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Article

Cudrania tricuspidata Combined with Lacticaseibacillus rhamnosus Modulate Gut Microbiota and Alleviate Obesity-Associated Metabolic Parameters in Obese Mice

by
Ju Kyoung Oh
1,†,
Robie Vasquez
1,
In-Chan Hwang
1,
Ye Na Oh
1,
Sang Hoon Kim
1,
Shin Ho Kang
2,
Jae Yeon Joung
2,
Nam Su Oh
3,
Sejeong Kim
4,
Yohan Yoon
4 and
Dae-Kyung Kang
1,*
1
Department of Animal Resources Science, Dankook University, Cheonan 31116, Korea
2
R&D Center, Seoul Dairy Cooperative, Ansan 15407, Korea
3
Department of Food and Biotechnology, Korea University Sejong Campus, Sejong 30019, Korea
4
Department of Food and Nutrition, Sookmyung Women’s University, Seoul 04310, Korea
*
Author to whom correspondence should be addressed.
†
Present address: Department of Microbiology, Tumor and Cell Biology, Centre for Translational Microbiome Research (CTMR), Karolinska Institutet, 171 65 Stockholm, Sweden.
Microorganisms 2021, 9(9), 1908; https://doi.org/10.3390/microorganisms9091908
Submission received: 7 July 2021 / Revised: 26 August 2021 / Accepted: 3 September 2021 / Published: 8 September 2021
(This article belongs to the Special Issue Dialogue of Probiotics with the Host)
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Versions Notes

Abstract

:
The aim of the presented study was to investigate the synbiotic effects of L. rhamnosus 4B15 and C. tricuspidata extract administration on the gut microbiota and obesity-associated metabolic parameters in diet-induced obese mice. Thirty-one 6-week-old male C57BL/N6 mice were divided into five diet groups: normal diet (ND, n = 7) group; high-fat diet (HFD, n = 6) group; probiotic (PRO, n = 5) group; prebiotic (PRE, n = 7) group; and synbiotic (SYN, n = 6) group. After 10 weeks, the percent of fat mass, serum triglyceride, and ALT levels were significantly reduced in SYN-fed obese mice, compared with other treatments. SYN treatment also modulated the abundance of Desulfovibrio, Dorea, Adlercreutzia, Allobaculum, Coprococcus, unclassified Clostridiaceae, Lactobacillus, Helicobacter, Flexispira, Odoribacter, Ruminococcus, unclassified Erysipelotrichaceae, and unclassified Desulfovibrionaceae. These taxa showed a strong correlation with obesity-associated indices. Lastly, the SYN-supplemented diet upregulated metabolic pathways known to improve metabolic health. Further investigations are needed to understand the mechanisms driving the synbiotic effect of C. tricuspidata and L. rhamnosus 4B15.

1. Introduction

In recent years, the strong relationship between the gut microbiota and the host’s physiology and health have been fairly established [1,2,3,4]. Several studies have demonstrated the role of the gut microbiota in the host’s metabolism, such that its modulation would affect its host beneficially or otherwise [2,3,4,5]. Aside from environment and genetic factors, diet plays a critical role in shaping the host’s gut microbiota [1,6]; thus, researchers have focused on modulating the gut microbiota through diet. The potential of probiotics and prebiotics in influencing the gut microbiota has been the subject of recent studies [7,8]. Probiotics are live, beneficial microorganisms that can improve microbial balance in the intestinal tract [9,10]. Probiotics have shown great potential for alleviating obesity, metabolic diseases, preventing food allergy and neurodegenerative disorders, as well as elimination of the pathogenic Helicobacter [11,12,13,14]. Hence, there is now a great interest in food containing probiotics, as well as fermented food [15,16]. Prebiotics, on the other hand, are a non-digestible substrate which may include oligosaccharides and dietary fibres [7,8,14,17]. The supplementation of prebiotics can beneficially affect host metabolism through gut microbiota modulation [14,18]. Multiple studies have shown the promising effect of both probiotics and prebiotics, and its combination (synbiotics) in the promotion of gut health, as well as the alleviation of metabolic disorders [7,14,19,20,21]. Synbiotic application in several animal models demonstrated promising results in the amelioration of intestinal inflammation and obesity, control of cholesterol, prevention of diarrhoea, and improvement of host metabolism [22,23,24,25,26].
Lacticaseibacillus rhamnosus 4B15, a lactobacillus strain recently isolated from infant faeces, has probiotic and anti-inflammatory potential [27]. On the other hand, Cudrania tricuspidata, a tree endemic to Korea and other parts of East Asia, is an important and widely used herbal medicine in Korea [28]. Investigations have shown that C. tricuspidata extracts can alleviate excess adiposity and serum triglyceride levels, and ameliorate insulin resistance and hyperglycaemia in murine models [28,29]. Another study revealed that C. tricuspidata extracts exhibit anti-obesity and antidiabetic potential by inhibiting protein-tyrosine phosphatase 1B (PTP1B), an important metabolic syndrome marker [30]. The synbiotic potential of L. rhamnosus 4B15 and C. tricuspidata in modulation of gut microbiota and the alleviation of metabolic disorders has not been studied. Therefore, the goal of this study was to investigate the effects of L. rhamnosus 4B15 and C. tricuspidata extract administration on the gut microbiota and obesity-associated metabolic parameters in diet-induced obese mice.

2. Material and Methods

2.1. Animals and Experimental Design

Thirty-one 6-week-old male C57BL/N6 mice were divided into five diet groups (5–7 mice per group, 2–3 mice per cage): normal diet (ND) group (n = 7); high-fat diet (HFD) group (n = 6); probiotic HFD (PRO) group (n = 5); prebiotic HFD (PRE) group (n = 7); and synbiotic HFD (SYN) group (n = 6). The ND group was provided a diet containing 10% fat, while the HFD group was provided an HFD (45% fat) (Table S1). The PRO, PRE, and SYN groups were also provided an HFD (45% fat) supplemented with either the probiotic L. rhamnosus 4B15, the prebiotic C. tricuspidata, or a combination of the prebiotic and probiotic (Figure S1). C. tricuspidata leaves were obtained from a local market in Sunchang, Jeollakbu-do, the Republic of Korea, while L. rhamnosus 4B15 was obtained from the Korean Culture Centre of Microorganisms (KCCM11983P). C. tricuspidata leaf extract and L. rhamnosus 4B15 were prepared according to previous methods [31,32]. The treatments were administered through oral gavage daily (200 µL/day). The concentrations of L. rhamnosus 4B15 and C. tricuspidata extract were 1.0 × 109 CFU/g and 1500 mg/mL, respectively (Figure S1). Mouse cages were equipped with a one-sided self-feeder and a nipple water-feeder for ad libitum access to feed and water throughout the experiment. The body weights of the mice were measured weekly. After 10 weeks of feeding, the mice were sacrificed; intestine, fat, and serum samples were collected. All animal protocols were approved by the Sookmyung Women’s University Animal Care Committee (SMWU-IACUC-1703-001-01; Seoul, South Korea; 26 December 2017).

2.2. Total Fat Mass and Serum Biochemical Analysis

After sacrifice, epididymal and inguinal fat were collected and weighed. The percent of fat mass was calculated using the formula: total fat mass/body weight × 100. The blood samples were placed in serum-separating tubes (Microtainer, Becton, Dickinson and Company, NJ, USA), followed by centrifugation at 2339× g for 10 min at 4 °C. The supernatants were then transferred to sterile micro-centrifuge tubes, immediately frozen and stored at −70 °C until further analyses. A chemistry analyser (Mindray, Shenzhen, China) was used to measure the serum triglyceride (TG), total cholesterol (T-Chol), and alanine aminotransferase activity (ALT) levels.

