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Effects of goat milk enriched with oligosaccharides on microbiota structures, and correlation between microbiota and short-chain fatty acids in the large intestine of the mouse

Open ArchivePublished:January 14, 2021DOI:https://doi.org/10.3168/jds.2020-19510

      ABSTRACT

      In this study, we explored the effects of combining goat milk and oligosaccharides on the large intestine environment of mice. A combination of goat milk with each of 3 oligosaccharides—stachyose, fructo-oligosaccharide (FOS), and a prebiotics mix—were independently fed to mice. We investigated composition changes in the microbiota of the large intestine using 16S rRNA gene sequencing; measured short-chain fatty acid content using gas chromatography–mass spectrometry; and performed a Spearman correlation analysis between microorganisms and short-chain fatty acids. Our results showed that microbial diversity in the large intestine decreased significantly in the FOS group. In terms of α diversity, microbial richness significantly declined in all 3 treatment groups; in terms of β diversity, the intestinal microbial structures clearly changed in the FOS group. The abundance of Bifidobacterium and Lactobacillus increased markedly in the FOS group compared with the other groups. Functional predictions showed that FOS reduced intestinal bacterial infections and improved the endocrine and immune systems. Spearman correlation analysis showed that propionic, isobutyric, and valeric acids were all positively correlated with certain microbiota. Our findings suggest that FOS-enriched goat milk is beneficial for improving the large intestine environment in the host.

      Key words

      INTRODUCTION

      Many microbial communities in the gut form a close and beneficial relationship with the host and constitute an open microbial ecosystem (
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      Goat milk has more minerals and vitamins than cow milk (
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      Short-chain fatty acids (SCFA) play important roles in maintaining the stability of the intestinal environment. They are volatile substances with fewer than 6 carbon atoms, and they are produced by colonic microbiota via the fermentation of complex oligosaccharides. Acetic, propionic, and butyric acids are the main SCFA that are absorbed up to 95% in the colon (
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      ). Studies have demonstrated that fermentable oligosaccharides cannot be digested by human enzymes in the small intestine but are widely fermented into SCFA in the large intestine. SCFA serve as energy sources for host cells and intestinal microflora, and they also reduce systemic inflammation and improve lipid and glucose metabolism by improving the function of the intestinal barrier (
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      The health of the large intestine is the focus of increasing attention because of its various pathological changes, including functional bowel disorders (irritable bowel syndrome, functional constipation, and functional diarrhea) and organic bowel diseases (Crohn's disease, ulcerative colitis, intestinal polyps, colon cancer, intestinal spasm, intestinal fistula, rectal cancer, and enteritis). One strategy for improved health is to adjust the compositions of the microflora and SCFA in the large intestine to improve its environment and prevent the development of disease. Given the functions of goat milk and oligosaccharides, integration of the 2 could be an optimal approach for improving the environment in the large intestine. A study on the effects of goat and cow milk powder-based diets with or without prebiotics on the gut microbial populations and fermentation products in newly weaned rats involved studying the effects on some targeted species of microorganisms (
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      The microbial communities in the large intestine are complex and can interact with SCFA; specifically, the types and content of SCFA are related to intestinal microbiota structures and the metabolic flux of host microorganisms in the gut. As well, SCFA can promote or inhibit the proliferation of some microbial species. Until now, only a few studies have explored the correlations between microbiota and SCFA in the large intestine of mice. The mechanisms of action of alterations in the gut environment after intake of goat milk enriched with oligosaccharides are poorly understood.
      The objectives of this study were to explore the combined effects of goat milk and oligosaccharides [including stachyose and fructo-oligosaccharide (FOS), as well as a prebiotic mix] on alternations in microflora structures in mouse large intestine and to investigate the relationships between SCFA and microbiota by 16S rRNA gene sequencing and GC-MS/MS analysis. It is well known that the use of different model species can produce inconsistent results (
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      ). We chose a murine animal model for this study because complete background information was available; because it offered good reproducibility, reliability, specificity, applicability, and controllability; and because it was economical. The findings of this study provide insights into the mechanisms of action of goat milk and oligosaccharides on the large intestine environment; our findings could also be used to establish an approach for developing goat-milk-based formula milk and functional foods.

