Search Tool For The Retrieval Of Interacting Genes/Proteins Research Articles

Analysis of Inflammation-Related Genes in Patients with Stanford Type A Aortic Dissection.

Background: Aortic dissection (AD) is a life-threatening cardiovascular disease. Pathophysiologically, it has been shown that aortic wall inflammation promotes the occurrence and development of aortic dissection. Thus, the aim of the current research was to determine the inflammation-related biomarkers in AD. Methods: In this study, we conducted differentially expressed genes (DEGs) analysis using the GSE153434 dataset containing 10 type A aortic dissection (TAAD) and 10 normal samples downloaded from the Gene Expression Omnibus (GEO) database. The intersection of DEGs and inflammation-related genes was identified as differential expressed inflammation-related genes (DEIRGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for DEIRGs. We then constructed the protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and identified hub genes using the Cytoscape plugin MCODE. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was used to construct a diagnostic model. Results: A total of 1728 DEGs were identified between the TAAD and normal samples. Thereafter, 61 DEIRGs are obtained by taking the intersection of DEGs and inflammation-related genes. The GO indicated that DEIRGs were mainly enriched in response to lipopolysaccharide, in response to molecules of bacterial origin, secretory granule membrane, external side of plasma, receptor ligand activity, and signaling receptor activator activity. KEGG analysis indicated that DEIRGs were mainly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, and proteoglycans in cancer. We identified MYC, SELL, HIF1A, EDN1, SERPINE1, CCL20, IL1R1, NOD2, TLR2, CD69, PLAUR, MMP14, and HBEGF as hub genes using the MCODE plug-in. The ROC indicated these genes had a good diagnostic performance for TAAD. Conclusion: In conclusion, our study identified 13 hub genes in the TAAD. This study will be of significance for the future development of a preventive therapy of TAAD.

The molecular mechanism of Xiaoxuming decoction in the treatment of ischemic stroke based on network pharmacology and molecular docking

AbstractXiaoxuming decoction is a traditional Chinese medicine that has been widely used in the clinical treatment of ischemic stroke (IS). This study employed network pharmacology to identify the bioactive molecules and therapeutic mechanism of Xiaoxuming decoction against IS. First, the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was utilized to obtain the ingredients and potential target proteins related to IS in the Xiaoxuming decoction. Subsequently, known target proteins related to IS were collected from online Mendelian inheritance in man (OMIM), Disgenet, and Gencards databases. The mechanism of Xiaoxuming decoction against IS was identified by enrichment analysis of gene ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG). Additionally, protein–protein interaction data were obtained from the search tool for the retrieval of interacting genes/proteins (STRING). The hub gene was further screened out from the gene expression omnibus (GEO) and verified by molecular docking. The study identified a total of 183 candidate molecules and 140 targets related to IS. These candidate targets regulate biological processes including inflammation, autophagy, oxidative stress, and vascular reaction. Our findings provide a comprehensive demonstration of the active compounds, key targets, main signaling pathways, and underlying molecular mechanisms of Xiaoxuming decoction in treating IS.

Exploration of the shared genes and signaling pathways between lung adenocarcinoma and idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF), a type of interstitial lung disease (ILD), is a chronic disease with an unknown etiology. The occurrence of lung cancer (LC) is one of the main causes of death in patients with IPF. However, the pathogenesis driving these malignant transformations remains unclear; therefore, this study aimed to identify the shared genes and functional pathways associated with both disease conditions. Data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. To identify overlapping genes in both diseases, the "limma" package in R software and weighted gene coexpression network analysis (WGCNA) were used. Venn diagrams were used to obtain the shared genes. The diagnostic value of the shared genes was assessed using receiver operating characteristic (ROC) curve analysis. Gene Ontology (GO) term enrichment was performed on the shared genes between lung adenocarcinoma (LUAD) and IPF, and the genes were also functionally enriched using Metascape. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Finally, the link between shared genes and common antineoplastic medicines was investigated using the CellMiner database. The coexpression modules associated with LUAD and IPF were discovered using WGCNA, and 148 genes were found to overlap. In addition, 74 upregulated and 130 downregulated overlapping genes were obtained via differential gene analysis. Functional analysis of the genes revealed that these genes are primarily engaged in extracellular matrix (ECM) pathways. Furthermore, COL1A2, POSTN, COL5A1, CXCL13, CYP24A1, CXCL14, and BMP2 were identified as potential biomarkers in patients with LUAD secondary to IPF showing good diagnostic values. ECM-related mechanisms may be the underlying link between LC and IPF. A total of 7 shared genes were identified as potential diagnostic markers and therapeutic targets for LUAD and IPF.

