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

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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NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

Nucleic Acids Research ◽

10.1093/nar/gkz240 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W234-W241 ◽

Cited By ~ 181

Author(s):

Guangyan Zhou ◽

Othman Soufan ◽

Jessica Ewald ◽

Robert E W Hancock ◽

Niladri Basu ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Visual Analytics ◽

Regulatory Networks ◽

Meta Analysis ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Perform Gene Expression ◽

And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

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A Pathway and Network Oriented Approach to Enlighten Molecular Mechanisms of Type 2 Diabetes Using Multiple Association Studies

10.1101/2020.01.14.905547 ◽

2020 ◽

Author(s):

Burcu Bakir-Gungor ◽

Miray Unlu Yazici ◽

Gokhan Goy ◽

Mustafa Temiz

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Protein Interaction ◽

Molecular Mechanisms ◽

Association Studies ◽

Genome Wide Association Studies ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Oriented Approach

AbstractDiabetes Mellitus (DM) is a group of metabolic disorder that is characterized by pancreatic dysfunction in insulin producing beta cells, glucagon secreting alpha cells, and insulin resistance or insulin in-functionality related hyperglycemia. Type 2 Diabetes Mellitus (T2D), which constitutes 90% of the diabetes cases, is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for type 2 diabetes (T2D) successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. However, traditional GWASs focus on the ‘the tip of the iceberg’ SNPs, and the SNPs with mild effects are discarded. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multi-genic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three meta-analysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in-silico approaches that proceed in top-down manner and bottom-up manner, and hence presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Our network and pathway-oriented approach is based on both the significance level of an affected pathway and its topological relationship with its neighbor pathways. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While, most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, protein-protein interaction networks into GWAS can dissect leading molecular pathways, which cannot be picked up using traditional analyses. We hope to bridge the knowledge gap from sequence to consequence.

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Analysis of the Molecular Mechanisms of the Effects of Prunella vulgaris against Subacute Thyroiditis Based on Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/9810709 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Xin Shen ◽

Rui Yang ◽

Jianpeng An ◽

Xia Zhong

Keyword(s):

Signaling Pathways ◽

Molecular Mechanisms ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Subacute Thyroiditis ◽

Network Pharmacology ◽

Pharmacokinetic Parameters ◽

Pathway Enrichment Analysis ◽

Prunella Vulgaris ◽

Protein Protein Interaction

Prunella vulgaris (PV) has a long history of application in traditional Chinese and Western medicine as a remedy for the treatment of subacute thyroiditis (SAT). This study applied network pharmacology to elucidate the mechanism of the effects of PV against SAT. Components of the potential therapeutic targets of PV and SAT-related targets were retrieved from databases. To construct a protein-protein interaction (PPI) network, the intersection of SAT-related targets and PV-related targets was input into the STRING platform. Gene ontology (GO) analysis and KEGG pathway enrichment analysis were carried out using the DAVID database. Networks were constructed by Cytoscape for visualization. The results showed that a total of 11 compounds were identified according to the pharmacokinetic parameters of ADME. A total of 126 PV-related targets and 2207 SAT-related targets were collected, and 83 overlapping targets were subsequently obtained. The results of the KEGG pathway and compound-target-pathway (C-T-P) network analysis suggested that the anti-SAT effect of PV mainly occurs through quercetin, luteolin, kaempferol, and beta-sitosterol and is most closely associated with their regulation of inflammation and apoptosis by targeting the PIK3CG, MAPK1, MAPK14, TNF, and PTGS2 proteins and the PI3K-Akt and TNF signaling pathways. The study demonstrated that quercetin, luteolin, kaempferol, and beta-sitosterol in PV may play a major role in the treatment of SAT, which was associated with the regulation of inflammation and apoptosis, by targeting the PI3K-Akt and TNF signaling pathways.

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High-Throughput Sequencing Reveals Diverse Sets of Conserved, Nonconserved, and Species-Specific miRNAs in Jute

International Journal of Genomics ◽

10.1155/2015/125048 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Md. Tariqul Islam ◽

Ahlan Sabah Ferdous ◽

Rifat Ara Najnin ◽

Suprovath Kumar Sarker ◽

Haseena Khan

Keyword(s):

