Molecular characterization of gut microbiota in high‑lipid diet‑induced hyperlipidemic rats treated with simvastatin
- Authors:
- Published online on: February 26, 2020 https://doi.org/10.3892/ijmm.2020.4516
- Pages: 1601-1615
Abstract
Introduction
Hyperlipidemia, which refers to abnormally elevated levels of lipids, such as cholesterol, and lipid proteins in the blood, increases the risk of arteriosclerosis, coronary heart disease, cerebral stroke and myocardial infarction in humans (1–3). Simvastatin (SV) is a lactone prodrug. It undergoes reversible de-esterification to its active 6-hydroxyl acid and serves as an inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A reductase to regulate hepatic cholesterol production (4). Additionally, SV is one of the most commonly prescribed statins, a safe and effective cholesterol-lowering drug for the clinical treatment of hyperlipidemia (5).
Other studies have suggested that metabolites produced by gut bacteria can enter the bloodstream by absorption, enterohepatic circulation or impaired gut barrier function (6,7), which can affect the metabolism of xenobiotics and serves an important role in disease onset and progression (8). On the other hand, the microbiota composition is also the result of the equilibrium between the ability of the host to withstand the selective pressure of the immune system and the ability to take advantage of available nutrients (9). In addition, microbial influences on drug response and bioavailability by their metabolic or peptide products on the host immune system or host metabolism, which is called indirect microbial effects, have attracted much attention (10,11). Although growing evidence has demonstrate that the gut microbiome is involved in variability of drug response and bioavailability, the underlying molecular mechanisms remain largely unknown (12,13). Zimmerman et al (13) measured the ability of diverse human gut bacteria to metabolize 271 oral drugs and found ~2/3 of the assayed drugs are metabolized by at least one strain, and 30 microbiome-encoded enzymes are validated. It is, therefore, possible to improve drug efficacy or minimize drug toxicity by manipulating the gut microbiome in the host (12,14).
In a previous study, variations in the therapeutic benefits and low-density lipoprotein cholesterol (LDL-C) reduction of SV therapy have been observed between ‘good’ and ‘poor’ responders (15). However, the mechanisms underlying the interindividual variation of cholesterol lowering during SV therapy are poorly understood. Therefore, Trupp et al and Kaddurah-Daouk et al (16,17) conducted a series of investigations to decipher the association between metabolic changes and efficacy of SV in ‘good’ and ‘poor’ responders using the metabolomic approach. They detected that plasma concentrations of SV are positively associated with microbially-synthesized secondary bile acids (18). The evidence highlighted the involvement of gut microbiota in affecting individual response to SV. Another study has demonstrated that one of the most significant differences in active fecal suspension is SV fragmentation (19). The data suggested that the degradation of SV by hydrolytic cleavage of methyl butanoic acid from the SV backbone may be associated with the intestinal microflora. Although statins have been implied to reduce growth and virulence in a number of bacterial pathogens due to their anti-inflammatory and immunomodulatory activities (20–22), the composition and metabolism characteristics of gut microbiota following SV treatment, particularly in a diseased state, are poorly understood. The present study was designed to obtain insights into the gut microbiota response to SV treatment in a hyperlipidemia disease model, as well as the underlying mechanisms of the metabolic pathways involved in gut flora interactions. Gut microbiota in fecal samples from high-lipid diet-fed rats following SV treatment were comprehensively evaluated by 16S rRNA gene high-throughput sequencing and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) algorithm analysis. The results may help obtain an improved understanding of the associations among gut microbiota, hyperlipidemia and the hypolipidemic efficacy of SV.
Materials and methods
Materials and reagents
SV (20 mg) was purchased from Merck Sharp & Dohme-Hoddesdon. DNA extraction was performed using the fast DNA stool mini kit (Qiagen GmbH). High-throughput sequencing was performed using Phusion High-Fidelity PCR Master mix (New England BioLabs, Inc.), the Qiagen Gel Extraction kit (Qiagen GmbH) and the TruSeq DNA PCR-Free Sample Preparation kit (Illumina, Inc.). Commercial assay kits for total cholesterol (TC), triglycerides (TG), LDL-C and high-density lipoprotein cholesterol (HDL-C) were purchased from Nanjing Jiancheng Bioengineering Institute.
Animal treatment and experimental design
All animal experiments were carried out in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals, and were approved by the Ethical Committee of Xi’an Jiaotong University School of Medical Sciences (approval no. 2017-288). A total of 24, 8-week-old male Sprague Dawley rats, weighing 180–220 g were purchased from the Experimental Animal Center of Xi’an Jiaotong University (Xi’an, China) and maintained under controlled specific pathogen-free environmental conditions (temperature, 24±2°C; relative humidity, 55±15%; 12-h light/dark cycle) with ad libitum access to food and water.
All 24 rats were acclimatized for 1 week prior to the experiments, and then fed with high-lipid diet enriched with 1% (w/w) cholesterol, 10% (w/w) lard, 5% egg yolk, 0.2% propylthiouracil, 1% sodium tauroglycocholate and 82.8% basic diet (23,24) during the entire study period. Following 4 weeks of high-lipid diet feeding, the levels of TC, TG, LDL-C and HDL-C in rat serum were determined and compared with those of the same rats prior to the experiment. Subsequently, the 24 rats in the hyperlipidemia disease model (24) were randomly divided into three groups (n=8). Two groups were treated with SV at a dose of 10 mg/kg−1/day−1 and 40 mg/kg−1/day−1 each, as previously described (25–27). The remaining group was considered to be the control group (0 mg/kg−1/day−1). The suspensions of SV (10 and 40 mg/kg−1) were prepared in sterile water and administered daily via gastric gavage at approximately the same time, at noon. Fecal samples were collected at the end of weeks 2 and 4 of the SV treatment period. As a consequence, a total of 48 fecal samples were divided into six groups: TW0mg, TW10mg and TW40mg (2-week SV treatment); and FW0mg, FW10mg and FW40mg (4-week SV treatment). TW0mg and FW0mg were the control groups, and the other four groups were considered to be treatment groups. All fecal samples were stored immediately at −80°C until DNA extraction for subsequent sequencing.
