Novel subtractive transcription-based amplification of mRNA (STAR) method and its application in search of rare and differentially expressed genes in AD brains

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DOIResolve DOI: http://doi.org/10.1186/1471-2164-7-286
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TypeArticle
Journal titleBMC Genomics
Volume7
Pages286
AbstractBackground: Alzheimer's disease (AD) is a complex disorder that involves multiple biological processes. Many genes implicated in these processes may be present in low abundance in the human brain. DNA microarray analysis identifies changed genes that are expressed at high or moderate levels. Complementary to this approach, we described here a novel technology designed specifically to isolate rare and novel genes previously undetectable by other methods. We have used this method to identify differentially expressed genes in brains affected by AD. Our method, termed Subtractive Transcription-based Amplification of mRNA (STAR), is a combination of subtractive RNA/DNA hybridization and RNA amplification, which allows the removal of non-differentially expressed transcripts and the linear amplification of the differentially expressed genes. Results: Using the STAR technology we have identified over 800 differentially expressed sequences in AD brains, both up- and down- regulated, compared to age-matched controls. Over 55% of the sequences represent genes of unknown function and roughly half of them were novel and rare discoveries in the human brain. The expression changes of nearly 80 unique genes were further confirmed by qRT-PCR and the association of additional genes with AD and/or neurodegeneration was established using an in-house literature mining tool (LitMiner). Conclusion: The STAR process significantly amplifies unique and rare sequences relative to abundant housekeeping genes and, as a consequence, identifies genes not previously linked to AD. This method also offers new opportunities to study the subtle changes in gene expression that potentially contribute to the development and/or progression of AD.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada; NRC Institute for Biological Sciences; NRC Institute for Information Technology
Peer reviewedNo
NRC numberLIU2006A
NPARC number9381684
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Record identifierc314a6f1-1a02-4ef2-ade0-c136c9a797df
Record created2009-07-10
Record modified2016-05-09
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