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. 2020 Aug 16;10(9):392. doi: 10.1007/s13205-020-02383-2

Intestinal mucosal bacterial diversity of antibiotic-associated diarrhea (AAD) mice treated with Debaryomyces hansenii and Qiweibaizhu powder

Haoqing Shao 1, Chenyang Zhang 1, Chunhui Wang 2,✉, Zhoujin Tan 1,✉
PMCID: PMC7429618  PMID: 32832342

Abstract

The aim was to investigate the combined effect of Debaryomyces hansenii and Qiweibaizhu powder (QWBZP) on the bacterial diversity of the intestinal mucosa of antibiotic-associated diarrhea (AAD) mice, for the potential treatment of diarrhea, especially which is induced by administration of antibiotics. Eighteen (18) mice were randomly assigned to three equal groups of six mice, namely Normal (mn group), Placebo control (mm group) and D. hansenii and QWBZP (DQ) treatment (mdq group). Mice were gavaged with a solution (23.33 mL·kg−1·day−1) consisting of gentamicin and cefradine to establish AAD. The DQ treatment group was gavaged with DQ for 4 days, and sterile water was used as a placebo control. The metagenome DNA of the intestinal mucosal microbiota was extracted, and the 16S rRNA gene was sequenced. Analysis showed that there were 288 OTUs for the normal group, 443 for the placebo control group, and 229 for the DQ treatment group. Phylogenetically, the gut microbiota of the DQ treatment group and the normal group were closer to each other than to the placebo control group. Both the DQ and placebo-treated groups included Stenotrophomonas, Robinsoniella, Bacteroidales S24-7 group norank, Citrobacter, and Glutamicibacter, but their abundances were significantly higher in the DQ treatment group than in the placebo control group. This suggested that the combined use of D. hansenii and QWBZP overcame the influence of dysbacteriosis and could lead to the recovery of intestinal mucosal microbiota homeostasis. This positive effect is likely related to short-chain fatty acid (SCFA)-producing bacteria, such as members of Micrococcaceae, Lachnospiraceae, and Bacteroidales S24-7 group, which could play beneficial roles in protecting the mucosal barrier and stimulating the immune response in mice.

Keywords: Debaryomyces hansenii, Qiweibaizhu powder, Intestinal mucosa, SCFA-producing bacteria, Antibiotic-associated diarrhea

Introduction

The intestinal micro-ecosystem has been recognized as the largest and the most important micro-ecosystem in the organism, and the normal gut microbiota as its core component plays a crucial role in the homeostasis of the gastrointestinal tract (Coyte and Rakoff-Nahoum 2019; Lozupone et al. 2012; Lynch and Pedersen 2016). As such, disturbances in the normal gut microbiota can lead to a variety of pathogenic states. The first step in understanding the symbiotic relationship between gut microbiota and its host is to characterize the baseline health microbiota and the differences that are associated with disease (Lozupone et al. 2012). Antibiotic-associated diarrhea (AAD), defined as diarrhea that occurs in association with the administration of antibiotics and without another apparent etiology, is one of the most common adverse drug events of antibiotic therapy (Bartlett 2002). AAD can affect up to a third of patients receiving a particular antibiotic (Mantegazza et al. 2018), but the incidence and severity of AAD vary from different antibiotics. We observed more serious diarrhea in mice received cephradine + gentamycin sulfate than lincomycin hydrochloride + ampicillin sodium and ceftriaxone sodium + erythromycin lactobionate (Zeng et al. 2012). Numerous studies reported that the mechanisms for AAD mainly laid on the changes or dysbiosis of microbial composition and function induced by antibiotics (Becattini et al. 2016; Nelson et al. 2017). Thus, reconstruction to a balanced baseline gut microbiota may be beneficial to prevent or treat AAD. However, the best treatment for micro-ecological disorders is not simply sterilizing and inhibiting but promoting the antagonistic action of the bacterial genus or species that is known as beneficial to remove pathogenic bacteria and restore normal intestinal flora structure. The mucosal surfaces are part of the first-line defense of the innate and adaptive immune systems, at which most of the microbe–host interactions take place (Spiljar et al. 2017). Emerging evidence indicates a central role as the microbiome in protection against colonization by pathogens, development of gut-associated lymphoid tissue, and regulation of the immune system (Ahluwalia et al. 2017). Based on the important role of the gut microbiome in the mucosal barrier and mucosal immunity, this study aims to explore the effect of D. hansenii and Qiweibaizhu Powder (DQ) on antibiotic-associated diarrhea from the perspective of intestinal mucosal microbiota.

