Leukotriene B4 Enhances NOD2-Dependent Innate Response against Influenza Virus Infection
Bibliographic record
Abstract
Leukotriene B4 (LTB4), a central mediator of inflammation, is well known for its chemoattractant properties on effectors cells of the immune system. LTB4 also has the ability to control microbial infection by improving host innate defenses through the release of antimicrobial peptides and modulation of intracellular Toll-like receptors (TLRs) expression in response to agonist challenge. In this report, we provide evidences that LTB4 acts on nucleotide-binging oligomerization domain 2 (NOD2) pathway to enhance immune response against influenza A infection. Infected mice receiving LTB4 show improved survival, lung architecture and reduced lung viral loads as compared to placebo-treated animals. NOD2 and its downstream adaptor protein IPS-1 have been found to be essential for LTB4-mediated effects against IAV infection, as absence of NOD2 or IPS-1 diminished its capacity to control viral infection. Treatment of IAV-infected mice with LTB4 induces an increased activation of IPS-1-IRF3 axis leading to an enhanced production of IFNβ in lungs of infected mice. LTB4 also has the ability to act on the RICK-NF-κB axis since administration of LTB4 to mice challenged with MDP markedly increases the secretion of IL-6 and TNFα in lungs of mice. TAK1 appears to be essential to the action of LTB4 on NOD2 pathway since pretreatment of MEFs with TAK1 inhibitor prior stimulation with IAV or MDP strongly abrogated the potentiating effects of LTB4 on both IFNβ and cytokine secretion. Together, our results demonstrate that LTB4, through its ability to activate TAK1, potentiates both IPS-1 and RICK axis of the NOD2 pathway to improve host innate responses.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".