Differential Contribution of NF-κB Signaling Pathways to CD4+ Memory T Cell Induced Activation of Endothelial Cells
Bibliographic record
Abstract
Endothelial cells (ECs) are important contributors to inflammation in immune-mediated inflammatory diseases (IMIDs). In this study, we examined whether CD4 + memory T (T m ) cells can drive EC inflammatory responses. Human T m cells produced ligands that induced inflammatory responses in human umbilical vein EC as exemplified by increased expression of inflammatory mediators including chemokines and adhesion molecules. NF-κB, a key regulator of EC activation, was induced by T m cell ligands. We dissected the relative contribution of canonical and non-canonical NF-κB signaling to T m induced EC responses using pharmacological small molecule inhibitors of IKKβ (iIKKβ) or NF-κB inducing kinase (iNIK). RNA sequencing revealed substantial overlap in IKKβ and NIK regulated genes (n=549) that were involved in inflammatory and immune responses, including cytokines (IL-1β, IL-6, GM-CSF) and chemokines (CXCL5, CXCL1). NIK regulated genes were more restricted, as 332 genes were uniquely affected by iNIK versus 749 genes by iIKKβ, the latter including genes involved in metabolism, proliferation and leukocyte adhesion (VCAM-1, ICAM-1). The functional importance of NIK and IKKβ in EC activation was confirmed by transendothelial migration assays with neutrophils, demonstrating stronger inhibitory effects of iIKKβ compared to iNIK. Importantly, iIKKβ – and to some extent iNIK - potentiated the effects of currently employed therapies for IMIDs, like JAK inhibitors and anti-IL-17 antibodies, on EC inflammatory responses. These data demonstrate that inhibition of NF-κB signaling results in modulation of T m cell-induced EC responses and highlight the potential of small molecule NF-κB inhibitors as a novel treatment strategy to target EC inflammatory responses in IMIDs.
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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".