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Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism

Nature Microbiology volume 7, pages 1605–1620 (2022)Cite this article

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Abstract

Pharmaceuticals have extensive reciprocal interactions with the microbiome, but whether bacterial drug sensitivity and metabolism is driven by pathways conserved in host cells remains unclear. Here we show that anti-cancer fluoropyrimidine drugs inhibit the growth of gut bacterial strains from 6 phyla. In both Escherichia coli and mammalian cells, fluoropyrimidines disrupt pyrimidine metabolism. Proteobacteria and Firmicutes metabolized 5-fluorouracil to its inactive metabolite dihydrofluorouracil, mimicking the major host mechanism for drug clearance. The preTA operon was necessary and sufficient for 5-fluorouracil inactivation by E. coli, exhibited high catalytic efficiency for the reductive reaction, decreased the bioavailability and efficacy of oral fluoropyrimidine treatment in mice and was prevalent in the gut microbiomes of colorectal cancer patients. The conservation of both the targets and enzymes for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug efficacy and side-effect profiles.

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Fig. 1: The anti-cancer drug 5-FU inhibits bacterial growth and is inactivated by the preTA operon.
Fig. 2: Shared and unique transcriptional response to the related fluoropyrimidines 5-FU and CAP during aerobic and anaerobic growth.
Fig. 3: E. coli PreTA more rapidly catalyses the reduction of pyrimidines.
Fig. 4: PreTA interferes with CAP efficacy in mice.
Fig. 5: Functional orthologues of the preTA operon are widespread in human gut bacterial strains from the Firmicutes and Proteobacteria phyla.
Fig. 6: The preTA operon is prevalent and functional in the gut microbiomes of CRC patients and controls, exhibiting marked inter-individual and temporal variation in abundance.

Data availability

The raw data for our mouse experiments as well as uncropped gel images and tumour photographs can be found in the source data section. The Genome Taxonomy Database95, Kyoto Encyclopedia of Genes and Genomes (KEGG) database96 and MIDAS v1.0 database65 are publicly available. Sequencing data have been deposited under the NCBI BioProjects PRJNA576932 (RNA-seq, 16S rRNA gene sequences and isolate genomes) and PRJNA720145 (GO Study metagenomic data). Source data are provided with this paper.

Code availability

Source code for our analyses of bacterial preTA can be found at https://bitbucket.org/pbradz/preta/.

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Acknowledgements

We thank the other members of the Gerona, Goga, Pollard and Turnbaugh labs for their helpful suggestions during the preparation of this manuscript; B. Yu, M. Tan and R. Sit from the Chan Zuckerberg Biohub for assistance with DNA sequencing; G. Wright and L. Ejim (McMaster University) for providing pINT1; J. Bisanz for advice with computational methods; K. Spitler in the UCSF Quantitative Metabolite Analysis Center for help with analytical methods; and the GO Study clinical research coordinators (D. Stanfield, P. Steiding and J. Whitman). This work was supported by the National Institutes of Health (R01HL122593; R21CA227232; R01CA223817), the Searle Scholars Program (SSP-2016-1352) and CDMRP W81XWH-18-1-0713. P.J.T. is a Chan Zuckerberg Biohub Investigator and was a Nadia’s Gift Foundation Innovator who was supported in part by the Damon Runyon Cancer Research Foundation (DRR-42-16). A.G. was supported in part by a MARK Foundation Endeavor Award. Fellowship support was provided by the Canadian Institutes of Health Research (P.S. and K.N.L.) and the NIH (J.V.L. - F32CA243548, T32CA108462; T.S.K. - F30CA257378). B.G.H.G. is a Connie and Bob Lurie Fellow of the Damon Runyon Cancer Research Foundation (DRG-2450-21).

Author information

Author notes

  1. These authors contributed equally: Peter Spanogiannopoulos, Than S. Kyaw.

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA

    Peter Spanogiannopoulos, Than S. Kyaw, Ben G. H. Guthrie, Kathy N. Lam, Daryll Gempis & Peter J. Turnbaugh

  2. Gladstone Institutes, San Francisco, CA, USA

    Patrick H. Bradley & Katherine S. Pollard

  3. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    Patrick H. Bradley

  4. Department of Cell and Tissue Biology, University of California San Francisco, San Francisco, CA, USA

    Joyce V. Lee & Andrei Goga

  5. Clinical Toxicology and Environmental Biomonitoring Laboratory, University of California San Francisco, San Francisco, CA, USA

    Jonathan Melamed, Ysabella Noelle Amora Malig & Roy R. Gerona

  6. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    Moriah Sandy, Wesley Kidder, Chloe E. Atreya & Alan Venook

