PPI network analysis of mRNA expression profile of ezrin knockdown in esophageal squamous cell carcinoma

RRM2 promotes the proliferation of chicken myoblasts, inhibits their differentiation and muscle regeneration

During myogenesis and regeneration, the proliferation and differentiation of myoblasts play key regulatory roles and may be regulated by many genes. In this study, we analyzed the transcriptomic data of chicken primary myoblasts at different periods of proliferation and differentiation with protein‒protein interaction network, and the results indicated that there was an interaction between cyclin-dependent kinase 1 (CDK1) and ribonucleotide reductase regulatory subunit M2 (RRM2). Previous studies in mammals have a role for RRM2 in skeletal muscle development as well as cell growth, but the role of RRM2 in chicken is unclear. In this study, we investigated the effects of RRM2 on skeletal muscle development and regeneration in chickens in vitro and in vivo. The interaction between RRM2 and CDK1 was initially identified by co-immunoprecipitation and mass spectrometry. Through a dual luciferase reporter assay and quantitative real-time PCR, we identified the core promoter region of RRM2, which is regulated by the SP1 transcription factor. In this study, through cell counting kit-8 assays, 5-ethynyl-2'-deoxyuridine incorporation assays, flow cytometry, immunofluorescence staining, and Western blot analysis, we demonstrated that RRM2 promoted the proliferation and inhibited the differentiation of myoblasts. In vivo studies showed that RRM2 reduced the diameter of muscle fibers and slowed skeletal muscle regeneration. In conclusion, these data provide preliminary insights into the biological functions of RRM2 in chicken muscle development and skeletal muscle regeneration.

Analysis of PPI networks of transcriptomic expression identifies hub genes associated with Newcastle disease virus persistent infection in bladder cancer

Bladder cancer cells can acquire persistent infection of oncolytic Newcastle disease virus (NDV) but the molecular mechanism(s) remain unelucidated. This poses a major barrier to the effective clinical translation of oncolytic NDV virotherapy of cancers. To improve our understanding of the molecular mechanism(s) associated with the development of NDV persistent infection in bladder cancer, we used mRNA expression profiles of persistently infected bladder cancer cells to construct PPI networks. Based on paths and modules in the PPI network, the bridges were found mainly in the upregulated mRNA-pathways of p53 signalling, ECM-receptor interaction, and TGF-beta signalling and downregulated mRNA-pathways of antigen processing and presentation, protein processing in endoplasmic reticulum, completement and coagulation cascades in persistent TCCSUPPi cells. In persistent EJ28Pi cells, connections were identified mainly through upregulated mRNA-pathways of renal carcinoma, viral carcinogenesis, Ras signalling and cell cycle and the downregulated mRNA-pathways of Wnt signalling, HTLV-I infection and pathways in cancers. These connections were mainly dependent on RPL8-HSPA1A/HSPA4 in TCCSUPPi cells and EP300, PTPN11, RAC1-TP53, SP1, CCND1 and XPO1 in EJ28Pi cells. Oncomine validation showed that the top hub genes identified in the networks that include RPL8, THBS1, F2 from TCCSUPPi and TP53 and RAC1 from EJ28Pi are involved in the development and progression of bladder cancer. Protein-drug interaction networks identified several putative drug targets that could be used to disrupt the linkages between the modules and prevent bladder cancer cells from acquiring NDV persistent infection. This novel PPI network analysis of differentially expressed mRNAs of NDV persistently infected bladder cancer cell lines provide an insight into the molecular mechanisms of NDV persistency of infection in bladder cancers and the future screening of drugs that can be used together with NDV to enhance its oncolytic efficacy.

Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock

Background

Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock.

Methods

We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model.

Results

A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls.

Conclusion

In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.

Transcriptomic analysis reveals hub genes and subnetworks related to ROS metabolism in Hylocereus undatus through novel superoxide scavenger trypsin treatment during storage

BACKGROUND

It was demonstrated in our previous research that trypsin scavenges superoxide anions. In this study, the mechanisms of storage quality improvement by trypsin were evaluated in H. undatus.