Traditional Chinese Medicine is an essential part of medical science worldwide, particularly in Asian countries. Qiweibaizhu powder (QWBZP) is formed by seven herbs, i.e., Radix Ginseng, Rhizoma Atractylodis Macrocephalae, Poria, Radix Aucklandiae, Folium Agastaches seu Pogostemonis, Radix Puerariae, and Radix Glycyrrhizae Preparata. QWBZP is under a well-established history for the treatment of infantile diarrhea in China. Our previous studies confirmed that the therapeutic effect of a 50% dose of ultra-fine QWBZP on diarrhea mice is equal to that of a full dose of traditional decoction (Deng et al. 2011). The antibacterial test proved that single herbs in QWBZP have varied degrees of antibacterial activity, and the main inhibitory components of QWBZP were Radix Ginseng and Rhizoma Atractylodis Macrocephalae. Besides, the minimum inhibitory concentration of ultra-fine QWBZP on Staphylococcus aureus, Aerobacter aerogenes, and Salmonella was lower than that of traditional decoction (Jiang et al. 2013). The yeast Debaryomyces hansenii is normally a non-pathogenic yeast with probiotic properties and potential immunostimulatory effects. Marine yeast D. hansenii strain CBS004 has proved to show antioxidant immune effects on head-kidney and spleen leukocytes (Angulo et al. 2017). β-Glucan from D. hansenii CBS 8339 recently has demonstrated to modulate signaling pathways and innate immune response in goat peripheral blood leukocytes (Angulo et al. 2018, 2019, 2020). We isolated D. hansenii from the gut of experimental mice and found it was related to the diversity of gut bacteria and the abundance of Lactobacillus and Bifidobacterium (He et al. 2017; Zeng et al. 2019). Both D. hansenii and QWBZP have positive effects on the recovery of bacterial lactase gene diversity (He et al. 2019; Long et al. 2018). The treatment effects for diarrhea of 25% dose of ultra-fine QWBZP + 25% dose of D. hansenii was equivalent to a 50% dose of ultra-fine QWBZP and reaching the effect of a full dose of traditional decoction (Guo et al. 2015). However, mechanisms of DQ in treating AAD have not been characterized yet. This study described the effects of DQ against changes or dysbiosis of intestinal mucosal microbiota induced by antibiotics and activates the gut mucosal immune function via microbiota.

Materials and methods

Reagents

Gentamycin sulfate injection (2 mL: 80 mg, Batch no. 5120106) and Cefradine Capsules (Mie Da) (0.25 g, Batch no. 110804) were obtained from Yichang Humanwell Pharmaceutical Co., Ltd and Suzhou Chung-Hwa Chemical Pharmaceutical Industrial Co., Ltd. Radix Ginseng (Origin: Jilin), Rhizoma Atractylodis Macrocephalae (Origin: Zhejiang), Poria (Origin: Yunnan), Radix Aucklandiae (Origin: Yunnan), Folium Agastaches seu Pogostemonis (Origin: Guangxi), Radix Puerariae (Origin: Hunan), and Radix Glycyrrhizae Preparata (Origin: Inner Mongolia) were purchased from The First Hospital of Hunan University of Chinese Medicine.

