jiangshui samples from Tianshui, Gansu sharply decreased, indicating the dominant fungal flora was prominent and diversity was the lowest. As shown in Figure 3C, the dilution curve for the bacterial community in the Jiangshui sample tended to be flat after the serial number was 16,000. That for the fungal community tended to be stable after the sequence number was 22,000. With an increase in sequencing depth, the curve tended to stabilize, indicating that sequencing included most of the sample information. Therefore, the dilution curve showed the sequencing data of microorganisms in Jiangshui was reasonable.

Figure 3
Rarefaction curves and Rank-Abundance curves of microbial communities in Jiangshui from different regions. The abscissa represents the ranking level of the number of species under the OTU taxonomic level, the ordinate represents the relative percentage content of the number of species under the taxonomic level, and the abscissa position of the extension end point of the sample curve is the number of species of the sample. (A) Bacterial community Rarefaction curves; (B) Bacterial community Rank-Abundance curves; (C) Fungal community Rarefaction curves; (D) Fungal community Rarefaction curves.

Microbial community α diversity analysis results

As shown in Table 1 and 2, the ACE and Chao1 indices show the microbial abundance of the bacterial community in Tianshui, Gansu was the largest and the fungal community in Ankang, Shaanxi was the largest. The Shannon and Simpson index showed the diversity of bacterial communities in Guyuan, Ningxia and Ankang, Shaanxi was higher, and the diversity of fungal communities in Ankang, Shaanxi and Tianshui, Gansu was higher. The sequencing depth index of Jiangshui samples from the three different regions was > 99%, indicating most species could be detected, the sample sequencing met the requirements, and sequencing results represent the actual condition of microorganisms in the samples.

Table 1
Detailed information for the samples used in the study.
Table 2
Alpha diversity index of Jiangshui in different regions.

Microbial community β diversity analysis results

As shown in Figure 4A, the explanatory value of the difference in sample composition was > 50% and the Jiangshui samples in Ankang, Shaanxi, were closely clustered to each other, indicating the bacterial microbial community composition of the Jiangshui samples from Ankang, Shaanxi, was highly similar, and the Jiangshui samples from Tianshui, Gansu, and Guyuan, Ningxia, were similar, indicating the species composition between the two regions was relatively similar. In addition, the fungal microbial community composition of several samples from the three different regions was highly similar, and the fungal species composition of the samples was generally different as shown in Figure 4B. These results indicate bacterial microbial and fungal microbial communities were significantly different in Jiangshui from different areas.

Figure 4
PCoA (principal coordinates analysis) map of microbial community diversity in Jiangshui from different regions. The x-axis and y-axis represent the two selected principal axes and the percentage represents the explanatory value of the principal axis to the difference in sample composition; the scale of x-axis and y-axis is a relative distance which has has no practical significance. (A) Bacterial community; (B) Fungal community.

3.2 Untargeted metabolomics

PCA results

To determine the overall distribution trend of Jiangshui samples from different regions, PCA was used to explore the correlation between samples. Under ESI+ and ESI conditions, the sum of the principal component contribution values of the PCA maps of the three different regions was > 50%, indicating the fitting of this model was reliable. As shown in Figure 5 and Figure S1, the similarity was high between Jiangshui samples from Guyuan, Ningxia, and Tianshui, Gansu, but significantly different from Ankang, Shaanxi, samples. In addition, as shown in Figure 5, the separation trend among the three regions was minimal with no abnormal points, indicating species composition structure from the three different regions was highly similar.

Figure 5
PCA scores of Jiangshui samples from different regions using ESI+. After dimension reduction analysis, relative coordinate points on the principal components p1 and p2 are found. The confidence ellipse indicates the “real” samples of this group were distributed in this area with 95% confidence.

OPLS-DA and permutation test results

Based on ESI+ and ESI-, OPLS-DA analysis results of Jiangshui samples from the three different regions are shown in Figure 6 and Figure S2. The samples of Jiangshui groups from the three different regions were evenly distributed on both sides of the origin, with clear boundaries and obvious separation, and large differences were observed between groups. To verify the reliability of the OPLS-DA model, R2X = 0.448, R2Y = 1, Q2 = 0.737, and Q2 were all > 0.5 and close to 1, indicating the model had good stability and prediction ability. Similarly, R2X = 0.607, R2Y = 0.966, Q2 = 0.759, and R2X = 0.613, R2Y = 0.996, Q2 = 0.808, indicating that the data of the model were reliable and can be used for subsequent screening analysis of metabolic difference.

