Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Adipose-derived mesenchymal stem cells-sourced exosomal microRNA-7846-3p suppresses proliferation and pro-angiogenic role of keloid fibroblasts by suppressing neuropilin 2

BACKGROUND

Exosomes (Exos) and their contained microRNAs (miRNAs) have been emergingly recognized as key regulators in spanning biological processes, including proliferation and angiogenesis.

AIM OF THE STUDY

This work investigates the function of Exos derived from adipose-derived mesenchymal stem cells (adMSCs) in viability of keloid fibroblasts (KFs).

METHODS

Abnormally expressed miRNAs in keloid tissues were screened using the GEO dataset GSE113620. Meanwhile, miRNAs enriched in adMSC-Exos were predicted by bioinformatics system. Exos were extracted from acquired adMSCs and identified, which were co-incubated with KFs. Uptake of Exos by KFs was examined by fluorescence staining. Viability, proliferation, and apoptosis of KFs were analyzed by CCK-8, EdU labeling, and TUNEL assays. Conditioned medium of KFs was collected to stimulate angiogenesis of human umbilical vein endothelial cells (HUVECs). Binding between miR-7846-3p and neuropilin 2 (NRP2) was validated by luciferase assay. Protein levels of NRP2 and the Hedgehog pathway molecules were analyzed by western blot analysis.

RESULTS

miR-7846-3p was predicted as an exosomal miRNA aberrantly expressed in keloids. AdMSC-Exos reduced viability, proliferation, and apoptosis resistance of KFs, and they blocked the angiogenesis of HUVECs. miR-7846-3p targeted NRP2 mRNA. miR-7846-3p upregulation in KFs suppressed NRP2 expression and reduced the expression of Hedgehog pathway molecules SHH, SMO, and GLI1. Either miR-7846-3p inhibition in Exos or NRP2 overexpression in KFs blocked the effects of Exos and restored the viability, proliferation, and pro-angiogenic role of KFs.

CONCLUSION

This work unravels that adMSC-Exos-derived miR-7846-3p suppresses NRP2 and inactivates the Hedgehog signaling to reduce proliferation and pro-angiogenic role of KFs.

Luteolin and triptolide: Potential therapeutic compounds for post-stroke depression via protein STAT

Post stroke depression (PSD) is a common neuropsychiatric complication following stroke closely associated with the immune system. The development of medications for PSD remains to be a considerable challenge due to the unclear mechanism of PSD. Multiple researches agree that the functions of gene ontology (GO) are efficient for the investigation of disease mechanisms, and DeepPurpose (DP) is extremely valuable for the mining of new drugs. However, GO terms and DP have not yet been applied to explore the pathogenesis and drug treatment of PSD. This study aimed to interpret the mechanism of PSD and discover important drug candidates targeting risk proteins, based on immune-related risk GO functions and informatics algorithms. According to the risk genes of PSD, we identified 335 immune-related risk GO functions and 37 compounds. Based on the construction of the GO function network, we found that STAT protein may be a pivot protein in underlying the mechanism of PSD. Additionally, we also established networks of Protein-Protein Interaction as well as Gene-GO function to facilitate the evaluation of key genes. Based on DP, a total of 37 candidate compounds targeting 7 key proteins were identified with a potential for the therapy of PSD. Furthermore, we noted that the mechanisms by which luteolin and triptolide acting on STAT-related GO function might involve three crucial pathways, including specifically hsa04010 (MAPK signaling pathway), hsa04151 (PI3K-Akt signaling pathway) and hsa04060 (Cytokine-cytokine receptor interaction). Thus, this study provided fresh and powerful information for the mechanism and therapeutic strategies of PSD.

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology

Background. Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanoma effectively with computational methods. Methods. Identification of genes associated with drug-resistant melanomas was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by GO and KEGG pathway enrichment analyses. The PPI network was constructed using STRING database and Cytoscape. GEPIA was used to perform the survival analysis and conduct the Kaplan-Meier curve. Drugs targeted at these genes were selected in Pharmaprojects. The binding affinity scores of drug-target interactions were predicted by DeepPurpose. Results. A total of 433 genes were found associated with drug-resistant melanomas by text mining. The most statistically differential functional enriched pathways of GO and KEGG analyses contained 348 genes, and 27 hub genes were further screened out by MCODE in Cytoscape. Six genes were identified with statistical differences after survival analysis and literature review. 16 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 11 ERBB2-targeted drugs with top affinity scores were predicted by DeepPurpose, including 10 ERBB2 kinase inhibitors and 1 antibody-drug conjugate. Conclusion. Text mining and bioinformatics are valuable methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the candidate drugs.

Effects of Apremilast on Induced Hypertrophic Scar of Rabbits

The present study aimed to assess the effect of Apremilast on experimentally induced hypertrophic scars in rabbits. A total of 40 healthy male New Zealand White rabbits between 6 and 12 months of age were assigned to four groups (n=10). Group I consists of apparently healthy control rabbits, in group II, the rabbits with an induced hypertrophic scar received no treatment, except for base gel. In group III, the rabbits with induced hypertrophic scar were treated with triamcinolone acetonide (TAC) 0.1% as standard medication. In group IV, rabbits with induced hypertrophic scars were treated with Apremilast 5%. On the first day, four surgical incisions were made using an 8-mm biopsy punch on the ventral surface of the rabbit ear down to cartilage. The TAC and Apremilast were topically administered to the developed scars on day 31. The results included an examination of skin histopathology, the level of transforming growth factor beta-1 (TGF-β1), and collagen III in skin tissue. In the treatments, the inflammatory score, scar index, as well as immunological scores of TGFβ1 and collagen III, significantly decreased, compared to the hypertrophic induced scar group (P≤ 0.001). Moreover, there was a significant reduction in fibroblast count, compared to the group of induced hypertrophic scars (P<0.05). Apremilast was efficacious in the treatment of hypertrophic scars due to its ability to reduce inflammations and fibroblast counts and scar index. Nonetheless, the reduction of immunological scores was almost comparable to that of topical TAC.