Abstract

Background

   Increasing evidence indicates that the gut microbiota contributes to
   the occurrence and development of metabolic diseases. However, little
   is known about the effects of commonly used antidiabetic agents on the
   gut microbiota. In this study, we investigated the roles of dipeptidyl
   peptidase-4 inhibitors (DPP-4i) and α-glucosidase inhibitor in
   modulating the gut microbiota.

Methods

   16S-rDNA sequencing was performed to analyse the effects of DPP-4i and
   acarbose on the gut microbiota in mice fed a high-fat diet (HFD). Fecal
   microbiota transplantation (FMT) from type 2 diabetes patients to
   germ-free mice was performed to investigate the contribution of the
   altered microbiome to antidiabetic effects of the drugs. Fecal
   metabolomics was also analysed by untargeted and targeted GC–MS
   systems.

Findings

   Although DPP-4i and α-glucosidase inhibitor both altered the gut
   microbial composition, only the microbiome modulation of DPP-4i
   contributed to its hypoglycemic effect. Specifically, the changes of
   68.6% genera induced by HFD were rescued by DPP-4i. FMT showed that the
   DPP-4i-altered microbiome improved glucose tolerance in colonized mice,
   while acarbose did not. Moreover, DPP-4i increased the abundance of
   Bacteroidetes, and also promoted a functional shift in the gut
   microbiome, especially increasing the production of succinate.

Interpretation

   Our findings demonstrate an important effect of DPP-4i on the gut
   microbiota, revealing a new hypoglycemic mechanism and an additional
   benefit of it. Furthermore, modulating the microbial composition, and
   the functional shift arising from changes in the microbiome, might be a
   potential strategy for improving glucose homeostasis.

Fund

   This work was supported by grants from the National Natural Science
   Foundation of China (No. 81700757, No. 81471039, No. 81700714 and No.
   81770434), the National Key R&D Program of China (No. 2017YFC1309602,
   No. 2016YFC1101100, No. 2017YFD0500503 and No. 2017YFD0501001), and the
   Natural Science Foundation of Chongqing (No. cstc2014jcyjjq10006, No.
   cstc2016jcyjA0093 and No. cstc2016jcyjA0518).

   Keywords: DPP-4i, Gut microbiota, Glucose tolerance, GF mice
     __________________________________________________________________

Research in context.

Evidence before this study

   Previous studies have found that dysbiosis of the gut microbiota is
   associated with the dysfunction of glucose metabolism, insulin
   resistance and lipid metabolism. Two major gut phyla, Bacteroidetes and
   Firmicutes share roles in modulating the response of the host to
   dynamic changes in the diet, and they are enriched with genes encoding
   enzymes that govern carbohydrate and lipid metabolism. In addition, the
   gut metabolite succinate has been indicated as a substrate for
   intestinal gluconeogenesis to improve glucose tolerance and insulin
   sensitivity.

Added value of this study

   DPP-4i altered the gut microbial composition, predominantly by
   increasing the abundance of Bacteroidetes, and the alterations
   contributed to its hypoglycemic effect. Moreover, DPP-4i obviously
   changed the pattern of fecal metabolites in HFD mice, especially
   increasing the production of succinate, which improved glucose
   tolerance.

Implications of all the available evidence

   Our study, along with studies by other groups, indicates that
   alterations of gut microbiota might be a new hypoglycemic mechanism of
   DPP-4i, and that modulating the bacterial composition and metabolites
   might be a potential strategy for improving glucose homeostasis.

   Alt-text: Unlabelled Box

1. Introduction

   The adult human intestine is home for >500 species of microbes [[65]1],
   and emerging evidence indicates that these microbes are heavily
   involved in modulating immunity, inflammation, gut-brain neural
   circuits, and metabolism [[66]2,[67]3]. Aberrant changes in the gut
   microbiota are associated with the occurrence and development of
   various diseases, such as immunological diseases [[68]4], inflamed
   intestinal diseases [[69]5], mental disorders [[70]6], and metabolic
   diseases [[71][7], [72][8], [73][9], [74][10]]. In the context of
   metabolic diseases, the gut microbiota has been reported to be
   associated with dysfunction of glucose metabolism, insulin resistance
   and lipid metabolism [[75]11,[76]12]. Consequently, the gut microbiota
   has been identified to be closely linked to diabetes and obesity, and
   interventions involving the intestinal bacterial flora are anticipated
   to be a new therapeutic strategy for these diseases.

