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Biomarkers for “Cause of Death”

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Forensic Medicine and Human Cell Research

Part of the book series: Current Human Cell Research and Applications ((CHCRA))

Abstract

For forensic pathologists, the most important practice is clarifying the “cause of death” of victims at autopsy. But it is sometimes difficult to determine the cause of death or forensic important matters (e.g., postmortem interval (PMI)) by only gross dissection and microscopic examinations.

Biomarkers are considered to be useful to achieve the purpose.

In this chapter, we introduce our part of works about trying to discover biomarkers for some causes of death (sudden cardiac death, heatstroke (HS), and polypharmacy) and PMI by using DNA analysis method or metabolomics analysis method using gas chromatography-tandem mass spectrometry (GC-MS/MS). In addition, we introduce hair imaging analysis for substance abuse by matrix-assisted laser desorption/ionization imaging MS.

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References

  1. Kalia M. Biomarkers for personalized oncology: recent advances and future challenges. Metabolism. 2015;64(3):S16–21.

    Article  CAS  Google Scholar 

  2. Zhao X, Modur V, Carayannopoulos LN, Laterza OF. Biomarkers in pharmaceutical research. Clin Chem. 2015;61(11):1343–53.

    Article  CAS  Google Scholar 

  3. Kuriachan VP, Sumner GL, Mitchell LB. Sudden cardiac death. Curr Probl Cardiol. 2015;40(4):133–200.

    Article  Google Scholar 

  4. Brion M, Sobrino B, Martinez M, Blanco-verea A, Carracedo A. Genetics massive parallel sequencing applied to the molecular autopsy in sudden cardiac death in the young. Forensic Sci Int Genet. 2015;18:160–70.

    Article  CAS  Google Scholar 

  5. Hertz CL, Ferrero-miliani L, Frank-hansen R, Morling N, Bundgaard H. A comparison of genetic findings in sudden cardiac death victims and cardiac patients: the importance of phenotypic classification. Europace. 2015;17:350–7.

    Article  Google Scholar 

  6. Methner DNR, Scherer SE, Welch K, Walkiewicz M, Eng CM, Belmont JW, et al. Postmortem genetic screening for the identification, verification, and reporting of genetic variants contributing to the sudden death of the young. Genome Res. 2016;26:1170–7.

    Article  Google Scholar 

  7. Neubauer J, Lecca MR, Russo G, Bartsch C, Medeiros-domingo A, Berger W, et al. Exome analysis in 34 sudden unexplained death (SUD) victims mainly identified variants in channelopathy-associated genes. Int J Legal Med. 2018;132(4):1057–65. https://doi.org/10.1007/s00414-018-1775-y.

    Article  PubMed  Google Scholar 

  8. Napolitano C, Bloise R, Monteforte N, Priori SG. Sudden cardiac death and genetic ion channelopathies: long QT, Brugada, short QT, catecholaminergic polymorphic ventricular tachycardia, and idiopathic ventricular fibrillation. Circulation. 2012;125(16):2027–34.

    Article  Google Scholar 

  9. Fernandez-Falgueras A, Sarquella-Brugada G, Brugada J, Brugada R, Campuzano O. Cardiac Channelopathies and sudden death: recent clinical and genetic advances. Biology. 2017;6(1):1–21.

    Google Scholar 

  10. Skinner JR, Crawford J, Smith W, Aitken A, Heaven D, Evans C, et al. Prospective, population-based long QT molecular autopsy study of postmortem negative sudden death in 1 to 40 year olds. Heart Rhythm. 2011;8(3):412–9.

    Article  Google Scholar 

  11. Bastiaenen R, Behr ER. Sudden death and ion channel disease: pathophysiology and implications for management. Heart. 2011;97(17):1365–72.

    Article  CAS  Google Scholar 

  12. Mu J, Zhang G, Xue D, Xi M, Qi J, Dong H. Sudden cardiac death owing to arrhythmogenic right ventricular cardiomyopathy: two case reports and systematic literature review. Medicine (Baltimore). 2017;96(47):e8808.

