Comprehensive Analysis of Common Different Gene Expression Signatures in the Neutrophils of Sepsis

The central component of sepsis pathogenesis is inflammatory disorder, which is related to dysfunction of the immune system. However, the specific molecular mechanism of sepsis has not yet been fully elucidated. The aim of our study was to identify genes that are significantly changed during sepsis development, for the identification of potential pathogenic factors. Differentially expressed genes (DEGs) were identified in 88 control and 214 septic patient samples. Gene ontology (GO) and pathway enrichment analyses were performed using David. A protein-protein interaction (PPI) network was established using STRING and Cytoscape. Further validation was performed using real-time polymerase chain reaction (RT-PCR). We identified 37 common DEGs. GO and pathway enrichment indicated that enzymes and transcription factors accounted for a large proportion of DEGs; immune system and inflammation signaling demonstrated the most significant changes. Furthermore, eight hub genes were identified via PPI analysis. Interestingly, four of the top five upregulated and all downregulated DEGs were involved in immune and inflammation signaling. In addition, the most intensive hub gene AKT1 and the top DEGs in human clinical samples were validated using RT-PCR. This study explored the possible molecular mechanisms underpinning the inflammatory, immune, and PI3K/AKT pathways related to sepsis development.

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Integrated aqueous humor ceRNA and miRNA-TF-mRNA network analysis reveals potential molecular mechanisms governing primary open-angle glaucoma pathogenesis

10.1101/2020.07.17.208397 ◽

2020 ◽

Author(s):

Xiaoqin Wang ◽

Ming Chen ◽

Liuzhi Zeng ◽

Longqian Liu

Keyword(s):

Gene Expression ◽

Aqueous Humor ◽

Molecular Mechanisms ◽

Primary Open Angle Glaucoma ◽

Open Angle Glaucoma ◽

Gene Expression Omnibus ◽

Open Angle ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Non Coding Rnas

AbstractPrimary open-angle glaucoma (POAG) is the leading cause of blindness globally, which develops through complex and poorly understood biological mechanisms. Herein, we conducted an integrated bioinformatics analysis of extant aqueous humor (AH) gene expression datasets in order to identify key genes and regulatory mechanisms governing POAG progression. We downloaded AH gene expression datasets (GSE101727 and GSE105269) corresponding to healthy controls and POAG patients from the Gene Expression Omnibus. We then identified mRNAs, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) that were differentially expressed (DE) between control and POAG patients. DEmRNAs and DElncRNAs were then subjected to pathway enrichment analyses, after which a protein-protein interaction (PPI) network was generated. This network was then expanded to establish lncRNA-miRNA-mRNA and miRNA-transcription factor(TF)-mRNA networks. In total, the GSE101727 dataset was used to identify 2746 DElncRNAs and 2208 DEmRNAs, while the GSE105269 dataset was used to identify 45 DEmiRNAs. We ultimately constructed a competing endogenous RNA (ceRNA) network incorporating 37, 5, and 14 of these lncRNAs, miRNAs and mRNAs, respectively. The proteins encoded by these 14 hub mRNAs were found to be significantly enriched for activities that may be linked to POAG pathogenesis. In addition, we generated a miRNA-TF-mRNA regulatory network containing 2 miRNAs (miR-135a-5p and miR-139-5p), 5 TFs (TGIF2, TBX5, HNF1A, TCF3, and FOS) and 5 mRNAs (SHISA7, ST6GAC2, TXNIP, FOS, and DCBLD2). The SHISA7, ST6GAC2, TXNIP, FOS, and DCBLD2 genes that may be viable therapeutic targets for the prevention or treatment of POAG, and regulated by the TFs (TGIF2, HNF1A, TCF3, and FOS).

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Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

10.21203/rs.3.rs-54056/v2 ◽

2021 ◽

Author(s):

Fu Jun Liao ◽

Peng-Fei Zheng ◽

Yao-Zong Guan ◽

Hong Wei Pan ◽

Wei Li

Keyword(s):

Molecular Mechanisms ◽

Acid Metabolism ◽

Unsaturated Fatty Acids ◽

Microarray Dataset ◽

Hub Genes ◽

Online Tool ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Qpcr Analysis ◽

Blue Module

Abstract Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.Methods: The microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.Results: The significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.Conclusions: SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

10.21203/rs.3.rs-54056/v1 ◽

2020 ◽

Author(s):

Fu Jun Liao ◽

Peng-Fei Zheng ◽

Yao-Zong Guan ◽

Wei Li

Keyword(s):