Animals

Eighteen 6-week-old-specific pathogen free (SPF) Kunming mice (half male and half female), weighing 20 ± 2 g, were purchased from Hunan SJA Laboratory Animal Co., Ltd (SYXK (Xiang) 2014-0012). The mice were raised under stable conditions (temperature 23–25, relative humidity 50–70%, 12 h light/dark cycles, free access to diet and water) in the laboratory animal center of Hunan University of Chinese Medicine. The process of animal experiments was conducted under animal protocols approved by the Animal Ethics and Welfare Committee of Hunan University of Chinese Medicine (No. 20171202).

Preparation of ultra-fine QWBZP liquid medicine

QWBZP was composed of seven herbs (Radix Ginseng 6 g, Rhizoma Atractylodis Macrocephalae 10 g, Poria 10 g, Radix Aucklandiae 6 g, Folium Agastaches seu Pogostemonis 10 g, Radix Puerariae 10 g, Radix Glycyrrhizae Preparata 3 g), with a total weight of 55 g. Herbs outlined above were shattered into ultra-fine powders and then brewed with boiling water. After cooling and low-speed centrifugation, the supernatant was collected and prepared into an initial solution (100% dose QWBZP) with a concentration of 2 g·mL−1, which was then diluted to a 25% dose (Guo et al. 2015). The prepared 25% dose of QWBZP liquid medicine was stored in a refrigerator at 4 °C and reheated to 25–30 °C before being used.

Preparation of D. hansenii suspension

D. hansenii, which has been isolated, identified, and preserved by our team from the intestinal contents of previous experimental animals, was inoculated into liquid potato sucrose medium and then cultured on a shaker (28 °C, 160 rpm) for 36 h. Then D. hansenii was collected by centrifugation (2000 rpm for 4 min). After washing twice repeatedly, D. hansenii was diluted with normal saline. The concentration of 100% dose D. hansenii suspension was 1010 cells·mL−1 as counted by a hemocytometer. Finally, the initial D. hansenii suspension was diluted to a 25% dose (Guo et al. 2015) and subsequently kept at 4 °C.

Preparation of antibiotic mixture solution

Six gentamicin sulfate injections and three cefradine capsules were mixed with normal saline to prepare an antibiotics mixture solution with a concentration of 62.5 g·L−1 (Zeng et al. 2012). The prepared antibiotics mixture solution was stored at 4 °C.

Process of animal experiment

After adapting to the environment for 2 days, eighteen mice were randomly assigned to three groups: Normal group (mn), Placebo control group (mm), and DQ treatment group (mdq). Each group was half male and half female. AAD model was established according to the methods groped by our research team in the early stage (Zeng et al. 2012). Specifically, mice in the placebo control and DQ treatment groups were fed with 23.33 mL·kg−1·day−1 (0.35 mL per time) the antibiotics mixture solution from the third day, twice a day, and then successfully developed diarrhea after 5 days. The evaluation criteria of the successful AAD model are the frequency of defecation increased (2–3 times per day), feces becomes wet and soft, and perianal area becomes dirty. After successful molding, mice in the DQ treatment group were treated with 0.35 mL DQ per time, twice a day, for four consecutive days from the eighth day. In the meanwhile, sterile water was used as a placebo in the placebo control group. The normal group was given the same frequency and amount of sterile water in the entire experiment. All mice were killed by cervical dislocation to collect intestinal mucosa samples on the twelfth day. In a sterile condition, intestinal mucosa was separately scraped by cover slips after rinsing its contents with normal saline. The intestinal mucosa of one male and one female in the same group was collected in one EP tube and then stored at 4 ℃.

