Reproducible chemical-induced changes in gene expression profiles in human hepatoma HepaRG cells under various experimental conditions

2009 ◽  
Vol 23 (3) ◽  
pp. 466-475 ◽  
Author(s):  
Carine B. Lambert ◽  
Catherine Spire ◽  
Marie-Pierre Renaud ◽  
Nancy Claude ◽  
Andre Guillouzo
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Hepatoma ◽  
Experimental Conditions ◽  
Heparg Cells ◽  
Induced Changes

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

scholarly journals Age and Vitamin E-Induced Changes in Gene Expression Profiles of T Cells

2006 ◽  
Vol 177 (9) ◽  
pp. 6052-6061 ◽  
Author(s):  
Sung Nim Han ◽  
Oskar Adolfsson ◽  
Cheol-Koo Lee ◽  
Tomas A. Prolla ◽  
Jose Ordovas ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Vitamin E ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes

scholarly journals ImmuSort, a database on gene plasticity and electronic sorting for immune cells

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Pingzhang Wang ◽  
Yehong Yang ◽  
Wenling Han ◽  
Dalong Ma
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Evaluation Scores ◽  
Cell Groups ◽  
Differential Gene ◽  
Disease Associated Genes

Abstract Gene expression is highly dynamic and plastic. We present a new immunological database, ImmuSort. Unlike other gene expression databases, ImmuSort provides a convenient way to view global differential gene expression data across thousands of experimental conditions in immune cells. It enables electronic sorting, which is a bioinformatics process to retrieve cell states associated with specific experimental conditions that are mainly based on gene expression intensity. A comparison of gene expression profiles reveals other applications, such as the evaluation of immune cell biomarkers and cell subsets, identification of cell specific and/or disease-associated genes or transcripts, comparison of gene expression in different transcript variants and probe set quality evaluation. A plasticity score is introduced to measure gene plasticity. Average rank and marker evaluation scores are used to evaluate biomarkers. The current version includes 31 human and 17 mouse immune cell groups, comprising 10,422 and 3,929 microarrays derived from public databases, respectively. A total of 20,283 human and 20,963 mouse genes are available to query in the database. Examples show the distinct advantages of the database. The database URL is http://immusort.bjmu.edu.cn/.

Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasites

Blood ◽  
2003 ◽  
Vol 102 (2) ◽  
pp. 672-681 ◽  
Author(s):  
Damien Chaussabel ◽  
Roshanak Tolouei Semnani ◽  
Mary Ann McDowell ◽  
David Sacks ◽  
Alan Sher ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Leishmania Donovani ◽  
Clustering Algorithm ◽  
Leishmania Major ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Infectious Agent ◽  
Experimental Conditions ◽  
Characteristic Functional

AbstractMonocyte-derived dendritic cells (DCs) and macrophages (Mϕs) generated in vitro from the same individual blood donors were exposed to 5 different pathogens, and gene expression profiles were assessed by microarray analysis. Responses to Mycobacterium tuberculosis and to phylogenetically distinct protozoan (Leishmania major, Leishmania donovani, Toxoplasma gondii) and helminth (Brugia malayi) parasites were examined, each of which produces chronic infections in humans yet vary considerably in the nature of the immune responses they trigger. In the absence of microbial stimulation, DCs and Mϕs constitutively expressed approximately 4000 genes, 96% of which were shared between the 2 cell types. In contrast, the genes altered transcriptionally in DCs and Mϕs following pathogen exposure were largely cell specific. Profiling of the gene expression data led to the identification of sets of tightly coregulated genes across all experimental conditions tested. A newly devised literature-based clustering algorithm enabled the identification of functionally and transcriptionally homogenous groups of genes. A comparison of the responses induced by the individual pathogens by means of this strategy revealed major differences in the functionally related gene profiles associated with each infectious agent. Although the intracellular pathogens induced responses clearly distinct from the extracellular B malayi, they each displayed a unique pattern of gene expression that would not necessarily be predicted on the basis of their phylogenetic relationship. The association of characteristic functional clusters with each infectious agent is consistent with the concept that antigen-presenting cells have prewired signaling patterns for use in the response to different pathogens.

scholarly journals Different statins produce highly divergent changes in gene expression profiles of human hepatoma cells: a pilot study.

2011 ◽  
Vol 58 (4) ◽  
Author(s):  
Agata Leszczynska ◽  
Monika Gora ◽  
Danuta Plochocka ◽  
Grazyna Hoser ◽  
Anna Szkopinska ◽  
...  
Keyword(s):  
Gene Expression ◽  
Statin Therapy ◽  
Hepg2 Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
High Dose ◽  
Human Hepatoma ◽  
Human Hepatoma Cells ◽  
Key Enzyme ◽  
Coa Reductase

Statins are inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), the key enzyme of the sterol biosynthesis pathway. Statin therapy is commonly regarded as well tolerated. However, serious adverse effects have also been reported, especially during high-dose statin therapy. The aim of our study was to investigate the effect of statins on gene expression profiles in human hepatoma HepG2 cells using Affymetrix Human Genome U133 Plus 2.0 arrays. Expression of 102, 857 and 1091 genes was changed substantially in HepG2 cells treated with simvastatin, fluvastatin and atorvastatin, respectively. Pathway and gene ontology analysis showed that many of the genes with changed expression levels were involved in a broad range of metabolic processes. The presented data clearly indicate substantial differences between the tested statins.

scholarly journals Reference Gene Selection for qPCR Normalization ofKosteletzkya virginicaunder Salt Stress

