Research ArticleGUT MICROBIOTA

Clostridioides difficile uses amino acids associated with gut microbial dysbiosis in a subset of patients with diarrhea

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Science Translational Medicine  24 Oct 2018:
Vol. 10, Issue 464, eaam7019
DOI: 10.1126/scitranslmed.aam7019
  • Fig. 1 A subset of patients with diarrhea have a dysbiotic gut microbiota.

    (A) β-Diversity (unweighted UniFrac) of the gut microbiota of healthy control individuals (n = 118) compared to patients with diarrhea clustered on the basis of partitioning around medoids (PAM) [cluster H (healthy-like), n = 78 and cluster D (dysbiotic), n = 37]. (B) Unweighted UniFrac distances between healthy-like and a dysbiotic gut microbiota from patients with diarrhea compared to a healthy control gut microbiota. The plotted median with interquartile range (IQR) and SD (Bonferroni-corrected P < 0.0001, t test) is shown. (C) α-Diversity as indicated by the Shannon diversity index is shown for dysbiotic and healthy-like gut microbiotas from patients with diarrhea. Plotted averages with SEM (***P < 0.0005, t test). (D) Heatmap showing significantly different microbial taxa between healthy-like and dysbiotic gut microbial communities. The operational taxonomic unit (OTU) number is featured after the genus (all Bonferroni-corrected P < 0.02, Wilcoxon rank-sum test). (E and F) β-Diversity (unweighted UniFrac) of the gut microbiota from (E) healthy control individuals (n = 118), dysbiotic patients with diarrhea (n = 37), and patients with C. difficile infection (n = 95); (F) unweighted UniFrac distance between the dysbiotic gut microbiota of patients with diarrhea versus the gut microbiota of healthy controls or those with CDI. Plotted median with IQR and SD (Bonferroni-corrected P < 0.0001, t test).

  • Fig. 2 Mice with a dysbiotic gut microbiota exhibit increased susceptibility to C. difficile infection.

    (A) C. difficile CFUs per milliliter of stool from germ-free mice colonized with either a healthy-like (n = 11) or dysbiotic (n = 10) gut microbiota from patients with diarrhea are shown. Data points represent individual animals with lines indicating average and SEM. Assay limit of detection (LOD) indicated by horizontal dotted line at 2 × 104 CFU/ml stool (**P < 0.005, ***P < 0.0005, ****P < 0.00005, Holm-Šídák test). (B) Stool consistency for germ-free mice transplanted with a healthy-like (n = 11) or dysbiotic (n = 10) gut microbiota, 2 days after C. difficile challenge. Plotted means and SEM (****P < 0.0001, Mann-Whitney test). (C) C. difficile toxin B concentrations measured by quantitative ELISA in stool from germ-free mice transplanted with a healthy-like or dysbiotic human gut microbiota, 6 days after C. difficile challenge. Plotted means and SEM; ND, not detected. (D) Average proximal colon inflammation score in germ-free mice transplanted with a healthy-like (n = 11) or dysbiotic (n = 10) human gut microbiota, after C. difficile challenge. Plotted means and SEM (***P < 0.0005, Mann-Whitney test). (E) IL-22 and IL-23 concentrations measured from full thickness tissue collected from the proximal colon of germ-free mice transplanted with a dysbiotic (n = 5) or healthy-like (n = 5) human gut microbiota before and 7 days after C. difficile challenge. Plotted means and SEM (*P < 0.05, **P < 0.005, Mann-Whitney test). (F) β-Diversity and (G) weighted UniFrac distance comparisons for dysbiotic and healthy-like human gut microbial communities after transplant into germ-free mice before and 2 days after C. difficile challenge [Bonferroni-corrected P = 1 (dysbiotic), Bonferroni-corrected P = 1 (healthy-like), Student’s t test].

  • Fig. 3 C. difficile exploits increased availability of amino acids in the dysbiotic gut microbiota.

    (A) A subset of pathway gene expression based on whole gut microbial community gene expression (RNA-seq) normalized using shallow metagenomic sequencing of stool from germ-free mice transplanted with healthy-like (n = 6) or dysbiotic (n = 6) human gut microbiota before C. difficile challenge. (B) Amino acid concentrations in stool from mice transplanted with a healthy-like (n = 5) or dysbiotic (n = 8) human gut microbiota. Plotted averages and SEM (*P < 0.05, **P < 0.005, Mann-Whitney test); ns, not significant; ND, not detected. (C) C. difficile growth kinetics in basal defined medium (BDM) containing 0, 0.01, or 0.1% deoxycholic acid (DCA) and amino acid concentrations at 100, 50, or 25% those of standard media concentrations. Plotted averages and SEM.

