Authors : Vataparthi Pravallika; Naga Jogayya.K; Satheesh Ampolu; Sheerin Bashar

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/yufxue5u

Scribd : https://tinyurl.com/pxthpfv8

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN963

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Advances in genetic sequencing technology, enhanced microbiological sample methods, and rapidly developing approaches in bioinformatics have all contributed to the meteoric emergence of microbiomics and metagenomics. Humans harbour vast microbial communities, both internally and outside, that are constantly exchanging information with and shaping their environs. These interactions may be crucial to forensics because information about them can be gleaned from human and environmental microbial profiles. Over the reports of hundred studies, as metagenome markers in forensic science is an emerging field of forensic science, microbiomes has a greater chance of becoming a specific tool kit for forensic science to provide answers for personal identification, cause and manner of death, interpretation of PMI and geolocation of a suspect or accused. Although the application of microbiomics to forensics has been extensively studied, most of its potential remains untapped because of insufficient sample numbers, inaccurate models, and unrepresentative experimental conditions. Law enforcement agencies are unlikely to benefit from the limited microbiomics data that is currently available. However, efforts are being made to find solutions to these problems, and evidence gleaned from the microbiome may one day aid in forensic investigations. Until more is learned about microbiomics, it is unlikely that this information will be useful to law enforcement. However, efforts are being made to find solutions to these problems, and it is possible that evidence gleaned from the microbiome can one day aid in forensic investigations.

Keywords : Microbiomics, Forensic Science, Law enforcement, Microbiology.

References :

1. MacCallum, W. G., and Hastings, T. W. (1899). A case of acute endocarditis caused by Micrococcus zymogenes (nov. spec.), with a description of the microorganism. J. Exp. Med. 4, 521–534. doi: 10.1084/jem.4.5-6.521
2. Smith, T. F., and Waterman, M. S. (1992). The continuing case of the Florida dentist. Science 256, 1155–1156. doi: 10.1126/science.256.5060.1155
3. van Belkum, A. (1994). DNA fingerprinting of medically important microorganisms by use of PCR. Clin. Microbiol. Rev. 7, 174–184. doi: 10.1128/cmr.7.2.174-184.1994
4. Bruce, R. G., and Dettmann, M. E. (1996). Palynological analyses of Australian surface soils and their potential in forensic science. Forensic Sci. Int. 81, 77–94. doi: 10.1016/s0379-0738(96)01973-1
5. Bryant, V. M., and Mildenhall, D. C. (1998). Forensic palynology: a new way to catch crooks. Contrib. Ser. Am. Assoc. Stratigraph. Palynol. 33, 145–155.
6. Budowle, B., Schutzer, S. E., Einseln, A., Kelley, L. C., Walsh, A. C., Smith, J. A., et al. (2003). Building microbial forensics as a response to bioterrorism. Science 301, 1852–1853. doi: 10.1126/science.1090083
7. Carter, D. O., Tomberlin, J. K., Benbow, M. E., and Metcalf, J. L. (eds) (2017). Forensic Microbiology. Hoboken, NJ: John Wiley & Sons.
8. Berglund, E. C., Kiialainen, A., and Syvänen, A. C. (2011). Next-generation sequencing technologies and applications for human genetic history and forensics. Investig. Genet. 2:23. doi: 10.1186/2041-2223-2-23
9. Kuiper, I. (2016). Microbial forensics: next-generation sequencing as catalyst. EMBO Rep. 17, 1085–1087. doi: 10.15252/embr.201642794
10. Clarke, T. H., Gomez, A., Singh, H., Nelson, K. E., and Brinkac, L. M. (2017). Integrating the microbiome as a resource in the forensics toolkit. Forensic Sci. Int. Genet. 30, 141–147. doi: 10.1016/j.fsigen.2017.06.008
11. Hampton-Marcell, J. T., Lopez, J. V., and Gilbert, J. A. (2017). The human microbiome: an emerging tool in forensics. Microb. Biotechnol. 10, 228–230. doi: 10.1111/1751-7915.12699
12. Statnikov, A., Henaff, M., Narendra, V., Konganti, K., Li, Z., Yang, L., et al. (2013). A comprehensive evaluation of multicategory classification methods for microbiomic data. Microbiome 1:11.
13. Capasso, L., Vento, G., Loddo, C., Tirone, C., Iavarone, F., Raimondi, F., et al. (2019). Oxidative stress and bronchopulmonary dysplasia: evidences from microbiomics, metabolomics and proteomics. Front. Pediatr. 7:30. doi: 10.3389/fped.2019.00030
14. Office for National Statistics (2019). Homicide in England and Wales: Year Ending March 2019. Available online at: https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/articles/homicideinenglandandwales/yearendingmarch2019 (accessed February 14, 2020).