      MATERIALS AND METHODS

      Goat Milk Preparation

      Milk from Saanen goats (Weinan city, Shaanxi Province, China), selected as the experimental milk, contained 3.6% protein, 5.8% fat, 4.6% lactose, and 0.86% minerals. The goat milk was collected from September to October 2018 and delivered to the laboratory in sterile glass containers with ice packs. The FOS used were produced by Grain Mill Food Group Co. Ltd. (Shenzhen, China). The stachyose (STS) and prebiotics mix were purchased from Zhonglitang Technology Co. Ltd. (Hebei, China). The prebiotics mix (FGS) consisted of FOS, galacto-oligosaccharides, and STS at a ratio of 1:1:1. The FOS, STS, and FGS were individually added to goat milk to a concentration of 5% (wt/vol; i.e., 50 g/L to prepare the different test milks).

      Animals

      Six-week-old male BALB/c mice (weight: 20 ± 2 g) were obtained from the Health Science Center of Xi'an Jiaotong University (Shaanxi, China). The mice were suckled by their dams for 4 wk until weaned, and then fed basic animal feed for 2 wk. Their environment was maintained at a temperature of 22 to 25°C, a humidity of 40 to 45%, and 12-h light/dark cycles. Mice were allowed to freely ingest goat milk (pasteurized at 95°C) for 15 min before the beginning of the experiment. After 1 wk of acclimation, mice were randomly divided into 4 groups: the control group, the STS treatment group, the FOS treatment group, and the FGS treatment group. Mice in the different groups were fed pasteurized goat milk as described above (control group) or milk enriched with different kinds of oligosaccharides (treatment groups) for 4 wk, using a 125-mL bottle. The entire experiment, involving animal use, was done in strict accordance with the Animal Care Guide of the Shaanxi Normal University, China.

      Microbial DNA Extraction and PCR Amplification

      Mice were euthanized by cervical dislocation under anesthesia. The contents of the large intestines of mice in each group were sampled for microflora analysis (n = 6 per group). To avoid external pollution, all experiments were carried out on a thoroughly cleaned workbench. All instruments, glassware, and test tubes were autoclaved before the experiment. A 200-mg sample was extracted from the large intestine of each mouse and collected in a sterile 1.5 mL microcentrifuge tube. Genomic DNA was extracted and purified from the large intestine contents using the E.Z.N.A. Soil DNA Kit (Omega Bio-tek, Norcross, GA) following the manufacturer's instructions. DNA concentration and mass were measured using an ND-1000 nanometer titration spectrophotometer (Thermo Fisher Scientific, Waltham, MA) and 0.8% agarose gel electrophoresis, respectively. To amplify and sequence the highly variable region V3–V4 of the 16S rRNA gene of target bacterial DNA, common gene primers 338F and 806R were used (forward primer 338F: 5′-ACTCCTACGGGAGGCAGCA-3′; reverse primer 806R: 5′-GGACTACHVGGGTWTCTAAT-3′). Unique 8-base barcodes were integrated into the primers for sample sequencing. A 20-μL reaction mixture was used in the PCR amplification system; it consisted of 4 μL of 5× FastPfu Buffer, 2 μL of 2.5 mM dNTP, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. The PCR thermal cycling was performed under the following conditions: an initial denaturation at 98°C for 2 min, 25 cycles of denaturation at 98°C for 15 s, annealing at 55°C for 30 s, extension at 72°C for 30 s, and a final elongation for 5 min at 72°C.

      16S rRNA Gene Illumina MiSeq Sequencing Analysis

      Each sample was subjected to 2% agarose gel electrophoresis to extract PCR amplicons. These amplicons were purified and quantified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA) and QuantiFluor-ST (Promega, Madison, WI), respectively, according to the manufacturer's instructions. Amplicon libraries were generated by pooling purified amplicons in equimolar amounts. Finally, the amplicons were sequenced on a MiSeq sequencing platform (Illumina Inc., San Diego, CA) in paired-end mode (2 × 300 bp). The raw data were displayed as fastq files, and the corresponding barcode sequences were matched with the samples. The original 16S rRNA data were demultiplexed, and then unmatched sequences were filtered using QIIME (version 1.8.0; http://qiime.org/). Sequence filtering met the following 3 criteria: low-quality reads with mass scores below 20 were all deleted based on a 50-bp sliding window; ambiguous and mismatched reads were removed; and overlapping sequences shorter than 10 bp were discarded. To remove noise and chimeras, the data were further processed through the USEARCH quality-filtering pipeline (version 11; http://drive5.com/usearch/manual/uparse_pipeline.html). Then, the readings were aggregated into operational taxonomic units (OTU) by UPARSE (version 7.1; http://drive5.com/uparse/) with 97% similarity or higher. The final OTU table was resampled to a depth of 30,886 sequences per sample to standardize the number of reads across samples. Then, 16S rRNA sequence classification was conducted using the Ribosomal Database Project RDP classifier (http://rdp.cme.msu.edu/), against the SILVA database (SSU 115; http://www.arb-silva.de/) at a confidence threshold of 70%.

