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Inactivation of the antidiabetic drug acarbose by human intestinal microbial-mediated degradation

Nature Metabolism volume 5, pages 896–909 (2023)Cite this article

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Abstract

Drugs can be modified or degraded by the gut microbiota, which needs to be considered in personalized therapy. The clinical efficacy of the antidiabetic drug acarbose, an inhibitor of α-glucosidase, varies greatly among individuals for reasons that are largely unknown. Here we identify in the human gut acarbose-degrading bacteria, termed Klebsiella grimontii TD1, whose presence is associated with acarbose resistance in patients. Metagenomic analyses reveal that the abundance of K. grimontii TD1 is higher in patients with a weak response to acarbose and increases over time with acarbose treatment. In male diabetic mice, co-administration of K. grimontii TD1 reduces the hypoglycaemic effect of acarbose. Using induced transcriptome and protein profiling, we further identify an acarbose preferred glucosidase, Apg, in K. grimontii TD1, which can degrade acarbose into small molecules with loss of inhibitor function and is widely distributed in human intestinal microorganisms, especially in Klebsiella. Our results suggest that a comparatively large group of individuals could be at risk of acarbose resistance due to its degradation by intestinal bacteria, which may represent a clinically relevant example of non-antibiotic drug resistance.

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Fig. 1: Flow diagram of patient enrolment.
Fig. 2: Discovery of an acarbose-degrading K. grimontii strain from microbiome of patients with type 2 diabetes.
Fig. 3: Influence of K. grimontii TD1 on the effects of acarbose in T2DM mouse model.
Fig. 4: Discovery of acarbose-degrading enzyme in K. grimontii TD1.
Fig. 5: Identification of acarbose-degrading products mediated by Apg.
Fig. 6: Characterization of Apg.
Fig. 7: Molecular modelling of the interaction between acarbose and Apg/human α-amylase.
Fig. 8: The prevalence and abundance analysis of apg+ bacteria.

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Data availability

Data supporting the findings of this research are available within the paper and its Supplementary Information files. The genome sequencing data have been deposited in the Sequence Read Archive (SRA) under accession code PRJNA801205. The RNA-seq and metagenomics data are available in NCBI under the SRA with Bioproject No. PRJNA801096 and SRP418540, respectively. Source data are provided with this paper.

Code availability

The generated source code is available at https://github.com/lovingstudy/Apg_gut.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (no. 31921006 to W.J. and no. 82170839 to G.Q.) and the Shanghai Super Post-doctoral Excellence Program (no. 2020458 to J.T.). We are grateful to Y. Wang, J. Xu, X. Xu and Y. Liu for HPLC and LC–MS/MS analysis. We thank Y. Zhang and H. Zhang for FPLC analysis. We also thank Z. Zhang for field emission scanning electron microscopy analysis.

Author information

Author notes

  1. These authors contributed equally: Jinzhong Tian, Chong Li, Zhixiang Dong, Yunpeng Yang, Jing Xing.

Authors and Affiliations

  1. Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences (CAS), Shanghai, PR China

