Dysregulated Macrophage-Inflammatory Protein-2 Expression Drives Illness in Bacterial Superinfection of Influenza
Bibliographic record
Abstract
Influenza virus infection is a leading cause of death and disability throughout the world. Influenza-infected hosts are vulnerable to secondary bacterial infection, however, and an ensuing bacterial pneumonia is actually the predominant cause of influenza-attributed deaths during pandemics. A number of mechanisms have been proposed by which influenza may predispose to superinfection with an unrelated or heterologous pathogen, but the subsequent interaction between the host, virus, and bacteria remains an understudied area. In this study, we develop and examine a novel model of heterologous pulmonary infection in which an otherwise subclinical Bordetella parapertussis infection synergizes with an influenza virus infection to yield a life-threatening secondary pneumonia. Despite a profound pulmonary inflammatory response and unaltered viral clearance, bacterial clearance was significantly impaired in heterologously infected mice. No deficits were observed in pulmonary or systemic adaptive immune responses or the viability or function of infiltrating inflammatory cells to explain this phenomenon, and we provide evidence that the onset of severe pulmonary inflammation actually precedes the increased bacterial burden, suggesting that exacerbated inflammation is independent of bacterial burden. To that end, neutralization of the ELR(+) inflammatory chemokine MIP-2 (CXCL2/GRO-beta) attenuated the inflammation, weight loss, and clinical presentation of heterologously infected mice without impacting bacterial burden. These data suggest that pulmonary inflammation, rather than pathogen burden, is the key threat during bacterial superinfection of influenza and that selective chemokine antagonists may be a novel therapeutic intervention in cases of bacterial superinfection of influenza.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".