Back

SOCS3 limits endotoxin-induced endothelial dysfunction by blocking a required autocrine interleukin 6 signal in human umbilical vein endothelial cells

Martino, N.; Bossardi Ramos, R.; Chuy, D.; Tomaszek, L.; Adam, A. P.

2022-04-25 cell biology
10.1101/2022.04.25.489373 bioRxiv
Show abstract

Increased circulating levels of soluble interleukin (IL)-6 receptor (sIL-6R) are commonly observed during inflammatory responses, allowing for IL-6 signaling to occur in cells that express the ubiquitous receptor subunit gp130 but not IL-6R, such as endothelial cells. Activation of Toll-like receptor (TLR)-4 or the tumor necrosis factor (TNF) receptor leads to NF-{kappa}B-dependent increases in endothelial IL-6 expression. Thus, we hypothesize that danger signals may induce autocrine IL-6 signaling within the endothelium via sIL-6R-mediated trans-signaling. In support of this hypothesis, we recently demonstrated that conditional deletion in the endothelium of the IL-6 signaling inhibitor SOCS3 leads to rapid mortality in mice challenged with the TLR-4 agonist endotoxin through increases in vascular leakage, thrombosis, leukocyte adhesion, and a type I-like interferon response. Here, we sought to directly test a role for sIL-6R in LPS-treated human umbilical vein endothelial cells. We show that cotreatment with sIL-6R dramatically increases the loss of barrier function and the expression of COX2 and tissue factor mRNA levels induced by LPS. This co-treatment led to a strong activation of STAT1 and STAT3 while not affecting LPS-induced activation of p38 and NF-{kappa}B signaling. Similar results were obtained when sIL-6R was added to a TNF challenge. JAK inhibition by pretreatment with ruxolitinib or by SOCS3 overexpression blunted LPS and sIL-6R synergistic effects, while SOCS3 knockdown further increased the response. Together, these findings demonstrate that IL-6 signaling downstream of NF-kB activation leads to a strong endothelial activation and may explain the acute endotheliopathy observed during critical illness.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.