Identification of genes encoding targets associated with adverse events in multiple myeloma.

1556 Background: Despite novel therapeutic options, understanding the toxicity profile of anti multiple myeloma (MM) drug regimens (AMDR) is vital. Data suggests adverse event (AE) profile is orchestrated by certain associated genes. The purpose of this study is to explore the interplay of genes, regimens, AEs and provide genetic evidence for a linkage between regimens and the presence of AEs. Methods: We examined a group of AMDRs retrieved from the FDA Adverse Event Reporting System. Drug-associated genes were obtained from the Drug Gene Interaction Database, DGIdb. Then, genes were imported to the Online Mendelian Inheritance in Man (OMIM) to search for the existing clinical synopses (features) in the peer reviewed biomedical material, or phenotypes when no data was available. Clinical synopses were then linked to MedDRA. Gene gene interaction was also determined through the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We then constructed a detailed large MM pharmacogenetic network to illustrate the linkage among drugs, genes and MM via various expert integrated resources. AMDR responses were then assessed at the DNA and protein levels by modularizing genetic causation into different scenarios i) a direct drug effect on gene expression; ii) the secondary or tertiary effect of drug(s) on subsequent drug metabolism or disposition altering gene expression pathways; iii) modulation of another biological network that disturbs drug effect on gene expression resulting in exacerbating AE occurrence. Results: We found multiple genes including CTLA4, NFKB1, PSMB8, CCND1 and HLAA were complicit/associated with different AEs including cardiac or hepatobiliary disorders or other serious events. Our results suggested the expression of CTLA4 was influenced by multiple AMDRs from combinations of bortezomib (V), lenalidomide (R), melphalan (M), or dexamethasone (d). When AMDRs involve these agents, the occurrence of pericardial disorders due to the treatment exhibited high confidence. We also found carfilzomib (K) combinations (Kd or KRd) were associated with PSMB8 which was likely to be the genetic factor concomitant with heart failures, cardiac arrhythmias, or hepatobiliary toxicities. Furthermore, our findings suggested NFKB1, thalidomide associated gene interacting with PSMB8, may present the same toxicity events in hepatobiliary disorders. Conclusions: Understanding the genomic basis of specific drug-AE combinations could reveal molecular mechanisms underlying drug-gene-AE associations and recognize the causality of AEs. When considering alternative agents in MM, our study offers insights for predicting treatment responses such as PSMB8 which could potentially cause an off target effect of K on cardiomyocyte 20S beyond MM cells. Our plan is to integrate this novel computational biology approach with translational medicine to further investigate our observation.

Astrocyte-derived complement C3 is activated in patients with tuberous sclerosis complex and mediates immune injury: an integrated bioinformatics analysis.

Tuberous sclerosis complex (TSC) is a genetic disorder associated with multiple neurological manifestations. Cortical tubers (CT) are recognized as the hallmark brain lesions of TSC and contribute to neurological and psychiatric symptoms. To understand the molecular mechanism of neuropsychiatric features of TSC, the differentially expressed genes (DEGs) in CT from patients with TSC and those in normal cortex (NC) from participants acting as healthy controls were investigated. The dataset of GSE16969, which had already been published and described (https://onlinelibrary.wiley.com/doi/10.1111/j.1750-3639.2009.00341.x), was downloaded from the Gene Expression Omnibus (GEO), including samples of 4 CT and 4 NC. The R package "limma" was used to screen DEGs in CT and NC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses of the DEGs were conducted using the R package "clusterProfiler". The online software Ingenuity Pathway Analysis (IPA) was used to explore activation/inaction of canonical pathways. The hub gene was selected based on the protein-protein interaction (PPI) network constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Subsequently, the hub genes at messenger (mRNA) and transcriptional levels were tested. We also explored immune cell-type enrichment using the online database xCell, and assessed the correlation between cell types and C3 expression. Then, we verified the source of C3 by constructing TSC2 knockout cells in the U87 astrocyte cell line. The human neuronal cell line SH-SY5Y was used to examine the effects of excessive complement C3 levels. A total of 455 DEGs were identified. A large number of pathways were involved in the immune response process based on the results of GO, KEGG, and IPA. C3 was identified as a hub gene. Complement C3 was also upregulated in the human CT and peripheral blood. Furthermore, based on the enrichment of functions and signaling pathways, complement C3 played a critical role in immune injury in CT of TSC. In the in vitro experiments, we found that excessive complement C3 was derived from TSC2 knockout U87 cells, and there was an increased intracellular reactive oxygen species (ROS) level in SH-SY5Y cells. Complement C3 is activated in patients with TSC and can mediate immune injury.