Bacterial genomic DNA extraction
Each fecal sample (~200 mg) was resuspended in Qiagen InhibitEX buffer (Qiagen GmbH) and thoroughly homogenized for 2 min. Bacterial genomic DNA was extracted from the supernatant using the QIAamp Fast DNA stool Mini kit (Qiagen GmbH), according to the manufacturer’s protocol. The extracted bacterial genomic DNA was estimated by measuring the absorbance at 260 nm using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Inc.) prior to downstream processing.
16S rRNA gene high-throughput sequencing
For each sample, the extracted bacterial genomic DNA was diluted to a 1 ng/μl working stock, which was used as a template for PCR. The barcoded primers flanked the V3–V4 region of the bacterial 16S rRNA gene. The primer sequences were as follows: 341F, 5′-CCTAYGGGRBGCASCAG-3′ and 806R, 5′-GGACTACNNGGGTATCTAAT-3′. All PCR reactions were carried out using Phusion High-Fidelity PCR Master Mix (New England BioLabs, Inc.). The thermocycling conditions were as follows: Pre-denaturation at 98°C for 1 min, followed by 30 cycles of denaturation at 98°C for 10 sec, annealing at 50°C for 30 sec and extension at 72°C for 30 sec; final extension at 72°C for 5 min. Resulting amplicons were confirmed in 2% agarose gel containing ethidium bromide and purified using a Qiagen Gel Extraction kit (Qiagen GmbH). Sequencing libraries were constructed using the TruSeq DNA PCR-Free Sample Preparation kit (Illumina, Inc.), according to the manufacturer’s protocol. Pooled amplicons were paired-end sequenced (2x250) on an Illumina HiSeq2500 platform, according to the manufacturer’s protocol. The samples were sent to Novogene Co., Ltd. for sequencing analysis.
Data analysis
Paired-end reads were merged using FLASH (version 1.2.7; http://ccb.jhu.edu/software/FLASH/) (28). Following merging, quality filtering on the raw tags was performed under specific filtering conditions to obtain the high-quality clean tags (29), according to the Quantitative Insights Into Microbial Ecology (QIIME; version 1.7.0) quality-control process (30). Using the UCHIME algorithm, chimera sequences were detected and then removed against the Gold reference database (31,32) and the effective tags were finally obtained. Similar sequences were assigned to operational taxonomic units (OTUs) using the threshold of 97% identity (UPARSE version 7.0.1001; http://drive5.com/uparse/). Each representative sequence was matched against the SSUrRNA database of SILVA using Mothur (version 1.39.3) to annotate taxonomic information (33,34). OTUs abundance information was normalized using a standard of sequence number corresponding to the sample with the least sequences.
Rarefaction and rank-abundance curves were drawn using the R software (version 2.15.3; https://www.r-project.org/). α diversity analysis including Chao1 index, abundance-based coverage estimator (ACE) index, Shannon index and Simpson index were estimated using QIIME software. Phylogenetic β diversity distances, including unweighted and weighted UniFrac distances, were calculated using QIIME software. Principal coordinates analysis (PCoA) was performed to visualize the similarities or dissimilarities of variables from complex multidimensional data. This analysis was visualized using the WGCNA package (version 1.51; https://cran.r-project.org/web/packages/WGCNA/), stats package (version 3.4.0; https://www.rdocumentation.org/packages/stats/versions/3.4.0) and the ggplot2 package (version 2.2.0; https://cran.r-project.org/web/packages/ggplot2/) in the R software. Unweighted pair-group method with arithmetic means (UPGMA) clustering was performed using QIIME software as a type of hierarchical clustering to interpret the distance matrix using average linkage. The significant differences of the bacterial community structure were tested by analysis of similarities (ANOSIM) and permutational multivariate analysis of variance using distance matrices (ADONIS) in the R software with the use of the vegan package (version 2.4-0; https://cran.r-project.org/web/packages/vegan/). Bacterial taxa with different abundances among different groups were detected using MetaStat analysis (35), and only taxa with mean relative abundance of >0.1% were considered. P-values were corrected for multiple comparisons with Benjamini and Hochberg false-discovery rate correction (q-value) (36).
The associations among microbial taxa could be captured by co-occurrence networks. Genera from 16S rRNA gene sequencing were used to calculate Spearman’s correlation coefficient for the TW0mg, TW10mg and TW40mg groups (37,38).
Functional annotation and profiling
The 16S rRNA gene study is a common method of identifying the bacterial taxonomic composition of fecal samples, but it has disadvantages in directly identifying the functional capabilities of the bacteria (39). PICRUSt algorithm analysis was used to analyze and predict the functional capacity of the intestinal bacterial community (39). The sequences were processed using QIIME. To make closed-reference picked OTUs compatible with PICRUSt, the Greengenes reference database (version 13.5; ftp://greengenes.microbio.me/greengenes_release/gg_13_5/gg_13_5_otus.tar.gz) was used. Following the filtering and normalizing of the OTU table, functional predictive assignment was performed coordinating with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (40).
Statistical analysis
Data are presented as the mean ± SD (levels of TC, TG, LDL-C and HDL-C ; average length and number of sequences) or mean ± standard error of the mean (relative abundance of operational taxonomic units at species level). The levels of TC, TG, LDL-C and HDL-C were compared with those of the same rats before the experiment using a paired-sample t-test. Significant differences between treated and untreated groups (TW10mg vs. TW0mg; TW40mg vs. TW0mg; FW10mg vs. FW0mg; FW40mg vs. FW0mg) in α diversity and functional prediction were tested by Kruskal-Wallis, followed by Dunn’s post hoc test. ANOSIM and ADONIS were used to test the existence of different compositions between treated and untreated groups. P<0.05 was considered to indicate a statistically significant difference. MetaStat analysis investigated the differentially abundant bacterial taxa between treated and untreated groups, and P<0.05 with q<0.05 was considered to indicate a statistically significant difference. All statistical analysis was carried out using SPSS (version 22.0; SPSS, Inc.) or R (version 2.15.3) software.
