Small secreted peptides (SSPs) are important signals for cell-to-cell communication in plant, involved in a variety of growth and developmental processes, as well as responses to stresses. While a large number of SSPs have been identified and characterized in various plant species, little is known about SSPs in wheat, one of the most important cereal crops. In this study, 4,981 putative SSPs were identified on the wheat genome, among which 1,790 TaSSPs were grouped into 38 known SSP families. The result also suggested that a large number of the putaitive wheat SSPs, Cys-rich peptides in particular, remained to be characterized. Several TaSSP genes were found to encode multiple SSP domains, including CLE, HEVEIN and HAIRPININ domains, and two potentially novel TaSSP family DYY and CRP8CI were identified manually among unpredicted TaSSPs. Analysis on the transcriptomic data showed that a great proportion of TaSSPs were expressed in response to abiotic stresses. Exogenous application of the TaCEPID peptide encoded by TraesCS1D02G130700 enhanced the tolerance of wheat plants to drought and salinity, suggesting porential roles of SSPs in regulating stress responses in wheat.

Background. This work used bioinformatic analysis to identify the relationship between periodontitis (PD) and aging, which could lead to new treatments for periodontal disease in the elderly. Method. Four microarray datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed in R language to identify differentially expressed genes (DEGs). The common DEGs of PD and aging were evaluated as key genes in this investigation by a Venn diagram. These common DEGs were analyzed through additional experiments and analysis, such as pathway analysis and enrichment analysis, and a network of protein-protein interactions (PPIs) was constructed. Cytoscape was used to visualize hub genes and critical modules based on the PPI network. Interaction of TF-genes and miRNAs with hub genes is identified. Result. 84 common DEGs were found between PD and aging. Cytohubba was performed on the PPI network obtained from STRING tool, and the top 10 genes (MMP2, PDGFRB, CTGF, CD34, CXCL12, VIM, IL2RG, ACTA2, COL4A2, and TAGLN) were selected as hub genes. VIM may be a potential biomarker in the analysis of linked hub gene regulatory networks, and hsa-mir-21 and hsa-mir-125b are predicted to be associated in PD and aging. Conclusion. This study investigated the key genes and pathways interactions between PD and aging, which may help reveal the correlation between PD and aging. The current research results are obtained by prediction, and follow-up biological experiments are required for further verification.

Background. The impaired osteoblastic differentiation of bone marrow mesenchymal stem cells (BMSCs) is a major cause of bone remodeling imbalance and osteoporosis. The bicaudal C homologue 1 (BICC1) gene is a genetic regulator of bone mineral density (BMD) and promotes osteoblast differentiation. The purpose of this study is to explore the probable function of BICC1 in osteoporosis and osteogenic differentiation of aged BMSCs. Methods. We examined the GSE116925 microarray dataset obtained from the Gene Expression Omnibus (GEO) database. The GEO2R algorithm identified differentially expressed genes (DEGs) in Sca-1+ BMSCs from young (3 months old) and old (18 months old) mice. Then, to identify the most crucial genes, we used pathway enrichment analysis and a protein-protein interaction (PPI) network. Furthermore, starBase v2.0 was used to generate the regulatory networks between BICC1 and related competing endogenous RNAs (ceRNAs). NetworkAnalyst was used to construct TF-gene networks and TF-miRNA-gene networks of BICC1 and ceRNA. Furthermore, we investigated the Bicc1 expression in aged Sca-1-positive BMSCs. Result. We detected 923 DEGs and discovered that epidermal growth factor receptor (EGFR) was the top hub gene with a high degree of linkage. According to the findings of the PPI module analysis, EGFR was mostly engaged in cytokine signaling in immune system and inflammation-related signaling pathways. 282 ceRNAs were found to interact with the BICC1 gene. EGFR was not only identified as a hub gene but also as a BICC1-related ceRNA. Then, we predicted 11 common TF-genes and 7 miRNAs between BICC1 and EGFR. Finally, we found that BICC1 mRNA and EGFR mRNA were significantly overexpressed in aged Sca-1-positive BMSCs. Conclusion. As a genetic gene that affects bone mineral density, BICC1 may be a new target for clinical treatment of senile osteoporosis by influencing osteogenic differentiation of BMSCs through EGFR-related signaling. However, the application of the results requires support from more experimental data.