      Bioinformatics Analyses

      Data visualization and statistical analysis of intestinal microbial communities was carried out using R (version 3.2.0; https://www.r-project.org/) unless otherwise noted. The rarefaction curves drawn using the Sobs and Shannon indices (OTU) showed the sequence data and high sampling depth. The Shannon and Chao indices were used in an α diversity analysis to determine the microbial diversity and richness of the intestinal flora. Each index was referred to MOTHUR (version 1.30.1; https://www.mothur.org). A β diversity analysis was performed using the abundance-weighted Jaccard distance metric to investigate the structural changes of microbial communities. Hierarchical clustering tree generation was conducted using the unweighted pair-group method with arithmetic mean algorithm to cluster the data sets. A principal coordinates analysis (PCoA) map was generated using the unweighted pair-group distance metric to describe the distances between treatment groups. A Venn diagram was created using R to visualize the unique and common OTU in multiple groups. A heat map was drawn using the vegan package in R to present species composition and abundance information for the community. Community composition maps were generated by QIIME at the phylum, family, genus, and species levels, and expressed 2 types of information intuitively: the dominant microorganisms contained in each sample and the relative abundance of each microorganism in the sample. To determine whether the microbial populations in the 4 groups were statistically different, a Kruskal–Wallis analysis was performed using R. Statistical significance was accepted at P < 0.05. The Tax4Fun software package (version 0.3.1; http://tax4fun.gobics.de/) was used to convert the 16S taxonomic pedigree based on the SILVA database to the classification lineage of prokaryotes in the Kyoto Encyclopedia of Genes and Genomes database (https://www.genome.jp/kegg/) to perform functional prediction.

      Detection of SCFA

      Based on GC-MS/MS detection data (Supplemental Figure S1; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ROXDVC), the targeted SCFA (acetic, propionic, butyric, isobutyric, isovaleric, valeric, isohexanoic, and hexanoic acids) in the large intestine contents of mice from the 4 groups were subjected to quantitative analysis (Supplemental Figure S2). Spearman correlation analysis was used to investigate the relationships between the significantly changed fatty acids and the significantly different OTU in terms of richness. For significance, the correlation coefficient was greater than 0.8 and the difference was less than 0.05.

      RESULTS

      Sequencing Depth and Taxonomic Diversity

      A total of 1,294,191 sequences were obtained by sequencing the 16S rRNA gene content of 24 samples from the large intestine. The length distribution of the selected sequences ranged from 401 to 440 bp, with an average length of 414 bp. Among these data, 604 OTU were clustered from non-repetitive sequences at 97%. The rarefaction curve was drawn by random sampling of the sequences. It was constructed based on the number of extracted sequences and their respective diversity indices, including the Sobs and Shannon indices (Supplemental Figure S3A and S3B; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ROXDVC). The results of the Shannon index rarefaction analysis showed that the number of samples provided an adequate and representative data set for dealing with the core microbiota.
      Differences in community diversity and richness were determined by α diversity analysis. Results showed that the diversity and richness of the large intestinal community were significantly different among the 4 groups. The Shannon index (Figure 1A) was significantly reduced in the FOS group compared with the control group (P < 0.01), but we found no significant differences between the STS and FGS groups and the control group. Shannon indices were significantly higher in the STS and FGS groups than in the FOS group, demonstrating that microflora in the large intestine were more diverse in the STS and FGS groups (P < 0.001). The community richness Chao index was significantly increased in the control group compared with the 3 treatment groups (P < 0.05 or P < 0.001; Figure 1B). The intake of sugars in the treatment groups might have downregulated the richness of the large intestinal microbiota. In addition, the richness of bacterial OTU in the intestinal microbiota was reduced in the FOS group compared with the STS and FSG groups.
      Figure thumbnail gr1
      Figure 1Evaluation of α and β diversity of mouse large intestinal microflora. (A) Richness abundance map generated by the Shannon index. (B) Diversity graph generated by the Chao index; **P < 0.01, ***P < 0.001. Error bars in these panels indicate SD. (C) Hierarchical cluster analysis of the abundance-weighted Jaccard distance metrics generated from the classification table at the operational taxonomic units (OTU) level. Lines of different colors represent samples from different treatment groups. (D) Principal coordinates analysis (PCoA) based on the abundance-weighted Jaccard distance metric. Shapes with different colors represent different grouping examples. FGS = prebiotics mix comprising FOS, galacto-oligosaccharides, and stachyose; FOS = fructo-oligosaccharide; STS = stachyose.
      A β diversity analysis was conducted to quantify the effect of different sugars in the large intestinal microflora. We used QIIME version 1.8.0 to calculate distance, and performed hierarchical clustering analysis. Visualization of the similarities and differences in large intestinal microbiota between the treatment groups was conducted by building tree structures based on the unweighted pair-group method with arithmetic mean algorithm. The abundance-weighted Jaccard distance metric was used for hierarchical cluster analysis of all samples. The results showed that the STS, FGS, and control groups were clustered, and a second cluster was composed mainly of samples from the FOS group (Figure 1C). Principal coordinates analysis was used to compare the intestinal microbiota among the different groups. On the PCoA map, large intestine microflora from all samples were divided into 4 groups. Although the STS, FGS, and control groups were observed as 3 independent groups, the distance between them was short. Thus, the STS, FGS, and control groups appeared to have similar intestinal microbiota, whereas the FOS group appeared to have different intestinal microbiota. In addition, the results of the PCoA showed that principal coordinates 1 and 2 accounted for 42.09 and 7.67% of the total variance, respectively (Figure 1D).