Results
Hyperlipidemia model
After a 4-week administration of a high-lipid diet, the levels of TC, TG, LDL-C and HDL-C (TC, 9.50±3.85 mmol/l; TG, 1.44±0.54 mmol/l; LDL-C, 6.34±2.35 mmol/l; HDL-C, 0.94±0.38 mmol/l; mean ± SD) were compared with those of the same rats before the experiment (data not shown). There were significant increases in the TC and LDL-C levels (P<0.001). TG was slightly increased (P<0.05), whereas HDL-C was decreased (P<0.05). The results clearly demonstrated that the hyperlipidemia model had been successfully established in these 24 Sprague Dawley rats.
Overview of sequencing analysis
A total of 4,082,278 raw sequences were generated in the present study (Table SI). Following sequence trimming, quality filtering and the removal of chimeras, 2,802,025 high-quality sequences remained, with an average length of 416±2 bp (mean ± SD). The mean number of sequences per sample was 58,376±4,050 (mean ± SD; data not shown). The rarefaction and rank abundance curves of all samples were stable, indicating that species representation in each sample had approached the plateau phase. This meant that no more bacteria would be detected with additional sequencing efforts (Fig. S1A and B).
These high-quality sequences were assigned to a total of 48,600 OTUs by the UPARSE pipeline based on 97% similarity, with an average of 1,012 OTUs per sample (Table SI). In total, 2,316 OTUs were singletons. A total of 99.98±0.11% (mean ± SD) high-quality sequences were classified to the phylum level, 98.71±0.47% (mean ± SD) to the family level, 71.80±7.84% (mean ± SD) to the genus level and 18.75±9.93% (mean ± SD) to the species level. The results demonstrated that the bacteria belonged to 29 phyla, 66 classes, 128 orders, 232 families, 362 genera and 111 species (data not shown).
Relative abundance of gut bacteria in different groups
The top 10 phyla and top 10 genera in gut bacteria relative abundance are shown in Fig. 1. Firmicutes (38.22–57.39%) and Bacteroidetes (30.64–54.80%) were the most prevalent phyla in all groups, followed by Proteobacteria and Verrucomicrobia. These phyla accounted for ~97.53, 98.04, 98.30, 97.76, 97.52 and 98.07% of the reads in the TW0mg, TW10mg, TW40mg, FW0mg, FW10mg and FW40mg groups, respectively (Table SII). Additionally, the phylum Actinobacteria is shown in Table SII. Only 0.029, 0.020, 0.021, 0.026, 0.023 and 0.013% of sequences were unclassified in the TW0mg, TW10mg, TW40mg, FW0mg, FW10mg and FW40mg groups, respectively. The relative abundance of Firmicutes in the SV treatment groups (TW10mg, 39.27%; TW40mg, 38.88%; FW10mg, 44.07%; FW40mg, 38.22%) was lower than that in the control groups (TW0mg, 57.39%; FW0mg, 46.36%; Fig. 1A; Table SII).
At the genus level, Bacteroides (7.70–24.25%) was the most predominant gut bacterial genus in the TW0mg, TW10mg, TW40mg, FW0mg and FW10mg groups; other major genera included Prevotella_9, Lachnospiraceae_NK4A136_group, Escherichia-Shigella, Helicobacter, Desulfovibrio, Prevotella_1, Akkermansia, Romboutsia and Prevotellaceae_Ga6A1_group (Fig. 1B). It was identified that the relative abundance of genus Bacteroides in the SV treatment groups (TW10mg, 24.25%; TW40mg, 18.42%; FW10mg, 11.85%; FW40mg, 10.23%) was higher than that in the control groups (TW0mg, 7.70% and FW0mg, 8.02%; Fig. 1B; Table SII).
Characterization of bacterial α diversity
The observed OTUs, Chao1, ACE, Shannon and Simpson indices were calculated for each sample to evaluate the richness, evenness and diversity of the microbial communities (Table SI). More specifically, as compared with the TW0mg group, the values of the OTUs and Shannon indices significantly decreased in the TW10mg and TW40mg groups, whereas the values of the Chao1 and ACE indices significantly decreased in the TW40mg group. Compared with the FW0mg group, the Shannon and Simpson indices were significantly decreased in the FW40mg group (P<0.05; Fig. 2A). In addition, Good’s coverage was 99.6% on average. This result indicated that the 16S rRNA sequences identified in these groups represented the major bacterial sequences in the samples.
Furthermore, Venn diagrams were used to compare the similarities and differences among the microbial communities in different groups. The TW0mg, TW10mg and TW40mg groups had 1,287 OTUs in common. These OTUs represented 68.35, 74.35 and 80.09% to their respective total OTUs. Additionally, the FW0mg, FW10mg and FW40mg groups had 1,326 OTUs in common. These OTUs represented 74.66, 75.68 and 76.29% to their respective total OTUs (Fig. S1C and D).
Characterization of bacterial β diversity
PCoA was performed to visualize the similarities or dissimilarities of the intestinal microbiota composition among different groups. According to PC1 and PC2 analysis (47.37 and 17.30% of variance explained, respectively), the microbial communities of the TW0mg, TW10mg and TW40mg groups were separated clearly from one another (Fig. 2B). Following 4 weeks of treatment, the microbial communities of the FW0mg, FW10mg and FW40mg groups were grouped into three distinct clusters based on PC1 and PC2 analysis (49.98 and 15.51% of variance explained, respectively; Fig. 2C). The PCoA plots demonstrated the dissimilarities of microbial community structure among these groups. Additionally, this observation was supported by UPGMA analysis based on the unweighted UniFrac distances (Fig. 2D). Furthermore, the results of the UPGMA analysis revealed that the composition of gut microbiota could be altered by SV treatment.
Furthermore, ANOSIM and ADONIS analyses were used to test the differences in bacterial communities between different groups. An R value of >0 was considered well separated by ANOSIM analysis. Higher R2 values suggested larger intergroup differences, as determined by ADONIS analysis (Fig. S2; Table I). The results of ANOSIM and ADONIS analyses revealed that there were significant differences in the fecal bacterial community between the treatment and control groups (P<0.05).
