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Goat milk fermented by lactic acid bacteria modulates small intestinal microbiota and immune responses
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Goat milk fermented by lactic acid bacteria modulates small intestinal microbiota and immune responses Xiaoxin Chena, Rui Zhengb, Rong Liub, Linqianag Lia, a b
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College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710119, China College of Life Science, Shaanxi Normal University, Xi'an 710119, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Lactic acid bacteria Fermented goat milk Gut microbiota 16S rRNA gene sequencing
The impact of fermented cow milk on gut microbiota has been extensively studied, however, the role of fermented goat milk (FGM) has not been well evaluated. The present study using 16S rRNA gene sequencing technology investigated the effect of FGM intake on small intestinal microbiota in mice. It was found that FGM intake decreased microbial diversity and significantly altered microbiota composition, particularly the genera Lactobacillus and Streptococcus. Moreover, metabolism and disease profiles were shifted by the FGM based on the function prediction. The expression of immune factors including tumor necrosis factor alpha (Tnfα), granzyme B (Gzmb), perforin (Prf) and aryl hydrocarbon receptor (Ahr) was also regulated by FGM treatment. Collectively, our findings reveal that FGM intake can exert beneficial effects on small intestinal microbiota and further modulate the host metabolism and immune responses. Therefore, the FGM can serve as an alternative functional food to improve health through gut bacterial management.
1. Introduction Lactic acid bacteria (LAB), a group of ancient organisms, produce lactic acid as one of the major metabolic end-products during carbohydrate fermentation (Lahtinen, Ouwehand, Salminen, & von Wright, 2011). The most common used LAB in the food industry include the genera Streptococcus, Lactococcus, Lactobacillus, Leuconostoc, Pediococcus and Bifidobacterium (Leroy & De Vuyst, 2004). Streptococcus in combination with Lactobacillus is the classic formulation of starter cultures used extensively for the production of fermented milk (Vinderola, Mocchiutti, & Reinheimer, 2002). While Bifidobacterium, a probiotic organism generally regarded as safe, is also widely utilized in dairy products for its health-enhancing benefits (Picard et al., 2005). The roles of LAB in enriching nutritional values (Gilliland, 1990), improving digestion of lactose (de Vrese et al., 2015; Hove, Nordgaard-Andersen, & Mortensen, 1994), controlling intestinal infections (Heyman, 2000; Maragkoudakis, Chingwaru, Gradisnik, Tsakalidou, & Cencic, 2010) and modulating immunity (Ren et al., 2016; Tsai, Cheng, Pan, & Biotechnology, , 2012), are proved to be effective. In the food industry, cow milk is the most common used matrix for LAB fermentation. However, in comparison with cow milk, goat milk presents a better digestibility, higher mineral bioavailability, more balanced protein and fat profiles (Zenebe, Ahmed, Kabeta, & Kebede, 2014; Park, 2006). Moreover, goat milk is considered to be much closer ⁎
in composition to breast milk than other sources of milk (Getaneh, Mebrat, Wubie, & Kendie, 2016). Due to a more reasonable proportion of nutrition and more expensive commercial values, goat milk is therefore becoming an essential niche in the total dairy industry sector (Haenlein, 2004). In these regards, goat milk has a great potential to provide an alternative basis for the LAB fermentation. The gut is inhabited by numerous microbes that form a widely diverse and immensely active ecological community (Veiga et al., 2014). A well-balanced and harmonious relationship between the host and intestinal microbes, driven by the diet, is of great importance to protect host health and prevent obesity (Ridaura et al., 2013; Zhang et al., 2009), inflammatory diseases (Round & Mazmanian, 2009; Morgan, 2012), neurological disorders (Scheperjans et al., 2015; Moos et al., 2016) and metabolic syndrome (Vijay-Kumar et al., 2010). LAB fermented milk is reported to exert myriad health benefits on the host gut (McKinley, 2005). Majority of studies have investigated the effects of fermented cow milk on host gut microbiota and the immune system. For example, Wang et al. (2012) reported that fermented cow milk supplemented with probiotics effectively altered the gut microbiota and immunity of host animals. Also, Veiga et al. (2010) discovered that Bifidobacterium animalis subsp. lactis fermented cow milk could reduce inflammation by altering a niche for colitogenic microbes. Nevertheless, the understanding of how LAB fermented goat milk (FGM) affects small intestinal microbiota is far from being satisfactory
Correspondence author. E-mail address: [email protected] (L. Li).
https://doi.org/10.1016/j.jff.2019.103744 Received 5 October 2019; Received in revised form 11 December 2019; Accepted 12 December 2019 1756-4646/ © 2019 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Please cite this article as: Xiaoxin Chen, et al., Journal of Functional Foods, https://doi.org/10.1016/j.jff.2019.103744
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2.3. Histology
and many aspects remain to be further explored. For the development of FGM products and the evaluation of its physiological function on human beings, it is important to determine the extent to which gut microbiomes are altered by LAB FGM. To address this issue, the alteration in microbial structure of mice gut treated with LAB FGM was analyzed using 16S rRNA high-throughput sequencing technology. The intestinal morphology was observed through H&E stain. The immune factors expression of tumor necrosis factor alpha (Tnfα), granzyme B (Gzmb), perforin (Prf) and aryl hydrocarbon receptor (Ahr) in mice small intestines was evaluated to further explore the influence of FGM consumption on the host immunity. Our results provide insight into the effects of FGM intake on small intestinal microbiota and host immune responses, which will drive a better understanding of the relationship between fermented foods diet and the host health. In addition, this study provides a new frame for developing FGM as a functional food.
Mice were sacrificed through cervical dislocation under anesthesia and small intestinal specimens were obtained under sterile conditions. The tissues were placed into 4% paraformaldehyde for fixation and dehydrated using a graded ethanol series. The processed tissues were embedded in paraffin which were subsequently sliced into 4-μm-thick sections and stained with H&E. Stained sections were observed and photographed under Nikon ECLIPSE 80 iMicroscope. 2.4. Microbial DNA extraction and PCR amplification Microbial genomic DNA were extracted from 200 mg small intestinal contents with the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer's protocol. DNA concentrations were determined by NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, U.S.), and their quality was measured by gel electrophoresis in 0.8% agarose. The extracted DNA underwent partial 16S rRNA gene amplification by PCR using the forward primer 338F (5́ -ACTCCTACGGGAGGCAGCA-3́ ) and the reverse primer 806R (5́ -GGACTACHVGGGTWTCTAAT-3́ ), which allowed the targeting of the V3-V4 hypervariable regions of the bacterial 16S rRNA. Unique eight-base barcodes were added to the primers for sorting of each samples from sequencing outcomes. Each reaction mixture of 20 μL contained 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer, 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. Thermocycling was performed in triplicate with an initial denaturation step at 98 °C for 2 min, followed by 25 cycles of denaturation at 98 °C for 15 sec, annealing at 55 °C for 30 sec, and extension at 72 °C for 30 sec, with a final extension at 72 °C for 5 min.
