scholarly journals Novel Comprehensive Bioinformatics Approaches to Determine the Molecular Genetic Susceptibility Profile of Moderate and Severe Asthma

2020 ◽  
Vol 21 (11) ◽  
pp. 4022
Author(s):  
Hatem Zayed
Keyword(s):  
Severe Asthma ◽  
Protein Interactions ◽  
Gene Networks ◽  
Molecular Genetic ◽  
Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Bioinformatics Pipeline ◽  
Moderate Asthma ◽  
Asthma Phenotypes ◽  
Chronic Inflammatory Condition

Background: Asthma is a chronic inflammatory condition linked to hyperresponsiveness in the airways. There is currently no cure available for asthma, and therapy choices are limited. Asthma is the result of the interplay between genes and the environment. The exact molecular genetic mechanism of asthma remains elusive. Aims: The aim of this study is to provide a comprehensive, detailed molecular etiology profile for the molecular factors that regulate the severity of asthma and pathogenicity using integrative bioinformatics tools. Methods: The GSE43696 omnibus gene expression dataset, which contains 50 moderate cases, 38 severe cases, and 20 healthy controls, was used to investigate differentially expressed genes (DEGs), susceptible chromosomal loci, gene networks, pathways, gene ontologies, and protein–protein interactions (PPIs) using an intensive bioinformatics pipeline. Results: The PPI network analysis yielded DEGs that contribute to interactions that differ from moderate-to-severe asthma. The combined interaction scores resulted in higher interactions for the genes STAT3, AGO2, COL1A1, CLCN6, and KSR for moderate asthma and JAK2, INSR, ERBB2, NR3C1, and PTK6 for severe asthma. Enrichment analysis (EA) demonstrated differential enrichment between moderate and severe asthma phenotypes; the ion transport regulation pathway was significantly enhanced in severe asthma phenotypes compared to that in moderate asthma phenotypes and involved PER2, GCR, IRS-2, KCNK7, KCNK6, NOX1, and SCN7A. The most enriched common pathway in both moderate and severe asthma is the development of the glucocorticoid receptor (GR) signaling pathway followed by glucocorticoid-mediated inhibition of proinflammatory and proconstrictory signaling in the airway of smooth muscle cell pathways. Gene sets were shared between severe and moderate asthma at 16 chromosome locations, including 17p13.1, 16p11.2, 17q21.31, 1p36, and 19q13.2, while 60 and 48 chromosomal locations were unique for both moderate and severe asthma, respectively. Phylogenetic analysis for DEGs showed that several genes have been intersected in phases of asthma in the same cluster of genes. This could indicate that several asthma-associated genes have a common ancestor and could be linked to the same biological function or gene family, implying the importance of these genes in the pathogenesis of asthma. Conclusion: New genetic risk factors for the development of moderate-to-severe asthma were identified in this study, and these could provide a better understanding of the molecular pathology of asthma and might provide a platform for the treatment of asthma.

scholarly journals Search for New Candidate Genes Involved in the Comorbidity of Asthma and Hypertension Based on Automatic Analysis of Scientific Literature

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Olga V. Saik ◽  
Pavel S. Demenkov ◽  
Timofey V. Ivanisenko ◽  
Elena Yu. Bragina ◽  
Maxim B. Freidin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Molecular Genetic ◽  
Genetic Networks ◽  
Positive Sign ◽  
Phenotypic Traits ◽  
Protein Protein Interactions ◽  
Scientific Publications ◽  
Associative Gene Networks

AbstractComorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits. Existing methods of prioritization consider genetic, expression and evolutionary data, molecular-genetic networks and other. In the case of molecular-genetic networks, as a rule, protein-protein interactions and KEGG networks are used. ANDSystem allows reconstructing associative gene networks, which include more than 20 types of interactions, including protein-protein interactions, expression regulation, transport, catalysis, etc. In this work, a set of genes has been prioritized to find genes potentially involved in asthma and hypertension comorbidity. The prioritization was carried out using well-known methods (ToppGene and Endeavor) and a cross-talk centrality criterion, calculated by analysis of associative gene networks from ANDSystem. The identified genes, including IL1A, CD40LG, STAT3, IL15, FAS, APP, TLR2, C3, IL13 and CXCL10, may be involved in the molecular mechanisms of comorbid asthma/hypertension. An analysis of the dynamics of the frequency of mentioning the most priority genes in scientific publications revealed that the top 100 priority genes are significantly enriched with genes with increased positive dynamics, which may be a positive sign for further studies of these genes.

scholarly journals A computational interactome and functional annotation for the human proteome

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
José Ignacio Garzón ◽  
Lei Deng ◽  
Diana Murray ◽  
Sagi Shapira ◽  
Donald Petrey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Enrichment Analysis ◽  
Human Proteome ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Functional Relationships ◽  
Structural Relationships ◽  
Human Proteins ◽  
Validation Tests

