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Impact of polyphenols combined with high-fat diet on rats' gut microbiota
Journal of Functional Foods 26 (2016) 763–771
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Impact of polyphenols combined with high-fat diet on rats’ gut microbiota Jiacheng Huang 1, Xiaohong Lin 1, Bin Xue, Jianming Luo, Lijuan Gao, Yong Wang, Shiyi Ou, Xichun Peng * Department of Food Science and Engineering, Jinan University, Guangzhou 510632, Guangdong, China
A R T I C L E
I N F O
A B S T R A C T
Article history:
Many individuals seek to control their body weight while simultaneously being able to satisfy
Received 15 July 2016
their appetite. It has been reported that polyphenols can restrain energy harvest by regu-
Received in revised form 21 August
lating gut microbiota. This study presents the co-action of a high-fat diet and polyphenols
2016
(quercetin and catechin) on rats’ gut microbiota and growth. Results show that the rats that
Accepted 27 August 2016
received catechin supplement had the lowest average body weight (P < 0.05). Supplement
Available online
of catechin, but not quercetin, distinctly altered the composition and down-regulated the diversity of rats’ gut microbiota without changing Firmicutes/Bacteroidetes ratio. However,
Keywords:
almost all serum biochemical indices were not significantly altered due to catechin. In con-
Quercetin
clusion, supplementing a high-fat diet with catechin can control BW by inhibiting one’s
Catechin
appetite. Furthermore, quercetin supplement did not change BW; however, it did result in
High fat diet
better serum indices, such as TG. Thus, supplementing with a mixture of various polyphe-
Gut microbiota
nols may be a better health choice.
Body weight
1.
Introduction
Obesity has become prevalent worldwide over the last 20–30 years. There are approximately 600 million obese people around the world as well as 1.9 billion overweight people (data from WHO, 2015). It is well known that being overweight or obese can lead to the development of various major diseases, such as hypertension, coronary heart disease, stroke, type 2 diabetes, cancer, and chronic kidney disease (Carmo et al., 2016). As a result, people are always seeking effective methods of con-
© 2016 Elsevier Ltd. All rights reserved.
trolling body weight. Various skills or functional foods have been developed to help weight loss (Torres-Fuentes, Schellekens, Dinan, & Cryan, 2015; Vine, Hargreaves, Briefel, & Orfield, 2013). Nonetheless, obesity still troubles billions of people every day (Carmo et al., 2016). Various factors can contribute to obesity, such as genetics, a high-fat diet, and lack of exercise (Horn, Turkheimer, Strachan, & Duncan, 2015; Langbein, Hofmann, Brunssen, Goettsch, & Morawietz, 2015). In particular, the relationship between gut microbiota and obesity/overweight has been frequently reported recently (Baothman, Zamzami, Taher,
* Corresponding author. Department of Food Science and Engineering, Jinan University, Guangzhou 510632, Guangdong, China. Fax: +86 020 85226630. E-mail address: [email protected] (X. Peng). 1 Two authors equally contributed to this paper. Abbreviations: BW, body weight; HFD, high-fat diet; HFD_Q, high-fat diet with quercetin; HFD_C, high-fat diet with catechin; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; OTUs, operational taxonomic units; TNF-α, tumour-necrosis-factor-α; RA, relative abundance http://dx.doi.org/10.1016/j.jff.2016.08.042 1756-4646/© 2016 Elsevier Ltd. All rights reserved.
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Abubaker, & Abu-Farha, 2016; John & Mullin, 2016; Li et al., 2015a, 2015b). Turnbaugh et al. (2006) proposed that a decreased ratio of Bacteroidetes/Firmicutes is responsible for obesity. However, more complex issues are reportedly involved in the development of obesity, and the phenomenon cannot only be explained by an imbalance in Bacteroidetes/ Firmicutes proportions and/or an interaction of these phyla. Some studies have even discovered that gut microbiome can induce fat deposition in animals (Wallace, Gohir, & Sloboda, 2016). Many factors can alter the human gut microbiota, such as diet, medicine, sports, genetics, or even mental stress (Ley, Peterson, & Gordon, 2006). From among these factors, diet is the most important (Laparra & Sanz, 2010). Many individuals consume high-energy diets, such as those containing high fat or sugar, because the foods in such diets are often considered very flavoursome. However, many individuals hope to stay fit while also being able to consume these high-energy foods. Thus, it would be beneficial if there were substances that an individual could consume that can neutralise or inhibit energy intake from high-energy foods. It is well known that a high-fat diet can lead to obesity as well as induce a high abundance of Firmicutes in the gut microbiota (Turnbaugh et al., 2006). A few studies have reported that plant polyphenols can regulate gut microbiota, lower blood lipids, and control body weight (Roopchand et al., 2015; Trigueros et al., 2013; Xue et al., 2016). However, prior to this study, the consequence of simultaneously consuming a highfat diet and polyphenols has never been investigated. This experiment attempts to discover the co-action of a high-fat diet and polyphenols on rats’ growth and gut microbiota.
2.
Methods
2.1.
Animals, diets, faecal samples, and serum samples
Eighteen Wistar rats (SPF grade), 5 weeks old, were obtained from the Guangdong Medical Laboratory Animal Center (Foshan, China). The animals were housed in polypropylene cages, to which they were allowed to adapt for 10 days in a room at the Institute of Laboratory Animal Science at Jinan University with a constant temperature (22 ± 2 °C) under a 12-h:12-h light/ dark cycle. During the adaption period, the rats were fed a standard diet (No. AIN-93). Then, they were randomly divided into 3 groups, 6 rats for each group, and were simultaneously fed with a high-fat diet (HFD) (Li et al., 2015a, 2015b) for 4 weeks. The differences between the group diets are as follows: (1) Group HFD; (2) Group HFD_Q: this group was fed with a high-fat diet supplemented with 150 mg/kg body weight · d quercetin by gavage; and (3) Group HFD_C: this group was fed with a highfat diet supplemented with 150 mg/kg · d catechin (HFD_C) by gavage. Quercetin (≥98% purity) and catechin (≥98% purity) were supplied by Sigma-Aldrich (St. Louis, MO, USA). All experiments were performed in agreement with the Ethical Committee of Jinan University, Guangzhou, China. The animals’ body weights were weighted each week. After all rats had adapted for 10 days, faecal samples were immediately collected from 6 rats at random as Group Con (consuming the standard diet: No. AIN-93). The other samples were collected
from each group at the end of the experiment. Freshly collected faecal samples in sterile plastic tubes were immediately stored at −80 °C until further treatment. All animal blood samples were collected from abdominal aorta and serum and were separated by centrifugation. Then, the rats were dislocated to cause death, which was followed by peeling the rats and weighing their abdominal adipose and epididymal fat.
2.2.
DNA extraction and PCR amplification
Bacterial genome DNA from rats’ faeces was extracted with a TIANamp Stool DNA Kit (Tiangen, Beijing, China) according to the manufacturer’s instructions. 1% agarose gel electrophoresis was used to examine the purity of the DNA. The V3 and V4 regions of the bacterial 16S rRNA gene were amplified in a PCR system (Bio-Rad, USA) with the following procedure: 95 °C for 2 min; followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s; and a final extension at 72 °C for 10 min using the primers F338 (5′-barcode-ACTCCTACGGGAGGCAGCA-3′) and R806 (5′GGACTACHVGGGTWTCTAAT-3′). The PCR reactions were performed in triplicate in a 20 µL mixture containing 2.5 mM dNTPs, 0.4 µL of each primer (5 µM), 0.4 µL of Fast Pfu polymerase, and 10 ng of template DNA.
2.3.
Illumina MiSeq sequencing
Sequencing was conducted on the Illumina MiSeq platform at Shanghai Majorbio Bio-pharm Technology. The sequencing protocol and the sequence processing were described in our previous study (Xue et al., 2016), and 20,000 sequences were determined for each sample. In brief, amplicons were extracted from 2% agarose gels and purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, USA) according to the manufacturer’s instructions and quantified with QuantiFluorTM-ST (Promega, USA). The purified amplicons were pooled in equimolar concentrations and then pairedend sequenced (2 × 250) on an Illumina MiSeq platform according to the standard protocols.
2.4. data
Processing and bioinformatics analysis of sequencing
The processing and bioinformatics analysis of the sequencing data are similar to that described in our previous study (Xue et al., 2016). Raw fastq files were demultiplexed and quality-filtered using QIIME (version 1.9.1) with the following criteria: (i) the 300 bp reads were truncated at any site receiving an average quality score <20 over a 50 bp sliding window, discarding the truncated reads that were shorter than 50 bp; (ii) exact barcode matching, 2 nucleotide mismatch in primer matching, and reads containing ambiguous characters were removed; and (iii) only sequences that overlap for longer than 10 bp were assembled according to their overlap sequences. Reads that could not be assembled were discarded. Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using Usearch (version 7.1, http://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. The taxonomy of each 16S rRNA gene sequence was analysed by RDP Classifier
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Journal of Functional Foods 26 (2016) 763–771
DBray-Curtis = 1 − 2 × ⎡⎣ ∑ min (SA ,i, SB,i )
(∑ S
A ,i
+ ∑ SB,i ) ⎤⎦
HFD_Con
1000
Feed intake (g)
(http://rdp.cme.msu.edu/) against the silva (SSU123) 16S rRNA database using a confidence threshold of 70% (Amato et al., 2013). Hierarchical clustering (Hcluster) analysis was performed according to the data matrix of the unweighted pair group method with arithmetic mean (UPGMA), and a tree-like structure was built to express and compare the similarity and difference between communities. The distance matrix was calculated by the Bray–Curtis method (Jiang et al., 2013):
HFD_Q
900
HFD_C
800 700 600
(1)
500
where SA,i represents the amount of sequences in No. i OUT of Sample A, and SB,i represents the amount of sequences in No. i OUT of Sample B. LEfSe analysis was performed with the method described by Zhang et al. (2013). The treatment groups were used as the class of subjects. A prediction of functional genes in each group was performed with PICRUSt software. PICRUSt can first normalise the abundance of OTUs by removing copies of the 16S marker gene in the genomics of species, and then it obtains and calculates the abundance of corresponding clusters of orthologous groups of proteins (COG) and the abundance of Kegg (Kyoto Encyclopedia of Genes and Genomes, KEGG) Orthology (KO) from the greengene id of each OUT. The function abundance profile can be individually parsed by scanning evolutionary genealogy of genes: non-supervised Orthologous Groups (eegNOG) from http://eggnog.embl.de based on the code of COG or analysing information of KO, pathway and enzyme code based on KEGG at http://www.genome.jp/kegg/.
throughout the whole experiment. As a result, Group HFD and Group HFD_Q grew at the same speed and finally achieved similar body weights (BW); however, Group HFD_C gained the least body weight (P < 0.05) (Extended Data Fig. S1 and Table 1). Meanwhile, the ratios of liver/BW, abdominal fat/BW, and epididymal fat/BW were the lowest in Group HFD_C with significance (P < 0.05) or no significance (P > 0.05) (Table 1). Additionally, the liver/BW and abdominal fat/BW ratios were lower in Group HFD_Q than in Group HFD (P > 0.05).
2.5.
3.2.
Serum biochemical analysis
Blood samples were collected from the tail vein after overnight fasting and centrifuged at 12,000 r.p.m. for 30 min to precipitate the blood cells, and the serum was stored at 80 °C until further analyses. The analyses of serum triglyceride (TG), high density lipoprotein (HDL), and low density lipoprotein (LDL) were performed via UV-vis spectrophotometer using Konelab 20XTi (Thermo Fisher Scientific, USA). The serum leptin, tumournecrosis-factor-α (TNF-α), and somatotropin concentrations were analysed by following the protocols of a rat leptin quantikine ELISA Kit (R&D Systems Co., USA), rat TNF-α kit (Shanghai Yinggong Co., China) and rat GH ELISA kit (Shanghai Yinggong Co., China), respectively.
1
2
3
4
Time (Weeks) Fig. 1 – Feed consumptions of groups. The group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C).
Serum biochemical parameters
Six serum biochemical parameters, including triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), leptin, TNF-α, and somatotropin, were determined (Table 2). The values of TG in Group HFD_Q and TNF-α in Group HFD_C were the lowest (P < 0.05 and P > 0.05, respectively). The somatotropin value in Group HFD_C was the highest (P > 0.05).
3.3.
Overall structural changes of gut microbiota
The switch from a standard diet to the high-fat diet changed the composition of the rats’ gut microbiota (Fig. 2, Extended
Table 1 – Rats’ body weight (BW) and fat.
3.
Results
3.1.
Feed consumption and animal growth
All groups experienced an adaption period to the new diet, a high-fat diet, and consumed less feed in the first two weeks (Fig. 1). However, each group experienced a different reduction in feed consumption. Group HFD_C consumed the least amount of feed, and the other groups consumed a similar amount of feed during the first week of switching to a new diet. After the first week, all groups recovered their appetites, but Group HFD_C continued to ingest the least amount of feed
Group BW (g)
Liver/BW (g/g)
Abdominal Epididymal fat/BW fat/BW (g/g) (g/g)
HFD 391.77 ± 33.16 2.97 ± 0.31% 2.55 ± 0.36% HFD_Q 377.20 ± 43.45 2.76 ± 0.10% 2.39 ± 0.31% HFD_C 344.11 ± 35.94* 2.51 ± 0.24%* 2.15 ± 0.49%
0.57 ± 0.05% 0.60 ± 0.03% 0.54 ± 0.03%
Note: The group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). * means P < 0.05, and ** means P < 0.01 when compared with Group HFD.
