scholarly journals Investigation of molecular mechanisms using integrated analysis of transcriptomes and cytokinome in dermatomyositis

2020 ◽  
Author(s):  
Jingjing Bai ◽  
Chanyuan Wu ◽  
Danli Zhong ◽  
Dong Xu ◽  
Qian Wang ◽  
...  
Keyword(s):  
Disease Activity ◽  
Signaling Pathways ◽  
Molecular Mechanisms ◽  
Type I Interferon ◽  
Interaction Network ◽  
Integrated Analysis ◽  
Type I ◽  
Hub Genes ◽  
Activity Assessment ◽  
Ifn Γ

Abstract Background Pathomechanism of dermatomyositis (DM) remains yet fully elucidated. While several cytokines have been proved to participate in the progress of DM, few studies provided a comprehensive analysis of cytokinome in different DM clinical-serological subgroups and correlation with disease activity as well as interaction with DM tissue lesions.Methods Transcriptome datasets of DM skin and muscle were obtained from public database. Hub genes and signaling pathways were filtered by bioinformatic software. Serum cytokinome was measured in DM patients with different clinical-serological subgroups and correlation with disease activity indicators was analyzed. Cytokine interaction network was constructed.Results 6 hub genes, including STAT1, MX1, ISG15, IFIT3, GBP1 and OAS2 were identified as IFN signature in DM. Differently expressed genes (DEGs) identified in the skin and muscle datasets were significantly enriched in the type I interferon signaling pathway, defense response to virus and chemotaxis. 11 cytokines were significantly elevated in patients positive for melanoma differentiation-associated protein (MDA5) antibody. IFN-α, IFN-γ, MIP-1α, IP-10, MCP1, GRO-α, IL-6, IL-18 and IL-1RA were correlated with disease activity. MCP1/MIP-1α/RANTES/MCP2/CCR1 axes were filtered from cytokine interaction network.Conclusions The complexity of DM immunopathogenesis is mediated through interactions of multiple cytokines and signaling pathways. Type I interferon is the core participant in DM tissue damage. Serum upregulation of IFN-α, IFN-γ, MIP-1α, IP-10, MCP1, GRO-α, IL-6, IL-18 and IL-1RA could be used for disease activity assessment in DM patients positive for MDA5 antibody. Finally, MCP1/MIP-1α/RANTES/MCP2/CCR1 axes mediated monocytes attraction might be novel therapeutic targets in DM by chemokine network analysis.

scholarly journals Investigation of molecular mechanisms using integrated analysis of transcriptomes and cytokinome in dermatomyositis

2020 ◽  
Author(s):  
Jingjing Bai ◽  
Chanyuan Wu ◽  
Danli Zhong ◽  
Dong Xu ◽  
Qian Wang ◽  
...  
Keyword(s):  
Disease Activity ◽  
Signaling Pathways ◽  
Molecular Mechanisms ◽  
Type I Interferon ◽  
Interaction Network ◽  
Integrated Analysis ◽  
Type I ◽  
Hub Genes ◽  
Activity Assessment ◽  
Ifn Γ

Abstract Background Pathomechanism of dermatomyositis (DM) remains yet fully elucidated. While several cytokines have been proved to participate in the progress of DM, few studies provided a comprehensive analysis of cytokinome in different DM clinical-serological subgroups and correlation with disease activity as well as interaction with DM tissue lesions.Methods Transcriptome datasets of DM skin and muscle were obtained from public database. Hub genes and signaling pathways were filtered by bioinformatic software. Serum cytokinome was measured in DM patients with different clinical-serological subgroups and correlation with disease activity indicators was analyzed. Cytokine interaction network was constructed.Results 6 hub genes, including STAT1, MX1, ISG15, IFIT3, GBP1 and OAS2 were identified as IFN signature in DM. Differently expressed genes (DEGs) identified in the skin and muscle datasets were significantly enriched in the type I interferon signaling pathway, defense response to virus and chemotaxis. 11 cytokines were significantly elevated in patients positive for melanoma differentiation-associated protein (MDA5) antibody. IFN-α, IFN-γ, MIP-1α, IP-10, MCP1, GRO-α, IL-6, IL-18 and IL-1RA were correlated with disease activity. MCP1/MIP-1α/RANTES/MCP2/CCR1 axes were filtered from cytokine interaction network.Conclusions The complexity of DM immunopathogenesis is mediated through interactions of multiple cytokines and signaling pathways. Type I interferon is the core participant in DM tissue damage. Serum upregulation of IFN-α, IFN-γ, MIP-1α, IP-10, MCP1, GRO-α, IL-6, IL-18 and IL-1RA could be used for disease activity assessment in DM patients positive for MDA5 antibody. Finally, MCP1/MIP-1α/RANTES/MCP2/CCR1 axes mediated monocytes attraction might be novel therapeutic targets in DM by chemokine network analysis.