   Some existing evidence has indicated that gut bacteria can be used in
   the treatment of metabolic diseases. Prebiotics (oligofructose,
   inulin-type fructans) and probiotics (Saccharomyces boulardii) have
   been shown to change the gut microbiota composition, and increase the
   quantities of Bifidobacterium and Lactobacillus while improving glucose
   tolerance and lipid metabolism [[77][13], [78][14], [79][15]].
   Additionally, as a promising treatment for diabetes and obesity,
   Roux-en-Y gastric bypass (RYGB) surgery improves the metabolic and
   inflammatory status partially by modifying the composition of the gut
   microbiome [[80]16]. Recently, the commonly used antidiabetic drug
   metformin has been reported to significantly change the composition of
   the gut microbiome and the concentration of intestinal short chain
   fatty acids (SCFAs) [[81][17], [82][18], [83][19]], which contributed
   to its therapeutic effects. Besides, other common hypoglycemic agents,
   such as acarbose, glucagon-like peptide 1 (GLP-1) agonists and
   dipeptidyl peptidase–4 inhibitors (DPP-4i), have also been reported to
   change the gut microbial community and metabolites when improving
   glucose metabolism [[84][20], [85][21], [86][22], [87][23], [88][24]].
   While, the role of alterations of the microbiome and metabolites in the
   hypoglycemic effect of these agents is not completely clear. As one
   fermentation product of bacterium strains derived from Actinoplanes sp.
   SE50, α-glucosidase inhibitor slows carbohydrate uptake and reduces
   postprandial hyperglycemia by inhibiting α-glucosidase activity in the
   brush border of small intestinal mucosa [[89]25]. DPP-4i is the most
   extensively used oral hypoglycemic agent worldwide, and it targets the
   DPP-4 enzyme and inhibits the degradation of GLP-1 to reduce blood
   glucose levels [[90]26]; its target DPP-4 has a high expression level
   in the small intestine [[91]27]. As such, these drugs might also have
   the ability to improve glucose homeostasis through affecting gut
   microbial composition.

   In the current study, we investigated the effects of DPP-4i and
   α-glucosidase inhibitor on the intestinal microbial flora, and the
   relationship between the altered microbiomes and the antidiabetic
   effects of the drugs. Additionally, although it is not clear how
   alterations in the gut microbiota improve glucose homeostasis in the
   host, a potential mechanism includes increased production of SCFAs and
   other organic acids [[92][28], [93][29], [94][30]]. Therefore, changes
   in fecal metabolomics were also observed in the present study.

   Here, we report that the DPP-4i-altered gut microbiota participates in
   its effect on glucose metabolism, while α-glucosidase inhibitor does
   not, indicating a novel hypoglycemic mechanism and an additional
   benefit of DPP-4i. Moreover, DPP-4i obviously altered the abundance of
   Bacteroidetes, and promoted a functional shift in the gut microbiome,
   which might be a potential therapeutic strategy for type 2 diabetes
   (T2D).

2. Materials and methods

2.1. Clinical study design

   Thirty newly diagnosed T2D patients were recruited at Xinqiao Hospital,
   Third Military Medical University (Chongqing, China), and were
   randomized into four groups: one group treated with acarbose (n = 8),
   one group with sitagliptin (Sit, n = 7), and the other two groups with
   the corresponding placebos (n = 7–8). The two drugs are in tablet and
   are available in the pharmaceutical market. Acarbose (Bayer
   Pharmaceutical Co., Germany) was administered at a start dose of
   150 mg/d. Sitagliptin (Merck Sharp & Dohme, USA) was administered at a
   dose of 100 mg/d. Furthermore, we recommended that all individuals
   maintain a reduced daily caloric intake of 25 kcal/kg, and perform
   regular physical exercise (2.5 h/week) throughout the entire treatment
   as in previous studies [[95]18]. We collected peripheral blood and
   fecal samples from the enrolled patients after treatment for two
   months. Each sample was frozen immediately at −80 °C or stored in
   personal −20 °C freezers before transport to the laboratory within
   24 h.