Changes in bacterial communities following SV treatment
At the phylum level, significant differences in the major taxonomical profiles (mean relative abundance of >0.1%) of fecal microbiota among different groups were further identified using MetaStat analysis. The relative abundances of phylum Firmicutes and Actinobacteria in the fecal microbiota were decreased following SV treatment, whereas that of Bacteroidetes was elevated. Specifically, as compared with the TW0mg group, the phylum Firmicutes and Actinobacteria in the TW10mg and TW40mg groups were significantly decreased (q<0.05). By contrast, there was a significant increase in the relative abundance of the phylum Bacteroidetes in the TW10mg, TW40mg and FW40mg groups (TW0mg vs. TW10mg, q<0.05; TW0mg vs. TW40mg, q<0.05; FW0mg vs. FW40mg, q<0.05; Fig. 3A; Table SII).
A total of 16 genera (average relative abundance of >0.1%; P<0.05; q<0.05) exhibiting different relative abundances were identified between the control and treatment groups (Table SII). The relative abundances of the Bacteroides and Phascolarctobacterium genera showed a significant increase following SV treatment for 2 weeks (TW10mg, TW40mg; q<0.05) compared with that in the TW0mg group (q<0.05), whereas that of Bifidobacterium and Tyzzerella displayed a significant decrease (q<0.05). In addition, the relative abundance of the genus Sutterella was significantly elevated in the TW10mg group compared with that in the TW0mg group (q<0.05); however, the genus Intestinimonas was less abundant in the TW10mg group than in the TW0mg group (q<0.05). Furthermore, the relative abundance of genus Ruminococcaceae_UCG-009 was significantly decreased in the TW40mg compared with in the TW0mg group. The genus Ruminococcaceae_NK4A214 was also decreased in the FW40mg group compared with in the FW0mg group (Fig. 3B).
A total of 35 sequences were classified to the species level, and certain species exhibited significant differences in relative abundance following SV treatment (Table II). A higher percentage of Bacteroides_caccae, Parabacteroides_distasonis, Escherichia_coli, Parasutterella_secunda and Bacteroides_fragilis was observed in the TW10mg group, as compared with the TW0mg group. In addition, the relative abundances of Bacteroides_intestinalis, Bacteroides_vulgatus and Bacteroides_uniformis were significantly increased in both the TW10mg and TW40mg groups, whereas Lachnospiraceae_bacterium_COE1 was less abundant. The percentage of Clostridiales_bacterium_CIEAF_020 decreased in the TW40mg and FW40mg groups compared with in the TW0mg and FW0mg groups, respectively.
 | Table IISignificant bacterial difference in relative abundance of operational taxonomic units at species level. |
Co-occurrence networks of bacterial genera
Based on the aforementioned analyses, the change of the intestinal microbiota was particularly caused following SV treatment for 2 weeks. The co-occurrence networks mainly focused on TW0mg, TW10mg and TW40mg groups. Based on Spearman’s correlation coefficient (r), the co-occurrence networks deduced from the genera were separately constructed for the rats of these three groups (Fig. 4). The results revealed that the predominant genera were from the Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria phyla. Samples from the TW0mg group had a lower network complexity than those from other groups. In addition, genera from Bacteroidetes in the TW10mg and TW40mg groups had a lower negative correlation with other genera, as compared with the TW0mg group, but genera from Firmicutes and Proteobacteria showed a higher positive correlation. Notably, genera from the Firmicutes showed an increased positive correlation in the TW10mg and TW40mg groups, mainly including genera within the Ruminococcaceae, Lachnospiraceae and Family_XIII families.
Functional annotation of the gut microbiota following SV treatment
The function of microbiota was investigated based on information from the 16S rRNA gene and OTUs using the PICRUSt algorithm. From the 6,112 predicted KEGG Orthology terms, 328 KEGG pathways were tested. The top 10 abundant categories at level 2 are shown in Fig. 5A. The third level of the KEGG pathway is shown in a heatmap (Fig. 5B). The SV-associated microbial genes involved in metabolism pathways (as described below) were emphasized. The abundances of pathways associated with ‘oxidative phosphorylation’ and ‘carbon fixation pathways in prokaryotes’ (energy metabolism; Fig. 5C), ‘pyruvate metabolism’ and ‘amino sugar and nucleotide sugar metabolism’ (carbohydrate metabolism; Fig. 5D), ‘amino acid related enzymes’ and ‘alanine, aspartate and glutamate metabolism’ (amino acid metabolism; Fig. 5E), and ‘purine metabolism’ and ‘pyrimidine metabolism’ (nucleotide metabolism; Fig. 5F) were significantly changed in the SV treatment groups compared with in the control groups (TW10mg vs. TW0mg; TW40mg vs. TW0mg; FW40mg vs. FW0mg; P<0.05). These results highlighted the importance of the gut microbiota in these metabolic pathways.
Discussion
Statin therapy has different lipid-lowering effects on hyperlipidemia in different individuals, while most efforts to understand statin pharmacodynamics have focused on genetic polymorphisms (41,42). However, growing evidences have suggested that the gut microbiome contributes to the variability in statin metabolism and statin response (19,43). A previous study reported that SV is metabolized by anaerobic bacteria in human fecal suspension (19). A recent study has revealed that antibiotic-modulated gut microbiota could attenuate the hypolipidemic effect of SV in high-fat diet (HFD)-fed mice (44). These results highlighted the potential interaction between gut microbiota and SV, both in vivo and in vitro. Although SV is considered one of the representative examples of microbial impact on drug bioavailability, the molecular characterization of gut microbiota in response to SV in high-lipid diet-induced hyperlipidemic rats is not fully understood. In the present study, the altered compositional and functional characteristics of gut microbiota in response to SV treatment were observed in high-lipid diet-fed rats.