  7. UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA

    Wesley Kidder, Erin L. Van Blarigan, Chloe E. Atreya, Alan Venook & Andrei Goga

  8. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA

    Erin L. Van Blarigan & Katherine S. Pollard

  9. Department of Urology, University of California San Francisco, San Francisco, CA, USA

    Erin L. Van Blarigan

  10. Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA

    Katherine S. Pollard

  11. Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA

    Katherine S. Pollard

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

    Katherine S. Pollard & Peter J. Turnbaugh

Authors
  1. Peter Spanogiannopoulos
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Contributions

P.J.T. conceived the project and was the primary supervisor for the study. R.R.G., A.G. and K.S.P. also supervised components of this work. P.S. led the in vitro screens and E. coli strain construction and established protocols for the pharmacokinetics and xenograft experiments. T.S.K. led the final pharmacokinetics, xenograft, transcriptomics and amplicon sequencing data generation and analysis. B.G.H.G. led the biochemical characterization of PreTA. P.H.B. led the bioinformatic analysis of preTA operons across genomes and microbiomes. J.M., Y.N.A.M., T.S.K., B.G.H.G. and M.S. performed mass spectrometry. K.N.L. sequenced and analysed the drug-resistant E. coli mutants. J.V.L. assisted with the tumour xenograft measurements. C.E.A., A.V. and W.K. (GO Study PI) oversaw the conception and design of the GO Study and contributed patient samples. E.L.V.B. contributed to developing the study protocol and supervision of data collection for the GO Study. D.G. designed the GO Study specimen collection kits and managed biospecimen collection, storage and retrieval. P.S. wrote the initial draft. T.S.K. and P.J.T. revised the manuscript with input from all authors.

Corresponding author

Correspondence to Peter J. Turnbaugh.

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Competing interests

P.J.T. is on the scientific advisory boards of Pendulum, Seed and SNIPRbiome; there is no direct overlap between the current study and these consulting duties. K.S.P. is on the scientific advisory board of Phylagen; there is no direct overlap between the current study and these consulting duties. C.E.A serves on the scientific advisory board of Pionyr Immunotherapeutics and has received research funding (institution) from Bristol Meyer Squibb, Guardant Health, Kura Oncology, Merck and Novartis; there is no direct overlap with the current study. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Bacterial taxa vary in their sensitivity to fluoropyrimidines despite the rapid emergence of resistance during in vitro growth.

(a) Simplified metabolic pathway for the bioactivation of the oral prodrug capecitabine (CAP) to 5-fluorouracil (5-FU), 5-fluorodeoxyuridine (FUDR), and downstream metabolites1. Red indicates chemical groups hydrolyzed during the conversion of CAP to 5-FU. Sequential reactions are indicated by multiple arrows. (b,c) Variation in (b) 5-FU and (c) FUDR minimal inhibitory concentration (MIC) at the phylum level. Bacterial strains where no MIC could be determined were set to the maximum tested concentration. Each dot represents a bacterial isolate. Red lines indicate the median. p-values, Kruskal-Wallis test with Dunn’s correction for multiple comparisons. (d) 5-FU MICs of parent and 5-FU-resistant strains of E. coli MG1655, B. fragilis DSM2151, and B. ovatus DSM1896 (see Supplementary Table 2). The type of selection is indicated by the strain identifier: A = agar; L = liquid. (e) Wild-type E. coli BW25113 was assayed for conversion of CAP to 5-FU by LC-MS/MS (n = 3 biological replicates per group). Open circles represent individual values, filled circles represent mean values. p-values, two-tailed paired t test comparing final vs. baseline values for each analyte.

Extended Data Fig. 2 Multiple fluoropyrimidines disrupt the pyrimidine metabolism pathway in E. coli.

(a) A working model for uracil and uridine import and metabolism in bacterial and mammalian cells. Underlined genes are essential for E. coli growth and metabolites with red circles indicate putative 5-FU metabolites. (b-d) Uracil rescues the growth of E. coli BW25113 in the presence of (b) 5-FU, (c) FUDR, and (d) CAP in a dose-dependent manner. M9MM plus glucose was used as the base media with added uracil from 0.1–10 µg/ml. (e-g) A loss-of-function mutation in uracil phosphoribosyltransferase gene (Δupp) rescues the growth of E. coli BW25113 in the presence of (e) 5-FU, (f) FUDR, and (g) CAP. MIC assays performed in M9MM plus glucose. Values in panels b-g are mean±stdev (n = 4 biological replicates per group).

Extended Data Fig. 3 The preTA operon is necessary and sufficient for the inactivation of 5-fluorouracil in an independent E. coli background and has a modest impact on growth.