Risk factors and predictors of urogenous sepsis after percutaneous nephrolithotomy for idiopathic calcium oxalate nephrolithiasis.

The aim of this study was to use bioinformatics approaches to screen and identify the key genes of idiopathic calcium oxalate nephrolithiasis, and explore its potential molecular mechanism. The GSE73680 kidney stone data set was downloaded from the Gene Expression Omnibus (GEO). R software (The R Foundation for Statistical Computing) was used to screen differentially expressed genes. GeneMANIA and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) databases were used to analyze related genes interacting with crucial genes, and a protein-protein interaction (PPI) network was constructed. The differential genes were then subjected to the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. The clinical data of 156 patients who received percutaneous nephrolithotomy (PCNL) therapy at our facility between January 2013 and December 2017 were retrospectively analyzed. The various parameters associated with postoperative urogenous sepsis were identified using multivariable logistic regression analysis. The study discovered one differentially expressed gene was nucleotide-binding oligomerization domain-containing protein 2 (NOD2). GO and KEGG analysis showed that NOD2 might affect the occurrence of idiopathic calcium oxalate kidney stones by affecting inflammation, receptor expression, immune environment, necrosis, apoptosis, and other pathways. The clinical parameter of patients who participated in the study, including preoperative urinary white blood cell (WBC) count, preoperative urinary nitrite, stone diameter, operation time, WBC count, and WBC D values, were statistically different between the systemic inflammatory response syndrome (SIRS) group and the urosepsis group. According to multivariate logistic regression analysis, the preoperative urine nitrite, calculus diameter, blood WBC, and NOD2 expression 3 hours after surgery were all independently associated with the urosepsis development. Preoperative urinary nitrite positive status, postoperative WBC count ≥2.98×109/L 3 hours after operation, stone diameter >6 cm, and low expression of NOD2 in renal papillary tissue are more likely to cause the urinary source of idiopathic calcium oxalate nephrolithiasis after PCNL urogenous sepsis. These parameters also offer a viable treatment paradigm for the perioperative management of PCNL in treating idiopathic calcium oxalate kidney stones.

Autoantibodies identify primary Sjögren’s syndrome in patients lacking serum IgG specific for Ro/SS-A and La/SS-B

ObjectiveIdentify autoantibodies in anti-Ro/SS-A negative primary Sjögren’s syndrome (SS).MethodsThis is a proof-of-concept, case-control study of SS, healthy (HC) and other disease (OD) controls. A discovery dataset of plasma samples (n=30...

Integrative analysis of gene expression and DNA methylation to identify biomarkers of non-genital warts induced by low-risk human papillomaviruses infection

BackgroundHuman papillomaviruses have been shown to dysregulate the gene expression and DNA methylation profiles of their host cells over the course of infection. However, there is a lack of information on the impact of low-risk HPV infection and wart formation on host cell's expression and methylation patterns. Therefore, the objective of this study is to analyse the genome and methylome of common warts using an integrative approach. MethodsIn the present study, gene expression (GSE136347) and methylation (GSE213888) datasets of common warts were obtained from the GEO database. Identification of the differentially expressed and differentially methylated genes was carried out using the RnBeads R package and the edgeR Bioconductor package. Next, functional annotation of the identified genes was obtained using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Network construction and analyses of the gene-gene, protein-protein, and signaling interactions of the differentially expressed and differentially methylated genes was performed using the GeneMANIA web interface, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the Signaling Network Open Resource 2.0 (SIGNOR 2.0), respectively. Lastly, significant hub genes were identified using the Cytoscape application CytoHubba. ResultsA total of 276 genes were identified as differentially expressed and differentially methylated in common warts, with 52% being upregulated and hypermethylated. Functional enrichment analysis identified extracellular components as the most enriched annotations, while network analyses identified ELN, ITGB1, TIMP1, MMP2, LGALS3, COL1A1 and ANPEP as significant hub genes. ConclusionsTo the best knowledge of the authors, this is the first integrative study to be carried out on non-genital warts induced by low-risk HPV types. Future studies are required to re-validate the findings in larger populations using alternative approaches.