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.

scholarly journals Network Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of the Drug Pair Astragalus-Angelica for Treatment of Male Infertility

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Feng Zhao ◽  
Yingjun Deng ◽  
Guanchao Du ◽  
Shengjing Liu ◽  
Jun Guo ◽  
...  
Keyword(s):  
Male Infertility ◽  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interactions ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Protein Protein Interactions ◽  
Drug Pair ◽  
Effective Components ◽  
New Ideas

Background. The traditional Chinese medicines Astragalus and Angelica are often combined to treat male infertility, but the specific therapeutic mechanism is not clear. Therefore, this study applies a network pharmacology approach to investigate the possible mechanism of action of the drug pair Astragalus-Angelica (PAA) in the treatment of male infertility. Methods. Relevant targets for PAA treatment of male infertility are obtained through databases. Protein-protein interactions (PPIs) are constructed through STRING database and screen core targets, and an enrichment analysis is conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target protein and the ligand of PAA. Results. The active ingredients of 112 PAA, 980 corresponding targets, and 374 effective targets of PAA for the treatment of male infertility were obtained, which are related to PI3K-Akt signaling pathway, HIF-1 signaling pathway, AGE-RAGE signaling pathway, IL-17 signaling pathway, and thyroid hormone signaling pathway. Conclusion. In this study, using a network pharmacology method, we preliminarily analyzed the effective components and action targets of the PAA. We also explored the possible mechanism of action of PAA in treating male infertility. They also lay a foundation for expanding the clinical application of PAA and provide new ideas and directions for further research on the mechanisms of action of the PAA and its components for male infertility treatment.

scholarly journals Glycyrrhizic Acid for COVID-19: Findings of Targeting Pivotal Inflammatory Pathways Triggered by SARS-CoV-2

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenjiang Zheng ◽  
Xiufang Huang ◽  
Yanni Lai ◽  
Xiaohong Liu ◽  
Yong Jiang ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Protein Interactions ◽  
Glycyrrhizic Acid ◽  
Enrichment Analysis ◽  
Host Factors ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Health Crisis ◽  
Inflammatory Pathways ◽  
Anti Inflammatory

Background: Coronavirus disease 2019 (COVID-19) is now a worldwide public health crisis. The causative pathogen is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Novel therapeutic agents are desperately needed. Because of the frequent mutations in the virus and its ability to cause cytokine storms, targeting the viral proteins has some drawbacks. Targeting cellular factors or pivotal inflammatory pathways triggered by SARS-CoV-2 may produce a broader range of therapies. Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 because of its anti-inflammatory and antiviral characteristics and possible ability to regulate crucial host factors. However, the mechanism underlying how GA regulates host factors remains to be determined.Methods: In our report, we conducted a bioinformatics analysis to identify possible GA targets, biological functions, protein-protein interactions, transcription-factor-gene interactions, transcription-factor-miRNA coregulatory networks, and the signaling pathways of GA against COVID-19.Results: Protein-protein interactions and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be key targets of GA for COVID-19. GO analysis indicated that the response to reactive oxygen species was significantly enriched. Pathway enrichment analysis showed that the IL-17, IL-6, TNF-α, IFN signals, complement system, and growth factor receptor signaling are the main pathways. The interactions of TF genes and miRNA with common targets and the activity of TFs were also recognized.Conclusions: GA may inhibit COVID-19 through its anti-oxidant, anti-viral, and anti-inflammatory effects, and its ability to activate the immune system, and targeted therapy for those pathways is a predominant strategy to inhibit the cytokine storms triggered by SARS-CoV-2 infection.

Integrated analysis of the expression, involved functions, and regulatory network of RUNX3 in melanoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yanxin Liu ◽  
Zhang Feng ◽  
Huaxia Chen
Keyword(s):  
Protein Interactions ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Protein Protein Interactions ◽  
Nf Kappab ◽  
Microarray Datasets