2.3. DNA Extraction and Sequencing

The mouse intestines were immediately transported to the laboratory at 4 °C, where they were stored in a deep freezer at −70 °C until further analysis. Genomic DNA was extracted using a QIAamp Power Fecal DNA Isolation Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. The concentration and purity of the extracted genomic DNA were confirmed by agarose gel electrophoresis and UV-visible spectrophotometry (Mecasys Co., Ltd., Daejeon, Republic of Korea). PCR amplification of 16S rRNA was performed using primers for the V3–V4 hypervariable region. The PCR conditions were as follows: initial denaturation at 94 °C for 5 min, followed by 30 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 45 s, and final extension at 72 °C for 1 min 30 s. The amplicons were purified using a NucleoSpin Clean-up Kit (Macherey-Nagel, Dueren, Germany) following the manufacturer’s instructions, and then used to construct sequencing libraries with an Illumina TruSeq DNA Sample Preparation Kit (Illumina, San Diego, CA, USA). For each sample, barcoded V3–V4 PCR amplicons were sequenced using the Illumina Miseq platform (MiSeq Reagent Kit v2, Illumina, CA, USA). Finally, amplification and sequencing of the V3–V4 region of the 16S rRNA gene was performed by ChunLab, Inc. (Seoul, Republic of Korea).

2.4. Microbial Community Analysis

Raw sequence data generated by the Illumina MiSeq platform were processed using the Quantitative Insights into Microbial Ecology (QIIME, version 1.9.0) pipeline [33]. Briefly, sequences that were less than 200 bp or greater than 600 bp in length, of low quality, or that had incorrect primer sequences and/or contained more than one ambiguous base were filtered out using the split_libraries.py script. Chimeric sequences were identified using the identify_chimeric_seqs.py and filter_fasta.py scripts, and removed from the sequence data. Operational taxonomic units were identified using the pick_open_reference.py script and the most recent Greengenes database (13_8); a 97% identity threshold was used [34].
Microbial composition data from mouse intestine samples were generated using the summarize_taxa_through_plots.py script. For alpha-diversity indices, the alpha_diversity.py script was employed to generate ACE, Chao1, Shannon–Weaver, and Simpson values. To illustrate the differences among the groups, a principal component analysis (PCA) based on the Bray–Curtis matrix was performed using the R software (v.4.0.2; R Development Core Team, Vienna, Austria). To compare the relative abundance of groups, boxplots were generated using the GraphPad Prism software (ver. 8.4.2; GraphPad Software Inc., San Diego, CA, USA). To identify and visualize the core microbiota for all groups, Venn diagrams were produced using an online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/; accessed on 6 May 2021).

2.5. Bioinformatic Analysis

To determine differential taxa, a linear discriminant analysis effect size (LEfSe) [35] analysis was performed using an online tool (https://huttenhower.sph.harvard.edu/galaxy/; accessed on 9 May 2021). Briefly, the LEfSe parameters were as follows: (1) the alpha value for the Kruskal–Wallis test of differences among classes <0.05; (2) the alpha value for the pairwise Wilcoxon test of differences among subclasses <than 0.05; (3) the threshold for the logarithmic linear discriminant analysis (LDA) score of 2.0; and (4) the all-against-all strategy for multi-class analysis. To predict functional pathways in the microbiome, phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) [36] was performed based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) (level 3) using an online tool (https://huttenhower.sph.harvard.edu/galaxy/; accessed on 4 May 2021).

2.6. Statistical Analysis

Statistical analyses were performed using GraphPad Prism (ver. 8.4.2). The normality of the data distribution was analysed using the Shapiro–Wilk test. A one-way ANOVA with a post-hoc Tukey HSD or Kruskal–Wallis test was used to analyse differences in body weight, metabolic parameters, relative abundance and alpha diversity. To determine whether the gut microbial distribution differed among groups, permutational multivariate analysis of variance (PERMANOVA) was performed using R (v.4.0.2). Significantly different KEGG pathways were identified using STAMP [37]. The correlation between obesity associated metabolic factors and gut microbiota was analysed using Spearman’s rank correlation in R (v.4.0.2). In all statistical calculations, differences were considered significant at p < 0.05, p < 0.01, and p < 0.001.

3. Results

3.1. Effect of Administration L. rhamnosus 4B15 and C. tricuspidata on Body Weight and Metabolic Parameters in Obese Mice

To examine the modulatory effects of L. rhamnosus 4B15 and C. tricuspidata on body weight and obesity-associated metabolic indices in HFD-induced obese mice, we administered L. rhamnosus 4B15, C. tricuspidata, and a combination of the two to diet-induced obese mice for 10 weeks (Figure 1). As expected, the HFD significantly increased the body weight and body weight gain (p < 0.01; Table S2), as well as total fat mass (p < 0.05), and serum levels of TG (p < 0.05) and ALT (p < 0.05) of the mice compared with the ND mice. Supplementation of L. rhamnosus 4B15 and C. tricuspidata, or its combination did not significantly affect the body weight and body weight gain of obese mice (p > 0.05; Figure 1a,b; Table S2). By contrast, the percent of fat mass was significantly reduced in SYN-fed obese mice (p < 0.05; Figure 1c), compared with HFD, PRO, and PRE groups. Similarly, the serum TG level was significantly lowered in the SYN group, in contrast with HFD and other treatments (p < 0.05; Figure 1d). Moreover, ALT levels in PRO, PRE, and SYN groups were significantly lower in contrast with the HFD group (p < 0.05; Figure 1f). On the other hand, serum T-Chol remained unaffected after administration of L. rhamnosus 4B15 and C. tricuspidata, or its combination (p > 0.05; Figure 1e).

3.2. Microbial Richness and Diversity

After administering L. rhamnosus 4B15 and C. tricuspidata, together and individually, we compared the intestinal species richness and diversity among the groups. No significant differences were observed in alpha diversity indices among treatments (ACE, Chao1, Shannon–Weaver, and Simpson) (Table S3). Meanwhile, PCA results revealed that PRO, PRE, and SYN groups clustered discretely from the HFD group (p < 0.001; Figure 2a). A pairwise comparison among the groups showed that HFD and SYN were significantly different (p < 0.05). Interestingly, these groups overlapped with the ND group, suggesting that they shared features with the ND group.

3.3. L. rhamnosus 4B15 and C. tricuspidata Altered the Gut Microbiota of Obese Mice

Consistent with the structural changes in the gut microbiota of the diet-induced obese mice, we also observed changes in the abundance of microbial communities at the phylum and genus levels. We identified 36 bacterial phyla in the ceca (Figure 2c; Table S4). Firmicutes and Bacteroidetes were the most abundant phyla, accounting for ~75% of sequence reads in all groups. The administration of SYN noticeably increased the abundance of Firmicutes (41%) compared with the HFD group. We did not observe any significant difference in the Firmicutes/Bacteroidetes ratio among the groups (Table S4). We found a 63% increase in the abundance of proteobacteria in the HFD group compared with the ND control. By contrast, the abundance of proteobacteria was lower in the PRE, PRO and SYN groups (79%, 54% and 40%, respectively), while actinobacteria expression was drastically elevated in the SYN group (800%; p < 0.01) compared with the HFD control.
At the genus level, we detected 440 bacterial genera in the mouse ceca (Table S5). Of the detected genera, 16, 6, 95, 20, and 56 were exclusive to the ND, HFD, PRO, PRE, and SYN groups, respectively, and 70 core genera were present in all groups (Figure 2b). The most abundant genera in all groups were unclassified Clostridiales, Bacteroides, and Akkermansia (Figure 2d). The HFD diet enriched several taxa such as Helicobacter, unclassified Erysipelotrichaceae and Desulfovibrionaceae, Flexispira, and Odoribacter. However, supplementation of SYN drastically decreased the abundances of Helicobacter (p < 0.01), unclassified Desulfovibrionaceae (p < 0.05), Flexispira (p < 0.01), and Odoribacter (p < 0.05) (Figure 3). In addition, we found that the SYN group mice had a significantly larger population of Desulfovibrio (p < 0.001), Adlercreutzia (p < 0.01), and unclassified Clostridiaceae (p < 0.05) compared with the HFD group (Figure 3). In addition, the abundances of Dorea, Allobaculum, Lactobacillus, Coprococcus, and Lactococcus were enriched by SYN supplementation, but not significantly so (Figure 3).
Next, we identified significantly different taxa between HFD and SYN groups using LEfSe [35] (Figure 4a). The LEfSe analysis revealed that L. rhamnosus 4B15 and C. tricuspidata preferentially increased the abundance of Desulfovibrio, Dorea, Allobaculum, Adlercreutzia, Coprococcus, Lactobacillus, Lactococcus, and unclassified Clostridiaceae and Christensenellaceae (p < 0.05). By contrast, the HFD elevated the abundance of Helicobacter, Flexispira, Anaeroplasma, Ruminococcus, Odoribacter and unclassified taxa under family Erysipelotrichaceae, Prevotellaceae, Desulfovibrionaceae, and Helicobacteriaceae (p < 0.05). The phylogenetic relationships between these taxa are depicted in a cladogram in Figure 4b.

