The Effect of Lactobacillus plantarum BW2013 on The Gut Microbiota in Mice Analyzed by 16S rRNA Amplicon Sequencing

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VOLUME 70 , ISSUE 2 (June 2021) > List of articles

The Effect of Lactobacillus plantarum BW2013 on The Gut Microbiota in Mice Analyzed by 16S rRNA Amplicon Sequencing

TONG TONG / XIAOHUI NIU / QIAN LI / YUXI LING / ZUMING LI * / JIA LIU / MICHAEL ZHANG / ZHIHUI BAI / RAN XIA / ZHICHAO WU / XIU LIU

Keywords : Lactobacillus plantarum, composition, gut microbiota, 16S rRNA amplicon sequencing, BALB/c mice

Citation Information : Polish Journal of Microbiology. Volume 70, Issue 2, Pages 235-243, DOI: https://doi.org/10.33073/pjm-2021-022

License : (CC-BY-NC-ND 4.0)

Received Date : 21-March-2021 / Accepted: 17-May-2021 / Published Online: 21-June-2021

ARTICLE

ABSTRACT

Lactobacillus plantarum BW2013 was isolated from the fermented Chinese cabbage. This study aimed to test the effect of this strain on the gut microbiota in BALB/c mice by 16S rRNA amplicon sequencing. The mice were randomly allocated to the control group and three treatment groups of L. plantarum BW2013 (a low-dose group of 108 CFU/ml, a medium-dose group of 109 CFU/ml, and a high-dose group of 1010 CFU/ml). The weight of mice was recorded once a week, and the fecal samples were collected for 16S rRNA amplicon sequencing after 28 days of continuous treatment. Compared with the control group, the body weight gain in the treatment groups was not significant. The 16S rRNA amplicon sequencing analysis showed that both the Chao1 and ACE indexes increased slightly in the medium-dose group compared to the control group, but the difference was not significant. Based on PCoA results, there was no significant difference in β diversity between the treatment groups. Compared to the control group, the abundance of Bacteroidetes increased in the low-dose group. The abundance of Firmicutes increased in the medium-dose group. At the genus level, the abundance of Alloprevotella increased in the low-dose group compared to the control group. The increased abundance of Ruminococcaceae and decreased abundance of Candidatus_Saccharimonas was observed in the medium-dose group. Additionally, the abundance of Bacteroides increased, and Alistipes and Candidatus_Saccharimonas decreased in the high-dose group. These results indicated that L. plantarum BW2013 could ameliorate gut microbiota composition, but its effects vary with the dose.

Graphical ABSTRACT

Introduction

Gut microbiota, a large and complex microbial community in the gastrointestinal tract, is essential to the host’s health and well-being (Koh et al. 2016; Liu et al. 2020). Gut microbiota can not only break down the indigestible carbohydrates in food (Schwalm and Groisman 2017), but also produce short-chain fatty acids (SFAs), which can provide nutrition for gut microbiota (Jia et al. 2020). The disturbance of gut microbiota induces inflammation, insulin resistance, diabetes, and osteoporosis (Ma et al. 2019; Bi et al. 2020). Even the novel coronavirus pneumonia was found associated with gut microbiota disturbance (He et al. 2020a).

Probiotics can modulate gut microbiota and cause favorable changes in the gut microbiota structure and functions (Hasan et al. 2019). When given enough dose, probiotics will reach the intestinal tract in an active state, thus improving intestinal microorganisms’ balance and producing beneficial effects on the host (Deng et al. 2020). Lactobacillus casei ZX633 may ameliorate the infant diarrhea microbiota, thus reducing the rate of infant bacterial diarrhea (Wang et al. 2020b). After treated with mixed lactic acid bacteria, Staphylococcus aureus infection could be prevented in mice, and the structure of intestinal microbiota could be improved (Ren et al. 2018).

Lactic acid bacteria and Bifidobacteria are the most commonly used probiotics (He et al. 2020b). Lactobacillus plantarum, a rod-shaped, facultative anaerobic, Gram-positive lactic acid bacterium, can effectively improve the health of the host by decreasing the level of bloodstream cholesterol, managing gastrointestinal disorders, and preventing diarrhea (Liu et al. 2015; Seddik et al. 2017a). (Wang et al. 2018) found that L. plantarum ZDY2013 remits ulcerative colitis by modifying of intestinal microbiota to regulate both oxidative stress and inflammatory mediators. Some functional activities are strain-specific (Biagioli et al. 2019). (Qiu et al. 2018) injected mice with potential probiotic strains, including L. plantarum ZDY04 (PLA04) and L. plantarum ZDY01 (PLA01). As a result, both serum trimethylamine N-oxide and cecal trimethylamine levels was reduced significantly only by L. plantarum ZDY04. (Li et al. 2019) demonstrated the loss of gut microbiota diversity induced by glycerol monolaurate could be remedied by L. plantarum T17, but the same effects were not found in the group of L. plantarum T34. L. plantarum BW2013 was isolated from fermented Chinese cabbage, and the influence of this strain on the gut microbiota is unknown.

Many methods were used to study the gut microbiota, such as the culture of gut microbiota, polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE), quantitative real-time polymerase chain reaction (qRT-PCR), 16S rRNA amplicon sequencing (Margiotta et al. 2020; Ling et al. 2020). As a relatively new technology, 16S rRNA amplicon sequencing opens out new potential avenues of research and facilitates in-depth studies exploring microbial populations and their dynamics in the animal gut (Peng and Zhang 2009; Kim and Isaacson 2015). The 16S rRNA technology has been widely used in biomedical research, linking the establishment between microbiota disorders and human disease (Evariste et al. 2019). For example, Zhu et al. (2020) used 16S rRNA amplicon sequencing to study the gut microbiota of ulcerative colitis with different glucocorticoid response types and found that they had different bacterial composition and function, which linked the microbiota disorders and ulcerative colitis. Analysis of 16S rRNA amplicon sequencing of intestinal microbiota found that high-calorie diet and lipopolysaccharide atomization synergistically promoted pneumonia process in rat pups, which is related to changes in the structure of intestinal flora (Bai et al. 2020).

Based on 16S rRNA amplicon sequencing, this study investigated the effects of different doses of L. plantarum BW2013 on gut microbiota composition in mice.

Experimental

Materials and Methods

Bacterial strains and cultural conditions. L. plantarum BW2013 was isolated from fermented Chinese cabbage and preserved by the China General Microbiological Culture Collection Center (CGMCC NO. 9462). L. plantarum BW2013 was grown anaerobically in the Man-Rogosa-Sharpe medium broth for 20 h at 37°C, and then centrifuged at 3,000 g for 15 min. The bacteria were washed twice and resuspended in sterile phosphate-buffered saline (PBS, pH 7.4).

