Upregulation of rate-limiting enzymes in cholesterol metabolism by PKCδ mediates endothelial apoptosis in diabetic wound healing.

Diabetic foot ulcer (DFU) is a prevalent complication of diabetes that poses significant challenges in terms of treatment and management. It is characterized by heightened endothelial apoptosis and impaired angiogenesis. In this study, we aimed to investigate the role of protein kinase Cδ (PKCδ) in regulating endothelial apoptosis in diabetic wounds by promoting cholesterol biosynthesis. The expression of PKCδ was increased in human umbilical vascular endothelial cells (HUVECs) cultivated in high glucose medium and skin tissue isolated from diabetic mice. High glucose-induced HUVECs apoptosis was reduced by PKCδ inhibition with siRNA or rottlerin. RNA-seq identified two enzymes, 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), as the downstream of PKCδ. PKCδ knockdown or inhibition suppressed the expression of HMGCS1 and HMGCR and lowered free cholesterol (FC) levels. Cholesterol restored high glucose-induced apoptosis in siRNA- or rottlerin-treated HUVECs. In vivo use of rosuvastatin calcium, an inhibitor of HMGCR, downregulated free cholesterol levels and accelerated the wound healing process. In conclusion, PKCδ expression in endothelial cells was activated by high glucose, which subsequently upregulates the expression of two enzymes catalyzing cholesterol biosynthesis, HMGCS1 and HMGCR. Enhanced cholesterol biosynthesis raises free cholesterol levels, promotes endothelial apoptosis, and finally delays wound healing.

KRT17 From Keratinocytes With High Glucose Stimulation Inhibit Dermal Fibroblasts Migration Through Integrin α11.

Objective: To investigate the effects of overexpressed keratin 17 (KRT17) on the biology of human dermal fibroblasts (HDFs) and to explore the mechanism of KRT17 in diabetic wound healing. Methods: KRT17 expression was tested in diabetic keratinocytes, animal models, and patient skin tissues (Huazhong University of Science and Technology Ethics Committee, [2022] No. 3110). Subsequently, HDFs were stimulated with different concentrations of KRT17 in vitro. Changes in the proliferation and migration of HDFs were observed. Then, identification of KRT17-induced changes in dermal fibroblast of RNA sequencing-based transcriptome analysis was performed. Results: KRT17 expression was upregulated under pathological conditions. In vitro stimulation of HDFs with different concentrations of KRT17 inhibited cell migration. RNA-seq data showed that enriched GO terms were extracellular matrix components and their regulation. KEGG analysis revealed that the highest number of enriched genes was PI3K-Akt, in which integrin alpha-11 (ITGA11) mRNA, a key molecule that regulates cell migration, was significantly downregulated. Decreased ITGA11 expression was observed after stimulation of HDFs with KRT17 in vitro. Conclusion: Increased expression of KRT17 in diabetic pathological surroundings inhibits fibroblast migration by downregulating the expression of ITGA11. Thus, KRT17 may be a molecular target for the treatment of diabetic wounds.

PKCδ regulates the vascular biology in diabetic atherosclerosis.

Diabetes mellitus, known for its complications, especially vascular complications, is becoming a globally serious social problem. Atherosclerosis has been recognized as a common vascular complication mechanism in diabetes. The diacylglycerol (DAG)-protein kinase C (PKC) pathway plays an important role in atherosclerosis. PKCs can be divided into three subgroups: conventional PKCs (cPKCs), novel PKCs (nPKCs), and atypical PKCs (aPKCs). The aim of this review is to provide a comprehensive overview of the role of the PKCδ pathway, an isoform of nPKC, in regulating the function of endothelial cells, vascular smooth muscle cells, and macrophages in diabetic atherosclerosis. In addition, potential therapeutic targets regarding the PKCδ pathway are summarized. Video Abstract.

An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2.

In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested them through the MobileNet V2 classification model. The test results showed that if we added the samples generated by EDCGAN to the original samples, the classification results would gradually improve. When the ratio reaches 80%, the overall classification result reaches the highest, achieving an accuracy rate of more than 99%. The experimental process proves the effectiveness of this method and can improve the quality of steel processing.

Revealing the role of tunable amino acid residues in elastin-like polypeptides (ELPs)-mediated biomimetic silicification.

Elastin-like polypeptides (ELPs) are attractive materials for the green preparation of silica nanoparticles via biomimetic silicification. However, the critical factors affecting the ELP-mediated silicification remain unclear. Herein, the role of tunable amino acid residues of ELPs in silicification was studied using three ELPs (ELPs[V9F-40], ELPs[KV8F-40], and ELPs[K5V4F-40]) and their fusion proteins (ELPs[V9F-40]-SpyCatcher, ELPs[KV8F-40]-SpyCatcher, and ELPs[K5V4F-40]-SpyCatcher) with different contents of lysine residues. Bioinformatics methods were employed for the first time to reveal the key physicochemical parameters correlated with silicification. The specific activity of ELPs was increased with the promotion of lysine content with a high correlation coefficient (R = 0.899). Furthermore, exogenous acidic protein SpyCatcher would hinder the interactions between the silica precursors and ELPs, leading to the significantly decrease in specific activity. The isoelectric point (pI) of ELPs presented the highest correlation to silicification with a coefficient of 0.963. The charges of the ELPs [K5V4F-40] at different pH were calculated based on the sequence or structure. Interestingly, the excellent correlation between charges based on structure and specific activity was obtained. Collectively, the novel methods developed here may pave a new way for rational design of ELPs or other peptides for efficient and green preparation of silica nanomaterials for biomedicine, biocatalysis, and biosensor.