Figure 6
(A-C) are the OPLS-DA scores using ESI+, the OPLS-DA score map filters out the information irrelevant to the group through orthogonal rotation to better distinguish the differences between groups and improve the efficiency of the model. Comp1 is the first prediction principal component decomposition degree and orthogonal Comp1 is the first orthogonal component decomposition degree. (A-C) are the OPLS-DA model validation; the abscissa represents the displacement retention degree of the displacement test (the proportion consistent with the order of Y variables of the original model, and the point with the displacement retention degree of 1 is the R2 and Q2 values of the original model), the ordinate represents the values of R2 (red circle) and Q2 (blue triangle) displacement test, and the two dashed lines, respectively, represent the regression lines of R2 and Q2. A and a: GS vs. NX; B and b: GS vs. SX; C and c: NX vs. SX.

Metabolite Venn analysis and differential metabolite cluster analysis results

As shown in Figure 7, there were 167 metabolites in the metabolic sets from Tianshui, Gansu, and Guyuan, Ningxia, 587 metabolites from Ankang, Shaanxi, and Tianshui, Gansu, 497 metabolites from Ankang, Shaanxi, and Guyuan, Ningxia, and 29 metabolites from the three regions. To determine the correlation and concentration change trend in different metabolites, the metabolites with the highest 30 abundances were selected. As shown in Figure 8, the abundance of metabolites in Jiangshui from Ankang, Shaanxi, was significantly greater compared to Guyuan, Ningxia, and Tianshui, Gansu. Based on hierarchical cluster analysis, the content of various metabolites in Jiangshui showed significant regional differences.

Figure 7
Venn diagram of each metabolic set of Jiangshui from the different regions. The overlapping area in the figure represents the number of metabolites common to multiple metabolic sets, the area without overlap represents the number of metabolites unique to the metabolic set, and the number represents the corresponding number of metabolites. The second histogram shows the number of metabolites contained in each metabolic set.
Figure 8
Cluster heatmap of metabolite cluster analysis among metabolic sets from the different regions. Each column in the figure represents a sample and each row represents a metabolite. The color represents the relative expression amount of metabolites in the group of samples. The left side is the tree diagram of metabolite clustering, the right side is the name of metabolites, the upper part is the tree view of sample clustering, and the lower part is the name of the sample.

Analysis results of differential volcano map

Based on ESI+, as shown in Figure 9 and Figure S3, 453 differential metabolites were detected in all Jiangshui samples from the three different regions and 119 were differential metabolites in samples from Tianshui, Gansu, and Guyuan, Ningxia; 28 were upregulated and 91 were downregulated. The number of differential metabolites from Ankang, Shaanxi, and Guyuan, Ningxia, was 291; 250 were upregulated and 41 were downregulated. The number of differential metabolites from Ankang, Shaanxi, and Tianshui, Gansu, was 351; 303 were upregulated and 48 were downregulated. Similarly, based on ESI-, 284 differential metabolites were detected in Jiangshui samples from the three regions. The results showed the metabolites in Jiangshui samples from the three regions were significantly different and the identification results are shown in Table S1.

Figure 9
ESI+ volcano map of the difference between the Jiangshui groups from different regions. The abscissa is the FC value of the metabolite expression difference between the two groups, (i.e., log2FC) and the ordinate is the statistical test value of the metabolite expression difference (i.e., -log10) (P-value). The values of the abscissa and the ordinate are logarithms. Each dot in the graph represents a specific metabolite and the size of the dot represents the VIP value. The points on the left are metabolites with differentially downregulated expression and the points on the right are metabolites with differentially upregulated expression.

Screening and analysis of differential metabolites

Based on analysis results of the OPLS-DA model, metabolites with P < 0.05, VIP > 1, and FC < 1 based on t-test were used as screening criteria. Based on the retention time and mass-to-charge ratio data, the differential metabolites were searched using ESI+ and the identification results are shown in Table 3. A total of 180 differential metabolites were screened out from Jiangshui samples from the different regions. A total of 94 differential metabolites were screened using ESI-.

Table 3
Difference of metabolites in Jiangshui samples from different regions under ESI+.

Differential metabolite pathway analysis

Pathway enrichment analysis was performed on the different metabolites based on the KEGG database. The differential metabolites were mainly distributed in 20 metabolic pathways. As shown in Figure 10, compared to Guyuan, Ningxia, Jiangshui from Tianshui, Gansu, had more pathways for protein digestion and absorption, aminoacyl-tRNA biosynthesis, central carbon metabolism in cancer, cyanoamino acid metabolism, biosynthesis of plant secondary metabolites, and cocaine addiction; the bubbles at protein digestion and absorption and aminoacyl-tRNA biosynthesis were the darkest and relatively large. A total of 7 detected differential metabolites were enriched in this metabolic pathway, including L-tyrosine, L-threonine, L-valine, histidine, phenylalanine, L-tryptophan, and L-glutamate.

Figure 10
Bubble diagram of KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis for samples from GS and NX. The abscissa is the enrichment significance P-value and the ordinate is the KEGG pathway. The size of bubbles represents the amount of metabolites enriched in the metabolic set in this pathway.

3.3 Combined analysis of microbial diversity and metabolomics in Jiangshui

Results of multiple regression analysis

O2PLS is predictive bi-directional modeling tool. Figure 11A shows the close average cluster around the origin. Two samples (Tianshui, Gansu 4, and Guyuan, Ningxia 4) deviated from the average cluster. Figure 11B and Figure S4 shows the Jiangshui samples from the two regions were relatively discrete, and the data matrix of microbiome and metabolome were closely separated, indicating the internal correlation between the microbiome and metabolome in samples from Tianshui, Gansu, and Guyuan, Ningxia, was the strongest.

Figure 11
O2PLS (the two-way orthogonal partial least squares to assess the intrinsic correlation between the two data sets) score map of the different regions. The shapes (squares and dots) represent different groups of samples, the colors represent different omics samples, where blue represents microbiome samples. The sample name is followed by t. The green represents metabolome samples, and the sample name is followed by u. The abscissa and ordinate represent the scores of the combination of metabolome and microbiome, u represents the scores of microbial samples, and u represents the scores of metabolome samples. A: GS and NX bacterial community; a: GS and NX fungal communities, and microbial communities.

Results of weighted gene coexpression network analysis

To further explore the relationship between microbiota and metabolome composition, the abundance of metabolites in Jiangshui samples from the three different regions was analyzed. As shown in Figure 12A and Figure S5, the correlation between each metabolite and microorganism was very high, and the positive correlation was higher than the negative correlation. A single metabolite/microorganism correlated with multiple microorganisms. Overall, there was a high correlation between the microbiome and metabolites.

Figure 12
WGCNA (weighted gene co-expression network analysis) correlation chord diagram of Jiangshui from different regions was used to decompose the metabolites into different metabolite modules. The metabolites in the modules were highly correlated, and the correlation analysis with the flora was performed. The left half circle of the chord diagram is the metabolite and the right half circle is the microorganism. Each string indicates the metabolite has a significantly high correlation with the microorganism. The red string represents a positive correlation and the green string represents a negative correlation. The wider the width of the string, the more counts associated with this metabolite or microorganism. A: Correlation between GS and NX bacterial flora and metabolites; a: correlation between GS and NX fungal flora and metabolites, and the correlation between microbial communities and metabolites in other regions are shown.

