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Angiopoietin-like protein 4/8 complex-mediated plasmin generation leads to cleavage of the complex and restoration of LPL activity.

Eugene Y ZhenYan Q ChenAnna M RussellMariam EhsaniRobert W SiegelYuewei QianRobert J Konrad
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
Triglyceride (TG) metabolism is highly regulated by angiopoietin-like protein (ANGPTL) family members [Y. Q. Chen et al. ,  J. Lipid Res. 61 , 1203-1220 (2020)]. During feeding, ANGPTL8 forms complexes with the fibrinogen-like domain-containing protein ANGPTL4 in adipose tissue to decrease ANGPTL3/8- and ANGPTL4-mediated lipoprotein lipase (LPL)-inhibitory activity and promote TG hydrolysis and fatty acid (FA) uptake. The ANGPTL4/8 complex, however, tightly binds LPL and partially inhibits it in vitro. To try to reconcile the in vivo and in vitro data on ANGPTL4/8, we aimed to find novel binding partners of ANGPTL4/8. To that end, we performed pulldown experiments and found that ANGPTL4/8 bound both tissue plasminogen activator (tPA) and plasminogen, the precursor of the fibrinolytic enzyme plasmin. Remarkably, ANGPTL4/8 enhanced tPA activation of plasminogen to generate plasmin in a manner like that observed with fibrin, while minimal plasmin generation was observed with ANGPTL4 alone. The addition of tPA and plasminogen to LPL-bound ANGPTL4/8 caused rapid, complete ANGPTL4/8 cleavage and increased LPL activity. Restoration of LPL activity in the presence of ANGPTL4/8 was also achieved with plasmin but was blocked when catalytically inactive plasminogen (S760A) was added to tPA or when plasminogen activator inhibitor-1 was added to tPA + plasminogen, indicating that conversion of plasminogen to plasmin was essential. Together, these results suggest that LPL-bound ANGPTL4/8 mimics fibrin to recruit tPA and plasminogen to generate plasmin, which then cleaves ANGPTL4/8, enabling LPL activity to be increased. Our observations thus reveal a unique link between the ANGPTL4/8 complex and plasmin generation.
Keyphrases
  • adipose tissue
  • type diabetes
  • machine learning
  • dna methylation
  • small molecule
  • deep learning
  • electronic health record
  • artificial intelligence