An orally administered drug prevents selection for antibiotic-resistant bacteria in the gut during daptomycin therapy.

Background and objectives: Previously, we showed proof-of-concept in a mouse model that oral administration of cholestyramine prevented enrichment of daptomycin-resistant Enterococcus faecium in the gastrointestinal (GI) tract during daptomycin therapy. Cholestyramine binds daptomycin in the gut, which removes daptomycin selection pressure and so prevents the enrichment of resistant clones. Here, we investigated two open questions related to this approach: (i) can cholestyramine prevent the enrichment of diverse daptomycin mutations emerging de novo in the gut? and (ii) how does the timing of cholestyramine administration impact its ability to suppress resistance? Methodology: Mice with GI E. faecium were treated with daptomycin with or without cholestyramine, and E. faecium was cultured from feces to measure changes in daptomycin susceptibility. A subset of clones was sequenced to investigate the genomic basis of daptomycin resistance. Results: Cholestyramine prevented the enrichment of diverse resistance mutations that emerged de novo in daptomycin-treated mice. Whole-genome sequencing revealed that resistance emerged through multiple genetic pathways, with most candidate resistance mutations observed in the clsA gene. In addition, we observed that cholestyramine was most effective when administration started prior to the first dose of daptomycin. However, beginning cholestyramine after the first daptomycin dose reduced the frequency of resistant E. faecium compared to not using cholestyramine at all. Conclusions and implications: Cholestyramine prevented the enrichment of diverse daptomycin-resistance mutations in intestinal E. faecium populations during daptomycin treatment, and it is a promising tool for managing the transmission of daptomycin-resistant E. faecium.

Changes in the Association Between Diagnostic Testing Method, Polymerase Chain Reaction Ribotype, and Clinical Outcomes From Clostridioides difficile Infection: One Institution's Experience.

BACKGROUND: In Clostridioides difficile infection (CDI), the relationship between clinical, microbial, and temporal/epidemiological trends, disease severity and adverse outcomes is incompletely understood. In a follow-up to our study from 2010-2013, we evaluate stool toxin levels and C. difficile polymerase chain reaction (PCR) ribotypes. We hypothesized that elevated stool toxins and infection with ribotype 027 associate with adverse outcomes. METHODS: In 565 subjects at the University of Michigan with CDI diagnosed by positive testing for toxins A/B by enzyme immunoassay (EIA) or PCR for the tcdB gene, we quantified stool toxin levels via a modified cell cytotoxicity assay (CCA), isolated C. difficile by anaerobic culture, and performed PCR ribotyping. Severe CDI was defined by Infectious Diseases Society of America (IDSA) criteria, and primary outcomes were all-cause 30-day mortality and a composite of colectomy, intensive care unit admission, and/or death attributable to CDI within 30 days. Analyses included bivariable tests and logistic regression. RESULTS: 199 samples were diagnosed by EIA; 447 were diagnosed by PCR. Toxin positivity associated with IDSA severity but not primary outcomes. In 2016, compared with 2010-2013, ribotype 106 newly emerged, accounting for 10.6% of strains, ribotype 027 fell from 16.5% to 9.3%, and ribotype 014-027 remained stable at 18.9%. Ribotype 014-020 associated with IDSA severity and 30-day mortality (P = .001). CONCLUSIONS: Toxin positivity by EIA and CCA associated with IDSA severity but not with subsequent adverse outcomes. The molecular epidemiology of C. difficile has shifted, which may have implications for the optimal diagnostic strategy for and clinical severity of CDI.

Anti-toxin antibody is not associated with recurrent Clostridium difficile infection.

Clostridium difficile infection (CDI) recurs in ∼20% of patients. Prior studies indicated that antibody responses directed against the C. difficile toxins A and B were potentially associated with lower risk of recurrent CDI. Here we tested the hypothesis that circulating anti-toxin IgG antibody levels associate with reduced risk of recurrent CDI. A cohort study with prospective enrollment and retrospective data abstraction examined antibody levels in 275 adult patients at the University of Michigan with CDI. We developed an enzyme linked immunosorbent assay to detect IgG antibodies against toxin A and toxin B in sera obtained at the time of diagnosis. Logistic regression examined the relationship between antibody levels and recurrence, and sensitivity tests evaluated for follow-up and survivor biases, history of CDI, and PCR ribotype. Follow-up data were available for 174 subjects, of whom 36 (20.7%) had recurrence. Comparing antibody levels vs. recurrence and CDI history, anti-toxin A levels were similar, while anti-toxin B levels had a greater range of values. In unadjusted analysis, detection of anti-toxin A antibodies, but not anti-toxin B antibodies, associated with an increased risk of recurrence (OR 2.71 [1.06, 8.37], P = .053). Adjusting for confounders weakened this association. The results were the same in sensitivity analyses. We observed a borderline increased risk of recurrence in patients positive for anti-toxin A antibodies, and sensitivity analyses showed this was not simply a reflection of prior exposure status. Future studies are needed to assess how neutralizing antibody or levels after treatment associate with recurrence.

Systemic Inflammatory Mediators Are Effective Biomarkers for Predicting Adverse Outcomes in Clostridioides difficile Infection.

Clostridioides difficile infection (CDI) can result in severe disease and death, with no accurate models that allow for early prediction of adverse outcomes. To address this need, we sought to develop serum-based biomarker models to predict CDI outcomes. We prospectively collected sera ≤48 h after diagnosis of CDI in two cohorts. Biomarkers were measured with a custom multiplex bead array assay. Patients were classified using IDSA severity criteria and the development of disease-related complications (DRCs), which were defined as ICU admission, colectomy, and/or death attributed to CDI. Unadjusted and adjusted models were built using logistic and elastic net modeling. The best model for severity included procalcitonin (PCT) and hepatocyte growth factor (HGF) with an area (AUC) under the receiver operating characteristic (ROC) curve of 0.74 (95% confidence interval, 0.67 to 0.81). The best model for 30-day mortality included interleukin-8 (IL-8), PCT, CXCL-5, IP-10, and IL-2Rα with an AUC of 0.89 (0.84 to 0.95). The best model for DRCs included IL-8, procalcitonin, HGF, and IL-2Rα with an AUC of 0.84 (0.73 to 0.94). To validate our models, we employed experimental infection of mice with C. difficile Antibiotic-treated mice were challenged with C. difficile and a similar panel of serum biomarkers was measured. Applying each model to the mouse cohort of severe and nonsevere CDI revealed AUCs of 0.59 (0.44 to 0.74), 0.96 (0.90 to 1.0), and 0.89 (0.81 to 0.97). In both human and murine CDI, models based on serum biomarkers predicted adverse CDI outcomes. Our results support the use of serum-based biomarker panels to inform Clostridioides difficile infection treatment.IMPORTANCE Each year in the United States, Clostridioides difficile causes nearly 500,000 gastrointestinal infections that range from mild diarrhea to severe colitis and death. The ability to identify patients at increased risk for severe disease or mortality at the time of diagnosis of C. difficile infection (CDI) would allow clinicians to effectively allocate disease modifying therapies. In this study, we developed models consisting of only a small number of serum biomarkers that are capable of predicting both 30-day all-cause mortality and adverse outcomes of patients at time of CDI diagnosis. We were able to validate these models through experimental mouse infection. This provides evidence that the biomarkers reflect the underlying pathophysiology and that our mouse model of CDI reflects the pathogenesis of human infection. Predictive models can not only assist clinicians in identifying patients at risk for severe CDI but also be utilized for targeted enrollment in clinical trials aimed at reduction of adverse outcomes from severe CDI.