16S rRNA gene amplicon and sequencing

The microbial metagenomic DNA of each sample was extracted as we previously described (Long et al. 2017; Wu et al. 2012). Measured the extracted metagenomic DNA by 1% agarose gel electrophoresis, amplicons targeting the 16S rRNA gene were generated with the barcoded primer pair 338F: (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R: (5′-GGACTACHVGGGTWTCTAAT-3′). Polymerase chain reaction (PCR) was performed using TransGen AP221-02: TransStart Fastpfu DNA Polymerase with ABI GeneAmp® 9700. Amplicons of the same sample (three replicates per sample) were mixed and then measured by 2% agarose gel electrophoresis. Amplicons were purified with AxyPrepDNA Gel Extraction Kit (AXYGEN Company) and quantified using QuantiFluorTM-ST Handheld Fluorometer with Blue Channel (Promega Company). Amplicons were pooled equally after the individual quantitative step. The pooled sample was then used to generate a library, and high-throughput sequencing was performed using the Illumina MiSeq platform at Wuhan Frasergen Genomic Medicine Co.,Ltd.

Bioinformatics and statistical analysis

Raw sequences were filtered and merged to obtain valid sequences. Valid sequences were then clustered to operational taxonomic units (OTUs) at 97% similarity, and the representative sequences of OTUs were defined by the taxonomy. Based on the taxonomy, statistical analysis of community structure was performed at various classification levels. Based on phylogeny, UniFrac analyses were carried out. For linear discriminant analysis effect size (LEfSe), non-parametric factorial Kruskal–Wallis sum-rank tests and unpaired Wilcoxon rank-sum tests were performed, followed by linear discriminant analysis (LDA) to assess the effect size of each differentially abundant taxon (Segata et al. 2011). In this study, a p value of < 0.05 was considered significant for both statistical methods. Bacteria with markedly increased numbers were defined as those with an LDA score (log10) of over 2. Based on the above analysis, a series of in-depth statistical and visual analysis of community structure and phylogeny were performed using R 3.6.3, and GraphPad Prism 8.

When appropriate, data were presented as mean and standard deviation (x¯ ± SD). SPSS 22.0 software was used to do statistical analysis. One-way analysis of variance (ANOVA) was performed to determine the effect of DQ on intestinal mucosal microbiota. Differences in characteristics of groups were considered significant at p < 0.05.

Results

Statistics of sequences and OTUs

A total of 335,706 valid sequences, with an average length of 443.68 bp, were detected from three groups of samples. Good’s coverages of samples were over 99.78% (from 0.997813 to 0.998993), which reflects that the sequence results can represent the true situation of the microorganisms in the sample. By randomly sampling the sequences, a rarefaction curve was constructed to compare the species richness in the samples as well as explain whether the sequence data of the samples were reasonable (Fig. 1a). There were 521 bacteria identified from three groups, and the total OTUs reached 288 in the normal group, 443 in the placebo control group, and 229 in the DQ treatment group (Fig. 1b). These bacteria came from 270 genera, 132 families, 73 orders, 33 classes, 18 phyla.

Fig. 1.

Fig. 1

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Rarefaction curve (a) and Venn diagram (b) of OTUs (at distance 0.03). a The abscissa represents the sequences randomly selected per sample, and the ordinate represents the number of OTUs found at the corresponding depth; b OTU numbers. mn: the normal group, mm: the placebo control group, mdq: the D. hansenii and QWBZP (DQ) treatment group

Community diversity of the mucosal microbiota

Alpha diversity, including a series of statistical indices, is regularly adopted to summarize the structure of an ecological community concerning its richness (number of taxonomic groups), evenness (distribution of abundance of the groups), or both (Willis 2019). The larger the Chao1 and ACE values, the richer the total number of species in the environment. The larger the Shannon value or the smaller the Simpson value, the more diverse the species in the environment. The alpha diversity indexes have no significant changes among the three treatments (p > 0.05), but the species richness and species diversity index of the mm group fluctuated greatly (Fig. 2).

Fig. 2.

