Synergistic effect of Mn-Si-COS on wound immune microenvironment by inhibiting excessive skin fibrosis mediated with ROS/TGF-β1/Smad7 signal
Source abstract
Abstract
Excessive oxidative stress and inflammation often impede wound healing and ultimately lead to excessive skin fibrosis formation. It was known that the structural properties of biomaterials can affect the healing and immune response of surrounding tissues. In this work, a composite structure of Mn-Si-chitooligosaccharides (COS) was designed (COS@Mn-MSN) and the ability of regulating wound microenvironment for inhibiting skin fibrosis was investigated. In order to reduce the negative effects of Mn, the nano-level Mn was doped into MSN to minimize its content. The results show that Mn in COS@Mn-MSN showed significant ability of scavenging excess intracellular ROS within 1 d. The Si released from COS@Mn-MSN can shift M2 macrophage polarization in the later stage (1-3 d), showing anti-inflammatory effect. Macrophage (RAW264.7) were activated alternatively by COS released from COS@Mn-MSN, with upregulated expression of anti-inflammatory factors (IL-10 and CD206) and downregulated expression of pro-inflammatory factors (TNF-α, CD80, and IL-1β) in the whole time. The expression of fibrosis associated factor TGF-β1 and CD26 in fibroblast cells (L929) were inhibited by COS and Si. Besides, the inflammatory microenvironment mediated by COS@Mn-MSN downregulated Smad-7 gene expression and upregulated Col-1α gene expression. With the function of reducing oxidative stress (0-1 d), the TGF-β1 inhibition (1-3 d) and anti-inflammatory effects (0-3 d), COS@Mn-MSN could inhibit excessive skin fibrosis formation mediated with ROS/TGF-β1/Smad7 signal. Therefore, the prepared COS@Mn-MSN shows great potential to active scarless wound therapy.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Wound Healing and Treatments · Periodontal Regeneration and Treatments · Pressure Ulcer Prevention and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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