2. Materials and methods 2.1. Fermented goat milk (FGM) preparation The FGM was prepared using Saanen goat milk (Weinan, Shaanxi, China). The goat milk was collected from September to October in 2018. The experiment was conducted as soon as the goat milk was delivered to the laboratory in the sterile glass containers with ice packs. The goat milk was heated at 95 °C for 15 min and cooled to 42 °C. Then 5% (v/v) bacteria strains were added to the milk. The incubation was carried out at 42 °C for 4 h, afterwards the FGM was stored at 2 °C overnight. All bacteria strains were bought from Changzhou ProbioPlus Biological Technology Co. Ltd. (Jiansu, China). These bacteria strains include Streptococcus salivarius subsp. thermophilus (S. thermophilus), Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Bifidobacterium animalis subsp. lactis (B. lactis). Two types of FGM treatment were used in this study: SL FGM (108 CFU S. thermophilus/g and L. bulgaricus/g) treatment and SLB FGM (108 CFU S. thermophilus/g, 107 CFU L. bulgaricus/g and 109 CFU B. lactis/g.) treatment. The viable counts of S. thermophilus, L. bulgaricus and B. lactis in the FGM were determined by plate counting. Serial decimal dilution of 10-g FGM were prepared in sterile peptone water (0.15%), and 1 mL appropriate dilutions were plated on culture media in duplicate for bacterial enumeration. Counts of S. thermophilus were enumerated on M17 agar supplemented with 5% sterile 10% (w/v) lactose, and the incubation was performed at 45 °C for 48 h under microaerophilic conditions. The enumeration of L. bulgaricus was conducted using MRS agar with pH adjusted to 5.2, and the plates were incubated at 45 °C for 72 h under anaerobic conditions. B. lactis were counted on Li-Mupirocin MRS supplemented with 0.05% L-cysteine-HCl and incubated at 45 °C for 72 h under anaerobic conditions.
2.5. 16S rRNA gene Illumine MiSeq sequencing analysis The amplified gene products were extracted from 2% agarose gels and purified with the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, U.S.). The purified amplicons were pooled in equimolar portions and sequenced in paired-end modus (2 × 300 bp) on the Illumine MiSeq platform. The resulting reads were de-multiplexed and quality filtered employing the Quantitative Insights Into Microbial Ecology (QIIME v1.8.0) software package. QIIME pre-processing involved removing reads with greater than two mismatches in the forward or reverse primer sequences, and then truncating primer sequences from the reads. Additional reads were filtered out if: (1) ambiguous and unmatched bases were detected, (2) overlap sequences were shorter than 10 bp, (3) average quality scores over a sliding window of 50 bp dropped below 20. Reads were then further processed by the USEARCH quality-filtering pipeline, which removed noise and chimeras. Finally, a total of 768,802 high-quality sequence reads with an average length of 424 bp were obtained. These sequences were clustered into 608 operational taxonomic units (OTUs) at 97% similarity by UPARSE (version 7.1 http://drive5.com/uparse/). A representative sequence was selected for each OTU and classified with the Ribosomal Database Project (RDP) classifier (http://rdp.cme.msu.edu/) against the Silva (SSU115) database based on a confidence threshold of 70%. Classified reads were then binned by taxonomy and normalized to generate a relative abundance table. The final OTU table was subsampled to a depth of 47,212 sequences per sample to standardize the number of reads across samples.
2.2. Animals Male BALB/c mice, aged 6 weeks old, were originally purchased from Xían Jiaotong University Health Science Center (Shaanxi, China) and subsequently housed in a standard animal facility under a 12 h light/dark cycle with controlled temperature (23 ± 2 °C) and humility (45 ± 5%). Mice had ad libitum access to animal chow and autoclaved water. After one-week acclimation period, mice were divided into three groups (n = 6 per group): control group (fed with fresh goat milk), SL group (SL FGM), and SLB group (SLB FGM). Instead of water, mice were provided free access to either fresh goat milk or FGM daily at a quantity of 5 mL/mouse. Treatments continued for 4 weeks. The mice body weight was monitored every two weeks. Animal experiments were approved and performed in accordance with the Animal Care Guidelines of Shaanxi Normal University (permission number: 2018SNNU046).
2.6. Bioinformatics analysis Data visualization and statistical analyses of gut microbiota were conducted using R software (version 3.2.0) unless otherwise noted. Alpha diversity analysis consisting of richness index Sobs (observed OTUs) and diversity index Simpson was carried out based on Miseq sequence data using MOTHUR (version v.1.30.1). Alpha diversity 2
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FGM treatment would not cause obvious histological changes in mice small intestines.
indices among groups were compared by Student’s t-test. Rarefaction curves using Sobs and Shannon indices were plotted for analyzing the sampling depth. Beta diversity was evaluated by Bray-Curtis distances with QIIME, and visualized via principal coordinate analysis (PCoA) and unweighted pair-group method with arithmetic means (UPGMA) hierarchical clustering tree. Venn diagrams and microbial community bar diagrams were generated by QIIME. Differences of taxa relative abundance in microbial profiles among groups were compared using Kruskal-Wallis test with false-discovery rate (FDR) correction. Based on the 16S sequencing data, closed-reference OUT tables were constructed by QIIME, and input to the PICRUSt. For the metagenome prediction, KEGG Orthology (KOs) was used as functional classification scheme in the PICRUSt. The PATHWAY/KEGG database (https://www.genome. jp/kegg/) includes most of the known metabolic pathways, some of regulatory pathways, diseases and drugs category. The present study used PATHWAY/KEGG database to generate the metabolism and disease profiles. Differences of microbial functional profiles among groups were determined by One-way ANOVA with Tukey post-test (SPSS version 23.0).
3.2. Sequencing depth and microbiota diversity Rarefaction curves measured using Sobs index (Fig. S1A) indicate that sampling effort was sufficient to reflect microbiota richness, which was also the case with Shannon index (Fig. S1B) in the curves. The results of alpha diversity analysis show that the differences of microbiota richness and diversity among groups were significant (p < 0.01 and p < 0.001). The microbiota richness (measured by Sobs index) in SLB group was observed greater than that in SL group (Fig. 2A), and a reduction of richness occurred in both treatment groups compared with the control. The microbiota diversity (measured by Simpson index) was the highest in SL group followed by SLB group and the control group (Fig. 2B), revealing that the microbial diversity of SL group was the lowest among three groups. In beta diversity analysis, samples were found tended to separate by the types of treatment, with distinct clusters observed on the PCoA plots (PC1 76.42% and PC2 21.03%, Fig. 2C). All groups were further clustered with hierarchical clustering tree on OTU level (Fig. 2D), and the results show that the control group and SLB group were assembled into one group by phylogenetic composition, whereas SL group was independent into another group.
2.7. RNA extraction and real-time PCR RNA was extracted from mice small intestines with TRIzol reagent (Invitrogen) and reverse transcribed into complementary DNA using High-Capacity cDNA Reverse Transcription Kit (StarScript Ⅱ First-stand cDNA Synthesis Mix, GenStar). All extraction samples were stored in −80 °C until further analysis. Real-time PCR was performed under the following conditions: 95 °C for 10 min; 40 cycles of 15 sec at 95 °C, 30 sec at the target-specific annealing temperature (50 °C for Gzmb, 54 °C for Tnfα, 58 °C for Ahr and Prf), and a final extension from 65 °C to 95 °C. All real-time PCR data were normalized to β-actin (Actb). The targeted primer sequences were as follows: Tnfα forward 5′-ACCCTCA CACTCAGATCATC-3′, reverse 5′-GAGTAGACAAGGTACAACCC-3′; Prf forward 5′-CCACTCCAAGGTAGCCAAT-3′, reverse 5′-GGAGATGAGCC TGTGGTAAG-3′; Gzmb forward 5′-CTGCTAAAGCTGAAGAGTAAGG-3′, reverse 5′-ACCTCTTGTAGCGTGTTTGAG-3′; Ahr forward 5′-GAGCACA AATCAGAGACTGG-3′, reverse 5′-TGGAGGAAGCATAGAAGACC-3′; Actb forward 5′-AAGATGACCCAGATCATGTTTGAGACC-3′, reverse 5′-AGCCAGTCCAGACGCAGGAT-3′. Relative expression of immune factors was calculated using the ΔΔCt algorithm. The expression differences were assessed by One-way ANOVA with Tukey post-test.