Fig. 2

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Comparison of alpha diversity index of three groups. Data are expressed as x¯±SD, n = 3, p > 0.05 (one-way ANOVA). mn: the normal group, mm: the placebo control group, mdq: the D. hansenii and QWBZP (DQ) treatment group

Beta diversity, which can be evaluated in many different ways, is broadly used to analyze the partitioning of biological diversity of environments or along a gradient (Legendre and Caceres 2013). Principal component analysis (PCA) was carried out to reflect the difference and distance of sample composition between groups (Fig. 3a). Unweighted uniFrac measure, which uses the presence/absence of bacteria or OTUs to compare community composition, is a qualitative measure (Lozupone et al. 2007). In this case, we used a hierarchical clustering method called unweighted pair group method with arithmetic mean (UPGMA) to create a tree structure describing the similarities and differences of microbial communities between samples (Fig. 3b). As shown in Fig. 3, the similarity of bacterial community composition of each sample in the mdq group is the highest, while that in the mm group is the lowest. Phylogenetically, the gut microbiota of the DQ treatment group and the normal group were closer to each other than to the placebo control group.

Fig. 3.

Fig. 3

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Beta diversity analysis of intestinal mucosal microbiota. a Principal component analysis (PCA). Points of different colors or shapes represent sample groups under different conditions. The scales on the horizontal and vertical axes are relative distances and have no practical significance. The more similar the sample composition, the closer the distance reflected in the PCA graph. b Unweighted pair group method with arithmetic mean (UPGMA) analysis based on unweighted uniFrac distance. The phylogenetic relationship between samples can be directly observed by the length of branches and the clustering distance. mn: the normal group, mm: the placebo control group, mdq: the D. hansenii and QWBZP (DQ) treatment group

Taxonomic composition of the mucosal microbiota

A total of 18 phyla of bacteria were identified from samples of three groups. The dominant phyla in the intestinal mucosa of mice were Firmicutes and Proteobacteria, followed by Bacteroidetes, and Actinobacteria (Fig. 4). The results of multiple comparisons showed that the relative abundance of Bacteroidetes in the mm group (12.66% vs 2.34%, p = 0.001) and Tenericutes in the mm (1.25% vs 0.01%, p = 0.002) and the mdq group (1.25% vs 4.63e−003%, p = 0.002) were significantly less than that in the mn group. Compared with the mm group, the relative abundances of Bacteroidetes (2.34% vs 14.32%, p = 0.000) and Actinobacteria (3.76% vs 12.25%, p = 0.01) in the mdq group increased dramatically. Gracilibacteria, Gemmatimonadetes, Fusobacteria, SBR1093, and Peregrinibacteria were identified only in the mm group. Acidobacteria and Deinococcus–Thermus were not detected in the mn group, and Deferribacteres was not detected in the mdq group.

Fig. 4.

Fig. 4

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Histogram of the intestinal mucosal bacterial composition at the phylum level. Data are expressed as x¯±SD, n = 3. *p < 0.05 (one-way ANOVA). mn: the normal group, mm: the Placebo control group, mdq: the D. hansenii and QWBZP (DQ) treatment group

A total of 270 genera of bacteria were detected from samples of three groups. The top 20 genera in the three groups are presented in Fig. 5. In the mdq group, genera with a relative abundance of more than 10% were Stenotrophomonas (17.62%), Robinsoniella (14.43%), Bacteroidales S24-7 group norank (14.28%), Citrobacter (11.98%), Glutamicibacter (11.37%) and Enterococcus (10.72%), while in mm group, only Lactobacillus (40.05%). In addition, Lactobacillus (44.16%), Bacteroidales S24-7 group norank (12.33%), Candidatus Arthromitus (10.61%), and Stenotrophomonas (10.41%) were the dominant genus in the mn group. Compared with placebo, the treatment of DQ significantly reduced the abundance of Lactobacillus (p = 0.009) and Bifidobacterium (p = 0.037) and in the meantime dramatically increased the abundance of Bacteroidales S24-7 group norank (p = 0.000), Citrobacter (p = 0.000), Glutamicibacter (p = 0.005), Robinsoniella (p = 0.000), and Carnobacterium (p = 0.001) (Table 1).

Fig. 5.