High Throughput Sequencing ◽

Target Genes ◽

Kegg Pathway ◽

Translational Repression ◽

Biological Processes ◽

Rt Pcr ◽

Stem Loop ◽

Evolutionarily Conserved ◽

Pathway Analyses ◽

Species Specific

MicroRNAs play a pivotal role in regulating a broad range of biological processes, acting by cleaving mRNAs or by translational repression. A group of plant microRNAs are evolutionarily conserved; however, others are expressed in a species-specific manner. Jute is an agroeconomically important fibre crop; nonetheless, no practical information is available for microRNAs in jute to date. In this study, Illumina sequencing revealed a total of 227 known microRNAs and 17 potential novel microRNA candidates in jute, of which 164 belong to 23 conserved families and the remaining 63 belong to 58 nonconserved families. Among a total of 81 identified microRNA families, 116 potential target genes were predicted for 39 families and 11 targets were predicted for 4 among the 17 identified novel microRNAs. For understanding better the functions of microRNAs, target genes were analyzed by Gene Ontology and their pathways illustrated by KEGG pathway analyses. The presence of microRNAs identified in jute was validated by stem-loop RT-PCR followed by end point PCR and qPCR for randomly selected 20 known and novel microRNAs. This study exhaustively identifies microRNAs and their target genes in jute which will ultimately pave the way for understanding their role in this crop and other crops.

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Identification of Therapeutic Targets and Prognostic Biomarkers Among Integrin Subunits in the Skin Cutaneous Melanoma Microenvironment

Frontiers in Oncology ◽

10.3389/fonc.2021.751875 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yeltai Nurzat ◽

Weijie Su ◽

Peiru Min ◽

Ke Li ◽

Heng Xu ◽

...

Keyword(s):

T Cells ◽

Cutaneous Melanoma ◽

Regulatory Networks ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Expression Levels ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Go Enrichment ◽

Underlying Mechanisms

The roles of different integrin alpha/beta (ITGA/ITGB) subunits in skin cutaneous melanoma (SKCM) and their underlying mechanisms of action remain unclear. Oncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, and Webgestalt analysis tools were used. The expression levels of ITGA3, ITGA4, ITGA6, ITGA10, ITGB1, ITGB2, ITGB3, ITGB4, and ITGB7 were significantly increased in SKCM tissues. The expression levels of ITGA1, ITGA4, ITGA5, ITGA8, ITGA9, ITGA10, ITGB1, ITGB2, ITGB3, ITGB5, ITGB6 and ITGB7 were closely associated with SKCM metastasis. The expression levels of ITGA1, ITGA4, ITGB1, ITGB2, ITGB6, and ITGB7 were closely associated with the pathological stage of SKCM. The expression levels of ITGA6 and ITGB7 were closely associated with disease-free survival time in SKCM, and the expression levels of ITGA6, ITGA10, ITGB2, ITGB3, ITGB6, ITGB7, and ITGB8 were markedly associated with overall survival in SKCM. We also found significant correlations between the expression of integrin subunits and the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells). Finally, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed, and protein-protein interaction (PPI) networks were constructed. We have identified abnormally-expressed genes and gene regulatory networks associated with SKCM, improving understanding of the underlying pathogenesis of SKCM.

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

BioMed Research International ◽

10.1155/2021/6655425 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhaojun Liu ◽

Yang Chen ◽

Tingting Pan ◽

Jialin Liu ◽

Rui Tian ◽

...

Keyword(s):

Immune System ◽

Molecular Mechanisms ◽

Clinical Samples ◽

Central Component ◽

Inflammatory Disorder ◽

Rt Pcr ◽

Hub Gene ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Pathogenic Factors

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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Peptide KRP Conjugated with Doxorubicin Exerts Anti-Tumor Activity by Regulating RPS6KA2 in Osteosarcoma

10.21203/rs.3.rs-765080/v1 ◽

2021 ◽

Author(s):

Mei Yu ◽

Jiameng Liu ◽

Zhiyuan Lu ◽

Yiyang Chen ◽

Dongjie Zhang ◽

...

Keyword(s):

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

S6 Kinase ◽

Protein Protein Interaction ◽

Qrt Pcr ◽

Ribosomal Protein S6 ◽

Ppi Networks ◽

Specific Tumor ◽

Network Analyst ◽

Anti Tumor Activity

Abstract Objectives: Osteosarcoma (OS) is the most common primary solid malignant tumor of the bone in adolescents. Conventional treatment of OS by surgery and chemotherapy is not effective and the prognosis is poor. Our previous study demonstrated that a novel cell-penetrating peptide (KRP) that, coupled to doxorubicin (DOX), allowed specific tumor targeting. However, the underlying molecular mechanisms of the KRP-DOX antitumor effect were not completely elucidated. Therefore, the present work aimed to identify key candidate genes by integrated bioinformatics analysis. Methods: Diﬀerentially expressed genes (DEGs) were screened using the Network Analyst. The functions and pathway involvements of the DEGs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, respectively. The protein-protein interaction (PPI) network was used to identify hub genes. In addition, quantitative RT-PCR (qRT-PCR) and Western blotting were performed to assess the expression level of candidate biomarkers in OS cells after KRP-DOX treatment. Results: A total of 790 DEGs were identiﬁed. GO functional analysis and KEGG pathway analysis demonstrated that the DEGs were mostly enriched in the ribosome. DEGs were visualized by PPI networks. After treatment of OS cells with KRP-DOX, the downregulated ribosomal protein S6 kinase A2 (RPS6KA2) was found to be closely related to inhibition of OS proliferation. In agreement with the bioinformatics analysis, qRT-PCR and western blot results showed low expression of RPS6KA2 in osteosarcoma cells in the KRP-DOX treatment group.Conclusions: RPS6KA2 is signiﬁcantly associated with the KRP-DOX anti-tumor effect and may serve as a candidate biomarker and therapeutic target for OS.