3.3. Unique and shared microbial taxa To study the microbial distribution of mice gut under different treatments, Venn diagram was generated based on the numbers of shared and unique OTUs among groups (Fig. S2). A shared microbiota of 173 OTUs were found in all 18 samples, suggesting that these microbes are common residents in mice gut. In addition, there were 95, 9 and 124 OTUs identified to be exclusive in the control group, SL group and SLB group, respectively. The OTU numbers shared between SLB group and control group was 329, which was higher than 184 shared between SL and control group. This implies that the kinship between SLB group and control group is much closer. 3.4. Composition of gut microbiota at various taxonomic levels A total of 13 phyla were detected in all samples. The most prevalent phyla was Firmicutes followed by Tenericutes, Proteobacteria, Bacteroidetes and Actinobacteria in a decreasing order of relative abundance (Fig. 3A). 10 phyla were observed to have a significant difference on abundance in at least one group (p < 0.05, p < 0.01 and p < 0.001; Fig. 3B). SL treatment appeared to have more obvious impacts on the microbial composition versus SLB treatment. The microbial profiles of mice treated with SL FGM exhibited an increase of Firmicutes and a decrease of Tenericutes and Proteobacteria. The Bacteroidetes and Actinobacteria were identified to be more abundant in the control while less popular in both treatment groups. Notably, the ratio of Firmicutes to Bacteroidetes increased remarkably after mice were treated with FGM (One-way ANOVA with Tukey post-test, p < 0.001; Fig. S3). In terms of genus level, 222 genera were characterized and the first 14 prevailing genera ranked by relative abundance are showed on Fig. 4A. There were 15 identified genera showed significant differences in abundance among three groups (p < 0.01 and p < 0.001; Fig. 4B). The genus Lactobacillus was the most abundant in the SLB group, and Streptococcus was the most prevalent in the SL group, while the norank_f_Clostridiales_vadinBB60_group was the main bacteria in the control group (Fig. 4B). The SL FGM treatment led to a more significant decline in the proportion of Mycoplasma, Pseudomonas, Clotridium_sensu_stricto_1 and Candidatus_Arthromitus than the SLB FGM treatment (p < 0.01 and p < 0.001). The norank_f_Bacteroidales_S24-7_group was notably decreased by both FGM treatments as compared with the control
2.8. Statistical analysis Differences between two groups were determined by two-tailed Student’s t-test. Multiple comparisons were carried out using KruskalWallis test or One-way ANOVA where appropriate. Before applying One-way ANOVA, we have assessed the normality and homoscedasticity. p < 0.05 was considered significant. 3. Results 3.1. Mice body weight and histological analysis Mice receiving FGM gained more body weight than the control mice (Table S1). Although no significant differences in body weight gain were found among three groups, the body weight values of the SLB group at week 2 and week 4 were apparently higher as compared with the control group (p < 0.05, Table S1). Histological alterations of the mice small intestine between the control and treatment groups are illustrated in Fig. 1. The intestine in the control had a normal histological structure, which was characterized by intact brush border and regularly arranged nucleus in homogeneous mucosal epithelial cells (Fig. 1A). No obvious alterations of the histological structure were observed in SL group and SLB group as compared with that in the control (Fig. 1B and C), which suggests that 3
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Fig. 1. Histological analysis of small intestines from mice with different treatments. (A) nontreatment group (control group). (B) SL treatment group (treated with FGM containing S. thermophilus, L. bulgaricus). (C) SLB treatment group (treated with FGM containing S. thermophilus, L. bulgaricus together with B. lactis). Scale bar = 50 µm.
Fig. 2. Alpha diversity and Beta diversity of gut microbial communities among the control group, SL group and SLB group. (A) The richness index Sobs (B) The diversity index Simpson (Student’s t-test, **p < 0.01 and ***p < 0.001). (C) Principal Coordinates Analysis (PCoA) based on Bray-Curtis distances on OUT level represents the differences in gut microbial structure among three groups. Shapes with different colors represent different grouped samples. (D) Hierarchical cluster analysis of Bray-Curtis distances generated from taxa tables summarized on OTU level. Points or lines with different colors represent a group of samples with different treatments.
than in the other groups. Moreover, it was noteworthy that an unclassified genus belonging to the order Lactobacillales (unclassified_o_Lactobacillales) was markedly promoted by SL treatment (p < 0.001).
(p < 0.001). The abundance of Ureaplasma, Enterococcus, Veillonella and Lachnoclostridium was found the lowest in the SL group compared with the SLB group and the control group. In addition, Staphylococcus and norank_c_Cyanobacteria were found more popular in the SLB group 4
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Fig. 3. Composition of gut microbiota at phylum level. (A) Relative abundances of dominant microbial phyla in mice gut. < 1% abundance of the phyla was merged into others. (B) The differences in microbial taxa among three groups at phylum level were determined by Kruskal-Wallis test (*p < 0.05, **p < 0.01 and ***p < 0.001).
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Fig. 4. Composition of gut microbiota at genus level. (A) Relative abundances of dominant microbial genera in mice gut. < 1% abundance of the genera was merged into others. (B) The differences in microbial taxa among three groups at genus level were determined by Kruskal-Wallis test (**p < 0.01 and ***p < 0.001).
metabolism, nucleotide metabolism, xenobiotics biodegradation metabolism and metabolism of cofactors and vitamins) was significantly downregulated in both treatment groups compared with the control (One-way ANOVA with Tukey post-test, p < 0.01) (Fig. 5A). While the
3.5. Functional prediction of gut microbiota Relative abundance of metabolic pathways (including amino acid metabolism, carbohydrate metabolism, energy metabolism, lipid 6
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Fig. 5. Functional prediction of metabolism (A) and disease (B) in FGM treatment groups and the control. Bars indicate the mean ± SD. Data were analyzed by oneway ANOVA with Tukey post-test, Asterisk indicates a statistically significant difference compared with the control, **p < 0.01.
but notably decreased in SLB group (p < 0.01). The distinct in the risk of infectious diseases may reflect the response of mice small intestines to B. lactis containing FGM.
