ObjectiveTo analyze the expression profile changes of osteogenic-related genes during spontaneous calcification of rat bone marrow mesenchymal stem cells (BMSCs).
MethodsBMSCs were isolated from 3-day-old healthy Sprague Dawley rats;cells at the 4th generation were used to establish the spontaneous calcification model in vitro. Spontaneous calcification process was recorded by inverted phase contrast microscope observation and alizarin red staining after 7 and 14 days of culture. For gene microarray analysis, cell samples were collected at 0, 7, and 14 days after culture; the differentially expressed genes were analyzed by bioinformatics methods and validated by real-time quantitative PCR (RT-qPCR) assay.
ResultsRat BMSCs calcified spontaneously in vitro. When cultured for 7 days, the cells began to aggregate and were weakly positive for alizarin red staining. After 14 days of culture, obvious cellular aggregation and typical mineralized nodules were observed, the mineralized nodules were brightly positive for alizarin red staining. A total of 576 gene probe-sets expressed differentially during spontaneous calcification, corresponding 378 rat genes. Among them, 359 gene probe-sets expressed differentially between at 0 and 7 days, while only 13 gene probe-sets expressed differentially between at 7 and 14 days. The 378 differentially expressed genes were divided into 6 modes according to their expression profiles. Moreover, according to their biological functions, differentially expressed genes related to bone cell biology could be classified into 7 major groups:angiogenesis, apoptosis, bone-related genes, cell cycle, development, cell communication, and signal pathways related to osteogenic differentiation. In cell cycle group, 12 down-regulated genes were linked with each other functionally. Matrix metalloproteinase 13 (Mmp13), secreted phosphoprotein 1 (Spp1), Cxcl12, Mmp2, Mmp3, Apoe, and Itga7 had more functional connections with other genes. The results of genes Spp1, Mgp, Mmp13, Wnt inhibitory factor 1, Cxcl12, and cyclin A2 by RT-qPCR were consistent with that of gene microarray.
ConclusionThe first 7 days after rat BMSCs were seeded are a key phase determining the fate of spontaneous calcification. Multiple genes related with cell communication, bone-related genes, cell cycle, transforming growth factor-β signaling pathway, mitogen-activated protein kinase signaling pathway, and Wnt signaling pathway are involved during spontaneous calcification.
Objective To investigate the expression levels of fatty acid metabolism-related genes in acute myeloid leukemia (AML) and construct a prognostic risk regression model for AML. Methods Gene expression data from control groups and AML patients were downloaded from the GTEx database and The Cancer Genome Atlas (TCGA) database, followed by screening for differentially expressed genes (DEGs) between AML patients and controls. Fatty acid metabolism-related genes were obtained from the MSigDB database. The intersection of DEGs and fatty acid metabolism-related genes yielded fatty acid metabolism-associated DEGs. A protein-protein interaction network was constructed using the STRING database. Hub genes were analyzed via random forest, Kaplan-Meier survival, and Cox proportional hazards regression based on TCGA clinical data to establish a prognostic model and evaluate their diagnostic and prognostic significance. Immune cell infiltration differences between high- and low-risk groups were assessed using CIBERSORT algorithms to explore immune microenvironment variations and correlations with risk scores. Results A total of 60 fatty acid metabolism-related DEGs were identified. Further screening revealed 15 hub genes, among which four genes (HPGDS, CYP4F2, ACSL1, and EHHADH) were selected via integrated random forest, Cox regression, and Kaplan-Meier analyses to construct an AML prognostic lipid metabolism gene signature. Heatmaps demonstrated statistically significant differences in tumor-infiltrating immune cell proportions between risk groups (P<0.05). Conclusion The constructed lipid metabolism gene prognostic model may serve as a biomarker for overall survival in AML patients and provide new insights for immunotherapy drug development.