scholarly journals Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Linjie Fang ◽  
Tingyu Tang ◽  
Mengqi Hu
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Type I ◽  
Hub Genes ◽  
Gene Set ◽  
Go Terms

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases’ lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.

scholarly journals Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Jing Xu ◽  
Yuejin Yang
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Operating Characteristics ◽  
Hub Genes ◽  
St Segment Elevation ◽  
Transcriptomic Signature ◽  
Selection Operator ◽  
Logistic Regression Algorithm

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF.Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867.Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF.Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

scholarly journals Integrated Analysis of lncRNAs, mRNAs, and TFs to Identify Regulatory Networks Underlying MAP Infection in Cattle

2021 ◽  
Vol 12 ◽  
Author(s):  
Maryam Heidari ◽  
Abbas Pakdel ◽  
Mohammad Reza Bakhtiarizadeh ◽  
Fariba Dehghanian
Keyword(s):  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Johne's Disease ◽  
Johne’S Disease ◽  
Functional Enrichment ◽  
Integrated Analysis ◽  
Complex Nature ◽  
Hub Genes ◽  
Cis And Trans

Johne’s disease is a chronic infection of ruminants that burdens dairy herds with a significant economic loss. The pathogenesis of the disease has not been revealed clearly due to its complex nature. In order to achieve deeper biological insights into molecular mechanisms involved in MAP infection resulting in Johne’s disease, a system biology approach was used. As far as is known, this is the first study that considers lncRNAs, TFs, and mRNAs, simultaneously, to construct an integrated gene regulatory network involved in MAP infection. Weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis were conducted to explore coexpression modules from which nonpreserved modules had altered connectivity patterns. After identification of hub and hub-hub genes as well as TFs and lncRNAs in the nonpreserved modules, integrated networks of lncRNA-mRNA-TF were constructed, and cis and trans targets of lncRNAs were identified. Both cis and trans targets of lncRNAs were found in eight nonpreserved modules. Twenty-one of 47 nonpreserved modules showed significant biological processes related to the immune system and MAP infection. Some of the MAP infection’s related pathways in the most important nonpreserved modules comprise “positive regulation of cytokine-mediated signaling pathway,” “negative regulation of leukocyte migration,” “T-cell differentiation,” “neutrophil activation,” and “defense response.” Furthermore, several genes were identified in these modules, including SLC11A1, MAPK8IP1, HMGCR, IFNGR1, CMPK2, CORO1A, IRF1, LDLR, BOLA-DMB, and BOLA-DMA, which are potentially associated with MAP pathogenesis. This study not only enhanced our knowledge of molecular mechanisms behind MAP infection but also highlighted several promising hub and hub-hub genes involved in macrophage-pathogen interaction.

Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

2020 ◽  
Author(s):  
Jing Xu ◽  
Yuejing Yang
Keyword(s):  
Machine Learning ◽  
Gene Ontology ◽  
Risk Stratification ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Hub Genes ◽  
Machine Learning Model ◽  
Transcriptomic Signature ◽  
Logistic Regression Algorithm ◽  
Geo Database

Abstract Objective To explore the molecular mechanism and search for the candidate biomarkers with predictive and prognostic potentiality that detectable in the whole blood of STEMI patients and post-STEMI HF patients.Methods In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. Differentially expressed genes (DEGs) of the datasets were investigated using R. Gene ontology and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. Protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. LASSO logistic regression algorithm and ROC analysis were performed to build machine learning models for predicting STEMI. Hub genes for further validated in post-STEMI HF patients from GSE59867.Results We identified 90 up-regulated DEGs and 9 down-regulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p value < 0.05). They were mainly enriched in Gene Ontology terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of 8 genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic biomarkers for post-STEMI HF.Conclusions We re-analyzed the integrated transcriptomic signature of STEMI patients showing predictive potentiality and revealed new insights and specific prospective biomarkers for STEMI risk stratification and HF development.

scholarly journals The Molecular Interactions of ZIKV and DENV with the Type-I IFN Response

Vaccines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 530
Author(s):  
Rosa C. Coldbeck-Shackley ◽  
Nicholas S. Eyre ◽  
Michael R. Beard
Keyword(s):  
Immune Responses ◽  
Molecular Interactions ◽  
Molecular Mechanisms ◽  
Type I Interferon ◽  
Control Measures ◽  
Type I ◽  
Type I Ifn ◽  
Adaptive Immune ◽  
Vaccination Strategies ◽  
Significant Disease

