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Chemoproteomic identification of a DPP4 homolog in Bacteroides thetaiotaomicron

Nature Chemical Biology volume 19, pages 1469–1479 (2023)Cite this article

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Abstract

Serine hydrolases have important roles in signaling and human metabolism, yet little is known about their functions in gut commensal bacteria. Using bioinformatics and chemoproteomics, we identify serine hydrolases in the gut commensal Bacteroides thetaiotaomicron that are specific to the Bacteroidetes phylum. Two are predicted homologs of the human dipeptidyl peptidase 4 (hDPP4), a key enzyme that regulates insulin signaling. Our functional studies reveal that BT4193 is a true homolog of hDPP4 that can be inhibited by FDA-approved type 2 diabetes medications targeting hDPP4, while the other is a misannotated proline-specific triaminopeptidase. We demonstrate that BT4193 is important for envelope integrity and that loss of BT4193 reduces B. thetaiotaomicron fitness during in vitro growth within a diverse community. However, neither function is dependent on BT4193 proteolytic activity, suggesting a scaffolding or signaling function for this bacterial protease.

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Fig. 1: Bioinformatics and activity-based protein profiling of B. thetaiotaomicron identify serine hydrolases specific to the Bacteroidetes phylum.
Fig. 2: BT4193 is a functional homolog of human DPP4 (hDPP4).
Fig. 3: Human DPP4 inhibitors target BT4193.
Fig. 4: Substrate specificity of BT4193 and BT3254 is driven by P1 Pro.
Fig. 5: BT4193 confers resistance to envelope stressors vancomycin and polymyxin B.
Fig. 6: BT4193, but not hDPP4-targeting drugs, impacts B. thetaiotaomicron fitness during in vitro growth in diverse communities.

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Data availability

Selection of serine hydrolase-associated Pfam domains was performed with the MEROPS (https://www.ebi.ac.uk/merops/) and ESTHER (https://bioweb.supagro.inrae.fr/ESTHER/general?what=index) databases. Proteomes for bioinformatic prediction of serine hydrolases were downloaded from UniProt, and accession codes for each proteome are included in Supplementary Table 2. Proteomes were annotated with Pfam domains using pfam_scan.pl (http://ftp.ebi.ac.uk/pub/databases/Pfam/). Proteomes of reference isolates from the NIH Human Microbiome Project gastrointestinal tract were downloaded from https://www.hmpdacc.org/hmp/HMRGD/. Carbohydrate-active enzymes were identified using the CAZyme database (http://www.cazy.org/). Raw proteomics data for this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD035963. Raw MSP–MS data can be obtained through massive.ucsd.edu under the dataset identifier numbers MSV000091339 (hDPP4), MSV000091338 (BT4193) and MSV000089969 (BT3254). Raw 16S rRNA data for community assembly experiments can be obtained at https://doi.org/10.25740/rn970zy4428. All other data are available in the source data provided with this paper.

Code availability

Code for bioinformatic analyses of serine hydrolases, analysis of fitness data, quantification of area under the growth curves and plotting of Venn diagrams can be obtained at https://doi.org/10.5281/zenodo.7835000. Code for analyzing community assembly experiments can be obtained at https://doi.org/10.5281/zenodo.7830074.

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Acknowledgements

We thank the laboratories of J. Sonnenburg and M. Howitt at Stanford University for the use of their equipment and W. Zhu at Vanderbilt University Medical Center for gifting the plasmids pKI_1 and pKI_2. L.J.K. was supported by the Stanford ChEM-H Chemistry/Biology Interface Predoctoral Training Program (T32 GM120007), a Stanford Molecular Pharmacology Training Grant (T32 GM113854), and a Stanford Graduate Fellowship. T.H.N. was supported by a National Science Foundation Graduate Research Fellowship (DGE-1656518). B.M.H. was supported by the UCSD Graduate Training Program in Cellular and Molecular Pharmacology through an institutional training grant from the National Institute of General Medical Sciences (T32 GM007752). M.L. was supported by Deutsche Forschungsgemeinschaft (DFG) for funding under the Walter Benjamin Program. R.C. was supported by the NIH Training (T32 HG000044) and a Stand Up 2 Cancer grant (to A.S.B.). F.F. was supported by a National Science Foundation Graduate Research Fellowship (DGE-1656518), a Stanford ChEM-H O’Leary-Thiry Graduate Fellowship, and Stanford’s Enhancing Diversity in Graduate Education Doctoral Fellowship Program. This work was supported by the NIH (grants R01 EB026332 and R01 EB026285 (to M.B.), R01 DK131005 (to A.J.O.), R01 AI148623 and R01 AI143757 (to A.S.B.) and RM GM135102 and R01 AI147023 (to K.C.H.)) and National Science Foundation (grant EF-2125383 (to K.C.H.)). K.C.H. is a Chan Zuckerberg Biohub Investigator.

