Lyn-Dependent Signaling Regulates the Innate Immune Response by Controlling Dendritic Cell Activation of NK Cells
Bibliographic record
Abstract
The innate immune response is a first line of defense against invading pathogens; however, the magnitude of this response must be tightly regulated, as hyper- or suboptimal responses can be detrimental to the host. Systemic inflammation resulting from bacterial infection can lead to sepsis, which remains a serious problem with high mortality rates. Lyn tyrosine kinase plays a key role in adaptive immunity, although its role in innate immunity remains unclear. In this study, we show that Lyn gain-of-function (Lyn(up/up)) mice display enhanced sensitivity to endotoxin and succumb to upregulated proinflammatory cytokine production at a dose well tolerated by control animals. Endotoxin sensitivity in Lyn(up/up) mice depends on dendritic cells (DCs) and NK cells and occurs though a mechanism involving increased maturation and activation of the DC compartment, leading to elevated production of IFN-γ by NK cells. We further show that modulation of endotoxin-induced signal transduction in DCs by Lyn involves the phosphatases Src homology 2 domain-containing phosphatase-1 and SHIP-1. Collectively, we demonstrate that Lyn regulates DC physiology such that alterations in Lyn-dependent signaling have profound effects on the nature and magnitude of inflammatory responses. Our studies highlight how perturbations in signaling pathways controlling DC/NK cell-regulated responses to microbial products can profoundly affect the magnitude of innate immune 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) | 0.000 | 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".