The Extracellular Matrix Promotes Diabetic Oral Wound Healing by Modulating the Microenvironment

Abstract

Oral wounds in diabetes mellitus (DM) often delay healing due to reduced angiogenesis and increased inflammatory response in the local microenvironment, even leading to graft necrosis and implant failure. Therefore, developing an effective program to promote healing is of great clinical value. Much of the current research is focused on promoting wound healing through surface adhesive materials that exert a pro-angiogenic, anti-inflammatory effect. However, the application of surface bonding materials in the oral cavity is very limited due to the humid and friction-prone environment. Decellularized extracellular adipose tissue (DAT) is an easily accessible and biocompatible material derived from adipose tissue. To further explore the potential of DAT, we used multi-omics to analyze its composition and possible mechanisms. Proteomic studies revealed that DAT contains anti-inflammatory, pro-angiogenic proteins that promote DM tissue regeneration. To adapt to the moist and chewing friction environment of the mouth, we modified DAT into a temperature-sensitive hydrogel material that can be injected intramucosally. DAT hydrogel has been verified to promote angiogenesis and exert anti-inflammatory effects through macrophage phenotypic transformation. Meanwhile, transcriptome analysis suggested that the inhibitory effect of DAT on the interleukin 17 signaling pathway might be a key factor in promoting DM oral wound healing. In conclusion, after multi-omic analysis, DAT hydrogel can exert good pro-angiogenic and anti-inflammatory effects through the interleukin 17 signaling pathway and can be adapted to the specific environment of the oral cavity. This provides a potential way to promote DM oral wound healing in a clinical setting.

Fig. 1.

Fabrication of decellularized extracellular adipose tissue (DAT) hydrogel and its promotional effect on diabetic oral wound healing with schematic mechanism. iNOS, inducible nitric oxide synthase; TNF-α, tumor necrosis factor alpha; IL-10, interleukin 10; Arg-1, arginase 1; α-SMA, alpha smooth muscle actin; IL-17, interleukin 17; IL-6, interleukin 6; Ptgs2, prostaglandin-endoperoxide synthase 2; AP-1, activator protein 1.

Fig. 2.

(A) Fabrication process of DAT hydrogels. (B) Comparison of DAT hydrogels before and after gel formation. (C) DNA content of adipose tissue and DAT. (D) The collagen content of adipose tissue and DAT. (E) Comparison plots of adipose tissue and DAT subjected to hematoxylin–eosin (HE) staining, Masson staining, Oil Red O staining (scale bar: 200 μm), and 4′,6-diamidino-2-phenylindole (DAPI) staining (scale bar: 50 μm). (F) Transmission electron microscopy (TEM) images of DAT hydrogels with different concentrations (scale bar: 5 μm). (G) TEM image porosity difference plots of DAT hydrogels with different concentrations. Statistical difference expression: ns, not significant; P > 0.05; **P < 0.01; ****P < 0.0001; analysis was performed using a t test with one-way analysis of variance (ANOVA), n = 3. hDAT, DAT hydrogel.

Fig. 3.

(A) Proteins and peptides were purified by gel electrophoresis and analyzed for approximate distribution. (B) The number of protein peptides that were extracted, analyzed, and quantified. (C) DAT’s top 30 Gene Ontology (GO)-enriched terms. (D) Chordal graph composed of GO terms related to angiogenesis and inflammation. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) bioprocess enrichment analysis of DAT. MAPK, mitogen-activated protein kinase; PI3K, phosphoinositide 3-kinase.

Fig. 4.

(A) Different hydrogel concentrations affect rabbit erythrocyte hemolysis, with phosphate-buffered saline (PBS) as the negative control and Triton X-100 as the positive control. (B and C) Cell Counting Kit-8 (CCK-8) assay to assess the cytotoxic/proliferative effects after 1, 3, and 5 d of co-incubation with different hydrogels (macrophages (B) and endothelial cells (C)). (D and E) Live/dead staining to assess the toxic effect of hydrogels (scale bar = 300 μm) (macrophages (D) and endothelial cells (E)). Statistical difference expression: ns, P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; analyses were performed using ANOVA, n = 3. HUVECs, human umbilical vein endothelial cells; PI, propidium iodide.

Fig. 5.

(A and C) Results of HUVEC migration ability assessed by wound healing assay (scale bar = 200 μm). (B) Determination of the invasive capacity of HUVECs by Transwell assay (scale bar = 200 μm). (D) Number of invasive cells in the Transwell assay. (E) Tube formation assay of HUVECs (scale bar = 200 μm). (F) Red-labeled (CD31) and green-labeled (α-SMA) immunostaining showed that DAT hydrogel-treated HUVECs exhibited enhanced angiogenic properties (scale bar = 50 μm). Statistical difference expression: ns, P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; analyses were performed using ANOVA, n = 3.

Fig. 6.

(A to E) Real-time reverse transcription polymerase chain reaction (RT-qPCR) results of the messenger RNA (mRNA) expression of anti-inflammatory factors (CD206, IL-10, and Arg-1) and inflammatory factors (iNOS and TNF-α). (F) Immunofluorescence of DAT hydrogels against iNOS expression (scale bar = 50 μm). (H) Immunofluorescence of CD206 expression by DAT hydrogel (scale bar = 50 μm). (G and I) Fluorescence intensity was quantified using ImageJ. (J and K) Antioxidant effect of DAT hydrogel on hydrogen peroxide-induced oxidative environment (scale bar = 300 μm). Statistical difference expression: ns, P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; analyses were performed using ANOVA, n = 3. LPS, lipopolysaccharide; DCFH-DA, dichlorodihydrofluorescein diacetate.

Fig. 7.

(A) Naked eye view of wound healing in DM rats at different times. (B) A plot of palatal wound area analysis was performed in DM rats at different time points. (C) Line graph of wound healing ratio over time. (D) HE-stained sections of palatal wounds of DM rats at different times. Black arrows point to inflammatory cells (scale bar = 1 mm, scale bar of enlarged diagrams = 50 μm [7 d] and 200 μm [14 d]). Statistical difference expression: ns, P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; analyses were performed using ANOVA, n = 3.

Fig. 8.

(A) Immunofluorescence staining of angiogenesis in the oral wounds of DM rats on the seventh day of healing (scale bar = 100 μm). (B) Anti-inflammatory immunofluorescence staining of the oral wounds of DM rats on the seventh day of healing (scale bar = 100 μm). (C to F) Fluorescence intensity was quantified using ImageJ. Statistical difference expression: **P < 0.01; ***P < 0.001; ****P < 0.0001; analyses were performed using ANOVA, n = 3. Inten, intensity.

Fig. 9.

(A) Volcano plots with |log fold change| > 1 and P values < 0.05 as thresholds. (B) Differential gene heat map. (C) The 30 most enriched entries in the GO analysis. (D) Heat map of inflammation-related differential genes. (E) The 30 most enriched pathways in KEGG pathway analysis. (F) Protein expression of IL-17 pathway-related proteins (AP-1, PTGS2, IL-6, and IL-17). (G) Grayscale semiquantitative analysis of related protein bands. Statistical difference expression: ns, P > 0.05; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; analyses were performed using ANOVA, n = 3. FC, fold change.