(a) Wild-type (preTA+), deletion (ΔpreTA), complemented (preTA++), and empty vector E. coli BW25113 strains were assayed for residual 5-FU using disk diffusion (0–48 hours incubation) and LC-QTOF/MS (48 hours). Complementation and empty vector were on the ΔpreTA background. (b) Wild-type, single gene deletion (ΔpreT, ΔpreA) and operon deletion (ΔpreTA) strains of E. coli BW25113 were assayed for conversion of 5-FU to DHFU by LC-MS/MS. Open circles represent individual values, filled circles represent means [n = 3; *p-value<0.05; 5-FU: 52 hr, p = 0.041; DHFU: 30 hr, p = 0.011; 45 hr, p = 0.004; 52 hr, p = 0.009, 2-way ANOVA with Tukey’s correction relative to baseline of the same analyte]. (c) 5-FU MIC determination of the E. coli strains shown in panel a and Fig. 1d in minimal (M9MM) media. Values are normalized to the growth control (no 5-FU) with darker colors indicating growth inhibition. Sterile media is shown in the final row.

Extended Data Fig. 4 Purification and biochemical characterization of E. coli PreTA.

(a) Purification workflow for E. coli PreTA including immobilized metal affinity chromatography (M: marker, I: insoluble fraction, S: soluble fraction, FT: flowthrough, W: wash, numbers across the top indicate imidazole concentration), TEV cleavage (M: marker, L: loaded, FT: flowthrough, W: wash, E: elute), size exclusion chromatography (M: marker, L: loaded, lanes are labeled by fraction from a 96 well plate collected via FPLC), and by UV–visible absorption spectroscopy where normalizing protein levels showed a ratio of greater than 0.35 for A280/A377 indicated holoenzyme. In all gels, numbers down the side indicate protein molecular weight in kDa and solid lines indicate lanes that were carried forward in the preparation. (b-c) Analytical size exclusion chromatography (SEC) traces (b) and analysis (c), characterizing the main peak as a heterotetramer. Grey shaded region indicates 95% confidence intervals of the linear model. (d) High pressure liquid chromatography (HPLC) of NADH, uracil (Ura), dihydrouracil (DHU), NAD+, 5-fluorouracil (5-FU), and dihydrofluorouracil (DHFU). (e) Liquid Chromatography Mass Spectrometry (LC-MS/MS) of enzymatic reaction with 5-FU confirms the presence of the exact mass of DHFU.

Source data

Extended Data Fig. 5 PreTA decreases the efficacy and oral bioavailability of capecitabine (CAP).

(a, g) Xenograft experiment (expt) 1 and 2 design, respectively. (b, h) Pre-treatment tumor volumes: (b) expt1, (h) expt2 (n = 8 mice ΔpreTA-CAP; n = 10 mice preTA++-Veh; n = 9 mice/group remainder expt1; n = 8 mice/group expt2; lines represent medians; 2-way ANOVA with Tukey’s correction). (c, i) Colonization levels of streptomycin-resistant (SmR) E. coli in colony-forming units (CFU)/gram stool in (c) expt1 and (i) expt2 (n = 6 mice/group expt1; n = 8 mice/group expt2; opaque dots and lines represent mean±SEM; 2-way ANOVA with Tukey’s correction). Zero values at baseline replaced with our limit of detection (1000 CFU/g). (d, j) Percentage of starting tumor volumes over time in (d) expt1 and (j) expt2 (n = 8 mice ΔpreTA-CAP group; n = 10 mice preTA++-Veh; n = 9 mice/group remainder expt1; n = 8 mice/group expt2; opaque dots and lines represent mean±SEM; 2-way ANOVA with Tukey’s correction did not reach significance). (e, k) Percentage of starting tumor volumes on (e) day 15 expt1 and (k) day 22 expt2 (n = 8 mice ΔpreTA-CAP group; n = 10 mice preTA++-Veh; n = 9 mice/group remainder expt1; n = 8 mice/group expt2; timepoints selected to capture all mice prior to euthanasia; lines represent medians; 2-way ANOVA with Tukey’s correction). (f, l) Percentage of mice reaching the humane endpoint in (f) expt1 and (l) expt2 (expt1: n = 8 mice ΔpreTA-CAP group; n = 10 mice preTA++-Veh; n = 9 mice/group remainder; expt2: n = 3 preTA++ groups, n = 4 ΔpreTA groups; log-rank Mantel-Cox test comparing ΔpreTA-CAP to all other groups. Eight mice were censored (black boxes) as they did not reach the endpoint in expt1. (m) LC-QTOF/MS quantification of 5-FU from pooled plasma samples following 500 mg/kg CAP administration in mice (same design as Fig. 4g; n = 5 mice/group pooled into n = 1 sample/group).