3.4. Synbiotic-Altered Microbiota Is Associated with Metabolic Parameters

Spearman’s correlation coefficients were calculated to determine the associations between the differential taxa and metabolic parameters in the HFD and SYN groups (Figure 4c). We found that Odoribacter (p < 0.05), Anaeroplasma (p < 0.05), Ruminococcus (p < 0.001), and unclassified Desulfovibrionaceae were positively associated with fat mass, while Ruminococcus (p < 0.05), unclassified Erysipelotrichaceae (p < 0.01), and Helicobacter (p < 0.05) were positively correlated with body weight. Moreover, Helicobacter (p < 0.01), Flexispira (p < 0.01, p < 0.05, respectively), Ruminococcus (p < 0.05), and unclassified Desulfovibrionaceae (p < 0.01, p < 0.05, respectively) were significantly positively correlated with TG and ALT. In contrast, SYN-enriched taxa, such as unclassified Clostridiaceae (p < 0.05), Desulfovibrio (p < 0.01), Lactococcus (p <0.01), and Adlercreutzia (p < 0.05) were significantly negatively correlated with adiposity. A significant negative association was observed between Desulfovibrio and body weight (p < 0.05). In addition, Desulfovibrio (p < 0.05) and Allobaculum (p < 0.01) were negatively correlated with ALT. Lastly, TG was negatively correlated with most of SYN-enriched taxa, most notably Allobaculum and Adlercreutzia (p < 0.01). Our results suggest a strong interaction between SYN-modulated taxa and obesity-associated metabolic factors.

3.5. Predicted KEGG Functional Pathways in the Gut Microbiome

Functional pathways of the gut microbiome based on KEGG pathways were identified using PICRUSt [36]. A total of 284 KEGG pathways were identified in the gut microbiome of the mice (Table S6), Significantly different pathways between HFD and SYN group are shown in Figure 5. Our results revealed that the administration of L. rhamnosus 4B15 and C. tricuspidata significantly upregulated several pathways involved in metabolism, including ABC transporters, starch and sucrose metabolism, galactose metabolism, fructose and mannose metabolism, glycerolipid metabolism, glycolysis/gluconeogenesis, energy metabolism, carbohydrate metabolism, and amino acid metabolism (p < 0.05). On the other hand, pathways such as the secretion system, oxidative phosphorylation, lipopolysaccharide biosynthesis, fatty acid biosynthesis, epithelial cell signalling in H. pylori, and bacterial toxin production were significantly upregulated in HFD mice (p < 0.05). This suggests that L. rhamnosus 4B15 and C. tricuspidata synbiotically influenced the functional pathways expressed in the gut microbiome of obese mice.

4. Discussion

Supplementation with L. rhamnosus 4B15 [38] or C. tricuspidata [29] have been individually shown to have a lowering effect on body weight in previous studies. Although a decreasing trend was observed, neither individual nor combined administration of L. rhamnosus 4B15 and C. tricuspidata resulted in significant reduction in the body weight of the mouse in the present study. Similarly, previous studies did not observe a significant reduction in body weights of obese mice after supplementation of C. tricuspidata [30,39]. Other studies on the effect of probiotic and synbiotic feeding on the body weight were inconclusive as well [40]. Feeding period, concentration, as well as ratio of the synbiotics used may explain the different result observed from the present study [40]. Meanwhile, feeding of HFD increased the fat mass in mice by 2.2-fold in contrast with ND. Excessive fat mass indicates the onset of obesity, and linked to the occurrence of insulin resistance and cardiometabolic diseases [41]. Triglycerides play a major role in the storage and transport of fatty acids, and an abnormal increase in the levels of triglycerides usually leads to cardiovascular disease [42,43,44]. Meanwhile, excessive serum activity of ALT is an indicator of liver damage, including non-alcoholic steatohepatitis [45,46]. In this study, we observed a reduction in fat mass, with serum TG and ALT in PRO, PRE, and SYN treatments. However, significant reduction in TG and fat mass was only observed on SYN-treated mice (p < 0.05). Previous data demonstrated the abilities of L. rhamnosus 4B15 and C. tricuspidata to reduce TG levels and fat mass in animal models [29,30,38]. The results of the present study suggest that the synergistic interaction between L. rhamnosus 4B15 and C. tricuspidata provides better protection against hypertriglyceridemia and excess adiposity than the probiotic or prebiotic treatment alone. Further investigations are necessary to validate the synbiotic potential of L. rhamnosus 4B15 and C. tricuspidata.
At phylum level we observed an increase in the abundance of Firmicutes in mice fed with C. tricuspidata, alone or in combination with L. rhamnosus 4B15. The presence of dietary fibres may explain the increase in abundance of Firmicutes, as several members of this phylum undergo fermentation [47,48]. On the other hand, HFDs can increase the abundance of proteobacteria and decrease the abundance of actinobacteria [49,50]. Many proteobacteria are pathogenic, and can produce endotoxins [51], while actinobacteria play important roles in gut homeostasis [52]. Our results suggest that L. rhamnosus 4B15 and C. tricuspidata supplementation altered the abundances of these phyla.
In the present study, we found that Desulfovibrio, Dorea, Adlercreutzia, Allobaculum, Coprococcus, unclassified Clostridiaceae, and Lactobacillus were enriched in obese mice fed with L. rhamnosus 4B15 and C. tricuspidata. Our correlation analysis also revealed that the abundances of these taxa have an inverse correlation with body weight and metabolic parameters. Desulfovibrio and Adlercreutzia were previously found to be enriched in lean hosts [53,54,55]. Adlercreutzia can alleviate diabetes and obesity by producing phenyl-γ-valerolactones; these biomolecules are associated with lean phenotypes [56,57]. Allobaculum, Lactobacillus, and Coprococcus were enriched in hamsters fed with resistant starch [58]. Xiang et al., [59] and Everard et al. [60] also reported that the metabolic syndrome was inversely correlated with the abundance of Allobaculum, Lactobacillus, and Coprococcus; these species produce short-chain fatty acids (SCFAs) [61,62], which are important fermentation by-products due to their roles in body weight regulation, gut permeability, and lipid and glucose metabolism [63,64,65]. In a recent study, Kong et al. [61] found that Lactococcus enrichment negatively correlated with obesity. Cross and colleagues [66] found out that Dorea negatively correlated with metabolic parameters such as TG, similar with our results.
We also found that Helicobacter, Flexispira, Odoribacter, Ruminococcus, unclassified Erysipelotrichaceae, and unclassified Desulfovibrionaceae were enriched in HFD-fed obese mice, and that enrichment of these genera exhibited a strong positive correlation with body weight and metabolic indices. Elevation in the population of Helicobacter and Flexispira in diet-induced obese animal models have been observed in previous studies [67,68,69]. Helicobacter and Flexispira are phylogenetically related, and are typically gastrointestinal pathogens [70,71]. In the present study, Helicobacter and Flexispira were less abundant in the gut of mice treated with L. rhamnosus 4B15 and C. tricuspidata. This observation is consistent with previous studies on the effect of synbiotics on Helicobacter [72]. Several studies have implicated the bacterial family of Erysipelotricaceae with metabolic disorders, such as hypercholesterolemia and gut inflammation [53,73,74]. We also observed an increase in the abundance of Odoribacter and Ruminococcus in HFD mice, which is in agreement with other studies [75,76]. Our results provide novel insights for the synbiotic potential of L. rhamnosus 4B15 and C. tricuspidata to modulate the gut bacteria and alleviate obesity-related metabolic syndrome.
Our PICRUSt analysis revealed that several metabolic pathways were significantly upregulated in SYN mice. ABC transporters, the most differential KEGG pathways between SYN and HFD mice, play key roles in the import of nutrients [77] and utilization of prebiotics [78,79]. The elevation of pathways such as galactose metabolism, starch and sucrose metabolism, fructose and mannose metabolism, and glycolysis/gluconeogenesis, as well as energy, carbohydrate, and amino acid metabolism, suggests that L. rhamnosus 4B15 and C. tricuspidata increased energy utilization, which in turn counteracted the obesogenic effects of the HFD [80,81,82]. Increased sugar and amino acid metabolism increases SCFA production, which in turn leads to a normal lipid profile and less inflammation in the gut [83,84]. SCFAs also play roles in glucose homeostasis and appetite regulation [65]. Glycerolipid metabolism has a vital role in the regulation of lipolysis and glucose homeostasis; dysregulation of the glycerolipid pathway can lead to obesity and type 2 diabetes [85]. This suggests that the administration of L. rhamnosus 4B15 and C. tricuspidata enriched metabolic pathways that can counteract the effects of HFDs.
We also observed the upregulation of several KEGG pathways in mice fed HFDs. Notably, lipopolysaccharide biosynthesis, epithelial cell signalling in H. pylori and bacterial toxin pathways were upregulated. In previous studies, the lipopolysaccharide biosynthesis pathway was enriched in patients with H. pylori infections [86], while the intensity of epithelial cell signalling in H. pylori pathway coincided with an increase in Helicobacter abundance [87]. These results reflect our observations of increase of Helicobacter, and its close relative Flexispira, as well as the upregulation of lipopolysaccharide biosynthesis and epithelial cell signalling pathways in the HFD group. We hypothesise that the high abundance of Helicobacter and Flexispira triggered the upregulation of the bacterial toxins pathway in the gut microbiota. Expectedly, there was an upregulation of fatty acid biosynthesis in HFD-fed mice. Excessive lipogenesis contributes to the pathogenesis of obesity and other related metabolic diseases. Thus, inhibition of integral enzymes, such as fatty acid synthase, is a potential therapeutic target for the treatment of obesity [88].