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Table 2 – Serum biochemical indices of rats. Group
TG (mmol/L)
HDL (mmol/L)
LDL (mmol/L)
Leptin (µg/L)
TNF-α (ng/L)
Somatotropin (µg/L)
HFD HFD_Q HFD_C
0.38 ± 0.06 0.25 ± 0.05** 0.31 ± 0.10
0.38 ± 0.04 0.41 ± 0.02 0.41 ± 0.05
0.32 ± 0.04 0.33 ± 0.03 0.32 ± 0.04
1.85 ± 0.52 1.94 ± 0.10 1.91 ± 0.28
84.23 ± 9.72 82.13 ± 5.67 76.61 ± 3.93
1.93 ± 0.34 2.12 ± 0.54 2.35 ± 0.67
Note: The group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C); triglyceride (TG); high density lipoprotein (HDL); low density lipoprotein (LDL); tumour-necrosis-factor-α (TNF-α). * means P < 0.05, and ** means P < 0.01 when compared with Group HFD.
Data Fig. S2 and Table 3). The composition of the gut microbiota in Group HFD_C presented more distinct alterations than that in Group HFD_Q and compared to that in Group Con and even Group HFD. The relative abundance (RA) of Firmicutes significantly increased (P < 0.01). In contrast, the RAs of Bacteroidetes and Proteobacteria decreased in all Groups with a high-fat diet (P < 0.01 or P < 0.05). The RAs of Proteobacteria in both Group HFD_Q and Group HFD_C were higher than that in Group HFD
(P < 0.01 or P < 0.05). Group HFD_Q had the lowest RA of Verrucomicrobia, and Group HFD_C had the lowest RA of Saccharibacteria. The RAs of other phyla also fluctuated in various groups but with no significance (P > 0.05). Moreover, the diversity of gut microbiota in each group also presented distinct differences (Extended Data Table S3). The microbiota diversities of Group HFD and Group HFD_Q were higher than those of the others, but that of Group HFD_C was lower than that of Group HFD (P < 0.05).
3.4. Specific phylotypes modulated by HFD and polyphenols
Fig. 2 – Cluster tree analysis of gut microbiota in four groups. The distance of a vertical line indicates the differences of various samples. The control group consuming a standard diet No. AIN-93 (Con), including samples of Con 1–6; the group consuming a high-fat diet (HFD), including samples of HFD 1–6; the group consuming a high-fat diet supplemented with quercetin (HFD_Q), including samples of HFD_Q 1–6; the group consuming a high-fat diet supplemented with catechin (HFD_C), including samples of HFD_C 1–6.
In Group HFD, the RAs of the top 100 genera were mostly upregulated after switching the feed, except for Allobaculum, Lachnoclostridium, Escherichia-Shigella, Anaerofilum, Bifidobacterium, Enterococcus, Pseudomonas, Rikenellaceae_RC9_gut_group, Bacillales_unclassified, and Mycoplasma. Rhodanobacter and Saccharibacteria_norank were only detected in Group HFD. Bacillales_unclassified was not detectable in all groups with the high-fat diet, and another two genera, Anaerostipes and Prevotella_9, were only undetectable in Group HFD_Q. As compared with Group HFD and Group HFD_Q, the RAs of some genera were enhanced in Group HFD_C, including Adlercreutzia, Candidatus_Stoquefichus, Coriobacteriaceae_UCG-002, Anaerostipes, Rothia, Pasteurella, Streptococcus, Erysipelotrichaceae_uncultured, Thalassospira, Prevotellaceae_NK3B31_group, [Eubacterium] _coprostanoligenes_group, Coprococcus_2, Flavonifractor, Allobaculum, Lachnoclostridium, Ruminococcaceae_unclassified, EscherichiaShigella, Anaerofilum, Gastranaerophilales_norank, Fusicatenibacter, Prevotellaceae_unclassified, Roseburia, Family_XIII_AD3011_group, Turicibacter, and Christensenellaceae_uncultured. In contrast, the RAs of some other genera were lowered, including Ruminiclostridium_6, Lachnospiraceae_UCG-001, Mollicutes _RF9_norank, Lachnospira, Ruminococcaceae_UCG-004, Lachnospiraceae_incertae_sedis, [Ruminococcus]_gauvreauii_group, Candidatus_Saccharimonas, Ruminococcaceae_UCG-013, Tyzzerella, Alistipes, Ruminococcaceae_UCG-010, Lachnospiraceae_ND3007 _group, Erysipelatoclostridium, Peptococcus, and Clostridiales_unclassified. The RAs of some genera in Group HFD_Q were also different from those in Group HFD (Fig. 3). Different amounts of OTUs were detected in various groups, including 482 in Group Con, 603 in Group HFD, 595 in Group HFD_Q, and 489 in Group HFD_C (Fig. 4). In all detected OTUs, 302 were shared by all groups. Additionally, 210 OTUs were discovered in Group HFD, but not in Group Con, and 145 and 115 OTUs survived in Group HFD_Q and Group HFD_C,
Journal of Functional Foods 26 (2016) 763–771
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Fig. 3 – The relative abundance of bacterial community at the taxa level of Genus in groups. The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). Only the top 100 genera presented.
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Table 3 – The amount of operational taxonomic units (OTUs) detected at the Phyla level. Phyla
Con
HFD
HFD_Q
HFD_C
Actinobacteria Bacteroidetes Cyanobacteria Deferribacteres Firmicutes Fusobacteria Proteobacteria Saccharibacteria Tenericutes Verrucomicrobia
60 ± 30 10,383 ± 1,828 11 ± 11 6.2 ± 7.4 3,834 ± 1,300 1.8 ± 3.68 3,885 ± 1,343 18 ± 11 28 ± 33 449 ± 418
66 ± 4 4,911 ± 1,353* 28 ± 32 2±4 12,212 ± 1,591** 0 840 ± 149** 67 ± 69 137 ± 109 413 ± 317
35 ± 22 4,818 ± 1,296** 12 ± 14 2±2 11,990 ± 1,294** 1±1 1,613 ± 571**,## 35 ± 31 128 ± 95* 44 ± 88**##
113 ± 73 5,436 ± 2,517** 169 ± 393 1±2 11,018 ± 3,162** 2 ± 2# 1,531 ± 2,023* 5 ± 7* 28 ± 18 372 ± 360
Note: The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a highfat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). * means P < 0.05, and ** means P < 0.01 when compared with Group Con; # means P < 0.05, and ## means P < 0.01 when compared with Group HFD.
respectively. Group HFD_Q and Group HFD_C respectively regained 41 and 30 OTUs that were detected in Group Con, but not in Group HFD. Group HFD_Q and Group HFD_C shared 431 OTUs. Additionally, each group possessed its own unique OTUs, including 26 in Group Con, 40 in Group HFD, 27 in Group HFD_Q, and 17 in Group HFD_C.
3.5.
genera, and several genera of Firmicutes were the most important ones. Similarly, the most important genera from among the 35 key genera in Group HFD_C also belonged to Firmicutes. Although almost all of the most important genera were Firmicutes in all of the groups consuming HFD, they were different from each other.
Key bacteria in each group
Different genera played key roles in various groups (Fig. 5 and Extended Data Fig. S5). Several genera of Bacteroidetes phyla in Group Con were the most important of the 38 key genera. There are only 21 key genera in Group HFD, from among which several genera of Firmicutes and Prevotellaceae of Bacteroidetes were the most important ones. Group HFD_Q recovered 41 key
Fig. 4 – Overlaps and differences of gut microbiota in four groups. The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). The plot was based on operational taxonomic units (OTUs) detected in all samples.
