Effects of defined gut microbial ecosystem components on virulence determinants of Clostridioides difficile
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".