4 Discussion

Jiangshui is a traditional fermented food widely consumed in Northwest China including Tianshui, Gansu, and Ankang, Shaanxi. It has high nutritional value and the fermentation process is simple. The functional compound probiotics in the serous water can directly or indirectly affect the healthy microorganisms in the intestinal system, showing its important role in the human gastrointestinal system (Kavas et al., 2022Kavas, N., Kavas, G., Kinik, Ö., Ateş, M., Kaplan, M., & Şatir, G. (2022). Symbiotic microencapsulation to enhance Bifidobacterium longum and Lactobacillus paracasei survival in goat cheese. Food Science and Technology, 42, e55620. http://dx.doi.org/10.1590/fst.55620.
http://dx.doi.org/10.1590/fst.55620...
). However, the microbial community structure of Jiangshui is complex (Chen et al., 2016bChen, P., Zhao, Y., Wu, Z., Liu, R., Xu, R., Yan, L., & Li, H. (2016b). Metagenomic data of fungal internal transcribed spacer from serofluid dish, a traditional Chinese fermented food. Genomics Data, 7, 134-136. http://dx.doi.org/10.1016/j.gdata.2015.12.028. PMid:26981389.
http://dx.doi.org/10.1016/j.gdata.2015.1...
). In this study, high-throughput sequencing technology was used to analyze the microbial community structure and diversity in Jiangshui from different regions, explore the differences between the dominant bacterial groups in different regions, and determine the flavor differences in Jiangshui from different regions based on metabolomics technology, to further improve flavor quality and nutritional value.

The abundance and diversity of microbial communities in Jiangshui samples changed in different regional environments (Fang et al., 2015Fang, R. S., Dong, Y. C., Chen, F., & Chen, Q. H. (2015). Bacterial diversity analysis during the fermentation processing of traditional Chinese yellow rice wine revealed by 16S rDNA 454 pyrosequencing. Journal of Food Science, 80(10), M2265-M2271. http://dx.doi.org/10.1111/1750-3841.13018. PMid:26409170.
http://dx.doi.org/10.1111/1750-3841.1301...
). The sequencing depth index was > 99% and the species accumulation curve showed the Jiangshui sequencing samples and sequencing data were reasonable. Lactobacillus was the dominant strain of the bacterial community at the species level (Liu et al., 2018Liu, X. J., Zhou, M., Jiaxin, C. X., Luo, Y., Ye, F. Z., Jiao, S., Hu, X. Z., Zhang, J. C., & Lu, X. (2018). Bacterial diversity in traditional sourdough from different regions in China. Lebensmittel-Wissenschaft + Technologie, 96, 251-259. http://dx.doi.org/10.1016/j.lwt.2018.05.023.
http://dx.doi.org/10.1016/j.lwt.2018.05....
), and the fungal community structure showed diversity. The laboratory strains isolated by Li et al. (2021)Li, S., Deng, B., Tian, S., Guo, M., Liu, H., & Zhao, X. (2021). Metabolic and transcriptomic analyses reveal different metabolite biosynthesis profiles between leaf buds and mature leaves in Ziziphus jujuba mill. Food Chemistry, 347, 129005. http://dx.doi.org/10.1016/j.foodchem.2021.129005. PMid:33482487.
http://dx.doi.org/10.1016/j.foodchem.202...
from the Jiangshui samples from Tianshui, Gansu, Northwest China, mainly belonged to Lactobacillus and Bifidobacterium (Li et al., 2021Li, S., Deng, B., Tian, S., Guo, M., Liu, H., & Zhao, X. (2021). Metabolic and transcriptomic analyses reveal different metabolite biosynthesis profiles between leaf buds and mature leaves in Ziziphus jujuba mill. Food Chemistry, 347, 129005. http://dx.doi.org/10.1016/j.foodchem.2021.129005. PMid:33482487.
http://dx.doi.org/10.1016/j.foodchem.202...
). Liang et al. (2018)Liang, H. P., Yin, L. G., Zhang, Y. H., Chang, C., & Zhang, W. X. (2018). Dynamics and diversity of a microbial community during the fermentation of industrialized Qingcai paocai, a traditional Chinese fermented vegetable food, as assessed by Illumina MiSeq sequencing, Dgge and qPCR assay. Annals of Microbiology, 68(2), 111-122. http://dx.doi.org/10.1007/s13213-017-1321-z.
http://dx.doi.org/10.1007/s13213-017-132...
also found that lactic acid bacteria were dominant in Northeast sauerkraut and Sichuan pickle. Li et al. (2022)Li, P., Ju, N., Zhang, S., Wang, Y., & Luo, Y. (2022). Evaluation of microbial diversity of Jiangshui from the Ningxia Hui autonomous region in China. Food Biotechnology, 36(2), 173-190. http://dx.doi.org/10.1080/08905436.2022.2054818.
http://dx.doi.org/10.1080/08905436.2022....
analyzed the microbial structure of Jiangshui from different regions using MIDI MIS and found the proportion of bacteria and fungi was as high as 93.05% and both play an important role in the formation of Jiangshui flavor and improvement of nutritional effect. Tadjine et al. (2021)Tadjine, D., Boudalia, S., Bousbia, A., Gueroui, Y., Symeon, G., Boudechiche, L. M., Tadjine, A., & Chemmam, M. (2021). Milk heat treatment affects microbial characteristics of cows’ and goats’ “Jben” traditional fresh cheeses. Food Science and Technology, 41(1), 136-143. http://dx.doi.org/10.1590/fst.00620.
http://dx.doi.org/10.1590/fst.00620...
found that cheese made from raw milk contains a variety of lactic acid bacteria, such as Lactococcus, Lactobacillus, Leuconostoc and Enterococcus (Tadjine et al., 2021Tadjine, D., Boudalia, S., Bousbia, A., Gueroui, Y., Symeon, G., Boudechiche, L. M., Tadjine, A., & Chemmam, M. (2021). Milk heat treatment affects microbial characteristics of cows’ and goats’ “Jben” traditional fresh cheeses. Food Science and Technology, 41(1), 136-143. http://dx.doi.org/10.1590/fst.00620.
http://dx.doi.org/10.1590/fst.00620...
). Therefore, although differences in fermentation processes and regions may affect microbial community diversity in the Jiangshui, Lactobacillus is the main bacterial community that determines the changes in the bacterial community in Jiangshui.