abundance of glycan biosynthesis and metabolism was upregulated in SL group as compared with the control. Moreover, it was found that FGM treatment could significantly reduce the risk of various diseases including cancers, metabolic diseases and neurodegenerative diseases (p < 0.01) (Fig. 5B). Interestingly, a subtle increase in the risk of immune system diseases was detected in both treatment groups. Meanwhile, the risk of infectious diseases was increased in SL group,
3.6. Expression of immune factors Tnfα, Prf, Gzmb and Ahr Intake of FGM regulated the relative mRNA expression of immune 7
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increase, while that of Bacteroidetes was detected to decrease in both treatment groups. A similar result was found in a recent study, which reported that the Firmicutes/Bacteroidetes ratio rose in mice gut after the supplement with kefir (also known as a fermented milk product) at a quantity of 2.15 g/kg/day (Hsu et al., 2018). The elevated ratio of Firmicutes to Bacteroidetes has been reported to be associated with increased body mass index (BMI) and obesity in mice and human (Ley et al., 2005). Hence, the increased ratio of Firmicutes to Bacteroidetes in our study is likely to imply the gain of mice body weight after FGM treatment. Indeed, it was found that the mice treated with FGM gained more body weight than the control mice according to the record of body weight during the experiment. Moreover, it was found that SL treatment induced a significant decreased abundance of Proteobacteria and Tenericutes. The phylum Proteobacteria is a member of facultative anaerobes in gastrointestinal tract where most resident microbes are obligate anaerobes. The increased prevalence of Proteobacteria has been reported to be a signature for the dysbiosis of gut microbiota and the occurrence of various diseases, such as metabolic disorders and intestinal inflammation (Shin, Whon, & Bae, 2015). Moreover, Phylum Tenericutes includes the genera Mycoplasma and Ureaplasma, of which certain species can be pathogenic to the host (Waites, Katz, & Schelonka, 2005). Therefore, it can be inferred that the intake of SL FGM probably could control the expansion of Proteobacteria during microbiota dysbiosis and might decrease the proportion of opportunistic pathogens. Genus Lactobacillus are fastidious Gram-positive bacteria that populate nutrient-rich habitats associated with food, plants, and mucosal surfaces of animals and humans (Duar et al., 2017). In gastrointestinal tracts, Lactobacillus are generally considered as commensal or indicative of healthy microbiota (Fijan, 2014). It has been demonstrated that Lactobacillus are capable to ferment oligo- and polysaccharides existed in the host diet (Gänzle & Follador, 2012), thereby producing shortchain fatty acids (SCFAs), which are vital metabolites for the fine tuning of immune responses (Rooks & Garrett, 2016). In addition, genus Streptococcus appears to be active resident in the small intestine, particularly in the duodenum and jejunum (Pei et al., 2004; Justesen, Nielsen, Jacobsen, Lave, & Rasmussen, 1984; Jandhyala et al., 2015). It has been reported that Streptococcus species play a vital role in degrading lactose, which are difficult to be digested by individuals with lactose intolerance (Fernandez et al., 2018). During the process of degrading lactose, Streptococcus species can produce lactate that can induce pathways involved in the production of mucus, which is a major component in the small intestinal barrier (Fernandez et al., 2018). In the present study, it was shown that SLB FGM treatment brought about a bloom of Lactobacillus while SL FGM treatment promoted the population of Streptococcus. Taken together, the results imply that intake of SL FGM might rise the amount of beneficial bacteria, and SLB FGM consumption seems to reinforce mucosal integrity of the small intestine. In this study, it was found that the FGM treatment could decrease the metabolism abundance (i.e., amino acid metabolism, carbohydrate metabolism, energy metabolism, nucleotide metabolism, metabolism of cofactors and vitamins, xenobiotics biodegradation metabolism and lipid metabolism), which was in line with others (McNulty et al., 2011). The FGM is particularly richer in peptides, amino acids, fatty acids and vitamins than the unfermented goat milk due to the bacterial fermentation. The intake of FGM provides more abundant substrates available for microbes to directly utilize and could reduce the metabolism activities required for the degradation of nutrient macromolecules (such as protein and lipid), leading to the decreased metabolism abundance. This reveals an adaptive function of the microflora in the small intestine. In addition, the Bacteroidales familiy is considered the predominant microorganisms that participate in the carbohydrate metabolism (Johnson, Heaver, Walters, & Ley, 2017). Our results showed that FGM treatment decreased the proportion of norank_f_Bacteroidales_S24-7_group, which was potentially linked with the reduced abundance of carbohydrate metabolism in FGM treatment groups. The more
Fig. 6. Differentially expressed immune-relative factors Tnfα, Prf, Gzmb and Ahr in treated mice small intestine compared with controls. Bars indicate the mean ± SD. Data were analyzed by one-way ANOVA with Tukey post-test, Asterisk indicates a statistically significant difference compared to the control, **p < 0.01.
factors Tnfα, Prf, Gzmb and Ahr (Fig. 6). The expression of Prf and Gzmb decreased markedly in mice small intestines after FGM treatment (Oneway ANOVA with Tukey post-test, p < 0.01). Ahr expression was down-regulated by two treatments, of which only SL treatment caused the significant difference in comparison of the control (p < 0.01). Interestingly, we detected an increase of Tnfα expression in both treatment groups (p < 0.01).
4. Discussion So far, most studies have examined the relationship between the intake of cow milk fermented products and the gut microbiota. However, few studies investigated effects of goat milk-based fermented foods on the small intestinal microbiota associated with nutrients assimilation. We gain insight into whether there is an interaction among FGM intake, small intestinal microbiota and immune system, and whether this interaction would be affected by the different LAB combination strategies. This study provides a microbe-based frame for evaluating host responses to FGM intake. In this study, it was found that intake of FGM decreased microbial diversity and altered the whole microbiota structure. Sobs index decreased and Simpson index increased in two treatment groups, suggesting that FGM consumption induced a decrease in alpha diversity. Rettedal, Altermann, Roy, and Dalziel (2019) demonstrated that there were no significant differences in alpha diversity between the treatments of unfermented and fermented sheep milk by detecting microbiota in the cecum (a compartment of the large intestine). Also, Usui et al. (2018) reported that alpha diversity of mice fecal microbiota had no obvious alterations after the long-term intake of LB81 yogurt. The contradiction between our results and previous studies could be explained by the fact that this work was performed with the small intestine, whose microbial environment is different from that in the large intestine and feces. In the small intestine, microbes are subject to more harsh stresses (i.e., low pH, faster transit time and bile acids), therefore the microbial diversity changes would be more obvious. Additionally, the results of beta diversity analysis indicate that the intake of FGM remarkably altered the overall microbiota structure. The PCoA plot and hierarchical clustering tree show that treatment groups and the control group were separated into three distinct clusters. The results were similar to a previous report by Zhang et al. (2017), revealing that the fermented milk containing Lactobacillus plantarum YW11 had pronounced influence on mice microbiota structure. At phylum level, the proportion of Firmicutes was observed to 8
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References
easily utilized substrates for microbiota in the FGM and the alterations in the microbial profiles might contribute to the changes in metabolism abundance. Recently, it has been demonstrated that the probiotic could activate the gut-associated natural killer cells to secret immune factors Tnfα which is a well-known pro-inflammatory cytokine (Aziz & Bonavida, 2016). Gzmb and Prf are cytotoxic factors involved in eliminating pathogenic bacteria during infection (Voskoboinik, Whisstock, & Trapani, 2015). In the present study, the expression of Tnfα was elevated following the FGM treatment. In contrast, Gzmb and Prf were downregulated by the FGM intake. The increased Tnfα expression and the declined Gzmb and Prf expression might be the host normal immune responses to exogenous probiotic bacteria consumption. Collectively, our results indicate that FGM intake would not elicit infection.
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5. Conclusion In summary, the intake of LAB FGM decreased microbiota diversity and significantly altered the overall microbiota composition in mice. The SL FGM elicited more intense alterations in microbial diversity and composition compared with SLB FGM. The treatment of SL FGM brought about a rise in Streptococcus, while that of SLB FGM resulted in the increase of Lactobacillus. Functional prediction analysis identified that FGM treatment decreased the abundance of most of important metabolism, such as amino acid metabolism, carbohydrate metabolism and energy metabolism. Mice receiving the FGM harbored the microflora with lower risks of cancers, metabolism diseases and neurodegenerative diseases. In addition, it was found that the expression level of immune factors could be regulated by both FGM treatments, especially the SL FGM treatment. Thus, our study provides evidence that the association of fermentation bacterial strains would influence the effect of FGM on gut microbial management, which can drive the downstream effects on metabolism and immune responses. The findings support the role of FGM microbes in gut microbial management and help to develop FGM as a functional food. Acknowledgments This work was financially supported by Shaanxi Provincial Science and Technology Department (2018ZDXM-NY-094) and Xi'an Science and Technology Bureau (20193038YF026NS026). Ethics statement All animal experiments were approved and performed in accordance with the Animal Care Guidelines of Shaanxi Normal University, China (Permission number: 2018SNNU046). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions L.L. conceived and designed the experiments. R.Z., R.L., and X.C. were responsible for the execution and quality control of the experiments. X.C. analyzed the data and wrote the paper. All authors critically revised the manuscript and approved the final paper. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jff.2019.103744. 9
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Goat milk fermented by lactic acid bacteria modulates small intestinal microbiota and immune responses Xiaoxin Chena, Rui Zhengb, Rong Liub, Linqianag Lia, a b
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College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710119, China College of Life Science, Shaanxi Normal University, Xi'an 710119, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Lactic acid bacteria Fermented goat milk Gut microbiota 16S rRNA gene sequencing
The impact of fermented cow milk on gut microbiota has been extensively studied, however, the role of fermented goat milk (FGM) has not been well evaluated. The present study using 16S rRNA gene sequencing technology investigated the effect of FGM intake on small intestinal microbiota in mice. It was found that FGM intake decreased microbial diversity and significantly altered microbiota composition, particularly the genera Lactobacillus and Streptococcus. Moreover, metabolism and disease profiles were shifted by the FGM based on the function prediction. The expression of immune factors including tumor necrosis factor alpha (Tnfα), granzyme B (Gzmb), perforin (Prf) and aryl hydrocarbon receptor (Ahr) was also regulated by FGM treatment. Collectively, our findings reveal that FGM intake can exert beneficial effects on small intestinal microbiota and further modulate the host metabolism and immune responses. Therefore, the FGM can serve as an alternative functional food to improve health through gut bacterial management.