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoli Tang ◽  
Hongyan Wang ◽  
Chuyang Shao ◽  
Hongbo Shao
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Reference Genes ◽  
Gene Selection ◽  
Expression Profiles ◽  
Elongation Factor ◽  
Gene Expression Profiles ◽  
Stable Reference Gene ◽  
Experimental Conditions ◽  
Suitable Reference

Kosteletzkya virginica(L.) is a newly introduced perennial halophytic plant. Presently, reverse transcription quantitative real-time PCR (qPCR) is regarded as the best choice for analyzing gene expression and its accuracy mainly depends on the reference genes which are used for gene expression normalization. In this study, we employed qPCR to select the most stable reference gene inK. virginicawhich showed stable expression profiles under our experimental conditions. The candidate reference genes were 18S ribosomal RNA (18SrRNA),β-actin (ACT),α-tubulin (TUA), and elongation factor (EF). We tracked the gene expression profiles of the candidate genes and analyzed their stabilities through BestKeeper, geNorm, and NormFinder software programs. The results of the three programs were identical and18SrRNAwas assessed to be the most stable reference gene in this study. However,TUAwas identified to be the most unstable. Our study proved again that the traditional reference genes indeed displayed a certain degree of variations under given experimental conditions. Importantly, our research also provides guidance for selecting most suitable reference genes and lays the foundation for further studies inK. virginica.

454 pyrosequencing-based analysis of gene expression profiles in the amphipod Melita plumulosa: Transcriptome assembly and toxicant induced changes

2014 ◽  
Vol 153 ◽  
pp. 73-88 ◽  
Author(s):  
Sharon E. Hook ◽  
Natalie A. Twine ◽  
Stuart L. Simpson ◽  
David A. Spadaro ◽  
Philippe Moncuquet ◽  
...  
Keyword(s):  
Gene Expression ◽  
454 Pyrosequencing ◽  
Transcriptome Assembly ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes

scholarly journals Gene expression profiling of the effects of organic dust in lung epithelial and THP-1 cells reveals inductive effects on inflammatory and immune response genes

2016 ◽  
Vol 48 (4) ◽  
pp. 281-289 ◽  
Author(s):  
Vijay Boggaram ◽  
David S. Loose ◽  
Koteswara R. Gottipati ◽  
Kartiga Natarajan ◽  
Courtney T. Mitchell
Keyword(s):  
Gene Expression ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Organic Dust ◽  
Monocytic Cells ◽  
Lung Epithelial ◽  
Poultry Dust ◽  
Induced Changes ◽  
Dust Extract

The intensification and concentration of animal production operations expose workers to high levels of organic dusts in the work environment. Exposure to organic dusts is a risk factor for the development of acute and chronic respiratory symptoms and diseases. Lung epithelium plays important roles in the control of immune and inflammatory responses to environmental agents to maintain lung health. To better understand the effects of organic dust on lung inflammatory responses, we characterized the gene expression profiles of A549 alveolar and Beas2B bronchial epithelial and THP-1 monocytic cells influenced by exposure to poultry dust extract by DNA microarray analysis using Illumina Human HT-12 v4 Expression BeadChip. We found that A549 alveolar and Beas2B bronchial epithelial and THP-1 cells responded with unique changes in the gene expression profiles with regulation of genes encoding inflammatory cytokines, chemokines, and other inflammatory proteins being common to all the three cells. Significantly induced genes included IL-8, IL-6, IL-1β, ICAM-1, CCL2, CCL5, TLR4, and PTGS2. Validation by real-time qRT-PCR, ELISA, Western immunoblotting, and immunohistochemical staining of lung sections from mice exposed to dust extract validated DNA microarray results. Pathway analysis indicated that dust extract induced changes in gene expression influenced functions related to cellular growth and proliferation, cell death and survival, and cellular development. These data show that a broad range of inflammatory mediators produced in response to poultry dust exposure can modulate lung immune and inflammatory responses. This is the first report on organic dust induced changes in expression profiles in lung epithelial and THP-1 monocytic cells.

scholarly journals Variability in statin-induced changes in gene expression profiles of pancreatic cancer

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Helena Gbelcová ◽  
Silvie Rimpelová ◽  
Tomáš Ruml ◽  
Marie Fenclová ◽  
Vítek Kosek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes

scholarly journals Open TG-GATEs: a large-scale toxicogenomics database

2014 ◽  
Vol 43 (D1) ◽  
pp. D921-D927 ◽  
Author(s):  
Yoshinobu Igarashi ◽  
Noriyuki Nakatsu ◽  
Tomoya Yamashita ◽  
Atsushi Ono ◽  
Yasuo Ohno ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Safety ◽  
Evaluation System ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Overview ◽  
Experimental Conditions ◽  
The Public ◽  
Target Organs

Abstract Toxicogenomics focuses on assessing the safety of compounds using gene expression profiles. Gene expression signatures from large toxicogenomics databases are expected to perform better than small databases in identifying biomarkers for the prediction and evaluation of drug safety based on a compound's toxicological mechanisms in animal target organs. Over the past 10 years, the Japanese Toxicogenomics Project consortium (TGP) has been developing a large-scale toxicogenomics database consisting of data from 170 compounds (mostly drugs) with the aim of improving and enhancing drug safety assessment. Most of the data generated by the project (e.g. gene expression, pathology, lot number) are freely available to the public via Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System). Here, we provide a comprehensive overview of the database, including both gene expression data and metadata, with a description of experimental conditions and procedures used to generate the database. Open TG-GATEs is available from https://toxico.nibiohn.go.jp/english/index.html.

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