Alterations in intestinal Proteobacteria and antimicrobial resistance gene burden in individuals administered microbial ecosystem therapeutic (MET-2) for recurrent <i>Clostridioides difficile</i> infection
Bibliographic record
Abstract
Abstract Intestinal colonisation with pathogens and antimicrobial resistant organisms (AROs) is associated with increased risk of infection. Fecal microbiota transplant (FMT) has successfully been used to cure recurrent Clostridioides difficile infection (rCDI) and to decolonise intestinal AROs. However, FMT has significant practical barriers to implementation. A microbial consortium, microbial ecosystem therapeutic (MET)-2, is an alternative to FMT for the treatment of rCDI. It is unknown whether MET-2 is associated with decreases in pathogens and antimicrobial resistance genes (ARGs). We conducted a post-hoc metagenomic analysis of stool collected from two interventional studies of MET-2 (published) and FMT (unpublished) for rCDI treatment to understand if MET-2 had similar effects to FMT for decreasing pathogens and ARGs as well as increasing anaerobes. Patients were included in the current study if baseline stool had Proteobacteria relative abundance ≥10% by metagenomic sequencing. We assessed pre- and post-treatment Proteobacteria, obligate anaerobe and butyrate-producer relative abundances and total ARGs. MET-2 and FMT were associated with decreases in Proteobacteria relative abundance as well as increases in obligate anaerobe and butyrate-producer relative abundances. The microbiota response remained stable over 4 or 6 months for MET-2 and FMT, respectively. MET-2, but not FMT, was associated with a decrease in the total number of ARGs. MET-2 is a potential therapeutic strategy for ARO/ARG decolonisation and anaerobe repletion.
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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 | 0.001 |
| 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".