Background: As a tumor suppressor or oncogenic gene, abnormal expression of RUNX family transcription factor 3 (RUNX3) has been reported in various cancers. Introduction: This study aimed to investigate the role of RUNX3 in melanoma. Methods: The expression level of RUNX3 in melanoma tissues was analyzed by immunohistochemistry and the Oncomine database. Based on microarray datasets GSE3189 and GSE7553, differentially expressed genes (DEGs) in melanoma samples were screened, followed by functional enrichment analysis. Gene Set Enrichment Analysis (GSEA) was performed for RUNX3. DEGs that co-expressed with RUNX3 were analyzed, and the transcription factors (TFs) of RUNX3 and its co-expressed genes were predicted. The protein-protein interactions (PPIs) for RUNX3 were analyzed utilizing the GeneMANIA database. MicroRNAs (miRNAs) that could target RUNX3 expression, were predicted. Results : RUNX3 expression was significantly up-regulated in melanoma tissues. GSEA showed that RUNX3 expression was positively correlated with melanogenesis and melanoma pathways. Eleven DEGs showed significant co-expression with RUNX3 in melanoma, for example, TLE4 was negatively co-expressed with RUNX3. RUNX3 was identified as a TF that regulated the expression of both itself and its co-expressed genes. PPI analysis showed that 20 protein-encoding genes interacted with RUNX3, among which 9 genes were differentially expressed in melanoma, such as CBFB and SMAD3. These genes were significantly enriched in transcriptional regulation by RUNX3, RUNX3 regulates BCL2L11 (BIM) transcription, regulation of I-kappaB kinase/NF-kappaB signaling, and signaling by NOTCH. A total of 31 miRNAs could target RUNX3, such as miR-326, miR-330-5p, and miR-373-3p. Conclusion: RUNX3 expression was up-regulated in melanoma and was implicated in the development of melanoma.

scholarly journals pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

2019 ◽  
Author(s):  
Sara Rahmati ◽  
Mark Abovsky ◽  
Chiara Pastrello ◽  
Max Kotlyar ◽  
Richard Lu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Enrichment Analysis ◽  
Human Model ◽  
Model Organisms ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Proteome Coverage ◽  
Pathway Annotation ◽  
Human Proteins ◽  
Integrated Pathway

Abstract PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein–protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

scholarly journals FLAME: A Web Tool for Functional and Literature Enrichment Analysis of Multiple Gene Lists

Biology ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 665
Author(s):  
Foteini Thanati ◽  
Evangelos Karatzas ◽  
Fotis A. Baltoumas ◽  
Dimitrios J. Stravopodis ◽  
Aristides G. Eliopoulos ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Enrichment Analysis ◽  
Experimental Colitis ◽  
Functional Enrichment ◽  
Protein Protein Interactions ◽  
Web Tool ◽  
Single List ◽  
Gene Ontologies ◽  
User Friendly ◽  
The Given

Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases, or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making a more combinatorial analysis more complicated and prone to errors. In this article, we present FLAME, a web tool for combining multiple lists prior to enrichment analysis. Users can upload several lists and use interactive UpSet plots, as an alternative to Venn diagrams, to handle unions or intersections among the given input files. Functional and literature enrichment, along with gene conversions, are offered by g:Profiler and aGOtool applications for 197 organisms. FLAME can analyze genes/proteins for related articles, Gene Ontologies, pathways, annotations, regulatory motifs, domains, diseases, and phenotypes, and can also generate protein–protein interactions derived from STRING. We have validated FLAME by interrogating gene expression data associated with the sensitivity of the distal part of the large intestine to experimental colitis-propelled colon cancer. FLAME comes with an interactive user-friendly interface for easy list manipulation and exploration, while results can be visualized as interactive and parameterizable heatmaps, barcharts, Manhattan plots, networks, and tables.

scholarly journals FLAME: a web tool for functional and literature enrichment analysis of multiple gene lists

2021 ◽  
Author(s):  
Foteini Thanati ◽  
Evangelos Karatzas ◽  
Fotis Baltoumas ◽  
Dimitrios J Stravopodis ◽  
Aristides G Eliopoulos ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Enrichment Analysis ◽  
Experimental Colitis ◽  
Functional Enrichment ◽  
Protein Protein Interactions ◽  
Web Tool ◽  
Single List ◽  
Gene Ontologies ◽  
User Friendly ◽  
The Given

Functional enrichment is a widely used method for interpreting experimental results by identifying classes of proteins/genes associated with certain biological functions, pathways, diseases or phenotypes. Despite the variety of existing tools, most of them can process a single list per time, thus making a more combinatorial analysis more complicated and prone to errors. In this article, we present FLAME, a web tool for combining multiple lists prior to enrichment analysis. Users can upload several lists of preference and use interactive UpSet plots, as an alternative to Venn diagrams, to handle unions or intersections among the given input files. Functional and literature enrichment along with gene conversions are offered by g:Profiler and aGOtool applications for 197 organisms. In its current version, FLAME can analyze genes/proteins for related articles, Gene Ontologies, pathways, annotations, regulatory motifs, domains, diseases, phenotypes while it can also generate protein-protein interactions derived from STRING. We have herein validated FLAME by interrogating gene expression data associated with the sensitivity of the distal part of the large intestine to experimental colitis-propelled colon cancer. The FLAME application comes with an interactive user-friendly interface which allows easy list manipulation and exploration, while results can be visualized as interactive and parameterizable heatmaps, barcharts, Manhattan plots, networks and tables. Availability: FLAME application: http://flame.pavlopouloslab.info Code: https://github.com/PavlopoulosLab/FLAME

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