Fig. 5

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Heatmap of the top 20 genera of three groups. The darker the red color, the higher the relative abundance is. The darker the blue color, the lower the relative abundance is. mn: the normal group, mm: the placebo control group, mdq: the D. hansenii and QWBZP (DQ) treatment group. mn the normal group, mm the Placebo control group, mdq the D. hansenii and QWBZP (DQ) treatment group

Table 1.

The abundance of dominant microorganisms or different microorganisms in three groups at the genus level

Genus mn mm mdq
Lactobacillus 12,719.33 ± 1696.46 11,535.67 ± 6008.81 424.33 ± 220.77**##
Bacteroidales S24-7 group norank 3550.33 ± 855.91 471 ± 225.96** 4112.33 ± 409.57##
Candidatus Arthromitus 3056.67 ± 509.94 71 ± 42.58** 9.33 ± 8.50**
Stenotrophomonas 2998.67 ± 687.74 2495.67 ± 1711.03 5073.67 ± 208.38#
Robinsoniella 26.67 ± 14.57 363.67 ± 251.56 4157 ± 783.68**##
Citrobacter 1399 ± 36.50 857.33 ± 628.02 3449.67 ± 383.58**##
Glutamicibacter 1233 ± 510.65 569 ± 349.48 3277.33 ± 1190.38*##
Enterococcus 864.67 ± 334.21 1591.67 ± 1484.52 3089 ± 365.14*
Bifidobacterium 639 ± 93.54 297 ± 200.73** 17.33 ± 10.41**#
Gastranaerophilales norank 1.67 ± 1.15 0* 0*
[Ruminococcus] torques group 0.67 ± 1.15 130.67 ± 96.92* 2 ± 1#
Enterobacteriaceae unclassified 16.67 ± 14.19 43.33 ± 36.55 0
Coprococcus 1 0 0 8.67 ± 1.53**##
Tyzzerella 4 0 0 3.67 ± 1.15**##
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*p < 0.05, **p < 0.01 versus the mn group (ANOVA). #p < 0.05, ##p < 0.01 versus the mm group (ANOVA)

mn the normal group, mm the placebo control group, mdq the D. hansenii and QWBZP (DQ) treatment group

Difference analysis of mucosal microbiota

LEfSe was performed to determine the microbiota with significant differences between different groups (Fig. 6). Robinsoniella (p = 0.027, belongs to Lachnospiraceae), Glutamicibacter (p = 0.039, belongs to Micrococcaceae), Tyzzerella 4 (p = 0.021, belongs to Lachnospiraceae) and Coprococcus 1 (p = 0.022, belongs to Lachnospiraceae) were main differential microbiota in the mdq group.[Ruminococcus] torques group (p = 0.043) and Enterobacteriaceae unclassified (p = 0.046) were key new-colonized bacteria in the mm group. Candidatus Arthromitus (p = 0.027), Bifidobacterium (p = 0.027), and Gastranaerophilales norank (p = 0.021) were prevalent microbiota in the mn group.

Fig. 6.

Fig. 6

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Differential bacteria based on Linear discriminant analysis effect size (LEfSe). LEfSe revealed a list of bacteria that enable discrimination between the normal, placebo control, and DQ treatment groups. a Linear discriminant analysis (LDA) histogram. A p value of < 0.05 was considered significant for Kruskal–Wallis and Wilcoxon tests. Bacteria with markedly increased numbers were defined as those with an LDA score (log10) of over 2. The length of the histogram represents the LDA score, that is, the effect size of bacteria with significant differences between different groups. b Cladogram. From the inside out is the bacterial taxonomic level of phylum, class, order, family, and genus. The red, green and blue nodes in the evolutionary tree represent the bacteria that play an important role in mdq, mm and mn groups respectively, while the yellow nodes represent the bacteria with no significant difference. mn the normal group, mm the placebo control group, mdq the D. hansenii and QWBZP (DQ) treatment group