Author information

Author notes

  1. Markus Lakemeyer

    Present address: Institute of Organic Chemistry and Macromolecular Chemistry, Friedrich-Schiller-University, Jena, Germany

  2. Matteo Guerra

    Present address: Department of Biochemical and Cellular Pharmacology, Genentech, San Francisco, CA, USA

Authors and Affiliations

  1. Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA

    Laura J. Keller

  2. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Taylor H. Nguyen & Kerwyn Casey Huang

  3. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA

    Lawrence J. Liu, Brianna M. Hurysz, Danielle J. Gelsinger & Anthony J. O’Donoghue

  4. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Markus Lakemeyer, Matteo Guerra, Phillip Ipock & Matthew Bogyo

  5. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Rachael Chanin & Ami S. Bhatt

  6. Divisions of Hematology and Blood and Marrow Transplantation, Department of Medicine, Stanford University, Stanford, CA, USA

    Rachael Chanin & Ami S. Bhatt

  7. Lundbeck La Jolla Research Center, Inc., San Diego, CA, USA

    Nhi Ngo, Kenneth M. Lum & Micah J. Niphakis

  8. Department of Chemistry, Stanford University, Stanford, CA, USA

    Franco Faucher

  9. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Kerwyn Casey Huang & Matthew Bogyo

  10. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Kerwyn Casey Huang

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Contributions

L.J.K. and M.B. conceived and designed the study. L.J.K. ran bioinformatic analyses, synthesized fluorogenic peptide substrates, purified recombinant enzymes and performed enzyme kinetics, gel-based ABPP and microbiology experiments. T.H.N. performed community assembly and fluorescent vancomycin microscopy experiments. L.J.L., B.M.H. and D.J.G. performed MSP–MS experiments. N.N., K.M.L. and M.J.N. performed MS-based ABPP experiments. R.C. performed B. thetaiotaomicron genetics. F.F. and P.I. synthesized FP-alkyne. P.I. assisted with bioinformatic analyses. L.J.K., T.H.N., L.J.L., B.M.H., M.L., M.G., D.J.G., P.I., A.J.O., K.C.H. and M.B. analyzed and interpreted data. L.J.K., T.H.N., K.C.H. and M.B. prepared the figures and wrote and edited the paper. All authors reviewed and revised the paper.

Corresponding author

Correspondence to Matthew Bogyo.

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Nature Chemical Biology thanks Michael Zimmermann and the other anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Bacteria from the phylum Bacteroidetes have a larger fraction of their proteome predicted to be serine hydrolases.

Percentage of the total proteome based on the number of proteins that are predicted serine hydrolases, colored by phylum. The horizontal line corresponds to the percentage of predicted human serine hydrolases as a reference.

Source data

Extended Data Fig. 2 hDPP4 inhibitors act on BT4193 but not BT3254.

a, Quantification of percent inhibition of GP-AMC cleavage in wild type (WT) B. thetaiotaomicron lysate (ex/em: 380/460 nm) after pretreatment with inhibitor for 30 min at 37 °C. Activity was normalized to DMSO treatment (mean ± SEM; n = 6 independent replicates). b, Apparent IC50 values of recombinant hDPP4 and BT4193 after treatment with inhibitor for 30 min prior to measuring activity via GP-AMC cleavage and the corresponding curves. Activity was normalized to DMSO treatment (100%) and no enzyme controls (0%), and fit with dose-dependent four parameter inhibition. Selectivity index (SI) is the ratio of BT4193 to hDPP4 apparent IC50 values (mean ± SD; n = 2 independent experiments, each calculated with 3 independent replicates). c, Quantification of inhibition of recombinant BT3254 by hDPP4 inhibitors after 30 min of pretreatment. Velocities were normalized to DMSO pretreatment. Data represent the mean ± SEM of 9 independent replicates.

Source data

Extended Data Fig. 3 Recombinant BT4193 and BT3254 prefer P1 Pro residues.

Quantification of cleavage velocity of di- and tripeptide fluorogenic peptides with ACC-containing substrates (ex/em: 355/460 nm) by recombinantly expressed and purified BT4193 and BT3254. Hydroxyproline is abbreviated as Hyp. Data represent the mean ± SEM of one representative experiment with 3 independent replicates. Statistical significance was determined using a two-tailed one-sample t-test compared with 0 (BT4193: AP-ACC, p = 0.0026; AA-ACC, p = 0.014; AL-ACC, p = 0.10; AS-ACC, p = 0.019; AT-ACC, p = 0.20; AHyp-ACC, p = 0.0049; BT3254: AAP-ACC, p = 0.0004; AAA-ACC, p = 0.0085; *P < 0.05; **P < 0.01; ***P < 0.001).

Source data

Extended Data Fig. 4 BT4193 confers resistance to deoxycholic acid and polymyxin B.

a, Growth of B. thetaiotaomicron strains during deoxycholic acid treatment, and quantification of area under the growth curve normalized to untreated bacteria. The ∆BT4193 ∆BT3254 strain exhibited decreased fitness in the presence of deoxycholic acid (mean ± SEM; 16 independent replicates). Statistical significance was determined using a one-way ANOVA test with post hoc Dunnett’s multiple comparisons tests compared with wild type (p = 0.0002; WT-∆BT4193, p = 0.14; WT-∆BT3254, p = 0.42; WT-∆BT4193 ∆BT3254, p < 0.0001; WT-∆BT4193::BT4193WT, p = 0.45; WT-∆BT4193::BT4193S606A, p = 0.72; ****P < 0.0001). b, Growth of B. thetaiotaomicron strains during polymyxin B treatment.

