氧化应激
丙二醛
急性肾损伤
药理学
谷胱甘肽
肾
医学
炎症
超氧化物歧化酶
谷胱甘肽过氧化物酶
化学
肌酐
内科学
内分泌学
生物化学
酶
作者
Mohammad-Reza Khajevand-Khazaei,Shekoofe Azimi,Ladan Sedighnejad,Sepide Salari,Atefeh Ghorbanpour,Tourandokht Baluchnejadmojarad,Parvaneh Mohseni‐Moghaddam,Safoura Khamse,Mehrdad Roghani
标识
DOI:10.1016/j.intimp.2019.01.026
摘要
Sepsis is a serious and life-threatening medical condition with a higher rate of patients' morbidity and mortality and with complications such as acute kidney injury (AKI). S-allyl cysteine (SAC) is the active constituent of the medicinal plant garlic (Allium sativum) with multiple beneficial effects including anti-inflammatory and antioxidant properties. In this research, we tried to determine the protective effect of SAC pretreatment in a mouse model of AKI. To induce AKI, lipopolysaccharide (LPS) was injected once (10 mg/kg, i.p.) and SAC was administered at doses of 25, 50, or 100 mg/kg (p.o.) 1 h before LPS. Treatment of LPS-challenged C56BL/6 animals with SAC lowered serum level of creatinine and blood urea nitrogen (BUN), partially restored renal oxidative stress-related biomarkers including malondialdehyde (MDA), glutathione (GSH), and activity of superoxide dismutase (SOD) and catalase in addition to improvement of mitochondrial membrane potential (MMP). Furthermore, SAC was capable to bring renal nuclear factor-kappaB (NF-κB), nuclear factor (erythroid-derived 2)-like 2 (Nrf2), toll-like receptor 4 (TLR4), cyclooxygenase-2 (COX2), tumor necrosis factor α (TNFα), interleukin-1β (IL-1β), interleukin-6 (IL-6), Annexin V, and DNA fragmentation partially back to their control levels. Additionally, SAC pretreatment was capable to exert a protective effect, as shown histologically by lower tubular injury and pathologic changes in the kidney. In summary, SAC is capable to alleviate LPS-induced AKI through mitigation of renal oxidative stress, inflammation, and apoptosis in addition to preservation of mitochondrial integrity and its favorable effect exhibits a dose-dependent pattern.
科研通智能强力驱动
Strongly Powered by AbleSci AI