15. Bureau for Justice Statistics. (2019). What is the Probability of Conviction for Felony Defendants? Available online at: https://www.bjs.gov/index.cfm?ty=qa&iid=403 (accessed February 14, 2020).
16. Sangero, B., and Halpert, M. (2007). Why a conviction should not be based on a single piece of evidence: a proposal for reform. Jurimetrics 48, 43–94.
17. Ingemann-Hansen, O., Brink, O., Sabroe, S., Sørensen, V., and Charles, A. V. (2008). Legal aspects of sexual violence—does forensic evidence make a difference? Forensic Sci. Int. 180, 98–104. doi: 10.1016/j.forsciint.2008.07.009
18. LaPorte, G. (2017). Wrongful convictions and DNA exonerations: Understanding the role of forensic science. NIJ J. 279:250705.
19. Walsh, K., Hussemann, J., Flynn, A., Yahner, J., and Golian, L. (2017). Estimating the Prevalence of Wrongful Convictions. Final Report 251115. (Washington, DC: Urban Institute), 1–13.
20. Innocent Project (2020). Exonerate the Innocent. Available online at: https://www. innocenceproject.org/exonerate/ (accessed September 18, 2020).
21. Desmond, A. U., Nicholas, O., and Emmanuel, O. O. (2018). Microbial forensics: forensic relevance of the individual person’s microbial signature. Int. J. Life Sci. Scient. Res. 2455, 2037–2043.
22. Sender, R., Fuchs, S., and Milo, R. (2016). Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14:e1002533. doi: 10.1371/journal.pbio.1002533
23. Vázquez-Baeza, Y., Callewaert, C., Debelius, J., Hyde, E., Marotz, C., Morton, J. T., et al. (2018). Impacts of the human gut microbiome on therapeutics. Annu. Rev. Pharmacol. Toxicol. 58, 253–270.
24. Noel, S., Martina-Lingua, M. N., Bandapalle, S., Pluznick, J., Hamad, A. R. A., Peterson, D. A., et al. (2014). Intestinal microbiota-kidney cross talk in acute kidney injury and chronic kidney disease. Nephron Clin. Pract. 127, 139–143. doi: 10.1159/000363209
25. Metcalf, J. L., Xu, Z. Z., Bouslimani, A., Dorrestein, P., Carter, D. O., and Knight, R. (2017). Microbiome tools for forensic science. Trends Biotechnol. 35, 814–823. doi: 10.1016/j.tibtech.2017.03.006
26. Schmedes, S. E., Woerner, A. E., and Budowle, B. (2017). Forensic human identification using skin microbiomes. Appl. Environ. Microbiol. 83:e01672-17.
27. Richardson, M., Gottel, N., Gilbert, J. A., and Lax, S. (2019). Microbial similarity between students in a common dormitory environment reveals the forensic potential of individual microbial signatures. mBio 10:e1054-19.
28. Phan, K., Barash, M., Spindler, X., Gunn, P., and Roux, C. (2020). Retrieving forensic information about the donor through bacterial profiling. Int. J. Legal Med. 134, 21–29. doi: 10.1007/s00414-019-02069-2
29. Goudarzi, M., Mak, T. D., Jacobs, J. P., Moon, B. H., Strawn, S. J., Braun, J., et al. (2016). An integrated multi-omic approach to assess radiation injury on the host-microbiome axis. Radiat. Res. 186, 219–234. doi: 10.1667/rr14306.1
30. Komaroff, A. L. (2018). The microbiome and risk for atherosclerosis. JAMA 319, 2381–2382. doi: 10.1001/jama.2018.5240
31. Morgan, R. M., and Levin, E. A. (2019). A crisis for the future of forensic science: lessons from the UK of the importance of epistemology for funding research and development. Forensic Sci. Int. 1, 243–252. doi: 10.1016/j.fsisyn.2019.09.002
32. Afshinnekoo, E., Meydan, C., Chowdhury, S., Jaroudi, D., Boyer, C., Bernstein, N., et al. (2015). Geospatial resolution of human and bacterial diversity from city-scale metagenomics. Cell Syst. 1, 72–87.
33. Rosenfeld, J., Reeves, D., Brugler, M. R., Narechania, A., Simon, S., Kolokotronis, S., et al. (2016). Genome assembly, annotation, and urban phylogenomics of the bedbug (Cimex lectularius). Nat. Commun. 7:10164.
