These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Reverse transcription-PCR methods significantly impact richness and composition measures of expressed fungal cellobiohydrolase I genes in soil and litter.
    Author: Weber CF, Kuske CR.
    Journal: J Microbiol Methods; 2011 Sep; 86(3):344-50. PubMed ID: 21704085.
    Abstract:
    The importance of soil fungi in complex carbon degradation and the recent identification of genes involved in this process have sparked considerable interest in examining fungal gene expression in situ. Expression of target eukaryotic genes is commonly examined using reverse transcription (RT)-PCR, during which single-stranded (ss) complementary DNA (cDNA) is synthesized from an oligo (dT) primer and the gene of interest is subsequently amplified by PCR using gene specific primers. Another method that is being increasingly employed in environmental gene expression studies is SMART PCR, which generates and amplifies double-stranded (ds) complementary DNA (cDNA) from sscDNA using PCR, prior to gene-specific PCR. We performed a replicated comparison of these two methods using RNA extracted from forest soil and litter to determine if the two approaches yielded comparable results. Richness, composition and reproducibility of gene expression profiles of the fungal glycosyl hydrolase family 7 (GH7) cellobiohydrolase I gene (cbhI) were examined when amplified from sscDNA or dscDNA synthesized using SMART PCR. In the dscDNA libraries from soil or litter samples, richness was significantly reduced and the composition was altered relative to sscDNA libraries. Library composition was significantly more reproducible among replicate sscDNA libraries than among parallel dscDNA libraries from litter. In sum, the reduced richness and altered composition produced in the dscDNA libraries could substantially influence ecological interpretations of the data. Defining the factors underpinning the methodological biases will potentially aid in optimizing the design of gene expression studies in soils and other complex environmental samples.
    [Abstract] [Full Text] [Related] [New Search]