Source data

Extended Data Fig. 6 Consistent shifts in the gut microbiota across groups.

(a) Quantification of streptomycin-resistant (SmR) E. coli in feces across time (n = 11 mice for for preTA++-Veh group and n = 10 mice/group for the rest; 2-way ANOVA test with Tukey’s correction; lines and ribbons represent mean±SEM). Zero values at baseline replaced with our limit of detection (1000 CFU/g). (b) Principal coordinate analysis of fecal microbiota from mice treated with CAP or vehicle (Veh) and colonized with ΔpreTA or preTA++ E. coli across time (Bray-Curtis distance matrix, permutational multivariate analysis of variance test with Benjamini-Hochberg correction using ADONIS statistical package). (c) Microbial community composition at the phylum level. Each bar represents stool from each mouse. Short horizontal lines within bars represent different amplicon sequence variants (ASV). (d) Number of ASVs over time for each treatment group (exact n values indicated in panel c; 2-way ANOVA with Tukey’s correction; lines and ribbons represent mean±SEM). (e, f) Proportion of (e) Bacteroidota and (f) E. coli over time for each treatment group (exact n values indicated in panel c; 2-way ANOVA with Tukey’s correction; lines and ribbons represent mean±SEM). (g) Volcano plots of differentially abundant sequence variants at day 17 (FDR < 0.1, |log2 fold-change|>1, Wald test with Benjamini-Hochberg correction, significance limits are marked with dash lines). Blue and red texts represent enriched and depleted taxa, respectively.

Source data

Extended Data Fig. 7 Distribution of preTA operons across gut metagenome-assembled genomes (MAGs).

A phylogenetic tree of species from IGGdb, made using a concatenated alignment of single-copy marker genes, is shown in black. Species in which a MAG contains a putative preTA operon are identified with colored circles, where the color of the circle corresponds to prevalence in the gut (blue: low prevalence; orange: high prevalence). All species displayed were detected at least once from 3,810 gut metagenome samples. Phylum-level annotations are shown as colored ring segments surrounding the tree for the ten phyla with the most gut MAGs. Data can be found in Supplementary Table 9.

Extended Data Fig. 8 Bacterial preTA operons from diverse strains, preTA encoding natural strains, and pooled isolates from patient samples are capable of 5-FU inactivation.

(a) preTA operons from Salmonella enterica LT2 (DSM17058), Oxalobacter formigenes ATCC35274, and Lactobacillus reuteri DSM20016 were amplified and integrated into the chromosome of E. coli MG1655 ∆preTA. Strains were incubated with 20 μg/ml 5-FU and assayed for residual drug using disk diffusion (0–48 hours incubation) and LC-QTOF/MS (48 hours). E. coli MG1655 preTA complementation and empty vector controls are also included on the ΔpreTA background. (b) We also tested strains predicted to encode the preTA operon: Salmonella enterica LT2, Anaerostipes caccae DSM14662, and Clostridium sporogenes DSM795. Residual 5-FU was assayed as in panel a. Of note, we did not detect either compound from A. caccae, suggesting that this strain may further metabolize DHFU. (c) Bacterial isolates from 22 CRC patient stool samples in the GO Study (Supplementary Table 12) were isolated on McConkey agar, pooled, and incubated in BHI+ with 5-FU for 4 days. Residual 5-FU levels were assessed using a disk diffusion assay. Colors indicate the treatment cohort: (A) CAP (yellow); (B) TAS-102 (blue); and (C) CAP plus immunotherapy (green). Red boxes in panels a-c indicate the first timepoint without a clear zone of inhibition.

Extended Data Fig. 9 preTA sources and abundance in CRC patients during fluoropyrimidine treatment.

(a) Fraction of total preTA reads mapping to different species in metagenomic samples from GO Study patients undergoing CAP treatment with or without pembrolizumab immunotherapy (IO). Heatmap values were linearly interpolated between samples (filled circles in panel b). (b) Total abundance, as log10 RPKG, of the preTA operon in the gut microbiome prior to and during treatment with the oral fluoropyrimidine CAP with or without IO, as shown in Fig. 6d. Lines connect measurements (filled circles) for the same patient. One zero RPKG value was replaced with half the minimum non-zero value prior to taking the logarithm. The first day of treatment is defined as day 1. Days are the same between panels.

Supplementary information

Source data

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

Unprocessed tumour images.

Source Data Extended Data Fig. 4

Unprocessed protein blots

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

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Spanogiannopoulos, P., Kyaw, T.S., Guthrie, B.G.H. et al. Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism. Nat Microbiol 7, 1605–1620 (2022). https://doi.org/10.1038/s41564-022-01226-5

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