34. Danko, D. C., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., et al. (2019b). Global genetic cartography of urban metagenomes and anti-microbial resistance. bioRxiv [Preprint]. doi: 10.1101/724526
35. Habtom, H., Pasternak, Z., Matan, O., Azulay, C., Gafny, R., and Jurkevitch, E. (2019). Applying microbial biogeography in soil forensics. Forensic Sci. Int. Genet. 38, 195–203. doi: 10.1016/j.fsigen.2018.11.010
36. Macdonald, C. A., Ang, R., Cordiner, S. J., and Horswell, J. (2011). Discrimination of soils at regional and local levels using bacterial and fungal T-RFLP profiling.
37. Jesmok, E. M., Hopkins, J. M., and Foran, D. R. (2016). Next-generation sequencing of the bacterial 16S rRNA gene for forensic soil comparison: a feasibility study.
38. Demanèche, S., Schauser, L., Dawson, L., Franqueville, L., and Simonet, P. (2017). Microbial soil community analyses for forensic science: application to a blind test. Forensic Sci. Int. 270, 153–158. doi: 10.1016/j.forsciint.2016.12.004
39. Young, J. M., Weyrich, L. S., Breen, J., Macdonald, L. M., and Cooper, A. (2015). Predicting the origin of soil evidence: high throughput eukaryote sequencing and MIR spectroscopy applied to a crime scene scenario. Forensic Sci. Int. 251, 22–31. doi: 10.1016/j.forsciint.2015.03.008
40. Sanachai, A., Katekeaw, S., and Lomthaisong, K. (2016). Forensic soil investigation from the 16S rDNA profiles of soil bacteria obtained by denaturing gradient gel electrophoresis. Chiang Mai J. Sci. 43, 748–755.
41. Pasternak, Z., Luchibia, A. O., Matan, O., Dawson, L., Gafny, R., Shpitzen, M., et al. (2019). Mitigating temporal mismatches in forensic soil microbial profiles. Aust. J. Forensic Sci. 51, 685–694. doi: 10.1080/00450618.2018.1450897
42. Pasternak, Z., Al-Ashhab, A., Gatica, J., Gafny, R., Avraham, S., Minz, D., et al. (2013). Spatial and temporal biogeography of soil microbial communities in arid and semiarid regions. PLoS One 8:e69705. doi: 10.1371/journal.pone. 0069705
43. Keet, J. H., Ellis, A. G., Hui, C., and Le Roux, J. J. (2019). Strong spatial and temporal turnover of soil bacterial communities in South Africa’s hyperdiverse fynbos biome. Soil Biol. Biochem. 136:107541. doi: 10.1016/j.soilbio.2019.107541
44. Carter, D. O., Metcalf, J. L., Bibat, A., and Knight, R. (2015). Seasonal variation of postmortem microbial communities. Forensic Sci. Med. Pathol. 11, 202–207. doi: 10.1007/s12024-015-9667-7
45. Adserias-Garriga, J., Hernández, M., Quijada, N. M., Lázaro, D. R., Steadman, D., and Garcia-Gil, J. (2017). Daily thanatomicrobiome changes in soil as an approach of postmortem interval estimation: an ecological perspective. Forensic Sci. Int. 278, 388–395. doi: 10.1016/j.forsciint.2017.07.017
46. Finley, S. J., Pechal, J. L., Benbow, M. E., Robertson, B. K., and Javan, G. T. (2016). Microbial signatures of cadaver gravesoil during decomposition. Microb. Ecol. 71, 524–529. doi: 10.1007/s00248-015-0725-1
47. Singh, B., Minick, K. J., Strickland, M. S., Wickings, K. G., Crippen, T. L., Tarone, A. M., et al. (2018). Temporal and spatial impact of human cadaver decomposition on soil bacterial and arthropod community structure and function. Front. Microbiol. 8:2616. doi: 10.3389/fmicb.2017.02616
48. Young, J. M., Austin, J. J., and Weyrich, L. S. (2017). Soil DNA metabarcoding and high-throughput sequencing as a forensic tool: considerations, potential limitations and recommendations. FEMS Microbiol. Ecol. 93:fiw207. doi: 10.1093/femsec/fiw207
49. Chase, J., Fouquier, J., Zare, M., Sonderegger, D. L., Knight, R., Kelley, S. T., et al. (2016). Geography and location are the primary drivers of office microbiome composition. mSystems 1:e00022-16.
