
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12764-8
10.1016/j.heliyon.2024.e36733
e36733
Research Article
Metagenomic and metabolite analysis reveals microbial community and metabolite dynamics in fermented Indigo naturalis
Yuan Xinyi a
Zhang Dayan b1
Li Duanyang a
Ji QiSen c
Gao Jihai gaojihai@cdutcm.edu.cn
a⁎
Hou Feixia houfeixia123@163.com
a⁎⁎
Chen Yang'er anty9826@163.com
b⁎⁎⁎
a School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
b College of Life Science, Sichuan Agricultural University, Ya’An, 625099, China
c Ya'an Xunkang Pharmaceutical Co., Ltd, Ya'an, Sichuan, 625000, China
⁎ Corresponding author. gaojihai@cdutcm.edu.cn
⁎⁎ Corresponding author. houfeixia123@163.com
⁎⁎⁎ Corresponding author. anty9826@163.com
1 The author is co-first author of the article.

23 8 2024
15 9 2024
23 8 2024
10 17 e367334 5 2024
13 8 2024
21 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The soaking and fermentation of the stems and leaves is an important intermediate step in the processing of Indigo Naturalis. However, the relationship between microbiota and Indigo Naturalis yields is still poorly understood. This study aimed to compare microbial communities and metabolite profiles at various stages of soaking fermentation, followed by validation of the results using HPLC. A total of 731 compounds were identified through metabolite analysis, with the levels of indigo and indirubin peaking after 36 h of fermentation. Metagenomes revealed Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria were identified as the most abundant microbial phyla in soaking fermentation. Correlation analysis indicated that the yields of indigo and indirubin may be affected by Lactococcus, Clostridium, and Enterobacter through the regulation of related synthetic enzymes. The findings offered novel perspectives on the relationship of microorganisms and Indigo Naturalis yields.

Graphical abstract

Image 1

Highlights

• The content of indigo reached its highest after 36 h.

• Clostridium, Escherichia and Lactobacillus was the dominant bacterial genera.

• It's possible to increase indigo yield by adjusting the content of microorganisms.

Keywords

Indigo Naturalis
Metagenome
Microbial community structure
Metabolites
==== Body
pmc1 Introduction

Indigo Naturalis, a commonly used Chinese herbal medicine for antiviral purposes, has the effects of clearing heat, cooling blood, and detoxifying [1,2]. The main components of Indigo Naturalis were indigo and indirubin. Modern pharmacological research has shown that indigo has effects such as improving immunity, anti-microbial activity, and liver protection [3,4]. And indirubin was utilized as an adjuvant drug for anti-tumor or tumor treatment [5]. Indigo was also a significant dye with global demand reaching 50,000 tonnes annually according to statistics [6]. With the development of the Indigo Naturalis industry, increasing the yields of indigo and indirubin becomes an urgent issue.

The immersion fermentation of the stems and leaves of the material was a crucial stage in the production of Indigo Naturalis. This process facilitated the release of substantial quantities of precursor compounds for indigo and indirubin, thereby aiding in the subsequent formation of indigo, indirubin and other related substances during the indigo beating process [7,8]. Microorganisms played an important role in this process, however, prior research on Indigo Naturalis has primarily concentrated on pharmacological actions, optimization of processing conditions, and enhancement of processing methods [[9], [10], [11], [12]]. Relevant studies have indicated that microorganisms, including Escherichia coli, could be utilized to synthesize indigo and indirubin [13,14]. Although associations between microorganisms and the synthesis of indigo have previously been reported, studies on how microorganisms regulate the transformation of indigo during soaking and fermentation process of Indigo Naturalis is insufficient.

This research utilized metagenomic sequencing and metabolite analysis to examine the dynamic changes in microbial communities and the conversion of active compounds during the soaking fermentation process of Indigo Naturalis. Additionally, the relationship between microorganisms and components transformation further explored and confirmed, with the aim of elucidating the molecular mechanism underlying the transformation of effective ingredients during Indigo Naturalis soaking fermentation and the role of microorganisms in this process. The ultimate goal was to increase indigo production, and to develop and utilize Indigo Naturalis resources, thereby laying a solid foundation for the advancement of the entire Indigo Naturalis industry chain.

