Effects of Prevotella copri on insulin, gut microbiota and bile acids

ABSTRACT

Obesity is becoming a major global health problem in children that can cause diseases such as type 2 diabetes and metabolic disorders, which are closely related to the gut microbiota. However, the underlying mechanism remains unclear. In this study, a significant positive correlation was observed between Prevotella copri (P. copri) and obesity in children (p = 0.003). Next, the effect of P. copri on obesity was explored by using fecal microbiota transplantation (FMT) experiment. Transplantation of P. copri. increased serum levels of fasting blood glucose (p < 0.01), insulin (p < 0.01) and interleukin-1β (IL-1β) (p < 0.05) in high-fat diet (HFD)-induced obese mice, but not in normal mice. Characterization of the gut microbiota indicated that P. copri reduced the relative abundance of the Akkermansia genus in mice (p < 0.01). Further analysis on bile acids (BAs) revealed that P. copri increased the primary BAs and ursodeoxycholic acid (UDCA) in HFD-induced mice (p < 0.05). This study demonstrated for the first time that P. copri has a significant positive correlation with obesity in children, and can increase fasting blood glucose and insulin levels in HFD-fed obese mice, which are related to the abundance of Akkermansia genus and bile acids.

KEYWORDS:

• Obesity
• prevotella copri
• insulin
• gut microbiota
• bile acids

1. Introduction

The incidence of obesity has increased worldwide in recent years. Statistical data show that approximately 39% of adults worldwide are overweight and approximately 13% are obese.¹ Childhood overweight and obesity are becoming major global health problems, and the global obesity rate of children is as high as 6.7%.² Childhood obesity contributes to many diseases, including persistent obesity in adulthood^3,4; increased incidence of type 2 diabetes, cardiovascular disease, chronic kidney disease, and cancer⁵; and increased mortality and premature death.^6,7 In China, the increasing trend of overweight and children with obesity is growing faster than in other countries.⁸ According to statistics on overweight in Chinese children and adults, from 1985 to 2014, the overweight rate among Chinese schoolchildren (over 7 years old) increased from 2.1 to 12.2%, while the obesity rate increased from 0.5 to 7.3%.⁹ Without any intervention measures, the incidence of overweight or obesity may increase to 28% by 2030.¹⁰

Obesity is the result of imbalanced energy intake and expenditure in the body, which may be caused by various factors such as poor diet, lack of exercise, psychological factors, and the environment.¹¹ Along with obesity, these factors can trigger insulin resistance, causing both high blood glucose and high insulin levels in the body.¹² Recent studies have suggested that gut microbiota may affect the host’s ability to store energy. Transplantation of the cecal microbiota in sterile C57BL/6J mice increases body fat content by 60%.¹³ The relative abundance of Bacteroidota in obese individuals was lower than that in lean individuals, whereas Firmicutes showed the opposite trend.¹⁴ Previous studies have suggested that short chain fatty acids (SCFAs) and bile acids (BAs) may play a critical role in glucolipid metabolism,^15,16 and SCFAs produced by microbial fermentation of dietary fiber can interact with G protein-coupled receptors to affect insulin sensitivity of adipocytes and peripheral organs.¹³

To explore the specific causes of childhood obesity caused by microorganisms, the gut microbiota of children was characterized, and the effect of P. copri on obesity was further explored using a high-fat diet (HFD)-induced mouse model.

2. Results

2.1. Difference in gut microbiota between obesity and normal children

Fecal samples from 194 children were grouped according to sex and the body mass index (BMI) (Supplemental Table S1) and analyzed by 16S rDNA sequencing. As shown in Figure 1(a), there was no significant difference in the principal component analysis (PCA), Chao1 index, Shannon index, observed specifications index, or Simpson index (p > .05) between obesity and normal children. Analysis on the composition of gut microbiota at the phylum level (Figures 1(b)) showed that the Firmicutes to Bacteroidetes (F/B) ratio was not significantly different among the groups (p > .05) (Figure 1(c)). However, P. copri-related OTU20 was significantly higher in the obesity group than the normal group based on linear discriminant analysis (LDA) (Figure 1(d)). Further analysis at the family level (Figure 1(f)) showed that the relative abundance of Prevotellaceae in the obesity group was higher than that in the normal weight group (p < .01), while Porphyromonadaceae, Bifidobacteriaceae and Coriobacteriaceae were significantly lower than the normal weight group (p < .05), based on the analysis of the top 12 bacteriaceae ranked by relative abundance (Figure 1(e)). At the genus level (Figures 1(g)), Prevotella (p < .01) was higher in the obesity group, whereas Parabacteroides (p < .01), Bifidobacterium (p < .05), and Oscillospira (p < .05) were lower (Figure 1(h)), as compared with the normal group.