RESULTS

Trypsin significantly delayed the weight loss and decreased the levels of ROS and membrane lipid peroxidation. Transcriptome profiles of H. undatus treated with trypsin revealed the pathways and regulatory mechanisms of ROS genes that were up- or downregulated following trypsin treatment by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. The current results showed that through the regulation of the expression of hub redox enzymes, especially thioredoxin-related proteins, trypsin can maintain low levels of endogenous active oxygen species, reduce malondialdehyde content and delay fruit aging. In addition, the results of protein-protein interaction networks suggested that the downregulated NAD(P) H and lignin pathways might be the key regulatory mechanisms governed by trypsin.

CONCLUSIONS

Trypsin significantly prolonged the storage life of H. undatus through regulatory on the endogenous ROS metabolism. As a new biopreservative, trypsin is highly efficient, safe and economical. Therefore, trypsin possesses technical feasibility for the quality control of fruit storage.

Transcriptomic Analysis Reveals Cu/Zn SODs Acting as Hub Genes of SODs in Hylocereus undatus Induced by Trypsin during Storage

It has been revealed by us that superoxide scavenging is a new activity of trypsin. In this study, the synergistic mechanisms of trypsin and superoxide dismutases (SODs) were evaluated in Hylocereus undatus (pitaya). Trypsin significantly improved the storage quality of H. undatus, including weight loss impediment and decrease of cellular injury. The regulatory mechanisms of 16 SOD genes by trypsin were revealed using transcriptomic analysis on H. undatus. Results revealed that important physiological metabolisms, such as antioxidant activities or metal ion transport were induced, and defense responses were inhibited by trypsin. Furthermore, the results of protein–protein interaction (PPI) networks showed that besides the entire ROS network, the tiny SODs sub-network was also a scale-free network. Cu/Zn SODs acted as the hub that SODs synergized with trypsin during the storage of H. undatus.

Identification and analysis of long non-coding RNA related miRNA sponge regulatory network in bladder urothelial carcinoma

Background

The aim of this study was to investigate the regulatory network of lncRNAs as competing endogenous RNAs (ceRNA) in bladder urothelial carcinoma (BUC) based on gene expression data derived from The Cancer Genome Atlas (TCGA).

Materials and methods

RNA sequence profiles and clinical information from 414 BUC tissues and 19 non-tumor adjacent tissues were downloaded from TCGA. Differentially expressed RNAs derived from BUC and non-tumor adjacent samples were identified using the R package “edgeR”. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the “clusterProfiler” package. Gene ontology and protein–protein interaction (PPI) networks were analyzed for the differentially expressed mRNAs using the “STRING” database. The network for the dysregulated lncRNA associated ceRNAs was then constructed for BUC using miRcode, miRTarBase, miRDB, and TargetScan. Cox regression analysis was performed to identify independent prognostic RNAs associated with BUC overall survival (OS). Survival analysis for the independent prognostic RNAs within the ceRNA network was calculated using Kaplan–Meier curves.

Results

Based on our analysis, a total of 666, 1819 and 157 differentially expressed lncRNAs, mRNAs and miRNAs were identified respectively. The ceRNA network was then constructed and contained 59 lncRNAs, 23 DEmiRNAs, and 52 DEmRNAs. In total, 5 lncRNAs (HCG22, ADAMTS9-AS1, ADAMTS9-AS2, AC078778.1, and AC112721.1), 2 miRNAs (hsa-mir-145 and hsa-mir-141) and 6 mRNAs (ZEB1, TMEM100, MAP1B, DUSP2, JUN, and AIFM3) were found to be related to OS. Two lncRNAs (ADAMTS9-AS1 and ADAMTS9-AS2) and 4 mRNA (DUSP2, JUN, MAP1B, and TMEM100) were validated using GEPIA. Thirty key hub genes were identified using the ranking method of degree. KEGG analysis demonstrated that the majority of the DEmRNAs were involved in pathways associated with cancer.

