scholarly journals Identification of hub genes related to the progression of type 1 diabetes by computational analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
G. Prashanth ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Immune System ◽  
Regulatory Network ◽  
Innate Immune System ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Innate Immune ◽  
Pcr Analysis ◽  
Rt Pcr ◽  
Hub Genes

Abstract Background Type 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known. Methods To identify the candidate genes in the progression of T1D, expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. A molecular docking study was performed. Results A total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be involved in the advancement of T1D. Molecular docking studies showed high docking score. Conclusions DEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

scholarly journals Bioinformatics analysis of RNA-seq data revealed critical genes in type 1 diabetes and molecular docking studies

2020 ◽  
Author(s):  
Prashanth G ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Molecular Docking ◽  
Immune System ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Docking Studies ◽  
Innate Immune ◽  
Pcr Analysis ◽  
Hub Genes ◽  
Molecular Docking Studies

Abstract Background: Type 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known.Methods: To identify the candidate genes in the progression of T1D, Expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. Molecular docking studies were performed.Results: A total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, POLR2A and PRKACA might be involved in the advancement of T1D. Molecular docking studies showed high docking score.Conclusions: DEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

scholarly journals Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Regulatory Network ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Pcr Analysis ◽  
Hub Genes ◽  
Ppi Networks ◽  
Neutrophil Degranulation

AbstractHeart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene - miRNA regulatory network and target gene - TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

scholarly journals Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Vijayakrishna Kolur ◽  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Shivakumar Kotturshetti ◽  
Anandkumar Tengli
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Regulatory Network ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Pcr Analysis ◽  
Hub Genes ◽  
Ppi Networks ◽  
Neutrophil Degranulation

Abstract Introduction Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. Methods The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein–protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene—miRNA regulatory network and target gene—TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. Results A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene—miRNA regulatory network and target gene—TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. Conclusion The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

scholarly journals A Novel Regulatory Player in the Innate Immune System: Long Non-Coding RNAs

2021 ◽  
Vol 22 (17) ◽  
pp. 9535
Author(s):  
Yuhuai Xie ◽  
Yuanyuan Wei
Keyword(s):  
Immune System ◽  
Immune Cells ◽  
Innate Immune System ◽  
Molecular Mechanisms ◽  
Innate Immune ◽  
Innate Immune Cells ◽  
Immune Mediated ◽  
Development And Differentiation ◽  
Non Coding Rnas ◽  
And Function

Long non-coding RNAs (lncRNAs) represent crucial transcriptional and post-transcriptional gene regulators during antimicrobial responses in the host innate immune system. Studies have shown that lncRNAs are expressed in a highly tissue- and cell-specific- manner and are involved in the differentiation and function of innate immune cells, as well as inflammatory and antiviral processes, through versatile molecular mechanisms. These lncRNAs function via the interactions with DNA, RNA, or protein in either cis or trans pattern, relying on their specific sequences or their transcriptions and processing. The dysregulation of lncRNA function is associated with various human non-infectious diseases, such as inflammatory bowel disease, cardiovascular diseases, and diabetes mellitus. Here, we provide an overview of the regulation and mechanisms of lncRNA function in the development and differentiation of innate immune cells, and during the activation or repression of innate immune responses. These elucidations might be beneficial for the development of therapeutic strategies targeting inflammatory and innate immune-mediated diseases.

scholarly journals Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Regulatory Network ◽  
Target Gene ◽  
Node Degree ◽  
Rt Pcr ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Histochemical Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Protein Protein Interaction ◽  
Stage Analysis

AbstractClear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis. This data was utilized in the construction of the protein-protein interaction network and module analysis was conducted using Human Integrated Protein-Protein Interaction rEference (HIPPIE) and Cytoscape software. In addation, target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analysed. Finally, hub genes were validated by survival analysis, expression analysis, stage analysis, mutation analysis, immune histochemical analysis, receiver operating characteristic (ROC) curve analysis, RT-PCR and immune infiltration analysis. The results of these analyses led to the identification of a total of 930 DEGs, including 469 up regulated and 461 down regulated genes. The pathwayes and GO found to be enriched in the DEGs (up and down regulated genes) were dTMP de novo biosynthesis, glycolysis, 4-hydroxyproline degradation, fatty acid beta-oxidation (peroxisome), cytokine, defense response, renal system development and organic acid metabolic process. Hub genes were identified from PPI network according to the node degree, betweenness centrality, stress centrality, closeness centrality and clustering coefficient. Similarly, targate genes were identified from target gene - miRNA regulatory network and target gene - TF regulatory network according to the node degree. Furthermore, survival analysis, expression analysis, stage analysis, mutation analysis, immune histochemical analysis, ROC curve analysis, RT-PCR and immune infiltration analysis revealed that CANX, SHMT2, IFI16, P4HB, CALU, CDH1, ERBB2, NEDD4L, TFAP2A and SORT1 may be associated in the tumorigenesis, advancement or prognosis of ccRCC. In conclusion, the 10 hub genes diagonised in the current study may help researchers in exemplify the molecular mechanisms linked with the tumorigenesis and advancement of ccRCC, and may be powerful and favorable candidate biomarkers for the prognosis, diagnosis and treatment of ccRCC.

scholarly journals RNA Sensor MDA5 Suppresses LINE-1 Retrotransposition By Regulating The Promoter Activity of LINE-1 5’-UTR

