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Exploration of the R code-based mathematical model for PMI estimation using profiling of RNA degradation in rat brain tissue at different temperatures

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Abstract

Precise estimation of postmortem interval (PMI) is crucial in some criminal cases. This study aims to find some optimal markers for PMI estimation and build a mathematical model that could be used in various temperature conditions. Different mRNA and microRNA markers in rat brain samples were detected using real-time fluorescent quantitative PCR at 12 time points within 144 h postmortem and at temperatures of 4, 15, 25, and 35 °C. Samples from 36 other rats were used to verify the animal mathematical model. Brain-specific mir-9 and mir-125b are effective endogenous control markers that are not affected by PMI up to 144 h postmortem under these temperatures, whereas the commonly used U6 is not a suitable endogenous control in this study. Among all the candidate markers, ΔCt (β-actin) has the best correlation coefficient with PMI and was used to build a new model using R software which can simultaneously manage both PMI and temperature parameters. This animal mathematical model is verified using samples from 36 other rats and shows increased accuracy for higher temperatures and longer PMI. In this study, β-actin was found to be an optimal marker to estimate PMI and some other markers were found to be suitable to act as endogenous controls. Additionally, we have used R code software to build a model of PMI estimation that could be used in various temperature conditions.

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References

  1. Henssge C, Madea B. Estimation of the time since death in the early post-mortem period. Forensic Sci Int. 2004;144:167–75.

    Article  CAS  PubMed  Google Scholar 

  2. Thyssen PJ, de Souza CM, Shimamoto PM, Salewski TDB, Moretti TC. Rates of development of immatures of three species of Chrysomya (Diptera: Calliphoridae) reared in different types of animal tissues: implications for estimating the postmortem interval. Parasitol Res. 2014;113:3373–80.

    Article  PubMed  Google Scholar 

  3. Kikuchi K, Kawahara KI, Biswas KK, Ito T, Tancharoen S, Shiomi N, et al. HMGB1: a new marker for estimation of the postmortem interval. Exp Ther Med. 2010;1:109–11.

    PubMed Central  CAS  PubMed  Google Scholar 

  4. Dorandeu A, Lorin DLGG. Contribution of the TUNEL method for post-mortem interval estimation: an experimental study. Ann Pathol. 2013;33:80–3.

    Article  PubMed  Google Scholar 

  5. Sampaio-Silva F, Magalhaes T, Carvalho F, Dinis-Oliveira RJ, Silvestre R. Profiling of RNA degradation for estimation of post morterm interval. PLoS One. 2013;8:e56507.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Li WC, Ma KJ, Lv YH, Zhang P, Pan H, Zhang H, et al. Postmortem interval determination using 18S-rRNA and microRNA. Sci Justice. 2014;54:307–10.

    Article  PubMed  Google Scholar 

  7. Lv YH, Ma KJ, Zhang H, He M, Zhang P, Shen YW, et al. A time course study demonstrating mRNA, microRNA, 18S rRNA, and U6 snRNA changes to estimate PMI in deceased rat’s spleen. J Forensic Sci. 2014;59:1286–94.

    Article  CAS  PubMed  Google Scholar 

  8. Chen PS, Su JL, Cha ST, Tarn WY, Wang MY, Hsu HC, et al. miR-107 promotes tumor progression by targeting the let-7 microRNA in mice and humans. J Clin Invest. 2011;121:3442–55.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Lardizabal MN, Nocito AL, Daniele SM, Ornella LA, Palatnik JF, Veggi LM. Reference genes for real-time PCR quantification of microRNAs and messenger RNAs in rat models of hepatotoxicity. PLoS One. 2012;7:e36323.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Tea M, Michael MZ, Brereton HM, Williams KA. Stability of small non-coding RNA reference gene expression in the rat retina during exposure to cyclic hyperoxia. Mol Vis. 2013;19:501–8.

    PubMed Central  CAS  PubMed  Google Scholar 

  11. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. miRBase: microRNA sequences, targets and gene nomenclature. Nucl Acids Res. 2006;34:D140–4.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Ecsedi M, Rausch M, Grosshans H. The let-7 microRNA directs vulval development through a single target. Dev Cell. 2015;32:335–44.

    Article  CAS  PubMed  Google Scholar 

  13. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55:611–22.

    Article  CAS  PubMed  Google Scholar 

  14. Birdsill AC, Walker DG, Lue L, Sue LI, Beach TG. Postmortem interval effect on RNA and gene expression in human brain tissue. Cell Tissue Bank. 2011;12:311–8.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Zhang H, Zhang P, Ma KJ, Lv YH, Li WC, Luo CL, et al. The selection of endogenous genes in human postmortem tissues. Sci Justice. 2013;53:115–20.