    Jinzhong Tian, Zhixiang Dong, Guoping Zhao, Yang Gu & Weihong Jiang

  2. Department of Endocrinology and Metabolism, First Affiliated Hospital of Zhengzhou University, Zhengzhou, PR China

    Chong Li, Ying Xin & Guijun Qin

  3. University of Chinese Academy of Sciences, Beijing, PR China

    Zhixiang Dong & Peijun Yu

  4. Institute of Comparative Medicine, College of Veterinary Medicine, Yangzhou University, Yangzhou, PR China

    Yunpeng Yang

  5. Jiangsu Co-innovation Center for Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, PR China

    Yunpeng Yang

  6. Lingang Laboratory, Shanghai, PR China

    Jing Xing

  7. Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, PR China

    Peijun Yu & Qiang Sun

  8. Department of Endocrinology and Metabolism, Hebi Coal (group) Ltd. General Hospital, Hebi, PR China

    Fengmei Xu

  9. Department of Endocrinology and Metabolism, Zhumadian Central Hospital, Zhumadian, PR China

    Lianwei Wang

  10. Department of Endocrinology and Metabolism, Huanghe Sanmenxia Hospital, Sanmenxia, PR China

    Yahui Mu

  11. Department of Endocrinology and Metabolism, Xinyang Central Hospital, Xinyang, PR China

    Xiangyang Guo

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Contributions

J.T., W.J., G.Q., Y.G., Q.S. and G.Z. conceived and initiated the project; J.T. and Z.D. performed the biochemical experiments; C.L., Y.X., F.X., L.W., Y.M., X.G. and G.Q. recruited volunteers and collected the sample; Y.Y., P.Y. and Q.S. performed the animal experiments; J.X. performed the docking experiments and prepared graphical illustrations; W.J., Y.G., J.T., Z.D., J.X., Y.Y., C.L. and Y.X. wrote the manuscript.

Corresponding authors

Correspondence to Yang Gu, Guijun Qin or Weihong Jiang.

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Nature Metabolism thanks Sofia Forslund, Michael Zimmerman and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary handling editor: Christoph Schmitt, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Identification of the isolates capable of metabolizing acarbose from human stool samples.

a, Gradually increased acarbose consumption of intestinal microbiota by enrichment using acarbose as the stress. The concentrations of acarbose used in the enrichment experiment were 0.01, 0.1, 1, 10 and 20 mg/ml for passage 1, 2, 3, 4 and 5, respectively. Data are represented as mean ± SD (n = 3). Error bars show SDs. b, Identification of the isolates capable of consuming acarbose. The isolates for testing were cultivated anaerobically in the mGAM medium supplemented with acarbose (1.5 mg/ml) for 24 h. Among the 76 tested isolates, 18 were found to efficiently metabolize acarbose and then identified as K. grimontii strains. c, d, The colony and cell morphology of K. grimontii TD1. The micro-structure of the K. grimontii TD1 cells was observed by scanning electron microscope. e, In vivo degradation of acarbose by K. grimonii TD1 in T2DM mice. A total of 12 T2DM mice were separated into two groups: the group I (n = 5) was gavaged with 50 mg/kg acarbose while the group II (n = 7) was gavaged with both acarbose (50 mg/kg) and K. grimontii TD1 (1011 cfu per mouse). The fecal samples were collected at 3, 6, 18 and 24 h. The residual acarbose contained in feces was assayed by HPLC analysis. Data are presented as mean ± SEM. The statistical significance between two groups was analysed using a two-tailed Student’s t-test.

Source data

Extended Data Fig. 2 Separation and purification of enzymes responsible for acarbose degradation in K. grimontii TD1.

a, Separation of total proteins of the K. grimontii TD1 cell lysate using HitrapTM Q HP by FPLC. The fractions 16, 17 and 18 showed catalytic activity towards acarbose. b, Separation of the protein mixture of the above three fractions (16–18) using HitrapTM SP HP by FPLC. The resulting fractions 27‒31 showed catalytic activity towards acarbose. c, A further separation of the protein mixture of the above fractions 27‒31 using Hiload 16/600 Superdex 200. The resulting fractions 28 and 29 showed catalytic activity towards acarbose. Each fraction was incubated with acarbose at 37 °C for 6 h and the concentrations of residual acarbose in the reaction mixture were assayed by HPLC. d, SDS-PAGE analysis of protein fractions from K. grimontii TD1 for testing acarbose catalytic activity. The fractions 27‒32 from size exclusion chromatography (c) were analysed by SDS-PAGE. Among them, the factions 28 and 29 exhibited obvious activity towards acarbose degradation. The red asterisks indicated the protein components with specific high content in these two factions. The marked band in fraction 28 was then used for the proteomic assay. e, Confirmation of the in vivo activity of Apg in E. coli. The E. coli strain that expressed apg was cultivated at 37 °C for 4 h with the supplementation of 1.8 mg/ml of acarbose. The initial (0 h) and residual (4 h) acarbose concentration were determined. Data are represented as the mean ± s. d. (n = 3). Error bars show s. d. Statistical analysis was performed by a two-tailed Student’s t-tes.

