Resveratrol, a polyphenol compound derived from various edible plants, protects against sepsis-induced acute kidney injury (AKI) via its anti-inflammatory activity, but the underlying mechanisms remain largely unknown. In this study, a rat model of sepsis was established by cecal ligation and puncture (CLP), 30 mg/kg resveratrol was intraperitoneally administrated immediately after the CLP operation. HK-2 cells treated by 1 μg/ml lipopolysaccharide, 0.2 μM tunicamycin, 2.5 mM irestatin 9389 and 20 μM resveratrol were used for in vitro study. The results demonstrated that resveratrol significantly improved the renal function and tubular epithelial cell injury and enhanced the survival rate of CLP-induced rat model of sepsis, which was accompanied by a substantial decrease of the serum content and renal mRNA expressions of TNF-α, IL-1β and IL-6. In addition, resveratrol obviously relieved the endoplasmic reticulum stress, inhibited the phosphorylation of inositol-requiring enzyme 1(IRE1) and nuclear factor-κB (NF-κB) in the kidney. In vitro studies showed that resveratrol enhanced the cell viability, reduced the phosphorylation of NF-κB and production of inflammatory factors in lipopolysaccharide and tunicamycin-induced HK-2 cells through inhibiting IRE1 activation. Taken together, administration of resveratrol as soon as possible after the onset of sepsis could protect against septic AKI mainly through inhibiting IRE1-NF-κB pathway-triggered inflammatory response in the kidney. Resveratrol might be a readily translatable option to improve the prognosis of sepsis.

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The complex traits of an organism are associated with a complex interplay between genetic factors (GFs) and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods and bioinformatic resource platform for understanding the associations between lncRNA and EF. In this study, we developed a novel computational method to identify potential associations between lncRNA and EF, and released LncEnvironmentDB, a user-friendly web-based database aiming to provide a comprehensive resource platform for lncRNA and EF. Topological analysis of EF-related networks revealed the small world, scale-free and modularity structure. We also found that lncRNA and EF significantly enriched interacting miRNAs are functionally more related by analyzing their related diseases, implying that the predicted lncRNA signature of EF can reflect the functional characteristics to some degree. Finally, we developed a random walk with a restart-based computational model (RWREFD) to predict potential disease-related EFs by integrating lncRNA-EF associations and EF-disease associations. The performance of RWREFD was evaluated by experimentally verified EF-disease associations based on leave-one-out cross-validation and achieved an AUC value of 0.71, which is higher than randomization test, indicating that the RWREFD method has a reliable and high accuracy of prediction. To the best of our knowledge, LncEnvironmentDB is the first attempt to predict and house the experimental and predicted associations between lncRNA and EF. LncEnvironmentDB is freely available on the web at http://bioinfo.hrbmu.edu.cn/lncefdb/.