Simulate gastrointestinal digestion. The simulated gastrointestinal juice was produced with following (Shinde et al. 2019), and amylase, pepsin, bovine bile, and trypsin were purchased from Sigma. First, bacteria (1 ml, 1 × 109 CFU/ml) were suspended in 5 ml simulated salivary juice for 5 min. Then, the samples were resuspended to 10 ml gastric juice and incubated for 2 h. Subsequently, the samples were resuspended to 10 ml intestinal juice and incubated for 2 h. The entire digestion procedure was performed at 37°C, with stirring to simulate peristaltic contraction. After the simulated digestion process, the bacteria cell suspensions were diluted and plated onto MRS agar plates. The number of colonies was counted after 24 h of incubation at 37°C according to the formula:

survivalrate(%)=N1/N0×100%

where N1 was the total viable count of strains after treatment and N0 was the total viable count of strains before treatment.

Adhesion to Caco-2 cell. Caco-2 cell cultures were determined by the method of Fonseca et al. (2021). After cultured in MRS broth for 24 h at 37°C and washed twice with phosphate-buffered solution, the bacteria were resuspended in DMEM approximately 109 CFU/ml. Then, 1 ml bacterial suspension was added to cells and incubated for 60 min at 37°C in a 5% CO2 atmosphere. Subsequently, the cells were washed three times with 1 ml of PBS to remove non-adherent bacterial cells and lysed with 1 ml of Triton-X solution at 37°C for 5 min. After the above procedures, the solution was serial diluted and plated on MRS agar to determine the bacterial counts.

Animal, rearing and grouping. The mice (8-week-old male) used in the experiment were purchased from Vital River Laboratories Inc. (Beijing, China). Mice were singly caged under specific pathogen-free conditions at 20–22°C, and relative humidity of 40–60%. Before intragastric administration, the mice were weighed, and the feces were collected for 16S rRNA gene amplicon sequencing analysis. Then the mice were randomly allocated to four groups (each group n = 10): control group (NC) and three treatment groups of L. plantarum BW2013: a low-dose (108 CFU/ml) group (LDG), a medium-dose (109 CFU/ml) group (MDG), and a high-dose group (1010 CFU/ml) (HDG). From 9 a.m. to 10 a.m. every day, the NC group was given sterile PBS (pH 7.4), and treatment groups were administered the corresponding of L. plantarum BW2013 suspension at 400 μl/d once daily over 28 days. All mice were weighed once a week. During the experiment, the mice were fed a normal diet.

16S rRNA gene amplicon sequencing. The fecal genomic DNA was extracted according to the manufacturer’s guidelines of DP712-Magnetic Bead Soil and Fecal Genomic DNA Extraction Kit (Tiangen, China). For 16S rRNA gene amplicon sequencing, the DNA samples were amplified with primers 27F (5’-AGAGTTTGATCMTGGCTCAG-3’) and 519R (5’-GWATTACCGCGGCKGCTG-3’), which targeted V3-V4 hypervariable regions of the bacterial 16S rRNA gene (Ranasinghe et al. 2012). PCR program was applied, as follows: the initial denaturation at 95°C for 15 min, the amplification of 34 cycles under various conditions (at 95°C for 30 s, 58°C for 30 s and 68°C for 1 min), and the final extension at 68°C for 5 min. Then the purified amplicons were sequenced with an Illumina Miseq sequencing platform at Novogene Bioinformatics Technology Co., Ltd. (Tianjin, China).

The same volume of 1 × loading buffer (contained SYB green) was mixed with PCR products, and electrophoresis was operated on 2% agarose gel for detection. PCR products were mixed in equal density ratios. Then, the mixture of PCR products was purified with Gene JETTM Gel Extraction Kit (Thermo Scientific).

Sequencing libraries were generated using Ion Plus Fragment Library Kit 48 rxns (Thermo Scientific) following the manufacturer’s recommendations. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific). At last, the library was sequenced on an Ion S5TM XL platform and 400 bp/600 bp single-end reads were generated.

Statistical analysis. The analysis of variance for multiple comparisons was performed in Prisma software (version 5). Statistical differences were evaluated by analysis of variance (ANOVA) and Dunnett-t pairwise comparisons. Cutadapt (V1.9.1) was used for quality control (Martin 2011). Uparse software (Uparse v7.0.1001, http://www.drive5.com/uparse/) was applied to cluster the clean reads to OTUs (Edgar 2013). Species annotation analysis was carried out using the Mothur method and SSSUrRNA database of SILVA132 (http://www.arb-silva.de/) to obtain taxonomic information at each taxonomic level (Quast et al. 2013).

Results

Tolerance to simulated digestion test and adhesion to Caco-2 cell. The survival rate of L. plantarum BW2013 after the simulated gastrointestinal digestion process was 2.90%, and the adhesion rates of L. plantarum BW2013 was 2.4%.

Effect of L. plantarum BW2013 on the body weight gain of mice. Before intragastric administration, the mice were weighed. Then body weight was recorded once a week. The weight changes of mice were shown in Fig. 1. There was no significant difference in body weight gain among the four groups.

Fig. 1.

Body weight variations of the mice after L. plantarum BW2013 gavage. The data are represented as mean ± SD.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

10.33073_pjm-2021-022-f001.jpg

Overall sequences and OTUs. An average of 85,386 reads was measured per sample by 16SrRNA amplicon sequencing, and an average of 80,298 clean reads was obtained after quality control. The clean reads of all samples were clustered by OTUs (operational taxonomic units) with 97% identity. A total of 1,120 OTUs were obtained, and 74 OTUs were annotated to the genus level.

Diversity indexes among the NC and treatment groups. Compared with NC group, the Chao 1 and ACE indexs (Table I) were slightly higher than those in the MDG group. But there were no significant differences for all the α-diversity indexes among the NC and treatment groups. Compared with the initial state, the Chao1 index of the NC and MDG groups increased significantly, respectively (p = 0.0379, p = 0.0267).

Table I

The α diversity index of gut microbiota in each group.

10.33073_pjm-2021-022-tbl1.jpg

The changes in gut microbiota among groups were examined by using principal coordinate analysis (PCoA). Based on weighted unifrac distance, PCoA analysis was conducted to compare the microbial community composition of different samples (Fig. 2). On the weighted unifrac PCoA score plot, the NC group’s symbols were separated from those of the treatment groups, which revealed that the microbiome composition of treatment groups was different from those of the NC group, but there was no significant difference. Additionally, there was no significant difference between initial state and NC group.

Fig. 2.