2 Materials and methods

2.1 Sample collect

The fermentation broth of Indigo Naturalis was collected in October 2023 from the fermenting tank at Ya'an Xunkang Pharmaceutical Co., Ltd. The conversion within indigo Naturalis primarily occurred during the processes of soaking fermentation and lime indigo treatment. It was observed that the leaves of Strobilanthes cusia (Nees) Bremek are usually soaked for approximately three days [7].

To investigate the succession of microbial communities and metabolites during the process, this study collected Indigo Naturalis fermentation broth four times, with 500 mL gathered per sample. Following collection, the broth was promptly frozen using liquid nitrogen and then transported to the laboratory preserved in dry ice. It was then stored in a −80 °C freezer. “1 d" after 24 h of fermentation, “1.5 d" after 36 h, and “2 d" after 48 h. Lime slurry was introduced for indigo beating from the afternoon of the third day. Following the completion of indigo beating, the solution with added lime slurry was collected and labeled as “dd”. The collected samples from four distinct time periods were freeze-dried for 48 h using a vacuum freeze-dryer(MM400 Retsch, Germany).

2.2 Metabolite detection and analysis

50 mg of the dried powder were weighed and combined with 1200 μL of methanol water internal standard extraction solution, which has been pre-cooled to −20 °C. The mixture was vortexed once every 30 min for 30 s, totaling 6 times. Following this, the sample was centrifuged at 12000 rpm for 3 min, and the supernatant was extracted, filtered using a microporous filter membrane (0.22 μm), and stored in an injection bottle. The sample extracts were then analyzed using a UPLC-MS/MS system (UPLC, Shim-pack UFLC SHIMADZU CBM30A system, USA; MS, Applied Biosystems 6500 Q TRAP,USA) analysis. The chromatographic conditions employed in the study included the use of an Agilent SB-C18 column (1.8 μm, 2.1 mm × 100 mm, Agilent, USA) with a mobile phase consisting of ultrapure water (with 0.1 % formic acid added) as phase A and acetonitrile (with 0.1 % formic acid added) as phase B. The total run time of chromatography was 25 min, the elution gradient was programmed as follows: at 0.00 min, the proportion of phase B was 5 %, which linearly increased linearly to 95 % over 9.00 min and was maintained at 95 % for 1 min. Subsequently, from 10.00 to 11.10 min, the proportion of phase B was reduced to 5 % and equilibrated at this level for 14 min. The flow rate was maintained at 0.35 mL/min, with a column temperature of 40 °C and an injection volume of 2 μL. The electrospray ionization (ESI) source temperature was maintained at 500 °C, and the ion spray voltage (IS) was set at 5500 V in positive ion mode and −4500 V in negative ion mode. Additionally, the ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were seted to 50, 60, and 25 psi, respectively, with collision-induced ionization parameters set to high. Quadrupole-quadrupole (QQ) scanning employed multiple reaction monitoring (MRM) mode, with collision gas (nitrogen) set to medium. The clustering potential (DP) and collision energy (CE) of each MRM ion pair were further optimized, and specific MRM ion pairs were monitored at each stage based on the eluted metabolites during the respective periods.

The qualitative analysis of primary and secondary MS data was conducted by querying the internal database through a self-compiled database provided by Wuhan MetWare Biological Science and Technology Co., Ltd. The Variable Importance of Projection (VIP) score from the OPLS-DA model was utilized to identify the most significant metabolites. Metabolites with significant differences in content were identified using thresholds of VIP ≥1. A Principal component analysis (PCA) was employed to assess variability both between and within groups. The functional annotation of differentially accumulated metabolites was performed based on KEGG pathways.

2.3 Genomic DNA extraction, library construction and metagenomic sequencing

Genomic DNA was extracted from collected samples using the ZYMO Soil Genomic DNA Extraction Kit (Quick-DNA HMW kit,USA) according to the manufacturer's instructions. The purity and integrity of the extracted DNA were evaluated using agarose gel electrophoresis, and concentration was precisely measured using Qubit. The validated DNA was stored at −80 °C in a refrigerator. Following the construction of the metagenomic DNA library, triplicate samples of extracted DNA were combined for downstream metagenomic sequencing. The DNA was then sheared into 300 bp fragments and sequenced on the MGI platform (MGISEQ-200,China).