Abstract 3158: Checkpoint Kinase I, Cyclin E1, and Exonuclease I genes play role in the survival of lung adenocarcinoma patients

Abstract According to the SEER program of NCI, lung, and bronchus cancer are the second most common cancer with an estimated 236,740 diagnoses in 2022. Fifty-four percent of the patients are expected to die of the disease in the United States alone. The five-year overall survival rate for these cancers is 22.9 percent. Lung adenocarcinoma (LUAD) accounts for forty percent of all lung cancer cases in the United States. Genetic biomarker-based early detection and precision treatment of LUAD patients can play a critical role in the reduction of the mortality rate. The purpose of this investigation was to use an integrated bioinformatics approach to identify differentially expressed Hub genes in LUAD with > 10 connections in the genetic interaction network(s). The higher number of genetic interactions potentially suggests their important role in patient survival. Out of 23,483 genes from seven published cancer studies and databases, we identified 107 significantly altered (up or down-regulated) genes that are common to all data sources. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed that many of the identified 107 genes were involved in DNA replication, DNA repair, ATP binding, and cancer pathways. A protein-protein interaction network was mapped with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). This analysis revealed 1116 protein-to-protein interactions (PPI) among the 107 genes. We selected 25 genes, which exhibited >30 PPI interactions each which showed Cox coefficients for gene expression, where a positive coefficient corresponds to a worse survival rate with higher levels of gene expression, while a negative coefficient corresponds to a better prognosis with lower levels of gene expression; it also provided a False Discovery Rate (FDR) with a corrected p-value. The Kaplan-Meier survival curves for genes that had an FDR of below 0.05 were graphed inside of the OncoLnc tool. Patients were subclassified into the low- and high-Hub gene expression level groups if the median expression was in the bottom 25 percentile or top 25 percentile. Hub genes isolated from Cytoscape were entered into OncoLnc to collect data on the relationship between LUAD patients (n=492) survival and gene expression, which resulted in the identification of three genes, Checkpoint Kinase 1, Cyclin E1, and Exonuclease 1. All three genes showed a significant correlation between increased differential expression in LUAD and worsened patient survival. These signature Hub genes (Checkpoint Kinase 1, Cyclin E1, and Exonuclease 1) and associated genetic networks can potentially be developed as early diagnostic markers or targets in precision gene therapies for improved prognosis of LUAD patients. Citation Format: Prateek Gupta, Leya Joykutty, Diaaidden Alwadi, Ana Ruas, Deodutta Roy, Quentin Felty, Alok Deoraj. Checkpoint Kinase I, Cyclin E1, and Exonuclease I genes play role in the survival of lung adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3158.

MiR-34a-5p as molecular hub of pathomechanisms in Huntington’s disease

BackgroundAlthough a pivotal role of microRNA (miRNA, miR) in the pathogenesis of Huntington’s disease (HD) is increasingly recognized, the molecular functions of miRNAs in the pathomechanisms of HD await further elucidation. One of the miRNAs that have been associated with HD is miR-34a-5p, which was deregulated in the mouse R6/2 model and in human HD brain tissues.MethodsThe aim of our study was to demonstrate interactions between miR-34a-5p and HD associated genes. By computational means we predicted 12 801 potential target genes of miR-34a-5p. An in-silico pathway analysis revealed 22 potential miR-34a-5p target genes in the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway “Huntington’s disease”.ResultsUsing our high-throughput miRNA interaction reporter assay (HiTmIR) we identified NDUFA9, TAF4B, NRF1, POLR2J2, DNALI1, HIP1, TGM2 and POLR2G as direct miR-34a-5p target genes. Direct binding of miR-34a-5p to target sites in the 3’UTRs of TAF4B, NDUFA9, HIP1 and NRF1 was verified by a mutagenesis HiTmIR assay and by determining endogenous protein levels for HIP1 and NDUFA9. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) analysis identified protein–protein interaction networks associated with HD like “Glutamine Receptor Signaling Pathway” and “Calcium Ion Transmembrane Import Into Cytosol”.ConclusionOur study demonstrates multiple interactions between miR-34a-5p and HD associated target genes and thereby lays the ground for future therapeutic interventions using this miRNA.