4.
Discussion
In this experiment, a high fat diet was fed to rats. Meanwhile, two polyphenols were supplemented in order to discover the co-impact of different polyphenols and the high-fat diet on rats’ growth and their gut microbiota. The rats that received the catechin supplement had the lowest average BW (P < 0.05), followed by the rats that received the quercetin supplement (P > 0.05). Furthermore, the liver/BW ratio also significantly declined after the rats’ diets were supplemented with catechin (P < 0.01). However, the serum biochemical indices were not significantly different between groups, except for TG in Group HFD_Q. The catechin supplement, but not quercetin, distinctly altered the composition and down-regulated the diversity of the rats’ gut microbiota without changing the Firmicutes/Bacteroidetes ratio. Some special genera were detected, and others fluctuated due to the catechin or quercetin supplement. Each group developed its own unique bacteria, and different genera played key roles in the gut metabolisms of the rats of various groups. Some researchers have proven that the ingestion of plant polyphenols, especially catechin, is responsible for controlling the BW of obese animal models (Hursel & Westerterp-Plantenga, 2013; Rastmanesh, 2011; Trigueros et al., 2013). Our experiments explored a different method of determining whether polyphenols control BW by separately and simultaneously supplementing a high-fat diet with two polyphenols and discovering their co-impact on gut microbiota. Our results are not in accordance with those of previous studies in which some serum biochemical indices, including TG, HDL, LDL, leptin, TNF-α, and somatotropin in obese models, are significantly different in animals that consume catechin or other polyphenols than from the same indices in the control group (Huang et al., 2016; Roopchand et al., 2015; Xu et al., 2015).
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Fig. 5 – LEfSe analysis of key genera of rats’ gut microbiota. The brown dots are unimportant bacteria in any groups, the other coloured dots are important bacteria in the group labelled with a same colour. The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C).
However, some alterations were observed, such as the insignificant reduction of serum leptin and TNF-α as well as the insignificant increment of serum somatotropin due to the supplement of catechin, or even quercetin. Many literatures have reported the impact of a high-fat diet on gut microbiota and have proposed that the Firmicutes/ Bacteroidetes ratio will increase when animals or humans consume a high-fat diet (Baothman et al., 2016; John & Mullin, 2016; Li et al., 2015a, 2015b; Turnbaugh et al., 2006). This is consistent with our results. Our results showed that a quercetin
or catechin supplement did not significantly change the Firmicutes/Bacteroidetes ratio induced by only a high fat diet; however, the diversity of gut microbiota was significantly downregulated. Furthermore, it has been reported that multiple mechanisms are involved in catechin’s control of a host’s BW, such as inhibiting energy intake, stimulating energy expenditure (Hursel & Westerterp-Plantenga, 2013), and even regulating gut microbiota (Roopchand et al., 2015; Trigueros et al., 2013; Xue et al., 2016). Our results demonstrated that supplementing a high fat diet with catechin can still control
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BW by inhibiting the host’s feed consumption, but it does not regulate the Firmicutes/Bacteroidetes ratio. Even though catechin reduced the gut microbiota diversity, the serum parameters, especially leptin, TNF-α, and somatotropin, did not respond to it. Some literatures have reported that leptin, TNFα, and somatotropin are closely related to gut microbiota (Chakraborti, 2015; Parnell & Reimer, 2012; Sanz & Moya-Pérez, 2014). Herein, the relationship between gut bacterial diversity and anti-obesity remains unclear. Additionally, the catechin supplement controlled the rats’ BW, but the serum TG, HDL, and LDL did not significantly decline. This indicates that a high fat diet can result in other health problems, even if one simultaneously supplements the diet with catechin. Furthermore, the consumption of quercetin did not change the BW, but it did result in better serum indices, such as TG. Thus, supplementing with a mixture of various polyphenols may be a better health choice.
5.
Conclusion
This experiment explored the co-impact of different polyphenols and a high fat diet on the growth of rats and the richness of the rats’ gut microbiota. It was discovered that the simultaneous ingestion of catechin and a high diet can control BW by inhibiting feed consumption, but it cannot regulate the Firmicutes/Bacteroidetes ratio. However, it is important to note that a high fat diet can still cause other health problems, even if the diet is being supplemented with catechin to control BW. Consumption of quercetin did not change the BW, but it did result in better serum indices, such as TG. Thus, supplementing with a mixture of various polyphenols may be a better health choice.
Acknowledgement The program was supported by the funds of the National Natural Science Funds (No. 31471589), Science and Technology Program of Guangzhou, China (No. 201510010115), Science and Technology Program of Nansha District, Guangzhou, China (2014GG05) and the Fundamental Research Funds for the Central Universities (No. 21615404). We thank Qiu Ruixia and Bin Yu from the Department of Food Science and Engineering, Jinan University, for their contribution to the experiments of this study.
Appendix: Supplementary material Supplementary data to this article can be found online at doi:10.1016/j.jff.2016.08.042.
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M., Gillis, M., Wilson, B. A., Nelson, K. E., White, B. A., & Leigh, S. R. (2013). Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. The ISME Journal, 7, 1344–1353. Baothman, O. A., Zamzami, M. A., Taher, I., Abubaker, J., & AbuFarha, M. (2016). The role of Gut Microbiota in the development of obesity and Diabetes. Lipids in Health and Disease, 15, 108. Carmo, J. M., Silva, A. A., Wang, Z., Fang, T., Aberdein, N., Rodriguez, C. E. P. L., & Hall, J. E. (2016). Obesity-induced hypertension: Brain signaling pathways. Current Hypertension Reports, 18, 58. Chakraborti, C. K. (2015). New-found link between microbiota and obesity. World Journal of Gastrointestinal Pathophysiology, 6, 110–119. Horn, E. E., Turkheimer, E., Strachan, E., & Duncan, G. E. (2015). Behavioral and environmental modification of the genetic influence on body mass index: A twin study. Behavior Genetics, 45(4), 409–426. Huang, C. C., Tung, Y. T., Huang, W. C., Chen, Y. M., Hsu, Y. J., & Hsu, M. C. (2016). Beneficial effects of cocoa, coffee, green tea, and garcinia complex supplement on diet induced obesity in rats. BMC Complementary and Alternative Medicine, 16, 100. Hursel, R., & Westerterp-Plantenga, M. S. (2013). Catechin- and caffeine-rich teas for control of body weight in humans. The American Journal of Clinical Nutrition, 98, 1682S–1693S. Jiang, X. T., Peng, X., Deng, G. H., Sheng, H. F., Wang, Y., Zhou, H. W., & Tam, N. F. Y. (2013). Illumina sequencing of 16S rRNA tag revealed spatial variations of bacterial communities in a mangrove wetland. Microbial Ecology, 66, 96–104. John, G. K., & Mullin, G. E. (2016). The gut microbiome and obesity. Current Oncology Reports, 18, 45. Langbein, H., Hofmann, A., Brunssen, C., Goettsch, W., & Morawietz, H. (2015). Impact of high-fat diet and voluntary running on body weight and endothelial function in LDL receptor knockout mice. Atherosclerosis. Supplements, 18, 59–66. Laparra, J. M., & Sanz, Y. (2010). Interactions of gut microbiota with functional food components and nutraceuticals. Pharmacological Research, 61, 219–225. Ley, R. E., Peterson, D. A., & Gordon, J. I. (2006). Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell, 124, 837–848. Li, S., Yingyi, G., Chen, L., Lijuan, L. G., Ou, S., & Peng, X. (2015a). Lean rats gained more body weight from a highfructooligosaccharide diet. Food & Function, 6, 2315–2321. Li, S., Zhang, C., Gu, Y., Chen, L., Ou, S., Wang, Y., & Peng, X. (2015b). Lean rats gained more body weight than obese ones from a high-fibre diet. The British Journal of Nutrition, 114, 1188– 1194. Parnell, J. A., & Reimer, R. A. (2012). Prebiotic fibres dosedependently increase satiety hormones and alter Bacteroidetes and Firmicutes in lean and obese JCR:LA-cp rats. The British Journal of Nutrition, 107, 601–613. Rastmanesh, R. (2011). High polyphenol, low probiotic diet for weight loss because of intestinal microbiota interaction. Chemico-Biological Interactions, 189, 1–8. Roopchand, D. E., Carmody, R. N., Kuhn, P., Moskal, K., RojasSilva, P., Turnbaugh, P. J., & Raskin, I. (2015). Dietary polyphenols promote growth of the gut bacterium Akkermansia muciniphila and attenuate high fat dietinduced metabolic syndrome. Diabetes, 64, 2847–2858. Sanz, Y., & Moya-Pérez, A. (2014). Microbiota, inflammation and obesity. Advances in Experimental Medicine and Biology, 817, 291– 317. Torres-Fuentes, C., Schellekens, H., Dinan, T. G., & Cryan, J. F. (2015). A natural solution for obesity: Bioactives for the prevention and treatment of weight gain. A review. Nutritional Neuroscience, 18, 49–65.
