Effect of xylitol on gut microbiota in an in vitro colonic simulation

Fecal sample collect and pre-treatment

Fecal samples were donated by six healthy adult women, ages ranging between 20 and 26, who did not receive antibiotic treatment for at least 6 months and voluntarily signed the informed consent form. Fecal samples were collected into a container and filled with a 80% N₂ and 20% CO₂ gas mixture. Under anaerobic conditions, 10 g of fresh fecal sample was collected and dissolved in 90 mL of 0.1 mol/L phosphate buffered saline solution (PBS, pH 4.7). The samples were then stirred thoroughly and filtered through three layers of gauze to discard large particulate residues. The filtrates (bacterial suspension) were collected and used as inoculum. All the media and buffer solutions were flushed with the gas mixture (80% N₂ and 20% CO₂) for 10 min before application.

In vitro single-stage colonic fermentation

In vitro single-stage, colonic reactors were constructed in Changdao Moni simulation (CDMN) in Zhejiang Gongshang University. Nutritive medium included (g/L): Peptone 3.0; Cornstarch 8.0; Yeast extract 4.5; Tryptone 3.0; Mucin 0.5; L-cysteine hydrochloride 0.8; Bile No.3 0.4; Heme 0.05; Sodium chloride 4.5; Tween 80 1.0; Potassium chloride 2.5; Potassium dihydrogen phosphate 0.4; Magnesium chloride hexahydrate 4.5; Calcium chloride hexahydrate 0.2; Magnesium sulfate heptahydrate 3.0; Ferrous sulfate heptahydrate 0.1; Calcium chloride dihydrate 0.1; Manganese chloride tetrahydrate 0.32; Cobalt sulfate heptahydrate 0.18; Copper sulfate pentahydrate 0.01; Zinc sulfate heptahydrate 0.18; Nickel chloride hexahydrate 0.092 [10]. The 10% fecal microbiota (v/v) (ca. 1% of fecal samples) were inoculated into first-stage (Figure 1) reactors with a total fermentation volume of about 600 mL. During the entire fermentation process, the mixing speed was set at 120 r/min to maintain broth uniformity. All reactors were kept at 37°C. The pH was maintained at 6.8 by adding at 0.5 mol/L either NaOH or HCl. The anaerobic condition was generated by flushing the headspace of all reactors and medium vessels with N₂ for 30 min three times daily. In the consecutive fed-batch fermentations the nutrient medium was replaced at a rate of 37.5 mL/h for a 24 h period.

Figure 1:

Changes of index and principal components analysis scores.

(A) Changes of index Chao1 (Alpha diversity) under Xylitol and Control for 8 days. (B) Principal component analysis scores plot based on the relative abundance of OTUs (97% similarity level). (C and D) Effect of Xylitol on bacterial phylum and genera alternation. Con and Xyl stand for the control and Xylitol groups, respectively.

After 7 days of continuous culturing, the fermentation broth was equally divided into two culture fermenters, which were experimental fermenters (Xyl) and control fermenters (Con). After addition of 5 g xylitol to nutrient medium tanks (3 L), in the two reactors continuous cultures were sustained for 8 days. The pH values of nutrient media were monitored. The waste liquid collected from each reactor was stored at −4°C before analysis.

Short chain fatty acid analysis

Gas chromatography–mass spectrometry (GC-MS) analysis was performed to determine the SCFAs, according to Tan et al. with modifications [11]. Ten milliliters batch cultures were harvested and centrifuged (TGL-16, Cence, Co., Ltd., China) at 8000 r/min for 2 min. The supernatant was collected and filtered through 0.45 um pore size membranes. 0.1 mL sulfuric acid solution (50%) and 1.0 mL ethyl-ether were added into the 0.5 mL treatment suspension. The mixture was shaken for 30 times, centrifuged at 10,000 r/min for 5 min, and stored at 4°C for 30 min. All GC-MS analyses were performed at room temperature, and each sample was placed on an FFAP elastic quartz capillary column (30 m×0.25 mm×0.25 μm). All parameters were set as following: Oven temperature programmed from 100°C (1 min) to 150°C (5 min) at 5°C per min; Source temperature at 200°C; interface temperature at 250°C; Carrier gas: high purity nitrogen (≥99.999%); carrier gas flow rate: 2 mL/min; inlet temperature: 270°C; injection method: no split injection; injection volume: 2.0 μL; detector temperature (FID): 280°C; mass spectrometer ion source type: electrospray ion Source (ESI); Ion Source Temperature: 350°C.

