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Neuropeptides, Inflammation, and Diabetic Wound Healing: Lessons from Experimental Models and Human Subjects

  • Lucas Mota,
  • Frank W. LoGerfo,
  • Aristidis Veves,
  • Leena Pradhan-Nabzdyk

摘要

Diabetic peripheral neuropathy and vascular disease, along with trauma, have long been recognized as major risk factors for the development of diabetic foot ulcerations (DFUs). More recently, chronic inflammation, abnormal extracellular matrix remodeling, and reduced wound neovascularization, as a result of dysregulated cell function with imbalanced secretion of cytokines, matrix metalloproteinases, and growth factors, have been implicated in DFU failure to heal. Therefore, researchers are now focusing their efforts on understanding the cellular and molecular mechanisms of diabetes-associated impaired wound healing, in an attempt to identify new targets and novel potential therapeutic approaches for DFUs, which remain a serious unmet clinical need. Recent advances in technologies such as single-cell sequencing are being implemented to further understand the pathology underlying DFU and design precise therapeutics. A growing body of evidence suggests an important role of neuropeptides in skin repair, particularly in diabetes, where neuropeptide levels are diminished. On the other hand, there is emerging interest in dissecting the mechanisms of dysregulated inflammation, namely, the changes in immune cells, such as macrophages and mast cells (MCs), in diabetic wound healing. Studies using in vitro and in vivo models of diabetic wound healing have considerably improved our understanding of the healing process. However, the currently available models have major caveats and are not ideal to study chronic, complicated, and multifactorial wounds, such as DFUs. In this chapter we summarize the involvement of neuropeptides and mast cells in diabetic wound healing, highlighting the most recent findings. We also discuss the benefits and limitations of the current wound healing models, emphasizing the need for confirmation and/or validation in multiple models and/or tissue specimens from human subjects.