Principal co-ordinates analysis (PCoA) of the microbial communities of different groups. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

10.33073_pjm-2021-022-f002.jpg

Relative abundance of gut microbiota at phylum and genus levels. The relative abundance of gut microbiota was measured at the phylum (Fig. 3) and genus (Fig. 4) levels.

Fig. 3.

Microbial community bar plot at the phylum level. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

10.33073_pjm-2021-022-f003.jpg
Fig. 4.

Microbial community bar plot at the genus level. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

10.33073_pjm-2021-022-f004.jpg

Examining the changes in the gut microbiota, the top ten bacterial phyla of the NC and treatment groups were evaluated. The results showed that Bacteroidetes was the most abundant phylum, followed by Firmicutes, Deferribacteres, and Proteobacteria in the NC and treatment groups. L. plantarum BW2013 mainly affected the abundance of Bacteroidetes and Firmicutes, which accounted for 97% of the total bacteria (Fig. 3). Compared with the NC group, the abundance of Bacteroidetes increased significantly in the LDG group (p = 0.04). Additionally, the relative abundance of Firmicutes increased significantly in the MDG group compared to the NC group (p = 0.01).

The four most prevalent bacterial genera in the guts of the NC and treatment groups were Alistipes, Alloprevotella, unidentified_Ruminococcaceae, and Bacteroides (Fig. 4). Compared to the NC group, Alistipes exhibited significantly decreased proportions in the HDG group (p = 0.038). In addition, the abundance of Candidatus_Saccharimonas decreased significantly in the MDG and HDG groups (p=0.01, p=0.007). By contrast, Alloprevotella, unidentified_Ruminococcaceae, and Bacteroides showed an upward trend. The abundance of Alloprevotella in the LDG group was significantly higher than that in the NC group (p=0.001). Moreover, compared with the NC group, the abundance of unidentified_Ruminococcaceae in the MDG group increased significantly (p = 0.014), while the abundance of Lactobacillus increased slightly, but there was no significant difference. In addition, the proportion of Bacteroides increased significantly in the HDG group compared to the NC group (p = 0.038).

Clustering analysis of species abundance. The heat map showed the relative abundance of the main identified bacteria at the genus level. As shown in Fig. 5, the clustering of gut microbiota was different in the groups. In the initial state, gut microbiota was mainly clustered in Firmicutes. In the NC group, Bacteroidetes were concentrated in the genus of Desulfovibrio. Both Parabacteroides and Alloprevotella from Bacteroidetes dominated the LDG group. In the MDG group, there were large quantities of Ruminococcaceae and Lachnospira in Firmicutes. In the HDG group, Bacteroidetes were concentrated in the genus of Bacteroides.

Fig. 5.

Heat map analysis of the gut microbiota at the phylum and genus levels. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

10.33073_pjm-2021-022-f005.jpg

Discussion

The consumption of probiotics has been reported to modulate the composition and structure of the gut microbiome and treat multiple diseases, but some functional activities are strain-specific (Biagioli et al. 2019; Hsu et al. 2019). There are also various methods for detecting gut microbiota, among which 16S rRNA amplicon sequencing is a faster and cheaper way to study the gut microbiome (Goldfeder et al. 2017). In this study, the effect of L. plantarum BW2013 on gut microbiota composition in BALB/c mice was investigated by 16S rRNA amplicon sequencing.

16S rRNA amplicon sequencing could be used to determine all microorganisms’ genetic composition and community function in environmental samples (Qi et al. 2019). In our study, the results of 16S rRNA amplicon sequencing showed that the ACE index and Chao1 index slightly increased in the MGD group compared to the NC group, but there was no significant difference. A previous study also showed that the microbial richness (Chao1 and Shannon) was not significantly improved after the L. plantarum LIP-1 treatment (Song et al. 2017), which is consistent with our results. Based on PCoA, there was no significant difference in β diversity in the treatment groups compared to the NC group. There was also no significant difference between the initial state and NC group.

At the phylum level, the abundance of Bacteroidetes and Firmicutes accounted for 97% of the total bacteria. Bacteroidetes can regulate the chaotic state of intestinal microorganisms to a balanced state (Wang et al. 2020a). Firmicutes may play important roles in gastrointestinal health, and affect the metabolism and function of gut microbes (Zhao et al. 2018). In this study, the relative abundance of Bacteroidetes in the LDG group was significantly higher than that in the NC group. (Li et al. 2017) found that L. casei CCFM419 increased the abundance of Bacteroidetes, which is similar to our result. Our study showed that the abundance of Firmicutes increased significantly in the MDG group compared with the NC group. L. plantarum 12 increased the relative abundance of Firmicutes (Sun et al. 2020), which is similar to our result. Contrary to our results, a strain of L. plantarum decreased the abundance of Firmicutes (Zhang et al. 2019).

At the genus level, L. plantarum BW2013 significantly increased the abundance of Alloprevotella and Ruminococcaceae, and significantly decreased the abundance of both Alistipes and Candidatus_Saccharimonas. Alistipes is pathogenic in colorectal cancer and is associated with mental signs of depression (Parker et al. 2020). Candidatu_Saccharimonas has been associated with inflammatory diseases, such as gingivitis and other periodontal dysfunctions (Cruz et al. 2020). Alloprevotella can produce vitamin B1 and folic acid, and an increase in the abundance of Alloprevotella was associated with the improvement of intestinal disorders Seddik et al. 2017; Qi et al. 2019). Based on our results, the abundance of Alloprevotella in the LDG group was significantly higher than in the NC group. Kong et al. (2018) found that the abundance of Alloprevotella increased significantly after probiotics treatment. This finding is similar to our result. Ruminococcaceae can produce butyrate, which can provide energy for intestinal epithelial cells (LeBlanc et al. 2017). In our study, compared with the NC group, the abundance of Ruminococcaceae in the MDG group increased significantly. Wang et al. (2018) found that the abundance of Ruminococcaceae showed a decreasing trend in L. plantarum ZDY2013 group, which is different from our result. It may be due to the different strains used in the experiment. Biagioli et al. (2019) mentioned that some functional activities are strain-specific. Our study showed that compared with the NC group, the abundance of Bacteroides in the HDG group increased significantly. Li et al. (2017) found that L. casei CCFM419 increased the abundance of Bacteroides, which is consistent with our result. Bacteroides are producers of short-chain fatty acids (SCFAs) (Du et al. 2020). SCFAs are produced by the fermentation of microorganisms in the gut and help regulate host energy homeostasis and physiological processes (Horiuchi et al. 2020). This correlation means that the presence of L. plantarum BW2013 can provide a positive impact on host health. In our study, Desulfovibrio was enriched in the NC group in the heat map, while Parabacteroides, Alloprevotella, Ruminococcaceae, Lachnospira, and Bacteroides were concentrated in the treatment groups. Our results collectively suggested that L. plantarum BW2013 the effect of ameliorating gut microbiota composition, but its effects vary with the dose.

