Dataset Information

Quantitative sense-specific determination of murine coronavirus RNA by reverse transcription polymerase chain reaction.

ABSTRACT: In many applications, it is useful to know the sense and amount of viral RNAs present in a sample. In theory, sense-specific measurement of viral RNAs may be achieved by reverse transcription polymerase chain reaction (RT-PCR) assays which utilize primers of defined polarity during the RT step. However, in practice, it has been shown that such assays are prone to artifacts, such as non-specific priming, which drastically diminish their reliability. Using murine coronavirus MHV-4 as a model, we describe and validate several modifications of the RT-PCR procedure which eliminate these artifacts. Key RT-PCR parameters which were optimized include the design of tagged primers, DNase treatment of in vitro transcribed RNA standards, specification of temperature differences between RT and PCR annealing steps, and use of competitive RNA templates for quantitative assays. The assays described may be used to determine the sense and abundance of any viral or host RNA of interest in complex biological specimens.

SUBMITTER: Schoenike B

PROVIDER: S-EPMC7119510 | biostudies-literature | 1999 Mar

REPOSITORIES: biostudies-literature

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Quantitative sense-specific determination of murine coronavirus RNA by reverse transcription polymerase chain reaction.

Schoenike B B Franta A K AK Fleming J O JO

Journal of virological methods 19990301 1-2

In many applications, it is useful to know the sense and amount of viral RNAs present in a sample. In theory, sense-specific measurement of viral RNAs may be achieved by reverse transcription polymerase chain reaction (RT-PCR) assays which utilize primers of defined polarity during the RT step. However, in practice, it has been shown that such assays are prone to artifacts, such as non-specific priming, which drastically diminish their reliability. Using murine coronavirus MHV-4 as a model, we d ...[more]

PMID: 10204695

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Project description:BACKGROUND: Wild peanut species (Arachis spp.) are a rich source of new alleles for peanut improvement. Plant transcriptome analysis under specific experimental conditions helps the understanding of cellular processes related, for instance, to development, stress response, and crop yield. The validation of these studies has been generally accomplished by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) which requires normalization of mRNA levels among samples. This can be achieved by comparing the expression ratio between a gene of interest and a reference gene which is constitutively expressed. Nowadays there is a lack of appropriate reference genes for both wild and cultivated Arachis. The identification of such genes would allow a consistent analysis of qRT-PCR data and speed up candidate gene validation in peanut. RESULTS: A set of ten reference genes were analyzed in four Arachis species (A. magna; A. duranensis; A. stenosperma and A. hypogaea) subjected to biotic (root-knot nematode and leaf spot fungus) and abiotic (drought) stresses, in two distinct plant organs (roots and leaves). By the use of three programs (GeNorm, NormFinder and BestKeeper) and taking into account the entire dataset, five of these ten genes, ACT1 (actin depolymerizing factor-like protein), UBI1 (polyubiquitin), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), 60S (60S ribosomal protein L10) and UBI2 (ubiquitin/ribosomal protein S27a) emerged as top reference genes, with their stability varying in eight subsets. The former three genes were the most stable across all species, organs and treatments studied. CONCLUSIONS: This first in-depth study of reference genes validation in wild Arachis species will allow the use of specific combinations of secure and stable reference genes in qRT-PCR assays. The use of these appropriate references characterized here should improve the accuracy and reliability of gene expression analysis in both wild and cultivated Arachis and contribute for the better understanding of gene expression in, for instance, stress tolerance/resistance mechanisms in plants.

Dataset Information

Quantitative sense-specific determination of murine coronavirus RNA by reverse transcription polymerase chain reaction.

Publications

Quantitative sense-specific determination of murine coronavirus RNA by reverse transcription polymerase chain reaction.

Similar Datasets

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