1. Introduction Lactic acid bacteria (LAB), a group of ancient organisms, produce lactic acid as one of the major metabolic end-products during carbohydrate fermentation (Lahtinen, Ouwehand, Salminen, & von Wright, 2011). The most common used LAB in the food industry include the genera Streptococcus, Lactococcus, Lactobacillus, Leuconostoc, Pediococcus and Bifidobacterium (Leroy & De Vuyst, 2004). Streptococcus in combination with Lactobacillus is the classic formulation of starter cultures used extensively for the production of fermented milk (Vinderola, Mocchiutti, & Reinheimer, 2002). While Bifidobacterium, a probiotic organism generally regarded as safe, is also widely utilized in dairy products for its health-enhancing benefits (Picard et al., 2005). The roles of LAB in enriching nutritional values (Gilliland, 1990), improving digestion of lactose (de Vrese et al., 2015; Hove, Nordgaard-Andersen, & Mortensen, 1994), controlling intestinal infections (Heyman, 2000; Maragkoudakis, Chingwaru, Gradisnik, Tsakalidou, & Cencic, 2010) and modulating immunity (Ren et al., 2016; Tsai, Cheng, Pan, & Biotechnology, , 2012), are proved to be effective. In the food industry, cow milk is the most common used matrix for LAB fermentation. However, in comparison with cow milk, goat milk presents a better digestibility, higher mineral bioavailability, more balanced protein and fat profiles (Zenebe, Ahmed, Kabeta, & Kebede, 2014; Park, 2006). Moreover, goat milk is considered to be much closer ⁎
in composition to breast milk than other sources of milk (Getaneh, Mebrat, Wubie, & Kendie, 2016). Due to a more reasonable proportion of nutrition and more expensive commercial values, goat milk is therefore becoming an essential niche in the total dairy industry sector (Haenlein, 2004). In these regards, goat milk has a great potential to provide an alternative basis for the LAB fermentation. The gut is inhabited by numerous microbes that form a widely diverse and immensely active ecological community (Veiga et al., 2014). A well-balanced and harmonious relationship between the host and intestinal microbes, driven by the diet, is of great importance to protect host health and prevent obesity (Ridaura et al., 2013; Zhang et al., 2009), inflammatory diseases (Round & Mazmanian, 2009; Morgan, 2012), neurological disorders (Scheperjans et al., 2015; Moos et al., 2016) and metabolic syndrome (Vijay-Kumar et al., 2010). LAB fermented milk is reported to exert myriad health benefits on the host gut (McKinley, 2005). Majority of studies have investigated the effects of fermented cow milk on host gut microbiota and the immune system. For example, Wang et al. (2012) reported that fermented cow milk supplemented with probiotics effectively altered the gut microbiota and immunity of host animals. Also, Veiga et al. (2010) discovered that Bifidobacterium animalis subsp. lactis fermented cow milk could reduce inflammation by altering a niche for colitogenic microbes. Nevertheless, the understanding of how LAB fermented goat milk (FGM) affects small intestinal microbiota is far from being satisfactory
Correspondence author. E-mail address: [email protected] (L. Li).
https://doi.org/10.1016/j.jff.2019.103744 Received 5 October 2019; Received in revised form 11 December 2019; Accepted 12 December 2019 1756-4646/ © 2019 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
Please cite this article as: Xiaoxin Chen, et al., Journal of Functional Foods, https://doi.org/10.1016/j.jff.2019.103744
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2.3. Histology
and many aspects remain to be further explored. For the development of FGM products and the evaluation of its physiological function on human beings, it is important to determine the extent to which gut microbiomes are altered by LAB FGM. To address this issue, the alteration in microbial structure of mice gut treated with LAB FGM was analyzed using 16S rRNA high-throughput sequencing technology. The intestinal morphology was observed through H&E stain. The immune factors expression of tumor necrosis factor alpha (Tnfα), granzyme B (Gzmb), perforin (Prf) and aryl hydrocarbon receptor (Ahr) in mice small intestines was evaluated to further explore the influence of FGM consumption on the host immunity. Our results provide insight into the effects of FGM intake on small intestinal microbiota and host immune responses, which will drive a better understanding of the relationship between fermented foods diet and the host health. In addition, this study provides a new frame for developing FGM as a functional food.
Mice were sacrificed through cervical dislocation under anesthesia and small intestinal specimens were obtained under sterile conditions. The tissues were placed into 4% paraformaldehyde for fixation and dehydrated using a graded ethanol series. The processed tissues were embedded in paraffin which were subsequently sliced into 4-μm-thick sections and stained with H&E. Stained sections were observed and photographed under Nikon ECLIPSE 80 iMicroscope. 2.4. Microbial DNA extraction and PCR amplification Microbial genomic DNA were extracted from 200 mg small intestinal contents with the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer's protocol. DNA concentrations were determined by NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, U.S.), and their quality was measured by gel electrophoresis in 0.8% agarose. The extracted DNA underwent partial 16S rRNA gene amplification by PCR using the forward primer 338F (5́ -ACTCCTACGGGAGGCAGCA-3́ ) and the reverse primer 806R (5́ -GGACTACHVGGGTWTCTAAT-3́ ), which allowed the targeting of the V3-V4 hypervariable regions of the bacterial 16S rRNA. Unique eight-base barcodes were added to the primers for sorting of each samples from sequencing outcomes. Each reaction mixture of 20 μL contained 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer, 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. Thermocycling was performed in triplicate with an initial denaturation step at 98 °C for 2 min, followed by 25 cycles of denaturation at 98 °C for 15 sec, annealing at 55 °C for 30 sec, and extension at 72 °C for 30 sec, with a final extension at 72 °C for 5 min.