2.4 Metagenomic bioinformatics analysis

The initial sequences underwent quality assessment using Fastp (v0.23.2). Bad sequences and sequence reads less than 100 bases with a median quality score below 20 were removed. Next, the sequences that met the quality standards were cross-referenced with plant and human genomes to eliminate potential contamination sequences that might have been introduced during the plant and experimental procedures, thereby yielding valid sequences suitable for subsequent analysis. The obtained sequences were processed to remove chimera sequences and clustered into OTU using usearch software, and compared using diamond (v2.0.15) to generate the OTU count and OTU table for each sample. Microbial diversity analysis and visualization were conducted using R software. For the analysis of microbial communities structure within metagenomic data, the software Kraken2 (v2.1.1) [15] and Bracken (v2.5) [16] were used. In contrast to the sequence assembly followed by species annotation approach, the species annotation method based on metagenomic sequencing reads provided more accurate species annotation results. To annotate microbial functional traits and metabolic pathways in metagenomic data, the HUMAnN (v3.0) [17] was used in conjunction with the UniRef 90 database.

2.5 Co-joint analysis of the metagenomic and metabolite

Based on the results of differential metabolite analysis in this experiment, we integrated the results from metagenomic analysis to map the key microorganisms and metabolites. Correlation analysis was performed using quantitative values of key microorganisms and differential metabolites across all samples. The correlation analysis was performed using the Cor function in R to calculate the Pearson correlation coefficient between microorganisms and metabolites. Subsequently, all differentially expressed microorganisms and metabolites were selected, and a correlation clustering heatmap was generated.

2.6 Simulated fermentation verification

Key microorganisms were selected for simulated fermentation validation trials utilizing correlation clustering heatmap, to analyze the levels of indigo and indirubin components to investigate the potential relationship between microorganisms and components transformation.

Strobilanthes cusia (Nees) Bremek plant stems and leaves were gathered and then distributed equally among five separate groups, with 3 replicates in each group. The control group was comprised of 50 g of stems and leaves, combined with 450 mL of water. The experimental group included 50 g of stems and leaves, mixed with 300 mL of water and a bacterial solution of 150 mL. Both the control group and the experimental group were concurrently exposed to fermentation trials conducted at ambient room temperature. On the third day of the fermentation process, the indigo beating procedure was conducted. 10 mg of unfermented stems and leaves, 1 day of fermentation broth, 2 days of fermentation broth, and samples after indigo were taken, and 4 mL of DMF (N, N - dimethylformamide) was added to a 5 mL volumetric flask. A 250-W ultrasound was applied for a duration of 30 min. The mixture was filtered and subsequently analyzed using HPLC.

The liquid phase conditions utilized the ZORBAX Eclipse chromatographic column (XDB-C18, 4.6 × 250 mm, USA) with water as mobile phase A and methanol as mobile phase B (70 % B and 30 % A) over a 20-min run time. The column was maintained at 30 °C, with the detection wavelength set at 286 nm.

2.7 Statistical analyses

All figures were plotted using R(v4.0.2) and Origin(v8.0) software. Statistical analysis was conducted using ANOVA and Duncan multiple comparison tests, as determined by SPSS(v22.0).

3 Results

3.1 Screening of differential metabolites

Using the UPLC-MS/MS detection platform and a self-constructed database, 731 metabolites were identified (Table S1). PCA results indicated significant inter-group differences among metabolic profiles, with minimal intra-group variation(Fig. 1a). The Variable Importance in Projection (VIP) obtained from the OPLS-DA model could be used to preliminarily screen metabolites that differ between different varieties or tissues. The screening criteria for differential metabolites in this study were: select metabolites with VIP >1 (P < 0.05). A total of 418 metabolites were identified, including 31 types of indole alkaloids (Table 1). The metabolic pathways involved in Kegg's annotation results (Fig. 1b) included biosynthesis of secondary metabolites, tryptophan metabolism, tyrosine metabolism, flavonoid and flavonol biosynthesis, phenylpropanoid biosynthesis, and other metabolic pathways.Fig. 1 The metabolic pathways results and the population differences of the indole alkaloids. (a) PCA plots based on LC–MS analysis. (b) The metabolic pathways involved in Kegg's annotation results. (c) The alterations of main active constituent at various stages. Different letters represent significant differences between groups (P < 0.05). (d) Population differences of the indole alkaloids.

Fig. 1

Table 1 Differential metabolites of indole compounds.