5. Conclusions

In conclusion, the results of the current study demonstrated that the combination of L. rhamnosus 4B15 and C. tricuspidata exerted a synbiotic effect on the gut microbiota and metabolic health of the obese mice. Administration of L. rhamnosus 4B15 and C. tricuspidata alleviated excess adiposity, and increased serum TG and ALT levels induced by HFD. The synbiotic treatment also modulated the gut microbiota, notably Desulfovibrio, Dorea, Adlercreutzia, Allobaculum, Coprococcus, unclassified Clostridiaceae, Lactobacillus, Helicobacter, Flexispira, Odoribacter, Ruminococcus, unclassified Erysipelotrichaceae, and unclassified Desulfovibrionaceae. These taxa revealed a strong correlation with body weight and metabolic parameters. Lastly, L. rhamnosus 4B15 and C. tricuspidata upregulated metabolic pathways known to improve metabolic health. Further investigations are needed to understand the mechanisms driving the synbiotic effect of C. tricuspidata and L. rhamnosus 4B15.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/microorganisms9091908/s1, Figure S1: Schematic diagram showing the schedule for the feeding experiment. Table S1: Composition of experimental mouse diets. Table S2: Weekly measurements of body weight and feed intake of experimental C57BL/N6 mice. Table S3: Comparison of alpha-diversity indices of the gut microbiota of C57BL/N6 mice. Table S4: Relative abundance of taxonomic groups at phylum level (cut-off > 0.1%). Table S5: Relative abundance of taxonomic groups at genus level (cut-off > 0.1%). Table S6: Relative abundance of predicted KEGG pathways in mice gut microbiota (cut-off > 0.1%).

Author Contributions

Conceptualization: N.S.O., Y.Y. and D.-K.K.; Formal analysis: J.K.O., I.-C.H., Y.N.O., S.H.K. (Sang Hoon Kim), S.H.K. (Shin Ho Kang), J.Y.J. and S.K.; Writing—original draft: J.K.O. and R.V.; Writing—review and editing: D.-K.K. All authors edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Seoul Dairy Cooperative, Republic of Korea; and by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through The Strategic Initiative for Microbiomes in Agriculture and Food, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (918001-04-4-WT011).

Institutional Review Board Statement

All animal protocols in this study were approved by Sookmyung Women’s University Animal Care Committee (SMWU-IACUC-1703-001-01, 26 December 2017).

Informed Consent Statement

Not applicable.

Data Availability Statement

All standard sequence format (.fastq) files generated by Illumina Miseq containing all raw sequence reads have been deposited at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) (BioProject accession number: PRJNA691724).

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ACEAbundance-based Estimator
HFDHigh fat diet
KEGGKyoto Encyclopedia of Genes and Genomes
LEfSeLinear Discriminant Analysis Effect Size
PCAPrincipal Component Analysis
PICRUStPhylogenetic Investigation of Communities by Reconstruction of Unobserved States
QIIMEQuantitative Insights Into Microbial Ecology