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Extended Data Fig. 5 Complementation with catalytically inactive BT4193 does not rescue loss of DPP4 activity in B. thetaiotaomicron lysate.

Quantification of initial velocities of fluorogenic peptide substrate cleavage (ex/em: 380/460 nm for AMC substrates; ex/em: 355/460 nm for ACC substrates) in lysate generated from wild type (WT), knockout, and complemented B. thetaiotaomicron strains. Data represent the mean ± SEM of 8 independent replicates for WT and ∆BT4193 and 9 independent replicates for ∆BT3254, ∆BT4193 ∆BT3254, ∆BT4193::BT4193WT, and ∆BT4193::BT4193S606A. Statistical significance was determined using a one-way ANOVA test with post hoc Dunnett’s multiple comparisons tests compared with wild type (GP-AMC: p < 0.0001; WT-∆BT4193, p < 0.0001; WT-∆BT3254, p = 0.55; WT-∆BT4193 ∆BT3254, p < 0.0001; WT-∆BT4193::BT4193WT, p = 0.0004; WT-∆BT4193::BT4193S606A, p < 0.0001; AP-ACC: p < 0.0001; WT-∆BT4193, p < 0.0001; WT-∆BT3254, p = 0.96; WT-∆BT4193 ∆BT3254, p < 0.0001; WT-∆BT4193::BT4193WT, p < 0.0001; WT-∆BT4193::BT4193S606A, p < 0.0001; AAP-ACC: p < 0.0001; WT-∆BT4193, p =  1.00; WT-∆BT3254, p < 0.0001; WT-∆BT4193 ∆BT3254, p < 0.0001; WT-∆BT4193::BT4193WT, p = 1.00; WT-∆BT4193::BT4193S606A, p = 0.77; ***P < 0.001; ****P < 0.0001).

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Extended Data Fig. 6 ∆BT4193 strains are not universally susceptible to stressors.

Quantification of the area under the growth curve of B. thetaiotaomicron strains under pH, ethanol, or sodium chloride stress, normalized to untreated bacteria. Deletion of BT4193 did not impact fitness in response to pH, ethanol treatment, or sodium chloride treatment (mean ± SEM; n = 8 independent replicates for pH 7.3, pH 6, pH 5, 0.25 M NaCl; n = 7 independent replicates for 5% EtOH WT, 5% EtOH ∆BT4193; n = 8 independent replicates for 5% EtOH ∆BT3254, 5% EtOH ∆BT4193 ∆BT3254). Lack of statistical significance was determined using a one-way ANOVA test (pH 7.3, p =  1.00; pH 6, p = 0.69; pH 5, p = 0.71; 5% EtOH, p = 0.48; 0.25 M NaCl, p = 0.11).

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Extended Data Fig. 7 Loss of BT4193 does not affect monoculture growth in rich media.

Representative growth of B. thetaiotaomicron strains in BHI or mGAM (mean ± SEM; n = 8 independent replicates).

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Extended Data Fig. 8 Overall community structure was not affected by deletion of BT4193.

Quantification of relative abundance of bacteria within the 15-member synthetic communities at the family level after 48 h of growth in BHI or mGAM. Each group of four bars represents replicates for a community with each strain and medium combination.

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Extended Data Fig. 9 Treatment with hDPP4-targeting drugs does not affect the fitness of bacteria from the phylum Bacteroidetes in stool-derived communities.

Quantification of the relative abundance of bacteria from the phylum Bacteroidetes in eight stool-derived communities after 48 h treatment with 10 µM saxagliptin or 10 µM sitagliptin in BHI or mGAM (mean ± SEM; n = 3 independent replicates). Lack of statistical significance was determined using a one-way ANOVA test (BHI: Community 1, p = 0.71; Community 2, p = 0.58; Community 3, p = 0.69; Community 4, p = 0.077; Community 5, p = 0.91; Community 6, p = 0.19; Community 7, p = 0.70; Community 8, p = 0.19; mGAM: Community 1, p = 0.15; Community 2, p = 1.00; Community 3, p = 0.10; Community 4, p = 0.25; Community 5, p = 0.33; Community 6, p = 0.20; Community 7, p = 0.16; Community 8, p = 0.91).

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13 and Note.

Reporting Summary

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Supplementary Tables 1–9.

Supplementary Data

Supporting data for Supplementary Figs. 3 and 7–13.

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Keller, L.J., Nguyen, T.H., Liu, L.J. et al. Chemoproteomic identification of a DPP4 homolog in Bacteroides thetaiotaomicron. Nat Chem Biol 19, 1469–1479 (2023). https://doi.org/10.1038/s41589-023-01357-8

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