Table 1Compounds	Q1(Da)	Q3(Da)	Molecular weight(Da)	Formula	CAS	VIP	
2,5-Dihydroxy-Indole	148.04	92.05	149.05	C8H7NO2	–	1.35	
N-Acetylisatin	188.04	144.05	189.04	C10H7NO3	574-17-4	1.35	
isatindigobisindoloside F	439.15	277.1	438.14	C23H22N2O7	–	1.34	
isatindigoside C	439.15	277.1	438.14	C23H22N2O7	–	1.34	
isatan B	294.1	134.06	293.09	C14H15NO6	20307-14-6	1.3	
3-Hydroxy-3-acetonyloxindole	206.08	118.07	205.07	C11H11NO3	33417-17-3	1.28	
Tryptanthrin	249.06	130.03	248.06	C15H8N2O2	13220-57-0	1.27	
insatindibisindolamide A	350.15	130.07	349.14	C20H19N3O3	–	1.27	
5-Hydroxytryptophol	178.09	160.08	177.08	C10H11NO2	154-02-9	1.25	
3-Indoleacetonitrile	157.08	130.07	156.07	C10H8N2	771-51-7	1.23	
bisindigotin	491.15	463.15	490.14	C32H18N4O2	–	1.23	
indiforine B	318.1	134.06	317.09	C16H15NO6	–	1.2	
2-(1H-Indole-3-carboxamido) benzoic acid	281.09	237.07	280.08	C16H12N2O3	171817-95-1	1.2	
isoindigotin	263.08	235.09	262.07	C16H10N2O2	476-34-6	1.17	
Tryptamine	161.11	144.08	160.1	C10H12N2	61-54-1	1.16	
Methoxyindoleacetic acid	206.08	145.05	205.07	C11H11NO3	3471-31-6	1.14	
Methyl dioxindole-3-acetate	222.08	130.06	221.07	C11H11NO4	57061-18-4	1.13	
Indole-5-carboxylic acid	160.04	116.05	161.05	C9H7NO2	1670-81-1	1.13	
3-Indolepropionic acid	190.09	118.07	189.08	C11H11NO2	830-96-6	1.13	
Indigo	263.08	235.09	262.07	C16H10N2O2	482-89-3	1.12	
Melatonin (N-Acetyl-5-methoxytryptamine)	233.13	174.09	232.12	C13H16N2O2	73-31-4	1.1	
isatindigotindoloside A	459.14	160.04	458.13	C22H22N2O9	–	1.1	
Indican	296.11	134.06	295.11	C14H17NO6	487-60-5	1.1	
isatan A	380.1	134.06	379.09	C17H17NO9	–	1.09	
5-Hydroxyindole-3-acetic acid	190.05	146.06	191.06	C10H9NO3	54-16-0	1.08	
5-Methoxytryptamine	191.12	159.07	190.11	C11H14N2O	608-07-1	1.07	
isoindygo	263.08	235.09	262.07	C16H10N2O2	–	1.07	
Isatin	148.04	102.03	147.03	C8H5NO2	91-56-5	1.07	
Indirubin	263.08	219.09	262.07	C16H10N2O2	479-41-4	1.06	
1-Methoxy-indole-3-acetamide	205.1	146.06	204.09	C11H12N2O2	–	1.06	
Indole-3-carboxylic acid	160.04	116.05	161.05	C9H7NO2	771-50-6	1.06	
Indole-3-carboxaldehyde	146.06	91.05	145.05	C9H7NO	487-89-8	1	
Note:Compounds=Names of substances; Q1(Da)=Molecular weight of the parent ion after the substance is ionized via the electrospray ion source; Q3(Da)=Characteristic fragment ion; Molecular Weight(Da)=Relative molecular weight; Formula=Molecular formula of the substance; CAS=Substance CAS Number;VIP=Variable Importance in Projection,the higher the VIP value of a variable, the greater its importance.

