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. 2016 Mar 18;44(5):2020-7.
doi: 10.1093/nar/gkw046. Epub 2016 Feb 3.

PON-mt-tRNA: a multifactorial probability-based method for classification of mitochondrial tRNA variations

Abhishek Niroula  1 , Mauno Vihinen  2
Affiliations

PON-mt-tRNA: a multifactorial probability-based method for classification of mitochondrial tRNA variations

Abhishek Niroula et al. Nucleic Acids Res. .

Abstract

Transfer RNAs (tRNAs) are essential for encoding the transcribed genetic information from DNA into proteins. Variations in the human tRNAs are involved in diverse clinical phenotypes. Interestingly, all pathogenic variations in tRNAs are located in mitochondrial tRNAs (mt-tRNAs). Therefore, it is crucial to identify pathogenic variations in mt-tRNAs for disease diagnosis and proper treatment. We collected mt-tRNA variations using a classification based on evidence from several sources and used the data to develop a multifactorial probability-based prediction method, PON-mt-tRNA, for classification of mt-tRNA single nucleotide substitutions. We integrated a machine learning-based predictor and an evidence-based likelihood ratio for pathogenicity using evidence of segregation, biochemistry and histochemistry to predict the posterior probability of pathogenicity of variants. The accuracy and Matthews correlation coefficient (MCC) of PON-mt-tRNA are 1.00 and 0.99, respectively. In the absence of evidence from segregation, biochemistry and histochemistry, PON-mt-tRNA classifies variations based on the machine learning method with an accuracy and MCC of 0.69 and 0.39, respectively. We classified all possible single nucleotide substitutions in all human mt-tRNAs using PON-mt-tRNA. The variations in the loops are more often tolerated compared to the variations in stems. The anticodon loop contains comparatively more predicted pathogenic variations than the other loops. PON-mt-tRNA is available at http://structure.bmc.lu.se/PON-mt-tRNA/.

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Figures

Figure 1.
Figure 1.
Schematic outline of PON-mt-tRNA. The method predicts the probability of pathogenicity using 2000 ML predictors and integrates with evidence of segregation, biochemistry and histochemistry. If the evidence is not known, PON-mt-tRNA predicts the pathogenicity based on the ML predictors only.
Figure 2.
Figure 2.
Distribution of predicted pathogenic variations in mt-tRNA structure. All possible single nucleotide substitutions at each position of the 22 mt-tRNAs are mapped to the three-dimensional structure of the yeast phenylalanine tRNA (pdb id: 1EHZ). (A) The numbers of predicted pathogenic mt-tRNA variations at each position. There are 66 possible variations per site except for sites which are missing from some mt-tRNAs. (B) The numbers of mt-tRNAs containing at least one predicted pathogenic variation at each position. Ac-stem, Anticodon stem; Ac-loop, Anticodon loop.
Figure 3.
Figure 3.
The distribution of predicted pathogenic variations in mt-tRNAs. All possible single nucleotide substitutions at each position of the 22 mt-tRNAs are classified using PON-mt-tRNA. There are three possible substitutions at each position. The secondary structures were obtained from mito-tRNAdb. Shading indicates the numbers of predicted pathogenic variants per site. Acc-stem, Acceptor stem; Ac-stem, Anticodon stem; Ac-loop, Anticodon loop; V-region, Variable region.
See this image and copyright information in PMC

References

    1. Kutter C., Brown G.D., Goncalves A., Wilson M.D., Watt S., Brazma A., White R.J., Odom D.T. Pol III binding in six mammals shows conservation among amino acid isotypes despite divergence among tRNA genes. Nat. Genet. 2011;43:948–955. - PMC - PubMed
    1. Kirchner S., Ignatova Z. Emerging roles of tRNA in adaptive translation, signalling dynamics and disease. Nat. Rev. Genet. 2015;16:98–112. - PubMed
    1. Anderson S., Bankier A.T., Barrell B.G., de Bruijn M.H.L., Coulson A.R., Drouin J., Eperon I.C., Nierlich D.P., Roe B.A., Sanger F., et al. Sequence and organization of the human mitochondrial genome. Nature. 1981;290:457–465. - PubMed
    1. Taylor R.W., Turnbull D.M. Mitochondrial DNA mutations in human disease. Nat. Rev. Genet. 2005;6:389–402. - PMC - PubMed
    1. Khrapko K., Coller H.A., André P.C., Li X.C., Hanekamp J.S., Thilly W.G. Mitochondrial mutational spectra in human cells and tissues. Proc. Natl. Acad. Sci. U.S.A. 1997;94:13798–13803. - PMC - PubMed

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