2021 ◽  
Author(s):  
Jiaxiu Yan ◽  
Yifei Zhao ◽  
Juan Du ◽  
Yu Wang ◽  
Shaohua Wang ◽  
...  
Keyword(s):  
Immune System ◽  
Innate Immune System ◽  
Promoter Activity ◽  
Feedback Loop ◽  
Innate Immune ◽  
Functional Region ◽  
Protein Levels ◽  
Long Interspersed Elements ◽  
Associated Proteins

Abstract Background: Type 1 long interspersed elements, or LINE-1, are the only active retroelements in human cells. The retrotransposition process of LINE-1 can trigger the activation of the innate immune system and has been proposed to play a role in the development of several autoimmune diseases, including Aicardi-Goutières syndrome (AGS). In contrast, all known AGS-associated proteins, except MDA5, have been reported to affect LINE-1 activity. Thus, MDA5 is likely to also function as a LINE-1 suppressor. Results: MDA5 was found to potently suppress LINE-1 activity in a reporter-based LINE-1 retrotransposition assay. Although MDA5 is an endogenous RNA sensor able to activate the innate immune system, increased interferon (IFN) expression only weakly contributed to MDA5-mediated LINE-1 suppression. Instead, MDA5 effectively reduced the levels of LINE-1 ORF1p and ORF2p, as a result of the MDA5-mediated downregulation of the promoter activity of LINE-1 5’-UTR, and the subsequent generation of LINE-1 RNA. Interestingly, despite MDA5 being a multi-domain protein, the N-terminal 2CARD domain alone is sufficient to inhibit LINE-1 activity. Conclusion: Our data reveal that MDA5 functions as a promoter regulator and suppresses the promoter activity of LINE-1 5’-UTR. Consequently, MDA5 reduces LINE-1 RNA and protein levels, and ultimately inhibits LINE-1 retrotransposition. In contrast, MDA5-induced IFN expression only plays a mild role in MDA5-mediated LINE-1 suppression. In addition, the N-terminal 2CARD domain was found to be a functional region for MDA5 upon inhibition of LINE-1 replication. Thus, our data suggest that besides being an initiator of the innate immune system, MDA5 is also an effector against LINE-1 activity, potentially forming a feedback loop by suppressing LINE-1-induced innate immune activation.

scholarly journals Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaofeng Ye ◽  
Tianshu Zeng ◽  
Wen Kong ◽  
Lu-lu Chen
Keyword(s):  
Type 1 Diabetes ◽  
Gene Network ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Healthy Controls ◽  
Ppi Network ◽  
Hub Genes ◽  
Fulminant Type 1 Diabetes ◽  
Fulminant Type

Objective. Fulminant type 1 diabetes (FT1D) is a type of type 1 diabetes, which is characterized by rapid onset of disease and severe metabolic disorders. We intend to screen for crucial genes and potential molecular mechanisms in FT1D in this study. Method. We downloaded GSE44314, which includes six healthy controls and five patients with FT1D, from the GEO database. Identification of differentially expressed genes (DEGs) was performed by NetworkAnalyst. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were screened by an online tool—Database for Annotation, Visualization, and Integration Discovery (DAVID). Protein-protein interaction (PPI) network and hub genes among DEGs were analyzed by NetworkAnalyst. And we also use NetworkAnalyst to find out the microRNAs (miRNAs) and transcription factors (TFs) which regulate the expression of DEGs. Result. We identified 130 DEGs (60 upregulated and 70 downregulated DEGs) between healthy controls and FT1D patients. GO analysis results revealed that DEGs were mostly enriched in generation of precursor metabolites and energy, neurohypophyseal hormone activity, and mitochondrial inner membrane. KEGG pathway analysis demonstrated that DEGs were mostly involved in nonalcoholic fatty liver disease. Results indicated that NCOA1, SRF, ERBB3, EST1, TOP1, UBE2S, INO80, COX7C, ITGAV, and COX6C were the top hub genes in the PPI network. Furthermore, we recognized that LDLR, POTEM, IFNAR2, BAZ2A, and SRF were the top hub genes in the miRNA-target gene network, and SRF, TSPAN4, CD59, ETS1, and SLC25A25 were the top hub genes in the TF-target gene network. Conclusion. Our study pinpoints key genes and pathways associated with FT1D by a sequence of bioinformatics analysis on DEGs. These identified genes and pathways provide more detailed molecular mechanisms of FT1D and may provide novel therapeutic targets.

scholarly journals LA RESPUESTA INMUNITARIA FRENTE AL VIRUS SARS-CoV-2

2020 ◽  
Vol 3 (9) ◽  
pp. 64-86
Author(s):  
SERGIO ROBERTO AGUILAR-RUIZ ◽  
FRANCISCO JAVIER SÁNCHEZ-PEÑA
Keyword(s):  
Immune System ◽  
Viral Entry ◽  
Immune Cells ◽  
Innate Immune System ◽  
Adaptive Immune System ◽  
Innate Immune ◽  
Adaptive Immune ◽  
Infected Cells ◽  
Systemic Pathology

The immune response against SARS-CoV-2 is similar to that against other viruses, where the innate immune system acts at early stages through the secretion of type 1 interferon (type 1 IFN), which prevents viral replication and the activation of natural killer (NK) cells. Later, the adaptive immune system acts through CD8+ cytotoxic T-lymphocytes and antibody production, which aim to destroy infected cells and block viral entry into cells. All the above leads to the elimination of the virus and mild symptomatology. However, in individuals with a weakened immune system, the viral infection spreads and leads to a potent inflammatory response, which leads to the recruitment of immune cells to the lungs, where they can cause severe pulmonary and even systemic pathology.

Sign in / Sign up

Export Citation Format

Share Document