Evidence-based medicine is the methodology of modern clinical research and plays an important role in guiding clinical practice. It has become an integral part of medical education. In the digital age, evidence-based medicine has evolved to incorporate innovative research models that utilize multimodal clinical big data and artificial intelligence methods. These advancements aim to address the challenges posed by diverse research questions, data methods, and evidence sources. However, the current teaching content in medical schools often fails to keep pace with the rapidly evolving disciplines, impeding students' comprehensive understanding of the discipline's knowledge system, cutting-edge theories, and development directions. In this regard, this article takes the opportunity of graduate curriculum reform to incorporate real-world data research, artificial intelligence, and bioinformatics into the existing evidence-based medicine curriculum, and explores the reform of evidence-based medicine teaching in the information age. The aim is to enable students to truly understand the role and value of evidence-based medicine in the development of medicine, while possessing a solid theoretical foundation, a broad international perspective, and a keen research sense, in order to cultivate talents for the development of the evidence-based medicine discipline.
Objective To screen the differentially expressed genes and pathways involved in rosacea using bioinformatics analysis. Methods The GSE65914 gene chipset was collected from the Gene Expression Omnibus (up to July 12th, 2021). It was searched according to the keyword “rosacea”. The data was analyzed by GEO2R platform. The common differential genes of three subtypes of rosacea were screened out. The online DAVID analysis tool was used to perform the gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction networks of differentially expressed genes were made by String and Cytoscape. The key modules and genes were screened by Mcode and Cytohubba. Results A total of 957 common differential genes were identified, including 533 up-regulated genes and 424 down-regulated genes. GO enrichment analysis showed that these genes were mainly involved in immune response, inflammatory response, intercellular signal transduction, positive regulation of T cell proliferation, chemokine signaling pathways, cell surface receptor signaling pathways, cellular response to interferon-γ, and other biological processes. KEGG pathway enrichment analysis mainly included cytokine-cytokine receptor interaction, rheumatoid arthritis, chemokine signaling pathway, PPAR signaling pathway, Toll-like receptor signaling pathway, nuclear transcription factor-κB signaling pathway, tumor necrosis factor signaling pathway and other signaling pathways. Cytohubba analysis revealed 10 key genes, including PTPRC, MMP9, CCR5, IL1B, TLR2, STAT1, CXCR4, CXCL10, CCL5 and VCAM1. Conclusion The key genes and related pathways may play an important role in the pathogenesis of rosacea.
ObjectiveA competing endogenous RNA (ceRNA) regulatory network associated with long non-coding RNA (lncRNA) specific for lung adenocarcinoma (LUAD) was constructed based on bioinformatics methods, and the functional mechanism of actinfilament-associated protein 1-antisense RNA1 (AFAP1-AS1) in LUAD was analyzed, in order to provide a new direction for the study of LUAD therapeutic targets. MethodsThe gene chip of LUAD was downloaded from the Gene Expression Omnibus (GEO), and lncRNA and mRNA with differential expression between LUAD and normal tissues were screened using GEO2R online software, and their target genes were predicted by online databases to construct ceRNA networks and perform enrichment analysis. In cell experiments, AFAP1-AS1 was genetically knocked down and siRNA was constructed and transfected into LUAD cells A549 by cell transfection. CCK8, transwell, scratch assay and flow cytometry were used to detect the ability of cells to proliferate, invade, migrate and apoptosis. ResultsA total of 6 differentially expressed lncRNA and 494 differentially expressed mRNA were identified in the microarray of LUAD. The ceRNA network involved a total of 6 lncRNA, 22 miRNA, and 55 mRNA. Enrichment analysis revealed that mRNA was associated with cancer-related pathways. In cell assays, knockdown of AFAP1-AS1 inhibited cell proliferation, invasion, and migration, and AFAP1-AS1 promoted apoptosis. ConclusionIn this study, we construct a lncRNA-mediated ceRNA network, which may help to further investigate the mechanism of action of LUAD. In addition, through cellular experiments, AFAP1-AS1 is found to have potential as a therapeutic target for LUAD.