50. Lax, S., Smith, D. P., Hampton-Marcell, J., Owens, S. M., Handley, K. M., Scott, N. M., et al. (2014). Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052. doi: 10.1126/science.1254529
51. Walker, A. R., and Datta, S. (2019). Identification of city specific important bacterial signature for the MetaSUB CAMDA challenge microbiome data. Biol. Direct 14:11.
52. Ryan, F. J. (2019). Application of machine learning techniques for creating urban microbial fingerprints. Biol. Direct 14:13.
53. Grantham, N. S., Reich, B. J., Laber, E. B., Pacifici, K., Dunn, R. R., Fierer, N., et al. (2019). Global forensic geolocation with deep neural networks. arXiv [Preprint]. Available online at: https://arxiv.org/abs/1905.11765 (accessed September 1, 2020).
54. Zeng, B., Zhao, J., Guo, W., Zhang, S., Hua, Y., Tang, J., et al. (2017). High-altitude living shapes the skin microbiome in humans and pigs. Front. Microbiol. 8:1929.doi: 10.3389/fmicb.2017.01929
55. Franzosa, E. A., Huang, K., Meadow, J. F., Gevers, D., Lemon, K. P., Bohannan, B. J., et al. (2015). Identifying personal microbiomes using metagenomic codes. Proc. Natl. Acad. Sci. U.S.A. 112, 2930–2938.
56. Wang, S., Song, F., Wang, Y., Huang, Y., Xie, B., and Luo, H. (2019). High resolution melting analysis (HRM) based on 16SrRNA as a tool for personal identification with the human oral microbiome. Forensic Sci. Int. Genet. 7, 161–163. doi: 10.1016/j.fsigss.2019.09.063
57. Woerner, A. E., Novroski, N. M., Wendt, F. R., Ambers, A., Wiley, R., Schmedes, S. E., et al. (2019). Forensic human identification with targeted microbiome markers using nearest neighbor classification. Forensic Sci. Int. Genet. 38, 130–139. doi: 10.1016/j.fsigen.2018.10.003
58. Watanabe, H., Nakamura, I., Mizutani, S., Kurokawa, Y., Mori, H., Kurokawa, K.,et al. (2018). Minor taxa in human skin microbiome contribute to the personal identification. PLoS One 13:e0199947. doi: 10.1371/journal.pone.0199947
59. Chan, P. L., Yu, P. H. F., Cheng, Y. W., Chan, C. Y., and Wong, P. K. (2009). Comprehensive characterization of indoor airborne bacterial profile. J. Environ. Sci. 21, 1148–1152. doi: 10.1016/s1001-0742(08)62395-5
60. Luongo, J. C., Barberán, A., Hacker-Cary, R., Morgan, E. E., Miller, S. L., and Fierer, N. (2017). Microbial analyses of airborne dust collected from dormitory rooms predict the sex of occupants. Indoor Air 27, 338–344. doi: 10.1111/ina.12302
61. Zhou, W., and Bian, Y. (2018). Thanatomicrobiome composition profiling as a tool for forensic investigation. Forensic Sci. Res. 3, 105–110. doi: 10.1080/20961790.2018.1466430
62. Bell, C. R., Wilkinson, J. E., Robertson, B. K., and Javan, G. T. (2018). Sex-related differences in the thanatomicrobiome in postmortem heart samples using bacterial gene regions V1-2 and V4. Lett. Appl. Microbiol. 67, 144–153. doi: 10.1111/lam.13005
63. Tridico, S. R., Murray, D. C., Addison, J., Kirkbride, K. P., and Bunce, M. (2014). Metagenomic analyses of bacteria on human hairs: a qualitative assessment for applications in forensic science. Investig. Genet. 5:16.