   Inclusion criteria were as follows: (i) age between 18 and 70 years;
   (ii) newly diagnosed T2D, based on World Health Organization 1998
   diagnostic criteria [[96]31]; (iii) HbA1c lower than 9%, and body mass
   index (BMI) <30 kg/m^2; (iv) not treatment with any antidiabetic agents
   before recruitment, and absence of other metabolic diseases; (v) native
   of Chongqing. Exclusion criteria were as follows: (i) pregnancy; (ii)
   current or recent cancer (1 year); (iii) use of antibiotics,
   prebiotics, probiotics or fiber supplements during the 3 months prior
   to enrollment; (iv) treatment with bariatric surgery; (iv) diagnosis of
   hypertension, coronary heart disease, cerebral infarction or other
   vascular related chronic diseases; (v) presence of gastrointestinal
   disorders or a history of chronic physical/mental disease, such as
   Alzheimer's disease or Parkinson's disease.

   Informed written consent was obtained from all participants. The
   experiment was approved by the Ethics Committee of Xinqiao Hospital of
   the Third Military Medical University. Complete clinical trial
   registration is deposited in the Chinese Clinical Trials Registry
   ([97]http://www.chictr.org.cn/index.aspx), and the registration number
   is ChiCTR-OPC-17010757.

2.2. Animal study

   C57BL/6 male mice (3–4 weeks old) were purchased from the Model Animal
   Research Center of Nanjing University. After a 2-week acclimatization
   period, mice were fed a normal diet (ND) or a high fat diet (HFD, 60%
   fat, 20% protein, 20% carbohydrate (kcal/100 g), D12492; Research
   Diets, New Brunswick, New Jersey, USA) for 14 weeks. The mice were then
   divided into different groups based on matched weights and fasting
   blood glucose levels. The different treatment groups were administered
   400 mg/kg of acarbose [[98]32], 4 g/kg of Sit [[99]33], or 300 mg/kg of
   saxagliptin [[100]34] (Sax, Onglyza®, AstraZeneca, Wilmington, DE)
   mixed with HFD for 4 weeks.

   For GLP-1 receptor agonist treatment, HFD mice received 200 μg/kg/day
   liraglutide (Novo Nordisk, HFD_Lira) or normal saline (HFD_NaCl) as a
   control through subcutaneous injection for 4 weeks. For antibiotic
   treatment, HFD mice were administered vancomycin (MedChemExpress,
   0.5 g/l) and bacitracin (Aladdin, 1.0 g/l) in drinking water for one
   week before and four weeks during Sit treatment (Ab_Sit), or
   administered Sit alone for 4 weeks as a control (C_Sit). The depletion
   of bacteria by antibiotics was verified using quantitative real-time
   PCR (qPCR) as previously described [[101]23]. For succinate treatment,
   5–6 weeks old germ-free (GF) Kunming male mice (Third Military Medical
   University Experimental Animal Research Center, Chongqing, China) were
   fed a HFD and administered sodium succinate (BBI Life Sciences) in
   drinking water (2.5%) for 6 weeks. After treatment, fecal samples were
   collected from the treated mice and frozen immediately at −80 °C. The
   experimental protocol was approved by the Third Military Medical
   University Institutional Animal Care and Use Committee.

2.3. Fecal genomic DNA extraction and 16S-rDNA sequencing

   Fecal genomic DNA was extracted from 0.1 g frozen fecal samples using
   an E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according
   to the manufacturer's protocol. The DNA concentration and purification
   were measured using a NanoDrop 2000 spectrophotometer (Thermo
   Scientific, Wilmington, USA), and the DNA quality was detected by 1%
   agarose gel electrophoresis. Amplicon libraries covering the V3-V4
   hypervariable regions of the bacterial 16S-rDNA gene were amplified
   using primers 341F: 5′-ACTCCTACGGGRSGCAGCAG-3′, and 806R: 5′-GGACTACVV
   GGGTATCTAATC-3′. PCR was performed in a 20 μl mixture containing 4 μl
   of 5 × FastPfu Buffer, 2 μl of 2.5 mmol/l dNTPs, 0.8 μl of each primer
   (5 μmol/l), 0.4 μl of FastPfu Polymerase and 10 ng of template DNA. PCR
   was conducted with an initial denaturation for 3 min at 95 °C, followed
   by 27 cycles of 30 s at 95 °C, 30 s for annealing at 55 °C, and 45 s
   for elongation at 72 °C, and a final extension at 72 °C for 10 min. The
   reactions were performed on a thermocycler PCR system (GeneAmp 9700,
   ABI, USA). All PCR products were purified using an AxyPrep DNA Gel
   Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified
   using QuantiFluor™-ST (Promega, USA). Purified and pooled amplicon
   libraries were paired-end sequenced (2 × 300) on the Illumina MiSeq
   platform (Illumina, San Diego, USA) according to the standard protocols
   by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