References

  1. Chang, C.-S.; Kao, C.-Y. Current understanding of the gut microbiota shaping mechanisms. J. Biomed. Sci. 2019, 26, 59. [Google Scholar] [CrossRef]
  2. Martin, A.M.; Sun, E.W.; Rogers, G.B.; Keating, D.J. The Influence of the Gut Microbiome on Host Metabolism Through the Regulation of Gut Hormone Release. Front. Physiol. 2019, 10, 428. [Google Scholar] [CrossRef]
  3. Visconti, A.; Le Roy, C.I.; Rosa, F.; Rossi, N.; Martin, T.C.; Mohney, R.P.; Li, W.; de Rinaldis, E.; Bell, J.T.; Venter, J.C.; et al. Interplay between the human gut microbiome and host metabolism. Nat. Commun. 2019, 10, 4505. [Google Scholar] [CrossRef] [Green Version]
  4. Marchesi, J.R.; Adams, D.H.; Fava, F.; Hermes, G.D.A.; Hirschfield, G.M.; Hold, G.; Quraishi, M.N.; Kinross, J.; Smidt, H.; Tuohy, K.M.; et al. The gut microbiota and host health: A new clinical frontier. Gut 2016, 65, 330–339. [Google Scholar] [CrossRef] [Green Version]
  5. Fong, W.; Li, Q.; Yu, J. Gut microbiota modulation: A novel strategy for prevention and treatment of colorectal cancer. Oncogene 2020, 39, 4925–4943. [Google Scholar] [CrossRef] [PubMed]
  6. Umu, Ö.C.O.; Rudi, K.; Diep, D.B. Modulation of the gut microbiota by prebiotic fibres and bacteriocins. Microb. Ecol. Health Dis. 2017, 28, 1348886. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Quigley, E.M.M. Prebiotics and Probiotics in Digestive Health. Clin. Gastroenterol. Hepatol. 2019, 17, 333–344. [Google Scholar] [CrossRef]
  8. Pandey, K.R.; Naik, S.R.; Vakil, B. V Probiotics, prebiotics and synbiotics—A review. J. Food Sci. Technol. 2015, 52, 7577–7587. [Google Scholar] [CrossRef]
  9. Williams, N.T. Probiotics. Am. J. Health Pharm. 2010, 67, 449–458. [Google Scholar] [CrossRef]
  10. Vandenplas, Y.; Huys, G.; Daube, G. Probiotics: An update. J. Pediatr. 2015, 91, 6–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Vyas, U.; Ranganathan, N. Probiotics, prebiotics, and synbiotics: Gut and beyond. Gastroenterol. Res. Pract. 2012, 2012, 872716. [Google Scholar] [CrossRef] [Green Version]
  12. Tilocca, B.; Pieroni, L.; Soggiu, A.; Britti, D.; Bonizzi, L.; Roncada, P.; Greco, V. Gut–Brain Axis and Neurodegeneration: State-of-the-Art of Meta-Omics Sciences for Microbiota Characterization. Int. J. Mol. Sci. 2020, 21, 4045. [Google Scholar] [CrossRef] [PubMed]
  13. Marzano, V.; Tilocca, B.; Fiocchi, A.G.; Vernocchi, P.; Levi Mortera, S.; Urbani, A.; Roncada, P.; Putignani, L. Perusal of food allergens analysis by mass spectrometry-based proteomics. J. Proteom. 2020, 215, 103636. [Google Scholar] [CrossRef]
  14. Markowiak, P.; Slizewska, K. Effects of Probiotics, Prebiotics, and Synbiotics on Human Health. Nutrients 2017, 9, 1021. [Google Scholar] [CrossRef] [PubMed]
  15. Sanders, M.E. Probiotics in 2015: Their Scope and Use. J. Clin. Gastroenterol. 2015, 49, S2–S6. [Google Scholar] [CrossRef]
  16. Tilocca, B.; Costanzo, N.; Morittu, V.M.; Spina, A.A.; Soggiu, A.; Britti, D.; Roncada, P.; Piras, C. Milk microbiota: Characterization methods and role in cheese production. J. Proteom. 2020, 210, 103534. [Google Scholar] [CrossRef]
  17. Holscher, H.D. Dietary fiber and prebiotics and the gastrointestinal microbiota. Gut Microbes 2017, 8, 172–184. [Google Scholar] [CrossRef]
  18. Bindels, L.B.; Delzenne, N.M.; Cani, P.D.; Walter, J. Towards a more comprehensive concept for prebiotics. Nat. Rev. Gastroenterol. Hepatol. 2015, 12, 303–310. [Google Scholar] [CrossRef] [PubMed]
  19. Patel, R.; DuPont, H.L. New approaches for bacteriotherapy: Prebiotics, new-generation probiotics, and synbiotics. Clin. Infect. Dis. 2015, 60 (Suppl. 2), S108–S121. [Google Scholar] [CrossRef]
  20. Ferrarese, R.; Ceresola, E.R.; Preti, A.; Canducci, F. Probiotics, prebiotics and synbiotics for weight loss and metabolic syndrome in the microbiome era. Eur. Rev. Med. Pharmacol. Sci. 2018, 22, 7588–7605. [Google Scholar] [CrossRef] [PubMed]
  21. Barengolts, E. Gut Microbiota, Prebiotics, Probiotics, and Synbiotics in Management of Obesity and Prediabetes: Review of Randomized Controlled Trials. Endocr. Pract. 2016, 22, 1224–1234. [Google Scholar] [CrossRef] [Green Version]
  22. Śliżewska, K.; Chlebicz, A. Synbiotics impact on dominant faecal microbiota and short-chain fatty acids production in sows. FEMS Microbiol. Lett. 2019, 366, fnz157. [Google Scholar] [CrossRef] [Green Version]
  23. Śliżewska, K.; Markowiak-Kopeć, P.; Żbikowski, A.; Szeleszczuk, P. The effect of synbiotic preparations on the intestinal microbiota and her metabolism in broiler chickens. Sci. Rep. 2020, 10, 4281. [Google Scholar] [CrossRef] [Green Version]
  24. Ke, X.; Walker, A.; Haange, S.-B.; Lagkouvardos, I.; Liu, Y.; Schmitt-Kopplin, P.; von Bergen, M.; Jehmlich, N.; He, X.; Clavel, T.; et al. Synbiotic-driven improvement of metabolic disturbances is associated with changes in the gut microbiome in diet-induced obese mice. Mol. Metab. 2019, 22, 96–109. [Google Scholar] [CrossRef] [PubMed]
  25. Sergeev, I.N.; Aljutaily, T.; Walton, G.; Huarte, E. Effects of Synbiotic Supplement on Human Gut Microbiota, Body Composition and Weight Loss in Obesity. Nutrients 2020, 12, 222. [Google Scholar] [CrossRef] [Green Version]
  26. Sarfraz, F.; Farooq, U.; Shafi, A.; Hayat, Z.; Akram, K.; Rehman, H.-U. Hypolipidaemic effects of synbiotic yoghurt in rabbits. Int. J. Dairy Technol. 2019, 72, 545–550. [Google Scholar] [CrossRef]
  27. Oh, N.S.; Joung, J.Y.; Lee, J.Y.; Kim, Y. Probiotic and anti-inflammatory potential of Lactobacillus rhamnosus 4B15 and Lactobacillus gasseri 4M13 isolated from infant feces. PLoS ONE 2018, 13, e0192021. [Google Scholar] [CrossRef] [PubMed]
  28. Kim, Y.S.; Lee, Y.; Kim, J.; Sohn, E.; Kim, C.S.; Lee, Y.M.; Jo, K.; Shin, S.; Song, Y.; Kim, J.H.; et al. Inhibitory Activities of Cudrania tricuspidata Leaves on Pancreatic Lipase In Vitro and Lipolysis In Vivo. Evid. Based. Complement. Alternat. Med. 2012, 2012, 878365. [Google Scholar] [CrossRef] [Green Version]
  29. Kim, O.-K.; Nam, D.-E.; Jun, W.; Lee, J. Cudrania tricuspidata water extract improved obesity-induced hepatic insulin resistance in db/db mice by suppressing ER stress and inflammation. Food Nutr. Res. 2015, 59, 29165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Kim, D.H.; Lee, S.; Chung, Y.W.; Kim, B.M.; Kim, H.; Kim, K.; Yang, K.M. Antiobesity and antidiabetes effects of a Cudrania tricuspidata hydrophilic extract presenting PTP1B inhibitory potential. Biomed. Res. Int. 2016, 2016, 8432759. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Dimitrijevic, R.; Ivanovic, N.; Mathiesen, G.; Petrusic, V.; Zivkovic, I.; Djordjevic, B.; Dimitrijevic, L. Effects of Lactobacillus rhamnosus LA68 on the immune system of C57BL/6 mice upon oral administration. J. Dairy Res. 2014, 81, 202–207. [Google Scholar] [CrossRef]
  32. Oh, N.S.; Lee, J.Y.; Kim, Y. The growth kinetics and metabolic and antioxidant activities of the functional synbiotic combination of Lactobacillus gasseri 505 and Cudrania tricuspidata leaf extract. Appl. Microbiol. Biotechnol. 2016, 100, 10095–10106. [Google Scholar] [CrossRef]