      Unique and Shared Microbial Taxa

      To count the number of common or unique species in multiple groups, OTU with 97% similarity were selected to draw a species Venn map. In total, 98 common OTU were found among the 24 bacterial samples by observing the similarity and overlap of species composition (Figure 2). We found 2, 5, 5, and 101 unique OTU in the STS, FOS, FGS, and control groups, respectively. The control group had the highest total number of OTU, consistent with the results of the Chao index, suggesting that community richness was also highest in the control group. The Venn map indicated that differences in microbial richness might have been caused by the addition of different kinds of sugars.
      Figure thumbnail gr2
      Figure 2Venn diagram showing the numbers of common and unique species based on operational taxonomic units in multiple groups or samples. Different colors represent different groups (or samples), and the region of overlap represents species shared by multiple groups (or samples). FGS = prebiotics mix comprising FOS, galacto-oligosaccharides, and stachyose; FOS = fructo-oligosaccharide; STS = stachyose.

      Composition of Gut Microbiota at Various Taxonomic Levels

      We have provided community species composition and species abundance information using heat maps. At the phylum level, by clustering the similarity of abundance among the 24 samples, we found that Firmicutes and Bacteroidetes had higher similarity and abundance than other bacteria, whereas Fusobacteria were the least abundant (Supplemental Figure S4; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ROXDVC). To further study the reactions of the microorganisms to sugar, community histograms were drawn at the phylum, family, genus, and species levels to analyze composition changes in the microflora of the large intestine. At the phylum level, Firmicutes and Bacteroidetes were most prevalent in the control, STS, and FGS groups, but in the FOS group, Bacteroidetes were decreased and Actinobacteria were dominant (Figure 3A). At the family level, 73 families were identified; Figure 3B shows the 27 most abundant families. Lachnospiraceae was predominant in all samples. The proportions of Prevotellaceae and Muribaculaceae were decreased in the FOS group, and Bifidobacteriaceae were increased. At the genus level, the proportions of Bifidobacterium, Blautia, and Anaerostipes were increased and Lachnospiraceae_NK4A136 were decreased in the FOS group (Figure 3C). At the species level, increased levels of unclassified Bifidobacterium and unclassified Blautia were clearly observed (Figure 3D).
      Figure thumbnail gr3a
      Figure 3Community bar charts showing large intestinal microbiota compositions at various taxonomic levels: (A) phylum, (B) family, (C) genus, and (D) species. Any phylum, family, genus, or species with less than 1% abundance was merged into others.
      Figure thumbnail gr3b
      Figure 3Community bar charts showing large intestinal microbiota compositions at various taxonomic levels: (A) phylum, (B) family, (C) genus, and (D) species. Any phylum, family, genus, or species with less than 1% abundance was merged into others.
      To observe significant changes in microbial community compositions, abundance differences in the top 15 microbial communities at the phylum, family, genus, and species levels were tested using the Kruskal–Wallis test. At the phylum level, 6 phyla were significantly different among the 4 groups (Figure 4A). Bacteroidetes, Cyanobacteria, Patescibacteria, and Deferribacteres were significantly decreased in the FOS group (P < 0.05 or P < 0.01). Compared with the control group, Actinobacteria were significantly enriched in the FOS and STS groups (P < 0.05). We found no significant differences in the relative abundance of other phyla. At the family level, 10 families showed significant differences in abundance among the 4 groups (Figure 4B). In the FOS group, the abundance of Bifidobacteriaceae, Atopobiaceae, Burkholderiaceae, and Streptococcaceae were significantly increased, whereas the abundance of Prevotellaceae, Ruminococcaceae, Rikenellaceae, and Desulfovibrionaceae were significantly decreased (P < 0.05 or P < 0.01). The abundance of Lactobacillaceae in the STS and FGS groups was significantly decreased (P < 0.05). At the genus level, 12 genera showed significant differences in abundance among the 4 groups (Figure 4C). In the FOS group, the abundance of Bifidobacterium, Lactobacillus, Blautia, Anaerostipes, and Coriobacteriaceae_UCG-002 were significantly increased, and the abundance of Muribaculaceae, Lachnospiraceae_NK4A136, Prevotellaceae_UCG-001, Alloprevotella, unclassified_f_Lachnospiraceae, norank_f_Lachnospiraceae, and Roseburia were significantly decreased (P < 0.05 or P < 0.01 or P < 0.001). At the species level, 14 species showed significant abundance differences among the 4 groups. The abundances of uncultured_bacterium_g__norank_f__Muribaculaceae, uncultured_Bacteroidales_bacterium_Prevotellaceae_UCG-001, and gut_metagenome_Alloprevotella were significantly decreased in the FOS treatment group (P < 0.05 or P < 0.01; Figure 4D).
      Figure thumbnail gr4
      Figure 4Differences in microbial taxa at the level of (A) phylum, (B) family, (C) genus, and (D) species as determined by the Kruskal–Wallis test; *P < 0.05, **P < 0.01, ***P < 0.001. FGS = prebiotics mix comprising FOS, galacto-oligosaccharides, and stachyose; FOS = fructo-oligosaccharide; STS = stachyose.

      Functional Prediction of Large Intestinal Microbiota

      To explore the potential functions of the microbiota in mouse large intestine, functional prediction based on the 16S RNA gene sequences were performed according to the classification lineage of prokaryotes in the Kyoto Encyclopedia of Genes and Genomes database. In the FOS group, genes involved in the metabolism of cofactors and vitamins, xenobiotics biodegradation and metabolism, and energy metabolism were downregulated, and genes involved in glycan biosynthesis and metabolism were upregulated (P < 0.05 or P < 0.01; Figure 5A). In the STS group, the abundance of genes involved in energy metabolism were significantly reduced (P < 0.05), and those involved in glycan biosynthesis and metabolism were increased. In the FGS group, the abundance of genes involved in metabolism of cofactors and vitamins were significantly increased (P < 0.01). In addition, disease prediction results showed that the intake of FOS significantly reduced the risk of bacterial infection in mice, but resulted in a higher risk of infection by parasites (P < 0.05 or P < 0.001; Figure 5B). In terms of system functional prediction, genes related to the endocrine, immune, and nervous systems were tested. Prediction results showed that the abundance of genes related to the endocrine and immune systems were significantly higher in the FOS group (P < 0.001, P < 0.01) than in the control group. Intake of STS also significantly increased the abundance of genes related to the endocrine system (P < 0.05, P < 0.01, or P < 0.001; Figure 5C).
      Figure thumbnail gr5
      Figure 5Functional predictions of (A) metabolism, (B) disease, and (C) system gene abundance in the control, STS, FOS, and FGS groups. The bar chart shows the mean ± SD. Data were analyzed by 1-way ANOVA with a Tukey post-test. Asterisks indicate statistically significant differences compared with the control group: *P < 0.05, **P < 0.01, ***P < 0.001. FGS = prebiotics mix comprising FOS, galacto-oligosaccharides, and stachyose; FOS = fructo-oligosaccharide; STS = stachyose.