64. Williams, D. W., and Gibson, G. (2017). Individualization of pubic hair bacterial communities and the effects of storage time and temperature. Forensic Sci. Int.Genet. 26, 12–20. doi: 10.1016/j.fsigen.2016.09.006
65. Phan, K., Barash, M., Spindler, X., Gunn, P., and Roux, C. (2020). Retrieving forensic information about the donor through bacterial profiling. Int. J. Legal Med. 134, 21–29. doi: 10.1007/s00414-019-02069-2
66. Koroglu, M., Gunal, S., Yildiz, F., Savas, M., Ozer, A., and Altindis, M. (2015). Comparison of keypads and touch-screen mobile phones/devices as potential risk for microbial contamination. J. Infect. Dev. Ctries. 9, 1308–1314. doi:10.3855/jidc.6171
67. Kõljalg, S., Mändar, R., Sõber, T., Rööp, T., and Mändar, R. (2017). High level bacterial contamination of secondary school students’ mobile phones. Germs 7, 73–77. doi: 10.18683/germs.2017.1111
68. Koscova, J., Hurnikova, Z., and Pistl, J. (2018). Degree of bacterial contamination of mobile phone and computer keyboard surfaces and efficacy of disinfection with chlorhexidine digluconate and triclosan to its reduction. Int. J. Environ. Res. Public Health 15:2238. doi: 10.3390/ijerph15102238
69. Kurli, R., Chaudhari, D., Pansare, A. N., Khairnar, M., Shouche, Y. S., and Rahi, P. (2018). Cultivable microbial diversity associated with cellular phones. Front. Microbiol. 9:1229. doi: 10.3389/fmicb.2018.01229
70. Lax, S., Hampton-Marcell, J. T., Gibbons, S. M., Colares, G. B., Smith, D., Eisen, J. A., et al. (2015). Forensic analysis of the microbiome of phones and shoes. Microbiome 3:21.
71. Coil, D. A., Neches, R. Y., Lang, J. M., Jospin, G., Brown, W. E., Cavalier, D.,et al. (2019). Bacterial communities associated with cell phones and shoes. PeerJ 8:e9235. doi: 10.7717/peerj.9235
72. Meadow, J. F., Altrichter, A. E., and Green, J. L. (2014). Mobile phones carry the personal microbiome of their owners. PeerJ 2:e447. doi: 10.7717/peerj.447
73. Kodama, W. A., Xu, Z., Metcalf, J. L., Song, S. J., Harrison, N., Knight, R., et al. (2019). Trace evidence potential in postmortem skin microbiomes: from death scene to morgue. J. Forensic Sci. 64, 791–798. doi: 10.1111/1556-4029.13949
74. Salzmann, A. P., Russo, G., Aluri, S., and Haas, C. (2019). Transcription and microbial profiling of body fluids using a massively parallel sequencing approach. Forensic Sci. Int. Genet. 43:102149. doi: 10.1016/j.fsigen.2019.10 2149
75. Dobay, A., Haas, C., Fucile, G., Downey, N., Morrison, H. G., Kratzer, A., et al. (2019). Microbiome-based body fluid identification of samples exposed to indoor conditions. Forensic Sci. Int. Genet. 40, 105–113. doi: 10.1016/j.fsigen. 2019.02.010
76. Hanssen, E. N., Avershina, E., Rudi, K., Gill, P., and Snipen, L. (2017). Body fluid prediction from microbial patterns for forensic application. Forensic Sci. Int. Genet. 30, 10–17. doi: 10.1016/j.fsigen.2017.05.009
77. Neckovic, A., van Oorschot, R. A., Szkuta, B., and Durdle, A. (2020). Investigation of direct and indirect transfer of microbiomes between individuals. Forensic Sci. Int. Genet. 45:102212. doi: 10.1016/j.fsigen.2019.102212
78. Advenier, A. S., Guillard, N., Alvarez, J. C., Martrille, L., and Lorin de la Grandmaison, G. (2016). Undetermined manner of death: an autopsy series. J. Forensic Sci. 61(Suppl. 1), S154–S158.
79. Lutz, H., Vangelatos, A., Gottel, N., Speed, E., Osculati, A., Visona, S., et al. (2019). Manner of death and demographic effects on microbial community composition in organs of the human cadaver. bioRxiv [Preprint]. doi: 10.1101/752576
80. Zhang, Y., Pechal, J. L., Schmidt, C. J., Jordan, H. R., Wang, W. W., Benbow, M. E., et al. (2019). Machine learning performance in a microbial molecular autopsy context: a cross-sectional postmortem human population study. PLoS One 14:e0213829. doi: 10.1371/journal.pone.0213829
81. Kaszubinski, S. F., Pechal, J. L., Smiles, K., Schmidt, C. J., Jordan, H. R., Meek, M. H., et al. (2020). Dysbiosis in the dead: human postmortem microbiome beta-dispersion as an indicator of manner and cause of death. Front. Microbiol. 11:555347. doi: 10.3389/fmicb.2020.555347
82. Christoffersen, S. (2015). The importance of microbiological testing for establishing cause of death in 42 forensic autopsies. Forensic Sci. Int. 250, 27–32. doi: 10.1016/j.forsciint.2015.02.020
83. Rambaud, C., Guibert, M., Briand, E., Grangeot-Keros, L., Coulomb-L’Herminé, A., and Dehan, M. (1999). Microbiology in sudden infant death syndrome (SIDS) and other childhood deaths. FEMS Immunol. Med. Microbiol. 25, 59–66. doi: 10.1111/j.1574-695x.1999.tb01327.x
84. Szydlowski, L., Skierska, A., Markiewicz-Loskot, G., Mazurek, B., Morka, A., and Undas, A. (2013). The role of Interleukin-6, its- 174 G > C polymorphism and C-reactive protein in idiopathic cardiac arrhythmias in children. Adv. Med. Sci. 58, 320–325. doi: 10.2478/ams-2013-0003
85. Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., et al. (2019). The NASA Twins Study: a multidimensional analysis of a year-long human spaceflight. Science 364:eaau8650.