DNA isolation, PCR, and 16s rRNA gene analysis

DNA from different samples was extracted using a MicroElute Genomic DNA Kit (D3096-01, Omega, Inc., USA) according to the manufacturer’s instructions. The total DNA was eluted in 50 μL of elution buffer by a modified version of the procedure described by the manufacturer (QIAGEN), and the samples were stored at −80°C until PCR amplification (LC-Bio Technology Co., Ltd., Hang Zhou, Zhejiang Province, China). Bacteria 16s rRNA gene sequences (V3-V4 regions) were amplified from the whole genome of samples via the primer pair (319F 5′-ACTCCTACGGGAGGCAGCAG-3′ and 806R 5′-GGACTACHVGGGTWTCTAAT-3′) according to a previously described method with a minor modification [12]. All reactions were conducted in 25 μL (total volume) mixtures including approximately 25 ng of a genomic DNA extract, 12.5 μL PCR Premix, 2.5 μL of each primer, and PCR-grade water to adjust the volume. PCR reactions were performed in a Mastercycler Gradient Thermocycler (Eppendorf, Hamburg, Germany) set to the following conditions: initial denaturation at 98°C for 30 s; 35 cycles of denaturation at 98°C for 10 s, annealing at 54/52°C for 30 s, and extension at 72°C for 45 s; and the final extension at 72°C for 10 min.

The PCR products were normalized by using an AxyPrep™ Mag PCR Normalizer (Axygen Biosciences, Union City, CA, USA), which allowed skipping the quantification step regardless of the PCR volume submitted for sequencing. The amplicon pools were prepared for sequencing with AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA), and the size and quantity of the amplicon library were assessed on the LabChip GX (Perkin Elmer, Waltham, MA, USA) and with the Library Quantification Kit for Illumina (Kapa Biosciences, Woburn, MA, USA), respectively. The PhiX Control library (V3) (Illumina) was combined with the amplicon library (expected at 30%). The library was clustered to a density of approximately 570 K/mm². The libraries were sequenced on the 300PE MiSeq runs, and one library was sequenced with both protocols using the standard Illumina sequencing primers, eliminating the need for a third (or fourth) index readings.

16s rRNA gene sequences were processed and modified as in previously described methods [13]. The reads were controlled and confirmed by QIIME (quantitative insights into microbial ecology, http://qiime.org/tutorials/processing_illumina_data.html), Vsearch (https://github.com/torognes/vsearch, v2.3.4), FASTQC, and FLASH (Fast Length Adjustment of Short reads, v1.2.8, http://ccb.jhu.edu/software/FLASH) quality filters. A Cd-hit method was introduced to select operational taxonomic units (OTUs) by the generation of an OTU table [14]. When the similarity of OTUs was over 97%, sequences were assigned to one unit. RDP (Ribosomal Database Project) classifiers were applied to distribute the 16s rRNA gene into distinct taxonomic categories by aligning representative sequences to taxonomically annotated sequences [15]. To calculate the alpha diversity, we rarefied the OTU table and calculated two metrics: The Chao1 metric, which estimates the richness, and the Shannon index. The heatmap of an important family of the gut microbiome was created by using Mev. 4-9-0. PICRUSt website to predict gene function on OTU tables (http://huttenhower.sph.harvard.edu/galaxy/). The principal component analysis and canonical correlation analysis of 16s rDNA sequencing results were performed by using Matlab software (v8.1.0.430). Principal component analysis (PCA) was carried out regarding all taxa relative abundances.

Statistics

The data were analyzed via two-way ANOVA and Duncan’s test for multiple comparisons with SPSS ver. 17.0. A value of p<0.05 was considered statistically significant.

Effect of xylitol on bacterial community

A total of 16 samples from Xyl and Con groups were chosen for microorganism profile analyses. After quality-filtering, the 16s rRNA sequencing results produced 4,146,899 reads, giving a mean sample depth of 129,590.6 reads with a standard deviation of 43,704.2 reads. A total of 789 operational taxonomic units (OTUs) were obtained, and the relative abundance of OTUs showed relative abundances of the genera in the samples. Chao1 is a commonly used alpha diversity index. The chao1 reflects the estimated number of OTU species in the sample (Figure 1A). Alpha-diversities of the microorganisms in Xyl group was significantly lower than that of the Con in the first 3 days, and the Chao1 index was only 201.92±17.79, 281.74±21.62 and 277.93±32.25, respectively. Chao1 Index tends to be consistent with the control group. Regarding this result, xylitol reduced the alpha diversity of microbes in the colon.