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Impact of polyphenols combined with high-fat diet on rats’ gut microbiota Jiacheng Huang 1, Xiaohong Lin 1, Bin Xue, Jianming Luo, Lijuan Gao, Yong Wang, Shiyi Ou, Xichun Peng * Department of Food Science and Engineering, Jinan University, Guangzhou 510632, Guangdong, China
A R T I C L E
I N F O
A B S T R A C T
Article history:
Many individuals seek to control their body weight while simultaneously being able to satisfy
Received 15 July 2016
their appetite. It has been reported that polyphenols can restrain energy harvest by regu-
Received in revised form 21 August
lating gut microbiota. This study presents the co-action of a high-fat diet and polyphenols
2016
(quercetin and catechin) on rats’ gut microbiota and growth. Results show that the rats that
Accepted 27 August 2016
received catechin supplement had the lowest average body weight (P < 0.05). Supplement
Available online
of catechin, but not quercetin, distinctly altered the composition and down-regulated the diversity of rats’ gut microbiota without changing Firmicutes/Bacteroidetes ratio. However,
Keywords:
almost all serum biochemical indices were not significantly altered due to catechin. In con-
Quercetin
clusion, supplementing a high-fat diet with catechin can control BW by inhibiting one’s
Catechin
appetite. Furthermore, quercetin supplement did not change BW; however, it did result in
High fat diet
better serum indices, such as TG. Thus, supplementing with a mixture of various polyphe-
Gut microbiota
nols may be a better health choice.
Body weight
1.
Introduction
Obesity has become prevalent worldwide over the last 20–30 years. There are approximately 600 million obese people around the world as well as 1.9 billion overweight people (data from WHO, 2015). It is well known that being overweight or obese can lead to the development of various major diseases, such as hypertension, coronary heart disease, stroke, type 2 diabetes, cancer, and chronic kidney disease (Carmo et al., 2016). As a result, people are always seeking effective methods of con-
© 2016 Elsevier Ltd. All rights reserved.
trolling body weight. Various skills or functional foods have been developed to help weight loss (Torres-Fuentes, Schellekens, Dinan, & Cryan, 2015; Vine, Hargreaves, Briefel, & Orfield, 2013). Nonetheless, obesity still troubles billions of people every day (Carmo et al., 2016). Various factors can contribute to obesity, such as genetics, a high-fat diet, and lack of exercise (Horn, Turkheimer, Strachan, & Duncan, 2015; Langbein, Hofmann, Brunssen, Goettsch, & Morawietz, 2015). In particular, the relationship between gut microbiota and obesity/overweight has been frequently reported recently (Baothman, Zamzami, Taher,
* Corresponding author. Department of Food Science and Engineering, Jinan University, Guangzhou 510632, Guangdong, China. Fax: +86 020 85226630. E-mail address: [email protected] (X. Peng). 1 Two authors equally contributed to this paper. Abbreviations: BW, body weight; HFD, high-fat diet; HFD_Q, high-fat diet with quercetin; HFD_C, high-fat diet with catechin; TG, triglyceride; HDL, high density lipoprotein; LDL, low density lipoprotein; OTUs, operational taxonomic units; TNF-α, tumour-necrosis-factor-α; RA, relative abundance http://dx.doi.org/10.1016/j.jff.2016.08.042 1756-4646/© 2016 Elsevier Ltd. All rights reserved.
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Abubaker, & Abu-Farha, 2016; John & Mullin, 2016; Li et al., 2015a, 2015b). Turnbaugh et al. (2006) proposed that a decreased ratio of Bacteroidetes/Firmicutes is responsible for obesity. However, more complex issues are reportedly involved in the development of obesity, and the phenomenon cannot only be explained by an imbalance in Bacteroidetes/ Firmicutes proportions and/or an interaction of these phyla. Some studies have even discovered that gut microbiome can induce fat deposition in animals (Wallace, Gohir, & Sloboda, 2016). Many factors can alter the human gut microbiota, such as diet, medicine, sports, genetics, or even mental stress (Ley, Peterson, & Gordon, 2006). From among these factors, diet is the most important (Laparra & Sanz, 2010). Many individuals consume high-energy diets, such as those containing high fat or sugar, because the foods in such diets are often considered very flavoursome. However, many individuals hope to stay fit while also being able to consume these high-energy foods. Thus, it would be beneficial if there were substances that an individual could consume that can neutralise or inhibit energy intake from high-energy foods. It is well known that a high-fat diet can lead to obesity as well as induce a high abundance of Firmicutes in the gut microbiota (Turnbaugh et al., 2006). A few studies have reported that plant polyphenols can regulate gut microbiota, lower blood lipids, and control body weight (Roopchand et al., 2015; Trigueros et al., 2013; Xue et al., 2016). However, prior to this study, the consequence of simultaneously consuming a highfat diet and polyphenols has never been investigated. This experiment attempts to discover the co-action of a high-fat diet and polyphenols on rats’ growth and gut microbiota.
2.
Methods
2.1.