The community richness of gut microbiota was calculated by the OTUs, Chao1 and ACE indices, while community diversity was explored by the Shannon and Simpson indices. As shown by the decrease in richness and diversity indices, SV therapy reduced not only the total number of species in gut microbiota, but also the heterogeneity in the 2-week treatment groups. In the 4-week treatment groups, no significant change was observed in the richness indices, whereas a significant decrease was observed in diversity indices. A previous 1-week SV study did not report appreciable changes in total bacteria by quantitative PCR (45). A previous 8-week SV study revealed that the hypolipidemic effect of SV is associated with the altered composition of the gut microbiota (44). The influence of SV (2- and 4-week therapy) on gut microbiota lacks information and is not fully understood. In the present study, the V3–V4 region of the 16S rRNA gene was sequenced following 2 and 4 weeks of SV treatment, and changes in richness and diversity of gut microbiota were identified. Consistent with the present study, Liu et al (43) and Sun et al (46) reported that variation in community richness and diversity was identified between statin-sensitive and -insensitive patients, which may suggest that the complexity of the fecal microbiome is closely associated with SV therapeutic actions. Therefore, the community composition of gut microbiota was analyzed at the phylum, genus and species levels, and the variations among groups were observed.
Recent studies have reported that, at the phylum level, Bacteroidetes and Firmicutes are predominant in the gut following statin treatment during HFD (44,47). In accordance with previous reports, the present data also demonstrated that Firmicutes and Bacteroidetes were the dominant phyla in all samples. From the co-occurrence networks, the increased positive correlation within the phylum Firmicutes suggested that the 2-week SV treatment induced strong interactions between Ruminococcaceae, Family_XIII, Lachnospiraceae and other bacteria within the phylum Firmicutes. This result indicated that these associated intestinal floras may by a novel therapeutic target for the improvement of SV efficacy. The abundance of Bacteroidetes suggested that a higher dose of SV treatment could cause the enrichment of Bacteroidetes in hyperlipidemic rats following 4 weeks of treatment. A decrease in the count of Firmicutes and an increase in that of Bacteroidetes caused the Firmicutes to Bacteroidetes ratio (F/B ratio) to drop from 2.05 to 0.70 following SV treatment. Consistent with the findings of the present study, a decreased F/B ratio induced by other two statins (atorvastatin and rosuvastatin) treatment was also observed (47). Since the F/B ratio is associated with metabolic disorders and is broadly considered to be an indicator of gut microbiota dysbiosis (48), the changes in Firmicutes and Bacteroidetes clearly suggested that SV treatment contributed to the decline of the F/B ratio in a high-lipid model, helping the gut microbiota regain its balance. The decrease in Firmicutes and the increase in Bacteroides in the two control groups (TW0mg and FW0mg) may be due to the high-lipid diet. This was also reported by Wu et al (49) and Gentile and Weir (50).
At the genus level, Ruminococcaceae_NK4A214, Ruminococcaceae_UCG-009 and Intestinimonas within the family Ruminococcaceae were decreased following SV treatment in the present study. It has been reported that the abundance of Ruminococcaceae is associated with a positive therapeutic effect of two statins, rosuvastatin (43) and atorvastatin (46). The results of the present study provided more evidence that demonstrated the interaction between Ruminococcaceae and statins. As shown in the co-occurrence networks, higher positive correlations of genera Ruminococcaceae_NK4A21, Ruminococcaceae_UCG-004, Ruminococcaceae_UCG-009, etc. were observed in the 2-week treatment groups, suggesting that the effect of SV may improve the interaction among these bacterial floras. Ruminococcaceae has been reported to be positively associated with the production of short chain fatty acids (SCFAs) (51) and bile acids (52), and possesses an extraordinary capacity for generating SCFAs and glucose (53). The genus Tyzzerella has been identified to be enriched in patients with a high cardiovascular disease risk profile (54); however, in the present study, a decreased level of Tyzzerella was observed in rats following SV treatment, suggesting that SV may reduce the risk of cardiovascular disease by influencing the genus Tyzzerella during the regulation of dyslipidemia. By contrast, the genus Phascolarctobacterium within the phylum Firmicutes was markedly increased following SV treatment. This genus is associated with both insulin sensitivity and secretion, and involved in the carbohydrate metabolism in overweight adults (55). This may be due to the production of SCFAs, including acetate and propionate (56). Briefly, the aforementioned Firmicutes members, which were identified to be significantly altered in the present study, suggested their potential associations with the metabolites of SCFAs and glucose. Since SCFAs regulate not only the gut barrier function but also lipid, glucose and cholesterol metabolism, changes in the SCFAs profile could impact the physiology of the host (57). These associations suggest that Firmicutes members considerably influence hypolipidemic drug efficacy or drug response by affecting the absorption of nutrients and maintaining energy balance.
At the species level, the present study detected that the proliferation of the genus Bacteroides was due to the expansion of the species, including Bacteroides_caccae, Bacteroides_fragilis, Bacteroides_vulgatus and Bacteroides_uniformis, as well as Bacteroides_intestinalis. As already reported, Bacteroides uniformis is associated with hepatic G6pase and farnesoid X receptor (FXR) (58). This association suggests a potential mechanisms through which this bacterium improves glucose tolerance (59). In addition, the species Bacteroides fragilis is positively associated with fasting blood glucose (60). Bacteroides vulgatus may also reduce gut microbial lipopolysaccharide production and inhibit atherosclerosis (61). Furthermore, Wang et al (62) reported that Parabacteroides distasonis within the phylum Bacteroidetes, as a promising probiotic, could modulate the host metabolism to alleviate obesity and metabolic dysfunctions by generating the secondary bile acids and succinate. Consequently, it has been accepted that these aforementioned microbial taxa at the species level may affect physiological functions in the host and serve a role in the statins-induced metabolic improvements. The likely functional capacity of microbiota genes that are modulated by SV treatment were predicted using PICRUSt. The PICRUSt approach may cause potential biases due to the infrequent update of GreenGene, and some OTUs could not be matched against GreenGene (63). Additionally, functional profiles of bacterial communities were based on the ‘common ancestor gene’. PICRUSt was based on the OTU tree to deduce the function of the common ancestor gene. There is an association between the phylogenetic relatedness of organisms and their complement of functional genes (39). Although potential biases exist, PICRUSt has been applied to samples collected from a wide range of habitats, including the human gastrointestinal tract (64). It must be remembered that PICRUSt predicts the functional attributes of a microbiome rather than identifying them directly from DNA sequences, the predictions made by PICRUSt can serve as useful hypothesis-generating tools and provide alternative ways to probe the structure of the microbiome (39,65). A previous study has demonstrated that the immune system balances the microbiota by directing the metabolism towards oxidative phosphorylation and fatty acids oxidation (66). The SV-associated microbiota likely exhibited enrichment in genes for energy metabolism, including oxidative phosphorylation in the present study. These observations suggested that SV may target the microbes for remediation of the immune system. Additionally, the results demonstrated that the microbiota exhibited enrichment in genes for the alanine, aspartate and glutamate metabolism pathway following SV treatment. Consistent with the present study, previous studies have reported that acetic acid could be produced by gut bacteria like Bacteroides spp., Bifidobacterium spp. and Ruminococuus spp. (67,68). In addition, propionic acid could be produced from alanine and aspartate, and acetic and butyric acid could be produced from glutamate (69). It is known that acetic, butyric and propionic acid are the main ingredients of SCFAs (70). SCFAs inhibit insulin signal transduction and control energy consumption by activating free fatty acid receptor 3 and free fatty acid receptor 2 (71). Of note, intestinal bacteria like Ruminococcaceae, Phascolarctobacterium and Bacteroides, which are important sources of SCFAs (72), showed significant changes following SV treatment in the present study. Previous studies have demonstrated that gut microbiota regulates triglyceride, cholesterol and bile acid homeostasis metabolism by influencing FXR (73,74). In the present study, the genera Bacteroides and Bifidobacterium, which have been identified to be associated with bile salt hydrolases (BSH) activity (75), were significantly altered. The BSH-catalyzed step is considered the ‘gateway reaction’ of microbiota-mediated bile acid metabolism (76). These findings suggested that the effect of SV on metabolic improvements could be explained by altered gut microbiota.