3.2 Analysis of differential metabolites related to the composition of Indigo Naturalis

Indigo Naturalis contains various derivatives in the biosynthesis pathway of its main active components. In conjunction with metabolites data, the alterations of each active constituent at various stages were identified and presented (Fig. 1c and d). It can be seen that the change in composition before and after beating indigo was more obvious. Before indigo beating, the main chemical components in the fermentation tank were indican, isoindygo, indigo, indirubin, tryptanthrin, melatonin, 5-Hydroxyindole-3-acetic acid, etc. After indigo beating, the main chemical components were colored tryptamine, 5-hydroxyindole-3-ethanol, 3-Indoleacetonitrile, methoxyindoleacetic acid, etc. Indigo reached its peak after 36 h of fermentation and decreased slightly after 48 h. The increase in time affected the accumulation of indigo. After indigo beating, the production of indigo further decreased. Therefore, appropriately reducing the soaking fermentation duration may improve the yields of indigo. Isatin, the precursor substance of indirubin, gradually increased, reaching its highest level after 36 h. After 48 h, the content slightly decreased, but rebounded slightly after indigo beating. The indole content followed the same trend as indigo and indirubin. Tryptamine gradually increased with fermentation time, reaching its peak at 48 h. During this period, it was postulated that the tryptophan pathway facilitated the conversion of indole to tryptamine, resulting in a reduction in the content of indigo and indirubin.

3.3 Microbial species analysis in each groups

Following sequencing on the MGISEQ-200 platform, a total of 39.44 gigabases(Gb) of raw sequencing data were obtained from four samples, with an average of 9.86 Gb per sample. After removing low-quality data during preprocessing, four groups of Clean Reads were obtained, resulting in clean reads of 75,411, 512 bp, 82,714, 368 bp, 99,447, 460 bp and 98,321, 584 bp, respectively (Table 2). Microbial diversity was commonly assessed using metrics such as the Shannon index and Simpson index. The diversity of microbial communities in different stages of fermentation broth (Fig. 2) data illustrated a progressive rise in colony diversity in the 48 h, reaching its peak at 48 h, and subsequently experienced a slight decline following the fermentation process. The fermentation pool exhibited the highest microbial abundance and slightly lower diversity after 36 h, with the lowest abundance and diversity observed after 24 h.Table 2 Overview of the sequencing data obtained from the 4 samples.

Table 2	Raw Reads(bp)	Clean Reads(bp)	GC(%)	Q30(%)	
1 d	95,132,474	75,411,512	36.55	85.98	
1.5 d	104,463,714	82,714,368	36.60	84.80	
2 d	126,362,720	99,447,460	36.82	87.20	
dd	124,757,752	98,321,584	36.14	87.13	

Fig. 2 Diversity index of microbial community (a) Analysis of microbial diversity, elevated values of the Shannon and Simpson indices typically indicate greater richness of microbial diversity within a given sample. (b) The total number of OTUs were 8533, with the number of OTUs in each sample being 6435, 6745, 7141, and 6976, respectively. Different letters represent significant differences between groups (P < 0.05).(c) Shared and unique microorganisms in each sample.

Fig. 2

After 24 h of fermentation, Firmicutes (70.39 %) was the dominant phylum, followed by Proteobacteria (24.74 %), Bacteroidetes (0.71 %), and Actinobacteria (0.37 %) (Fig. 3a). In the early stage of fermentation, Firmicutes dominated, while Proteobacteria and Bacteroidetes had higher content. As time increases, Firmicutes showed a downward trend. The phylum Proteobacteria showed a trend of first increasing and then decreasing. The phylum Bacteroidetes was showed an increasing trend. At the genus level, 19 genera were identified (>0. 1 %), and at each stage of fermentation, the genera Lactococcus and Clostridium had an absolute advantage (Fig. 3b). After 24 h, Lactococcus(38.18 %) was the dominant genus, followed by Clostridium(31.32 %), Citrobacter(6.50 %), Pectobacterium(4.57 %), Streptococcus(4.04 %), Enterobacter(2.84 %), Klebsiella (1.70 %), Enterococcus (1.63 %). At the species level, 40 species(>0.1 %) were identified (Fig. 3c). At 24 h, the dominant bacterial genus was Lactobacillus lactis (21.36 %), followed by Clostridium beijerinckii(17.52 %), Lactococcus raffinolactis(9.21 %), Clostridium saccharobutylicum (8.40 %), Citrobacter freundii (2.76 %), Citrobacter portucalensis (1.95 %), Escherichia coli (1.56 %) and Lactococcus cremoris (1.51 %).Fig. 3 Metataxonomic of sample represented the abundance in relative percentage (%) distributed at different taxa level. (a) Phylum. (b) Genus. (c) Species. Minority of <0.1 % (Phylum <0.01 %) were grouped as others.