  • Fig. 4 Proline affects C. difficile colonization in germ-free mice transplanted with a dysbiotic or healthy-like human gut microbiota.

    (A) C. difficile growth kinetics indicated optical density (OD) at 600 nm in the presence or absence of proline in basal defined medium without glucose. Plotted means and SEM. (B) D-proline reductase A ( prdA) expression normalized to metagenomic read counts in the healthy-like and dysbiotic gut microbiota of transplanted mice before C. difficile challenge. (C) ΔprdB mutant C. difficile CFU/ml stool from germ-free mice transplanted with a dysbiotic (n = 7) or healthy-like (n = 8) human gut microbiota after C. difficile challenge is shown. Colonization of the transplanted mice with wild-type (WT) C. difficile from Fig. 2A is also shown. Data points represent individual animals with lines indicating average and SEM. Assay limit of detection (LOD) indicated by a horizontal dotted line at 2 × 104 CFU/ml stool (***P < 0.005, ****P < 0.0005, two-way ANOVA). (D) C. difficile toxin B concentrations were measured by quantitative ELISA in the stool of mice transplanted with a dysbiotic (n = 7) or a healthy-like (n = 8) human gut microbiota 6 days after challenge with ΔprdB mutant C. difficile or wild-type C. difficile (data from Fig. 2C). Plotted means and SEM (***P < 0.0005, Mann-Whitney test).

  • Fig. 5 Fecal microbiota transplant from healthy individuals reduces free proline and susceptibility of transplanted mice to C. difficile infection.

    (A) β-Diversity (weighted UniFrac) of mice transplanted with a dysbiotic human gut microbiota, before and after a fecal microbiota transplant (FMT) from healthy individuals (n = 6). (B) Distances (weighted UniFrac) between FMT healthy donors and mice transplanted with a dysbiotic gut microbiota were significantly decreased after FMT (Bonferroni-corrected P < 0.0001, Student’s t test). (C) α-Diversity of the dysbiotic gut microbiota in transplanted mice before and after FMT (n = 6). Plotted averages and SEM (***P < 0.0005, Student’s t test). (D) Proline concentrations in stool from mice transplanted with a dysbiotic gut microbiota, before and after FMT (n = 6). Plotted averages and SEM (**P < 0.005, Mann-Whitney test).

  • Fig. 6 Five clinical risk factors may predict gut microbial dysbiosis and susceptibility to C. difficile infection in patients with diarrhea.

    (A) Receiver operating characteristic (ROC) curve based on five clinical risk factors that may be predictive of gut microbiota dysbiosis. Recent antibiotics (OR, 5.21; 95% CI, 2.14 to 12.71; P < 0.001), immunosuppression (OR, 2.87; 95% CI, 1.27 to 6.48; P = 0.012), current hospitalization (OR, 6.17; 95% CI, 2.22 to 17.15; P < 0.001), recent hospitalization (OR, 4.87; 95% CI, 1.72 to 13.74; P = 0.003), and prior C. difficile infection (OR, 9.26; 95% CI, 2.37 to 36.20; P = 0.001). Area under the curve (AUC), 0.78 (see table S4). (B) ROC curve based on five clinical risk factors that may be predictive of C. difficile infection. Recent antibiotics (OR, 3.35; 95% CI, 2.78 to 4.03; P = 6.21 × 10−37), immunosuppression (OR, 2.47; 95% CI, 2.06 to 2.96; P = 8.42 × 10−23), current hospitalization (OR, 2.94; 95% CI, 2.40 to 3.61; P = 8.45 × 10−25), recent hospitalization (OR, 3.32; 95% CI, 2.72 to 4.06; P = 1.85 × 10−31), and prior C. difficile infection (OR, 5.84; 95% CI, 4.42 to 7.72; P = 1.66 × 10−35). AUC, 0.71.

