Cargando…

Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation

Bacteria acts as the main decomposer during the process of biodegradation by microbial communities in the ecosystem. Numerous studies have revealed the bacterial succession patterns during carcass decomposition in the terrestrial setting. The machine learning algorithm-generated models based on such...

Descripción completa

Detalles Bibliográficos
Autores principales: Dmitrijs, Finkelbergs, Guo, Juanjuan, Huang, Yecao, Liu, Yafei, Fang, Xinyue, Jiang, Kankan, Zha, Lagabaiyila, Cai, Jifeng, Fu, Xiaoliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356301/
https://www.ncbi.nlm.nih.gov/pubmed/35942315
http://dx.doi.org/10.3389/fmicb.2022.951707
_version_ 1784763486842126336
author Dmitrijs, Finkelbergs
Guo, Juanjuan
Huang, Yecao
Liu, Yafei
Fang, Xinyue
Jiang, Kankan
Zha, Lagabaiyila
Cai, Jifeng
Fu, Xiaoliang
author_facet Dmitrijs, Finkelbergs
Guo, Juanjuan
Huang, Yecao
Liu, Yafei
Fang, Xinyue
Jiang, Kankan
Zha, Lagabaiyila
Cai, Jifeng
Fu, Xiaoliang
author_sort Dmitrijs, Finkelbergs
collection PubMed
description Bacteria acts as the main decomposer during the process of biodegradation by microbial communities in the ecosystem. Numerous studies have revealed the bacterial succession patterns during carcass decomposition in the terrestrial setting. The machine learning algorithm-generated models based on such temporal succession patterns have been developed for the postmortem interval (PMI) estimation. However, the bacterial succession that occurs on decomposing carcasses in the aquatic environment is poorly understood. In the forensic practice, the postmortem submersion interval (PMSI), which approximately equals to the PMI in most of the common drowning cases, has long been problematic to determine. In the present study, bacterial successions in the epinecrotic biofilm samples collected from the decomposing swine cadavers submerged in water were analyzed by sequencing the variable region 4 (V4) of 16S rDNA. The succession patterns between the repeated experimental settings were repeatable. Using the machine learning algorithm for establishing random forest (RF) models, the microbial community succession patterns in the epinecrotic biofilm samples taken during the 56-day winter trial and 21-day summer trial were determined to be used as the PMSI predictors with the mean absolute error (MAE) of 17.87 ± 2.48 ADD (≈1.3 day) and 20.59 ± 4.89 ADD (≈0.7 day), respectively. Significant differences were observed between the seasons and between the substrates. The data presented in this research suggested that the influences of the environmental factors and the aquatic bacterioplankton on succession patterns of the biofilm bacteria were of great significance. The related mechanisms of such influence need to be further studied and clarified in depth to consider epinecrotic biofilm as a reliable predictor in the forensic investigations.
format Online
Article
Text
id pubmed-9356301
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-93563012022-08-07 Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation Dmitrijs, Finkelbergs Guo, Juanjuan Huang, Yecao Liu, Yafei Fang, Xinyue Jiang, Kankan Zha, Lagabaiyila Cai, Jifeng Fu, Xiaoliang Front Microbiol Microbiology Bacteria acts as the main decomposer during the process of biodegradation by microbial communities in the ecosystem. Numerous studies have revealed the bacterial succession patterns during carcass decomposition in the terrestrial setting. The machine learning algorithm-generated models based on such temporal succession patterns have been developed for the postmortem interval (PMI) estimation. However, the bacterial succession that occurs on decomposing carcasses in the aquatic environment is poorly understood. In the forensic practice, the postmortem submersion interval (PMSI), which approximately equals to the PMI in most of the common drowning cases, has long been problematic to determine. In the present study, bacterial successions in the epinecrotic biofilm samples collected from the decomposing swine cadavers submerged in water were analyzed by sequencing the variable region 4 (V4) of 16S rDNA. The succession patterns between the repeated experimental settings were repeatable. Using the machine learning algorithm for establishing random forest (RF) models, the microbial community succession patterns in the epinecrotic biofilm samples taken during the 56-day winter trial and 21-day summer trial were determined to be used as the PMSI predictors with the mean absolute error (MAE) of 17.87 ± 2.48 ADD (≈1.3 day) and 20.59 ± 4.89 ADD (≈0.7 day), respectively. Significant differences were observed between the seasons and between the substrates. The data presented in this research suggested that the influences of the environmental factors and the aquatic bacterioplankton on succession patterns of the biofilm bacteria were of great significance. The related mechanisms of such influence need to be further studied and clarified in depth to consider epinecrotic biofilm as a reliable predictor in the forensic investigations. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9356301/ /pubmed/35942315 http://dx.doi.org/10.3389/fmicb.2022.951707 Text en Copyright © 2022 Dmitrijs, Guo, Huang, Liu, Fang, Jiang, Zha, Cai and Fu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Dmitrijs, Finkelbergs
Guo, Juanjuan
Huang, Yecao
Liu, Yafei
Fang, Xinyue
Jiang, Kankan
Zha, Lagabaiyila
Cai, Jifeng
Fu, Xiaoliang
Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation
title Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation
title_full Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation
title_fullStr Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation
title_full_unstemmed Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation
title_short Bacterial Succession in Microbial Biofilm as a Potential Indicator for Postmortem Submersion Interval Estimation
title_sort bacterial succession in microbial biofilm as a potential indicator for postmortem submersion interval estimation
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356301/
https://www.ncbi.nlm.nih.gov/pubmed/35942315
http://dx.doi.org/10.3389/fmicb.2022.951707
work_keys_str_mv AT dmitrijsfinkelbergs bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT guojuanjuan bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT huangyecao bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT liuyafei bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT fangxinyue bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT jiangkankan bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT zhalagabaiyila bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT caijifeng bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation
AT fuxiaoliang bacterialsuccessioninmicrobialbiofilmasapotentialindicatorforpostmortemsubmersionintervalestimation