Twenty-five male C57BL/6 mice (8 weeks old) (Shanghai SLAC Laboratory Animal Co., Ltd.) were purchased and caged under specified pathogen-free (SPF) conditions (Experimental Animal Center, Hubei Campus, Tongji University, Shanghai, China) at 22 ± 2°C with 55 ± 15% humidity and a 12-h dark/12-h light cycle. All the mice had free access to normal diet (Ralston Purina, St. Louis, Missouri, USA). Then, a continuous 7-day colitis induction was performed using 3% DSS (36–50 kDa, Sigma, US, LOT NO: S3045) dissolved in drinking water (Liu et al., 2016). Meanwhile, the mice were randomly assigned to 5 different groups before treatment: the blank group (mice gavaged with PBS only), the control group (mice treated with DSS and gavaged with PBS), the probiotics group (mice treated with DSS and gavaged with probiotic cocktail), the FMT group (mice treated with DSS and gavaged with fecal suspension from healthy mice), and the ASA group [mice treated with DSS and gavaged with 5-ASA (100 mg/kg) (JiaxingSiCheng Chemical Co., Ltd., China) dissolved in 0.5% sodium carboxymethylcellulose (China Jiaxing Sicheng Chemical Co., Ltd.)]. The probiotic cocktail (Shanghai Tongquan Biotechnology, Shanghai, China) contains Bifidobacterium animalis subsp. lactis HN019, Bifidobacterium longum BI-05, Lactobacillus acidophilus NCFM, Lactobacillus rhamnosus Lr-32, Lactobacillus plantarum Lp-115, Lactobacillus salivarius Ls-33, Lactobacillus paracasei 37, Bifidobacterium animalis spp. lactis 420, Lactobacillus casei Lc-11, and Lactobacillus gasseri 36, mixed in equal amounts. The total intervening amount was 8×10¹⁰ CFU per mouse per day for consecutive 7 days.

The animal protocols were approved by the Ethics Committee of Shanghai Tenth People’s Hospital affiliated to Tongji University (SHDSYY-2018-KY0008).

(1) Twenty grams of fresh feces was collected from 25 healthy 6-week C57BL/6 mice using anal massage, 5 consecutive days before the experiment; (2) the feces were immediately mixed with 100 ml of sterile 10% glycerite and PBS mixture after the collection to get the feces mixture; (3) the fecal microbiota suspension was obtained by grinding the feces mixture with a standard mortar and pestle; (4) to remove the insoluble impurities, the fecal microbiota suspension was filtered through screens with the following diameters: 0.4 mm, 0.2 mm, and 0.1 mm; and (5) the suspension was collected and stored at −80°C (Wong et al., 2017).

Disease activity index (DAI) was used to assess the severity of colitis, including items of weight loss, stool consistency, and the presence of hematochezia, which were recorded every day during the experiment. DAI score calculation: (1) Bodyweight: 0 points were recorded if the bodyweight showed no decrease; 1 point was recorded when the bodyweight showed a 1%–5% decrease; 2 points were recorded when the bodyweight showed a 5%–10% decrease; 3 points were recorded when the bodyweight showed a 10%–15% decrease; and 4 points were recorded when the bodyweight showed more than 15% decrease. (2) Fecal traits: normal stool = 0 points; loose stool (not adhering to the anal paste of semi-formed stool) = 2 points; watery stool (can adhere to the anus watery stool) = 4 points. (3) Fecal occult blood result: no fecal occultation or naked eye blood stool = 0 points; fecal occult blood positive = 2 points; naked eye blood stool = 4 points. Finally, add the above three scores to get the DAI of each mouse to evaluate the severity of colitis (Tian et al., 2016; Li et al., 2019). The colon length and body weight alteration were also measured to evaluate the disease progression (Chen et al., 2020). We anesthetized mice with an inhalation anesthetic using 3% isoflurane during induction and 1.5% isoflurane during maintenance, then the serum was obtained by centrifuging blood using heart puncture drawn for 10 min at 3,000 rpm.