Conclusion

In this study, our results showed that treatment with L. plantarum BW2013 exerted an effect on the gut microbiota composition in mice. At the phylum level, the abundance of Bacteroidetes increased in the LDG group compared with the NC group, while the abundance of Firmicutes increased in the MDG group. At the genus level, the abundance of Alloprevotella was higher in the LDG group compared with the NC group. By contrast, the abundance of Ruminococcaceae increased in the MDG group, but Candidatus_Saccharimonas decreased. In addition, Bacteroides abundance increased in the HDG group, but Alistipes and Candidatus_Saccharimonas decreased. These results indicated that L. plantarum BW2013 had the effect of ameliorating the composition of gut mice, but its effect varies with dosing.

Financial disclosure

Ethical approval

This project was approved by the Ethics Committee of Functional Inspection Center of Health Food of Applied Science and Humanities in Beijing Union University (No. 2019-04).

Acknowledgments

This paper was supported by Beijing Natural Science Foundation [grant number 6173033], Beijing Union University Foundation [grant number 12213611605-001], Academic Research Projects of Beijing Union University [grant number ZK70202003], and Internal Trade Food Science and Technology (Beijing) Co., Ltd Cooperation Projects [grant number 202116].

Conflict of interest

The authors do not report any financial or personal connections with other persons or organizations, which might negatively affect the contents of this publication and/or claim authorship rights to this publication.

