scholarly journals Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea R. Daamen ◽  
Prathyusha Bachali ◽  
Katherine A. Owen ◽  
Kathryn M. Kingsmore ◽  
Erika L. Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Host Response ◽  
Cell Activation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Myeloid Cell ◽  
Comprehensive Analysis ◽  
Expression Data ◽  
Disease Pathogenesis ◽  
Cytotoxic Cells

AbstractSARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

scholarly journals Comprehensive Transcriptomic Analysis of COVID-19 Blood, Lung, and Airway

2020 ◽  
Author(s):  
Andrea R. Daamen ◽  
Prathyusha Bachali ◽  
Katherine A. Owen ◽  
Kathryn M. Kingsmore ◽  
Erika L. Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Host Response ◽  
Cell Activation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Myeloid Cell ◽  
Comprehensive Analysis ◽  
Expression Data ◽  
Disease Pathogenesis ◽  
Cytotoxic Cells

AbstractSARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.Graphical Abstract

scholarly journals Gene Expression Profiles Analyzed Using Integrating RNA Sequencing, and Microarray Reveals Increased Inflammatory Response, Proliferation, and Osteoclastogenesis in Pigmented Villonodular Synovitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Zhao ◽  
Jiaoyun Lv ◽  
Hongwei Zhang ◽  
Jiawei Xie ◽  
Hui Dai ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell Activation ◽  
Cell Activation ◽  
Expression Profiles ◽  
Osteoclast Differentiation ◽  
Gene Expression Profiles ◽  
Myeloid Cell ◽  
Pigmented Villonodular Synovitis ◽  
Villonodular Synovitis ◽  
Pigmented Villonodular

BackgroundPigmented villonodular synovitis (PVNS) is a rare condition that involves benign proliferation of the synovial tissue and is characterized by severe joint destruction and high recurrence even after surgical resection. However, poor understanding of the pathogenesis limits its effective therapy.MethodIn this study, gene expression profiles of six patients with PVNS, 11 patients with osteoarthritis (OA), nine patients with rheumatoid arthritis (RA) (E-MTAB-6141), and three healthy subjects (GSE143514) were analyzed using integrating RNA sequencing (RNA-seq) and microarray to investigate the PVNS transcriptome. Gene ontology, string, and cytoscape were used to determine the gene functional enrichment. Cell functional molecules were detected using flow cytometry or immunohistochemical test to identify the cell subset and function. CD14+ cells were isolated and induced to osteoclast to evaluate the monocyte/macrophage function.ResultsThe most obvious local manifestations of PVNS were inflammation, including increased immune cells infiltration and cytokine secretion, and tumor phenotypes. High proportion of inflammatory cells, including T cells, natural killer (NK) cells, NKT cells, and B cells were recruited from the blood. Th17 and monocytes, especially classical monocytes but not nonclassical monocytes, increased in PVNS synovium. An obvious increase in osteoclastogenesis and macrophage activation was observed locally. Elevated expression of MMP9, SIGLEC 15, and RANK were observed in myeloid cell of PVNS than OA. When compared with RA, osteoclast differentiation and myeloid cell activation are PVNS-specific characters, whereas T cell activation is shared by PVNS and RA.ConclusionThe transcriptional expression characteristics of PVNS showed increased immune response, cell migration, and osteoclastogenesis. Osteoclast differentiation is only observed in PVNS but not RA, whereas T-cell activation is common in inflammatory arthritis.

scholarly journals Time Dependent Gene Expression Changes in the Liver of Mice Treated with Benzene

2008 ◽  
Vol 3 ◽  
pp. BMI.S590 ◽  
Author(s):  
Han-Jin Park ◽  
Jung Hwa Oh ◽  
Seokjoo Yoon ◽  
S.V.S. Rana
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
General Purpose ◽  
Time Dependent ◽  
Expression Data ◽  
Benzene Exposure ◽  
Microarray Analyses ◽  
First Time ◽  
Affymetrix Gene Chip

Benzene is used as a general purpose solvent. Benzene metabolism starts from phenol and ends with p-benzoquinone and o-benzoquinone. Liver injury inducted by benzene still remains a toxicologic problem. Tumor related genes and immune responsive genes have been studied in patients suffering from benzene exposure. However, gene expression profiles and pathways related to its hepatotoxicity are not known. This study reports the results obtained in the liver of BALB/C mice (SLC, Inc., Japan) administered 0.05 ml/100 g body weight of 2% benzene for six days. Serum, ALT, AST and ALP were determined using automated analyzer (Fuji., Japan). Histopathological observations were made to support gene expression data. c-DNA microarray analyses were performed using Affymetrix Gene-chip system. After six days of benzene exposure, twenty five genes were down regulated whereas nineteen genes were up-regulated. These gene expression changes were found to be related to pathways of biotransformation, detoxification, apoptosis, oxidative stress and cell cycle. It has been shown for the first time that genes corresponding to circadian rhythms are affected by benzene. Results suggest that gene expression profile might serve as potential biomarkers of hepatotoxicity during benzene exposure.

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

scholarly journals Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis

2015 ◽  
Vol 11 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Aakash Chavan Ravindranath ◽  
Nolen Perualila-Tan ◽  
Adetayo Kasim ◽  
Georgios Drakakis ◽  
Sonia Liggi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ligand Binding ◽  
Gene Expression Data ◽  
Mechanism Of Action ◽  
In Silico ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Connectivity Map ◽  
Action Analysis

Integrating gene expression profiles with certain proteins can improve our understanding of the fundamental mechanisms in protein–ligand binding.

scholarly journals A comprehensive analysis of gene expression profiles in distal parts of the mouse renal tubule

2010 ◽  
Vol 460 (6) ◽  
pp. 925-952 ◽  
Author(s):  
Sylvain Pradervand ◽  
Annie Zuber Mercier ◽  
Gabriel Centeno ◽  
Olivier Bonny ◽  
Dmitri Firsov
Keyword(s):  
Gene Expression ◽  
Renal Tubule ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

scholarly journals GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 675 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Zhang ◽  
Guo
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

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