Molecular Mechanisms ◽

Acid Metabolism ◽

Unsaturated Fatty Acids ◽

Kegg Pathway ◽

Hub Genes ◽

Data Set ◽

Online Tool ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Qpcr Analysis

Abstract Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.Methods: The microarray data set of GSE66676 obtained from patients with hyperlipidaemia was downloaded. The weighted gene co‑expression network (WGCNA) analysis was used to analyze the gene expression profile and royalblue module was considered as the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royalblue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.Results: The significant module (royalblue) identified was associated with TC, TG and Non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royalblue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis of unsaturated fatty acids pathways. SQLE (degree = 17) was revealed as key molecules that associated with hypercholesterolemia (HCH) and SCD was revealed as key molecules that associated with hypertriglyceridemia (HTG). Meanwhile, RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.Conclusions: SQLE and SCD are related to hyperlipidaemia, SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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Large-scale genomic study reveals robust activation of the immune system following advanced Inner Engineering meditation retreat

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2110455118 ◽

2021 ◽

Vol 118 (51) ◽

pp. e2110455118

Author(s):

Vijayendran Chandran ◽

Mei-Ling Bermúdez ◽

Mert Koka ◽

Brindha Chandran ◽

Dhanashri Pawale ◽

...

Keyword(s):

Immune Response ◽

Immune System ◽

Large Scale ◽

Molecular Mechanisms ◽

Positive Impact ◽

Well Being ◽

Core Network ◽

Protein Protein Interaction ◽

Genomic Study ◽

Meditation Retreat

The positive impact of meditation on human well-being is well documented, yet its molecular mechanisms are incompletely understood. We applied a comprehensive systems biology approach starting with whole-blood gene expression profiling combined with multilevel bioinformatic analyses to characterize the coexpression, transcriptional, and protein–protein interaction networks to identify a meditation-specific core network after an advanced 8-d Inner Engineering retreat program. We found the response to oxidative stress, detoxification, and cell cycle regulation pathways were down-regulated after meditation. Strikingly, 220 genes directly associated with immune response, including 68 genes related to interferon signaling, were up-regulated, with no significant expression changes in the inflammatory genes. This robust meditation-specific immune response network is significantly dysregulated in multiple sclerosis and severe COVID-19 patients. The work provides a foundation for understanding the effect of meditation and suggests that meditation as a behavioral intervention can voluntarily and nonpharmacologically improve the immune response for treating various conditions associated with excessive or persistent inflammation with a dampened immune system profile.

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

BMC Endocrine Disorders ◽

10.1186/s12902-021-00709-6 ◽

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.

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Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

10.1101/2021.09.24.461631 ◽

2021 ◽

Author(s):

Basavaraj Mallikarjunayya Vastrad ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Immune System ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Hub Gene ◽

Gene Regulatory ◽

Key Genes

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

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Identification of key candidate genes and pathways in axial Spondyloarthritis through integrated bioinformatics analysis

10.1101/2020.03.17.995134 ◽

2020 ◽

Author(s):

Zhen-zhen Zhang ◽

Jing Zeng ◽

Hai-hong Li ◽

Yu-cong Zou ◽

Shuang Liang ◽

...

Keyword(s):

Immunohistochemical Staining ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Axial Spondyloarthritis ◽

Kegg Pathway ◽

Transcriptional Regulatory Network ◽

Rt Pcr ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Pathway Analyses

AbstractBackgroundRadiographic axial Spondyloarthritis (r-axSpA) is the prototypic form of seronegative spondyloarthritis (SpA). In the present study, we evaluated the key genes related with r-axSpA, and then elucidated the possible molecular mechanisms of r-axSpA.Material/MethodsThe gene expression GSE13782 was downloaded from the GEO database contained five proteoglycan-induced spondylitis mice and three naïve controls. The differentially expressed genes (DEGs) were identified with the Bioconductor affy package in R. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were built with the DAVID program followed by construction of a protein-protein interaction (PPI) network performed with Cytoscape. WebGestalt was performed to construct transcriptional regulatory network and microRNAs-target regulatory networks. RT-PCR and immunohistochemical staining were performed to testify the expression of hub genes, transcription factors (TFs) and microRNAs.ResultsA total of 230 DEGs were identified. PPI networks were constructed by mapping DEGs into STRING, in which 20 hub proteins were identified. KEGG pathway analyses revealed that the chemokine, NOD-like receptor, IL-17, and TNF signalling pathways were altered. GO analyses revealed that DEGs were extensively involved in the regulation of cytokine production, the immune response, the external side of the plasma membrane, and G-protein coupled chemoattractant receptor activity. The results of RT-PCR and immunohistochemical staining demonstrated that the expression of DEGs, TFs and microRNAs in our experiment were basically consistent with the predictions.ConclusionsThe results of this study offer insight into the pathomechanisms of r-axSpA and provide potential research directions.

