Author(s): Ajaz S, Czajka A, Malik A
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Abstract We describe a protocol to accurately measure the amount of human mitochondrial DNA (MtDNA) in peripheral blood samples which can be modified to quantify MtDNA from other body fluids, human cells, and tissues. This protocol is based on the use of real-time quantitative PCR (qPCR) to quantify the amount of MtDNA relative to nuclear DNA (designated the Mt/N ratio). In the last decade, there have been increasing numbers of studies describing altered MtDNA or Mt/N in circulation in common nongenetic diseases where mitochondrial dysfunction may play a role (for review see Malik and Czajka, Mitochondrion 13:481-492, 2013). These studies are distinct from those looking at genetic mitochondrial disease and are attempting to identify acquired changes in circulating MtDNA content as an indicator of mitochondrial function. However, the methodology being used is not always specific and reproducible. As more than 95 \% of the human mitochondrial genome is duplicated in the human nuclear genome, it is important to avoid co-amplification of nuclear pseudogenes. Furthermore, template preparation protocols can also affect the results because of the size and structural differences between the mitochondrial and nuclear genomes. Here we describe how to (1) prepare DNA from blood samples; (2) pretreat the DNA to prevent dilution bias; (3) prepare dilution standards for absolute quantification using the unique primers human mitochondrial genome forward primer (hMitoF3) and human mitochondrial genome reverse primer(hMitoR3) for the mitochondrial genome, and human nuclear genome forward primer (hB2MF1) and human nuclear genome reverse primer (hB2MR1) primers for the human nuclear genome; (4) carry out qPCR for either relative or absolute quantification from test samples; (5) analyze qPCR data; and (6) calculate the sample size to adequately power studies. The protocol presented here is suitable for high-throughput use.
This article was published in Methods Mol Biol
and referenced in Journal of Computer Science & Systems Biology