Fig. 3

3.4 Enrichment analysis of microbial metabolic pathways

The HUMAnN(V3.0) software was utilized to assess the metabolic functions of microorganisms, identifying 628 metabolic pathways across four stages (Table S2). The dominant metabolic pathways (Fig. 4a) included ANAGLYCOLYSIS-PWY: Glycolysis III (from glucose), CALPIN-PWY: Calvin cycle, PWY-3001: Super pathway I for L-isoleucine biosynthesis, PWY-6317: D-galactose degradation I (Leloir pathway), PWY-7111: Acetate fermentation to produce isobutanol (engineered), PWY-7221: Guanosine ribonucleotide de novo biosynthesis, PWY-7238: Sucrose biosynthesis II, PWY-7977: L-methionine biosynthesis IV, PWY-8178: pentose phosphate pathway (non oxidative branch) II, VALSYN-PWY: valine biosynthesis, etc. After 24 h of fermentation (Fig. 4b), the abundance of Lactococcus was most pronounced, with the associated metabolic pathway being VALSYN-PWY: Valine biosynthesis. This may be likely due to the essential role of valine, in the growth of Lactococcus. When fermenting for 36 h, the abundance of various metabolic pathways was high, indicating that the microbial community was most active during this period.Fig. 4 Changes and functional analysis in microbial species at different stages. (a) Functional analysis of metagenome data for predictive metabolic pathways. (b) Changes in microbial species at different stages.

Fig. 4

3.5 Correlation analysis between differentially microbiota and metabolites

It can be seen (Fig. 5a) that the formation of indigo was positively correlated with Escherichia coli, Clostridium beijerinkii, Enterobacter cloacae, and Pectibacter odoriferum. TnaA was a secretory enzyme essential for degrading tryptophan to indole. Research has shown that Escherichia coli contains the TnaA gene, which could convert tryptophan into indole, thereby indirectly increasing the yields of indigo [18]. Indigo appeared to be a negative correlation with Clostridium perfringens, possibly due to its consumption of carbohydrates. This may decrease one of the precursor substances of indigo (UDP-glucose), thereby reducing indigo production [19].Fig. 5 Correlation between microbiota and metabolites. (a) Heatmap of correlations among microbial species and metabolites. Correlation strength and correlation significance values are shown as shaded colors (red, positive correlation; green, negative correlation). Heatmap values range from +1.0 to −1.0. Values above/below zero represent positive/negative correlations, respectively, between genera and parameters. (b) Changes in the Content of Indigo and indirubin in HPLC experiments. (c) The number of shared and unique genes at different stages. (d) Relative content of genes related to indigo synthesis in key microorganisms. From inside to outside, each circle represents Escherichia coli, Lactococcus lactis, Clostridium beijerinckii, and Klebsiella pneumoniae in sequence. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Selected Escherichia coli, Clostridium beijerinckii, Klebsiella pneumoniae and Lactococcus lactis strains that exhibited high correlation coefficients with indigo and indirubin, and added them to the fermentation broth for simulated fermentation experiments. Based on the simulated fermentation verification experiment(Fig. 5b), the indigo content in the strain-added groups was significantly higher than in the control group after one day of fermentation. After two days of fermentation, the indigo content in the Clostridium beijerinckii group increased by 1.19 % compared to the one-day fermentation group. In the Escherichia coli group, the indirubin content increased by 0.01 % after one day of fermentation compared to the control group. After two days of fermentation, the blank group showed the highest increase in indirubin at 0.058 %, as well as an increase in the Clostridium beijerinckii group (0.029 %). The content of indirubin increased across all groups after 3 days of fermentation. The control group showed the most significant increase, measuring 0.338 %, followed by the Clostridium beijerinckii group with an increase of 0.129 %.