Source data

Extended Data Fig. 3 A full maximum likelihood phylogenetic tree of Apg homologues across different species and genera.

The numbers on the branches indicate bootstrap supports. The BLASTp search identified 5,000 putative Apg-like enzymes (amino acid identity ≥ 70%, coverage ≥ 80%, e-value ≤ 1e − 10) from 11 genera (Cedecea, Citrobacter, Enterobacter, Klebsiella, Leclercia, Lelliottia, Raoultella, Salmonella, Scandinavium, Shigella and Trabulsiella) as reference.

Source data

Extended Data Fig. 4 Abundance analysis of apg+ bacteria in human intestinal microbiota.

Prevalence and abundance of apg+ bacteria in the intestinal microbiota of healthy human (blue, from GMrepo, n = 5,372) and T2DM patients (pink, from GMrepo, n = 247). Embraced numbers indicate the sample size of the categories. All the T2DM patients were treated with acarbose.

Source data

Extended Data Fig. 5 The prevalence and abundance of apg+ bacteria among healthy human populations of different gender, country, body mass and age groups.

a, The prevalence of apg+ bacteria in male and female samples. Two-tailed Fisher exact test was performed between the two gender groups. b, The abundance of apg+ bacteria in positive samples of male and female. c, The prevalence of apg+ bacteria among different countries. Two-tailed Fisher exact tests were performed between a specific country and the general ratio derived from all samples. d, The abundance of apg+ bacteria in positive samples from different countries. e, The prevalence of apg+ bacteria among groups of different body mass. Two-tailed Fisher exact tests were performed between the normal group and each abnormal group. f, The abundance of apg+ bacteria in positive samples with different grades of body mass. The body mass categories are defined by the BMI as suggested by the Centers for Disease Control and Prevention of the United States. Underweight: BMI < 18.5, normal: BMI of 18.5~24.9, overweight: BMI of 25.0~29.9, and obesity: BMI ≥ 30. g, The prevalence of apg+ bacteria among different age groups. Two-tailed Fisher exact tests were performed between a specific age group and the general ratio derived from all samples. h, The abundance of apg+ bacteria in positive samples from different age groups. In a, c, e, and g, apg+ is defined as the abundance of apg homolog containing bacteria greater than 0.01%. Embraced numbers indicate the sample size of the categories. P-values are labelled in the scientific notion (for example, 4.58E − 05 means 4.58 times ten raised to the power of −5). In the enhanced box plots of b, d, f, and h, the vertical line in the middle of each distribution indicates the median value. The diamond dots indicate outliers, which are 0.07% outside of a fitted normal distribution. The number of samples in each group is indicated in brackets.

Source data

Extended Data Fig. 6 The prevalence of the apg homologs in the intestinal microbiota of T2DM patients.

a, The prevalence of the apg homologs in the intestinal microbiota of 393 enrolled T2DM patients included in the metagenomic analyses. LR: low drug responders; HR: high drug responders. b, The prevalence of the apg homologs in the intestinal microbiota of 22 randomly selected T2DM patients with high or low response to the drug (11 from each group). The intestinal microorganisms in fecal samples of the patients were tested in acarbose metabolism using an ex vivo system. The parameters used to assess the metagenomic abundance of apg homologs in T2DM patients are as follows: identity ≥ 80%, e-value ≤1e − 10 and percent coverage of query and target alignment ≥ 80%.

Source data

Supplementary information

Reporting Summary

Supplementary Data 1

Information of patients with T2DM.

Supplementary Data 2

Raw data of RNA-seq.

Supplementary Data 3

Distribution of Apg-like enzymes in microbial communities.

Supplementary Data 4

Comparison of Apg with homologous proteins for key residues.

Supplementary Tables 1–6

Supplementary Tables 1–6.

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Tian, J., Li, C., Dong, Z. et al. Inactivation of the antidiabetic drug acarbose by human intestinal microbial-mediated degradation. Nat Metab 5, 896–909 (2023). https://doi.org/10.1038/s42255-023-00796-w

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