Dataset Information

Effects of defined gut microbial ecosystem components on virulence determinants of Clostridioides difficile.

ABSTRACT: Many cases of Clostridioides difficile infection (CDI) are poorly responsive to standard antibiotic treatment strategies, and often patients suffer from recurrent infections characterized by severe diarrhea. Our group previously reported the successful cure of two patients with recurrent CDI using a standardized stool-derived microbial ecosystem therapeutic (MET-1). Using an in vitro model of the distal gut to support bacterial communities, we characterized the metabolite profiles of two defined microbial ecosystems derived from healthy donor stool (DEC58, and a subset community, MET-1), as well as an ecosystem representative of a dysbiotic state (ciprofloxacin-treated DEC58). The growth and virulence determinants of two C. difficile strains were then assessed in response to components derived from the ecosystems. CD186 (ribotype 027) and CD973 (ribotype 078) growth was decreased upon treatment with DEC58 metabolites compared to ciprofloxacin-treated DEC58 metabolites. Furthermore, CD186 TcdA and TcdB secretion was increased following treatment with ciprofloxacin-treated DEC58 spent medium compared to DEC58 spent medium alone. The net metabolic output of C. difficile was also modulated in response to spent media from defined microbial ecosystems, although several metabolite levels were divergent across the two strains examined. Further investigation of these antagonistic properties will guide the development of microbiota-based therapeutics for CDI.

SUBMITTER: Carlucci C

PROVIDER: S-EPMC6351598 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effects of defined gut microbial ecosystem components on virulence determinants of Clostridioides difficile.

Carlucci Christian C Jones Carys S CS Oliphant Kaitlyn K Yen Sandi S Daigneault Michelle M Carriero Charley C Robinson Avery A Petrof Elaine O EO Weese J Scott JS Allen-Vercoe Emma E

Scientific reports 20190129 1

Many cases of Clostridioides difficile infection (CDI) are poorly responsive to standard antibiotic treatment strategies, and often patients suffer from recurrent infections characterized by severe diarrhea. Our group previously reported the successful cure of two patients with recurrent CDI using a standardized stool-derived microbial ecosystem therapeutic (MET-1). Using an in vitro model of the distal gut to support bacterial communities, we characterized the metabolite profiles of two defined ...[more]

PMID: 30696914

Similar Datasets

Project description:BackgroundClostridioides difficile infection (CDI) is the most common hospital acquired infection in the USA, with recurrence rates > 15%. Although primary CDI has been extensively linked to gut microbial dysbiosis, less is known about the factors that promote or mitigate recurrence. Moreover, previous studies have not shown that microbial abundances in the gut measured by 16S rRNA amplicon sequencing alone can accurately predict CDI recurrence.ResultsWe conducted a prospective, longitudinal study of 53 non-immunocompromised participants with primary CDI. Stool sample collection began pre-CDI antibiotic treatment at the time of diagnosis, and continued up to 8 weeks post-antibiotic treatment, with weekly or twice weekly collections. Samples were analyzed using (1) 16S rRNA amplicon sequencing, (2) liquid chromatography/mass-spectrometry metabolomics measuring 1387 annotated metabolites, and (3) short-chain fatty acid profiling. The amplicon sequencing data showed significantly delayed recovery of microbial diversity in recurrent participants, and depletion of key anaerobic taxa at multiple time-points, including Clostridium cluster XIVa and IV taxa. The metabolomic data also showed delayed recovery in recurrent participants, and moreover mapped to pathways suggesting distinct functional abnormalities in the microbiome or host, such as decreased microbial deconjugation activity, lowered levels of endocannabinoids, and elevated markers of host cell damage. Further, using predictive statistical/machine learning models, we demonstrated that the metabolomic data, but not the other data sources, can accurately predict future recurrence at 1 week (AUC 0.77 [0.71, 0.86; 95% interval]) and 2 weeks (AUC 0.77 [0.69, 0.85; 95% interval]) post-treatment for primary CDI.ConclusionsThe prospective, longitudinal, and multi-omic nature of our CDI recurrence study allowed us to uncover previously unrecognized dynamics in the microbiome and host presaging recurrence, and, in particular, to elucidate changes in the understudied gut metabolome. Moreover, we demonstrated that a small set of metabolites can accurately predict future recurrence. Our findings have implications for development of diagnostic tests and treatments that could ultimately short-circuit the cycle of CDI recurrence, by providing candidate metabolic biomarkers for diagnostics development, as well as offering insights into the complex microbial and metabolic alterations that are protective or permissive for recurrence. Video Abstract.

Dataset Information

Effects of defined gut microbial ecosystem components on virulence determinants of Clostridioides difficile.

Publications

Effects of defined gut microbial ecosystem components on virulence determinants of Clostridioides difficile.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets