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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Assessing Teratogenic Changes in a Zebrafish Model of Fetal Alcohol Exposure

Assessing Teratogenic Changes in a Zebrafish Model of Fetal Alcohol Exposure

Published on: March 20, 2012

Microarray analysis of ethanol-induced changes in gene expression.

Robnet T Kerns¹, Michael F Miles

¹Department of Pharmacology/Toxicology, Virginia Commonwealth University, Richmond, VA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|March 29, 2008

Summary

This summary is machine-generated.

DNA microarray studies reveal gene networks affected by ethanol exposure. Gene network analysis aids understanding of ethanol

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Area of Science:

Genomics
Molecular Biology
Neuroscience

Background:

DNA microarray studies enable unbiased analysis of whole genome messenger ribonucleic acid (mRNA) expression patterns.
Numerous studies utilize this approach to investigate ethanol's effects in cell cultures and animal models.
Expression profiling has identified novel gene networks responsive to ethanol and variations across different animal strains.

Purpose of the Study:

To discuss the experience of performing and analyzing DNA microarray studies on ethanol.
To highlight the importance of high-quality standards in microarray data generation and analysis.
To emphasize the potential of gene network analysis for understanding ethanol's molecular mechanisms.

Main Methods:

Focus on short oligonucleotide microarrays (Affymetrix) for ethanol studies.
General principles applicable to other microarray platforms and experimental designs.
Discussion of gene network analysis for interpreting expression profiling data.

Main Results:

Identified novel gene networks responding to ethanol.
Revealed differences in gene expression across animal strains with varying ethanol responses.
Demonstrated the benefit of meta-analysis for combining microarray data from different laboratories.

Conclusions:

Gene network analysis provides unique insights into molecular mechanisms of ethanol response, toxicity, and addiction.
High-quality microarray studies are crucial for realizing the potential of meta-analysis.
This work may lead to novel therapeutic targets for pharmacotherapy.