Altered oral microbiota of drug-resistant organism carriers exhibit impaired gram-negative pathogen inhibition
Bibliographic record
Abstract
Abstract The oral microbiome has been understudied as a reservoir for clinical pathogens, including drug-resistant strains. Understanding how alterations in microbiome functioning render this site vulnerable to colonization is essential, as multidrug-resistant organisms (MDRO) carriage is a major risk factor for developing serious infections. To advance our knowledge of oral MDRO carriage and protection against pathogen colonization conferred by native microbiota, we examined microbiomes from individuals colonized by MDROs (n=33) and non-colonized age-matched controls (n=30). Shotgun metagenomic analyses of oral swabs from study participants revealed significant differences in microbial communities with depletion of Streptococcus spp. among those colonized by multidrug-resistant gram-negative bacilli (RGNB), compared to non-carriers. We utilized metagenomic sequencing to characterize the oral resistome and find antimicrobial resistance genes are present in higher abundance among RNGB carriers versus non-carriers. High-throughput co-culture screening revealed oral bacteria isolated from MDRO non-carriers demonstrate greater inhibition of gram-negative pathogens, compared to isolates from carriers. Moreover, biosynthetic gene clusters from streptococci are found in higher abundance from non-carrier microbiomes, compared to RGNB carrier microbiomes. Bioactivity-guided fractionation of extracts from Streptococcus isolate SID2657 demonstrated evidence of strong E. coli and A. baumannii inhibition in a murine model of infection. Together, this provides evidence that oral microbiota shape this dynamic microbial community and may serve as an untapped source for much-needed antimicrobial small-molecules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".