    Article  CAS  PubMed  Google Scholar 

  16. Smart JL, Kaliszan M. Use of a finite element model of heat transport in the human eye to predict time of death. J Forensic Sci. 2013;58(Suppl 1):S69–77.

    Article  PubMed  Google Scholar 

  17. Muggenthaler H, Sinicina I, Hubig M, Mall G. Database of post-mortem rectal cooling cases under strictly controlled conditions: a useful tool in death time estimation. Int J Legal Med. 2012;126:79–87.

    Article  PubMed  Google Scholar 

  18. Vennemann M, Koppelkamm A. mRNA profiling in forensic genetics I: possibilities and limitations. Forensic Sci Int. 2010;203:71–5.

    Article  CAS  PubMed  Google Scholar 

  19. Odriozola A, Riancho JA, de la Vega R, Agudo G, Garcia-Blanco A, de Cos E, et al. miRNA analysis in vitreous humor to determine the time of death: a proof-of-concept pilot study. Int J Legal Med. 2013;127:573–8.

    Article  PubMed  Google Scholar 

  20. Zapico CS, Menendez ST, Nunez P. Cell death proteins as markers of early postmortem interval. Cell Mol Life Sci. 2014;71:2957–62.

    Article  CAS  Google Scholar 

  21. Inoue H, Kimura A, Tuji T. Degradation profile of mRNA in a dead rat body: basic semi-quantification study. Forensic Sci Int. 2002;130:127–32.

    Article  CAS  PubMed  Google Scholar 

  22. Gonzalez-Herrera L, Valenzuela A, Marchal JA, Lorente JA, Villanueva E. Studies on RNA integrity and gene expression in human myocardial tissue, pericardial fluid and blood, and its postmortem stability. Forensic Sci Int. 2013;232:218–28.

    Article  CAS  PubMed  Google Scholar 

  23. Young ST, Wells JD, Hobbs GR, Bishop CP. Estimating postmortem interval using RNA degradation and morphological changes in tooth pulp. Forensic Sci Int. 2013;229:161–3.

    Article  Google Scholar 

  24. Vennemann M, Koppelkamm A. Postmortem mRNA profiling II: practical considerations. Forensic Sci Int. 2010;203:76–82.

    Article  CAS  PubMed  Google Scholar 

  25. Mocellin S, Rossi CR, Pilati P, Nitti D, Marincola FM. Quantitative real-time PCR: a powerful ally in cancer research. Trends Mol Med. 2003;9:189–95.

    Article  CAS  PubMed  Google Scholar 

  26. Neville MJ, Collins JM, Gloyn AL, McCarthy MI, Karpe F. Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Obesity (Silver Spring). 2011;19:888–92.

    Article  CAS  Google Scholar 

  27. Burke JE, Sashital DG, Zuo X, Wang YX, Butcher SE. Structure of the yeast U2/U6 snRNA complex. RNA. 2012;18:673–83.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Benz F, Roderburg C, Vargas CD, Vucur M, Gautheron J, Koch A, et al. U6 is unsuitable for normalization of serum miRNA levels in patients with sepsis or liver fibrosis. Exp Mol Med. 2013;45:e42.

    Article  PubMed Central  PubMed  Google Scholar 

  29. Xiang M, Zeng Y, Yang R, Xu H, Chen Z, Zhong J, et al. U6 is not a suitable endogenous control for the quantification of circulating microRNAs. Biochem Biophys Res Commun. 2014;454:210–4.

    Article  CAS  PubMed  Google Scholar 

  30. Munoz-Barus JI, Rodriguez-Calvo MS, Suarez-Penaranda JM, Vieira DN, Cadarso-Suarez C, Febrero-Bande M. PMICALC: an R code-based software for estimating post-mortem interval (PMI) compatible with Windows, Mac and Linux operating systems. Forensic Sci Int. 2010;194:49–52.

    Article  PubMed  Google Scholar 

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Correspondence to Kaijun Ma or Long Chen.

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Ma, J., Pan, H., Zeng, Y. et al. Exploration of the R code-based mathematical model for PMI estimation using profiling of RNA degradation in rat brain tissue at different temperatures. Forensic Sci Med Pathol 11, 530–537 (2015). https://doi.org/10.1007/s12024-015-9703-7

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  • DOI: https://doi.org/10.1007/s12024-015-9703-7

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