In the present study, LC-MS untargeted metabolomics analysis was used to study the different metabolites in Jiangshui from three regions (Liu et al., 2021Liu, X., Fan, H. M., Liu, D. H., Liu, J., Shen, Y., Zhang, J., Wei, J., & Wang, C. L. (2021). Transcriptome and metabolome analyses provide insights into the watercore disorder on “Akibae” pear fruit. International Journal of Molecular Sciences, 22(9), 4911. http://dx.doi.org/10.3390/ijms22094911. PMid:34066340.
http://dx.doi.org/10.3390/ijms22094911...
). Santos et al. (2021)Santos, L. S., Fernandes, C. C., Santos, L. S., Deus, I. P. B., Sousa, T. L., & Miranda, M. L. D. (2021). Ethanolic extract from Capsicum chinense Jacq. ripe fruits: phenolic compounds, antioxidant activity and development of biodegradable films. Food Science and Technology, 41(2), 497-504. http://dx.doi.org/10.1590/fst.08220.
http://dx.doi.org/10.1590/fst.08220...
also used LC-MS technology to analyze the mature fruits of EECC (Ethanolic extract from Capsicum chinese) and identified 10 phenolic compounds (Santos et al., 2021Santos, L. S., Fernandes, C. C., Santos, L. S., Deus, I. P. B., Sousa, T. L., & Miranda, M. L. D. (2021). Ethanolic extract from Capsicum chinense Jacq. ripe fruits: phenolic compounds, antioxidant activity and development of biodegradable films. Food Science and Technology, 41(2), 497-504. http://dx.doi.org/10.1590/fst.08220.
http://dx.doi.org/10.1590/fst.08220...
). PCA showed that the distance between regions directly affected the aggregation degree of Jiangshui samples. The discriminant and permutation tests of OPLS-DA showed the model was highly reliable, improved the effectiveness and analytical ability of the model, could better distinguish the differences between groups, and could be used for subsequent differential metabolite analysis (Rao et al., 2016Rao, G., Sui, J., & Zhang, J. (2016). Metabolomics reveals significant variations in metabolites and correlations regarding the maturation of walnuts (Juglans regia L.). Biology Open, 5(6), 829-836. http://dx.doi.org/10.1242/bio.017863. PMid:27215321.
http://dx.doi.org/10.1242/bio.017863...
). Based on VIP > 1, P-value < 0.05, and FC < 1, the identification of differential metabolites and pathway enrichment analysis were performed. For example, compared to Jiangshui from Guyuan, Ningxia, Jiangshui from Tianshui, Gansu, had more metabolic pathways for protein digestion and absorption, aminoacyl-tRNA biosynthesis, central carbon metabolism in cancer, cyanoamino acid metabolism, biosynthesis of plant secondary metabolites, and cocaine addiction. There were seven differential metabolites enriched in the metabolic pathways of protein digestion and absorption and aminoacyl-tRNA biosynthesis, including L-tyrosine, L-threonine, L-valine, histidine, phenylalanine, L-tryptophan, and L-glutamate. These amino acids contribute to the main flavor of Jiangshui caused by metabolic utilization of Jiangshui in different regions. The same metabolites simultaneously participate in multiple metabolic pathways, indicating the different metabolites have a significant effect on the pathway. Various effects on the above metabolic pathways can change the content of metabolic substances in the Jiangshui from various regions as well as improve the taste quality, nutritional value, and efficacy of the Jiangshui. Finally, based on joint analysis of microbial diversity and metabolomics, the Jiangshui samples from the different regions were highly correlated (McHardy et al., 2013McHardy, I. H., Goudarzi, M., Tong, M., Ruegger, P. M., Schwager, E., Weger, J. R., Graeber, T. G., Sonnenburg, J. L., Horvath, S., Huttenhower, C., McGovern, D. P. B., Fornace, A. J. Jr., Borneman, J., & Braun, J. (2013). Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome, 1(1), 17. http://dx.doi.org/10.1186/2049-2618-1-17. PMid:24450808.
http://dx.doi.org/10.1186/2049-2618-1-17...
).