      Spearman Correlation Analysis Between the Microbial Community and SCFA

      In the FOS group, the Spearman correlations between SCFA concentrations (Supplemental Figure S7, Supplemental Tables S1 and S2; https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ROXDVC) changed significantly; furthermore, significant differences in OTU richness showed that some microbiota were significantly interrelated with certain kinds of SCFA (P < 0.05), and the correlation coefficients were all greater than 0.8 at the family (Supplemental Figure S5A), genus (Supplemental Figure S5B), and species levels (Supplemental Figure S5E). As shown in Figure 6A and Supplemental Figure S6A, isobutyric acid was positively correlated with Papillibacter, Alloprevotella, Lachnospiraceae FCS020, Candidatus-Saccharimonas, Oscillibacter, and Lachnospiraceae UCG-001 (P < 0.05 or P < 0.01), and negatively correlated with Clostridium innocuum at the genus level (P < 0.05; Supplemental Figure S6A). Firmicutes bacterium M10–2 was negatively correlated with propionic, valeric, and isobutyric acids, but positively correlated with the mouse gut metagenome at the species level (Supplemental Figure S6B).
      Figure thumbnail gr6
      Figure 6Heat maps displaying the relation between short-chain fatty acids (SCFA) and microflora: (A) control group; (B) fructo-oligosaccharide (FOS) group.