3.6 Integrated analysis of indigo-related biosynthesis genes and key microorganism

The results of the metagenomic functional annotation indicated that a total of 4996 genes were annotated (Fig. 5c and d). The key genes involved in the transformation of Indigo Naturalis components were as follows: Tryptophan synthase α (TSA), Tryptophan Synthase β (TSB), Indole-3-glycerol phosphate synthase (IGPS), TnaA enzyme, UDP-glucuronosyltransferase (UGT). IGPS and TSA increased with fermentation time, reaching thier peak at 48 h. Concurrently, metabolite data showed a gradual increase in indole content up to 36 h, followed by a decrease at 48 h. This decrease may be attributed to the conversion of indole into other substances, such as tryptamine, through the tryptophan pathway during this period. IGPS catalysed the formation of indole glycerophosphate from ortho aminobenzoic acid, which could then be synthesized into indole by TSA. The synthesis of indole involved microorganisms such as Escherichia coli, Lactococcus lactis, Clostridium beijerinckii, and Clostridium saccharobutylicum. The abundance of TnaA decreased with increasing fermentation time, reaching its highest level at 24 h. TnaA enzyme decomposed tryptophan into indole. Microorganisms involved in this process include Enterobacter sp. Bisph 2, Escherichia coli, etc. The abundance of UGT and GLU genes increased with fermentation time, reaching thier peak at 48 h. UGT catalysed the conversion of indolxy to indican. GLU catalysed the conversion of indican to indoxyl, which is a precursor substance of indigo and indirubin. This process primarily involved Clostridium and Escherichia coli. Indican was a highly water-soluble component, and its content was higher 48 h in fermentation, possibly due to the gradual dissolution of indican from the leaves during this period. The abundance of TSB decreased with increasing fermentation time, reaching its highest level at 24 h. TSB converted indole into tryptophan, and microorganisms associated with this process included Escherichia coli, Clostridium butyricum, Klebsiella pneumoniae, etc.

4 Discussion

The modern method of preparing indigo involved soaking the stems and leaves of the raw material in water, filtering out impurities, adjusting the pH of the filtrate with ammonia water, introducing air, adding lime, stirring, allowing the mixture to settle, filtering, precipitating, drying to obtain crude indigo, and refining it with water to obtain the final indigo product [20]. The soaking and fermentation process was a critical step in the production of indigo and blue dyeing, with microorganisms played a significant role [21]. This step was essential for producing high-quality indigo dye and Indigo Naturalis. Despite there have been studies on the relationship between microorganisms and indigo synthesis, there remains a lack of systematic investigation into the transformation mechanism of indigo during soaking fermentation.

In this study, metagenomic sequencing revealed a gradual increase in colony numbers in the pool as the fermentation process progressed. In the early stages of fermentation, Firmicutes dominated, while Proteobacteria and Bacteroidetes were present at higher levels. As fermentation progressed, microorganisms ferment carbohydrated to produce acid and gas. The environment in the fermentation tank becomed increasingly acidic and low in oxygen, leading to a decline in Firmicutes. Proteobacteria exhibited a pattern of initial growth followed by decline. This decline may be attributed to the challenges faced by certain aerobic bacteria within the phylum when exposed to low oxygen, acidic, and high-alcohol environments. Bacteroidetes showed an increasing trend, with most of them being anaerobic bacteria that could tolerate hypoxic environments. Additionally, Bacteroidetes were also one of the most oxygen-tolerant anaerobic bacteria, capable of withstanding atmospheric oxygen concentrations for up to three days [22]. Therefore, after indigo beating, the numbers still slightly increased. Upon analyzing the functional contribution of each bacterium, it concluded that Clostridium, Lactococcus, and Enterococcus played significant roles in nearly all metabolic pathways. This might be attributed to their dominant positions in the sample. Lactobacillus metabolized to produce organic acids, such as lactic acid. Enterococcus exhibited good biological safety and probiotic properties, and were commonly used to accelerate the fermentation process in production [23,24].

From the perspective of fermentation period, the most active phase for the transformation of Indigo Naturalis components occurred between 36 and 48 h. During this stage, the composition of each component varied significantly, and there was a high abundance of transformation-related enzymes. After 48 h, content of indole, indigo, and indirubin decreased, potentially due to the predominant synthesis of tryptamine via the tryptophan pathway during this timeframe. Simultaneously, the enzyme tryptophan facilitated the reverse conversion of tryptophan into indole [[25], [26], [27]]. This indicated that the soaking fermentation time might affect the distribution of indole content in various branching pathways of indole components in subsequent steps. From the perspective of microbial species, Clostridium, Lactococcus, and Escherichia coli played important roles. In the microbial tryptophan pathway, HSUTM and colleagues discovered the gene responsible for encoding the glucosyltransferase PtUGT1 from the plant Polygonum multiflorum [28]. This gene was subsequently expressed in Escherichia coli through heterologous expression. Currently, Escherichia coli was used as an expression vector for the synthesis of indigo compounds [29]. This suggested that these microorganisms may promote the transformation of indigo.