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Identification of the key differentially expressed genes and pathways involved in neutrophilia

Innate Immunity ◽

10.1177/1753425919887411 ◽

2019 ◽

Vol 26 (4) ◽

pp. 270-284

Author(s):

Chengcheng He ◽

Yingchun Zhang ◽

Hongwei Luo ◽

Bo Luo ◽

Yancheng He ◽

...

Keyword(s):

Molecular Mechanisms ◽

Polymorphonuclear Neutrophils ◽

Phenotypic Characteristics ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Tnf Α ◽

The Common ◽

Protein Protein Interaction Networks ◽

Microarray Datasets ◽

Multiple Signaling

Polymorphonuclear neutrophils (PMNs) are the most important determinants in the acute inflammatory response. Pathologically increased numbers of PMNs in the circulation or specific tissues (or both) lead to neutrophilia. However, the genes expressed and pathways involved in neutrophilia have yet to be elucidated. By analysis of three public microarray datasets related to neutrophilia (GSE64457, GSE54644, and GSE94923) and evaluation by gene ontology, pathway enrichment, protein–protein interaction networks, and hub genes analysis using multiple methods (DAVID, PATHER, Reactome, STRING, Reactome FI Plugin, and CytoHubba in Cytoscape), we identified the commonly up-regulated and down-regulated different expressed genes. We also discovered that multiple signaling pathways (IL-mediated, LPS-mediated, TNF-α, TLR cascades, MAPK, and PI3K-Akt) were involved in PMN regulation. Our findings suggest that the commonly expressed genes involved in regulation of multiple pathways were the underlying molecular mechanisms in the development of inflammatory, autoimmune, and hematologic diseases that share the common phenotypic characteristics of increased numbers of PMNs. Taken together, these data suggest that these genes are involved in the regulation of neutrophilia and that the corresponding gene products could serve as potential biomarkers and/or therapeutic targets for neutrophilia.

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Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

Nutrition & Metabolism ◽

10.1186/s12986-021-00555-2 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Fu-Jun Liao ◽

Peng-Fei Zheng ◽

Yao-Zong Guan ◽

Hong-Wei Pan ◽

Wei Li

Keyword(s):

Molecular Mechanisms ◽

Acid Metabolism ◽

Unsaturated Fatty Acids ◽

Microarray Dataset ◽

Hub Genes ◽

Online Tool ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Qpcr Analysis ◽

Blue Module

Abstract Background The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms. Methods The microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov). A protein–protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software. Results The significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples. Conclusions SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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Radiation Therapy in Patients With Brain Cancer: Post-proteomics Interpretation

Journal of lasers in medical sciences ◽

10.15171/jlms.2019.s11 ◽

2019 ◽

Vol 10 (5) ◽

pp. S59-S63

Author(s):

Hamid Asadzadeh-Aghdaei ◽

Farshad Okhovatian ◽

Zahra Razzaghi ◽

Mohammadhossein Heidari ◽

Reza Vafaee ◽

...

Keyword(s):

Radiation Therapy ◽

Brain Cancer ◽

Molecular Mechanisms ◽

Early Stage ◽

Skin Lesions ◽

Network Stability ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Complementary Study

Introduction: Radiation therapy (RT) as a common method for cancer treatment could result in some side effects. The molecular investigation is one of the approaches that could assist in decrypting the molecular mechanisms of this incident. For this aim, protein-protein interaction (PPI) network analysis as a complementary study of the proteome is conducted to explore the RT effect on brain cancer after the early stage of exposure prior to the appearance of the skin lesion. Methods: Cytoscape 3.7.2 and its plug-ins were used to analyze the network of differential expression of proteins (DEPs) in the treatment condition, and the centrality and pathway enrichment was conducted by the use of NetworkAnalyzer and ClueGO+CluePedia. Results: A network of 15 DEPs indicated that 6 nodes were key players in the network stability and SERPINC1 and F5 were from the query proteins. The pathways of post-translational protein phosphorylation, platelet degranulation, and complement and coagulation cascades were the most highlighted ones for the central nodes that could be affected in RT. Conclusion: The central proteins of the network of early-stage treatments could have additional importance in the mechanisms of radiotherapy response prior to skin lesions. Introduced biomarkers can be used for the patients’ follow-up. These candidates are worth precise attention for this type of therapy after approving by validation studies.

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