2. Materials and methods 2.1. Fermented goat milk (FGM) preparation The FGM was prepared using Saanen goat milk (Weinan, Shaanxi, China). The goat milk was collected from September to October in 2018. The experiment was conducted as soon as the goat milk was delivered to the laboratory in the sterile glass containers with ice packs. The goat milk was heated at 95 °C for 15 min and cooled to 42 °C. Then 5% (v/v) bacteria strains were added to the milk. The incubation was carried out at 42 °C for 4 h, afterwards the FGM was stored at 2 °C overnight. All bacteria strains were bought from Changzhou ProbioPlus Biological Technology Co. Ltd. (Jiansu, China). These bacteria strains include Streptococcus salivarius subsp. thermophilus (S. thermophilus), Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Bifidobacterium animalis subsp. lactis (B. lactis). Two types of FGM treatment were used in this study: SL FGM (108 CFU S. thermophilus/g and L. bulgaricus/g) treatment and SLB FGM (108 CFU S. thermophilus/g, 107 CFU L. bulgaricus/g and 109 CFU B. lactis/g.) treatment. The viable counts of S. thermophilus, L. bulgaricus and B. lactis in the FGM were determined by plate counting. Serial decimal dilution of 10-g FGM were prepared in sterile peptone water (0.15%), and 1 mL appropriate dilutions were plated on culture media in duplicate for bacterial enumeration. Counts of S. thermophilus were enumerated on M17 agar supplemented with 5% sterile 10% (w/v) lactose, and the incubation was performed at 45 °C for 48 h under microaerophilic conditions. The enumeration of L. bulgaricus was conducted using MRS agar with pH adjusted to 5.2, and the plates were incubated at 45 °C for 72 h under anaerobic conditions. B. lactis were counted on Li-Mupirocin MRS supplemented with 0.05% L-cysteine-HCl and incubated at 45 °C for 72 h under anaerobic conditions.
2.5. 16S rRNA gene Illumine MiSeq sequencing analysis The amplified gene products were extracted from 2% agarose gels and purified with the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, U.S.). The purified amplicons were pooled in equimolar portions and sequenced in paired-end modus (2 × 300 bp) on the Illumine MiSeq platform. The resulting reads were de-multiplexed and quality filtered employing the Quantitative Insights Into Microbial Ecology (QIIME v1.8.0) software package. QIIME pre-processing involved removing reads with greater than two mismatches in the forward or reverse primer sequences, and then truncating primer sequences from the reads. Additional reads were filtered out if: (1) ambiguous and unmatched bases were detected, (2) overlap sequences were shorter than 10 bp, (3) average quality scores over a sliding window of 50 bp dropped below 20. Reads were then further processed by the USEARCH quality-filtering pipeline, which removed noise and chimeras. Finally, a total of 768,802 high-quality sequence reads with an average length of 424 bp were obtained. These sequences were clustered into 608 operational taxonomic units (OTUs) at 97% similarity by UPARSE (version 7.1 http://drive5.com/uparse/). A representative sequence was selected for each OTU and classified with the Ribosomal Database Project (RDP) classifier (http://rdp.cme.msu.edu/) against the Silva (SSU115) database based on a confidence threshold of 70%. Classified reads were then binned by taxonomy and normalized to generate a relative abundance table. The final OTU table was subsampled to a depth of 47,212 sequences per sample to standardize the number of reads across samples.
2.2. Animals Male BALB/c mice, aged 6 weeks old, were originally purchased from Xían Jiaotong University Health Science Center (Shaanxi, China) and subsequently housed in a standard animal facility under a 12 h light/dark cycle with controlled temperature (23 ± 2 °C) and humility (45 ± 5%). Mice had ad libitum access to animal chow and autoclaved water. After one-week acclimation period, mice were divided into three groups (n = 6 per group): control group (fed with fresh goat milk), SL group (SL FGM), and SLB group (SLB FGM). Instead of water, mice were provided free access to either fresh goat milk or FGM daily at a quantity of 5 mL/mouse. Treatments continued for 4 weeks. The mice body weight was monitored every two weeks. Animal experiments were approved and performed in accordance with the Animal Care Guidelines of Shaanxi Normal University (permission number: 2018SNNU046).
2.6. Bioinformatics analysis Data visualization and statistical analyses of gut microbiota were conducted using R software (version 3.2.0) unless otherwise noted. Alpha diversity analysis consisting of richness index Sobs (observed OTUs) and diversity index Simpson was carried out based on Miseq sequence data using MOTHUR (version v.1.30.1). Alpha diversity 2
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FGM treatment would not cause obvious histological changes in mice small intestines.
indices among groups were compared by Student’s t-test. Rarefaction curves using Sobs and Shannon indices were plotted for analyzing the sampling depth. Beta diversity was evaluated by Bray-Curtis distances with QIIME, and visualized via principal coordinate analysis (PCoA) and unweighted pair-group method with arithmetic means (UPGMA) hierarchical clustering tree. Venn diagrams and microbial community bar diagrams were generated by QIIME. Differences of taxa relative abundance in microbial profiles among groups were compared using Kruskal-Wallis test with false-discovery rate (FDR) correction. Based on the 16S sequencing data, closed-reference OUT tables were constructed by QIIME, and input to the PICRUSt. For the metagenome prediction, KEGG Orthology (KOs) was used as functional classification scheme in the PICRUSt. The PATHWAY/KEGG database (https://www.genome. jp/kegg/) includes most of the known metabolic pathways, some of regulatory pathways, diseases and drugs category. The present study used PATHWAY/KEGG database to generate the metabolism and disease profiles. Differences of microbial functional profiles among groups were determined by One-way ANOVA with Tukey post-test (SPSS version 23.0).
3.2. Sequencing depth and microbiota diversity Rarefaction curves measured using Sobs index (Fig. S1A) indicate that sampling effort was sufficient to reflect microbiota richness, which was also the case with Shannon index (Fig. S1B) in the curves. The results of alpha diversity analysis show that the differences of microbiota richness and diversity among groups were significant (p < 0.01 and p < 0.001). The microbiota richness (measured by Sobs index) in SLB group was observed greater than that in SL group (Fig. 2A), and a reduction of richness occurred in both treatment groups compared with the control. The microbiota diversity (measured by Simpson index) was the highest in SL group followed by SLB group and the control group (Fig. 2B), revealing that the microbial diversity of SL group was the lowest among three groups. In beta diversity analysis, samples were found tended to separate by the types of treatment, with distinct clusters observed on the PCoA plots (PC1 76.42% and PC2 21.03%, Fig. 2C). All groups were further clustered with hierarchical clustering tree on OTU level (Fig. 2D), and the results show that the control group and SLB group were assembled into one group by phylogenetic composition, whereas SL group was independent into another group.
2.7. RNA extraction and real-time PCR RNA was extracted from mice small intestines with TRIzol reagent (Invitrogen) and reverse transcribed into complementary DNA using High-Capacity cDNA Reverse Transcription Kit (StarScript Ⅱ First-stand cDNA Synthesis Mix, GenStar). All extraction samples were stored in −80 °C until further analysis. Real-time PCR was performed under the following conditions: 95 °C for 10 min; 40 cycles of 15 sec at 95 °C, 30 sec at the target-specific annealing temperature (50 °C for Gzmb, 54 °C for Tnfα, 58 °C for Ahr and Prf), and a final extension from 65 °C to 95 °C. All real-time PCR data were normalized to β-actin (Actb). The targeted primer sequences were as follows: Tnfα forward 5′-ACCCTCA CACTCAGATCATC-3′, reverse 5′-GAGTAGACAAGGTACAACCC-3′; Prf forward 5′-CCACTCCAAGGTAGCCAAT-3′, reverse 5′-GGAGATGAGCC TGTGGTAAG-3′; Gzmb forward 5′-CTGCTAAAGCTGAAGAGTAAGG-3′, reverse 5′-ACCTCTTGTAGCGTGTTTGAG-3′; Ahr forward 5′-GAGCACA AATCAGAGACTGG-3′, reverse 5′-TGGAGGAAGCATAGAAGACC-3′; Actb forward 5′-AAGATGACCCAGATCATGTTTGAGACC-3′, reverse 5′-AGCCAGTCCAGACGCAGGAT-3′. Relative expression of immune factors was calculated using the ΔΔCt algorithm. The expression differences were assessed by One-way ANOVA with Tukey post-test.
