Abstract
Introduction: Septic patients have low levels of high-density lipoproteins (HDLs), which is a risk factor. Replenishing HDLs with synthetic HDLs (sHDLs) has shown promise as a therapy for sepsis. This study aimed to develop a computational approach to design and test new types of sHDLs for sepsis treatment. Methods: We used a three-step computational approach to design sHDL nanoparticles based on the structure of HDLs and their binding to endotoxins. We tested the efficacy of these sHDLs in two sepsis mouse models-cecal ligation and puncture (CLP)-induced and P. aeruginosa-induced sepsis models-and assessed their impact on inflammatory signaling in cells. Results: We designed four sHDL nanoparticles: two based on the ApoA-I sequence (YGZL1 and YGZL2) and two based on the ApoE sequence (YGZL3 and YGZL4). We demonstrated that an ApoE-based sHDL nanoparticle, YGZL3, provides effective protection against CLP- and P. aeruginosa-induced sepsis. The sHDLs effectively suppressed inflammatory signaling in HEK-blue or RAW264 cells. Conclusions: Unlike earlier approaches, we developed a new approach that employs computational simulations to design a new type of sHDL based on HDL's structure and function. We found that YGZL3, an ApoE sequence-based sHDL, provides effective protection against sepsis in two mouse models.