  33. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Pena, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  34. DeSantis, T.Z.; Hugenholtz, P.; Larsen, N.; Rojas, M.; Brodie, E.L.; Keller, K.; Huber, T.; Dalevi, D.; Hu, P.; Andersen, G.L. Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB. Appl. Environ. Microbiol. 2006, 72, 5069–5072. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Langille, M.G.I.; Zaneveld, J.; Caporaso, J.G.; McDonald, D.; Knights, D.; Reyes, J.A.; Clemente, J.C.; Burkepile, D.E.; Vega Thurber, R.L.; Knight, R.; et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 2013, 31, 814–821. [Google Scholar] [CrossRef]
  37. Parks, D.H.; Tyson, G.W.; Hugenholtz, P.; Beiko, R.G. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 2014, 30, 3123–3124. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Kim, S.; Joung, J.Y.; Kang, D.; Oh, N.S.; Yoon, Y. Anti-obesity effects of Lactobacillus rhamnosus 4B15, and its synergy with hydrolysed lactose skim milk powder. Int. Dairy J. 2021, 123, 104997. [Google Scholar] [CrossRef]
  39. Oh, J.K.; Amoranto, M.B.C.; Oh, N.S.; Kim, S.; Lee, J.Y.; Oh, Y.N.; Shin, Y.K.; Yoon, Y.; Kang, D.-K. Synergistic effect of Lactobacillus gasseri and Cudrania tricuspidata on the modulation of body weight and gut microbiota structure in diet-induced obese mice. Appl. Microbiol. Biotechnol. 2020, 104, 6273–6285. [Google Scholar] [CrossRef]
  40. Vallianou, N.; Stratigou, T.; Christodoulatos, G.S.; Tsigalou, C.; Dalamaga, M. Probiotics, Prebiotics, Synbiotics, Postbiotics, and Obesity: Current Evidence, Controversies, and Perspectives. Curr. Obes. Rep. 2020, 9, 179–192. [Google Scholar] [CrossRef]
  41. Goossens, G.H. The Metabolic Phenotype in Obesity: Fat Mass, Body Fat Distribution, and Adipose Tissue Function. Obes. Facts 2017, 10, 207–215. [Google Scholar] [CrossRef]
  42. Alves-Bezerra, M.; Cohen, D.E. Triglyceride Metabolism in the Liver. Compr. Physiol. 2017, 8, 1–8. [Google Scholar] [CrossRef]
  43. Budoff, M. Triglycerides and Triglyceride-Rich Lipoproteins in the Causal Pathway of Cardiovascular Disease. Am. J. Cardiol. 2016, 118, 138–145. [Google Scholar] [CrossRef] [Green Version]
  44. Del-Río-Navarro, B.E.; Miranda-Lora, A.L.; Huang, F.; Hall-Mondragon, M.S.; Leija-Martínez, J.J. Effect of supplementation with omega-3 fatty acids on hypertriglyceridemia in pediatric patients with obesity. J. Pediatr. Endocrinol. Metab. 2019, 32, 811–819. [Google Scholar] [CrossRef]
  45. Jadaho, S.B.; Yang, R.-Z.; Lin, Q.; Hu, H.; Anania, F.A.; Shuldiner, A.R.; Gong, D.-W. Murine alanine aminotransferase: cDNA cloning, functional expression, and differential gene regulation in mouse fatty liver. Hepatology 2004, 39, 1297–1302. [Google Scholar] [CrossRef]
  46. Sookoian, S.; Pirola, C.J. Liver enzymes, metabolomics and genome-wide association studies: From systems biology to the personalized medicine. World J. Gastroenterol. 2015, 21, 711–725. [Google Scholar] [CrossRef]
  47. Dos Indiani, C.M.S.P.; Rizzardi, K.F.; Castelo, P.M.; Ferraz, L.F.C.; Darrieux, M.; Parisotto, T.M. Childhood Obesity and Firmicutes/Bacteroidetes Ratio in the Gut Microbiota: A Systematic Review. Child. Obes. 2018, 14, 501–509. [Google Scholar] [CrossRef] [PubMed]
  48. Sanmiguel, C.; Gupta, A.; Mayer, E.A. Gut Microbiome and Obesity: A Plausible Explanation for Obesity. Curr. Obes. Rep. 2015, 4, 250–261. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Simpson, H.L.; Campbell, B.J. Review article: Dietary fibre-microbiota interactions. Aliment. Pharmacol. Ther. 2015, 42, 158–179. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Wang, J.; Tang, H.; Zhang, C.; Zhao, Y.; Derrien, M.; Rocher, E.; van-Hylckama Vlieg, J.E.T.; Strissel, K.; Zhao, L.; Obin, M.; et al. Modulation of gut microbiota during probiotic-mediated attenuation of metabolic syndrome in high fat diet-fed mice. ISME J. 2015, 9, 1–15. [Google Scholar] [CrossRef]
  51. Chang, C.-J.; Lin, C.-S.; Lu, C.-C.; Martel, J.; Ko, Y.-F.; Ojcius, D.M.; Tseng, S.-F.; Wu, T.-R.; Chen, Y.-Y.M.; Young, J.D.; et al. Ganoderma lucidum reduces obesity in mice by modulating the composition of the gut microbiota. Nat. Commun. 2015, 6, 7489. [Google Scholar] [CrossRef] [Green Version]
  52. Binda, C.; Lopetuso, L.R.; Rizzatti, G.; Gibiino, G.; Cennamo, V.; Gasbarrini, A. Actinobacteria: A relevant minority for the maintenance of gut homeostasis. Dig. Liver Dis. 2018, 50, 421–428. [Google Scholar] [CrossRef] [PubMed]
  53. Zietak, M.; Kovatcheva-Datchary, P.; Markiewicz, L.H.; Stahlman, M.; Kozak, L.P.; Backhed, F. Altered microbiota contributes to reduced diet-induced obesity upon cold exposure. Cell Metab. 2016, 23, 1216–1223. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Caesar, R.; Tremaroli, V.; Kovatcheva-Datchary, P.; Cani, P.D.; Backhed, F. Crosstalk between gut microbiota and dietary lipids aggravates WAT inflammation through TLR signaling. Cell Metab. 2015, 22, 658–668. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Jiao, X.; Wang, Y.; Lin, Y.; Lang, Y.; Li, E.; Zhang, X.; Zhang, Q.; Feng, Y.; Meng, X.; Li, B. Blueberry polyphenols extract as a potential prebiotic with anti-obesity effects on C57BL/6 J mice by modulating the gut microbiota. J. Nutr. Biochem. 2019, 64, 88–100. [Google Scholar] [CrossRef]
  56. Rodríguez-Daza, M.-C.; Daoust, L.; Boutkrabt, L.; Pilon, G.; Varin, T.; Dudonné, S.; Levy, É.; Marette, A.; Roy, D.; Desjardins, Y. Wild blueberry proanthocyanidins shape distinct gut microbiota profile and influence glucose homeostasis and intestinal phenotypes in high-fat high-sucrose fed mice. Sci. Rep. 2020, 10, 2217. [Google Scholar] [CrossRef] [Green Version]
  57. Zhang, X.; Zhao, A.; Sandhu, A.K.; Edirisinghe, I.; Burton-Freeman, B.M. Functional Deficits in Gut Microbiome of Young and Middle-Aged Adults with Prediabetes Apparent in Metabolizing Bioactive (Poly)phenols. Nutrients 2020, 12, 3595. [Google Scholar] [CrossRef]
  58. Li, T.; Teng, H.; An, F.; Huang, Q.; Chen, L.; Song, H. The beneficial effects of purple yam (Dioscorea alata L.) resistant starch on hyperlipidemia in high-fat-fed hamsters. Food Funct. 2019, 10, 2642–2650. [Google Scholar] [CrossRef]
  59. Li, X.; Wang, H.; Wang, T.; Zheng, F.; Wang, H.; Wang, C. Dietary wood pulp-derived sterols modulation of cholesterol metabolism and gut microbiota in high-fat-diet-fed hamsters. Food Funct. 2019, 10, 775–785. [Google Scholar] [CrossRef]
  60. Everard, A.; Lazarevic, V.; Derrien, M.; Girard, M.; Muccioli, G.M.G.; Neyrinck, A.M.; Possemiers, S.; vDan Holle, A.; Francois, P.; de Vos, W.M.; et al. Responses of Gut Microbiota and Glucose and Lipid Metabolism to Prebiotics in Genetic Obese and Diet-Induced Leptin-Resistant Mice. Diabetes 2011, 60, 3307. [Google Scholar] [CrossRef] [Green Version]
  61. Kong, C.; Gao, R.; Yan, X.; Huang, L.; Qin, H. Probiotics improve gut microbiota dysbiosis in obese mice fed a high-fat or high-sucrose diet. Nutrition 2019, 60, 175–184. [Google Scholar] [CrossRef]
