Hypoxia-Induced Mitogenic Factor Promotes Vascular Adhesion Molecule-1 Expression via the PI-3K/Akt–NF-κB Signaling Pathway
Bibliographic record
Abstract
Hypoxia-induced mitogenic factor (HIMF), also known as FIZZ1 (found in inflammatory zone 1), is an important player in lung inflammation. However, the effects of HIMF on cell adhesion molecules involved in lung inflammation remain largely unknown. In the present work, we tested whether HIMF modulates vascular adhesion molecule (VCAM)-1 expression, and dissected the possible signaling pathways that link HIMF to VCAM-1 upregulation. Recombinant HIMF protein, instilled intratracheally into adult mouse lungs, results in a significant increase of VCAM-1 production in vascular endothelial, alveolar type II, and airway epithelial cells. In cultured mouse endothelial SVEC 4-10 and lung epithelial MLE-12 cells, we demonstrated that HIMF induces VCAM-1 expression via the phosphatidylinositol-3 kinase (PI-3K)/Akt-nuclear factor (NF)-kappaB signaling pathway. Knockdown of HIMF expression by small interference RNA attenuated LPS-induced VCAM-1 expression in vitro. We showed that HIMF induced phosphorylation of the IkappaB kinase signalsome and, subsequently, IkappaBalpha, leading to activation of NF-kappaB. Meanwhile, VCAM-1 production was correspondingly upregulated. Blocking NF-kappaB signaling pathway by expression of dominant-negative mutants of IkappaB kinase and IkappaBalpha suppressed HIMF-induced VCAM-1 upregulation. HIMF also strongly induced phosphorylation of Akt. A dominant-negative mutant of PI-3K, Deltap85, as well as PI-3K inhibitor, LY294002, also blocked HIMF-induced NF-kappaB activation and attenuated VCAM-1 production. Furthermore, LY294002 pretreatment abolished HIMF-enhanced mononuclear cells adhesion to endothelial and epithelial cells. Our findings connect HIMF to signaling pathways that regulate inflammation, and thus reveal the critical roles that HIMF plays in lung inflammation.
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 | 0.000 |
| 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".