Microbial community succession on submerged vertebrate carcasses in a tidal river habitat: Implications for aquatic forensic investigations.
bacterial community succession
carrion decomposition
epinecrotic
post mortem submersion interval
salinity
tidal river
Journal
Journal of forensic sciences
ISSN: 1556-4029
Titre abrégé: J Forensic Sci
Pays: United States
ID NLM: 0375370
Informations de publication
Date de publication:
Nov 2021
Nov 2021
Historique:
revised:
13
07
2021
received:
26
03
2021
accepted:
20
07
2021
pubmed:
1
9
2021
medline:
19
11
2021
entrez:
31
8
2021
Statut:
ppublish
Résumé
Death investigations in aquatic ecosystems are challenging due to abiotic and biotic factors that may influence the estimation of a postmortem submersion interval (PMSI). In this study, we examined bacterial changes throughout the decomposition process on porcine carcasses submerged in a tidal-influenced river and identified predictors of epinecrotic community succession. Fetal porcine (Sus scrofa) carcasses (N = 6) were submerged with epinecrotic samples collected every 3 days (6 collections) over a period of 19 days (~7415 accumulated degree hours (ADH)). Amplicon sequencing was performed using the Illumina MiSeq platform (16S V4 region, 2 × 250 bp format) to identify changes in bacterial relative abundance and diversity. To match bacterial succession with rough taphonomy, carcasses were visually assessed at each sampling time point to determine the decomposition stage. Notably, the three most abundant families were Moraxellaceae, Burkholderiaceae (Proteobacteria), and Clostridiaceae (Firmicutes), though communities composition varied significantly across decomposition stages. Greater bacterial phylogenetic diversity was observed in in latter decomposition stages (advanced floating decay, sunken remains). Random Forest Models were built to predict ADH and explained 77%-80.8% of variation in ADH with an error rate of +/-1943.2 ADH (Root Mean Square Error) or approx. ±2.7 days at the mean water temperature of this study. This study provided a useful model that could be used to estimate a PMSI in this river system utilizing bacterial community succession, and thus, potentially improve the accuracy of such estimations to be used in the court of law.
Identifiants
pubmed: 34462924
doi: 10.1111/1556-4029.14869
doi:
Substances chimiques
DNA, Bacterial
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2307-2318Subventions
Organisme : Gordon Guyer Fellowship
Organisme : Merritt Endowed Fellowship
Organisme : Niemeyer-Hodgson Grant
Organisme : Millersville Student Investigator Grant
Organisme : Millesville University Faculty Grant
Organisme : Millersville University Student Research Grant
Informations de copyright
© 2021 American Academy of Forensic Sciences.
Références
Wallace JR, Merritt RW. The role of aquatic organisms in forensic investigations. In: Byrd JH, Tomberlin JK, editors. Forensic entomology: The utility of arthropods in legal investigations. Boca Raton, FL: CRC Press; 2020. p. 156-86.
Benbow ME, Receveur J, Lamberti GA. Death and decomposition in aquatic ecosystems. Front Ecol Evol. 2020;8:17. https://doi.org/10.3389/fevo.2020.00017.
Benbow ME, Barton PS, Ulyshen MD, Beasley JC, DeVault TL, Strickland MS, et al. Necrobiome framework for bridging decomposition ecology of autotrophically and heterotrophically-derived organic matter. Ecol Monogr. 2019;89(1):e01331. https://doi.org/10.1002/ecm.1331.
Nuorteva PH. Sarcosaprophagous insects as forensic indicators. In: Tedeschi CG, Eckert WG, Tedeschi LG, editors. Forensic medicine: A study in trauma and environmental hazards. Philadelphia, PA: WB Saunders Publishing; 1977. p. 1096-8.
Rodriguez WC, Bass WM. Insect activity and its relationship to decay rates of human cadavers in East Tennessee. J Forensic Sci. 1983;28(2):423-32. https://doi.org/10.1520/JFS11524J.
Catts E, Goff ML. Forensic entomology in criminal investigations. Annu Rev Entomol. 1992;37(1):253-72. https://doi.org/10.1146/annurev.en.37.010192:00134.
Keh B. Scope and applications of forensic entomology. Annu Rev Entomol. 1985;30:137-54. https://doi.org/10.1146/annurev.en.30.010185.001033.
Benecke M. Arthropods and corpses. In: Tsokos M, editor. Forensic pathology, Rev, vol. II. Totowa, NJ: Humana Press; 2004. p. 207-40.
Haefner JN, Wallace JR, Merritt RW. Pig decomposition in lotic aquatic systems: The potential use of algal growth in establishing a postmortem submersion interval (PMSI). J Forensic Sci. 2004;49(2):330-6.
Zimmerman K, Wallace JR. Estimating a postmortem submersion interval using algal diversity on mammalian carcasses in brackish waters. J Forensic Sci. 2008;53(4):935-41. https://doi.org/10.1111/j.1556-4029.2008.00748.x.
Anderson GS. Decomposition and invertebrate colonization of cadavers in coastal marine environments. Current concepts in forensic entomology. Dordrecht, Netherlands: Springer; 2009. p. 223-72. https://doi.org/10.1007/978-1-4020-9684-6_12.