    Article  Google Scholar 

  13. Bauce B, Nava A, Beffagna G, Basso C, Lorenzon A, Smaniotto G, et al. Multiple mutations in desmosomal proteins encoding genes in arrhythmogenic right ventricular cardiomyopathy/dysplasia. Heart Rhythm. 2010;7(1):22–9.

    Article  Google Scholar 

  14. Zhang M, Xue A, Shen Y, Bosco J, Li L, Zhao Z, et al. Mutations of desmoglein-2 in sudden death from arrhythmogenic right ventricular cardiomyopathy and sudden unexplained death. Forensic Sci Int. 2015;255:85–8.

    Article  CAS  Google Scholar 

  15. Nishio H, Iwata M, Suzuki K. Postmortem molecular screening for cardiac ryanodine receptor type 2 mutations in sudden unexplained death R420W mutated case with characteristics of status Thymico-Lymphaticus. Circ J. 2006;70:1402–6.

    Article  CAS  Google Scholar 

  16. Nishio H, Iwata M, Tamura A, Miyazaki T, Tsuboi K, Suzuki K. Identification of a novel mutation V2321M of the cardiac ryanodine receptor gene of sudden unexplained death and a phenotypic study of the gene mutations. Legal Med. 2008;10(4):196–200.

    Article  CAS  Google Scholar 

  17. Nishio H, Kuwahara M, Tsubone H, Koda Y, Sato T, Fukunishi S, Tamura A, Suzuki K. Identification of an ethnic-specific variant (V207M) of the KCNQ1 cardiac potassium channel gene in sudden unexplained death and implications from a knock-in mouse model. Int J Legal Med. 2009;123(3):253–7.

    Article  Google Scholar 

  18. Sato T, Nishio H, Suzuki K. Sudden death during exercise in a juvenile with arrhythmogenic right ventricular cardiomyopathy and desmoglein-2 gene substitution: a case report. Legal Med. 2011;13(6):298–300.

    Article  CAS  Google Scholar 

  19. Sato T, Nishio H, Suzuki K. Identification of arrhythmogenic right ventricular cardiomyopathy-causing gene mutations in young sudden unexpected death autopsy cases. J Forensic Sci. 2015;60(2):457–61.

    Article  CAS  Google Scholar 

  20. Ibarra MCA, Wu S, Murayama K, Minami N, Ichihara Y, Kikuchi H, Noguchi S, Hayashi YK, Ochiai R, Nishino I. Malignant hyperthermia in Japan: mutation screening of the entire ryanodine receptor type 1 gene coding region by direct sequencing. Anesthesiology. 2006;104(6):1146–54.

    Article  Google Scholar 

  21. Robinson R, Carpenter D, Shaw MA, Halsall J, Hopkins P. Mutations in RYR1 in malignant hyperthermia and central core disease. Hum Mutat. 2006;27(10):977–89.

    Article  CAS  Google Scholar 

  22. Nishio H, Sato T, Fukunishi S, Tamura A, Iwata M, Tsuboi K, Suzuki K. Identification of malignant hyperthermia-susceptible ryanodine receptor type 1 gene (RYR1) mutations in a child who died in a car after exposure to a high environmental temperature. Legal Med. 2009;11(3):142–3.

    Article  CAS  Google Scholar 

  23. Brandom BW, Muldoon SM. Unexpected MH deaths without exposure to inhalation anesthetics in pediatric patients. Paediatr Anaesth. 2013;23(9):851–4.

    Article  Google Scholar 

  24. Sato T, Nishio H, Iwata M, Tsuboi K, Tamura A, Miyazaki T, Suzuki K. Postmortem molecular screening for mutations in ryanodine receptor type 1 (RYR1) gene in psychiatric patients suspected of having died of neuroleptic malignant syndrome. Forensic Sci Int. 2010;194(1–3):77–9.

    Article  CAS  Google Scholar 

  25. Takahashi M, Sato T, Nishiguchi M, Suzuki K, Nishio H. Postmortem genetic analysis for a sudden death case complicated with Marfan syndrome. Legal Med. 2010;12(6):305–7.

