High-Resolution Melting of 12S rRNA and Cytochrome <i>b</i> DNA Sequences for Discrimination of Species within Distinct European Animal Families

<div><p>The cheap and easy identification of species is necessary within multiple fields of molecular biology. The use of high-resolution melting (HRM) of DNA provides a fast closed-tube method for analysis of the sequence composition of the mitochondrial genes 12S rRNA and cytochrome <i>b</i>. We investigated the potential use of HRM for species identification within eleven different animal groups commonly found in Europe by animal-group-specific DNA amplification followed by DNA melting. Influence factors as DNA amount, additional single base alterations, and the existence of mixed samples were taken into consideration. Visual inspection combined with mathematical evaluation of the curve shapes did resolve nearly all species within an animal group. The assay can therefore not only be used for identification of animal groups and mixture analysis but also for species identification within the respective groups. The use of a universal 12S rRNA system additionally revealed a possible approach for species discrimination, mostly by exclusion. The use of the HRM assay showed to be a reliable, fast, and cheap method for species discrimination within a broad range of different animal species and can be used in a flexible “modular” manner depending on the question to be solved.</p></div

Normalized melt curves using the universal 12S rRNA primers.

<p>The curves of two runs (necessary due to sample number) were merged. A distribution of the T_ms over 3°C can be observed as well as special melt curve features resulting in direct discrimination, e.g. sheep. Compare to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115575#pone.0115575.s002" target="_blank">S2 Fig.</a></p

Reproducibility of HRM curves within and between three runs.

<p>The RMSE values between all measured samples were calculated and the mean values of all triplicate means (grand mean RMSE), as well as the maximal observed RMSE are presented.</p><p>Reproducibility of HRM curves within and between three runs.</p

Melt curves and RMSE values for animal-family specific PCR and HRM of the Leporidae.

<p>A) Normalized HRM results for 12S rRNA. The SNP represents an A>G transition. B) RMSE value range for the comparison of all curves presented in A. C) Normalized HRM results for cytb. D) RMSE value range for the comparison of all curves presented in C. Lower values were calculated for samples of the same species and species differentiation is possible. The SNP does not fit into the range obtained for the 12S rRNA rabbit fragments, but does not interfere with the hare curves.</p

Summary of possible visual discriminations within the animal groups analyzed.

<p>Except for the pairs roe deer/red deer and beech marten/otter, discrimination was always possible by the analysis of at least one gene. Within the Mustelidae, the discrimination relies mostly on 12S rRNA, as no successful amplification for cytb was possible for all species.</p

Mixture analysis within and between animal groups.

<p>A) Mixtures of pig and human DNA representing animals of two different groups can be successfully resolved. B–D) Mixtures between samples of the same animal group i.e. family. The success of mixture resolution varied between the animal groups.</p

Melt curves and RMSE values for animal-family specific PCR and HRM of the Phasianidae.

<p>A) Normalized HRM results for 12S rRNA. B) RMSE value range for the comparison of all curves presented in A. C) Normalized HRM results for cytb. D) RMSE value range for the comparison of all curves presented in C. Lower values were calculated for samples of the same species within 12S rRNA and species differentiation is possible. Analyzing cytb only would lead to more inconclusive results, although a slightly different melting behavior is also visible.</p

Demographic and clinical characteristics.

<p>Demographic and clinical characteristics.</p

Flow of participants through study.

<p>Flow of participants through study.</p

Regression models of behavioural predictor variables on self report outcome variables.

<p>Regression models of behavioural predictor variables on self report outcome variables.</p