Activation of TLR4 induces inflammatory muscle injury via mTOR and NF-κB pathways in experimental autoimmune myositis mice

Abstract

Idiopathic inflammatory myopathy (IIM) is an autoimmune disease that invades skeletal muscle; however, the etiology of IIM is still poorly understood. Toll-like receptor (TLR) 4 has been widely reported to take part in the autoimmune inflammation of IIMs. The mammalian target of rapamycin, mTOR, is a key central substance which mediates immune responses and metabolic changes, and also has been confirmed to be involved in the pathogenesis of IIMs. However, the interconnectedness between TLR4 and mTOR in IIM inflammation has not been fully elucidated. We hypothesized that TLR4 may play an important role in IIM inflammatory muscle injury by regulating mTOR. Mice were divided into four groups: a normal control group, IIM animal model (experimental autoimmune myositis, EAM) group, TAK242 intervention group and rapamycin (RAPA) intervention group. The results of EAM mice showed that TLR4, mTOR, nuclear factor-kappa B (NF-κB) and inflammatory factors interleukin-17A (IL-17A) and interferon γ (IFN-γ) mRNA levels were significantly upregulated. These factors were positively correlated with the degree of muscle inflammatory injury. When EAM mice were given the antagonist TAK242 to inhibit the TLR4 pathway, the results demonstrated that both mTOR and NF-κB were downregulated in the muscle of the mice. Muscle staining showed that the inflammatory injury was alleviated and the EAM mouse muscle strength was improved. Then, RAPA was used to inhibit the mTOR pathway, and the inflammatory factors IL-17A and IFN-γ were downregulated in EAM mouse muscle and serum. Consistently, muscle inflammatory injury was significantly reduced, and muscle strength was significantly improved. Our results suggest that TLR4 may regulate inflammatory muscle injury in EAM by activating the mTOR and NF-κB pathways, which provides both an experimental complement for the pathological mechanism of IIM and an encouraging target for the selection of effective treatments.