In conclusion, the findings of the present study revealed that SV treatment could reduce gut microbial community richness and diversity. The SV treatment also contributed to the remodeling of the fecal bacterial community composition, mainly including a decrease in the phylum Firmicutes and an increase in the phylum Bacteroidetes. Furthermore, the SV-associated modulation in gut microbiota genes has a profound influence on the energy, carbohydrate, amino acid and nucleotide metabolism pathways. Understanding the causal links between gut microbiota gene content and associated-metabolic activities may provide novel insights into potential drug targets for preventing hyperlipidemia.
Supplementary Information
Acknowledgements
Not applicable.
Funding
The present study was supported by the National Natural Science Foundation of China (grant no. NSFC81730056).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors’ contributions
SZ designed and executed the experiments, analyzed the data and wrote the manuscript. HL, LY, JZ, LH, RL, RW, YS, NM and SU helped perform the experiments. JX conceived and designed the study, critically reviewed and drafted the manuscript. All authors take responsibility for the integrity of the work as a whole. All authors read and approved the final manuscript.
Ethics approval and consent to participate
All animal experiments were carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and performed with the approval of the Ethical Committee of Xi’an Jiaotong University School of Medical Sciences (approval no. 2017-288).
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
|
Jain KS, Kathiravan MK, Somani RS and Shishoo CJ: The biology and chemistry of hyperlipidemia. Bioorg Med Chem. 15:4674–4699. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Farnier M and Davignon J: Current and future treatment of hyperlipidemia: The role of statins. Am J Cardiol. 82:3J–10J. 1998. View Article : Google Scholar : PubMed/NCBI | |
|
Fazio S and Linton MF: The role of fibrates in managing hyperlipidemia: Mechanisms of action and clinical efficacy. Curr Atheroscler Rep. 6:148–157. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Vickers S, Duncan CA, Chen IW, Rosegay A and Duggan DE: Metabolic disposition studies on simvastatin, a cholesterol-lowering prodrug. Drug Metab Dispos. 18:138–145. 1990.PubMed/NCBI | |
|
Pedersen TR, Kjekshus J, Berg K, Haghfelt T, Faergeman O, Faergeman G, Pyörälä K, Miettinen T, Wilhelmsen L, Olsson AG, et al: Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: The scandinavian simvastatin survival study (4S). 1994. Atheroscler Suppl. 5:81–87. 1994. View Article : Google Scholar | |
|
Parks BW, Nam E, Org E, Kostem E, Norheim F, Hui ST, Pan C, Civelek M, Rau CD, Bennett BJ, et al: Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab. 17:141–152. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC and Siuzdak G: Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci USA. 106:3698–3703. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, Gordon JI, Relman DA, Fraser-Liggett CM and Nelson KE: Metagenomic analysis of the human distal gut microbiome. Science. 312:1355–1359. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Hooper LV, Littman DR and Macpherson AJ: Interactions between the microbiota and the immune system. Science. 336:1268–1273. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Mannerås-Holm L, Ståhlman M, Olsson LM, Serino M, Planas-Fèlix M, et al: Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 23:850–858. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC, Karpinets TV, Prieto PA, Vicente D, Hoffman K, Wei SC, et al: Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 359:97–103. 2018. View Article : Google Scholar | |
|
Jia W, Li H, Zhao L and Nicholson JK: Gut microbiota: A potential new territory for drug targeting. Nat Rev Drug Discov. 7:123–129. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R and Goodman AL: Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 570:462–467. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Nicholson JK, Elaine H and Wilson ID: Gut microorganisms, mammalian metabolism and personalized health care. Nat Rev Microbiol. 3:431–438. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Simon JA, Lin F, Hulley SB, Blanche PJ, Waters D, Shiboski S, Rotter JI, Nickerson DA, Yang H, Saad M and Krauss RM: Phenotypic predictors of response to simvastatin therapy among African-Americans and caucasians: The cholesterol and pharmacogenetics (CAP) study. Am J Cardiol. 97:843–850. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Trupp M, Zhu H, Wikoff WR, Baillie RA, Zeng ZB, Karp PD, Fiehn O, Krauss RM and Kaddurah-Daouk R: Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS One. 7:e383862012. View Article : Google Scholar : PubMed/NCBI | |
|
Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, Nguyen UT, Watkins SM and Krauss RM: Lipidomic analysis of variation in response to simvastatin in the cholesterol and pharmacogenetics study. Metabolomics. 6:191–201. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, Nguyen UT, Wojnoonski K, Watkins SM, Trupp M and Krauss RM: Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One. 6:e254822011. View Article : Google Scholar : PubMed/NCBI | |
|
Aura AM, Mattila I, Hyötyläinen T, Gopalacharyulu P, Bounsaythip C, Orešič M and Oksman-Caldentey KM: Drug metabolome of the simvastatin formed by human intestinal microbiota in vitro. Mol Biosyst. 7:437–446. 2011. View Article : Google Scholar | |
|
Rodriguez AL, Wojcik BM, Wrobleski SK, Myers DD Jr, Wakefield TW and Diaz JA: Statins, inflammation and deep vein thrombosis: A systematic review. J Thromb Thrombolysis. 33:371–382. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Greenwood J, Steinman L and Zamvil SS: Statin therapy and autoimmune disease: From protein prenylation to immunomodulation. Nat Rev Immunol. 6:358–370. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Hennessy E, Adams C, Reen FJ and O’Gara F: Is there potential for repurposing Statins as novel antimicrobials? Antimicrobial Agents Chemother. 60:5111–5121. 2016. View Article : Google Scholar | |