    Article  CAS  Google Scholar 

  26. Farrugia A, Keyser C, Hollard C, Raul JS, Muller J, Ludes B. Targeted next generation sequencing application in cardiac channelopathies: analysis of a cohort of autopsy-negative sudden unexplained deaths. Forensic Sci Int. 2015;254:5–11.

    Article  CAS  Google Scholar 

  27. Hertz CL, Christiansen SL, Dahl M, Weeke PE, et al. Next-generation sequencing of 100 candidate genes in young victims of suspected sudden cardiac death with structural abnormalities of the heart. Int J Legal Med. 2016;130(1):91–102.

    Article  CAS  Google Scholar 

  28. Nunn LM, Lopes LR, Syrris P, Murphy C, Plagnol V, Firman E, et al. Diagnostic yield of molecular autopsy in patients with sudden arrhythmic death syndrome using targeted exome sequencing. Europace. 2016;18(6):888–96.

    Article  Google Scholar 

  29. Suktitipat B, Sathirareuangchai S, Roothumnong E, et al. Molecular investigation by whole exome sequencing revealed a high proportion of pathogenic variants among Thai victims of sudden unexpected death syndrome. PLoS One. 2017;12(7):e0180056.

    Article  Google Scholar 

  30. Neubauer J, Haas C, Bartsch C, et al. Post-mortem whole-exome sequencing (WES) with a focus on cardiac disease-associated genes in five young sudden unexplained death (SUD) cases. Int J Legal Med. 2016;130(4):1011–21.

    Article  Google Scholar 

  31. Anderson JH, Tester DJ, Will ML, Ackerman MJ. Whole-exome molecular autopsy after exertion-related sudden unexplained death in the young. Circ Cardiovasc Genet. 2016;9(3):259–65.

    Article  Google Scholar 

  32. Bagnall RD, Das KJ, Du J, Semsarian C. Exome analysis—based molecular autopsy in cases of sudden unexplained death in the young. Heart Rhythm. 2014;11(4):655–62.

    Article  Google Scholar 

  33. Michaud K, Mangin P, Elger BS. Genetic analysis of sudden cardiac death victims: a survey of current forensic autopsy practices. Int J Legal Med. 2011;125(3):359–66.

    Article  Google Scholar 

  34. Kauferstein S, Kiehne N, Jenewein T, Biel S, Kopp M, Erkapic D, et al. Genetic analysis of sudden unexplained death: a multidisciplinary approach. Forensic Sci Int. 2013;229(1–3):122–7.

    Article  CAS  Google Scholar 

  35. Nicholson JK, Lindon JC. Metabonomics. Nature. 2008;455(7216):1054–6.

    Article  CAS  Google Scholar 

  36. Rižner TL. Discovery of biomarkers for endometrial cancer: current status and prospects. Expert Rev Mol Diagn. 2016;16(12):1315–36.

    Article  Google Scholar 

  37. Newgard CB. Metabolomics and metabolic diseases: where do we stand ? Cell Metab. 2017;25(1):43–56.

    Article  CAS  Google Scholar 

  38. Hirayama A, Nakashima E, Sugimoto M, Akiyama S, Sato W, Maruyama S, Matsuo S, Tomita M, Yuzawa Y, Soga T. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal Bioanal Chem. 2012;404(10):3101–9.

    Article  CAS  Google Scholar 

  39. Wuolikainen A, Moritz T, Marklund SL, Antti H, Andersen PM. Disease-related changes in the cerebrospinal fluid metabolome in amyotrophic lateral sclerosis detected by GC/TOFMS. PLoS One. 2011;6(4):e17947.

    Article  CAS  Google Scholar 

  40. Wilkins JM, Trushina E. Application of metabolomics in Alzheimer’s disease. Front Neurol. 2018;8(719):1–20.

    Google Scholar 

  41. Sethi S, Brietzke E. Omics-based biomarkers: application of metabolomics in neuropsychiatric disorders. Int J Neuropsychopharmacol. 2015;19(3):pyv096.