ObjectiveTo lay a theoretical foundation for the research of regulation of Hyperpolarization activated cyclic nucleotide gated channel 1 (HCN1) gene expression and its involvement in the pathogenesis of Mesio-temporal lobe epilepsy (MTLE) and other related diseases, the bioinformatics methods were used to analyze sequence characteristic, transcription factors and their binding sites in the promoter region of human HCN1 gene, and the physicochemical properties, signal peptides, hydrophobicity, transmembrane regions, protein structure, interacting proteins and functions of HCN1 proteins.MethodBiological software and website, such as Protparam, Protscale, MHMM, SignalP 5.0, NetPhos 3.1, Swiss-Model, Promoter 2.0, AliBaba2.1 and EMBOSS were used to analyze and predict physicochemical properties, structural functions, localized expression, phylogenetic relationships and protein interactions with human HCN1 protein, and promoter, CpG island and transcription factor characteristics of HCN1 gene.ResultsThe evolutionary analysis of HCN1 protein showed that the genetic distance between human and Pongo abelii was the smallest, indicating the closest genetic relationship between human and Pongo abelii. Human HCN1 protein was an unstable hydrophilic protein located on the plasma membrane, which contained two transmembrane structure. However, the predicted results showed that there was no signal peptide and nuclear localization sequence in this protein. The secondary structure of HCN1 protein was mostly random coil and alpha helix, and it contained multiple potential phosphorylation sites. The ontology analysis results of HCN1 protein were showed as follows. The cellular component of HCN1 protein was located in the plasma membrane (GO:0005886); the molecular functionof HCN1 protein were cyclic adenosine monophosphate binding (GO:0030552) and voltage-gated ion channel activity (GO:0005244); the biological process of this protein were reacting to cAMP (GO:0071320) and transmembrane transport of potassium (GO:0071805). The analysis results of String database showed that the proteins that had close interaction with human HCN1 protein mainly included the ten proteins (HCN2, HCN4, PEX5L, MARCH7, KCTD3, GNAT3, SHKBP1, KCNQ2, FLNA and NEDD4L). These proteins were mainly involved in regulation of ion transport and transmembrane transport of potassium (GO:0071805). The HCN1 gene was located at 5p12 and contained 8 exons and 7 introns.There were at least three promoter regions in the nucleotide sequence of 2 000 bp from the upstream of the HCN1 gene to the 5 'flanks, and contained a 158 bp CpG island in the promoter region and one TATA boxes and one CAAT boxes in the 5' regulation region ofHCN1 gene; niceteen transcription factors, including NF-κB, NF-1, AP-1, TBP, IRF-1, c-Ets-1, Elf-1, HNF-3, HNF-1, YY1, GATA-1, RXR-α, GR, AP-2αA, ENKTF-1, C/EBPβ, C/EBPα, c-Fos and c-Jun, binding in the promoter region of the HCN1 gene were predicted by both softwares (AliBaba2.1 and PROMO2).ConclusionThe analysis results provide important information for further studies on the role of HCN1. Bioinformatics analysis of the promoter region can improve the research efficiency of gene promoters, and provide theoretical basis for subsequent experiments to construct expression vectors of HCN1 gene promoters and identify their functions.
Objective To explore the mode and role of differential expression of circular RNAs (circRNAs) in myelodysplastic syndrome (MDS). Methods We preprocessed and analyzed the circRNA expression profile datasets GSE163386, GSE94591, and GSE81173 in the GEO (Gene Expression Omnibus) database. By using the circBank database and the ENCORI, miRDB, and miRWalk databases to predict microRNAs (miRNAs) that interacted with differentially expressed circRNAs and messenger RNAs (mRNAs), the circRNA-miRNA-mRNA axis was constructed. We retrieved miRNAs related to MDS in PubMed and further obtained competing endogenous RNA (ceRNA) networks related to MDS by taking intersections. Results Through analysis, 128 differentially expressed circRNAs were identified, 48 highly expressed, and 80 low expressed. Among differentially expressed circRNAs with multiple differences>10, 3 were upregulated and 11 were downregulated. Through analysis, 101 differentially expressed mRNA were identified, with 9 upregulated and 92 downregulated. Intersecting with the MDS related miRNAs retrieved by PubMed, we further obtained the MDS related ceRNA network, namely circRNA (has_circ_0061137)-miRNA (has-miR-16-5p)-mRNA (RUBCNL, TBC1D9, SLC16A6) and circRNA (has_circ_0061137)-miRNA (has-miR-125b-5p)-mRNA (CCR5, SLC16A6, IRF4), all of which were downregulated. Conclusion The ceRNA networks revealed in this study may help elucidate the circRNA mechanism in MDS.