   Raw sequence reads were demultiplexed, quality-filtered, merged and
   clustered into OTUs with a 97% similarity cutoff using UPARSE (version
   7.1, [102]http://drive5.com/uparse/), and chimeric sequences were
   identified and removed using UCHIME. The taxonomy of the acquired OTUs
   was analysed using the RDP Classifier Bayesian algorithm
   ([103]http://rdp.cme.msu.edu/) against the SILVA database (version128)
   with a confidence threshold of 70%.

2.4. Untargeted metabolomics analyses

   Fecal samples (60 mg) were spiked with 40 μl of internal standards
   (0.3 mg/ml 2-chlorophenylalanine in methanol) and 360 μl of cold
   methanol (Merck, Darmstadt, Germany) solution, and then extracted
   according to the manufacturer's instructions (Majorbio Bio-Pharm
   Technology Co., Ltd., Shanghai, China). The extracted samples were
   detected using a 7890A-5975C gas chromatograph-mass spectrometry
   (GC–MS) detection system (Agilent Technologies, Santa Clara, CA). The
   QC sample was a pooled sample in which aliquots of all the extracted
   samples were mixed, and then analysed using the same method as used for
   the analytic samples.

2.5. Targeted SCFAs analyses

   Fecal samples (10 mg) were supplemented with 10 μl of internal
   standards (0.0125 μl/μl 2-ethylbutyric acid, Sigma-Aldrich) and 500 μl
   of methanol, and then extracted according to the manufacturer's
   protocol (Majorbio Bio-Pharm Technology Co., Ltd., Shanghai, China).
   The extracted samples were detected using a 6890A-5973C GC–MS system
   (Agilent Technologies, Santa Clara, CA). SCFAs standards were mixtures
   of acetate, propionate, butyrate, isobutyrate, valerate and
   isovalerate. All the standards, excluding isovalerate (Sigma-Aldrich),
   were purchased from Merck (Darmstadt, Germany).

2.6. Fecal microbiota transplantation (FMT)

   5–6 weeks old GF Kunming male mice were fed a HFD (60% fat, 20%
   protein, 20% carbohydrate, kcal/100 g) for 8 weeks before FMT. The
   transplant materials were derived from 12 donors: 3 from Sit-treated
   T2D patients, 3 from acarbose-treated patients, and 6 from matched
   placebo-treated patients. Each fecal sample (100 mg) was suspended in
   1.0 ml sterile phosphate-buffered saline under anaerobic condition (80%
   N2:10% CO2:10% H2), and colonized to 3 GF mice by oral gavage with
   200 μl of the suspended fecal microbiota as in our previous study
   [[104]6]. After FMT, the colonized mice were separately housed in
   different gnotobiotic isolators to prevent normalization of the gut
   microbiota, and continued feeding with a HFD. Body weight was measured
   for all the colonized mice every week and glucose tolerance 2 weeks
   after FMT. One mouse transplanted with Sit-matched placebo-treated
   microbiota, and another transplanted with acarbose-treated microbiota
   died after FMT, and both were excluded from the analysis. The
   experimental protocol was approved by the Third Military Medical
   University Institutional Animal Care and Use Committee.

2.7. Glucose tolerance test (GTT) and insulin secretion test

   The treated mice and colonized mice were fasted overnight hours for GTT
   as in previous studies [[105]17,[106]30], and then administered glucose
   at 1 g/kg of body weight via i.p. injection. Tail blood glucose
   concentrations were measured at 0, 15, 30, 60 and 120 min after glucose
   injection using an Accu-Check glucometer (Roche, Basel, Switzerland).
   Insulin secretion levels were measured at 0, 15 and 30 min during a GTT
   using a Rat/Mouse Insulin ELISA kit (Millipore, Bedford, MA, USA). The
   homeostasis model assessment of insulin resistance (HOMA-IR) was
   calculated as previously reported: HOMA-IR = (fasting glucose
   (mmol/l) × fasting insulin (mU/l))/22.5 [[107]35].

