Unveiling Macrophage-Biofilm Interactions: Implications for Cellular Metabolism and Wound Healing
Bibliographic record
Abstract
Chronic wounds are challenging to treat, cause significant pain, prolong hospitalization, and in the most severe cases, may lead to infection and/or amputations.Understanding the pathophysiology of chronic wounds is crucial for creating novel therapies that promote healing.Bacterial biofilms have been shown to impair wound healing and promote a low-grade inflammatory response.In chronic wounds, macrophages are chronically activated in a pro-inflammatory state and are unable to promote tissue repair.It is unclear what interactions occur between biofilms and macrophages to drive this persistent pro-inflammatory activation.Emerging evidence suggests that mitochondrial reprogramming plays a key role in fine-tuning the macrophage inflammatory response to bacterial infection.In this study, we found that treatment of bone marrow-derived macrophages with conditioned medium containing secreted factors from single-species biofilms of Staphylococcus aureus or Pseudomonas aeruginosa resulted in different patterns of mitochondrial reprogramming and inflammatory responses.S. aureus conditioned media induced a low-grade inflammatory response, associated with a transient reprogramming of the mitochondria to support mitochondrial reactive oxygen species and inflammatory cytokine production.Alternatively, P. aeruginosa conditioned media induced a stronger inflammatory response associated with sustained mitochondrial reprogramming that resulted in prolonged accumulation of mtROS, which eventually resulted in cell death.When macrophages were stimulated with an anti-inflammatory signal, IL-4, they were unable to repolarize to an antiinflammatory state and demonstrated terminal reprogramming towards sustained inflammation.Our findings imply that secreted factors from biofilms (e.g., LPS) may alter mitochondrial function to rewire macrophages to promote prolonged inflammation in chronic wounds.The bacterial species that are present in wounds have a significant impact on this reprogramming.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".