Anderson GS, Bell LS. Impact of marine submergence and season on faunal colonization and decomposition of pig carcasses in the Salish Sea. PLoS One. 2016;11(3):e0149107. https://doi.org/10.1371/journal.pone.0149107.
Benbow ME, Pechal JL, Lang JM, Erb R, Wallace JR. The potential of high-throughput metagenomic sequencing of aquatic bacterial communities to estimate the postmortem submersion interval. J Forensic Sci. 2015;60(6):1500-10. https://doi.org/10.1111/1556-4029.12859.
Lang J, Erb R, Pechal J, Wallace J, McEwan R, Benbow ME. Microbial biofilm community variation in flowing habitats: Potential utility as bioindicators of postmortem submersion intervals. Microorganisms. 2016;4(1):1. https://doi.org/10.3390/microorganisms4010001.
Kaszubinski SF, Receveur JP, Wydra B, Smiles K, Wallace JR, Babcock NJ, et al. Cold case experiment demonstrates the potential utility of aquatic microbial community assembly in estimating a postmortem submersion interval. J Forensic Sci. 2020;65(4):1210-20. https://doi.org/10.1111/1556-4029.14303.
Cartozzo C, Simmons T, Swall J, Singh B. Postmortem submersion interval (PMSI) estimation from the microbiome of Sus scrofa bone in a freshwater river. Forensic Sci Int. 2021;318: https://doi.org/10.1016/j.forsciint.2020.110480.
Dickson GC, Poulter RTM, Maas EW, Probert PK, Kieser JA. Marine bacterial succession as a potential indicator of postmortem submersion interval. Forensic Sci Int. 2011;209(1-3):1-10. https://doi.org/10.1016/j.forsciint.2010.10.016.
Fenoglio S, Merritt RW, Cummins KW. Why do no specialized necrophagous species exist among aquatic insects? Freshw Sci. 2014;33(3):711-5. https://doi.org/10.1086/677038.
Kaushik NK, Hynes HBN. The fate of the dead leaves that fall into streams. Arch fur Hydrobiol. 1971;68:465-515.
Suberkropp K, Klug MJ. Fungi and bacteria associated with leaves during processing in a woodland stream. Ecology. 1976;57:707-19. https://doi.org/10.2307/1936184.
Suberkropp K, Klug MJ. The maceration of deciduous leaf litter by aquatic hyphomycetes. Can J Bot. 1980;58:1025-31. https://doi.org/10.1139/b80-126.
Weston NB, Vile MA, Neubauer SC, Velinsky DJ. Accelerated microbial organic matter mineralization following salt-water intrusion into tidal freshwater marsh soils. Biogeochem. 2011;102:135-51. https://doi.org/10.1007/s110533-010-9427.
Newell SY. Established and potential impacts of eukaryotic mycelial decomposers in marine/terrestrial ecotones. J Exp Mar Biol Ecol. 1996;200(1-2):187-206. https://doi.org/10.1016/S0022-0981(96)02643-3.
Allan JD. Stream ecology - Structure and function of running waters. London, U.K.: Chapman & Hall Publishers; 2007. p. 135-61.
Wallace JB, Eggert SL, Meyer JL, Webster JR. Multiple trophic levels of a forest stream linked to terrestrial inputs. Science. 1997;277(5322):102-4. https://doi.org/10.1126/science.277.5322.102
Parmenter RR, Lamarra VA. Nutrient cycling in a freshwater marsh: The decomposition of fish and waterfowl. Limnol Oceanogr. 1991;36(5):976-87. https://doi.org/10.4319/lo.1991.36.5.0976.
Burkepile DE, Parker JD, Woodson CB, Mills HJ, Kubanek J, Sobecky PA, et al. Chemically mediated competition between microbes and animals: Microbes as consumers in food webs. Ecology. 2006;87:2821-31.
Smith CR, Baco AR. Ecology of whale falls at the deep-sea floor. Oceanogr Mar Biol. 2003;41:311-54.
DeBruyn JM, Hauther KA. Postmortem succession of gut microbial communities in deceased human subjects. PeerJ. 2017;5:e3437. https://doi.org/10.7717/peerj.3437.
Javan GT, Finley SJ, Can I, Wilkinson JE, Hanson JD, Tarone AM. Human thanatomicrobiome succession and time since death. Sci Rep. 2016;6:29598. https://doi.org/10.1038/srep29598.
Metcalf Jl, Xu ZZ, Weiss S, et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science. 2016;351(6269):158-62. https://doi.org/10.1126/science.aad2646.
Lv X, Ma B, Yu J, Chang SX, Xu J, Li Y, et al. Bacterial community structure and function shift along a successional series of tidal flats in the Yellow River Delta. Sci Rep. 2016;6:36550. https://doi.org/10.1038/srep36550.
Pechal JL, Crippen TL, Benbow ME, Tarone AM, Dowd S, Tomberlin JK. The potential use of bacterial community succession in forensics as described by high throughput metagenomic sequencing. Int J Legal Med. 2014;128(1):193-205. https://doi.org/10.1007/s00414-013-0872-1.