The research group compiled the findings and created a transformation map of the active ingredients in Indigo Naturalis to explain the transformation process of indigo and the role of microorganisms(Fig. 6). By integrating changes in metabolites, microbial communities, and indigo-related biosynthesis genes, we infered that microorganisms such as Escherichia coli, Lactococcus lactis, Clostridium beijerinckii and Clostridium saccharobutylicum utilized through IGPS and TSA to increase indole content. Escherichia coli also enhanced indole content through TnaA enzyme at 48 h during fermentation. Indole could subsequently transform into indolxy, indirectly increasing the abundance of indole constituents. Additionally, indolxy could reversibly also be converted into indican with the assistance of microorganisms. Clostridium and Escherichia coli utilized through GLU to catalyze the conversion of indolxy, thereby increasing the content of indole components.Fig. 6 Speculation of fermentation components transformation. Blue font represents microorganisms, while black font represents enzymes or components. The blue color block indicates that the process occurred in the fermentation tank, while the green color block represents that the process occurred in living plants. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

In order to verify the effect of microorganisms on indigo transformation and yields, the experimental team conducted a simulated fermentation validation experiment. We found that Escherichia coli, Clostridium beijerinckii, Klebsiella pneumoniae, and Lactococcus lactis increased the content of indigo and indirubin during fermentation. The effect was more significant after one day of indigo fermentation, although the increase in indirubin was relatively minor compared to the blank group. Therefore, in the production process, the yields of different products could be optimized by controlling the proportion of microorganisms and adjusting the fermentation conditions [30]. If indigo production is required, control the oxygen content, add relevant microorganisms and system pH during the enzymatic hydrolysis process to enhance indigo formation.

This study provided valuable insights into the changes in indole and indigo components throughout the fermentation process of Indigo Naturalis. However, several unresolved issues remain: the lack of research on the enzymes responsible for directing indole components towards different branching pathways during soaking fermentation, and the unclear identification of the enzymes that facilitate the conversion of isatin to indirubin and leucoindigo to indigo within the indigo synthesis pathway. Furthermore, the role of the indigo synthesis pathway within the plant organism necessitates further exploration [31].

5 Conclusion

Briefly, our data revealed the association between microbiota and metabolite yields of the soaking and fermentation processes from a multi-omics perspective. Microorganisms played a significant role in the conversion of Indigo Naturalis constituents during the fermentation stage. Clostridium and Lactobacillus,the dominant bacterial genera during the fermentation process, might promote the transformation of indigo by regulating genes related to the synthesis of indigo and indirubin. This study enhanced understanding of the structure and function of the microbial community during the soaking and fermentation processes of Indigo Naturalis. The research was valuable for guiding enterprises in improving the quality of indigo and developing Indigo Naturalis resources.

Data availability statement

The raw sequence data has been deposited and available in the China National GeneBank DataBase under accession number CNP0006054.

CRediT authorship contribution statement

Xinyi Yuan: Writing – original draft, Data curation, Conceptualization. Dayan Zhang: Writing – review & editing, Formal analysis, Data curation. Duanyang Li: Data curation. QiSen Ji: Visualization, Validation, Project administration. Jihai Gao: Project administration, Funding acquisition. Feixia Hou: Funding acquisition, Formal analysis. Yang'er Chen: Funding acquisition, Formal analysis, Data curation.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Jihai Gao reports financial support was provided by 10.13039/501100004829 Science and Technology Department of Sichuan Province . Jihai Gao reports financial support was provided by Sichuan Province Pharmaceutical Administration. Jihai Gao reports financial support was provided by National Administration of Traditional Chinese Medicine. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Acknowledgements

The authors are grateful for the financial support from Research on the preparation process and industrial application of characteristic fermented traditional Chinese medicine decoction pieces (2023ZHCG0073 ), National Interdisciplinary Innovation Team of Traditional Chinese Medicine (ZYYCXTD-D-202209 ) and Multi-dimensional evaluation of characteristic traditional Chinese medicine resources and product development innovation team (2022C001 ). We are also thankful to all our laboratory colleagues and research staff members for their constructive advice and help.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36733.
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