  62. Zhang, W.; Xu, J.-H.; Yu, T.; Chen, Q.-K. Effects of berberine and metformin on intestinal inflammation and gut microbiome composition in db/db mice. Biomed. Pharmacother. 2019, 118, 109131. [Google Scholar] [CrossRef]
  63. Canfora, E.E.; Jocken, J.W.; Blaak, E.E. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat. Rev. Endocrinol. 2015, 11, 577–591. [Google Scholar] [CrossRef] [PubMed]
  64. den Besten, G.; Bleeker, A.; Gerding, A.; van Eunen, K.; Havinga, R.; van Dijk, T.H.; Oosterveer, M.H.; Jonker, J.W.; Groen, A.K.; Reijngoud, D.-J.; et al. Short-Chain Fatty Acids Protect Against High-Fat Diet–Induced Obesity via a PPARγ-Dependent Switch From Lipogenesis to Fat Oxidation. Diabetes 2015, 64, 2398–2408. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Morrison, D.J.; Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 2016, 7, 189–200. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Cross, T.-W.L.; Zidon, T.M.; Welly, R.J.; Park, Y.-M.; Britton, S.L.; Koch, L.G.; Rottinghaus, G.E.; de Godoy, M.R.C.; Padilla, J.; Swanson, K.S.; et al. Soy Improves Cardiometabolic Health and Cecal Microbiota in Female Low-Fit Rats. Sci. Rep. 2017, 7, 9261. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Liu, D.; Wen, B.; Zhu, K.; Luo, Y.; Li, J.; Li, Y.; Lin, H.; Huang, J.; Liu, Z. Antibiotics-induced perturbations in gut microbial diversity influence metabolic phenotypes in a murine model of high-fat diet-induced obesity. Appl. Microbiol. Biotechnol. 2019, 103, 5269–5283. [Google Scholar] [CrossRef] [PubMed]
  68. Xu, H.; Zhao, C.; Li, Y.; Liu, R.; Ao, M.; Li, F.; Yao, Y.; Tao, Z.; Yu, L. The ameliorative effect of the Pyracantha fortuneana (Maxim.) H. L. Li extract on intestinal barrier dysfunction through modulating glycolipid digestion and gut microbiota in high fat diet-fed rats. Food Funct. 2019, 10, 6517–6532. [Google Scholar] [CrossRef]
  69. Zhao, M.; Cai, H.; Jiang, Z.; Li, Y.; Zhong, H.; Zhang, H.; Feng, F. Glycerol-Monolaurate-Mediated Attenuation of Metabolic Syndrome is Associated with the Modulation of Gut Microbiota in High-Fat-Diet-Fed Mice. Mol. Nutr. Food Res. 2019, 63, 1801417. [Google Scholar] [CrossRef]
  70. Wang, S.; Huang, M.; You, X.; Zhao, J.; Chen, L.; Wang, L.; Luo, Y.; Chen, Y. Gut microbiota mediates the anti-obesity effect of calorie restriction in mice. Sci. Rep. 2018, 8, 13037. [Google Scholar] [CrossRef] [Green Version]
  71. Schauer, D.B.; Ghori, N.; Falkow, S. Isolation and characterization of “Flexispira rappini” from laboratory mice. J. Clin. Microbiol. 1993, 31, 2709–2714. [Google Scholar] [CrossRef] [Green Version]
  72. Pourmasoumi, M.; Najafgholizadeh, A.; Hadi, A.; Mansour-Ghanaei, F.; Joukar, F. The effect of synbiotics in improving Helicobacter pylori eradication: A systematic review and meta-analysis. Complement. Ther. Med. 2019, 43, 36–43. [Google Scholar] [CrossRef]
  73. Kaakoush, N.O. Insights into the role of Erysipelotrichaceae in the human host. Front. Cell. Infect. Mi. 2015, 5, 84. [Google Scholar] [CrossRef] [Green Version]
  74. Truax, A.D.; Chen, L.; Tam, J.W.; Cheng, N.; Guo, H.; Koblansky, A.A.; Chou, W.C.; Wilson, J.E.; Brickey, W.J.; Petrucelli, A.; et al. The inhibitory innate immune sensor NLRP12 maintains a threshold against obesity by regulating gut microbiota homeostasis. Cell Host Microbe 2018, 24, 364–378. [Google Scholar] [CrossRef] [Green Version]
  75. Wu, T.; Gao, Y.; Hao, J.; Geng, J.; Zhang, J.; Yin, J.; Liu, R.; Sui, W.; Gong, L.; Zhang, M. Capsanthin extract prevents obesity, reduces serum TMAO levels and modulates the gut microbiota composition in high-fat-diet induced obese C57BL/6J mice. Food Res. Int. 2020, 128, 108774. [Google Scholar] [CrossRef]
  76. Liang, H.; Jiang, F.; Cheng, R.; Luo, Y.; Wang, J.; Luo, Z.; Li, M.; Shen, X.; He, F. A high-fat diet and high-fat and high-cholesterol diet may affect glucose and lipid metabolism differentially through gut microbiota in mice. Exp. Anim. 2021, 70, 73–83. [Google Scholar] [CrossRef]
  77. Davidson, A.L.; Dassa, E.; Orelle, C.; Chen, J. Structure, function, and evolution of bacterial ATP-binding cassette systems. Microbiol. Mol. Biol. Rev. 2008, 72, 317–364. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Saito, Y.; Shigehisa, A.; Watanabe, Y.; Tsukuda, N.; Moriyama-Ohara, K.; Hara, T.; Matsumoto, S.; Tsuji, H.; Matsuki, T. Multiple Transporters and Glycoside Hydrolases Are Involved in Arabinoxylan-Derived Oligosaccharide Utilization in Bifidobacterium pseudocatenulatum. Appl. Environ. Microbiol. 2020, 86, e01782-20. [Google Scholar] [CrossRef]
  79. Ji, X.; Hou, C.; Gao, Y.; Xue, Y.; Yan, Y.; Guo, X. Metagenomic analysis of gut microbiota modulatory effects of jujube (Ziziphus jujuba Mill.) polysaccharides in a colorectal cancer mouse model. Food Funct. 2020, 11, 163–173. [Google Scholar] [CrossRef] [PubMed]
  80. Zhang, X.; Chen, Y.; Zhu, J.; Zhang, M.; Ho, C.-T.; Huang, Q.; Cao, J. Metagenomics Analysis of Gut Microbiota in a High Fat Diet-Induced Obesity Mouse Model Fed with (-)-Epigallocatechin 3-O-(3-O-Methyl) Gallate (EGCG3″Me). Mol. Nutr. Food Res. 2018, 62, e1800274. [Google Scholar] [CrossRef]
  81. Sanguinetti, E.; Guzzardi, M.A.; Tripodi, M.; Panetta, D.; Selma-Royo, M.; Zega, A.; Telleschi, M.; Collado, M.C.; Iozzo, P. Microbiota signatures relating to reduced memory and exploratory behaviour in the offspring of overweight mothers in a murine model. Sci. Rep. 2019, 9, 12609. [Google Scholar] [CrossRef] [Green Version]
  82. Lee, H.; Ko, G. Effect of metformin on metabolic improvement and gut microbiota. Appl. Environ. Microbiol. 2014, 80, 5935–5943. [Google Scholar] [CrossRef] [Green Version]
  83. Maya-Lucas, O.; Murugesan, S.; Nirmalkar, K.; Alcaraz, L.D.; Hoyo-Vadillo, C.; Pizano-Zárate, M.L.; García-Mena, J. The gut microbiome of Mexican children affected by obesity. Anaerobe 2019, 55, 11–23. [Google Scholar] [CrossRef]
  84. Louis, P.; Flint, H.J. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 2017, 19, 29–41. [Google Scholar] [CrossRef] [Green Version]
  85. Prentki, M.; Madiraju, S.R.M. Glycerolipid Metabolism and Signaling in Health and Disease. Endocr. Rev. 2008, 29, 647–676. [Google Scholar] [CrossRef] [Green Version]
  86. Chen, L.; Xu, W.; Lee, A.; He, J.; Huang, B.; Zheng, W.; Su, T.; Lai, S.; Long, Y.; Chu, H.; et al. The impact of Helicobacter pylori infection, eradication therapy and probiotic supplementation on gut microenvironment homeostasis: An open-label, randomized clinical trial. EBioMedicine 2018, 35, 87–96. [Google Scholar] [CrossRef] [Green Version]
  87. Yuan, L.; Li, X.; He, S.; Gao, C.; Wang, C.; Shao, Y. Effects of Natural Flavonoid Isoorientin on Growth Performance and Gut Microbiota of Mice. J. Agric. Food Chem. 2018, 66, 9777–9784. [Google Scholar] [CrossRef]
  88. Angeles, T.S.; Hudkins, R.L. Recent advances in targeting the fatty acid biosynthetic pathway using fatty acid synthase inhibitors. Expert Opin. Drug Discov. 2016, 11, 1187–1199. [Google Scholar] [CrossRef]
Microorganisms 09 01908 g001 550