Comprehensive bioinformatics analysis on exportins in lung adenocarcinoma and lung squamous cell carcinoma.

Lung cancer is one of the most common malignant tumors in the world. Exportins are closely associated with the cellular activity and disease progression in a variety of different tumors. However, the expression level, genetic variation, immune infiltration, and biological function of different exportins in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), as well as their relationship with the prognosis of patients with LUAD and LUSC have not been fully clarified. To analyze the differential expression, prognostic value, genetic variation, biological function, and immune cell infiltration of exportins in patients with LUAD and LUSC, the ONCOMINE; UALCAN; Human Protein Atlas (HPA); Kaplan-Meier plotter; cBioPortal; Search Tool for the Retrieval of Interacting Genes/Proteins (STRING); Database for Annotation, Visualization, and Integrated Discovery (DAVID); Tumor Immune Estimation Resource (TIMER); and LinkedOmics databases were used in this study. The transcriptional and protein expression levels of CSE1L and XPO1/5/6/7 were increased in patients with LUAD and LUSC, and the increased transcriptional levels of CSE1L and XPO5/6/7 were related to worse prognosis. An increased transcriptional level of XPO1 was associated with a better prognosis. These results indicated that CSE1L and XPO1/5/6/7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Moreover, the high mutation rate of exportins in non-small cell lung cancer was 50.48%, and the largest proportion of mutations included high messenger RNA expression. The expression of exportins was significantly correlated with the infiltration of various immune cells. Differentially expressed exportins could regulate the occurrence and development of LUAD and LUSC by involving a variety of microRNAs and transcription factor E2F1. Our study provides novel insights into the selection of prognostic biomarkers of exportins in LUAD and LUSC.

Peptidomic changes in human serous colorectal cancer patients.

Colorectal cancer (CRC) is the third most common cancer worldwide and one of the leading causes of cancer-related death. Peptidomics, considered a novel branch of proteomics, has an increasing range of applications in the screening, diagnosis, prognosis, and even monitoring of cancer. However, there is little information for peptidomics analysis in CRC. In this study, a comparative peptidomic profiling was analyzed in 3 CRC tissue samples and 3 adjacent intestinal epithelial tissue samples by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Among the total 133 nonredundant peptides identified, 59 were significantly differentially expressed in the CRC samples and benign colonic epithelium conditions [fold change (FC) >2, P<0.05]. Totals of 25 up-regulated and 34 down-regulated peptides were detected, respectively. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were applied to predict the possible functions of these relevant precursor proteins. To clearly identify the potential interaction network of peptide precursors, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to determine protein interactions and a possible central role in CRC. Our results for the first time revealed the differentially expressed peptides between the serous CRC tissue and the adjacent intestinal epithelial tissue samples, and these prominently variable peptides might have an important potential role in occurrence and development of CRC.

Discovering genetic linkage between periodontitis and type 1 diabetes: A bioinformatics study.

Background: Relationship between periodontitis (PD) and type 1 diabetes (T1D) has been reported, but the detailed pathogenesis requires further elucidation. This study aimed to reveal the genetic linkage between PD and T1D through bioinformatics analysis, thereby providing novel insights into scientific research and clinical treatment of the two diseases. Methods: PD-related datasets (GSE10334, GSE16134, GSE23586) and T1D-related datasets(GSE162689)were downloaded from NCBI Gene Expression Omnibus (GEO). Following batch correction and merging of PD-related datasets as one cohort, differential expression analysis was performed (adjusted p-value <0.05 and ∣log2 fold change| > 0.5), and common differentially expressed genes (DEGs) between PD and T1D were extracted. Functional enrichment analysis was conducted via Metascape website. The protein-protein interaction (PPI) network of common DEGs was generated in The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Hub genes were selected by Cytoscape software and validated by receiver operating characteristic (ROC) curve analysis. Results: 59 common DEGs of PD and T1D were identified. Among these DEGs, 23 genes were commonly upregulated, and 36 genes were commonly downregulated in both PD- and T1D-related cohorts. Functional enrichment analysis indicated that common DEGs were mainly enriched in tube morphogenesis, supramolecular fiber organization, 9 + 0 non-motile cilium, plasma membrane bounded cell projection assembly, glomerulus development, enzyme-linked receptor protein signaling pathway, endochondral bone morphogenesis, positive regulation of kinase activity, cell projection membrane and regulation of lipid metabolic process. After PPI construction and modules selection, 6 hub genes (CD34, EGR1, BBS7, FMOD, IGF2, TXN) were screened out and expected to be critical in linking PD and T1D. ROC analysis showed that the AUC values of hub genes were all greater than 70% in PD-related cohort and greater than 60% in T1D-related datasets. Conclusion: Shared molecular mechanisms between PD and T1D were revealed in this study, and 6 hub genes were identified as potential targets in treating PD and T1D.