86. Elhaik, E. (2016). a “Wear and tear” Hypothesis to explain sudden infant death syndrome. Front. Neurol. 7:180. doi: 10.3389/fneur.2016.00180
87. Elhaik, E. (2019). Neonatal circumcision and prematurity are associated with sudden infant death syndrome (SIDS). J. Clin. Transl. Res. 4, 136–151.
88. Domínguez, A. M., Pino, J., García, A., García, I., and González, I. (2018). PW 1649 Drowning prevention campaigns, do they really work? Injury Prev. 24:A153.
89. Cenderadewi, M., Franklin, R. C., Peden, A. E., and Devine, S. (2019). Pattern of intentional drowning mortality: a total population retrospective cohort study in Australia, 2006–2014. BMC Public Health 19:207. doi: 10.1186/s12889-019-6476-z
90. Kakizaki, E., Kozawa, S., Sakai, M., and Yukawa, N. (2009). Bioluminescent bacteria have potential as a marker of drowning in seawater: two immersed cadavers retrieved near estuaries. Leg. Med. 11, 91–96. doi: 10.1016/j.legalmed.2008.10.004
91. Huys, G., Coopman, V., Van Varenbergh, D., and Cordonnier, J. (2012). Selective culturing and genus-specific PCR detection for identification of Aeromonas in tissue samples to assist the medico-legal diagnosis of death by drowning. Forensic Sci. Int. 221, 11–15. doi: 10.1016/j.forsciint.2012.03.017
92. Aoyagi, M., Iwadate, K., Fukui, K., Abe, S., Sakai, K., Maebashi, K., et al. (2009). A novel method for the diagnosis of drowning by detection of Aeromonas sobria with PCR method. Leg. Med. 11, 257–259. doi: 10.1016/j.legalmed.2009.07.003
93. Uchiyama, T., Kakizaki, E., Kozawa, S., Nishida, S., Imamura, N., and Yukawa, N. (2012). A new molecular approach to help conclude drowning as a cause of death: simultaneous detection of eight bacterioplankton species using real-time PCR assays with TaqMan probes. Forensic Sci. Int. 222, 11–26. doi: 10.1016/j.forsciint.2012.04.029
94. Rutty, G. N., Bradley, C. J., Biggs, M. J., Hollingbury, F. E., Hamilton, S. J., Malcomson, R. D., et al. (2015). Detection of bacterioplankton using PCR probes as a diagnostic indicator for drowning; the Leicester experience. Leg. Med. 17, 401–408. doi: 10.1016/j.legalmed.2015.06.001
95. Voloshynovych, V. M., Kasala, R. O., Stambulska, U. Y., and Voloshynovych, M. S. (2019). Determination the presence of amplification products of 16s rRNA microcystis aeruginosa as a biomarker of drowning. Rom. J. Leg. Med. 27, 16–21. doi: 10.4323/rjlm.2019.16
96. Lee, S. Y., Woo, S. K., Lee, S. M., Ha, E. J., Lim, K. H., Choi, K. H., et al. (2017). Microbiota composition and pulmonary surfactant protein expression as markers of death by drowning. J. Forensic Sci. 62, 1080–1088. doi: 10.1111/1556-4029.13347
97. J. Forensic Sci. 56, 61–69. doi: 10.1111/j.1556-4029.2010.01542.x Marella, G. L., Feola, A., Marsella, L. T., Mauriello, S., Giugliano, P., and Arcudi, G. (2019). Diagnosis of drowning, an everlasting challenge in forensic medicine: review of the literature and proposal of a diagnostic algorithm. Acta Med. 35, 900–919.