References


  1. Bai C, Liu T, Xu J, Ma X, Huang L, Liu S, Yu H, Chen J, Gu X. Effect of high calorie diet on intestinal flora in LPS-induced pneumonia rats. Sci Rep. 2020 Feb 3;10(1):1701. https://doi.org/10.1038/s41598-020-58632-0
    [PUBMED] [CROSSREF]
  2. Bi R, Gao J, Pan L, Lai X. Progress in the treatment of diabetes mellitus based on intestinal flora homeostasis and the advancement of holistic analysis methods. Nat Prod Commun. 2020;15(4):1–11. https://doi.org/10.1177/1934578X20918418
  3. Biagioli M, Capobianco D, Carino A, Marchianò S, Fiorucci C, Ricci P, Distrutti E, Fiorucci S. Divergent effectiveness of multispecies probiotic preparations on intestinal microbiota structure depends on metabolic properties. Nutrients. 2019 Feb 2;11(2):325.
    [CROSSREF]
  4. Cruz BCDS, Conceição LLD, Mendes TAO, Ferreira CLLF, Gonçalves RV, Peluzio MDCG. Use of the synbiotic VSL#3 and yacon-based concentrate attenuates intestinal damage and reduces the abundance of Candidatus Saccharimonas in a colitis-associated carcinogenesis model. Food Res Int. 2020 Nov;137:109721. https://doi.org/10.1016/j.foodres.2020.109721
  5. Deng X, Tian H, Yang R, Han Y, Wei K, Zheng C, Liu Z, Chen T. Oral probiotics alleviate intestinal dysbacteriosis for people receiving bowel preparation. Front Med (Lausanne). 2020 Feb 28;7:73. https://doi.org/10.3389/fmed.2020.00073
    [PUBMED] [CROSSREF]
  6. Du X, Xiang Y, Lou F, Tu P, Zhang X, Hu X, Lyu W, Xiao Y. Microbial community and short-chain fatty acid mapping in the intestinal tract of quail. Animals (Basel). 2020 Jun 9;10(6):1006. https://doi.org/10.3390/ani10061006
    [CROSSREF]
  7. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013 Oct;10(10):996–998. https://doi.org/10.1038/nmeth.2604
    [PUBMED] [CROSSREF]
  8. Evariste L, Barret M, Mottier A, Mouchet F, Gauthier L, Pinelli E. Gut microbiota of aquatic organisms: A key endpoint for ecotoxicological studies. Environ Pollut. 2019 May;248:989–999. https://doi.org/10.1016/j.envpol.2019.02.101
    [PUBMED] [CROSSREF]
  9. Fonseca HC, de Sousa Melo D, Ramos CL, Dias DR, Schwan RF. Probiotic properties of lactobacilli and their ability to inhibit the adhesion of enteropathogenic bacteria to Caco-2 and HT-29 cells. Probiotics Antimicrob Proteins. 2021 Feb;13(1):102–112. https://doi.org/10.1007/s12602-020-09659-2
    [PUBMED] [CROSSREF]
  10. Goldfeder RL, Wall DP, Khoury MJ, Ioannidis JPA, Ashley EA. Human genome sequencing at the population scale: a primer on high-throughput dna sequencing and analysis. Am J Epidemiol. 2017 Oct 15;186(8):1000–1009. https://doi.org/10.1093/aje/kww224
    [PUBMED] [CROSSREF]
  11. Hasan N, Yang H. Factors affecting the composition of the gut microbiota, and its modulation. PeerJ. 2019 Aug 16;7:e7502. https://doi.org/10.7717/peerj.7502
    [PUBMED] [CROSSREF]
  12. He LH, Ren LF, Li JF, Wu YN, Li X, Zhang L. Intestinal flora as a potential strategy to fight SARS-CoV-2 infection. Front Microbiol. 2020a Jun 9;11:1388. https://doi.org/10.3389/fmicb.2020.01388
    [CROSSREF]
  13. He Y, Xu R, Wang W, Zhang J, Hu X. Probiotics, prebiotics, antibiotic, Chinese herbal medicine, and fecal microbiota transplantation in irritable bowel syndrome: Protocol for a systematic review and network meta-analysis. Medicine (Baltimore). 2020b Aug 7; 99(32):e21502. https://doi.org/10.1097/MD.0000000000021502
    [CROSSREF]
  14. Horiuchi H, Kamikado K, Aoki R, Suganuma N, Nishijima T, Nakatani A, Kimura I. Bifidobacterium animalis subsp. lactis GCL2505 modulates host energy metabolism via the short-chain fatty acid receptor GPR43. Sci Rep. 2020 Mar 5;10(1):4158. https://doi.org/10.1038/s41598-020-60984-6
    [PUBMED] [CROSSREF]
  15. Hsu CN, Hou CY, Chan JYH, Lee CT, Tain YL. Hypertension programmed by perinatal high-fat diet: effect of maternal gut microbiota-targeted therapy. Nutrients. 2019 Dec 2;11(12):2908. https://doi.org/10.3390/nu11122908
    [CROSSREF]
  16. Jia Q, Wang L, Zhang X, Ding Y, Li H, Yang Y, Zhang A, Li Y, Lv S, Zhang J. Prevention and treatment of chronic heart failure through traditional Chinese medicine: Role of the gut microbiota. Pharmacol Res. 2020 Jan;151:104552. https://doi.org/10.1016/j.phrs.2019.104552
  17. Kim HB, Isaacson RE. The pig gut microbial diversity: Understanding the pig gut microbial ecology through the next generation high throughput sequencing. Vet Microbiol. 2015 Jun 12;177(3–4):242–251. https://doi.org/10.1016/j.vetmic.2015.03.014
    [PUBMED] [CROSSREF]
  18. Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell. 2016 Jun 2;165(6):1332–1345. https://doi.org/10.1016/j.cell.2016.05.041
    [PUBMED] [CROSSREF]
  19. Kong C, Gao R, Yan X, Huang L, Qin H. Probiotics improve gut microbiota dysbiosis in obese mice fed a high-fat or high-sucrose diet. Nutrition. 2019 Apr;60:175–184. https://doi.org/10.1016/j.nut.2018.10.002
    [PUBMED] [CROSSREF]
  20. LeBlanc JG, Chain F, Martín R, Bermúdez-Humarán LG, Courau S, Langella P. Beneficial effects on host energy metabolism of short-chain fatty acids and vitamins produced by commensal and probiotic bacteria. Microb Cell Fact. 2017 May 8;16(1):79. https://doi.org/10.1186/s12934-017-0691-z
    [PUBMED] [CROSSREF]
  21. Li X, Wang E, Yin B, Fang D, Chen P, Wang G, Zhao J, Zhang H, Chen W. Effects of Lactobacillus casei CCFM419 on insulin resistance and gut microbiota in type 2 diabetic mice. Benef Microbes. 2017 May 30;8(3):421–432. https://doi.org/10.3920/BM2016.0167
    [PUBMED] [CROSSREF]
  22. Li Y, Liu T, Zhang X, Zhao M, Zhang H, Feng F. Lactobacillus plantarum helps to suppress body weight gain, improve serum lipid profile and ameliorate low-grade inflammation in mice administered with glycerol monolaurate. J Funct Food. 2019;53: 54–61. https://doi.org/10.1016/j.jff.2018.12.015
    [CROSSREF]
  23. Ling Y, Li W, Tong T, Li Z, Li Q, Bai Z, Wang G, Chen J, Wang Y. Assessing the microbial communities in four different daqus by using PCR-DGGE, PLFA, and Biolog analyses. Pol J Microbiol. 2020;69(1):27–37. https://doi.org/10.33073/pjm-2020-004
