Microbiome (Nov 2017)

Identifying predictive features of Clostridium difficile infection recurrence before, during, and after primary antibiotic treatment

  • Sepideh Pakpour,
  • Amit Bhanvadia,
  • Roger Zhu,
  • Abhimanyu Amarnani,
  • Sean M. Gibbons,
  • Thomas Gurry,
  • Eric J. Alm,
  • Laura A. Martello

DOI
https://doi.org/10.1186/s40168-017-0368-1
Journal volume & issue
Vol. 5, no. 1
pp. 1 – 10

Abstract

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Abstract Background Colonization by the pathogen Clostridium difficile often occurs in the background of a disrupted microbial community. Identifying specific organisms conferring resistance to invasion by C. difficile is desirable because diagnostic and therapeutic strategies based on the human microbiota have the potential to provide more precision to the management and treatment of Clostridium difficile infection (CDI) and its recurrence. Methods We conducted a longitudinal study of adult patients diagnosed with their first CDI. We investigated the dynamics of the gut microbiota during antibiotic treatment, and we used microbial or demographic features at the time of diagnosis, or after treatment, to predict CDI recurrence. To check the validity of the predictions, a meta-analysis using a previously published dataset was performed. Results We observed that patients’ microbiota “before” antibiotic treatment was predictive of disease relapse, but surprisingly, post-antibiotic microbial community is indistinguishable between patients that recur or not. At the individual OTU level, we identified Veillonella dispar as a candidate organism for preventing CDI recurrence; however, we did not detect a corresponding signal in the conducted meta-analysis. Conclusion Although in our patient population, a candidate organism was identified for negatively predicting CDI recurrence, results suggest the need for larger cohort studies that include patients with diverse demographic characteristics to generalize species that robustly confer colonization resistance against C. difficile and accurately predict disease relapse.