Discussion

The gut microbiota is immensely diverse, varies from individuals but its core microbiome is stable and conservative (Lozupone et al. 2012). Studies have addressed that the administration of antibiotics resulting in decreases in species diversity. A 2-week course of ampicillin, streptomycin, and clindamycin can significantly reduce the microbial diversity in the cecum and large intestine contents of treated mice (Grazul et al. 2016). The use of cefotaxime sodium and lincomycin hydrochloride had similar effects (Li et al. 2020). Probiotics or a moderate-dose Hetiao Jianpi Decoction played a big part in remodeling the diversity (Grazul et al. 2016; Li et al. 2020). We also found that D. hansenii treatment was able to adjust the diversity and composition of intestinal mucosal microbiota of AAD mice (He et al. 2019). Similar to the previous study, the differences between individuals in species richness and species diversity of the DQ treatment group are smaller than that of the placebo control group. Moreover, compared with the placebo control group, the mucosal microbiota of the DQ treatment group has a closer phylogenetic relationship with that of healthy individuals. We speculate that DQ helps to reshape the diversity of microbiota after antibiotics administration and regulate homeostasis.

At the phylum level, DQ treatment significantly promoted the recolonization of Bacteroides and Actinomyces. At the genus level, Stenotrophomonas, Robinsoniella, Bacteroidales S24-7 group norank, Citrobacter, and Glutamicibacter were priority populations of the DQ treatment group and were significantly richer than that of the placebo control group. Also, the effect of DQ on intestinal mucosal microbiota in AAD mice is not consistent with the application of QWBZP or D. hansenii alone. Our previous study found that the priority bacteria were Lactobacillus, Enterococcus, Bacteroidales S24-7 group norank, and Stenotrophomonas in AAD mice treated with QWBZP or D. hansenii alone (Long et al. 2020; Zeng et al. 2019). Contrary to a previous study, we observed a high concentration of Lactobacillus in the placebo control group, which was associated with colonization resistance and mucosa barrier (Xie et al. 2019). This may be related to the resilience of intestinal microbiota to a short-term broad-spectrum antibiotic intervention (Palleja et al. 2018). However, this resilience cannot completely remodel the microbiota composition to baseline (Haak et al. 2019). Numerous studies have confirmed that antibiotic administrations resulted in long-lasting and profound changes in the composition and function of host–microbiota (Becattini et al. 2016; Scott et al. 2018).

Interestingly, Bacteroidales S24-7 group norank was the only genus with high abundance enriched in both the normal group and the DQ treatment group but strikingly diminished in the placebo control group. This finding is consistent with the effect of DQ on intestinal lumen microbiota (Xie et al. 2020) and has attracted our attention. Bacteroidales S24-7 group norank was recognized as butyrate-producing bacteria (Tang et al. 2018). Butyrate is a major short-chain fatty acid (SCFA) that is metabolized by gut bacteria from indigestible fiber-rich diets. SCFA is essential for maintaining the intestinal homeostasis and modulating the immune response in the organism (Serino 2019). A study has shown there were inverse correlations between mucin O-glycan levels and the production of n-butyrate in UC patients, while n-butyrate production exhibited an enhance in mucin-fed rodents leading to the expansion of RORγt + Treg cells and IgA-producing cells in colonic lamina propria (Yamada et al. 2019). In our studies, the DQ treatment group exhibited a significant increase in the abundance of Bacteroidales S24-7 group norank, and the abundance of this genus was strikingly diminished in the Placebo control group. This result suggests an important role for the butyrate-producing bacteria in the efficacies of DQ.