Western blot assay in colon tissues was performed following the method reported previously (Kong et al., 2021b). Total protein from animal colon tissues was lysed with lysis buffer. The protein samples were loaded onto a 10% SDS–polyacrylamide gel and transferred to a polyvinylidene difluoride membrane (Simuwu-Biotechnology Co., Ltd, China, SD0043 or SD0044) before blocking with 5% skim milk powder for 4 h. After that, samples were incubated with the primary antibody overnight at 4°C: JAM-1 (Abcam, USA, ref#ab270446, 1:1,000), Occludin (Abcam, USA, ref#ab216327, 1:1,000), ZO-1 (Thermo Fisher Scientific, USA, ref#61-7300, 1:1,000), and β-Actin (Simuwu-Biotechnology Co., Ltd, China, ref#SD0034; 1:2,000). Next, the membrane was incubated with a horseradish peroxidase-conjugated secondary antibody (Simuwu-Biotechnology Co., Ltd, China, SD0038) for 1 h at room temperature. Immunodetection was performed using the Tanon 4600 chemiluminescence instrument (Tanon, Shanghai, China).

Total RNA was extracted using Trizol reagent (Invitrogen, USA, ref#15596018). An RT reagent kit (TAKARA, Japan, ref#RR047A) was used to reverse-transcribe RNA into cDNA. A TB Green qPCR kit (TAKARA, Japan, ref#RR420A) was used to carry out the real-time qPCR reactions. The process was controlled and the result was analyzed on the qPCR instrument (Roche, LightCycler^® 480II). All the experiments were performed in triplicate and β-Actin was selected as the reference gene for mRNA. The specific primer sequences (Occludin, ZO-1, JAM-1, and β-Actin) used for amplification are listed in Table 1.

The levels of cytokines (IL-4, IL-10, IL-17, IL-23, and IFN-γ) in the serum of mice were measured with the corresponding ELISA kit (Simuwu-Biotechnology Co., Ltd., ref#SDM0006 96T, ref#SDM0010 96T, ref#SDM0012 96T, ref#SDM0117 96T, and ref#SDM0115 96T). The whole process was carried out according to the protocol. (1) Dilute the animal serum 10 times with diluent and then add 100 μl to each well, mix the reaction plate, and leave the plate at 37°C for 40 min; (2) wash the reaction plate 3 times with washing liquid, and invert the plate on a filter paper to dry it; (3) add distilled water and 50 μl of the first antibody working liquid to each well (except blank), thoroughly mix the reaction plate, and place the plate at 37°C for 20 min; (4) wash the reaction plate 3 times with washing liquid as described before; (5) add 100 μl of enzyme-labeled antibody working solution to each well and place the reaction plate for 10 min at 37°C; (6) add 100 μl of the substrate working solution to each well and place the plate at 37°C in the dark for 15 min for reaction; (7) add 100 μl of the stop solution to each well and mix them well; (8) measure the absorbance value at 450 nm with a microplate reader (Tecan, F50) within 30 min.

The fecal samples were prepared according to the manufacturer’s instructions and the DNA was extracted from fecal samples as previously described (Kong et al., 2021a). A Nanodrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, MA, USA) and 1% agarose gel electrophoresis were utilized to examine the concentrations and quality of the DNA samples. The specific forward primer 341F (5’-CCTACGGGRSGCAGCAG-3’) and reverse primer 806R (5’-GGACTACVVGGGTATCTAATC-3’) were designed to amplify the V3–V4 hypervariable regions of the bacteria 16S rDNA gene on a thermocycler PCR system. PCR products were detected by 2% agarose gel electrophoresis and were gelled and recovered by an AxyPrep DNA gel recovery kit (Axygen Biosciences, Union City, CA, USA). After quantification and homogenization, the DNA products underwent paired-end sequencing with Illumina NovaSeq PE250 (Illumina, San Diego, CA, USA). Statistical analysis was conducted in R (v3.5.1).