Animals, diets, faecal samples, and serum samples
Eighteen Wistar rats (SPF grade), 5 weeks old, were obtained from the Guangdong Medical Laboratory Animal Center (Foshan, China). The animals were housed in polypropylene cages, to which they were allowed to adapt for 10 days in a room at the Institute of Laboratory Animal Science at Jinan University with a constant temperature (22 ± 2 °C) under a 12-h:12-h light/ dark cycle. During the adaption period, the rats were fed a standard diet (No. AIN-93). Then, they were randomly divided into 3 groups, 6 rats for each group, and were simultaneously fed with a high-fat diet (HFD) (Li et al., 2015a, 2015b) for 4 weeks. The differences between the group diets are as follows: (1) Group HFD; (2) Group HFD_Q: this group was fed with a high-fat diet supplemented with 150 mg/kg body weight · d quercetin by gavage; and (3) Group HFD_C: this group was fed with a highfat diet supplemented with 150 mg/kg · d catechin (HFD_C) by gavage. Quercetin (≥98% purity) and catechin (≥98% purity) were supplied by Sigma-Aldrich (St. Louis, MO, USA). All experiments were performed in agreement with the Ethical Committee of Jinan University, Guangzhou, China. The animals’ body weights were weighted each week. After all rats had adapted for 10 days, faecal samples were immediately collected from 6 rats at random as Group Con (consuming the standard diet: No. AIN-93). The other samples were collected
from each group at the end of the experiment. Freshly collected faecal samples in sterile plastic tubes were immediately stored at −80 °C until further treatment. All animal blood samples were collected from abdominal aorta and serum and were separated by centrifugation. Then, the rats were dislocated to cause death, which was followed by peeling the rats and weighing their abdominal adipose and epididymal fat.
2.2.
DNA extraction and PCR amplification
Bacterial genome DNA from rats’ faeces was extracted with a TIANamp Stool DNA Kit (Tiangen, Beijing, China) according to the manufacturer’s instructions. 1% agarose gel electrophoresis was used to examine the purity of the DNA. The V3 and V4 regions of the bacterial 16S rRNA gene were amplified in a PCR system (Bio-Rad, USA) with the following procedure: 95 °C for 2 min; followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s; and a final extension at 72 °C for 10 min using the primers F338 (5′-barcode-ACTCCTACGGGAGGCAGCA-3′) and R806 (5′GGACTACHVGGGTWTCTAAT-3′). The PCR reactions were performed in triplicate in a 20 µL mixture containing 2.5 mM dNTPs, 0.4 µL of each primer (5 µM), 0.4 µL of Fast Pfu polymerase, and 10 ng of template DNA.
2.3.
Illumina MiSeq sequencing
Sequencing was conducted on the Illumina MiSeq platform at Shanghai Majorbio Bio-pharm Technology. The sequencing protocol and the sequence processing were described in our previous study (Xue et al., 2016), and 20,000 sequences were determined for each sample. In brief, amplicons were extracted from 2% agarose gels and purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, USA) according to the manufacturer’s instructions and quantified with QuantiFluorTM-ST (Promega, USA). The purified amplicons were pooled in equimolar concentrations and then pairedend sequenced (2 × 250) on an Illumina MiSeq platform according to the standard protocols.
2.4. data
Processing and bioinformatics analysis of sequencing
The processing and bioinformatics analysis of the sequencing data are similar to that described in our previous study (Xue et al., 2016). Raw fastq files were demultiplexed and quality-filtered using QIIME (version 1.9.1) with the following criteria: (i) the 300 bp reads were truncated at any site receiving an average quality score <20 over a 50 bp sliding window, discarding the truncated reads that were shorter than 50 bp; (ii) exact barcode matching, 2 nucleotide mismatch in primer matching, and reads containing ambiguous characters were removed; and (iii) only sequences that overlap for longer than 10 bp were assembled according to their overlap sequences. Reads that could not be assembled were discarded. Operational taxonomic units (OTUs) were clustered with a 97% similarity cutoff using Usearch (version 7.1, http://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. The taxonomy of each 16S rRNA gene sequence was analysed by RDP Classifier
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DBray-Curtis = 1 − 2 × ⎡⎣ ∑ min (SA ,i, SB,i )
(∑ S
A ,i
+ ∑ SB,i ) ⎤⎦
HFD_Con
1000
Feed intake (g)
(http://rdp.cme.msu.edu/) against the silva (SSU123) 16S rRNA database using a confidence threshold of 70% (Amato et al., 2013). Hierarchical clustering (Hcluster) analysis was performed according to the data matrix of the unweighted pair group method with arithmetic mean (UPGMA), and a tree-like structure was built to express and compare the similarity and difference between communities. The distance matrix was calculated by the Bray–Curtis method (Jiang et al., 2013):
HFD_Q
900
HFD_C
800 700 600
(1)
500
where SA,i represents the amount of sequences in No. i OUT of Sample A, and SB,i represents the amount of sequences in No. i OUT of Sample B. LEfSe analysis was performed with the method described by Zhang et al. (2013). The treatment groups were used as the class of subjects. A prediction of functional genes in each group was performed with PICRUSt software. PICRUSt can first normalise the abundance of OTUs by removing copies of the 16S marker gene in the genomics of species, and then it obtains and calculates the abundance of corresponding clusters of orthologous groups of proteins (COG) and the abundance of Kegg (Kyoto Encyclopedia of Genes and Genomes, KEGG) Orthology (KO) from the greengene id of each OUT. The function abundance profile can be individually parsed by scanning evolutionary genealogy of genes: non-supervised Orthologous Groups (eegNOG) from http://eggnog.embl.de based on the code of COG or analysing information of KO, pathway and enzyme code based on KEGG at http://www.genome.jp/kegg/.
throughout the whole experiment. As a result, Group HFD and Group HFD_Q grew at the same speed and finally achieved similar body weights (BW); however, Group HFD_C gained the least body weight (P < 0.05) (Extended Data Fig. S1 and Table 1). Meanwhile, the ratios of liver/BW, abdominal fat/BW, and epididymal fat/BW were the lowest in Group HFD_C with significance (P < 0.05) or no significance (P > 0.05) (Table 1). Additionally, the liver/BW and abdominal fat/BW ratios were lower in Group HFD_Q than in Group HFD (P > 0.05).
2.5.
3.2.
Serum biochemical analysis
Blood samples were collected from the tail vein after overnight fasting and centrifuged at 12,000 r.p.m. for 30 min to precipitate the blood cells, and the serum was stored at 80 °C until further analyses. The analyses of serum triglyceride (TG), high density lipoprotein (HDL), and low density lipoprotein (LDL) were performed via UV-vis spectrophotometer using Konelab 20XTi (Thermo Fisher Scientific, USA). The serum leptin, tumournecrosis-factor-α (TNF-α), and somatotropin concentrations were analysed by following the protocols of a rat leptin quantikine ELISA Kit (R&D Systems Co., USA), rat TNF-α kit (Shanghai Yinggong Co., China) and rat GH ELISA kit (Shanghai Yinggong Co., China), respectively.
1
2
3
4
Time (Weeks) Fig. 1 – Feed consumptions of groups. The group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C).