Excepting Bacteroidales S24-7 group norank, Blautia, and Erysipelotrichaceae norank also were the predominant genera in the intestinal lumen of the DQ treatment group (Xie et al. 2020), which differ from the composition of intestinal mucosa bacteria. The differences in bacterial composition of the intestinal lumen and the intestinal mucosa proved that the spatial heterogeneity of bacteria in their host displays along not only the length of gut (from mouth to rectum) (Martinez-Guryn et al. 2019) but also the cross section (from lumen to mucosa). This finding, on the other hand, also lays a foundation for us to further exploring the spatial heterogeneity and functional heterogeneity between bacteria and their host (Tropini et al. 2017). Moreover, Blautia and Erysipelotrichaceae are butyrate-producing bacteria (Liu et al. 2019; Wang et al. 2018). It is further confirmed that the effects of DQ on AAD mice are related to butyrate-producing bacteria.

In our previous study, we found that Paeniclostridium and Ruminococcus gauvreauii group were the key biomarkers of intestinal mucosal microbiota in AAD mice treated with D. hansenii (Zeng et al. 2019). Different from findings using D. hansenii alone for treatment, LEfSe analysis showed that Robinsoniella, Tyzzerella 4, Coprococcus 1, and Glutamicibacter were the key differential bacteria of the DQ treatment group. The first three are members of the Lachnospiraceae family, and the last one belongs to the Micrococcaceae family. The loss of Lachnospiraceae and Ruminococcaceae families caused by cefoperazone, clindamycin, and vancomycin has been proved to be associated with susceptibility of C. difficile in the large intestine and significant loss of secondary bile acids (Theriot et al. 2016). An investigation of microbes that exist in utero and interact with the intestinal immune system found that Micrococcaceae and Lactobacillus were the most abundant families in fetal meconium, and fetal intestines dominated by Micrococcaceae exhibited distinct patterns of T cell composition and epithelial transcription (Rackaityte et al. 2020). Early-life vancomycin treatment resulted in airway inflammation by prominently decreasing the abundances of Micrococcaceae and Clostridiaceae-1 (Yang et al. 2019). Bunker et al. demonstrated that microbiota from a sub-set of human individuals encoded two protein “superantigens” expressed on the surface of commensal bacteria of the family Lachnospiraceae such as Ruminococcus gnavus that bind IgA variable regions and stimulate potent IgA responses in mice (Bunker et al. 2019). Meanwhile, Lachnospiraceae family members such as Coprococcus are major butyrate-producing bacteria (Wang et al. 2018; Yamada et al. 2019). In this study, the abundance of Micrococcaceae family in the intestinal mucosa decreased in the placebo control group but have a significant up-regulation in the DQ treatment group. Populations of Lachnospiraceae family including Coprococcus 1 and [Ruminococcus] gnavus group were promoted by DQ in the intestinal mucosa. These results indicate that DQ may promote a mucosal immune response by up-regulating Micrococcaceae and Lachnospiraceae family in AAD mice from dysbiosis.

Conclusion

In summary, the combined use of D. hansenii and QWBZP overcame the influence of dysbacteriosis and could lead to the recovery of intestinal mucosal microbiota homeostasis. This positive effect is likely related to short-chain fatty acids (SCFAs)-producing bacteria, such as members of Micrococcaceae, Lachnospiraceae, and Bacteroidales S24-7 group, which could play beneficial roles in protecting the mucosal barrier and stimulating the immune response in mice.

Acknowledgements

Thanks for the sequencing service provided by Wuhan Frasergen Genomic Medicine Co., Ltd.

Author contributions

ZT designed the study; CZ performed the experiments; HS analyzed the data and wrote the manuscript; CW and ZT checked the manuscript. All authors read and approved the final manuscript. The decision to submit the manuscript for publication was made by all the authors.

Funding

This work was supported by the National Natural Science Foundation of China (No. 81573951) and the Key Research and Development Program of Hunan Provincial (No. 2019NK2192).

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Human and animal rights

All animal work was carried out in accordance within the guidelines of the Institutional Animal Care and Use Committee of Hunan University of Chinese Medicine (No. 20171202).

Contributor Information

Chunhui Wang, Email: wch998@126.com.

Zhoujin Tan, Email: tanzhjin@sohu.com.

References

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Associated Data

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

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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