2.8. Statistical analysis

   Data are expressed as the mean ± SEM. The significance of differences
   between two groups was evaluated using Student's t-test. For comparing
   multiple groups, the differences were analysed by one-way ANOVA with
   FDR correction or Tukey's test. 't- A p value <.05 was defined as
   statistically significant.

3. Results

3.1.1. DPP-4i and acarbose alter the composition of the gut microbial
community

   To observe the effects of these drugs on the gut microbiota, HFD mice
   were treated with the α-glucosidase inhibitor acarbose or DPP-4i at
   corresponding doses according to previous studies [[108]32,[109]33]. As
   expected, in comparison with ND mice, HFD mice showed increased weight
   gain, and impaired glucose tolerance (Supplementary Fig. S1). Compared
   with HFD control mice, HFD_Sit and HFD_AC mice showed a significant
   improvement in glucose tolerance (Supplementary Fig. S1b, c). In
   contrast to their effects on glucose metabolism, acarbose and Sit
   treatment had no effect on body weight (Supplementary Fig. S1a),
   indicating that the improvements in glucose tolerance are not related
   to body weight.

   We next determined the effect of acarbose and Sit on the composition of
   the gut microbiota using 16S-rDNA sequencing. The results showed that
   >80% of the obtained operational taxonomic units (OTUs) were assigned
   to Bacteroidetes and Firmicutes, consistent with previous studies
   [[110]36]. As shown in Supplementary Fig. S2a, principal components
   analysis (PCA) revealed a difference in distribution of the gut
   microbial community between the acarbose treated (HFD_AC) and HFD
   groups. However, in the heatmap analysis, although the two groups were
   roughly clustered, two acarbose-treated samples (HFD_AC2 and HFD_AC7,
   labeled with green color) were classified into the cluster that
   included all samples of the HFD group (Supplementary Fig. S2b).
   Interestingly, although acarbose is generally considered most likely to
   affect the microbiome based on its functional mechanism [[111]25], Sit
   actually showed a more pronounced effect on the gut microbiota compared
   with acarbose. The HFD_Sit samples formed a cluster that was completely
   distinct from that of the HFD samples ([112]Fig. 1a). The HFD and ND
   groups also formed different clusters, which is consistent with
   previous reports [[113]17,[114]37]. Moreover, hierarchical clustering
   of the heatmap revealed striking changes in the genera resulting in
   distinct clustering of the samples ([115]Fig. 1b), further suggesting
   that Sit has a more definitive regulatory effect on the microbiota
   compared with acarbose. Currently, several kinds of DPP-4i are applied
   clinically, and we then sought to investigate whether the effect on the
   gut microbiota is universal for all DPP-4i as a pharmaceutical class
   effect. Administration of Sax to HFD mice also led to similar changes
   in gut microbial community structure ([116]Fig. 1c, d). Taken together,
   these results demonstrate that DPP-4i and acarbose both alter the
   composition of the gut microbiota, while the effects of DPP-4i seemed
   to be more pronounced. Additionally, the modulatory effects of DPP-4i
   on the gut microbiota may not be restricted to one inhibitor of the
   family, but is rather a class effect.

Fig. 1.

   [117]Fig. 1
   [118]Open in a new tab

   DPP-4i alters the composition of the gut microbiota in HFD mice. (a)
   Cluster analysis of the ND (n = 6), HFD (n = 8) and HFD_Sit (n = 8)
   groups using PCA. The first two principal components (PC1 and PC2) from
   PCA are plotted for each sample. The percentage variation covered in
   the plotted principal components is marked on the axes. Each spot
   represents one sample, and each group of mice is labeled by a different
   symbol. (b) Heatmap analysis of species abundance clustering at the
   genus level in the ND, HFD and HFD_Sit groups. The heatmap shows the
   top 30 genera ranked on the basis of abundance. Each column in the
   heatmap represents one sample, and each row represents one genus. The
   color bar showing blue to red indicates the relative abundance of each
   genus. (c) Cluster analysis of the HFD (n = 8) and HFD_Sax (n = 7)
   groups by PCA. (d) Heatmap analysis of species abundance clustering at
   the genus level in the HFD and HFD_Sax groups. The heatmap shows the
   top30 ranked genera. The range of colors from green to red indicates
   the relative abundance of each genus. (For interpretation of the
   references to color in this figure legend, the reader is referred to