ObjectiveThe role of ferroptosis-related genes in the occurrence and development of lung injury caused by sepsis was investigated by bioinformatics methods, and the closely related genes were predicted. MethodsThe Dataset GSE154653 was downloaded from the gene expression database (GEO), and a total of 8 cases of microarray gene set were included in normal group and lipopolysaccharide (LPS)-induced sepsis lung tissue. The differential expression genes (DEGs) were screened out under conditions of |log2 FC|>1 and P.adj<0.05. Meanwhile, the selected DEGs were combined with the driver and suppressor genes of ferroptosis downloaded from the ferroptosis database (FerrDb) to obtain the differential genes associated with ferroptosis in sepsis (Fe-DEGs). These Fe-DEGs were further analyzed using R language, DAVID, and STRING online tools to identify GO-KEGG functions and pathways, and the construction of PPI network. Results The Bioinformatics approach screened out 3533 DEGs and intersected 53 key genes related to ferroptosis. The further biological process (BP) of GO enrichment analysis mainly involves the positive regulation of transcription, the positive regulation of RNA polymerase II promoter transcription, the cytokine mediated signaling pathway, and the positive regulation of angiogenesis. The molecular function (MF) mainly involves the same protein binding, transcriptional activation activity and REDOX enzyme activity. The pathways are enriched in iron death, HIF-1 signaling pathway and AGE-RAGE signaling pathway. Five key Fe-DEGs genes were screened by constructing PPI network, including CYBB, LCN2, HMOX1, TIMP1 and CDKN1A. Conclusion CYBB、LCN2、HMOX1、TIMP1 and CDKNIA genes may be key genes involved in ferroptosis of lung tissue caused by sepsis.
Objective
To investigate specific changes of T cell repertoire in convalescent patients infected by influenza A (H7N9) virus.
Methods
Peripheral blood samples from 8 convalescent patients infected by H7N9 virus and 10 healthy donors were collected. After extracting whole DNA from these samples, arm-PCR were performed and the products were submitted to Illumina HiSeq2000 platform to produce deep sequencing data of the nucleotide sequences of complementary determining region 3 of T cell receptor β chain (TRB). Differences were compared in TRB diversity and V-D-J gene usage and similarities of sequences between the patients and the healthy donors.
Results
Frequency of V-D-J gene usage was different between the H7N9 patient group and the healthy group, such as TRBV30, TRBV27, and TRBV18 (Student's t test, P < 05). Main component analysis showed V-J pairing pattern was significantly different between two groups, which may have potential in identifying patients from healthy people. A considerable number of shared CDR3s were found in patient-patient pairs and normal-normal pairs, while seldom were found in patient-normal pairs. The similarity between patients was also confirmed by overlap distance analysis. Indexes for assessing diversity of immune repertoires, Shannon-Weiner index and Simpson index, were both lower in the patients (Student's t test, P < 05), suggesting that the immune system of the patients had not recovered 6 months after H7N9 infection. Compared with the healthy donors, the number of hyper-expression clones increased in the patient group, and some of them showed similarity among patients.
Conclusions
TRB repertoires are less diverse in patients with increased hyper-expressed clones and identifiable V-J usage pattern, which is identifiable from normal population. These results suggest that there are H7N9-specific changes in TRB repertoires of H7N9 infected patients in convalescent phase, which have potential implication in diagnosis and therapeutic T cell development.