|
Zhang Q, Wang GJ, A JY, Wu D, Zhu LL, Ma B and Du Y: Application of GC/MS-based metabonomic profiling in studying the lipid-regulating effects of Ginkgo biloba extract on diet-induced hyperlipidemia in rats. Acta Pharmacol Sin. 30:1674–1687. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Xu QY, Liu YH, Zhang Q, Ma B, Yang ZD, Liu L, Yao D, Cui GB, Sun JJ and Wu ZM: Metabolomic analysis of simvastatin and fenofibrate intervention in high-lipid diet-induced hyperlipidemia rats. Acta Pharmacol Sin. 35:1265–1273. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Ozansoy G, Guven C, Ceylan A, Can B, Aktan F, Oz E and Gönül B: Effects of simvastatin treatment on oxidant/antioxidant state and ultrastructure of streptozotocin-diabetic rat lung. Cell Biochem Funct. 23:421–426. 2006. View Article : Google Scholar | |
|
He Q, Qin S, Ma K, Luo S and Zhang X: Effects of simvastatin on angiogenesis and the expression of Ang1 after myocardial infarction in rats. Heart. 96:A142010. View Article : Google Scholar | |
|
Bracht L, Barbosa CP, Caparroz-Assef SM, Cuman RK, Ishii-Iwamoto EL, Bracht A and Bersani-Amado CA: Effects of simvastatin, atorvastatin, ezetimibe, and ezetimibe+simvastatin combination on the inflammatory process and on the liver metabolic changes of arthritic rats. Fundam Clin Pharmacol. 26:722–734. 2012. View Article : Google Scholar | |
|
Magoč T and Salzberg SL: FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 27:2957–2963. 2011. View Article : Google Scholar | |
|
Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA and Caporaso JG: Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 10:57–59. 2013. View Article : Google Scholar | |
|
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, et al: QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 7:335–336. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Edgar RC, Haas BJ, Clemente JC, Quince C and Knight R: UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 27:2194–2200. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G, Ciulla D, Tabbaa D, Highlander SK, Sodergren E, et al: Chimeric 16S rRNA sequence formation and detection in sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21:494–504. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Wang Q, Garrity GM, Tiedje JM and Cole JR: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 73:5261–5267. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J and Glöckner FO: The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41:D590–D596. 2013. View Article : Google Scholar : | |
|
White JR, Nagarajan N and Pop M: Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol. 5:e10003522009. View Article : Google Scholar : PubMed/NCBI | |
|
Benjamini Y and Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J Royal Statist Soc. 57:289–300. 1995. | |
|
Jiao S, Liu Z, Lin Y, Yang J, Chen W and Wei G: Bacterial communities in oil contaminated soils: Biogeography and co-occurrence patterns. Soil Biol Biochem. 98:64–73. 2016. View Article : Google Scholar | |
|
Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, et al: A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 490:55–60. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, et al: Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 31:814–821. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M and Tanabe M: Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42:D199–D205. 2014. View Article : Google Scholar : | |
|
Mega JL, Morrow DA, Brown A, Cannon CP and Sabatine MS: Identification of genetic variants associated with response to statin therapy. Arterioscler Thromb Vasc Biol. 29:1310–1315. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Barber MJ, Mangravite LM, Hyde CL, Chasman DI, Smith JD, McCarty CA, Li X, Wilke RA, Rieder MJ, Williams PT, et al: Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS One. 5:e97632010. View Article : Google Scholar : PubMed/NCBI | |
|
Liu Y, Song X, Zhou H, Zhou X, Xia Y, Dong X, Zhong W, Tang S, Wang L, Wen S, et al: Gut microbiome associates with lipid-lowering effect of rosuvastatin in vivo. Front Microbiol. 9:5302018. View Article : Google Scholar : | |
|
He X, Zheng N, He J, Liu C, Feng J, Jia W and Li H: Gut microbiota modulation attenuated the hypolipidemic effect of simvastatin in high-fat/cholesterol-diet fed mice. J Proteome Res. 16:1900–1910. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Catry E, Pachikian BD, Salazar N, Neyrinck AM, Cani PD and Delzenne NM: Ezetimibe and simvastatin modulate gut microbiota and expression of genes related to cholesterol metabolism. Life Sci. 132:77–84. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Sun B, Li L and Zhou X: Comparative analysis of the gut microbiota in distinct statin response patients in East China. J Microbiol. 56:886–892. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Kim J, Lee H, An J, Song Y, Lee CK, Kim K and Kong H: Alterations in gut microbiota by statin therapy and possible intermediate effects on hyperglycemia and hyperlipidemia. Front Microbiol. 10:19472019. View Article : Google Scholar : PubMed/NCBI | |
|
Ley RE, Turnbaugh PJ, Klein S and Gordon JI: Microbial ecology: Human gut microbes associated with obesity. Nature. 444:1022–1023. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R, et al: Linking long-term dietary patterns with gut microbial enterotypes. Science. 334:105–108. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Gentile CL and Weir TL: The gut microbiota at the intersection of diet and human health. Science. 362:776–780. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Daniel H, Gholami AM, Berry D, Desmarchelier C, Hahne H, Loh G, Mondot S, Lepage P, Rothballer M, Walker A, et al: High-fat diet alters gut microbiota physiology in mice. ISME J. 8:295–308. 2014. View Article : Google Scholar : | |