Paraffin-embedded colon tissues were cut into 5-mm sections. After being dewaxed in xylene and dehydrated in gradient alcohol, the sections were stained with hematoxylin for 8 min and with eosin for 5 min. Then, the sections were dehydrated and sealed. The histopathology evaluation was performed by two researchers who are blinded to section information. The following evaluation criteria were used: Epithelium (E): 0, normal morphology; 1, loss of goblet cells; 2, loss of goblet cells in large areas; 3, loss of crypts; 4, loss of crypts in large areas. Infiltration (I): 0, no infiltrate; 1, infiltrate around crypt basis; 2, infiltrate reaching to L. muscularis mucosae; 3, extensive infiltration reaching the L. muscularis mucosae and thickening of mucosa with abundant edema; 4, infiltration of the L. submucosa. The total histological score is defined as the sum of the epithelium and infiltration score (total score = E + I) (Obermeier et al., 1999).

The feces samples were prepared for metabolomic analysis according to the manufacturer’s instructions (Metabo-Profile Biotechnology, Shanghai). An ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA) was used to quantitate the 7 main kinds of SCFAs (propanoic acid, isovaleric acid, isobutyric acid, butyric acid, valeric acid, hexanoic acid, and acetic acid) in this project as previously described (Liu Y et al., 2020). The raw data files generated by UPLC-MS/MS were processed using the MassLynx software (v4.1, Waters, Milford, MA, USA) to perform peak integration, calibration, and quantitation for each metabolite. Statistical analysis was conducted in Prism 8 (GraphPad) and R (v3.5.1).

All data were expressed as mean ± standard deviation (SD). The SPSS 22.0 software (SPSS Inc., Chicago, IL, USA) was utilized for statistical analyses. The Mann–Whitney U test and one-way ANOVA test were used to identify the significant differences between groups. Mann–Whitney U test and Pearson’s chi-square were utilized to identify the continuous and categorical variables, respectively. p < 0.05 was considered statistically significant.

As shown in Figures 1A, B, compared to the control group, the probiotic cocktail treatment significantly slowed down the weight loss of colitis mice, whereas the body weight among the FMT, 5-ASA, and control groups had no statistical difference. In evaluation of the severity of colitis, the DAI scores were found to significantly decrease in the probiotic cocktail, ASA, and FMT groups (Figures 1C, D), with the probiotic cocktail group obtaining the lowest score (Figures 1C, D). Furthermore, the probiotic cocktail notably increased the colon length of colitis mice compared to the control and ASA groups (Figures 1E, F). The images of representative hematoxylin–eosin (H&E) staining for colon and histological evaluation (Figures 2G–L) indicated that the probiotic cocktail performed best in alleviating the mucosal inflammation of DSS-induced colitis mice than any other treatment (red arrows represent enriched inflammatory cell and crypt for the probiotics group).

To further clarify the anti-inflammatory effect of the probiotic cocktail, we detected several inflammatory cytokines in the serum samples from colitis mice. As shown in Figures 2A–E, compared with the control group, the probiotic cocktail, FMT, and 5-ASA groups significantly decreased the level of pro-inflammatory IFN-γ and IL-17. The trend of decreased pro-inflammatory IL-23 and increased anti-inflammatory IL-10 and IL-4 was also observed in colitis mice after treatment of probiotic cocktail, although there was no significant difference (Figures 2C–E). Of note, the probiotic cocktail had a better inhibitory effect in serum IFN-γ and IL-17 production than FMT and 5-ASA (Figures 2A, B). Since our previous study revealed that probiotics played a crucial role in protecting the intestinal barrier (Liu et al., 2014; Yin et al., 2018), we also detected the expression of several tight junction proteins (JAM-1, ZO-1, and Occludin) in the colon tissues from colitis mice. As shown in Figures 2F–H, the probiotic cocktail significantly increased the mRNA expression of ZO-1 as compared with the control group, and the upward trend was also observed in JAM-1 and Occludin although not significant. Meanwhile, the following Western blot confirmed the upregulation of ZO-1, JAM-1, and Occludin at the protein level in the probiotic cocktail group (Figure 2I).

