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Influence of N-acetylcysteine on apoptosis in skeletal muscle of rats with chronic heart failure

Grant number: 12/23936-9
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): March 01, 2013
Effective date (End): February 28, 2015
Field of knowledge:Health Sciences - Medicine - Medical Clinics
Principal researcher:Paula Felippe Martinez
Grantee:Vinicius Milani Lopes
Home Institution: Faculdade de Medicina (FMB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

There are few studies evaluating mechanisms involved in skeletal muscle fibers apoptosis during heart failure (HF). Oxidative stress has a well-established role in apoptosis intracellular signaling pathways. Previous works have shown a link between glutathione and apoptosis; however, this relationship has not been defined in skeletal muscle during HF. N-acetylcysteine (NAC) is an important source of cysteine for glutathione synthesis. NAC administration restored total glutathione levels and decreased oxidative stress markers in myocardium of infarcted rats. The aim of this study is to verify whether NAC administration normalizes total levels and redox status of glutathione and inhibits or attenuates apoptosis in skeletal muscle of rats with myocardial infarction (MI)-induced HF. Wistar rats will be assorted in three groups: Sham, MI-C (without treatment), and MI-NAC (treated with NAC for two months). Gastrocnemius muscle will be analyzed six months after MI induction. Ventricular dysfunction and HF will be confirmed by echocardiography and pathologic features, respectively. Fibers cross sectional area from gastrocnemius muscle (white portion) will be measured in histological sections stained with hematoxylin-eosin. Muscular glutathione levels and DNA fragmentation will be analyzed by available commercial kits. Protein expression of caspases, apoptosis markers, and apoptosis repressor with caspase recruitment domain (ARC), an antiapoptotic protein, will be quantified by Western blot. Statistical analysis: ANOVA and Bonferroni test.