  • Table 1 Absolute counts of prdA gene expression in dysbiotic and healthy-like mice at day 2 after C. difficile challenge by taxonomic group.
    DysbioticHealthy-like
    Mouse IDM1M2M3M4M5M6M1M2M3M4M5M6
    Clostridium difficile531451003798182357742764000000
    Clostridium
    hylemonae
    000000518004124600
    Dorea longicatena000000467802171000
    Lachnospiraceae
    bacterium
    5_1_57FAA
    000000001266034250
  • Table 2 Demographics of patients who developed C. difficile infection after initially testing negative.
    Developed CDI
    (n = 493)
    Did not
    developed CDI
    (n = 16,697)
    P value
    Sex, n (%)
      Male252 (51)7391 (44)0.003
      Female241 (49)9306 (56)
    Age (year)
      Mean (SD)56.8 (17.7)57.1 (18.6)0.726
      Range18–9518–106
    BMI
      Mean (SD)29 (17.7)28 (7.3)0.002
      Range14–7910–100
  • Table 3 Risk factors predictive of C. difficile infection.
    Risk factorOdds ratio (95% CI)P value
    Antibiotics3.35 (2.78–4.03)6.2 × 10−37
    Immunosuppression2.47 (2.06–2.96)8.4 × 10−23
    Recent hospitalization3.32 (2.72–4.06)1.8 × 10−31
    Current hospitalization2.94 (2.40–3.61)8.4 × 10−25
    Prior CDI5.84 (4.42–7.72)1.6 × 10−35
    Any one risk factor1.16 (0.74–1.83)0.51
    Any two risk factors3.89 (2.76–5.48)7.4 × 10−15
    Any three risk factors5.00 (3.59–6.96)1.2 × 10−21
    Any four risk factors9.28 (6.67–12.91)5.4 × 10−40
    All five risk factors24.24 (12.96–45.32)1.8 × 10−23

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/10/464/eaam7019/DC1

    Materials and Methods

    Fig. S1. Etiology and cluster analysis of patients with diarrhea.

    Fig. S2. Microbial community and host phenotypes of healthy-like and dysbiotic mice.

    Fig. S3. Technical and biological replication of healthy-like and dysbiotic microbial communities.

    Fig. S4. Metatranscriptomics and metabolomics indicate that SCFAs and secondary bile acids inhibit C. difficile colonization.

    Fig. S5. Metabolic properties of healthy-like and dysbiotic human stool samples.

    Fig. S6. Dietary intervention reduces early C. difficile colonization in dysbiotic mice.

    Fig. S7. FMT decreases susceptibility to CDI and normalizes the metabolic milieu.

    Fig. S8. Distribution of healthy-like and dysbiotic individuals by risk factors that are predictive of dysbiosis.

    Table S1. Patient cohort demographics.

    Table S2. Taxonomic differences between healthy-like and dysbiotic individuals.

    Table S3. Etiology of diarrhea by community type.

    Table S4. Efficiency of transfer of human gut microbiota to mice at the family level.

    Table S5. Colon inflammation scores.

    Table S6. Custom diet formulations.

    Table S7. Demographics and risk factors predictive of dysbiosis.

    Data file S1. Untargeted metabolomics with metabolites increased in healthy-like mice.

    Data file S2. Untargeted metabolomics with metabolites increased in dysbiotic mice.

    Data file S3. Source data for main figures and tables.

    Data file S4. Source data for supplementary figures and tables.

    References (5562)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Etiology and cluster analysis of patients with diarrhea.
    • Fig. S2. Microbial community and host phenotypes of healthy-like and dysbiotic mice.
    • Fig. S3. Technical and biological replication of healthy-like and dysbiotic microbial communities.
    • Fig. S4. Metatranscriptomics and metabolomics indicate that SCFAs and secondary bile acids inhibit C. difficile colonization.
    • Fig. S5. Metabolic properties of healthy-like and dysbiotic human stool samples.
    • Fig. S6. Dietary intervention reduces early C. difficile colonization in dysbiotic mice.
    • Fig. S7. FMT decreases susceptibility to CDI and normalizes the metabolic milieu.
    • Fig. S8. Distribution of healthy-like and dysbiotic individuals by risk factors that are predictive of dysbiosis.
    • Table S1. Patient cohort demographics.
    • Table S2. Taxonomic differences between healthy-like and dysbiotic individuals.
    • Table S3. Etiology of diarrhea by community type.
    • Table S4. Efficiency of transfer of human gut microbiota to mice at the family level.
    • Table S5. Colon inflammation scores.
    • Table S6. Custom diet formulations.
    • Table S7. Demographics and risk factors predictive of dysbiosis.
    • References (5562)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). Untargeted metabolomics with metabolites increased in healthy-like mice.
    • Data file S2 (Microsoft Excel format). Untargeted metabolomics with metabolites increased in dysbiotic mice.
    • Data file S3 (Microsoft Excel format). Source data for main figures and tables.
    • Data file S4 (Microsoft Excel format). Source data for supplementary figures and tables.

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