|
Lin H, An Y, Hao F, Wang Y and Tang H: Correlations of fecal metabonomic and microbiomic changes induced by high-fat diet in the pre-obesity state. Sci Rep. 6:216182016. View Article : Google Scholar : PubMed/NCBI | |
|
Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF and Gordon JI: The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA. 101:15718–15723. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Kelly TN, Bazzano LA, Ajami NJ, He H, Zhao J, Petrosino JF, Correa A and He J: Gut microbiome associates with lifetime cardiovascular disease risk profile among bogalusa heart study participants. Circ Res. 119:956–964. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Muñiz Pedrogo DA, Jensen MD, Van Dyke CT, Murray JA, Woods JA, Chen J, Kashyap PC and Nehra V: Gut microbial carbohydrate metabolism hinders weight loss in overweight adults undergoing lifestyle intervention with a volumetric diet. Mayo Clin Proc. 93:1104–1110. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Wu F, Guo X, Zhang J, Zhang M, Ou Z and Peng Y: Phascolarctobacterium faecium abundant colonization in human gastrointestinal tract. Exp Ther Med. 14:3122–3126. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Canfora EE, Jocken JW and Blaak EE: Short-chain fatty acids in control of body weight and insulin sensitivity. Nat Rev Endocrinol. 11:577–591. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Rodrigues RR, Greer RL, Dong X, Dsouza KN, Gurung M, Wu JY, Morgun A and Shulzhenko N: Antibiotic-induced alterations in gut microbiota are associated with changes in glucose metabolism in healthy mice. Front Microbiol. 8:23062017. View Article : Google Scholar : PubMed/NCBI | |
|
Gauffin Cano P, Santacruz A, Moya A and Sanz Y: Bacteroides uniformis CECT 7771 ameliorates metabolic and immunological dysfunction in mice with high-fat-diet induced obesity. PLoS One. 7:e410792012. View Article : Google Scholar : PubMed/NCBI | |
|
Kim MS, Hwang SS, Park EJ and Bae JW: Strict vegetarian diet improves the risk factors associated with metabolic diseases by modulating gut microbiota and reducing intestinal inflammation. Environ Microbiol Rep. 5:765–775. 2013.PubMed/NCBI | |
|
Yoshida N, Emoto T, Yamashita T, Watanabe H, Hayashi T, Tabata T, Hoshi N, Hatano N, Ozawa G, Sasaki N, et al: Bacteroides vulgatus and bacteroides dorei reduce gut microbial lipopolysaccharide production and inhibit atherosclerosis. Circulation. 138:2486–2498. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Wang K, Liao M, Zhou N, Bao L, Ma K, Zheng Z, Wang Y, Liu C, Wang W, Wang J, et al: Parabacteroides distasonis alleviates obesity and metabolic dysfunctions via production of succinate and secondary bile acids. Cell Rep. 26:222–235. e2252019. View Article : Google Scholar : PubMed/NCBI | |
|
Balvočiūtė M and Huson DH: SILVA, RDP, greengenes, NCBI and OTT-how do these taxonomies compare? BMC Genomics. 18(Suppl 2): 1142017. View Article : Google Scholar | |
|
Gevers D, Kugathasan S, Denson LA, Vazquez-Baeza Y, Van Treuren W, Ren B, Schwager E, Knights D, Song SJ, Yassour M, et al: The treatment-naive microbiome in new-onset crohn’s disease. Cell Host Microbe. 15:382–392. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Douglas GM, Beiko RG and Langille MGI: Predicting the functional potential of the microbiome from marker genes using PICRUSt. Methods Mol Biol. 1849:169–177. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Litvak Y, Byndloss MX and Baumler AJ: Colonocyte metabolism shapes the gut microbiota. Science. 362:eaat9076. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Louis P, Hold GL and Flint HJ: The gut microbiota, bacterial metabolites and colorectal cancer. Nat Rev Microbiol. 12:661–672. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Rey FE, Faith JJ, Bain J, Muehlbauer MJ, Stevens RD, Newgard CB and Gordon JI: Dissecting the in vivo metabolic potential of two human gut acetogens. J Biol Chem. 285:22082–22090. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Davila AM, Blachier F, Gotteland M, Andriamihaja M, Benetti PH, Sanz Y and Tomé D: Intestinal luminal nitrogen metabolism: Role of the gut microbiota and consequences for the host. Pharmacol Res. 68:95–107. 2013. View Article : Google Scholar | |
|
Kasubuchi M, Hasegawa S, Hiramatsu T, Ichimura A and Kimura I: Dietary gut microbial metabolites, short-chain fatty acids, and host metabolic regulation. Nutrients. 7:2839–2849. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Kimura I, Ozawa K, Inoue D, Imamura T, Kimura K, Maeda T, Terasawa K, Kashihara D, Hirano K, Tani T, et al: The gut microbiota suppresses insulin-mediated fat accumulation via the short-chain fatty acid receptor GPR43. Nat Commun. 4:18292013. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang J, Guo Z, Xue Z, Sun Z, Zhang M, Wang L, Wang G, Wang F, Xu J, Cao H, et al: A phylo-functional core of gut microbiota in healthy young chinese cohorts across lifestyles, geography and ethnicities. ISME J. 9:1979–1990. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Li T and Chiang JY: Bile acid signaling in metabolic disease and drug therapy. Pharmacol Rev. 66:948–983. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Sayin SI, Wahlström A, Felin J, Jäntti S, Marschall HU, Bamberg K, Angelin B, Hyötyläinen T, Orešič M and Bäckhed F: Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist. Cell Metab. 17:225–235. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Long SL, Gahan CGM and Joyce SA: Interactions between gut bacteria and bile in health and disease. Mol Aspects Med. 56:54–65. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Parasar B, Zhou H, Xiao X, Shi Q, Brito IL and Chang PV: Chemoproteomic profiling of gut microbiota-associated bile salt hydrolase activity. ACS Cent Sci. 5:867–873. 2019.PubMed/NCBI |