5 Conclusion

In the present study, 16S rRNA high-throughput sequencing and untargeted metabolomics were used to analyze the microbial community diversity and differential metabolites in 18 Jiangshui samples from Tianshui, Gansu, Guyuan, Ningxia, and Ankang, Shaanxi. The results showed several regional differences in microorganisms in Jiangshui from the three different areas. The bacterial community structure was relatively stable and diversity was low. Lactobacillus was the dominant genus in Jiangshui from the three regions; however, the fungal community structure was complex and changeable. Furthermore, 100 genera were detected in the Jiangshui samples from the three regions at the genus classification level. The dominant genera in the different regions had significant differences in species and abundance. Based on VIP > 1, P < 0.05, and FC < 1, 78 different metabolites were screened out in the Jiangshui from the three regions using ESI+. Among them, the key metabolites L-tyrosine, L-threonine, L-valine, histidine, phenylalanine, L-tryptophan, and L-glutamate were enriched in protein digestion and absorption and biosynthesis of aminoacyl-tRNA. These differential metabolites formed a highly sensory pleasant flavor of the Jiangshui (Jun et al., 2018Jun, Z., Shuaishuai, W., Lihua, Z., Qilong, M., Xi, L., Mengyang, N., Tong, Z., & Hongli, Z. (2018). Culture-dependent and -independent analysis of bacterial community structure in Jiangshui, a traditional Chinese fermented vegetable food. LWT, 96, 244-250. http://dx.doi.org/10.1016/j.lwt.2018.05.038.
http://dx.doi.org/10.1016/j.lwt.2018.05....
; Moon et al., 2018Moon, S. H., Kim, C. R., & Chang, H. C. (2018). Heterofermentative lactic acid bacteria as a starter culture to control kimchi fermentation. Lebensmittel-Wissenschaft + Technologie, 88, 181-188. http://dx.doi.org/10.1016/j.lwt.2017.10.009.
http://dx.doi.org/10.1016/j.lwt.2017.10....
). Based on joint analysis, the microorganisms and metabolites in the Jiangshui from the three different regions correlated, and the metabolites/microorganisms were significantly associated with various microorganisms/metabolites. Therefore, our results clarify the differences between Jiangshui from different regions and provide a theoretical basis for future studies on the composition and content of metabolites in Jiangshui from other regions.

  • Practical Application: Jiangshui is a traditional fermented food in northwest China, which has high nutritional value and good flavor. The lactic acid bacteria in Jiangshui can degrade nitrite, and has special pharmacological effects, which can promote digestion, regulate viscera and urinate, and reduce cholesterol. It has become an indispensable part of the traditional fermented food diet in China. In this study, 16S rRNA high-throughput sequencing technology and untargeted metabolomics technology were used to determine the differences of microbial communities in different regions.

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Publication Dates

  • Publication in this collection
    27 Feb 2023
  • Date of issue
    2023

History

  • Received
    23 Oct 2022
  • Accepted
    09 Dec 2022
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