To investigate the altered gut microbiota after different treatments, 16S rDNA sequencing was performed. Alpha diversity indexes were calculated to assess the differences in bacterial diversity among groups. Compared to the control group, the FMT group had a significantly higher Chao1, Shannon, and Simpson index (all p < 0.05), which was opposite for the probiotics group (Figures 3A–C). This finding suggested that the number and abundance of other species decreased after the probiotics were dominant. Meanwhile, the principal coordinate analysis based on weighted UniFrac distance revealed the significant differentiation in the composition of gut microbiota among groups (Figure 3D).

At the phylum level, the relative abundance of Verrucomicrobia was increased in the probiotics group but decreased in the FMT and 5-ASA groups (Control: 28.99%, Probiotics: 42.95%, FMT: 22.48%, ASA: 15.94%, Blank: 38.66%), while Firmicutes changed in an opposite manner (Control: 13.98%, Probiotics: 8.78%, FMT: 31.41%, ASA: 32.12%, Blank: 39.08%) (Figure 3E). In addition, the relative abundance of Proteobacteria was decreased in the probiotics and FMT groups as compared with that of the control group (Control: 18.66%, Probiotics: 9.78%, FMT: 5.69%, ASA: 18.85%, Blank: 0.91%). At the genus level, the relative abundance of Akkermansia and Bifidobacterium was increased in the probiotics group but decreased in the ASA group (Control: 31.98%, Probiotics: 50.84%, FMT: 34.58%, ASA: 19.40%, Blank: 68.37%; Control: 0.17%, Probiotics: 0.86%, FMT: 0.04%, ASA: 0.01%, Blank: 1.24%, respectively). Meanwhile, the relative abundance of Parasutterella was decreased in the probiotics, FMT, and ASA groups as compared with the control group (Control: 15.73%, Probiotics: 3.03%, FMT: 6.12%, ASA: 2.82%, Blank: 2.82%) (Figure 3F).

Furthermore, LEfSe analysis was utilized to figure out the key elements among different groups. At the genus level, DSS treatment significantly reduced the beneficial bacteria including Bifidobacterium and Blautia, which were reported to inhibit inflammation and enhance gut barrier (Chen et al., 2021). Notably, these two genera were obviously upregulated after the probiotic cocktail treatment. Moreover, we observed that the key genera in the FMT group were Dorea, Oscillibacter, Desulfovibrio, Butyricicoccus, Micipirllum, Intestinimonas, Clostridium IV, and XIVb, whereas the dominant bacteria in the ASA group were Escherichia_Shigella, Clostridium sensu stricto, and Romboustia (Figures 4A, B).

Emerging evidence suggests that gut microbiota exerts an anti-inflammatory effect through producing SCFAs. Accordingly, we performed targeted metabolomics to detect the levels of several SCFAs (propionic acid, isovaleric acid, isobutyric acid, butyric acid, valeric acid, hexanoic acid, and acetic acid) in the fecal samples of DSS-induced colitis mice (Figures 5A–G). Compared with the control group, the probiotic cocktail rather than FMT and 5-ASA significantly promoted the production of propanoic acid and isobutyric acid (Figures 5A, C). The upward trend of isovaleric acid, butyric acid, valeric acid, hexanoic acid, and acetic acid was also observed in the probiotic cocktail group though not significant (Figures 5B, D–G). Additionally, Spearman rank correlation analysis was used to test correlations between bacteria and SCFA abundance (Figure 5H). Unsurprisingly, butyrate-producing bacteria Bifidobacterium and Roseburia were positively correlated with most SCFAs, while it was opposite for some pro-inflammatory bacteria such as Parasutterella and Bacterioides. Taken together, the results suggested that the probiotic cocktail effectively promoted the accumulation of both butyrate-producing probiotics and SCFAs.