Figure 1. Difference in gut microbiota between obesity and normal children. PCA, Chao1 index, Shannon index, observed specification index, and Simpson index of the gut microbiota in obesity and normal weight children (a). Composition of gut microbiota at the phylum (b), family (e), and genus (g) levels in obesity and normal weight children. OTU clustering and LDA analysis of gut microbiota in obesity and normal weight children, with the length of the bar graph indicating the impact of each OTU (d). The proportion of Firmicutes and Bacteroidetes in the gut microbiota of obesity and normal weight children (c). The difference in intestinal microflora between children with obesity and normal weight children at the family level (f) and the genus level (h). Obese, children with obesity (n = 87); Normal weight, normal weight children(n = 107). Data are shown as the means ± SEMs. ANOSIM: R = 0.135, p = 0.049, *p < 0.05.

Analysis on the relationship between gut microbiota and BMI showed that among the four genera with significant changes, only g_Prevotella had a positive correlation with BMI (Figure 2(a)), whereas the other three genera were negatively correlated with human BMI (Figure 2(b–d)).

Figure 2. Correlation analysis between different bacteria and BMI. P. copri was positively correlated with BMI (a); Paraacteroides was negatively correlated with BMI (b); Bifidobacterium was negatively correlated with BMI (c); Oscillospira was negatively correlated with BMI (d). p values were obtained after Pearson’s correlation test.

2.2. Effects of P. copri on body weight and insulin level in mice

The HFD-induced C57BL/6J mouse model was used to further explore the effect of P. copri on obesity (Figure 3(a)). The growth curves (Figure 3(b)), final body weight (Figure 3(c)), and abdominal fat rate (Figure 3(d,e)) of mice in the HFD group were significantly higher than the CTL group (p < .01). Measurements on fasting blood glucose and insulin levels showed that HFD+P group exhibited a significant increase in the glucose (Figure 3(f)) and insulin (Figure 3(g)) levels as compared to the HFD group (p < .01). Accordingly, the HFD+P group showed a higher calculated homeostasis model assessment of insulin resistance (HOMA-IR) index as compared to the HFD and CTL+P groups (p < .01) (Figure 3(h)). In addition, serum level of interleukin-1β (IL-1β) in HFD+P group was higher than HFD group (Supplemental Figure S2).

Figure 3. Effects of P. copri on body weight and insulin sensitivity in mice. Experimental design (a); growth curve of mice (b); final body weight (c); representative image of abdominal fat section (d); abdominal fat rate (e), fasting blood glucose (f), fasting insulin (g) and HOMA-IR index (h). Data are shown as the means±SEMs (n = 10). *p < .05, **p < .01.

2.3. Modulation of gut microbiota by P. copri in mice

Next, the effect of P. copri on gut microbiota was further explored by 16S rDNA gene sequencing. As shown in Figure 4(a), HFD had a significant effect on the structure of gut microbiota (R² = 0.242, p = .004). The ACE index of the HFD group was lower than the CTL group (p < .05), while the Shannon, Chao 1, and Simpson indices showed no significant difference (Figure 4(b)). Analysis on the gut microbiota at the phylum level showed that P. copri significantly downregulated the relative abundance of Verrucomicrobiota (p < .05) (Figure 4(c,d)). Figure 4(f) showed the microbes that significantly modulated by P. copri at the family and genus levels. Among them, the Akkemansiaceae family contained the top 8 bacteria in the intestinal tract (Figure 4(e)), and the relative abundance of the Akkermansia genus in the CTL group was higher than other three groups (Figures 4(g) and S3). Although the difference between the HFD and HFD+P groups was not significant, the relative abundance of Akkermansia genus in the HFD+P group was decreased to a very low level.

Figure 4. Modulation of P. copri transplantation on intestinal microflora in mice. Cluster analysis by using PCA (a). Shannon index, Chao 1 index, Simpson index and Ace index (b). Effect of Prevotella on the relative abundance of the main microbes at the phylum level (c) and Verrucomicrobiota phylum (d). Effect of P. copri on the relative abundance of the main microbes at the family level (e). Microorganisms with significant differences at the family level and genus level, with the left indicating microorganisms with differences at the family level and the right indicating micro elevation with differences at the genus level (f), Akkermansia genus (g). Data are shown as the means±SEMs (n = 6). *p < .05, **p < .01.

