Defining the activity of pro-reparative extracellular vesicles in wound healing based on miRNA payloads and cell type-specific lineage mapping.

Small extracellular vesicles (EVs) are released by cells and deliver biologically active payloads to coordinate the response of multiple cell types in cutaneous wound healing. Here we used a cutaneous injury model as a donor of pro-reparative EVs to treat recipient diabetic obese mice, a model of impaired wound healing. We established a functional screen for microRNAs (miRNAs) that increased the pro-reparative activity of EVs and identified a down-regulation of miR-425-5p in EVs in vivo and in vitro associated with the regulation of adiponectin. We tested a cell type-specific reporter of a tetraspanin CD9 fusion with GFP to lineage map the release of EVs from macrophages in the wound bed, based on the expression of miR-425-5p in macrophage-derived EVs and the abundance of macrophages in EV donor sites. Analysis of different promoters demonstrated that EV release under the control of a macrophage-specific promoter was most abundant and that these EVs were internalized by dermal fibroblasts. These findings suggested that pro-reparative EVs deliver miRNAs, such as miR-425-5p, that stimulate the expression of adiponectin that has insulin-sensitizing properties. We propose that EVs promote intercellular signaling between cell layers in the skin to resolve inflammation, induce proliferation of basal keratinocytes, and accelerate wound closure.

Serpin-loaded extracellular vesicles promote tissue repair in a mouse model of impaired wound healing.

Chronic metabolic diseases such as diabetes are characterized by delayed wound healing and a dysregulation of the inflammatory phase of wound repair. Our study focuses on changes in the payload of extracellular vesicles (EVs) communicating between immune cells and stromal cells in the wound bed, which regulate the rate of wound closure. Adoptive transfer of EVs from genetically defined mouse models are used here to demonstrate a functional and molecular basis for differences in the pro-reparative biological activity of diabetic (db/db) vs. wildtype EVs in wound healing. We identify several members of the Serpin family of serine protease inhibitors that are absent in db/db EVs, then we overexpress Serpin A1, F2 and G1 in EVs to evaluate their effect on wound healing in db/db mice. Serpins have an important role in regulating levels of elastase, plasmin and complement factors that coordinate immune cell signaling in full thickness wounds in a diabetic model. Here, we establish a novel therapeutic approach by engineering the payload of EVs based on proteomic analysis. Serpin-loaded EVs were used to rescue the Serpin deficiency identified by proteomics and promote wound healing in db/db mice, as well as evaluated how EVs affected extracellular matrix remodeling and the resolution of tissue injury. Therefore, we propose that the identification of EV payloads that are downregulated in diabetic wounds can be systematically analyzed for their functional activity and potential as a therapeutic, based on whether their re-expression in engineered EVs restores normal kinetics of tissue repair in chronic wounds.

Objective quantification of upper extremity motor functions in Unified Parkinson's Disease Rating Scale Test.

Two tri-axial accelerometers were placed on the wrists (one on each hand) of the patients with Parkinson's disease (PD) and a non-PD control group. Subjects were asked to perform three of the upper extremity motor function tasks from the Unified Parkinson's Disease Rating Scale (UPDRS) test. The tasks were: 1) finger tapping, 2) opening and closing of palms, and 3) pronation-supination movements of the forearms. The inertia signals were wirelessly received and stored on a computer for further off-line analysis. Various features such as range, standard deviation, entropy, time to accomplish the task, and maximum frequency present in the signal were extracted and compared. The results showed that among the studied population, "standard deviation", "range", "entropy", "time" and "max frequency" are the best to worst features, respectively, to distinguish between the non-PD and PD subjects. Furthermore, using the mentioned features, it is more probable to distinguish between the non-PD and PD subjects from tasks 2 and 3 as opposed to task 1.