MATERIALS AND METHODS: The different primer sets were developed using bioinformatics software DNASTAR. The E. coli cells were used for recombinant protein expression.
RESULTS: The NiV 'G' region primers were designed and amplified for 1 kb fragment and cloned. The NiV 'G' fragments were sub-cloned in pET-28(+) B and pGEX-5x-1. Recombinant protein thus obtained in soluble form in both the cases was essayed using western blot. The result showed the protein expression yield was more in pET-28(+) B with low stability and vice versa for pGEX-5x-1.
CONCLUSION: The antibodies raised from the protein can be used as diagnostic reagent for detection of NiV. Thus, a new diagnostic technique can be industrialized.
METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.
RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.
DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.
METHODS: Total RNA was extracted from formalin-fixed paraffin-embedded tissue biopsies of 20 OPMD cases with known clinical outcomes (10 MT vs. 10 NT). Samples were assessed for quantity, quality and integrity of RNA prior to sequencing. Analysis for differential gene expression between MT and NT was performed using statistical packages in R. Genes were considered to be significantly differentially expressed if the False Discovery Rate corrected P-value was 1.90). Analysis of RNA-Sequencing outputs revealed 41 genes (34 protein-coding; 7 non-coding) that were significantly differentially expressed between MT and NT cases. The log2 fold change for the statistically significant differentially expressed genes ranged from -2.63 to 2.48, with 23 protein-coding genes being downregulated and 11 protein-coding genes being upregulated in MT cases compared to NT cases.
CONCLUSION: Several candidate genes that may play a role in malignant transformation of OPMD have been identified. Experiments to validate these candidates are underway. It is anticipated that this work will contribute to better understanding of the etiopathogenesis of OPMD and development of novel biomarkers.
METHOD: By using the keywords "acute lymphoblastic leukemia", and "microarray", a total of 280 and 275 microarray datasets were found listed in Gene Expression Omnibus database GEO and ArrayExpress database respectively. Further manual inspection found that only three studies (GSE18497, GSE28460, GSE3910) were focused on gene expression profiling of paired diagnosis-relapsed pediatric B-ALL. These three datasets which comprised of a total of 108 matched diagnosis-relapsed pediatric B-ALL samples were then included for this meta-analysis using RankProd approach.
RESULTS: Our analysis identified a total of 1795 upregulated probes which corresponded to 1527 genes (pfp 1), and 1493 downregulated probes which corresponded to 1214 genes (pfp gene (pfp gene ontology biological process annotation, the upregulated genes were most enriched in cell cycle processes (enrichment score = 15.3), whilst the downregulated genes were clustered in transcription regulation (enrichment score = 12.6). Elevated expression of cell cycle regulators (e.g kinesins, AURKA, CDKs) was the key genetic defect implicated in relapsed ALL, and serve as attractive targets for therapeutic intervention.
CONCLUSION: We identified S100A8 as the most overexpressed gene, and the cell cycle pathway as the most promising biomarker and therapeutic target for relapsed childhood B-ALL. The validity of the results warrants further investigation.