Serum biochemical parameters
Six serum biochemical parameters, including triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), leptin, TNF-α, and somatotropin, were determined (Table 2). The values of TG in Group HFD_Q and TNF-α in Group HFD_C were the lowest (P < 0.05 and P > 0.05, respectively). The somatotropin value in Group HFD_C was the highest (P > 0.05).
3.3.
Overall structural changes of gut microbiota
The switch from a standard diet to the high-fat diet changed the composition of the rats’ gut microbiota (Fig. 2, Extended
Table 1 – Rats’ body weight (BW) and fat.
3.
Results
3.1.
Feed consumption and animal growth
All groups experienced an adaption period to the new diet, a high-fat diet, and consumed less feed in the first two weeks (Fig. 1). However, each group experienced a different reduction in feed consumption. Group HFD_C consumed the least amount of feed, and the other groups consumed a similar amount of feed during the first week of switching to a new diet. After the first week, all groups recovered their appetites, but Group HFD_C continued to ingest the least amount of feed
Group BW (g)
Liver/BW (g/g)
Abdominal Epididymal fat/BW fat/BW (g/g) (g/g)
HFD 391.77 ± 33.16 2.97 ± 0.31% 2.55 ± 0.36% HFD_Q 377.20 ± 43.45 2.76 ± 0.10% 2.39 ± 0.31% HFD_C 344.11 ± 35.94* 2.51 ± 0.24%* 2.15 ± 0.49%
0.57 ± 0.05% 0.60 ± 0.03% 0.54 ± 0.03%
Note: The group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). * means P < 0.05, and ** means P < 0.01 when compared with Group HFD.
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Table 2 – Serum biochemical indices of rats. Group
TG (mmol/L)
HDL (mmol/L)
LDL (mmol/L)
Leptin (µg/L)
TNF-α (ng/L)
Somatotropin (µg/L)
HFD HFD_Q HFD_C
0.38 ± 0.06 0.25 ± 0.05** 0.31 ± 0.10
0.38 ± 0.04 0.41 ± 0.02 0.41 ± 0.05
0.32 ± 0.04 0.33 ± 0.03 0.32 ± 0.04
1.85 ± 0.52 1.94 ± 0.10 1.91 ± 0.28
84.23 ± 9.72 82.13 ± 5.67 76.61 ± 3.93
1.93 ± 0.34 2.12 ± 0.54 2.35 ± 0.67
Note: The group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C); triglyceride (TG); high density lipoprotein (HDL); low density lipoprotein (LDL); tumour-necrosis-factor-α (TNF-α). * means P < 0.05, and ** means P < 0.01 when compared with Group HFD.
Data Fig. S2 and Table 3). The composition of the gut microbiota in Group HFD_C presented more distinct alterations than that in Group HFD_Q and compared to that in Group Con and even Group HFD. The relative abundance (RA) of Firmicutes significantly increased (P < 0.01). In contrast, the RAs of Bacteroidetes and Proteobacteria decreased in all Groups with a high-fat diet (P < 0.01 or P < 0.05). The RAs of Proteobacteria in both Group HFD_Q and Group HFD_C were higher than that in Group HFD
(P < 0.01 or P < 0.05). Group HFD_Q had the lowest RA of Verrucomicrobia, and Group HFD_C had the lowest RA of Saccharibacteria. The RAs of other phyla also fluctuated in various groups but with no significance (P > 0.05). Moreover, the diversity of gut microbiota in each group also presented distinct differences (Extended Data Table S3). The microbiota diversities of Group HFD and Group HFD_Q were higher than those of the others, but that of Group HFD_C was lower than that of Group HFD (P < 0.05).
3.4. Specific phylotypes modulated by HFD and polyphenols
Fig. 2 – Cluster tree analysis of gut microbiota in four groups. The distance of a vertical line indicates the differences of various samples. The control group consuming a standard diet No. AIN-93 (Con), including samples of Con 1–6; the group consuming a high-fat diet (HFD), including samples of HFD 1–6; the group consuming a high-fat diet supplemented with quercetin (HFD_Q), including samples of HFD_Q 1–6; the group consuming a high-fat diet supplemented with catechin (HFD_C), including samples of HFD_C 1–6.
In Group HFD, the RAs of the top 100 genera were mostly upregulated after switching the feed, except for Allobaculum, Lachnoclostridium, Escherichia-Shigella, Anaerofilum, Bifidobacterium, Enterococcus, Pseudomonas, Rikenellaceae_RC9_gut_group, Bacillales_unclassified, and Mycoplasma. Rhodanobacter and Saccharibacteria_norank were only detected in Group HFD. Bacillales_unclassified was not detectable in all groups with the high-fat diet, and another two genera, Anaerostipes and Prevotella_9, were only undetectable in Group HFD_Q. As compared with Group HFD and Group HFD_Q, the RAs of some genera were enhanced in Group HFD_C, including Adlercreutzia, Candidatus_Stoquefichus, Coriobacteriaceae_UCG-002, Anaerostipes, Rothia, Pasteurella, Streptococcus, Erysipelotrichaceae_uncultured, Thalassospira, Prevotellaceae_NK3B31_group, [Eubacterium] _coprostanoligenes_group, Coprococcus_2, Flavonifractor, Allobaculum, Lachnoclostridium, Ruminococcaceae_unclassified, EscherichiaShigella, Anaerofilum, Gastranaerophilales_norank, Fusicatenibacter, Prevotellaceae_unclassified, Roseburia, Family_XIII_AD3011_group, Turicibacter, and Christensenellaceae_uncultured. In contrast, the RAs of some other genera were lowered, including Ruminiclostridium_6, Lachnospiraceae_UCG-001, Mollicutes _RF9_norank, Lachnospira, Ruminococcaceae_UCG-004, Lachnospiraceae_incertae_sedis, [Ruminococcus]_gauvreauii_group, Candidatus_Saccharimonas, Ruminococcaceae_UCG-013, Tyzzerella, Alistipes, Ruminococcaceae_UCG-010, Lachnospiraceae_ND3007 _group, Erysipelatoclostridium, Peptococcus, and Clostridiales_unclassified. The RAs of some genera in Group HFD_Q were also different from those in Group HFD (Fig. 3). Different amounts of OTUs were detected in various groups, including 482 in Group Con, 603 in Group HFD, 595 in Group HFD_Q, and 489 in Group HFD_C (Fig. 4). In all detected OTUs, 302 were shared by all groups. Additionally, 210 OTUs were discovered in Group HFD, but not in Group Con, and 145 and 115 OTUs survived in Group HFD_Q and Group HFD_C,
Journal of Functional Foods 26 (2016) 763–771
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Fig. 3 – The relative abundance of bacterial community at the taxa level of Genus in groups. The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). Only the top 100 genera presented.