Conclusion

Our findings provide an understanding of the important role of lncRNA–related ceRNAs in BUC. Additional experimental and clinical validations are required to support our findings.

Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach

Osteoporosis (OP) is an age-related disease, and osteoporotic fracture is one of the major causes of disability and mortality in elderly patients (>70 years old). As the pathogenesis and molecular mechanism of OP remain unclear, the identification of disease biomarkers is important for guiding research and providing therapeutic targets. In the present study, core genes and microRNAs (miRNAs) associated with OP were identified. Differentially expressed genes (DEGs) between human mesenchymal stem cell specimens from normal osseous tissues and OP tissues were detected using the GEO2R tool of the Gene Expression Omnibus database and Morpheus. Network topological parameters were determined using NetworkAnalyzer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, and ClueGO. Cytoscape with the Search Tool for the Retrieval of Interacting Genes and Molecular Complex Detection plug-in was used to visualize protein-protein interactions (PPIs). Additionally, miRNA-gene regulatory modules were predicted using CyTargetLinker in order to guide future research. In total, 915 DEGs were identified, including 774 upregulated and 141 downregulated genes. Enriched GO terms and pathways were determined, including 'nervous system development', 'regulation of molecular function', 'glutamatergic synapse pathway' and 'pathways in cancer'. The node degrees of DEGs followed power-law distributions. A PPI network with 541 nodes and 1,431 edges was obtained. Overall, 3 important modules were identified from the PPI network. The following 10 genes were identified as core genes based on high degrees of connectivity: Albumin, PH domain leucine-rich repeat-containing protein phosphatase 2 (PHLPP2), DNA topoisomerase 2-α, kininogen 1 (KNG1), interleukin 2 (IL2), leucine-rich repeats and guanylate kinase domain containing, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit γ (PIK3CG), leptin, transferrin and RNA polymerase II subunit A (POLR2A). Additionally, 15 miRNA-target interactions were obtained using CyTargetLinker. Overall, 7 miRNAs co-regulated IL2, 3 regulated PHLPP2, 3 regulated KNG1, 1 regulated PIK3CG and 1 modulated POLR2A. These results indicate potential biomarkers in the pathogenesis of OP and therapeutic targets.

Identification and analysis of key genes associated with ulcerative colitis based on DNA microarray data

This study aimed to identify genes that may play a role in development of ulcerative colitis (UC) and gain insight into its pathogenesis.Gene expression profiling data, including samples collected from 13 early-stage UC (EUC), 8 advanced-stage UC (AUC), and 5 control subjects, were downloaded from the Gene Expression Omnibus database under the accession number of GSE9452. Differentially expressed genes (DEGs) were identified in EUC and AUC compared with controls. DEGs for EUC and AUC, as well as AUC-specific DEGs were subjected to pathway enrichment analysis. Random Walk with Restart (RWR) was used to identify DEGs that are critical in UC based on a protein-to-protein interaction (PPI) network and the inflammatory bowel disease (IBD) pathway downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. IL17 and transforming growth factor beta TGF-β) expression levels in colonic tissue from patients with UC and normal colonic mucosa from healthy adults were analyzed by immunohistochemistry (IHC).A total of 3511 and 911 DEGs were identified in AUC and EUC, respectively. The overlapping DEGs and the AUC-specific DEGs were both enriched in pathways related to immunity, such as antigen processing and presentation. AUC-specific DEGs were related to cell migration, such as ECM-receptor interaction. Following DEG prioritization, TLR4 and STAT1 were linked with EUC, AUC, and CD. The upregulated gene TGFB increased the number of Th17 cells, as verified by IHC. Furthermore, PIK3R1, CREBBP, and STAT1 were part of high-degree nodes in the PPI sub-network.The upregulated gene TGFB may regulate IL17 expression in UC. PIK3R1 may participate in immunity and CREBBP may interact with STAT1 in the development and progression of UC.