    [CROSSREF]
  24. Liu WH, Yang CH, Lin CT, Li SW, Cheng WS, Jiang YP, Wu CC, Chang CH, Tsai YC. Genome architecture of Lactobacillus plantarum PS128, a probiotic strain with potential immunomodulatory activity. Gut Pathog. 2015 Aug 15;7:22. https://doi.org/10.1186/s13099-015-0068-y
    [PUBMED] [CROSSREF]
  25. Liu Z, Luo G, Du R, Sun W, Li J, Lan H, Chen P, Yuan X, Cao D, Li Y, et al. Effects of spaceflight on the composition and function of the human gut microbiota. Gut Microbes. 2020 Jul 3;11(4):807–819. https://doi.org/10.1080/19490976.2019.1710091
    [PUBMED] [CROSSREF]
  26. Ma Q, Li Y, Li P, Wang M, Wang J, Tang Z, Wang T, Luo L, Wang C, Wang T, et al. Research progress in the relationship between type 2 diabetes mellitus and intestinal flora. Biomed Pharmacother. 2019 Sep;117:109138. https://doi.org/10.1016/j.biopha.2019.109138
  27. Margiotta E, Miragoli F, Callegari ML, Vettoretti S, Caldiroli L, Meneghini M, Zanoni F, Messa P. Gut microbiota composition and frailty in elderly patients with Chronic Kidney Disease. PLoS One. 2020 Apr 1;15(4):e0228530. https://doi.org/10.1371/journal.pone.0228530
    [PUBMED] [CROSSREF]
  28. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011 May;7(1):10–12. https://doi.org/10.14806/ej.17.1.200
    [CROSSREF]
  29. Parker BJ, Wearsch PA, Veloo ACM, Rodriguez-Palacios A. The genus Alistipes: Gut bacteria with emerging implications to inflammation, cancer, and mental health. Front Immunol. 2020 Jun 9;11:906. https://doi.org/10.3389/fimmu.2020.00906
    [PUBMED] [CROSSREF]
  30. Peng H, Zhang J. Commercial high-throughput sequencing and its applications in DNA analysis. Biologia. 2009;64(1): 20–26. https://doi.org/10.2478/s11756-009-0028-4
    [CROSSREF]
  31. Qi H, Liu Y, Qi X, Liang H, Chen H, Jiang P, Wang D. Dietary recombinant phycoerythrin modulates the gut microbiota of H22 tumor-bearing mice. Mar Drugs. 2019 Nov 26;17(12):665. https://doi.org/10.3390/md17120665
    [CROSSREF]
  32. Qiu L, Tao X, Xiong H, Yu J, Wei H. Lactobacillus plantarum ZDY04 exhibits a strain-specific property of lowering TMAO via the modulation of gut microbiota in mice. Food Funct. 2018 Aug 15; 9(8):4299–4309. https://doi.org/10.1039/c8fo00349a
    [PUBMED] [CROSSREF]
  33. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013 Jan 1;41(D1):D590–D596. https://doi.org/10.1093/nar/gks1219
    [PUBMED] [CROSSREF]
  34. Ranasinghe PD, Satoh H, Oshiki M, Oshima K, Suda W, Hattori M, Mino T. Revealing microbial community structures in large- and small-scale activated sludge systems by barcoded pyrosequencing of 16S rRNA gene. Water Sci Technol. 2012;66(10):2155–2161. https://doi.org/10.2166/wst.2012.428
    [PUBMED] [CROSSREF]
  35. Ren D, Gong S, Shu J, Zhu J, Liu H, Chen P. Effects of mixed lactic acid bacteria on intestinal microbiota of mice infected with Staphylococcus aureus. BMC Microbiol. 2018 Sep 6;18(1):109. https://doi.org/10.1186/s12866-018-1245-1
    [PUBMED] [CROSSREF]
  36. Schwalm ND 3rd, Groisman EA. Navigating the gut buffet: control of polysaccharide utilization in Bacteroides spp. Trends Microbiol. 2017 Dec;25(12):1005–1015. https://doi.org/10.1016/j.tim.2017.06.009
    [PUBMED] [CROSSREF]
  37. Seddik HA, Bendali F, Gancel F, Fliss I, Spano G, Drider D. Lactobacillus plantarum and its probiotic and food potentialities. Probiotics Antimicrob Proteins. 2017 Jun;9(2):111–122. https://doi.org/10.1007/s12602-017-9264-z
    [PUBMED] [CROSSREF]
  38. Shinde T, Vemuri R, Shastri MD, Perera AP, Tristram S, Stanley R, Eri R. Probiotic Bacillus coagulans MTCC 5856 spores exhibit excellent in vitro functional efficacy in simulated gastric survival, mucosal adhesion and immunomodulation. J Funct Food. 2019;52:100–108. https://doi.org/10.1016/j.jff.2018.10.031
    [CROSSREF]
  39. Song JJ, Tian WJ, Kwok LY, Wang YL, Shang YN, Menghe B, Wang JG. Effects of microencapsulated Lactobacillus plantarum LIP-1 on the gut microbiota of hyperlipidaemic rats. Br J Nutr. 2017 Oct; 118(7):481–492. https://doi.org/10.1017/S0007114517002380
    [PUBMED] [CROSSREF]
  40. Sun M, Liu Y, Song Y, Gao Y, Zhao F, Luo Y, Qian F, Mu G, Tuo Y. The ameliorative effect of Lactobacillus plantarum-12 on DSS-induced murine colitis. Food Funct. 2020 Jun 24;11(6):5205–5222. https://doi.org/10.1039/d0fo00007h
    [PUBMED] [CROSSREF]
  41. Wang C, Zhao J, Zhang H, Lee YK, Zhai Q, Chen W. Roles of intestinal bacteroides in human health and diseases. Crit Rev Food Sci Nutr. 2020a Aug 6:1–19. https://doi.org/10.1080/10408398.2020.1802695
  42. Wang X, Zhang M, Wang W, Lv H, Zhang H, Liu Y, Tan Z. The in vitro effects of the probiotic strain, Lactobacillus casei ZX633 on gut microbiota composition in infants with diarrhea. Front Cell Infect Microbiol. 2020b Sep 10;10:576185. https://doi.org/10.3389/fcimb.2020.576185
  43. Wang Y, Guo Y, Chen H, Wei H, Wan C. Potential of Lactobacillus plantarum ZDY2013 and Bifidobacterium bifidum WBIN03 in relieving colitis by gut microbiota, immune, and anti-oxidative stress. Can J Microbiol. 2018 May;64(5):327–337. https://doi.org/10.1139/cjm-2017-0716
    [PUBMED] [CROSSREF]
  44. Zhang F, Li Y, Wang X, Wang S, Bi D. The impact of Lactobacillus plantarum on the gut microbiota of mice with DSS-induced colitis. Biomed Res Int. 2019 Feb 20;2019:3921315. https://doi.org/10.1155/2019/3921315
  45. Zhao LL, Yin HC, Lu TF, Niu YJ, Zhang YY, Li SQ, Wang YP, Chen HY. Application of high-throughput sequencing for microbial diversity detection in feces of specific-pathogen-free ducks. Poult Sci. 2018 Jul 1;97(7):2278–2286. https://doi.org/10.3382/ps/pex348
    [PUBMED] [CROSSREF]
  46. Zhu Y, Luo J, Yang Z, Miao Y. High-throughput sequencing analysis of differences in intestinal microflora between ulcerative colitis patients with different glucocorticoid response types. Genes Genomics. 2020 Oct;42(10):1197–1206. https://doi.org/10.1007/s13258-020-00986-w
    [PUBMED] [CROSSREF]
XML PDF Share

