Toxic Effects of Endocrine Disruptor Exposure on Collagen-Induced Arthritis
Abstract
Endocrine disruptors (EDs) are chemical substances capable of affecting endocrine system functioning and interfering with organ morphogenesis and physiological functions. The development and regeneration of bone tissues have a complex hormonal regulation, and therefore, bone tissue cells can be considered potential targets for endocrine disruptors. In that regard, the aim of this research was to investigate the impact of ED exposure on the inflammatory response and oxidative stress in an experimental model of collagen-induced arthritis (CIA). Arthritis was induced by an emulsion of type II collagen (CII) and complete Freund's adjuvant, which was administered intradermally on days 0 and 21. Mice from day 21 to day 35 received the following EDs by oral gavage: cypermethrin (CP), diethyl phthalate (DEP), vinclozolin (VCZ), 17α-ethinylestradiol (EE), perfluorooctanesulfonic acid (PFOS) and atrazine (ATR). ED exposure caused worsening of clinical signs (erythema and edema in the hind paws), histological and radiographic changes, as well as behavioral deficits, induced by CII injections. Furthermore, ED exposure significantly increased the degree of inflammation and oxidative damage induced by arthritis; this upregulation was more evident after exposure to ATR than to other EDs. The results from our study suggest that exposure to EDs may play a deleterious role in the progression of RA; therefore, exposure to EDs should be limited.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.