According to 16s rDNA sequence of the control group and mix group by principal components analysis (Figure 1B), the primary components accounted for 90.43% while the secondary components accounted for 2.85%. These results can comprehensively reflect the distribution of the sample in the control group. After the addition of prebiotics, the distance between the control group and xylitol group increased, indicating that those two groups differed from each other. However, after 3 days, the difference tended to disappear.

The composition of gut microbiota changed quickly after the addition of prebiotics (Figure 1C, D), which can be seen from the high relative content of Firmicutes and the low relative content of Bacteroides. Furthermore, the prebiotics stimulated the relative amount of Escherichia spp. and Clostridium spp. at the beginning. The proportion of Escherichia spp. and Clostridium spp. were significantly high at the first day in the xylitol group, by 62.37% and 12.94%, respectively. Besides, xylitol also enhanced the relative abundances of Lactobacillus spp., which was observed on the second day, to the extent of 7.2%. In the bar chart of both phyla and genera, it can be seen that the modulation of the influence of prebiotics was reversible and only lasted in a short time. After 3 days the composition of gut microbiota reversed to the initial condition.

Effect of xylitol on short-chain fatty acid (SCFAs)

In SCFA bar chart (Figure 2), production of SCFAs in the first 2 days was low, being only 0.66 mg/L and 0.55 mg/L, followed by a dramatic increase. Xylitol had a lagging impact on short-chain fatty acids production. In the following 5 days, the concentration of SCFAs in the mix group was significantly higher than that in the control group. On the 4^th day, the content of SCFAs reached the peak (4.93 mg/L); by comparison, the control group was only 2.67 mg/L. In day 8, there were no significantly differences between xylitol groups (1.00 mg/L) and control groups (0.99 mg/L). Xylitol also promoted the production of propionic acid and butyric acid.

Figure 2:

Concentrations of SCFAs in Con and Xyl.

The relationship between microbiota, SCFA, and xylitol

The correlation between gut microbiota can be seen after performing Spearman correlation analysis and plotting with Cytoscape. Red means a positive correlation and green means a negative correlation (Figure 3A). The solid line represented a significant correlation and the dashed line represented non-significant. Form Figure 3A, it can be concluded that Bilophila spp. and Dorea spp. are significantly and positively correlated. Bacteroides spp. were significantly negatively correlated with Escherichia. spp, Bifidobacterium spp., Pseudomonas spp., and Clostridium spp. Beyond our expectation, we observed that Escherichia had a positive relationship with Bifidobacterium.

Figure 3:

(A) The correlation between gut microbiota. (B) Canonical correspondence analysis (Label “1–24” stand for Bacteroides, Escherichia, Desulfovibrio, Parabacteroides, Pseudomonas, Clostridium, Phascolarctobacterium, Sutterella, Oscillospira, Acinetobacter, Clostridium, Collinsella, Roseburia, Lactobacillus, Blautia, Cloacibacillus, Comamonas, Bilophila, Stenotrophomonas, Defluviitalea, Dorea, Bifidobacterium, Ruminococcus and Clostridium, respectively). (C) Heatmap and hierarchical clustering (Euclidean distance) on the taxonomic genus level.

Regarding the CCA (canonical correspondence analysis) (Figure 3B), we found bacteria under xylitol supplementation had an acute angle with different SCFA, indicating a positive relationship. According to the heatmap (Figure 3C), xylitol supplemented samples on the 1^st, 2^nd, and 3^rd day were clustered together, indicating that xylitol can only affect microbiota in the first 3 days.

Function prediction of gut microbiota via PICRUSt

In this study, 14 primary metabolic pathways, 24 secondary metabolic pathways, and 263 tertiary metabolic pathways of KEGG (Kyoto Encyclopedia of Genes and Genomes) were obtained through PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) prediction. We found that the majority of secondary metabolisms were not affected by xylitol. Among the tertiary metabolic pathways (Figure 4) xylitol promoted the glycolysis (Ko00010), DNA replication proteins (Ko03032), amino sugar and nucleotide sugar metabolisms (Ko00520) of gut bacteria, as well as DNA repair and recombination (Ko03400) pathways. On the other hand, xylitol interfered propanoate metabolism (Ko00640) and inhibited ion channel pathways (Ko04040) along with arginine and proline metabolism (Ko00330). Beyond our expectation, the expression of porphyrin and chlorophyll metabolism (Ko00860) pathways were not operating significantly in the xylitol group.

Figure 4:

Comparisons of the predominant gene pathways of the microbiota in Con and Xyl groups the first day after adding xylitol.