      DISCUSSION

      The results of the present study showed clear differences in α diversity indices among the treatment groups. The Shannon index significantly decreased in the FOS group (P < 0.01), consistent with the results from a study on the role of FOS in modulating cecal microbiota (
      • Huazano-García A.
      • Shin H.
      • López M.
      Modulation of gut microbiota of overweight mice by agavins and their association with bodyweight loss.
      ). This finding suggested that intake of FOS decreased the diversity of large intestinal microflora. Because FOS is a fermentable prebiotic that can be used as a substrate for the microbial community in the large intestine to stimulate changes to the α and β diversity indices (
      • Laffin M.
      • Perry T.
      • Park H.
      • Hotte N.
      • Fedorak R.N.
      • Thiesen A.
      • Dicken B.
      • Madsen K.L.
      Prebiotic supplementation following ileocecal resection in a murine model is associated with a loss of microbial diversity and increased inflammation.
      ), it can be concluded that FOS-enriched goat milk reduces the diversity and richness of large intestine microbiota.
      In terms of the β diversity index analysis, we observed significant separations between the FOS and control groups, and between the FGS and control groups through PCoA analysis, but we found a poor separation effect between the STS and control groups. Meanwhile, the hierarchical clustering tree showed that the OTU in the FOS and the FGS groups formed a cluster by phylogenetic composition; this was consistent with previous findings on the effects of FOS or FGS intake on the overall composition of gut microbiota (
      • Mao B.
      • Li D.
      • Zhao J.
      • Liu X.
      • Gu Z.
      • Chen Y.Q.
      • Zhang H.
      • Chen W.
      Metagenomic insights into the effects of fructo-oligosaccharides (FOS) on the composition of fecal microbiota in mice.
      ;
      • Bruno-Barcena J.M.
      • Azcarate-Peril M.A.
      Galacto-oligosaccharides and colorectal cancer: Feeding our intestinal probiome.
      ). Because FOS is a component of FGS, these results demonstrate that FOS treatment has remarkable effects on regulating microbiota structures in the large intestine.
      Our results for the composition of gut microbiota at various taxonomic levels showed that the 3 different oligosaccharides used to enrich goat milk caused significant differences in the composition of the large intestinal microbiota at the phylum and family levels. At the phylum level, the intake of FOS led to increased colonization by Actinobacteria, in line with the findings of a previous study (
      • Wang L.
      • Hu L.
      • Yan S.
      • Jiang T.
      • Fang S.
      • Wang G.
      • Zhao J.
      • Zhang H.
      • Chen W.
      Effects of different oligosaccharides at various dosages on the composition of gut microbiota and short-chain fatty acids in mice with constipation.
      ). Interestingly, a recent study found that the ratio of Firmicutes to Bacteroidetes increased in the FOS group, suggesting that FOS treatment could promote the production of SCFA in the large intestine and reduce the risk of host infection (
      • Molist F.
      • Manzanilla E.G.
      • Pérez J.F.
      • Nyachoti C.M.
      Coarse, but not finely ground, dietary fibre increases intestinal firmicutes:bacteroidetes ratio and reduces diarrhoea induced by experimental infection in piglets.
      ). Additionally, the ratio of Firmicutes to Bacteroidetes is also positively correlated with body mass index (
      • Koliada A.
      • Syzenko G.
      • Moseiko V.
      • Budovska L.
      • Puchkov K.
      • Perederiy V.
      • Gavalko Y.
      • Dorofeyev A.
      • Romanenko M.
      • Tkach S.
      • Sineok L.
      • Lushchak O.
      • Vaiserman A.
      Association between body mass index and Firmicutes/Bacteroidetes ratio in an adult Ukrainian population.
      ). Furthermore, the results of the present study showed that intake of FOS significantly increased the abundance of Lactobacillaceae and Bifidobacteriaceae at the family level compared with other groups (P < 0.01 or P < 0.05), similar to the findings of previous studies (