98. Lucci, A., Campobasso, C. P., Cirnelli, A., and Lorenzini, G. (2008). A promising microbiological test for the diagnosis of drowning. Forensic Sci. Int. 182, 20–26. doi: 10.1016/j.forsciint.2008. 09.004
99. Javan, G. T., Finley, S. J., Abidin, Z., and Mulle, J. G. (2016). The thanatomicrobiome: a missing piece of the microbial puzzle of death. Front. Microbiol. 7:225. doi: 10.3389/fmicb.2016.00225
100. Burcham, Z. M., Pechal, J. L., Schmidt, C. J., Bose, J. L., Rosch, J. W., Benbow, M. E., et al. (2019). Bacterial community succession, transmigration, and differential gene transcription in a controlled vertebrate decomposition model. Front. Microbiol. 10:745. doi: 10.3389/fmicb.2019.00745.
101. Javan, G. T., Finley, S. J., Tuomisto, S., Hall, A., Benbow, M. E., and Mills, D. (2019). An interdisciplinary review of the thanatomicrobiome in human decomposition. Forensic Sci. Med. Pathol. 15, 75–83. doi: 10.1007/s12024-018-0061-0
102. Adserias-Garriga, J., Hernández, M., Quijada, N. M., Lázaro, D. R., Steadman, D., and Garcia-Gil, J. (2017). Daily thanatomicrobiome changes in soil as an approach of postmortem interval estimation: an ecological perspective. Forensic Sci. Int. 278, 388–395. doi: 10.1016/j.forsciint.2017.07.017.
103. Metcalf, J. L., Parfrey, L. W., Gonzalez, A., Lauber, C. L., Knights, D., Ackermann, G., et al. (2013). A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. eLife 2:e01104.
104. Pechal, J. L., Crippen, T. L., Benbow, M. E., Tarone, A. M., Dowd, S., and Tomberlin, J. K. (2014). The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int. J. Legal Med. 128, 193–205. doi: 10.1007/s00414-013-0872-1
105. Pechal, J. L., Schmidt, C. J., Jordan, H. R., and Benbow, M. E. (2018). A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition. Sci. Rep. 8:5724.
106. Callahan, B. J., McMurdie, P. J., and Holmes, S. P. (2017). Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISMEJ. 11, 2639–2643. doi: 10.1038/ismej.2017.119
107. J. Forensic Sci. 61, 607–617. doi: 10.1111/1556-4029.13049 Johnson, H. R., Trinidad, D. D., Guzman, S., Khan, Z., Parziale, J. V., DeBruyn,
108. Belk, A., Xu, Z. Z., Carter, D. O., Lynne, A., Bucheli, S., Knight, R., et al. (2018). Microbiome data accurately predicts the postmortem interval using random forest regression models. Genes 9, 104–128. doi: 10.3390/genes9020104
109. Javan, G. T., Finley, S. J., Smith, T., Miller, J., and Wilkinson, J. E. (2017). Cadaver thanatomicrobiome signatures: the ubiquitous nature of Clostridium species in human decomposition. Front. Microbiol. 8:2096. doi: 10.3389/fmicb.2017. 02096
110. Zhou, X., Jiang, X., Yang, C., Ma, B., Lei, C., Xu, C., et al. (2016). Cecal microbiota of Tibetan Chickens from five geographic regions were determined by 16S rRNA sequencing. Microbiologyopen 5, 753–762. doi: 10.1002/mbo3.367
111. Zhao, J., Yao, Y., Li, D., Xu, H., Wu, J., Wen, A., et al. (2018). Characterization of the gut microbiota in six geographical populations of Chinese Rhesus macaques (Macaca mulatta), implying an adaptation to high-altitude environment. Microb. Ecol. 76, 565–577. doi: 10.1007/s00248-018-1146-8
112. McKenzie, V. J., Bowers, R. M., Fierer, N., Knight, R., and Lauber, C. L. (2012). Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 6, 588–596. doi: 10.1038/ismej.2011.129
113. Avena, C. V., Parfrey, L. W., Leff, J. W., Archer, H. M., Frick, W. F., Langwig, K. E., et al. (2016). Deconstructing the bat skin microbiome: influences of the host and the environment. Front. Microbiol. 7:1753. doi: 10.3389/fmicb.2016.01753
114. Erwin, P. M., Rhodes, R. G., Kiser, K. B., Keenan-Bateman, T. F., McLellan, W. A., and Pabst, D. A. (2017). High diversity and unique composition of gut microbiomes in pygmy (Kogia breviceps) and dwarf (K. sima) sperm whales. Sci. Rep. 7:7205.