Pearman JK, Keeley NB, Wood SA, Laroche O, Zaiko A, Thomson-Laing G, et al. Comparing sediment DNA extraction methods for assessing organic enrichment associated with marine aquaculture. PeerJ. 2020;8:e10231. https://doi.org/10.7717/peerj.10231.
Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci. 2011;108(Suppl 1):4516-22. https://doi.org/10.1073/pnas.1000080107.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. App Environ Microbiol. 2013;79(17):5112-20. https://doi.org/10.1128/AEM.01043-13.
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581-3. https://doi.org/10.1038/nmeth.3869.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: A versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. https://doi.org/10.7717/peerj.2584.
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotech. 2019;37(8):852-7. https://doi.org/10.1038/s41587-019-0209-9.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2012;41(D1):D590-6. https://doi.org/10.1093/nar/gks1219.
R Core Development Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 2011;5(2):169-72. https://doi.org/10.1038/ismej.2010.133.
Oksanen J, Blanchet FG, Kindt R, et al. Package ‘vegan’.Community Ecolology Package, version 2(9). 2013. p. 1-295. https://scholar.google.com/citations?view_op=view_citation&hl=de&user=2WBRFVIAAAAJ&citation_for_view=2WBRFVIAAAAJ:mB3voiENLucC. Accessed 20 July 2021.
Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2(3):18-22.
Wright MN, Ziegler A, ranger: A fast implementation of random forests for high dimensional data in C++ and R. 2015;77(1). https://doi.org/10.18637/jss.v077.i01.
Dang H, Lovell CR. Microbial surface biofilm development in marine environments. Microbio Mol Biol Rev. 2016;80(1):91-138. https://doi.org/10.1128/MMBR.00037-15.
Wallace JR. Aquatic vertebrate carrion decomposition. In: Benbow ME, Tomberlin JK, Tarone AM, editors. Carrion ecology, evolution and their applications. Boca Raton, FL: CRC Press; 2015. p. 247-71.
Moore JC, Berlow EL, Coleman DC, de Ruiter PC, Dong Q, Hastings A, et al. Detritus, trophic dynamics and biodiversity. Ecol Lett. 2004;7(7):584-600. https://doi.org/10.1111/j.1461-0248.2004.00606.x.
Polis GA, Anderson WB, Holt RD. Toward an integration of landscape and food web ecology: The dynamics of spatially subsidized food webs. Ann Rev Ecol System. 1997;28:289-316. https://doi.org/10.1146/annurev.ecolays.28.1.289.
Khandeparker L, Eswaran R, Gardade L, Kuchi N, Mapari K, Naik SD, et al. Elucidation of the tidal influence on bacterial populations in a monsoon influenced estuary through simultaneous observations. Environ Monit Assess. 2017;189:41. https://doi.org/10.1007/s10661-016-5687-3.
Bishop AH. The signatures of microorganisms and of human and environmental biomes can now be used to provide evidence in legal cases. FEMS Microbiol Lett. 2019;366(3): https://doi.org/10.1093/femsle/fnz021.
Zheng B, Wang L, Liu L. Bacterial community structure in its regulating factors in the intertidal sediment along the Liaodong Bay of Bohai Sea. China. Microbiol Res. 2014;169:585-92. https://doi.org/10.1016/j.micres.2013.09.019.
Byrd JH, Tomberlin JK. Insects of forensic importance. In: Byrd JH, Tomberlin JK, editors. Forensic entomology: The utility of arthropods in legal investigations, 2nd edn. Boca Raton, FL: CRC Press; 2020. p. 15-62.
Higley LG, Haskell NH. Insect development and forensic entomology. In: Byrd JH, Castner JL, editors. Forensic entomology: The utility of arthropods in legal investigations. Boca Raton, FL: CRC Press; 2010. p. 287-302.
Wells JD, Lamotte LR. Estimating the postmortem interval. In: Byrd JH, Tomberlin JK, editors. Forensic entomology: The utility of arthropods in legal investigations, 2nd edn. Boca Raton, FL: CRC Press; 2020. p. 213-24.
Julian P II, Osborne TZ. From lake to estuary, the tale of two waters. A study of aquatic continuum biogeochemistry. Environ Monit Assess. 2018;190(2): https://doi.org/10.1007/s10661-017-6455-8.
Belk A, Xu ZZ, Carter DD, Lynne A, Bucheli S, Knight R, et al. Microbiome data accurately predicts postmortem interval using random forest regression models. Genes. 2018;9(2):104. https://doi.org/10.3390/genes9020104.
Knights D, Costello EK, Knight R. Supervised classification of human microbiota. FEMS Microbiol Rev. 2011;35(2):343-59. https://doi.org/10.1111/j.1574-6976.2010.00251.x.
Morrissey EM, Gillespie JL, Morina JC, Franklin RB. Salinity affects microbial activity and soil organic matter content in tidal wetlands. Glob Chang Biol. 2013;20(4):1351-62. https://doi.org/10.1111/gcb.12431.