Figure 1. Effect of administration of L. rhamnosus 4B15 and C. tricuspidata on body weight and metabolic parameters. Comparison of the effect of each treatment on the (a) body weight, (b) body weight gain, (c) percent fat mass, (d) triglyceride, (e) total cholesterol, and (f) alanine aminotransferase, ALT of high-fat diet-induced obese mice. Values are presented as mean ± SEM. The significance values [p < 0.05] and [p < 0.001] are denoted as *, ***, respectively, in comparison with ND. The significance value [p < 0.05] is denoted as # in comparison with HFD. Statistical analyses were performed using one-way ANOVA with post-hoc Tukey HSD. ND: normal diet; HFD: high-fat diet; PRO: high-fat diet + L. rhamnosus 4B15; PRE: high-fat diet + C. tricuspidata; SYN: high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
Figure 1. Effect of administration of L. rhamnosus 4B15 and C. tricuspidata on body weight and metabolic parameters. Comparison of the effect of each treatment on the (a) body weight, (b) body weight gain, (c) percent fat mass, (d) triglyceride, (e) total cholesterol, and (f) alanine aminotransferase, ALT of high-fat diet-induced obese mice. Values are presented as mean ± SEM. The significance values [p < 0.05] and [p < 0.001] are denoted as *, ***, respectively, in comparison with ND. The significance value [p < 0.05] is denoted as # in comparison with HFD. Statistical analyses were performed using one-way ANOVA with post-hoc Tukey HSD. ND: normal diet; HFD: high-fat diet; PRO: high-fat diet + L. rhamnosus 4B15; PRE: high-fat diet + C. tricuspidata; SYN: high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
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Figure 2. Administration of L. rhamnosus 4B15 and C. tricuspidata altered the gut microbiota composition. (a) Clustering patterns of each group in PCA plot based on Bray–Curtis distance matrix. (b) Venn diagram showing the core microbiota of the groups, and unique microbial community of each group. Comparison of gut microbiota composition among all groups at (c) phylum and (d) genus levels.. Abundance cut-off was set at >0.1%. Abbreviations: ND, normal diet; HFD, high-fat diet; PRO, high-fat diet + L. rhamnosus 4B15; PRE, high-fat diet + C. tricuspidata; SYN, high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
Figure 2. Administration of L. rhamnosus 4B15 and C. tricuspidata altered the gut microbiota composition. (a) Clustering patterns of each group in PCA plot based on Bray–Curtis distance matrix. (b) Venn diagram showing the core microbiota of the groups, and unique microbial community of each group. Comparison of gut microbiota composition among all groups at (c) phylum and (d) genus levels.. Abundance cut-off was set at >0.1%. Abbreviations: ND, normal diet; HFD, high-fat diet; PRO, high-fat diet + L. rhamnosus 4B15; PRE, high-fat diet + C. tricuspidata; SYN, high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
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Figure 3. Relative abundances of selected taxa. The significance values [p < 0.05], [p < 0.01], and [p < 0.001] are denoted as *, **, ***, respectively, in comparison with HFD. Values are presented as mean ± SEM. Statistical analyses were performed using Kruskal–Wallis non-parametric test. ND: normal diet; HFD: high-fat diet; PRO: high-fat diet + L. rhamnosus 4B15; PRE: high-fat diet + C. tricuspidata; SYN: high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
Figure 3. Relative abundances of selected taxa. The significance values [p < 0.05], [p < 0.01], and [p < 0.001] are denoted as *, **, ***, respectively, in comparison with HFD. Values are presented as mean ± SEM. Statistical analyses were performed using Kruskal–Wallis non-parametric test. ND: normal diet; HFD: high-fat diet; PRO: high-fat diet + L. rhamnosus 4B15; PRE: high-fat diet + C. tricuspidata; SYN: high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
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Figure 4. Association between gut microbiota and metabolic parameters. (a) Linear discriminant analysis effect size (LEfSe) histogram showing significant differential taxa between HFD (red) and SYN (green) groups, and (b) cladogram plot from LEfSe showing the phylogenetic relationships among differential taxa in HFD (red) and SYN (green) groups. (c) Heatmap based on Spearman’s correlation between gut microbiota and obesity-associated metabolic parameters. The significance values [p < 0.05], [p < 0.01], and [p < 0.001] is denoted as *, **, and ***, respectively. HFD: high-fat diet; SYN: high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
Figure 4. Association between gut microbiota and metabolic parameters. (a) Linear discriminant analysis effect size (LEfSe) histogram showing significant differential taxa between HFD (red) and SYN (green) groups, and (b) cladogram plot from LEfSe showing the phylogenetic relationships among differential taxa in HFD (red) and SYN (green) groups. (c) Heatmap based on Spearman’s correlation between gut microbiota and obesity-associated metabolic parameters. The significance values [p < 0.05], [p < 0.01], and [p < 0.001] is denoted as *, **, and ***, respectively. HFD: high-fat diet; SYN: high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
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Figure 5. KEGG functional pathways predicted in the gut microbiome of HFD and SYN groups using PICRUSt. Significant differential KEGG pathways between HFD (blue) and SYN (yellow). Statistical analysis was carried out using STAMP software. Abbreviations: HFD, high-fat diet; SYN, high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
Figure 5. KEGG functional pathways predicted in the gut microbiome of HFD and SYN groups using PICRUSt. Significant differential KEGG pathways between HFD (blue) and SYN (yellow). Statistical analysis was carried out using STAMP software. Abbreviations: HFD, high-fat diet; SYN, high-fat diet + L. rhamnosus 4B15 + C. tricuspidata.
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Oh, J.K.; Vasquez, R.; Hwang, I.-C.; Oh, Y.N.; Kim, S.H.; Kang, S.H.; Joung, J.Y.; Oh, N.S.; Kim, S.; Yoon, Y.; et al. Cudrania tricuspidata Combined with Lacticaseibacillus rhamnosus Modulate Gut Microbiota and Alleviate Obesity-Associated Metabolic Parameters in Obese Mice. Microorganisms 2021, 9, 1908. https://doi.org/10.3390/microorganisms9091908

AMA Style

Oh JK, Vasquez R, Hwang I-C, Oh YN, Kim SH, Kang SH, Joung JY, Oh NS, Kim S, Yoon Y, et al. Cudrania tricuspidata Combined with Lacticaseibacillus rhamnosus Modulate Gut Microbiota and Alleviate Obesity-Associated Metabolic Parameters in Obese Mice. Microorganisms. 2021; 9(9):1908. https://doi.org/10.3390/microorganisms9091908

Chicago/Turabian Style

Oh, Ju Kyoung, Robie Vasquez, In-Chan Hwang, Ye Na Oh, Sang Hoon Kim, Shin Ho Kang, Jae Yeon Joung, Nam Su Oh, Sejeong Kim, Yohan Yoon, and et al. 2021. "Cudrania tricuspidata Combined with Lacticaseibacillus rhamnosus Modulate Gut Microbiota and Alleviate Obesity-Associated Metabolic Parameters in Obese Mice" Microorganisms 9, no. 9: 1908. https://doi.org/10.3390/microorganisms9091908

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