New Mouse Model for Chronic Infections by Gram-Negative Bacteria Enabling the Study of Anti-Infective Efficacy and Host-Microbe Interactions
Bibliographic record
Abstract
ABSTRACT Only a few, relatively cumbersome animal models enable long-term Gram-negative bacterial infections that mimic human situations, where untreated infections can last for weeks. Here, we describe a simple murine cutaneous abscess model that enables chronic or progressive infections, depending on the subcutaneously injected bacterial strain. In this model, Pseudomonas aeruginosa cystic fibrosis epidemic isolate LESB58 caused localized high-density skin and soft tissue infections and necrotic skin lesions for up to 10 days but did not disseminate in either CD-1 or C57BL/6 mice. The model was adapted for use with four major Gram-negative nosocomial pathogens, Acinetobacter baumannii , Klebsiella pneumoniae , Enterobacter cloacae , and Escherichia coli . This model enabled noninvasive imaging and tracking of lux -tagged bacteria, the influx of activated neutrophils, and production of reactive oxygen-nitrogen species at the infection site. Screening antimicrobials against high-density infections showed that local but not intravenous administration of gentamicin, ciprofloxacin, and meropenem significantly but incompletely reduced bacterial counts and superficial tissue dermonecrosis. Bacterial RNA isolated from the abscess tissue revealed that Pseudomonas genes involved in iron uptake, toxin production, surface lipopolysaccharide regulation, adherence, and lipase production were highly upregulated whereas phenazine production and expression of global activator gacA were downregulated. The model was validated for studying virulence using mutants of more-virulent P. aeruginosa strain PA14. Thus, mutants defective in flagella or motility, type III secretion, or siderophore biosynthesis were noninvasive and suppressed dermal necrosis in mice, while a strain with a mutation in the bfiS gene encoding a sensor kinase showed enhanced invasiveness and mortality in mice compared to controls infected with wild-type P. aeruginosa PA14. IMPORTANCE More than two-thirds of hospital infections are chronic or high-density biofilm infections and difficult to treat due to adaptive, multidrug resistance. Unfortunately, current models of chronic infection are technically challenging and difficult to track without sacrificing animals. Here we describe a model of chronic subcutaneous infection and abscess formation by medically important nosocomial Gram-negative pathogens that is simple and can be used for tracking infections by imaging, examining pathology and immune responses, testing antimicrobial treatments suitable for high-density bacterial infections, and studying virulence. We propose that this mouse model can be a game changer for modeling hard-to-treat Gram-negative bacterial chronic and skin infections.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".