2.4. Modulation of intestinal BAs and SCFAs by P. copri in mice

The effect of P. copri on BAs and SCFAs were analyzed by metabolomic analysis. As shown in Figure 5(a), the primary BAs content in HFD+P group was significantly higher than those in the CTL+P group (p < .05) (Figure 5(b)). Additionally, ursodeoxycholic acid (UDCA) content in the HFD+P group was significantly higher than that in other groups (Figure 5(c) and Supplemental Table S3).

Figure 5. Modulation of P. copri on intestinal SCFAs and bile acids in mice. The composition of BAs (a). The content of total BAs, primary BAs and secondary BAs (b). The content of UDCA (c). The composition of SCFAs (d). Content of acetic acid (e). Content of butanoic acid (f). Data are shown as the means±SEMs (n = 6). *p < .05, **p < .01.

The composition of SCFAs in the feces is shown in Figure 5(d) and Supplemental Table S4. The acetic acid content in HFD and HFD+P groups was significantly lower than that in CTL group (p < .05) (Figure 5(e)). In addition, the butanoic acid content in HFD group was significantly higher than that in CTL group (p < .05) (Figure 5(f)).

3. Discussion

P. copri is considered as the most representative and widely prevalent strain within the Prevotella genus. Nevertheless, the specific function of P. copri in the human intestinal microbiota is a topic of debate and necessitates additional research.^17,18 Several studies propose that P. copri could serve as a pathogen, potentially influencing the onset of conditions such as rheumatoid arthritis,^19,20 hypertension, insulin resistance and glucose intolerance.²¹ Conversely, other studies have indicated that a decrease in the abundance of P. copri may be associated with the onset of diseases such as Parkinson’s disease and autism.^22,23 Furthermore, P. copri has been suggested to potentially enhance immune response.^24–29

Increasing evidences have suggested that there is a link between gut microbiota and human health,^30,31 and recent studies have revealed that the fecal microbiota of patients with type 2 diabetes showed an increased abundance of P. copri,³² which may aggravate insulin resistance by promoting the synthesis of branched chain amino acids (BCAAs) by intestinal microbiota,²¹ and the concentration of BCAAs is positively correlated with insulin resistance and type 2 diabetes.³³ Supplementation of BCAAs can promote insulin resistance by disrupting skeletal muscle insulin signaling in animals and humans,³⁴ and high levels of BCAAs may activate mTORC1 to cause insulin resistance through phosphorylation of insulin receptor substrate 1.³⁵ However, BCAA supplementation can induce insulin resistance in obese mice but not in normal mice,³⁶ and thus the effect of P. copri may depend on the diet.²⁴ In this study, P. copri increased the fasting blood glucose and insulin levels in HFD-fed obese mice, but not in basal diet-fed mice, which suggesting that P. copri exhibits conditional pathogenicity in metabolic disorders associated with obesity. However, due to the limitation of detection method, the successful engraftment of P. copri was not analyzed in this study, future studies based on metagenomics will provide important reference for understanding the interaction between P. copri and other microbial species.

Interestingly, P. copri caused significant change in the relative abundance of Akkermansia in this study, which may indicate an antagonistic phenomenon between P. copri and Akkermansia. Akkermansia has been proven to be one of the few dominant bacteria closely related to the development of obesity.³⁷ It may regulate the host energy expenditure related to glucose and lipid metabolism, thereby affecting the development of obesity. Akkermansia supplementation can reverse metabolic disorders such as metabolic endotoxemia and insulin resistance caused by HFD in mice,³⁸ but cannot alter plasma lipids in basal diet-fed mice.³⁹ In this study, P. copri simultaneously decreased the relative abundance of Akkermansia in both HFD-fed mice and basal diet-fed mice but increased fasting blood glucose and insulin only in HFD-fed mice, which may relate to the varying impacts of Akkermansia.

