Metal Uptake Systems Underpin <i>Enterococcus faecalis</i> Virulence in Both Non-Diabetic and Diabetic Wound Infection Models
Bibliographic record
Abstract
ABSTRACT Wound infections remain an important medical problem, which is aggravated by the prevalence of multidrug resistant bacteria. Among them, Enterococcus faecalis is a major pathogen of surgical site incisional and of diabetic chronic wounds, but factors driving its colonization and persistence in wounds remain poorly understood. Iron, manganese, and zinc are essential cofactors in cellular processes, prompting the host to restrict their availability through mobilization of metal-sequestering proteins, a defense known as nutritional immunity. Previously, we showed that E. faecalis strains lacking key iron (Δ5Fe), manganese (Δ3Mn), or zinc (Δ2Zn) uptake systems have impaired virulence. Here, we used an excisional wound model in normoglycemic (C57Bl/6J or B6) and diabetic (C57Bl/6J lepR⁻/⁻ or DB) mice to examine the role of these metal import systems in wounds. The strong upregulation of metal import genes and reduced wound colonization by Δ3Mn, Δ5Fe, and Δ2Zn strains in B6 mice indicate that iron, manganese, and zinc are limited during wound infection. While Δ2Zn and Δ3Mn strains showed no improved colonization in diabetic wounds, the Δ5Fe exhibited a temporary colonization advantage over non-diabetic mice. Quantifications of metal-sequestering proteins lactoferrin, transferrin, calprotectin and psoriasin from intact skin and infected wounds indicated that nutritional immunity, especially iron restriction, is delayed in diabetes. In conclusion, this study underscores the crucial role of trace metal acquisition in E. faecalis wound colonization and suggests differences in metal bioavailability between diabetic and non-diabetic wounds, helping to explain the increased susceptibility of diabetic wounds to chronic infection.
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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 | 0.001 |
| 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".