Inhibiting antibiotic-resistant Enterobacteriaceae by microbiota-mediated intracellular acidification
Bibliographic record
Abstract
Klebsiella pneumoniae, Escherichia coli, and other members of the Enterobacteriaceae family are common human pathogens that have acquired broad antibiotic resistance, rendering infection by some strains virtually untreatable. Enterobacteriaceae are intestinal residents, but generally represent <1% of the adult colonic microbiota. Antibiotic-mediated destruction of the microbiota enables Enterobacteriaceae to expand to high densities in the colon, markedly increasing the risk of bloodstream invasion, sepsis, and death. Here, we demonstrate that an antibiotic-naive microbiota suppresses growth of antibiotic-resistant clinical isolates of Klebsiella pneumoniae, Escherichia coli, and Proteus mirabilis by acidifying the proximal colon and triggering short chain fatty acid (SCFA)–mediated intracellular acidification. High concentrations of SCFAs and the acidic environment counter the competitive edge that O2 and NO3 respiration confer upon Enterobacteriaceae during expansion. Reestablishment of a microbiota that produces SCFAs enhances clearance of Klebsiella pneumoniae, Escherichia coli, and Proteus mirabilis from the intestinal lumen and represents a potential therapeutic approach to enhance clearance of antibiotic-resistant pathogens.
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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".