3.3. Unique and shared microbial taxa To study the microbial distribution of mice gut under different treatments, Venn diagram was generated based on the numbers of shared and unique OTUs among groups (Fig. S2). A shared microbiota of 173 OTUs were found in all 18 samples, suggesting that these microbes are common residents in mice gut. In addition, there were 95, 9 and 124 OTUs identified to be exclusive in the control group, SL group and SLB group, respectively. The OTU numbers shared between SLB group and control group was 329, which was higher than 184 shared between SL and control group. This implies that the kinship between SLB group and control group is much closer. 3.4. Composition of gut microbiota at various taxonomic levels A total of 13 phyla were detected in all samples. The most prevalent phyla was Firmicutes followed by Tenericutes, Proteobacteria, Bacteroidetes and Actinobacteria in a decreasing order of relative abundance (Fig. 3A). 10 phyla were observed to have a significant difference on abundance in at least one group (p < 0.05, p < 0.01 and p < 0.001; Fig. 3B). SL treatment appeared to have more obvious impacts on the microbial composition versus SLB treatment. The microbial profiles of mice treated with SL FGM exhibited an increase of Firmicutes and a decrease of Tenericutes and Proteobacteria. The Bacteroidetes and Actinobacteria were identified to be more abundant in the control while less popular in both treatment groups. Notably, the ratio of Firmicutes to Bacteroidetes increased remarkably after mice were treated with FGM (One-way ANOVA with Tukey post-test, p < 0.001; Fig. S3). In terms of genus level, 222 genera were characterized and the first 14 prevailing genera ranked by relative abundance are showed on Fig. 4A. There were 15 identified genera showed significant differences in abundance among three groups (p < 0.01 and p < 0.001; Fig. 4B). The genus Lactobacillus was the most abundant in the SLB group, and Streptococcus was the most prevalent in the SL group, while the norank_f_Clostridiales_vadinBB60_group was the main bacteria in the control group (Fig. 4B). The SL FGM treatment led to a more significant decline in the proportion of Mycoplasma, Pseudomonas, Clotridium_sensu_stricto_1 and Candidatus_Arthromitus than the SLB FGM treatment (p < 0.01 and p < 0.001). The norank_f_Bacteroidales_S24-7_group was notably decreased by both FGM treatments as compared with the control
2.8. Statistical analysis Differences between two groups were determined by two-tailed Student’s t-test. Multiple comparisons were carried out using KruskalWallis test or One-way ANOVA where appropriate. Before applying One-way ANOVA, we have assessed the normality and homoscedasticity. p < 0.05 was considered significant. 3. Results 3.1. Mice body weight and histological analysis Mice receiving FGM gained more body weight than the control mice (Table S1). Although no significant differences in body weight gain were found among three groups, the body weight values of the SLB group at week 2 and week 4 were apparently higher as compared with the control group (p < 0.05, Table S1). Histological alterations of the mice small intestine between the control and treatment groups are illustrated in Fig. 1. The intestine in the control had a normal histological structure, which was characterized by intact brush border and regularly arranged nucleus in homogeneous mucosal epithelial cells (Fig. 1A). No obvious alterations of the histological structure were observed in SL group and SLB group as compared with that in the control (Fig. 1B and C), which suggests that 3
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Fig. 1. Histological analysis of small intestines from mice with different treatments. (A) nontreatment group (control group). (B) SL treatment group (treated with FGM containing S. thermophilus, L. bulgaricus). (C) SLB treatment group (treated with FGM containing S. thermophilus, L. bulgaricus together with B. lactis). Scale bar = 50 µm.
Fig. 2. Alpha diversity and Beta diversity of gut microbial communities among the control group, SL group and SLB group. (A) The richness index Sobs (B) The diversity index Simpson (Student’s t-test, **p < 0.01 and ***p < 0.001). (C) Principal Coordinates Analysis (PCoA) based on Bray-Curtis distances on OUT level represents the differences in gut microbial structure among three groups. Shapes with different colors represent different grouped samples. (D) Hierarchical cluster analysis of Bray-Curtis distances generated from taxa tables summarized on OTU level. Points or lines with different colors represent a group of samples with different treatments.
than in the other groups. Moreover, it was noteworthy that an unclassified genus belonging to the order Lactobacillales (unclassified_o_Lactobacillales) was markedly promoted by SL treatment (p < 0.001).
(p < 0.001). The abundance of Ureaplasma, Enterococcus, Veillonella and Lachnoclostridium was found the lowest in the SL group compared with the SLB group and the control group. In addition, Staphylococcus and norank_c_Cyanobacteria were found more popular in the SLB group 4
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Fig. 3. Composition of gut microbiota at phylum level. (A) Relative abundances of dominant microbial phyla in mice gut. < 1% abundance of the phyla was merged into others. (B) The differences in microbial taxa among three groups at phylum level were determined by Kruskal-Wallis test (*p < 0.05, **p < 0.01 and ***p < 0.001).
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Fig. 4. Composition of gut microbiota at genus level. (A) Relative abundances of dominant microbial genera in mice gut. < 1% abundance of the genera was merged into others. (B) The differences in microbial taxa among three groups at genus level were determined by Kruskal-Wallis test (**p < 0.01 and ***p < 0.001).
metabolism, nucleotide metabolism, xenobiotics biodegradation metabolism and metabolism of cofactors and vitamins) was significantly downregulated in both treatment groups compared with the control (One-way ANOVA with Tukey post-test, p < 0.01) (Fig. 5A). While the
3.5. Functional prediction of gut microbiota Relative abundance of metabolic pathways (including amino acid metabolism, carbohydrate metabolism, energy metabolism, lipid 6
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Fig. 5. Functional prediction of metabolism (A) and disease (B) in FGM treatment groups and the control. Bars indicate the mean ± SD. Data were analyzed by oneway ANOVA with Tukey post-test, Asterisk indicates a statistically significant difference compared with the control, **p < 0.01.
but notably decreased in SLB group (p < 0.01). The distinct in the risk of infectious diseases may reflect the response of mice small intestines to B. lactis containing FGM.