    Article  Google Scholar 

  42. Lewitt PA, Li J, Lu M, Beach TG, Adler CH, et al. 3-Hydroxykynurenine and other Parkinson’s disease biomarkers discovered by metabolomic analysis. Mov Disord. 2013;28(12):1653–60.

    Article  CAS  Google Scholar 

  43. Kang J, Zhu L, Lu J, Zhang X. Application of metabolomics in autoimmune diseases: insight into biomarkers and pathology. J Neuroimmunol. 2015;279(1):25–32.

    Article  CAS  Google Scholar 

  44. Fujieda Y, Ueno S, Ogino R, Kuroda M, Jönsson TJ, Guo L, Bamba T, Fukusaki E. Metabolite profiles correlate closely with neurobehavioral function in experimental spinal cord injury in rats. PLoS One. 2012;7(8):e43152.

    Article  CAS  Google Scholar 

  45. Weljie AM, Newton J, Mercier P, Carlson E, Slupsky CM. Targeted profiling: quantitative analysis of 1 H NMR metabolomics data. Anal Chem. 2006;78(13):4430–42.

    Article  CAS  Google Scholar 

  46. Juo C, Chiu DT, Shiao M. Liquid chromatography-mass spectrometry in metabolite profiling. Biofactors. 2008;34(2):159–69.

    Article  CAS  Google Scholar 

  47. Agnolet S, Wiese S, Verpoorte R, Staerk D. Comprehensive analysis of commercial willow bark extracts by new technology platform: combined use of metabolomics, high-performance liquid chromatography—solid-phase extraction—nuclear magnetic resonance spectroscopy and high-resolution radical sca. J Chromatogr A. 2012;1262:130–7.

    Article  CAS  Google Scholar 

  48. Deng M, Zhang M, Sun F, Ma J, Hu L, Yang X, et al. A gas chromatography-mass spectrometry based study on urine metabolomics in rats chronically poisoned with hydrogen sulfide. Biomed Res Int. 2015;2015:295241.

    PubMed  PubMed Central  Google Scholar 

  49. Bando K, Kunimatsu T, Sakai J, Kimura J, et al. GC-MS-based metabolomics reveals mechanism of action for hydrazine induced hepatotoxicity in rats. J Appl Toxicol. 2011;31(6):524–35.

    Article  CAS  Google Scholar 

  50. Lendoiro E, Cordeiro C, Rodríguez-Calvo MS, Vieira DN, Suárez-Peñaranda JM, López-Rivadulla M, Muñoz-Barus JI. Applications of tandem mass spectrometry (LC—MSMS) in estimating the post-mortem interval using the biochemistry of the vitreous humour. Forensic Sci Int. 2012;223(1–3):160–4.

    Article  CAS  Google Scholar 

  51. Kawamoto O, Michiue T, Ishikawa T, Maeda H. Comprehensive evaluation of pericardial biochemical markers in death investigation. Forensic Sci Int. 2013;224(1–3):73–9.

    Article  CAS  Google Scholar 

  52. Boaks A, Siwek D, Mortazavi F. The temporal degradation of bone collagen: a histochemical approach. Forensic Sci Int. 2014;240:104–10.

    Article  CAS  Google Scholar 

  53. Kaliszan M. Studies on time of death estimation in the early post mortem period—application of a method based on eyeball temperature measurement to human bodies. Legal Med. 2013;15(5):278–82.

    Article  Google Scholar 

  54. Sampaio-Silva F, Magalhães T, Carvalho F, Dinis-Oliveira RJ, Silvestre R. Profiling of RNA degradation for estimation of post mortem [corrected] interval. PLoS One. 2013;8(2):e56507.

    Article  CAS  Google Scholar 

  55. Hansen J, Lesnikova I, Funder AM, Banner J. DNA and RNA analysis of blood and muscle from bodies with variable postmortem intervals. Forensic Sci Med Pathol. 2014;10(3):322–8.

    Article  CAS  Google Scholar 

  56. Zapico SC, Menéndez ST, Núñez P. Cell death proteins as markers of early postmortem interval. Cell Mol Life Sci. 2014;71(15):2957–62.