FIGURES & TABLES

Fig. 1.

Body weight variations of the mice after L. plantarum BW2013 gavage. The data are represented as mean ± SD.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

Full Size   |   Slide (.pptx)

Fig. 2.

Principal co-ordinates analysis (PCoA) of the microbial communities of different groups. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

Full Size   |   Slide (.pptx)

Fig. 3.

Microbial community bar plot at the phylum level. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

Full Size   |   Slide (.pptx)

Fig. 4.

Microbial community bar plot at the genus level. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

Full Size   |   Slide (.pptx)

Fig. 5.

Heat map analysis of the gut microbiota at the phylum and genus levels. Initial state (IS) stands for mice before intragastric administration.

NC – control group, LDG – low-dose group, MDG – medium-dose group, HDG – high-dose group.

Full Size   |   Slide (.pptx)

REFERENCES

  1. Bai C, Liu T, Xu J, Ma X, Huang L, Liu S, Yu H, Chen J, Gu X. Effect of high calorie diet on intestinal flora in LPS-induced pneumonia rats. Sci Rep. 2020 Feb 3;10(1):1701. https://doi.org/10.1038/s41598-020-58632-0
    [PUBMED] [CROSSREF]
  2. Bi R, Gao J, Pan L, Lai X. Progress in the treatment of diabetes mellitus based on intestinal flora homeostasis and the advancement of holistic analysis methods. Nat Prod Commun. 2020;15(4):1–11. https://doi.org/10.1177/1934578X20918418
  3. Biagioli M, Capobianco D, Carino A, Marchianò S, Fiorucci C, Ricci P, Distrutti E, Fiorucci S. Divergent effectiveness of multispecies probiotic preparations on intestinal microbiota structure depends on metabolic properties. Nutrients. 2019 Feb 2;11(2):325.
    [CROSSREF]
  4. Cruz BCDS, Conceição LLD, Mendes TAO, Ferreira CLLF, Gonçalves RV, Peluzio MDCG. Use of the synbiotic VSL#3 and yacon-based concentrate attenuates intestinal damage and reduces the abundance of Candidatus Saccharimonas in a colitis-associated carcinogenesis model. Food Res Int. 2020 Nov;137:109721. https://doi.org/10.1016/j.foodres.2020.109721
  5. Deng X, Tian H, Yang R, Han Y, Wei K, Zheng C, Liu Z, Chen T. Oral probiotics alleviate intestinal dysbacteriosis for people receiving bowel preparation. Front Med (Lausanne). 2020 Feb 28;7:73. https://doi.org/10.3389/fmed.2020.00073
    [PUBMED] [CROSSREF]
  6. Du X, Xiang Y, Lou F, Tu P, Zhang X, Hu X, Lyu W, Xiao Y. Microbial community and short-chain fatty acid mapping in the intestinal tract of quail. Animals (Basel). 2020 Jun 9;10(6):1006. https://doi.org/10.3390/ani10061006
    [CROSSREF]
  7. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013 Oct;10(10):996–998. https://doi.org/10.1038/nmeth.2604
    [PUBMED] [CROSSREF]
  8. Evariste L, Barret M, Mottier A, Mouchet F, Gauthier L, Pinelli E. Gut microbiota of aquatic organisms: A key endpoint for ecotoxicological studies. Environ Pollut. 2019 May;248:989–999. https://doi.org/10.1016/j.envpol.2019.02.101
    [PUBMED] [CROSSREF]
  9. Fonseca HC, de Sousa Melo D, Ramos CL, Dias DR, Schwan RF. Probiotic properties of lactobacilli and their ability to inhibit the adhesion of enteropathogenic bacteria to Caco-2 and HT-29 cells. Probiotics Antimicrob Proteins. 2021 Feb;13(1):102–112. https://doi.org/10.1007/s12602-020-09659-2
    [PUBMED] [CROSSREF]
  10. Goldfeder RL, Wall DP, Khoury MJ, Ioannidis JPA, Ashley EA. Human genome sequencing at the population scale: a primer on high-throughput dna sequencing and analysis. Am J Epidemiol. 2017 Oct 15;186(8):1000–1009. https://doi.org/10.1093/aje/kww224
    [PUBMED] [CROSSREF]
  11. Hasan N, Yang H. Factors affecting the composition of the gut microbiota, and its modulation. PeerJ. 2019 Aug 16;7:e7502. https://doi.org/10.7717/peerj.7502
    [PUBMED] [CROSSREF]
  12. He LH, Ren LF, Li JF, Wu YN, Li X, Zhang L. Intestinal flora as a potential strategy to fight SARS-CoV-2 infection. Front Microbiol. 2020a Jun 9;11:1388. https://doi.org/10.3389/fmicb.2020.01388
    [CROSSREF]
  13. He Y, Xu R, Wang W, Zhang J, Hu X. Probiotics, prebiotics, antibiotic, Chinese herbal medicine, and fecal microbiota transplantation in irritable bowel syndrome: Protocol for a systematic review and network meta-analysis. Medicine (Baltimore). 2020b Aug 7; 99(32):e21502. https://doi.org/10.1097/MD.0000000000021502
    [CROSSREF]
  14. Horiuchi H, Kamikado K, Aoki R, Suganuma N, Nishijima T, Nakatani A, Kimura I. Bifidobacterium animalis subsp. lactis GCL2505 modulates host energy metabolism via the short-chain fatty acid receptor GPR43. Sci Rep. 2020 Mar 5;10(1):4158. https://doi.org/10.1038/s41598-020-60984-6
    [PUBMED] [CROSSREF]
  15. Hsu CN, Hou CY, Chan JYH, Lee CT, Tain YL. Hypertension programmed by perinatal high-fat diet: effect of maternal gut microbiota-targeted therapy. Nutrients. 2019 Dec 2;11(12):2908. https://doi.org/10.3390/nu11122908
    [CROSSREF]
  16. Jia Q, Wang L, Zhang X, Ding Y, Li H, Yang Y, Zhang A, Li Y, Lv S, Zhang J. Prevention and treatment of chronic heart failure through traditional Chinese medicine: Role of the gut microbiota. Pharmacol Res. 2020 Jan;151:104552. https://doi.org/10.1016/j.phrs.2019.104552
  17. Kim HB, Isaacson RE. The pig gut microbial diversity: Understanding the pig gut microbial ecology through the next generation high throughput sequencing. Vet Microbiol. 2015 Jun 12;177(3–4):242–251. https://doi.org/10.1016/j.vetmic.2015.03.014
    [PUBMED] [CROSSREF]
  18. Koh A, De Vadder F, Kovatcheva-Datchary P, Bäckhed F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell. 2016 Jun 2;165(6):1332–1345. https://doi.org/10.1016/j.cell.2016.05.041
    [PUBMED] [CROSSREF]
  19. Kong C, Gao R, Yan X, Huang L, Qin H. Probiotics improve gut microbiota dysbiosis in obese mice fed a high-fat or high-sucrose diet. Nutrition. 2019 Apr;60:175–184. https://doi.org/10.1016/j.nut.2018.10.002
    [PUBMED] [CROSSREF]
  20. LeBlanc JG, Chain F, Martín R, Bermúdez-Humarán LG, Courau S, Langella P. Beneficial effects on host energy metabolism of short-chain fatty acids and vitamins produced by commensal and probiotic bacteria. Microb Cell Fact. 2017 May 8;16(1):79. https://doi.org/10.1186/s12934-017-0691-z
    [PUBMED] [CROSSREF]
  21. Li X, Wang E, Yin B, Fang D, Chen P, Wang G, Zhao J, Zhang H, Chen W. Effects of Lactobacillus casei CCFM419 on insulin resistance and gut microbiota in type 2 diabetic mice. Benef Microbes. 2017 May 30;8(3):421–432. https://doi.org/10.3920/BM2016.0167
    [PUBMED] [CROSSREF]