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Absorbance or Organization into Ankylosis: A Microarray Analysis of Haemarthrosis in a Sheep Model of Temporomandibular Joint Trauma

10.21203/rs.3.rs-846555/v1 ◽

2021 ◽

Author(s):

Mai-Ning Jiao ◽

Tong-Mei Zhang ◽

Kun Yang ◽

Zhao-Yuan Xu ◽

Guan-Meng Zhang ◽

...

Keyword(s):

Temporomandibular Joint ◽

Molecular Mechanisms ◽

Contralateral Side ◽

Sheep Model ◽

Tmj Ankylosis ◽

Protein Protein Interaction ◽

Ppi Networks ◽

New Ideas ◽

Or Organization ◽

Joint Trauma

Abstract Background: Traumatic haemarthrosis was hypothesized to be the etiology of temporomandibular joint (TMJ) ankylosis. Here, taking haematoma absorbance as a control, we aimed to reveal the molecular mechanisms involved in haematoma organizing into ankylosis using transcriptome microarray profiles.Material/Methods: Disk removal was performed to building haematoma absorbance (HA) in one side of TMJ, while removal of disk and articular fibrous layers was performed to induced TMJ ankylosis through haematoma organization (HO) in the contralateral side in a sheep model. Haematoma tissues harvested at days 1, 4 and 7 postoperatively were examined by histology, and analyzed by Affymetrix OviGene-1_0-ST microarrays. The DAVID were recruited to perform the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis for the different expression genes (DEGs). The DEGs were also typed into protein–protein interaction (PPI) networks to get the interaction data. Six significant genes screened from PPI analysis, were confirmed by real-time PCR.Results: We found 268, 223 and 17 DEGs at least 2-fold at days 1, 4 and 7, respectively. At day 1, genes promoting collagen ossification (POSTN, BGN, LUM, SPARC), cell proliferation (TGF-β), and osteogenic differentiation of mesenchymal stem cells (BMP-2) were up-regulated in the HO side. At day 4, several genes involved in angiogenesis (KDR, FIT1, TEK) shower higher expression in the HO side. While HA was characterized by a continuous immune and inflammatory reaction.Conclusions: Our results provide a comprehensive understanding of the role of haematoma in the onset and progress of TMJ ankylosis. Further study of key genes may provide new ideas for future treatment of the disease.

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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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Transcriptome Changes in the Mink Uterus during Blastocyst Dormancy and Reactivation

International Journal of Molecular Sciences ◽

10.3390/ijms20092099 ◽

2019 ◽

Vol 20 (9) ◽

pp. 2099

Author(s):

Xinyan Cao ◽

Jiaping Zhao ◽

Yong Liu ◽

Hengxing Ba ◽

Haijun Wei ◽

...

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Regulatory Networks ◽

Molecular Mechanisms ◽

Embryo Implantation ◽

Global Gene Expression ◽

Gene Clusters ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Maternal Regulation

Embryo implantation in the mink follows the pattern of many carnivores, in that preimplantation embryo diapause occurs in every gestation. Details of the gene expression and regulatory networks that terminate embryo diapause remain poorly understood. Illumina RNA-Seq was used to analyze global gene expression changes in the mink uterus during embryo diapause and activation leading to implantation. More than 50 million high quality reads were generated, and assembled into 170,984 unigenes. A total of 1684 differential expressed genes (DEGs) in uteri with blastocysts in diapause were compared to the activated embryo group (p < 0.05). Among these transcripts, 1527 were annotated as known genes, including 963 up-regulated and 564 down-regulated genes. The gene ontology terms for the observed DEGs, included cellular communication, phosphatase activity, extracellular matrix and G-protein couple receptor activity. The KEGG pathways, including PI3K-Akt signaling pathway, focal adhesion and extracellular matrix (ECM)-receptor interactions were the most enriched. A protein-protein interaction (PPI) network was constructed, and hub nodes such as VEGFA, EGF, AKT, IGF1, PIK3C and CCND1 with high degrees of connectivity represent gene clusters expected to play an important role in embryo activation. These results provide novel information for understanding the molecular mechanisms of maternal regulation of embryo activation in mink.

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