    Article  CAS  Google Scholar 

  57. Mao S, Fu G, Seese RR, Wang ZY. Estimation of PMI depends on the changes in ATP and its degradation products. Legal Med. 2013;15(5):235–8.

    Article  CAS  Google Scholar 

  58. Wells JD, Lecheta MC, Moura MO, Lamotte LR. An evaluation of sampling methods used to produce insect growth models for postmortem interval estimation. Int J Legal Med. 2015;129(2):405–10.

    Article  Google Scholar 

  59. Sato T, Zaitsu K, Tsuboi K, Nomura M, et al. A preliminary study on postmortem interval estimation of suffocated rats by GC-MS/MS-based plasma metabolic profiling. Anal Bioanal Chem. 2015;407(13):3659–65.

    Article  CAS  Google Scholar 

  60. Li C, Li Z, Tuo Y, Ma D, Shi Y, Zhang Q, et al. MALDI-TOF MS as a novel tool for the estimation of postmortem interval in liver tissue samples. Sci Rep. 2017;7(1):4887.

    Article  Google Scholar 

  61. Li C, Ma D, Deng K, Chen Y, Huang P, Wang Z. Application of MALDI-TOF MS for estimating the postmortem interval in rat muscle samples. J Forensic Sci. 2017;62(5):1345–50.

    Article  Google Scholar 

  62. Sucholeiki R. Heatstroke. Semin Neurol. 2005;25(3):307–14.

    Article  Google Scholar 

  63. Palmiere C, Mangin P. Hyperthermia and postmortem biochemical investigations. Int J Legal Med. 2013;127(1):93–102.

    Article  Google Scholar 

  64. Zhang F, Wang D, Li X, Li Z, Chao J, Qin X. Metabolomic study of the fever model induced by baker’s yeast and the antipyretic effects of aspirin in rats using nuclear magnetic resonance and gas chromatography—mass spectrometry. J Pharm Biomed Anal. 2013;81–82:168–77.

    Article  Google Scholar 

  65. Jolly K, Gammage MD, Cheng KK, Bradburn P, Banting MV, Langman MJS. Sudden death in patients receiving drugs tending to prolong the QT interval. Br J Clin Pharmacol. 2009;68(5):743–51.

    Article  CAS  Google Scholar 

  66. Stirnimann G, Petitprez S, Abriel H, Schwick NG. Brugada syndrome ECG provoked by the selective serotonin reuptake inhibitor fluvoxamine. Europace. 2010;12(2):282–3.

    Article  Google Scholar 

  67. Sicouri S, Antzelevitch C. Sudden cardiac death secondary to antidepressant and antipsychotic drugs. Expert Opin Drug Saf. 2008;7(2):181–94.

    Article  CAS  Google Scholar 

  68. Kaplan KA, Chiu VM, Lukus PA, Zhang X, Siems WF, Schenk JO, et al. Neuronal metabolomics by ion mobility mass spectrometry: cocaine effects on glucose and selected biogenic amine metabolites in the frontal cortex, striatum, and thalamus of the rat. Anal Bioanal Chem. 2013;405(6):1959–68.

    Article  CAS  Google Scholar 

  69. Assié M, Carilla-durand E, Bardin L, Maraval M, Aliaga M, Malfètes N, et al. The antipsychotics clozapine and olanzapine increase plasma glucose and corticosterone levels in rats: comparison with aripiprazole, ziprasidone, bifeprunox and F15063. Eur J Pharmacol. 2008;592(1–3):160–6.

    Article  Google Scholar 

  70. De Hert M, Detraux J, Van Winkel R, Yu W, Correll CU. Metabolic and cardiovascular adverse effects associated with antipsychotic drugs. Nat Rev Endocrinol. 2011;8(2):114–26.

    Article  Google Scholar 

  71. Zhao J, Jung Y, Jang C, Chun K, Kwon SW, Lee J. Metabolomic identification of biochemical changes induced by fluoxetine and imipramine in a chronic mild stress mouse model of depression. Sci Rep. 2015;5:8890.