Identification of Timm13 protein translocase of the mitochondrial inner membrane as a potential mediator of liver fibrosis based on bioinformatics and experimental verification

ObjectiveTo explore the association between translocase of the inner mitochondrial membrane 13 (Timm13) and liver fibrosis.MethodsGene expression profiles of GSE167033 were collected from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between liver disease and normal samples were analyzed using GEO2R. Gene Ontology and Enrichment function were performed, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and the hub genes of the PPI network were calculated by MCODE plug-in in Cytoscape. We validated the transcriptional and post-transcriptional expression levels of the top correlated genes using fibrotic animal and cell models. A cell transfection experiment was conducted to silence Timm13 and detect the expression of fibrosis genes and apoptosis genes.Results21,722 genes were analyzed and 178 DEGs were identified by GEO2R analysis. The top 200 DEGs were selected and analyzed in STRING for PPI network analysis. Timm13 was one of the hub genes via the PPI network. We found that the mRNA levels of Timm13 in fibrotic liver tissue decreased (P < 0.05), and the mRNA and protein levels of Timm13 also decreased when hepatocytes were stimulated with transforming growth factor-β1. Silencing Timm13 significantly reduced the expression of profibrogenic genes and apoptosis related genes.ConclusionsThe results showed that Timm13 is closely related to liver fibrosis and silencing Timm13 significantly reduced the expression of profibrogenic genes and apoptosis related genes, which will provide novel ideas and targets for the clinical diagnosis and treatment of liver fibrosis.

SNHG 12 and hsa-miR-140-5P may play an important role in the ceRNA network related to hypertrophic cardiomyopathy.

Competing endogenous RNA (ceRNA) networks play important roles in the mechanism and development of a variety of diseases. This study aimed to construct a ceRNA network of hypertrophic cardiomyopathy (HCM). We searched the Gene Expression Omnibus (GEO) database and then analyzed the RNAs of 353 samples to explore differentially expressed lncRNAs (DELs), microRNAs (miRNAs; DEMs), and messenger RNAs (DEmRNAs) during the progression of HCM. Weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and transcription factor (TF) prediction of miRNAs were also performed, and the GO terms, KEGG pathway terms, protein-protein interaction (PPI) network, and Pearson correlation network of the DEGs were visualized with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and through Pearson analysis. In addition, a ceRNA network related to HCM was constructed on the basis of the DELs, DEMs, and DEs. Finally, the function of the ceRNA network was explored via GO and KEGG enrichment analyses. Through our analysis, 93 DELs (77 upregulated and 16 downregulated), 163 DEMs (91 upregulated and 72 downregulated), and 432 DEGs (238 upregulated and 194 downregulated) were screened. The functional enrichment analysis results for miRNAs showed that the miRNAs were mainly related to the VEGFR signaling network and the INFr pathway and were mainly regulated by TFs such as SOX1, TEAD1, and POU2F1. Gene set enrichment analysis (GSEA), GO analysis, and KEGG enrichment analysis showed that the DEGs were enriched in the Hedgehog signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. In addition, a ceRNA network including 8 lncRNAs (e.g., LINC00324, SNHG12, and ALMS1-IT1), 7 miRNAs (e.g., hsa-miR-217, hsa-miR-184, and hsa-miR-140-5p), and 52 mRNAs (e.g., IGFBP5, TMED5, and MAGT1) was constructed. The results revealed that SNHG12, hsa-miR-140-5p, hsa-miR-217, TFRC, HDAC4, TJP1, IGFBP5, and CREB5 may form an important network involved in the pathology of HCM. The novel ceRNA network that we have demonstrated will provide new research points about molecular mechanisms of HCM.