  22. Li Y, Liu T, Zhang X, Zhao M, Zhang H, Feng F. Lactobacillus plantarum helps to suppress body weight gain, improve serum lipid profile and ameliorate low-grade inflammation in mice administered with glycerol monolaurate. J Funct Food. 2019;53: 54–61. https://doi.org/10.1016/j.jff.2018.12.015
    [CROSSREF]
  23. Ling Y, Li W, Tong T, Li Z, Li Q, Bai Z, Wang G, Chen J, Wang Y. Assessing the microbial communities in four different daqus by using PCR-DGGE, PLFA, and Biolog analyses. Pol J Microbiol. 2020;69(1):27–37. https://doi.org/10.33073/pjm-2020-004
    [CROSSREF]
  24. Liu WH, Yang CH, Lin CT, Li SW, Cheng WS, Jiang YP, Wu CC, Chang CH, Tsai YC. Genome architecture of Lactobacillus plantarum PS128, a probiotic strain with potential immunomodulatory activity. Gut Pathog. 2015 Aug 15;7:22. https://doi.org/10.1186/s13099-015-0068-y
    [PUBMED] [CROSSREF]
  25. Liu Z, Luo G, Du R, Sun W, Li J, Lan H, Chen P, Yuan X, Cao D, Li Y, et al. Effects of spaceflight on the composition and function of the human gut microbiota. Gut Microbes. 2020 Jul 3;11(4):807–819. https://doi.org/10.1080/19490976.2019.1710091
    [PUBMED] [CROSSREF]
  26. Ma Q, Li Y, Li P, Wang M, Wang J, Tang Z, Wang T, Luo L, Wang C, Wang T, et al. Research progress in the relationship between type 2 diabetes mellitus and intestinal flora. Biomed Pharmacother. 2019 Sep;117:109138. https://doi.org/10.1016/j.biopha.2019.109138
  27. Margiotta E, Miragoli F, Callegari ML, Vettoretti S, Caldiroli L, Meneghini M, Zanoni F, Messa P. Gut microbiota composition and frailty in elderly patients with Chronic Kidney Disease. PLoS One. 2020 Apr 1;15(4):e0228530. https://doi.org/10.1371/journal.pone.0228530
    [PUBMED] [CROSSREF]
  28. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011 May;7(1):10–12. https://doi.org/10.14806/ej.17.1.200
    [CROSSREF]
  29. Parker BJ, Wearsch PA, Veloo ACM, Rodriguez-Palacios A. The genus Alistipes: Gut bacteria with emerging implications to inflammation, cancer, and mental health. Front Immunol. 2020 Jun 9;11:906. https://doi.org/10.3389/fimmu.2020.00906
    [PUBMED] [CROSSREF]
  30. Peng H, Zhang J. Commercial high-throughput sequencing and its applications in DNA analysis. Biologia. 2009;64(1): 20–26. https://doi.org/10.2478/s11756-009-0028-4
    [CROSSREF]
  31. Qi H, Liu Y, Qi X, Liang H, Chen H, Jiang P, Wang D. Dietary recombinant phycoerythrin modulates the gut microbiota of H22 tumor-bearing mice. Mar Drugs. 2019 Nov 26;17(12):665. https://doi.org/10.3390/md17120665
    [CROSSREF]
  32. Qiu L, Tao X, Xiong H, Yu J, Wei H. Lactobacillus plantarum ZDY04 exhibits a strain-specific property of lowering TMAO via the modulation of gut microbiota in mice. Food Funct. 2018 Aug 15; 9(8):4299–4309. https://doi.org/10.1039/c8fo00349a
    [PUBMED] [CROSSREF]
  33. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013 Jan 1;41(D1):D590–D596. https://doi.org/10.1093/nar/gks1219
    [PUBMED] [CROSSREF]
  34. Ranasinghe PD, Satoh H, Oshiki M, Oshima K, Suda W, Hattori M, Mino T. Revealing microbial community structures in large- and small-scale activated sludge systems by barcoded pyrosequencing of 16S rRNA gene. Water Sci Technol. 2012;66(10):2155–2161. https://doi.org/10.2166/wst.2012.428
    [PUBMED] [CROSSREF]
  35. Ren D, Gong S, Shu J, Zhu J, Liu H, Chen P. Effects of mixed lactic acid bacteria on intestinal microbiota of mice infected with Staphylococcus aureus. BMC Microbiol. 2018 Sep 6;18(1):109. https://doi.org/10.1186/s12866-018-1245-1
    [PUBMED] [CROSSREF]
  36. Schwalm ND 3rd, Groisman EA. Navigating the gut buffet: control of polysaccharide utilization in Bacteroides spp. Trends Microbiol. 2017 Dec;25(12):1005–1015. https://doi.org/10.1016/j.tim.2017.06.009
    [PUBMED] [CROSSREF]
  37. Seddik HA, Bendali F, Gancel F, Fliss I, Spano G, Drider D. Lactobacillus plantarum and its probiotic and food potentialities. Probiotics Antimicrob Proteins. 2017 Jun;9(2):111–122. https://doi.org/10.1007/s12602-017-9264-z
    [PUBMED] [CROSSREF]
  38. Shinde T, Vemuri R, Shastri MD, Perera AP, Tristram S, Stanley R, Eri R. Probiotic Bacillus coagulans MTCC 5856 spores exhibit excellent in vitro functional efficacy in simulated gastric survival, mucosal adhesion and immunomodulation. J Funct Food. 2019;52:100–108. https://doi.org/10.1016/j.jff.2018.10.031
    [CROSSREF]
  39. Song JJ, Tian WJ, Kwok LY, Wang YL, Shang YN, Menghe B, Wang JG. Effects of microencapsulated Lactobacillus plantarum LIP-1 on the gut microbiota of hyperlipidaemic rats. Br J Nutr. 2017 Oct; 118(7):481–492. https://doi.org/10.1017/S0007114517002380
    [PUBMED] [CROSSREF]
  40. Sun M, Liu Y, Song Y, Gao Y, Zhao F, Luo Y, Qian F, Mu G, Tuo Y. The ameliorative effect of Lactobacillus plantarum-12 on DSS-induced murine colitis. Food Funct. 2020 Jun 24;11(6):5205–5222. https://doi.org/10.1039/d0fo00007h
    [PUBMED] [CROSSREF]
  41. Wang C, Zhao J, Zhang H, Lee YK, Zhai Q, Chen W. Roles of intestinal bacteroides in human health and diseases. Crit Rev Food Sci Nutr. 2020a Aug 6:1–19. https://doi.org/10.1080/10408398.2020.1802695
  42. Wang X, Zhang M, Wang W, Lv H, Zhang H, Liu Y, Tan Z. The in vitro effects of the probiotic strain, Lactobacillus casei ZX633 on gut microbiota composition in infants with diarrhea. Front Cell Infect Microbiol. 2020b Sep 10;10:576185. https://doi.org/10.3389/fcimb.2020.576185
  43. Wang Y, Guo Y, Chen H, Wei H, Wan C. Potential of Lactobacillus plantarum ZDY2013 and Bifidobacterium bifidum WBIN03 in relieving colitis by gut microbiota, immune, and anti-oxidative stress. Can J Microbiol. 2018 May;64(5):327–337. https://doi.org/10.1139/cjm-2017-0716
    [PUBMED] [CROSSREF]
  44. Zhang F, Li Y, Wang X, Wang S, Bi D. The impact of Lactobacillus plantarum on the gut microbiota of mice with DSS-induced colitis. Biomed Res Int. 2019 Feb 20;2019:3921315. https://doi.org/10.1155/2019/3921315
  45. Zhao LL, Yin HC, Lu TF, Niu YJ, Zhang YY, Li SQ, Wang YP, Chen HY. Application of high-throughput sequencing for microbial diversity detection in feces of specific-pathogen-free ducks. Poult Sci. 2018 Jul 1;97(7):2278–2286. https://doi.org/10.3382/ps/pex348
    [PUBMED] [CROSSREF]
  46. Zhu Y, Luo J, Yang Z, Miao Y. High-throughput sequencing analysis of differences in intestinal microflora between ulcerative colitis patients with different glucocorticoid response types. Genes Genomics. 2020 Oct;42(10):1197–1206. https://doi.org/10.1007/s13258-020-00986-w
    [PUBMED] [CROSSREF]

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