Impact of <i>Actinobacillus pleuropneumoniae</i> biofilm mode of growth on the lipid A structures and stimulation of immune cells
Bibliographic record
Abstract
Actinobacillus pleuropneumoniae (APP), the etiologic agent of porcine pleuropneumonia, forms biofilms on biotic and abiotic surfaces. APP biofilms confers resistance to antibiotics. To our knowledge, no studies have examined the role of APP biofilm in immune evasion and infection persistence. This study was undertaken to (i) investigate biofilm-associated LPS modifications occurring during the switch to biofilm mode of growth; and (ii) characterize pro-inflammatory cytokines expression in porcine pulmonary alveolar macrophages (PAMs) and proliferation in porcine PBMCs challenged with planktonic or biofilm APP cells. Extracted lipid A samples from biofilm and planktonic cultures were analyzed by HPLC high-resolution, accurate mass spectrometry. Biofilm cells displayed significant changes in lipid A profiles when compared with their planktonic counterparts. Furthermore, in vitro experiments were conducted to examine the inflammatory response of PAMs exposed to UV-inactivated APP grown in biofilm or in suspension. Relative mRNA expression of pro-inflammatory genes IL1, IL6, IL8 and MCP1 decreased in PAMs when exposed to biofilm cells compared to planktonic cells. Additionally, the biofilm state reduced PBMCs proliferation. Taken together, APP biofilm cells show a weaker ability to stimulate innate immune cells, which could be due, in part, to lipid A structure modifications.
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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".