115. Torres, S., Clayton, J. B., Danzeisen, J. L., Ward, T., Huang, H., Knights, D., et al. (2017). Diverse bacterial communities exist on canine skin and are impacted by cohabitation and time. PeerJ 5:e3075. doi: 10.7717/peerj.3075
116. Engel, K., Sauer, J., Jünemann, S., Winkler, A., Wibberg, D., Kalinowski, J., et al. (2018). Individual-and species-specific skin microbiomes in three different estrildid finch species revealed by 16S amplicon sequencing. Microb. Ecol. 76, 518–529. doi: 10.1007/s00248-017-1130-8
117. Russo, C. D., Weller, D. W., Nelson, K. E., Chivers, S. J., Torralba, M., and Grimes, D. J. (2018). Bacterial species identified on the skin of bottlenose dolphins off southern California via next generation sequencing techniques. Microb. Ecol. 75, 303–309. doi: 10.1007/s00248-017-1071-2
118. Song, S. J., Lauber, C., Costello, E. K., Lozupone, C. A., Humphrey, G., Berg-Lyons, D., et al. (2013). Cohabiting family members share microbiota with one another and with their dogs. eLife 2:e00458.
119. Daubert v. Merrell Dow Pharmaceuticals Inc (1993). 509 U.S. p. 579. Available online at: https://supreme.justia.com/cases/federal/us/509/579/ (accessed September 1, 2020).
120. Willis, S. M., McKenna, L., McDermott, S., O’Donell, G., Barrett, A., Rasmusson, B., et al. (2015). ENFSI Guideline for Evaluative Reporting in Forensic Science. Wiesbaden: European Network of Forensic Science Institutes.
121. Habtom, H., Pasternak, Z., Matan, O., Azulay, C., Gafny, R., and Jurkevitch, E. (2019). Applying microbial biogeography in soil forensics. Forensic Sci. Int. Genet. 38, 195–203. doi: 10.1016/j.fsigen.2018.11.010
122. President’s Cou Pasternak, Z., Luchibia, A. O., Matan, O., Dawson, L., Gafny, R., Shpitzen, M., et al. (2019). Mitigating temporal mismatches in forensic soil microbial profiles. Aust. J. Forensic Sci. 51, 685–694. doi: 10.1080/00450618.2018.1450897
123. ncil of Advisors on Science and Technology (US) (2016). Report to the President. Forensic Science in Criminal Courts: Ensuring scientific validity of Feature-Comparison Methods. Washington, DC: Executive Office of the President of the United States.
124. Pasternak, Z., Al-Ashhab, A., Gatica, J., Gafny, R., Avraham, S., Minz, D., et al. (2013). Spatial and temporal biogeography of soil microbial communities in arid and semiarid regions. PLoS One 8:e69705. doi: 10.1371/journal.pone. 0069705
125. Mayuoni-Kirshenbaum, L., Waiskopf, O., Finkelstein, N., and Pasternak, Z. (2020). How did the DNA of a suspect get to the crime scene? A practical study in DNA transfer during lock-picking. Aust. J. Forensic Sci. 52, 1–11. doi: 10.1080/00450618.2020.1793384
126. Shamarina, D., Stoyantcheva, I., Mason, C. E., Bibby, K., and Elhaik, E. (2017). Communicating the promise, risks, and ethics of large-scale, open space microbiome and metagenome research. Microbiome 5, 132–141.
127. Daniel, R., and van Oorschot, R. A. (2011). An investigation of the presence of DNA on unused laboratory gloves. Forensic Sci. Int. 3, e45–e46.
128. Ogilvie, L. A., and Jones, B. V. (2015). The human gut virome: a multifaceted majority. Front. Microbiol. 6:918. doi: 10.3389/fmicb.2015.00918
129. McIntyre, A. B. R., Ounit, R., Afshinnekoo, E., Prill, R. J., Hénaff, E., Alexander, N., et al. (2017). Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 18:182.
130. Danko, D. C., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D. J., et al. (2019b). Global genetic cartography of urban metagenomes and anti-microbial resistance. bioRxiv [Preprint]. doi: 10.1101/724526
131. Foox, J., Tighe, S. W., Nicolet, C. M., Zook, J. M., Herbert, Z. T., Warner, D., et al. (2020). Multi-platform assessment of DNA sequencing performance using human and bacterial reference genomes in the ABRF next-generation sequencing study. bioRxiv [Preprint]. doi: 10.1101/2020.07.23.218602
132. McIntyre, A. B. R., Alexander, N., Grigorev, K., Bezdan, D., Sichtig, H., Chiu, C. Y., et al. (2019). Single-molecule sequencing detection of N6-methyladenine in microbial reference materials. Nat. Commun. 10:579.