Zika Virus (ZIKV) and Dengue Virus (DENV) are related viruses of the Flavivirus genus that cause significant disease in humans. Existing control measures have been ineffective at curbing the increasing global incidence of infection for both viruses and they are therefore prime targets for new vaccination strategies. Type-I interferon (IFN) responses are important in clearing viral infection and for generating efficient adaptive immune responses towards infection and vaccination. However, ZIKV and DENV have evolved multiple molecular mechanisms to evade type-I IFN production. This review covers the molecular interactions, from detection to evasion, of these viruses with the type-I IFN response. Additionally, we discuss how this knowledge can be exploited to improve the design of new vaccine strategies.

scholarly journals Splicing factor SRSF1 limits IFN-γ production via RhoH and ameliorates experimental nephritis

Rheumatology ◽  
2020 ◽  
Vol 60 (1) ◽  
pp. 420-429
Author(s):  
Takayuki Katsuyama ◽  
Hao Li ◽  
Suzanne M Krishfield ◽  
Vasileios C Kyttaris ◽  
Vaishali R Moulton
Keyword(s):  
T Cells ◽  
T Cell ◽  
Molecular Mechanisms ◽  
Elevated Serum ◽  
Splicing Factor ◽  
Type I ◽  
Human T Cells ◽  
Experimental Nephritis ◽  
Ifn Γ

Abstract Objective CD4 T helper 1 (Th1) cells producing IFN-γ contribute to inflammatory responses in the pathogenesis of SLE and lupus nephritis. Moreover, elevated serum type II IFN levels precede the appearance of type I IFNs and autoantibodies in patient years before clinical diagnosis. However, the molecules and mechanisms that control this inflammatory response in SLE remain unclear. Serine/arginine-rich splicing factor 1 (SRSF1) is decreased in T cells from SLE patients, and restrains T cell hyperactivity and systemic autoimmunity. Our objective here was to evaluate the role of SRSF1 in IFN-γ production, Th1 differentiation and experimental nephritis. Methods T cell-conditional Srsf1-knockout mice were used to study nephrotoxic serum-induced nephritis and evaluate IFN-γ production and Th1 differentiation by flow cytometry. RNA sequencing was used to assess transcriptomics profiles. RhoH was silenced by siRNA transfections in human T cells by electroporation. RhoH and SRSF1 protein levels were assessed by immunoblots. Results Deletion of Srsf1 in T cells led to increased Th1 differentiation and exacerbated nephrotoxic serum nephritis. The expression levels of RhoH are decreased in Srsf1-deficient T cells, and silencing RhoH in human T cells leads to increased production of IFN-γ. Furthermore, RhoH expression was decreased and directly correlated with SRSF1 in T cells from SLE patients. Conclusion Our study uncovers a previously unrecognized role of SRSF1 in restraining IFN-γ production and Th1 differentiation through the control of RhoH. Reduced expression of SRSF1 may contribute to pathogenesis of autoimmune-related nephritis through these molecular mechanisms.

Ingested IFN-α has biological effects in humans with relapsing-remitting multiple sclerosis

1997 ◽  
Vol 3 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Staley A Brod ◽  
Ronald H Kerman ◽  
Laura D Nelson ◽  
Gailen D Marshall ◽  
Evelyn M Henninger ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Disease Activity ◽  
Biological Effects ◽  
Intercellular Adhesion Molecule ◽  
Type I ◽  
Recombinant Type ◽  
Relapsing Remitting ◽  
Ifn Γ ◽  
Remitting Multiple Sclerosis ◽  
Relapsing Remitting Multiple Sclerosis

Parenterally administered human recombinant type I interferons (hrIFN) in relapsing-remitting multiple sclerosis (RRMS) decrease relapses and spontaneous in vitro IFN-γ production, reduce clinical progression, and decrease magnetic resonance imaging (MRI)-defined disease activity and lesions. Parenterally administered type I IFN use is limited by clinical and chemical toxicities, and the induction of antibodies that abrogate their activity in vivo correlated with the loss of clinical benefit. Therefore, we determined whether ingested IFN-α was non-toxic and had biological effects in humans. ingested hrIFN-α showed no toxicity in normal volunteers or patients with RRMS at doses ranging from 300 to 100 000 units. In subjects with RRMS, a significant decrease in Con A-mediated proliferation and serum soluble intercellular adhesion molecule-I (sICAM-I), a surrogate measure for disease activity in MS, was found after ingesting 10 000 and 30 000 units IFN-α The RRMS subjects also showed decreased IL-2 secretion after ingesting 10 000 units IFN-α, and decreased IFN-γ, TGF-β and IL-10 production after ingesting 30 000 units IFN-α. The decreased secretion of IFN-γ and IL-2 by ingested IFN-α suggests that oral IFN-α may cause a functional inhibition of Th I-like T helper cells in RRMS, a potential site of intervention at the level of effector T cells in MS. Our studies support the oral use of human IFN-α as a biological response modifier in humans.

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