BAs are amphipathic steroid molecules that synthesized from cholesterol in perivenous hepatocytes surrounding the hepatic central vein through the action of 15 enzymes. A fraction of BAs undergoes reabsorption in the distal intestine and is subsequently transported back to the liver via the vein circulation. The remaining portion of BAs either bypasses enterohepatic circulation to reach peripheral organs through systemic circulation or is excreted in feces. Although most BAs (95%) delivered to the intestine are reabsorbed into the enterohepatic circulation, the analysis of fecal BAs can offer some advantages in the search of biomarkers.⁴⁰ Bile acids is also correlated with inflammation,⁴¹ which can downregulate the expression of the intestinal farnesol X receptor to reduce intestinal bile acid binding protein expression and the organic solute subunits (OSTα & OSTβ) of the BA efflux transporter protein.^42,43 In this study, the primary bile acid level was increased in the HFD+P group, which may relate to the inflammatory response. Additionally, P. copri increased the concentration of UDCA, which may accelerate the enterohepatic circulation of BAs,⁴⁴ and improve insulin resistance and liver steatosis by accelerating lipid flow from the liver to the feces in HFD-fed mice.⁴⁵

Collectively, this study demonstrated a significant positive correlation between P.copri and obesity in children. Moreover, P.copri enhanced fasting blood glucose and insulin levels with increased inflammation in HFD-fed obese mice by modulating gut microbiota and bile acids.

4. Materials and methods

4.1. Children’s research subjects

A total of 194 children aged 6–15 years from Changsha City, Hunan Province, China, were selected for fecal sample collection. Obesity was determined using BMI cutoff for overweight and obesity in Chinese children and adolescents aged 2–18 years, which is recommended by the Obesity Working Group China (WGOC). The experimental subjects were excluded from taking antibiotics, drugs, or health foods that may regulate the intestinal microbiota within one month. The study was approved by the Medical Ethics Committee of Hunan Children’s Hospital (Permission No. HCHLL-2018-30). All guardians provided informed consent and signed an informed consent form.

4.2. Characterization of gut microbiota by 16S rDNA gene sequencing

The gut microbiota was characterized by 16S rDNA gene sequencing as described previously.⁴⁶ According to the instructions of E.Z.N. A ®soil DNA kit (Omega Bio tek, Norcross, GA, U.S.), the total DNA of the fecal microbiota, and the DNA extraction quality was detected by 1% agarose gel electrophoresis. A NanoDrop2000 was used to determine the concentration and purity of DNA. PCR amplification of the V3-V4 region of the 16S rDNA gene was performed using 338F (5’- ACTCCTACGGGGAGGCAGCAG-3’) and 806 R (5’- GGACTACHVGGGTWTCTAAT-3’). The PCR products of the same sample were mixed and 2% agarose gel was used to recover the PCR products using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) to purify the recovered product, 2% agarose gel electrophoresis was used for detection, and a Quantum ™ Fluorometer (Promega, USA) was used to detect and quantify the recovered products. A NEXTFLEX Rapid DNA Seq Kit was used to build the database. Sequencing was performed on the MiSeq PE300 platform (Illumina).

Fastp software was used for quality control of the original sequencing sequence and Flash software was used for splicing. Using the Uparse software, OTU clustering was performed on the sequence based on a similarity threshold of 97%, and chimeras were removed. Based on the Silva 16S rDNA database (v138), the RDP classifier was used to annotate the OTU representative sequences in the species taxonomy, set the confidence threshold to 0.7, and obtain the species taxonomy annotation results.

4.3. Materials and reagents

P. copri (DSM 18,205) was purchased from the ATCC. The medium used was Gifu Anaerobic Medium (GAM) with vitamin K and heme chloride. P. copri species are gram-negative anaerobic bacteria⁴⁷ that need to grow under anaerobic conditions at 37°C. After subculturing, the bacteria reached a plateau after 8 h. At this time, the bacterial solution in the culture bottle was poured into a 50 ml centrifuge tube and centrifuged at 3000 rpm for 10 min, the supernatant was poured out, and normal saline was added to the precipitate in the test tube to prepare a bacterial solution.

4.4. Experimental design and diets of mice

Forty male mice (C57BL/6J, 6 weeks of age) were raised in automatic feeding cabinets (24°C and 12:12 h light – dark cycle) with free access to feed and water. Mice and sterilized poplar bedding were purchased from the Hunan Slake Jingda Laboratory Animal Co., Ltd. (Changsha, Hunan, China). Mice were randomly allocated into four treatments with 10 repetitions in each treatment and one mouse in each repetition and fed a basal diet (control group, CTL), a high-fat diet (HFD), a basal diet and P. copri (CTL + P), or a high-fat diet and P. copri (HFD + P). Single-cage feeding of mice. The trial period lasted 12 weeks. Mice in the CTL+P and HFD+P groups were gavaged with 200 μL bacterial solution(5 × 10⁸ CFU/ml) daily, while other mice were gavaged with 200 μL saline instead. Animal protocols were performed in accordance with the guidelines of the Animal Care and Use Committee of Hunan Agricultural University (Permission No. 2021–055).

