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Multi-template polymerase chain reaction

View Article: PubMed Central - PubMed

ABSTRACT

PCR is a formidable and potent technology that serves as an indispensable tool in a wide range of biological disciplines. However, due to the ease of use and often lack of rigorous standards many PCR applications can lead to highly variable, inaccurate, and ultimately meaningless results. Thus, rigorous method validation must precede its broad adoption to any new application. Multi-template samples possess particular features, which make their PCR analysis prone to artifacts and biases: multiple homologous templates present in copy numbers that vary within several orders of magnitude. Such conditions are a breeding ground for chimeras and heteroduplexes. Differences in template amplification efficiencies and template competition for reaction compounds undermine correct preservation of the original template ratio. In addition, the presence of inhibitors aggravates all of the above-mentioned problems. Inhibitors might also have ambivalent effects on the different templates within the same sample. Yet, no standard approaches exist for monitoring inhibitory effects in multitemplate PCR, which is crucial for establishing compatibility between samples.

No MeSH data available.


Related in: MedlinePlus

A simulation of the fluctuations in the microbial community structure induced by the treatment in the course of an experiment and the effect of such change on the quantification of microbial load. Apart from the multi-template PCR burden of artifacts and biases this assay often uses untrustworthy target sequences, e.g. 16S or 18S rDNA. The numbers of these sequences vary among microbial species. Imagine a situation when single specie with 10 copies of 16S rDNA grew above the LOD at the beginning of the experiment. A treatment stimulates a shift within microbial community and four other bacterial species grew above the LOD, while the former one was suppressed under the new conditions.Load before treatment: 1 microbial strain grew above LOD × 105 CFU × 10 copies of 16SrDNA/cell = 106 copies of 16S rDNA ought to be detected (although really there are only).Load after treatment: 4 microbial strains grew above LOD × 105 CFU × 1 copy of 16SrDNA/cell = 4 × 105 copies of 16S rDNA ought to be detected under new conditions.So, the analysis based on the 16S rDNA would show one order of magnitude decrease in bacterial load after treatment. While in fact, the load of microbes before treatment was 105 and after treatment – 4 × 105. The rate of the possible error is much higher than the twofold difference in number of DNA molecules that real-time PCR usually aims to detect.
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fig0020: A simulation of the fluctuations in the microbial community structure induced by the treatment in the course of an experiment and the effect of such change on the quantification of microbial load. Apart from the multi-template PCR burden of artifacts and biases this assay often uses untrustworthy target sequences, e.g. 16S or 18S rDNA. The numbers of these sequences vary among microbial species. Imagine a situation when single specie with 10 copies of 16S rDNA grew above the LOD at the beginning of the experiment. A treatment stimulates a shift within microbial community and four other bacterial species grew above the LOD, while the former one was suppressed under the new conditions.Load before treatment: 1 microbial strain grew above LOD × 105 CFU × 10 copies of 16SrDNA/cell = 106 copies of 16S rDNA ought to be detected (although really there are only).Load after treatment: 4 microbial strains grew above LOD × 105 CFU × 1 copy of 16SrDNA/cell = 4 × 105 copies of 16S rDNA ought to be detected under new conditions.So, the analysis based on the 16S rDNA would show one order of magnitude decrease in bacterial load after treatment. While in fact, the load of microbes before treatment was 105 and after treatment – 4 × 105. The rate of the possible error is much higher than the twofold difference in number of DNA molecules that real-time PCR usually aims to detect.

Mentions: Apart from the problem surrounding sample dilution, another technical difficulty might arise from the complex nature of the samples used in multi-template assays. Treatment might not only change the concentration of particular microorganisms but also the community structure as a whole. As a result, not only the quantities but also the qualities of the original templates might vary between treatments. The prevailing groups of microorganisms after different treatments might differ in the number of target sequences on their genomes. There is evidence that the number of 16S rDNA loci correlates with the rate with which bacteria respond to the availability of resources and that the species responding to stimuli faster will have a higher copy number [153]. Thus, the measurement of 16S rDNA quantities might lead to wrong conclusions about the total bacterial number if species with low copy number dominate in one treatment while those with higher copy number in another (Fig. 4). A warning was given that the failure to compare DNA from similar groups of bacteria and possessing similar growth rates, readily leads to an under- or over-estimation of the amount of DNA by one order of magnitude [149]. The rate of the possible error is much higher than the twofold difference in number of DNA molecules that real-time PCR usually aims to detect [97]. Nadkarni and co-workers [149] recommended that a DNA standard representing those bacteria most likely to predominate in a given habitat should be used for a more accurate determination of total bacterial load. However, this sensible recommendation is difficult to follow in most of the environmental studies. On the other hand, ignoring it often makes the results questionable.