Peripheral Blood Gene Expression Profile of Infants with Atopic Dermatitis

To enhance the understanding of molecular mechanisms and mine previously unidentified biomarkers of pediatric atopic dermatitis, PBMC gene expression profiles were generated by RNA sequencing in infants with atopic dermatitis and age-matched controls. A total of 178 significantly differentially expressed genes (DEGs) (115 upregulations and 63 downregulations) were seen, compared with those in healthy controls. The DEGs identified included IL1β, TNF, TREM1, IL18R1, and IL18RAP. DEGs were validated by real-time RT- qPCR in a larger number of samples from PBMCs of infants with atopic dermatitis aged <12 months. Using the DAVID (Database for Annotation, Visualization and Integrated Discovery) database, functional and pathway enrichment analyses of DEGs were performed. Gene ontology enrichment analysis showed that DEGs were associated with immune responses, inflammatory responses, regulation of immune responses, and platelet activation. Pathway analysis indicated that DEGs were enriched in cytokine‒cytokine receptor interaction, immunoregulatory interactions between lymphoid and nonlymphoid cells, hematopoietic cell lineage, phosphoinositide 3-kinase‒protein kinase B signaling pathway, NK cell‒mediated cytotoxicity, and platelet activation. Furthermore, the protein‒protein interaction network was predicted using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database and visualized with Cytoscape software. Finally, on the basis of the protein‒protein interaction network, 18 hub genes were selected, and two significant modules were obtained. In conclusion, this study sheds light on the molecular mechanisms of pediatric atopic dermatitis and may provide diagnostic biomarkers and therapeutic targets.

Yersiniomics, a Multi-Omics Interactive Database for Yersinia Species.

The genus Yersinia includes a large variety of nonpathogenic and life-threatening pathogenic bacteria, which cause a broad spectrum of diseases in humans and animals, such as plague, enteritis, Far East scarlet-like fever (FESLF), and enteric redmouth disease. Like most clinically relevant microorganisms, Yersinia spp. are currently subjected to intense multi-omics investigations whose numbers have increased extensively in recent years, generating massive amounts of data useful for diagnostic and therapeutic developments. The lack of a simple and centralized way to exploit these data led us to design Yersiniomics, a web-based platform allowing straightforward analysis of Yersinia omics data. Yersiniomics contains a curated multi-omics database at its core, gathering 200 genomic, 317 transcriptomic, and 62 proteomic data sets for Yersinia species. It integrates genomic, transcriptomic, and proteomic browsers, a genome viewer, and a heatmap viewer to navigate within genomes and experimental conditions. For streamlined access to structural and functional properties, it directly links each gene to GenBank, the Kyoto Encyclopedia of Genes and Genomes (KEGG), UniProt, InterPro, IntAct, and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and each experiment to Gene Expression Omnibus (GEO), the European Nucleotide Archive (ENA), or the Proteomics Identifications Database (PRIDE). Yersiniomics provides a powerful tool for microbiologists to assist with investigations ranging from specific gene studies to systems biology studies. IMPORTANCE The expanding genus Yersinia is composed of multiple nonpathogenic species and a few pathogenic species, including the deadly etiologic agent of plague, Yersinia pestis. In 2 decades, the number of genomic, transcriptomic, and proteomic studies on Yersinia grew massively, delivering a wealth of data. We developed Yersiniomics, an interactive web-based platform, to centralize and analyze omics data sets on Yersinia species. The platform allows user-friendly navigation between genomic data, expression data, and experimental conditions. Yersiniomics will be a valuable tool to microbiologists.

Identification of hub genes associated with osteoporosis development by comprehensive bioinformatics analysis.

Osteoporosis (OP) is a systemic bone disease caused by various factors, including, the decrease of bone density and quality, the destruction of bone microstructure, and the increase of bone fragility. It is a disease with a high incidence in a large proportion of the world's elderly population. However, osteoporosis lacks obvious symptoms and sensitive biomarkers. Therefore, it is extremely urgent to discover and identify disease-related biomarkers for early clinical diagnosis and effective intervention for osteoporosis. In our study, the Linear Models for Microarray Data (LIMMA) tool was used to screen differential expressed genes from transcriptome sequencing data of OP blood samples downloaded from the GEO database, and cluster Profiler was used for enriching analysis of differently expressed genes. In order to analyzed the relevance of gene modules, clinical symptoms, and the most related module setting genes associated with disease progression, we adapted Weighted Gene Co-expression Network Analysis (WGCNA) to screen and analyze the related pathways and relevant molecules. We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to construct protein interaction network of key modules, and Cytoscape software was used to complete network visualization and screen of core genes in the network. Various plug-in algorithms of cytoHubba were used to identify key genes of OP. Finally, correlation analysis and single-gene gene probe concentration analysis (GSEA) analysis were performed for each core gene. Results of a total of 8 key genes that were closely related to the occurrence and development of OP were screened out, which provided a brand-new idea for the clinical diagnosis and early prevention of OP. Quantitative real-time PCR (qRT-PCR) was performed for validation, the expression levels of CUL1, PTEN and STAT1 genes in the OS group were significantly higher than in the non-OS groups. Receiver operating characteristic analysis demonstrated that CUL1, PTEN and STAT1 displayed considerable diagnostic accuracy for OS.