4.5. Analysis of lipids, interleukins and insulin in serum

Venous blood was collected and centrifuged at 3000 rpm for 10 min, and the serum was stored at − 80°C until testing. Glucose, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were measured using the respective Assay Kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China), while the levels of insulin, interleukin (IL)-10, IL-6, IL-1β, and tumor necrosis factor-α (TNF-α) were detected using their respective ELISA kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).

4.6. Metabolomics

Chromatographic conditions: Two microliters of sample was separated using an HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm) and then subjected to mass spectrometry detection. The mobile phase consisted of 0.1% formic acid in water:acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile:isopropanol:water (47.5:47.5:5, v/v) (solvent B). The solvent gradient changed according to the following conditions: from 0 to 0.1 min, 0% B to 5% B; from 0.1 to 2 min, 5% B to 25% B; from 2 to 9 min, 25% B to 100% B; from 9 to 13 min, 100% B to 100% B; from 13 to 13.1 min, 100% B to 0% B; and from 13.1 to 16 min, 0% B to 0% B for equilibrating the systems. The sample injection volume was 2 µL and the flow rate was set to 0.4 mL/min. The column temperature was maintained at 40°C. During the analysis period, all samples were stored at 4°C.

MS conditions: Mass spectrometric data were collected using a Thermo UHPLC-Q Exactive Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in either positive or negative ion mode. The optimal conditions were set as follows: heater temperature, 400°C; capillary temperature, 320°C; sheath gas flow rate, 40 arb; Aux gas flow rate, 10 arb; ion-spray voltage floating (ISVF), −2800 V in negative mode and 3500 V in positive mode; and normalized collision energy, 20-40-60 V rolling for MS/MS. The full MS resolution was 70,000 and the MS/MS resolution was 17,500. Data acquisition was performed in the data-dependent acquisition (DDA) mode. Detection was performed over a mass range of 70–1050 m/z.

After the completion of the computer operation, LC‒MS raw data were imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for analysis. The UHPLC-Q Exactive system (Thermo Fisher Scientific) was used for LC MS analysis.

4.7. Statistical analysis

Results are expressed as the mean ± SEM. Significant differences between groups were determined using the t-test or one-way analysis of variance (ANOVA), followed by Fisher’s least significant difference (LSD) and Duncan’s Multiple Range test (SPSS25, IBM Corp., Armonk, NY, USA). The correlation between gut microbiota and BMI was determined using Pearson’s correlation analysis. PCA was used to reflect the differences and distances between samples. p < 0.05 indicates statistical significance, and p < 0.01 indicates extremely significance.

Authors’ contributions

Jiatai Gong, Shusong Wu and Lijun Peng were the primary investigators of this study. Qianjin Zhang, Ruizhi Hu, Xizi Yang and Chengkun Fang participated in the animal experiments. Long Wang, Mingkun Shi, Liping Yao and Jing Lv participated in sample analysis and statistical data analysis. Hognfu Zhang, De-Xing Hou, Yulong Yin and Jianhua He revised the manuscript. Shusong Wu and Lijun Peng designed the study and wrote the manuscript as corresponding authors.

Consent for publication

Permission to publish this article has been obtained from all experimental participants, and copies of the consent form can be viewed at any stage.

Ethics approval and consent to participate

Animal protocols were performed in accordance with the guidelines of the Animal Care and Use Committee of Hunan Agricultural University (Permission No. 2021–055). The children’s study was approved by the Medical Ethics Committee of Hunan Children’s Hospital (Permission No. HCHLL-2018-30). All child guardians provided informed consent and signed an informed consent form.

Acknowledgments

We thank Meiji Biotechnology for their assistance in microbial sequencing and Mr. Zhang Yulong for providing the P. copri.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated in the current study are available in the NCBI repository (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA992675/)^{.[PRJNA992675]}

The datasets generated in the current study are available in the NCBI repository (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA992648^{.[PRJNA992648]}

The datasets supporting the conclusions of this study are included within the article and its additional files.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2024.2340487

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (32102578, U22A20515), National Key R&D Program of China (2023YFD1302300, 2023YFD1301200), Changsha Natural Science Foundation (kq2208092), and the Hunan Provincial Health Commission (C202306017634).