      • Xu Z.R.
      • Hu C.H.
      • Xia M.S.
      • Zhan X.A.
      • Wang M.Q.
      Effects of dietary fructooligosaccharide on digestive enzyme activities, intestinal microflora and morphology of male broilers.
      ;
      • Gómez B.
      • Gullón B.
      • Remoroza C.
      • Schols H.A.
      • Parajó J.C.
      • Alonso J.L.
      Purification, characterization, and prebiotic properties of pecticoligosaccharides from orange peel wastes.
      ;
      • Tandon D.
      • Haque M.M.
      • Gote M.
      • Jain M.
      • Bhaduri A.
      • Dubey A.K.
      • Mande S.S.
      A prospective randomized, double-blind, placebo-controlled, dose-response relationship study to investigate efficacy of fructo-oligosaccharides (fos) on human gut microflora.
      ). Lactobacillaceae is a family of lactic acid bacteria, and it is believed to be beneficial for the intestinal environment (
      • Kawakami S.
      • Ito R.
      • Maruki-Uchida H.
      • Kamei A.
      • Yasuoka A.
      • Toyoda T.
      • Ishijima T.
      • Nishimura E.
      • Morita M.
      • Sai M.
      • Abe K.
      • Okada S.
      Intake of a mixture of sake cake and rice malt increases mucin levels and changes in intestinal microbiota in mice.
      ). Because the Bifidobacteriaceae family belongs to the phylum Actinobacteria, an increase in the former corresponds to an increase in the latter, as described above for the FOS group. Bifidobacteriaceae benefit health by their involvement in the prevention and treatment of certain diseases, such as intestinal barrier dysfunction, constipation, steatohepatitis, and pathogen-related diseases (
      • Flores-Maltos D.A.
      • Mussatto S.I.
      • Contreras-Esquivel J.C.
      • Rodriguez-Herrera R.
      • Teixeira J.A.
      • Aguilar C.N.
      Biotechnological production and application of fructooligosaccharides.
      ;
      • Ling X.
      • Linglong P.
      • Weixia D.
      • Hong W.
      Protective effects of Bifidobacterium on intestinal barrier function in LPS-induced enterocyte barrier injury of Caco-2 monolayers and in a rat NEC model.
      ;
      • Meksawan K.
      • Chaotrakul C.
      • Leeaphorn N.
      • Gonlchanvit S.
      • Eiam-Ong S.
      • Kanjanabuch T.
      Effects of fructo-oligosaccharide supplementation on constipation in elderly continuous ambulatory peritoneal dialysis patients.
      ;
      • Matsumoto K.
      • Ichimura M.
      • Tsuneyama K.
      • Moritoki Y.
      • Tsunashima H.
      • Omagari K.
      • Hara M.
      • Yasuda I.
      • Miyakawa H.
      • Kikuchi K.
      Fructo-oligosaccharides and intestinal barrier function in a methionine–choline-deficient mouse model of nonalcoholic steatohepatitis.
      ). Bifidobacteriaceae are widely used as soluble probiotics in the preparation of functional foods for humans (
      • Valdés-Varela L.
      • Ruas-Madiedo P.
      • Gueimonde M.
      In vitro fermentation of different fructo-oligosaccharides by Bifidobacterium strains for the selection of synbiotic combinations.
      ), because FOS is the preferred carbon source for probiotics to promote the growth of beneficial gut microbiota (
      • Koleva P.T.
      • Valcheva R.S.
      • Sun X.
      • Gänzle M.G.
      • Dieleman L.A.
      Inulin and fructo-oligosaccharides have divergent effects on colitis and commensal microbiota in HLA-B27 transgenic rats.
      ). In summary, our results suggest that goat milk enriched with FOS can increase the number of beneficial bacteria, restore normal microbiota, and reduce the occurrence of various diseases in the large intestine.
      The correlation analyses between microbiota and SCFA showed that isobutyric, propionic, and valeric acids were the 3 main kinds of SCFA in the large intestines of mice, consistent with findings from
      • Paturi G.
      • Butts C.A.
      • Hedderley D.
      • Stoklosinski H.
      • Martell S.
      • Dinnan H.
      • Carpenter E.A.
      Goat and cow milk powder-based diets with or without prebiotics influence gut microbial populations and fermentation products in newly weaned rats.
