Background Keratinocytes and fibroblasts represent the major cell types in the epidermis and dermis of the skin and play a significant role in maintenance of skin homeostasis. However, the biological characteristics of keratinocytes and fibroblasts remain to be elucidated. The purpose of this study was to compare the gene expression pattern between keratinocytes and fibroblasts and to explore novel biomarker genes so as to provide potential therapeutic targets for skin-related diseases such as burns, wounds, and aging. Methods Skin keratinocytes and fibroblasts were isolated from newborn mice. To fully understand the heterogeneity of gene expression between keratinocytes and fibroblasts, differentially expressed genes (DEGs) between the two cell types were detected by RNA-seq technology. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the known genes of keratinocytes and fibroblasts and verify the RNA-seq results. Results Transcriptomic data showed a total of 4309 DEGs (fold-change > 1.5 and q-value < 0.05). Among them, 2197 genes were highly expressed in fibroblasts and included 10 genes encoding collagen, 16 genes encoding transcription factors, and 14 genes encoding growth factors. Simultaneously, 2112 genes were highly expressed in keratinocytes and included 7 genes encoding collagen, 14 genes encoding transcription factors, and 8 genes encoding growth factors. Furthermore, we summarized 279 genes specifically expressed in keratinocytes and 33 genes specifically expressed in fibroblasts, which may represent distinct molecular signatures of each cell type. Additionally, we observed some novel specific biomarkers for fibroblasts such as Plac8 (placenta-specific 8), Agtr2 (angiotensin II receptor, type 2), Serping1 (serpin peptidase inhibitor, clade G, member 1), Ly6c1 (lymphocyte antigen 6 complex, locus C1), Dpt (dermatopontin), and some novel specific biomarkers for keratinocytes such as Ly6a (lymphocyte antigen 6 complex, locus A) and Lce3c (late cornified envelope 3C), Ccer2 (coiled-coil glutamate-rich protein 2), Col18a1 (collagen, type XVIII, alpha 1) and Col17a1 (collagen type XVII, alpha 1). In summary, these data provided novel identifying biomarkers for two cell types, which can provide a resource of DEGs for further investigations.
Deer velvet antlers are the young horns of male deer that are not ossified and densely overgrown. Velvet antler and its preparations have been widely used in the treatment of postmenopausal osteoporosis (PMOP) in recent years, although its mechanism of action in the human body remains unclear. To screen the effective ingredients and targets of velvet antler in the treatment of PMOP using network pharmacology and to explore the potential mechanisms of velvet antler action in such treatments, we screened the active ingredients and targets of velvet antler in the BATMAN-TCM database. We also screened the relevant targets of PMOP in the GeneCards and OMIM databases and then compared the targets at the intersection of both velvet antler and PMOP. We used Cytoscape 3.7.2 software to construct a network diagram of “disease-drug-components-targets” and a protein-protein interaction (PPI) network through the STRING database and screened out the core targets; the R language was then used to analyze the shared targets between antler and PMOP for GO-enrichment analysis and KEGG pathway-annotation analysis. Furthermore, we used the professional software Maestro 11.1 to verify the predictive analysis based on network pharmacology. Hematoxylin-eosin (H&E) staining and micro-CT were used to observe the changes in trabecular bone tissue, further confirming the results of network pharmacological analysis. The potentially effective components of velvet antler principally include 17β-E2, adenosine triphosphate, and oestrone. These components act on key target genes such as AKT1, IL6, MAPK3, TP53, EGFR, SRC, and TNF and regulate the PI3K/Akt-signaling and MAPK-signaling pathways. These molecules participate in a series of processes such as cellular differentiation, apoptosis, metabolism, and inflammation and can ultimately be used to treat PMOP; they reflect the overall regulation, network regulation, and protein interactions.
ABSTRACT. This study used DNA microarray data to identify differentially expressed genes of osteoporosis and provide useful information for treatments of the disease. We downloaded gene expression data of Osteoporosis GSE35956 from the Gene Expression Omnibus database, which included five normal and five osteoporosis samples. We then identified the differentially expressed genes between normal and disease samples using the R language software, and constructed the protein interaction network. DAVID was used to perform the biological process enrichment and KEGG pathway cluster analyses. We used the Cytoscape plug-in unit, Cluster ONE, to perform cluster module analysis to find hub proteins of the network module and to analyze their Gene Ontology (GO) functions. A total of 294 genes were found to be differentially expressed between normal and disease samples, which were used to construct the differential gene-protein interaction network. GO function analysis revealed that the genes' functions were mainly involved in the intracellular signaling cascade. KEGG pathway analysis suggested that the main metabolic pathways of these genes were those of cancer: the neurotrophin/T cell/Fc epsilon RI/B cell/ ErbB/p53 signaling pathway, the cell cycle pathway, and the chronic myeloid leukemia pathway. Screening analysis of hub proteins revealed that KRT18 had the highest hub degree. In conclusion, we found differentially expressed genes related to osteoporosis. GO biological process enrichment and KEGG pathway enrichment analyses identified significant osteoporosis genes and their molecular functions. Finally, module analysis of hub proteins in interaction networks showed that cell death was one of the main biological processes of osteoporosis genes.
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