    Article  CAS  Google Scholar 

  72. Boyda HN, Tse L, Procyshyn RM, Honer WG, Barr AM. Preclinical models of antipsychotic drug-induced metabolic side effects. Trends Pharmacol Sci. 2010;31(10):484–97.

    Article  CAS  Google Scholar 

  73. Barbui C, Bighelli I, Carr G, Castellazzi M, Lucii C. Antipsychotic dose mediates the association between polypharmacy and corrected QT interval. PLoS One. 2016;11(2):e0148212.

    Article  Google Scholar 

  74. Shima N, Miyawaki I, Bando K, Horie H, Zaitsu K, Katagi M, et al. Influences of methamphetamine-induced acute intoxication on urinary and plasma metabolic profiles in the rat. Toxicology. 2011;287(1–3):29–37.

    Article  CAS  Google Scholar 

  75. Zaitsu K, Miyawaki I, Bando K, Horie H, Shima N, Katagi M, Tatsuno M, Bamba T, Sato T, Ishii A, Tsuchihashi H, Suzuki K, Fukusaki E. Metabolic profiling of urine and blood plasma in rat models of drug addiction on the basis of morphine, methamphetamine, and cocaine-induced conditioned place preference. Anal Bioanal Chem. 2014;406(5):1339–54.

    Article  CAS  Google Scholar 

  76. Kintz P. Value of hair analysis in postmortem toxicology. Forensic Sci Int. 2004;142(2–3):127–34.

    Article  CAS  Google Scholar 

  77. Kintz P. Bioanalytical procedures for detection of chemical agents in hair in the case of drug-facilitated crimes. Anal Bioanal Chem. 2007;388:1467–74.

    Article  CAS  Google Scholar 

  78. Miki A, Katagi M, Shima N, et al. Imaging of methamphetamine incorporated into hair by MALDI-TOF mass spectrometry. Forensic Toxicol. 2011;29:111–6.

    Article  CAS  Google Scholar 

  79. Miki A, Katagi M, Kamata T, Zaitsu K, Tatsuno M, Nakanishi T, et al. MALDI-TOF and MALDI-FTICR imaging mass spectrometry of methamphetamine incorporated into hair. J Mass Spectrom. 2011;46(4):411–6.

    Article  CAS  Google Scholar 

  80. Kamata T, Shima N, Sasaki K, Matsuta S, Takei S, Katagi M, et al. Time-course mass spectrometry imaging for depicting drug incorporation into hair. Anal Chem. 2015;87(11):5476–81.

    Article  CAS  Google Scholar 

  81. Shima N, Sasaki K, Kamata T, Matsuta S, et al. Single-hair analysis of zolpidem on the supposition of its single administration in drug-facilitated crimes. Forensic Toxicol. 2015;33:122–30.

    Article  CAS  Google Scholar 

  82. Shima N, Sasaki K, Kamata T, Matsuta S, Wada M, Kakehashi H, et al. Incorporation of Zolpidem into hair and its distribution after a single administration. Drug Metab Dispos. 2017;45(3):286–93.

    Article  CAS  Google Scholar 

  83. Flinders B, Cuypers E, Zeijlemaker H, Heeren RMA. Preparation of longitudinal sections of hair samples for the analysis of cocaine by MALDI-MS/MS and TOF-SIMS imaging. Drug Test Anal. 2015;7(10):859–65.

    Article  CAS  Google Scholar 

  84. Nakanishi T, Nirasawa T, Takubo T. Quantitative mass barcode-like image of nicotine in single longitudinally sliced hair sections from long-term smokers by matrix-assisted laser desorption time-of-flight mass spectrometry imaging. J Anal Toxicol. 2014;38(6):349–53.

    Article  CAS  Google Scholar 

  85. Wabuyele SL, Colby JM, McMillin GA. Detection of drug-exposed newborns. Ther Drug Monit. 2018;40(2):166–85.

    Article  CAS  Google Scholar 

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Sato, T., Suzuki, K. (2019). Biomarkers for “Cause of Death”. In: Ishikawa, T. (eds) Forensic Medicine and Human Cell Research. Current Human Cell Research and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-2297-6_1

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