      . The present study further demonstrated that isobutyric acid was positively correlated with Papillibacter, Alloprevotella, Lachnospiraceae FCS020, Candidatus-Saccharimonas, Oscillibacter, and Lachnospiraceae UCG-001 and negatively correlated with Clostridium innocuum at the genus level; whereas propionic, valeric, and isobutyric acids were negatively correlated with Firmicutes bacterium M10–2 and positively correlated with the mouse gut metagenome at the species level. These results reveal the interaction mechanisms between microbiota and SCFA. As well, acetate is an important SCFA in the gut, and a byproduct of fermentation (
      • Scheppach W.
      • Bartram P.
      • Richter A.
      • Richter F.
      • Liepold H.
      • Dusel G.
      • Hofstetter G.
      • Ruthlein J.
      • Kasper H.
      Effect of short-chain fatty acids on the human colonic mucosa in vitro.
      ), but it was not shown in our correlation analysis of microbiota and SCFA. It is worth noting that Bifidobacteria, which were greatly increased in the FOS group, can produce large amounts of acetate. Acetate confers anti-inflammatory and antiapoptotic effects (
      • Fukuda S.
      • Toh H.
      • Hase K.
      • Oshima K.
      • Nakanishi Y.
      • Yoshimura K.
      • Tobe T.
      • Clarke J.M.
      • Topping D.L.
      • Suzuki T.
      • Taylor T.D.
      • Itoh K.
      • Kikuchi J.
      • Morita H.
      • Hattori M.
      • Ohno H.
      Bifidobacteria can protect from entero-pathogenic infection through production of acetate.
      ), and plays an important role in protecting the colon from pathogen infection and carcinoma (
      • Jan G.
      • Belzacq A.S.
      • Haouzi D.
      • Rouault A.
      • Métivier D.
      • Kroemer G.
      • Brenner C.
      Propionibacteria induce apoptosis of colorectal carcinomacells via short-chain fatty acids acting on mitochondria.
      ;
      • Knol J.
      • Scholtens P.
      • Kafka C.
      • Steenbakkers J.
      • Gro S.
      • Helm K.
      • Klarczyk M.
      • Schöpfer H.
      • Böckler H.-M.
      • Wells J.
      Colon microflora in infants fed formula with galacto- and fructo-oligosaccharides: More like breast-fed infants.
      ). Thus, goat milk enriched with FOS can improve the large intestinal environment and benefit host health compared with goat milk enriched with STS or FGS. However, although the effects of oligosaccharides on gut flora have been evaluated using mice as a model because of their short life cycle and low cost, colonic microflora in mice, rats, and humans are different. Therefore, the effects of goat milk enriched with oligosaccharides on the human large intestinal environment needs to be validated in further studies.

      CONCLUSIONS

      Regarding α diversity, the addition of FOS to goat milk significantly reduced the diversity and richness of microbiota in the large intestine compared with pure goat milk or goat milk enriched with STS or FGS. We also found clear changes in microbial community structures in the FOS group compared with the other groups in terms of β diversity, in which the abundances of well-known beneficial bacteria (including Bifidobacteria and Lactobacillus) greatly increased. Through functional prediction, we observed that FOS in goat milk regulated metabolism, reduced the risk of intestinal bacterial infection, and improved the endocrine and immune systems. Propionic, isobutyric, and valeric acids were positively correlated with certain beneficial microbiota. Taken together, these findings show that supplementing goat milk with FOS has great potential to optimize large intestine microbiota structures and improve the large intestinal environment. These findings could be of great importance for developing functional food based on goat milk combined with oligosaccharides, and for revealing their respective mechanisms of action.

      ACKNOWLEDGMENTS

      This work was financially supported by the major research plan program of Shaanxi province (2018ZDXM-NY-094; 2019NY-131), the science and technology plan program of Xi'an city (20193038YF026NS026), the National Natural Science Foundation of China (31901657), and the Shaanxi Normal University program (number KY2019ZD009). The authors declare that they have no conflicts of interest.

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