The PPI network analysis of mRNA expression profile of uterus from primary dysmenorrheal rats

To elucidate the mechanisms of molecular regulations underlying primary dysmenorrhea (PD), we used our previously published mRNA expression profile of uterus from PD syndrome rats to construct protein-protein interactions (PPI) network via STRING Interactome. Consequently, 34 subnetworks, including a "continent" (Subnetwork 1) and 33 "islands" (Subnetwork 2-34) were generated. The nodes, with relative expression ratios, were visualized in the PPI networks and their connections were identified. Through path and module exploring in the network, the bridges were found from pathways of cellular response to calcium ion, SMAD protein signal transduction, regulation of transcription from RNA polymerase II promoter in response to stress and muscle stretch that were significantly enriched by the up-regulated mRNAs, to the cascades of cAMP metabolic processes and positive regulation of cyclase activities by the down-regulated ones. This link is mainly dependent on Fos/Jun - Vip connection. Our data, for the first time, report the PPI network analysis of differentially expressed mRNAs in the uterus of PD syndrome rats, to give insight into screening drugs and find new therapeutic strategies to relieve PD.

Ezrin Inhibition Up-regulates Stress Response Gene Expression

Ezrin is a member of the ERM (ezrin/radixin/moesin) family of proteins that links cortical cytoskeleton to the plasma membrane. High expression of ezrin correlates with poor prognosis and metastasis in osteosarcoma. In this study, to uncover specific cellular responses evoked by ezrin inhibition that can be used as a specific pharmacodynamic marker(s), we profiled global gene expression in osteosarcoma cells after treatment with small molecule ezrin inhibitors, NSC305787 and NSC668394. We identified and validated several up-regulated integrated stress response genes including PTGS2, ATF3, DDIT3, DDIT4, TRIB3, and ATF4 as novel ezrin-regulated transcripts. Analysis of transcriptional response in skin and peripheral blood mononuclear cells from NSC305787-treated mice compared with a control group revealed that, among those genes, the stress gene DDIT4/REDD1 may be used as a surrogate pharmacodynamic marker of ezrin inhibitor compound activity. In addition, we validated the anti-metastatic effects of NSC305787 in reducing the incidence of lung metastasis in a genetically engineered mouse model of osteosarcoma and evaluated the pharmacokinetics of NSC305787 and NSC668394 in mice. In conclusion, our findings suggest that cytoplasmic ezrin, previously considered a dormant and inactive protein, has important functions in regulating gene expression that may result in down-regulation of stress response genes.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Establishment of a new OSCC cell line derived from OLK and identification of malignant transformation-related proteins by differential proteomics approach

Oral squamous cell carcinoma (OSCC) is usually preceded by the oral premalignant lesions, mainly oral leukoplakia (OLK) after repeated insults of carcinogens, tobacco. B(a)P and DMBA are key carcinogens in tobacco smoke. In the present study, for the first time we established the cancerous cell line OSCC-BD induced by B(a)P/DMBA mixture and transformed from dysplastic oral leukoplakia cell line DOK. Cell morphology, proliferation ability, migration ability, colony formation, and tumorigenicity were studied and confirmed the malignant characteristics of OSCC-BD cells. We further identified the differential proteins between DOK and OSCC-BD cells by stable isotope dimethyl labeling based quantitative proteomic method, which showed 18 proteins up-regulated and 16 proteins down-regulated with RSD < 8%. Differential proteins are mainly related to cell cycle, cell proliferation, DNA replication, RNA splicing and apoptosis. Abberant binding function, catalysis activity and transportor activity of differential proteins might contribute to the malignant transformation of OLK. Of the 34 identified differential proteins with RSD < 8%, 13 novel cancer-related proteins were reported in the present study. This study might provide a new insight into the mechanism of OLK malignant transformation and the potent biomarkers for early diagnosis, meanwhile further facilitate the application of the quantification proteomics to carcinogenesis research.