abundance of glycan biosynthesis and metabolism was upregulated in SL group as compared with the control. Moreover, it was found that FGM treatment could significantly reduce the risk of various diseases including cancers, metabolic diseases and neurodegenerative diseases (p < 0.01) (Fig. 5B). Interestingly, a subtle increase in the risk of immune system diseases was detected in both treatment groups. Meanwhile, the risk of infectious diseases was increased in SL group,
3.6. Expression of immune factors Tnfα, Prf, Gzmb and Ahr Intake of FGM regulated the relative mRNA expression of immune 7
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increase, while that of Bacteroidetes was detected to decrease in both treatment groups. A similar result was found in a recent study, which reported that the Firmicutes/Bacteroidetes ratio rose in mice gut after the supplement with kefir (also known as a fermented milk product) at a quantity of 2.15 g/kg/day (Hsu et al., 2018). The elevated ratio of Firmicutes to Bacteroidetes has been reported to be associated with increased body mass index (BMI) and obesity in mice and human (Ley et al., 2005). Hence, the increased ratio of Firmicutes to Bacteroidetes in our study is likely to imply the gain of mice body weight after FGM treatment. Indeed, it was found that the mice treated with FGM gained more body weight than the control mice according to the record of body weight during the experiment. Moreover, it was found that SL treatment induced a significant decreased abundance of Proteobacteria and Tenericutes. The phylum Proteobacteria is a member of facultative anaerobes in gastrointestinal tract where most resident microbes are obligate anaerobes. The increased prevalence of Proteobacteria has been reported to be a signature for the dysbiosis of gut microbiota and the occurrence of various diseases, such as metabolic disorders and intestinal inflammation (Shin, Whon, & Bae, 2015). Moreover, Phylum Tenericutes includes the genera Mycoplasma and Ureaplasma, of which certain species can be pathogenic to the host (Waites, Katz, & Schelonka, 2005). Therefore, it can be inferred that the intake of SL FGM probably could control the expansion of Proteobacteria during microbiota dysbiosis and might decrease the proportion of opportunistic pathogens. Genus Lactobacillus are fastidious Gram-positive bacteria that populate nutrient-rich habitats associated with food, plants, and mucosal surfaces of animals and humans (Duar et al., 2017). In gastrointestinal tracts, Lactobacillus are generally considered as commensal or indicative of healthy microbiota (Fijan, 2014). It has been demonstrated that Lactobacillus are capable to ferment oligo- and polysaccharides existed in the host diet (Gänzle & Follador, 2012), thereby producing shortchain fatty acids (SCFAs), which are vital metabolites for the fine tuning of immune responses (Rooks & Garrett, 2016). In addition, genus Streptococcus appears to be active resident in the small intestine, particularly in the duodenum and jejunum (Pei et al., 2004; Justesen, Nielsen, Jacobsen, Lave, & Rasmussen, 1984; Jandhyala et al., 2015). It has been reported that Streptococcus species play a vital role in degrading lactose, which are difficult to be digested by individuals with lactose intolerance (Fernandez et al., 2018). During the process of degrading lactose, Streptococcus species can produce lactate that can induce pathways involved in the production of mucus, which is a major component in the small intestinal barrier (Fernandez et al., 2018). In the present study, it was shown that SLB FGM treatment brought about a bloom of Lactobacillus while SL FGM treatment promoted the population of Streptococcus. Taken together, the results imply that intake of SL FGM might rise the amount of beneficial bacteria, and SLB FGM consumption seems to reinforce mucosal integrity of the small intestine. In this study, it was found that the FGM treatment could decrease the metabolism abundance (i.e., amino acid metabolism, carbohydrate metabolism, energy metabolism, nucleotide metabolism, metabolism of cofactors and vitamins, xenobiotics biodegradation metabolism and lipid metabolism), which was in line with others (McNulty et al., 2011). The FGM is particularly richer in peptides, amino acids, fatty acids and vitamins than the unfermented goat milk due to the bacterial fermentation. The intake of FGM provides more abundant substrates available for microbes to directly utilize and could reduce the metabolism activities required for the degradation of nutrient macromolecules (such as protein and lipid), leading to the decreased metabolism abundance. This reveals an adaptive function of the microflora in the small intestine. In addition, the Bacteroidales familiy is considered the predominant microorganisms that participate in the carbohydrate metabolism (Johnson, Heaver, Walters, & Ley, 2017). Our results showed that FGM treatment decreased the proportion of norank_f_Bacteroidales_S24-7_group, which was potentially linked with the reduced abundance of carbohydrate metabolism in FGM treatment groups. The more
Fig. 6. Differentially expressed immune-relative factors Tnfα, Prf, Gzmb and Ahr in treated mice small intestine compared with controls. Bars indicate the mean ± SD. Data were analyzed by one-way ANOVA with Tukey post-test, Asterisk indicates a statistically significant difference compared to the control, **p < 0.01.
factors Tnfα, Prf, Gzmb and Ahr (Fig. 6). The expression of Prf and Gzmb decreased markedly in mice small intestines after FGM treatment (Oneway ANOVA with Tukey post-test, p < 0.01). Ahr expression was down-regulated by two treatments, of which only SL treatment caused the significant difference in comparison of the control (p < 0.01). Interestingly, we detected an increase of Tnfα expression in both treatment groups (p < 0.01).
4. Discussion So far, most studies have examined the relationship between the intake of cow milk fermented products and the gut microbiota. However, few studies investigated effects of goat milk-based fermented foods on the small intestinal microbiota associated with nutrients assimilation. We gain insight into whether there is an interaction among FGM intake, small intestinal microbiota and immune system, and whether this interaction would be affected by the different LAB combination strategies. This study provides a microbe-based frame for evaluating host responses to FGM intake. In this study, it was found that intake of FGM decreased microbial diversity and altered the whole microbiota structure. Sobs index decreased and Simpson index increased in two treatment groups, suggesting that FGM consumption induced a decrease in alpha diversity. Rettedal, Altermann, Roy, and Dalziel (2019) demonstrated that there were no significant differences in alpha diversity between the treatments of unfermented and fermented sheep milk by detecting microbiota in the cecum (a compartment of the large intestine). Also, Usui et al. (2018) reported that alpha diversity of mice fecal microbiota had no obvious alterations after the long-term intake of LB81 yogurt. The contradiction between our results and previous studies could be explained by the fact that this work was performed with the small intestine, whose microbial environment is different from that in the large intestine and feces. In the small intestine, microbes are subject to more harsh stresses (i.e., low pH, faster transit time and bile acids), therefore the microbial diversity changes would be more obvious. Additionally, the results of beta diversity analysis indicate that the intake of FGM remarkably altered the overall microbiota structure. The PCoA plot and hierarchical clustering tree show that treatment groups and the control group were separated into three distinct clusters. The results were similar to a previous report by Zhang et al. (2017), revealing that the fermented milk containing Lactobacillus plantarum YW11 had pronounced influence on mice microbiota structure. At phylum level, the proportion of Firmicutes was observed to 8
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References
easily utilized substrates for microbiota in the FGM and the alterations in the microbial profiles might contribute to the changes in metabolism abundance. Recently, it has been demonstrated that the probiotic could activate the gut-associated natural killer cells to secret immune factors Tnfα which is a well-known pro-inflammatory cytokine (Aziz & Bonavida, 2016). Gzmb and Prf are cytotoxic factors involved in eliminating pathogenic bacteria during infection (Voskoboinik, Whisstock, & Trapani, 2015). In the present study, the expression of Tnfα was elevated following the FGM treatment. In contrast, Gzmb and Prf were downregulated by the FGM intake. The increased Tnfα expression and the declined Gzmb and Prf expression might be the host normal immune responses to exogenous probiotic bacteria consumption. Collectively, our results indicate that FGM intake would not elicit infection.
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5. Conclusion In summary, the intake of LAB FGM decreased microbiota diversity and significantly altered the overall microbiota composition in mice. The SL FGM elicited more intense alterations in microbial diversity and composition compared with SLB FGM. The treatment of SL FGM brought about a rise in Streptococcus, while that of SLB FGM resulted in the increase of Lactobacillus. Functional prediction analysis identified that FGM treatment decreased the abundance of most of important metabolism, such as amino acid metabolism, carbohydrate metabolism and energy metabolism. Mice receiving the FGM harbored the microflora with lower risks of cancers, metabolism diseases and neurodegenerative diseases. In addition, it was found that the expression level of immune factors could be regulated by both FGM treatments, especially the SL FGM treatment. Thus, our study provides evidence that the association of fermentation bacterial strains would influence the effect of FGM on gut microbial management, which can drive the downstream effects on metabolism and immune responses. The findings support the role of FGM microbes in gut microbial management and help to develop FGM as a functional food. Acknowledgments This work was financially supported by Shaanxi Provincial Science and Technology Department (2018ZDXM-NY-094) and Xi'an Science and Technology Bureau (20193038YF026NS026). Ethics statement All animal experiments were approved and performed in accordance with the Animal Care Guidelines of Shaanxi Normal University, China (Permission number: 2018SNNU046). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions L.L. conceived and designed the experiments. R.Z., R.L., and X.C. were responsible for the execution and quality control of the experiments. X.C. analyzed the data and wrote the paper. All authors critically revised the manuscript and approved the final paper. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jff.2019.103744. 9
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