Structural basis for the allosteric behaviour and substrate specificity of Lactococcus lactis Prolidase.

Prolidase (EC 3.4.13.9) is an enzyme that specifically hydrolyzes Xaa-Pro dipeptides into free amino acids. We previously studied kinetic behaviours and solved the crystal structure of wild-type (WT) Lactococcus lactis prolidase (Llprol), showing that this homodimeric enzyme has unique characteristics: allosteric behaviour and substrate inhibition. In this study, we focused on solving the crystal structures of three Llprol mutants (D36S, H38S, and R293S) which behave differently in v-S plots. The D36S and R293S Llprol mutants do not show allosteric behaviour, and the Llprol mutant H38S has allosteric behaviour comparable to the WT enzyme (Hill constant 1.52 and 1.58, respectively). The crystal structures of Llprol variants suggest that the active site of Llprol formed with amino acid residues from both monomers, i.e., located in an interfacial area of dimer. The comparison between the structure models of Llprol indicated that the two monomers in the dimers of Llprol variants have different relative positions among Llprol variants. They showed different interatomic distances between the amino acid residues bridging the two monomers and varied sizes of the solvent-accessible interface areas in each Llprol variant. These observations indicated that Llprol could adapt to different conformational states with distinctive substrate affinities. It is strongly speculated that the domain movements required for productive substrate binding are restrained in allosteric Llprol (WT and H38S). At low substrate concentrations, only one out of the two active sites at the dimer interface could accept substrate; as a result, the asymmetrical activated dimer leads to allosteric behaviour.

AGAP2-AS1 May Promote the Occurrence and Development of Glioblastoma by Sponging miR-9-5p: Evidence From a ceRNA Network.

Glioblastoma (GBM), the primary malignant brain tumor, is typically associated with a poor prognosis and poor quality of life, mainly due to the lack of early diagnostic biomarkers and therapeutic targets. However, gene sequencing technologies and bioinformatics analysis are currently being actively utilized to explore potential targets for the diagnosis and management of malignancy. Herein, based on a variety of bioinformatics tools for the reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of AGAP2-AS1-miR-9-5p-MMP2/MMP9 was constructed, and a potential therapeutic target for GBM was identified. Enrichment analysis predicted that the ceRNA regulatory network participates in the processes of cell proliferation, differentiation, and migration.

ceRNA Network Analysis Shows That lncRNA CRNDE Promotes Progression of Glioblastoma Through Sponge mir-9-5p.

Glioblastoma accounts for 45.2% of central nervous system tumors. Despite the availability of multiple treatments (e.g., surgery, radiotherapy, chemotherapy, biological therapy, immunotherapy, and electric field therapy), glioblastoma has a poor prognosis, with a 5-year survival rate of approximately 5%. The pathogenesis and prognostic markers of this cancer are currently unclear. To this end, this study aimed to explore the pathogenesis of glioblastoma and identify potential prognostic markers. We used data from the GEO and TCGA databases and identified five genes (ITGA5, MMP9, PTPRN, PTX3, and STX1A) that could affect the survival rate of glioblastoma patients and that were differentially expressed between glioblastoma patients and non-tumors groups. Based on a variety of bioinformatics tools for reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of messenger RNA (STX1A, PTX3, MMP9)-microRNA (miR-9-5p)-long non-coding RNA (CRNDE) was constructed. Finally, we identified five potential therapeutic drugs (bacitracin, hecogenin, clemizole, chrysin, and gibberellic acid) that may be effective treatments for glioblastoma.

High gene expression levels of VEGFA and CXCL8 in the peritumoral brain zone are associated with the recurrence of glioblastoma: A bioinformatics analysis.

The present study aimed to identify differentially regulated genes between the peritumoral brain zone (PBZ) and tumor core (TC) of glioblastoma (GBM), to elucidate the underlying molecular mechanisms and provide a target for the treatment of tumors. The GSE13276 and GSE116520 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for the PBZ and TC were obtained using the GEO2R tool. The bioinformatics and evolutionary genomics online tool Venn was used to identify common DEGs between the two datasets. The Database for Annotation, Visualization, and Integrated Discovery online tool was used to analyze enriched pathways of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The Search Tool for the Retrieval of Interacting Genes/Proteins online tool was used to construct a protein-protein interaction (PPI) network of DEGs. Hub genes were identified using Cytohubba, a plug-in for Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was utilized to perform survival analysis. In total, 75 DEGs, including 12 upregulated and 63 downregulated genes, were identified. In the GO term analysis, these DEGs were mainly enriched in 'regulation of angiogenesis' and 'central nervous system development'. Furthermore, in the KEGG pathway analysis, the DEGs were mainly enriched in 'bladder cancer' and 'endocytosis'. When filtering the results of the PPI network analysis using Cytohubba, a total of 10 hub genes, including proteolipid protein 1, myelin associated oligodendrocyte basic protein, contactin 2, myelin oligodendrocyte glycoprotein, myelin basic protein, myelin associated glycoprotein, SRY-box transcription factor 10, C-X-C motif chemokine ligand 8 (CXCL8), vascular endothelial growth factor A (VEGFA) and plasmolipin, were identified. These hub genes were further subjected to GO term and KEGG pathway analysis, and were revealed to be enriched in 'central nervous system development', 'bladder cancer' and 'rheumatoid arthritis'. These hub genes were used to perform survival analysis using the GEPIA database, and it was determined that VEGFA and CXCL8 were significantly associated with a reduction in the overall survival of patients with GBM. In conclusion, the results suggest that the recurrence of GBM is associated with high gene expression levels VEGFA and CXCL8, and the development of the central nervous system.