Biomarker identification of immune-related genes in pheochromocytoma and paraganglioma.

Although we have a good understanding of the diagnosis and treatment of pheochromocytoma and paraganglioma (PPGL), the underlying pathogenesis and molecular pathways of PPGL need to be further studied. This study aimed to use bioinformatics to analyze the role of immune-related genes (IRGs) in the pathogenesis of PPGL. GSE19422 and GSE60459 microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the "limma" package in R, and genes overlapping with IRGs were screened using the "VennDiagram" package. A protein-protein interaction (PPI) network was constructed in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the core genes were identified by Cytoscape, followed by enrichment analysis and receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic efficacy of the core genes. In addition, the level of immune cell infiltration of PPGL was analyzed and the target drug of the core gene was predicted. A total of 1,105 DEGs were identified from the 2 datasets, of which 94 were IRGs, suggesting that the occurrence of PPGL involved immune-related pathways. Through PPI and Cytoscape, a total of 2 core genes: fibroblast growth factor 2 (FGF2), FYN proto-oncogene (FYN), and vascular cell adhesion molecule 1 (VCAM1) were identified, and the ROC curve showed that these 3 core genes had good efficacy in the diagnosis of PPGL, and more than 50 potential therapeutic drugs could be predicted based on these 3 core genes. Subsequent immunoinfiltration analysis showed that mast cells activated were significantly elevated in patients with PPGL, negatively correlated with macrophages M2, and positively correlated with the level of dendritic cells activated. This study found that immunity is closely related to the occurrence of PPGL, and that FGF2, FYN, and VCAM1 may be potential biomarkers and therapeutic targets of PPGL.

Screening for diagnostic targets in tuberculosis and study on its pathogenic mechanism based on mRNA sequencing technology and miRNA-mRNA-pathway regulatory network.

Tuberculosis is common infectious diseases, characterized by infectivity, concealment and chronicity, and the early diagnosis is helpful to block the spread of tuberculosis and reduce the resistance of Mycobacterium tuberculosis to anti-tuberculosis drugs. At present, there are obvious limitations in the application of clinical detection methods used for the early diagnosis of tuberculosis. RNA sequencing (RNA-Seq) has become an economical and accurate gene sequencing method for quantifying transcripts and detecting unknown RNA species. A peripheral blood mRNA sequencing was used to screen the differentially expressed genes between healthy people and tuberculosis patients. A protein-protein interaction (PPI) network of differentially expressed genes was constructed through Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The potential diagnostic targets of tuberculosis were screened by the calculation of degree, betweenness and closeness in Cytoscape 3.9.1 software. Finally, the functional pathways and the molecular mechanism of tuberculosis were clarified in combination of the prediction results of key gene miRNAs, and by Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia Genes and Genomes (KEGG) pathway annotation analysis. 556 Differential genes of tuberculosis were screened out by mRNA sequencing. Six key genes (AKT1, TP53, EGF, ARF1, CD274 and PRKCZ) were screened as the potential diagnostic targets for tuberculosis by analyzing the PPI regulatory network and using three algorithms. Three pathways related to the pathogenesis of tuberculosis were identified by KEGG pathway analysis, and two key miRNAs (has-miR-150-5p and has-miR-25-3p) that might participate in the pathogenesis of tuberculosis were screened out by constructing a miRNA-mRNA pathway regulatory network. Six key genes and two important miRNAs that could regulate them were screened out by mRNA sequencing. The 6 key genes and 2 important miRNAs may participate in the pathogenesis of infection and invasion of Mycobacterium tuberculosis through herpes simplex virus 1 infection, endocytosis and B cell receptor signaling pathways.