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Table 3 – The amount of operational taxonomic units (OTUs) detected at the Phyla level. Phyla
Con
HFD
HFD_Q
HFD_C
Actinobacteria Bacteroidetes Cyanobacteria Deferribacteres Firmicutes Fusobacteria Proteobacteria Saccharibacteria Tenericutes Verrucomicrobia
60 ± 30 10,383 ± 1,828 11 ± 11 6.2 ± 7.4 3,834 ± 1,300 1.8 ± 3.68 3,885 ± 1,343 18 ± 11 28 ± 33 449 ± 418
66 ± 4 4,911 ± 1,353* 28 ± 32 2±4 12,212 ± 1,591** 0 840 ± 149** 67 ± 69 137 ± 109 413 ± 317
35 ± 22 4,818 ± 1,296** 12 ± 14 2±2 11,990 ± 1,294** 1±1 1,613 ± 571**,## 35 ± 31 128 ± 95* 44 ± 88**##
113 ± 73 5,436 ± 2,517** 169 ± 393 1±2 11,018 ± 3,162** 2 ± 2# 1,531 ± 2,023* 5 ± 7* 28 ± 18 372 ± 360
Note: The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a highfat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). * means P < 0.05, and ** means P < 0.01 when compared with Group Con; # means P < 0.05, and ## means P < 0.01 when compared with Group HFD.
respectively. Group HFD_Q and Group HFD_C respectively regained 41 and 30 OTUs that were detected in Group Con, but not in Group HFD. Group HFD_Q and Group HFD_C shared 431 OTUs. Additionally, each group possessed its own unique OTUs, including 26 in Group Con, 40 in Group HFD, 27 in Group HFD_Q, and 17 in Group HFD_C.
3.5.
genera, and several genera of Firmicutes were the most important ones. Similarly, the most important genera from among the 35 key genera in Group HFD_C also belonged to Firmicutes. Although almost all of the most important genera were Firmicutes in all of the groups consuming HFD, they were different from each other.
Key bacteria in each group
Different genera played key roles in various groups (Fig. 5 and Extended Data Fig. S5). Several genera of Bacteroidetes phyla in Group Con were the most important of the 38 key genera. There are only 21 key genera in Group HFD, from among which several genera of Firmicutes and Prevotellaceae of Bacteroidetes were the most important ones. Group HFD_Q recovered 41 key
Fig. 4 – Overlaps and differences of gut microbiota in four groups. The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C). The plot was based on operational taxonomic units (OTUs) detected in all samples.
4.
Discussion
In this experiment, a high fat diet was fed to rats. Meanwhile, two polyphenols were supplemented in order to discover the co-impact of different polyphenols and the high-fat diet on rats’ growth and their gut microbiota. The rats that received the catechin supplement had the lowest average BW (P < 0.05), followed by the rats that received the quercetin supplement (P > 0.05). Furthermore, the liver/BW ratio also significantly declined after the rats’ diets were supplemented with catechin (P < 0.01). However, the serum biochemical indices were not significantly different between groups, except for TG in Group HFD_Q. The catechin supplement, but not quercetin, distinctly altered the composition and down-regulated the diversity of the rats’ gut microbiota without changing the Firmicutes/Bacteroidetes ratio. Some special genera were detected, and others fluctuated due to the catechin or quercetin supplement. Each group developed its own unique bacteria, and different genera played key roles in the gut metabolisms of the rats of various groups. Some researchers have proven that the ingestion of plant polyphenols, especially catechin, is responsible for controlling the BW of obese animal models (Hursel & Westerterp-Plantenga, 2013; Rastmanesh, 2011; Trigueros et al., 2013). Our experiments explored a different method of determining whether polyphenols control BW by separately and simultaneously supplementing a high-fat diet with two polyphenols and discovering their co-impact on gut microbiota. Our results are not in accordance with those of previous studies in which some serum biochemical indices, including TG, HDL, LDL, leptin, TNF-α, and somatotropin in obese models, are significantly different in animals that consume catechin or other polyphenols than from the same indices in the control group (Huang et al., 2016; Roopchand et al., 2015; Xu et al., 2015).
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Fig. 5 – LEfSe analysis of key genera of rats’ gut microbiota. The brown dots are unimportant bacteria in any groups, the other coloured dots are important bacteria in the group labelled with a same colour. The control group consuming a standard diet No. AIN-93 (Con); the group consuming a high-fat diet (HFD); the group consuming a high-fat diet supplemented with quercetin (HFD_Q); the group consuming a high-fat diet supplemented with catechin (HFD_C).
However, some alterations were observed, such as the insignificant reduction of serum leptin and TNF-α as well as the insignificant increment of serum somatotropin due to the supplement of catechin, or even quercetin. Many literatures have reported the impact of a high-fat diet on gut microbiota and have proposed that the Firmicutes/ Bacteroidetes ratio will increase when animals or humans consume a high-fat diet (Baothman et al., 2016; John & Mullin, 2016; Li et al., 2015a, 2015b; Turnbaugh et al., 2006). This is consistent with our results. Our results showed that a quercetin
or catechin supplement did not significantly change the Firmicutes/Bacteroidetes ratio induced by only a high fat diet; however, the diversity of gut microbiota was significantly downregulated. Furthermore, it has been reported that multiple mechanisms are involved in catechin’s control of a host’s BW, such as inhibiting energy intake, stimulating energy expenditure (Hursel & Westerterp-Plantenga, 2013), and even regulating gut microbiota (Roopchand et al., 2015; Trigueros et al., 2013; Xue et al., 2016). Our results demonstrated that supplementing a high fat diet with catechin can still control
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BW by inhibiting the host’s feed consumption, but it does not regulate the Firmicutes/Bacteroidetes ratio. Even though catechin reduced the gut microbiota diversity, the serum parameters, especially leptin, TNF-α, and somatotropin, did not respond to it. Some literatures have reported that leptin, TNFα, and somatotropin are closely related to gut microbiota (Chakraborti, 2015; Parnell & Reimer, 2012; Sanz & Moya-Pérez, 2014). Herein, the relationship between gut bacterial diversity and anti-obesity remains unclear. Additionally, the catechin supplement controlled the rats’ BW, but the serum TG, HDL, and LDL did not significantly decline. This indicates that a high fat diet can result in other health problems, even if one simultaneously supplements the diet with catechin. Furthermore, the consumption of quercetin did not change the BW, but it did result in better serum indices, such as TG. Thus, supplementing with a mixture of various polyphenols may be a better health choice.
5.
Conclusion
This experiment explored the co-impact of different polyphenols and a high fat diet on the growth of rats and the richness of the rats’ gut microbiota. It was discovered that the simultaneous ingestion of catechin and a high diet can control BW by inhibiting feed consumption, but it cannot regulate the Firmicutes/Bacteroidetes ratio. However, it is important to note that a high fat diet can still cause other health problems, even if the diet is being supplemented with catechin to control BW. Consumption of quercetin did not change the BW, but it did result in better serum indices, such as TG. Thus, supplementing with a mixture of various polyphenols may be a better health choice.
Acknowledgement The program was supported by the funds of the National Natural Science Funds (No. 31471589), Science and Technology Program of Guangzhou, China (No. 201510010115), Science and Technology Program of Nansha District, Guangzhou, China (2014GG05) and the Fundamental Research Funds for the Central Universities (No. 21615404). We thank Qiu Ruixia and Bin Yu from the Department of Food Science and Engineering, Jinan University, for their contribution to the experiments of this study.
Appendix: Supplementary material Supplementary data to this article can be found online at doi:10.1016/j.jff.2016.08.042.
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