Host Fate is Rapidly Determined by Innate Effector-Microbial Interactions During Acinetobacter baumannii Bacteremia
Bibliographic record
Abstract
BACKGROUND: Acinetobacter baumannii is one of the most antibiotic-resistant pathogens. Defining mechanisms driving pathogenesis is critical to enable new therapeutic approaches. METHODS: We studied virulence differences across a diverse panel of A. baumannii clinical isolates during murine bacteremia to elucidate host-microbe interactions that drive outcome. RESULTS: We identified hypervirulent strains that were lethal at low intravenous inocula and achieved very high early, and persistent, blood bacterial densities. Virulent strains were nonlethal at low inocula but lethal at 2.5-fold higher inocula. Finally, relatively avirulent (hypovirulent) strains were nonlethal at 20-fold higher inocula and were efficiently cleared by early time points. In vivo virulence correlated with in vitro resistance to complement and macrophage uptake. Depletion of complement, macrophages, and neutrophils each independently increased bacterial density of the hypovirulent strain but insufficiently to change lethality. However, disruption of all 3 effector mechanisms enabled early bacterial densities similar to hypervirulent strains, rendering infection 100% fatal. CONCLUSIONS: The lethality of A. baumannii strains depends on distinct stages. Strains resistant to early innate effectors are able to establish very high early bacterial blood density, and subsequent sustained bacteremia leads to Toll-like receptor 4-mediated hyperinflammation and lethality. These results have important implications for translational efforts to develop therapies that modulate host-microbe interactions.
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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".