Multi-template polymerase chain reaction
A simulation of the fluctuations in the microbial community structure induced by the treatment in the course of an experiment and the effect of such change on the quantification of microbial load. Apart from the multi-template PCR burden of artifacts and biases this assay often uses untrustworthy target sequences, e.g. 16S or 18S rDNA. The numbers of these sequences vary among microbial species. Imagine a situation when single specie with 10 copies of 16S rDNA grew above the LOD at the beginning of the experiment. A treatment stimulates a shift within microbial community and four other bacterial species grew above the LOD, while the former one was suppressed under the new conditions.Load before treatment: 1 microbial strain grew above LOD × 105 CFU × 10 copies of 16SrDNA/cell = 106 copies of 16S rDNA ought to be detected (although really there are only).Load after treatment: 4 microbial strains grew above LOD × 105 CFU × 1 copy of 16SrDNA/cell = 4 × 105 copies of 16S rDNA ought to be detected under new conditions.So, the analysis based on the 16S rDNA would show one order of magnitude decrease in bacterial load after treatment. While in fact, the load of microbes before treatment was 105 and after treatment – 4 × 105. The rate of the possible error is much higher than the twofold difference in number of DNA molecules that real-time PCR usually aims to detect.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5121205&req=5

fig0020: A simulation of the fluctuations in the microbial community structure induced by the treatment in the course of an experiment and the effect of such change on the quantification of microbial load. Apart from the multi-template PCR burden of artifacts and biases this assay often uses untrustworthy target sequences, e.g. 16S or 18S rDNA. The numbers of these sequences vary among microbial species. Imagine a situation when single specie with 10 copies of 16S rDNA grew above the LOD at the beginning of the experiment. A treatment stimulates a shift within microbial community and four other bacterial species grew above the LOD, while the former one was suppressed under the new conditions.Load before treatment: 1 microbial strain grew above LOD × 105 CFU × 10 copies of 16SrDNA/cell = 106 copies of 16S rDNA ought to be detected (although really there are only).Load after treatment: 4 microbial strains grew above LOD × 105 CFU × 1 copy of 16SrDNA/cell = 4 × 105 copies of 16S rDNA ought to be detected under new conditions.So, the analysis based on the 16S rDNA would show one order of magnitude decrease in bacterial load after treatment. While in fact, the load of microbes before treatment was 105 and after treatment – 4 × 105. The rate of the possible error is much higher than the twofold difference in number of DNA molecules that real-time PCR usually aims to detect.
Mentions: Apart from the problem surrounding sample dilution, another technical difficulty might arise from the complex nature of the samples used in multi-template assays. Treatment might not only change the concentration of particular microorganisms but also the community structure as a whole. As a result, not only the quantities but also the qualities of the original templates might vary between treatments. The prevailing groups of microorganisms after different treatments might differ in the number of target sequences on their genomes. There is evidence that the number of 16S rDNA loci correlates with the rate with which bacteria respond to the availability of resources and that the species responding to stimuli faster will have a higher copy number [153]. Thus, the measurement of 16S rDNA quantities might lead to wrong conclusions about the total bacterial number if species with low copy number dominate in one treatment while those with higher copy number in another (Fig. 4). A warning was given that the failure to compare DNA from similar groups of bacteria and possessing similar growth rates, readily leads to an under- or over-estimation of the amount of DNA by one order of magnitude [149]. The rate of the possible error is much higher than the twofold difference in number of DNA molecules that real-time PCR usually aims to detect [97]. Nadkarni and co-workers [149] recommended that a DNA standard representing those bacteria most likely to predominate in a given habitat should be used for a more accurate determination of total bacterial load. However, this sensible recommendation is difficult to follow in most of the environmental studies. On the other hand, ignoring it often makes the results questionable.

View Article: PubMed Central - PubMed

ABSTRACT

PCR is a formidable and potent technology that serves as an indispensable tool in a wide range of biological disciplines. However, due to the ease of use and often lack of rigorous standards many PCR applications can lead to highly variable, inaccurate, and ultimately meaningless results. Thus, rigorous method validation must precede its broad adoption to any new application. Multi-template samples possess particular features, which make their PCR analysis prone to artifacts and biases: multiple homologous templates present in copy numbers that vary within several orders of magnitude. Such conditions are a breeding ground for chimeras and heteroduplexes. Differences in template amplification efficiencies and template competition for reaction compounds undermine correct preservation of the original template ratio. In addition, the presence of inhibitors aggravates all of the above-mentioned problems. Inhibitors might also have ambivalent effects on the different templates within the same sample. Yet, no standard approaches exist for monitoring inhibitory effects in multitemplate PCR, which is crucial for establishing compatibility between samples.

No MeSH data available.


Related in: MedlinePlus