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Bone MS, Legrand TPRA, Harvey ML, Wos-Oxley ML, Oxley APA. Aquatic conditions & bacterial communities as drivers of the decomposition of submerged remains. Forensic Sci Int 2024; 361:112072. [PMID: 38838610 DOI: 10.1016/j.forsciint.2024.112072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 06/07/2024]
Abstract
Aquatic decomposition, as a forensic discipline, has been largely under-investigated as a consequence of the highly complex and influential variability of the water environment. The limitation to the adaptability of scenario specific results justifies the necessity for experimental research to increase our understanding of the aquatic environment and the development of post-mortem submersion interval (PMSI) methods of estimation. This preliminary research aims to address this contextual gap by assessing the variation in the bacterial composition of aquatic biofilms as explained by water parameter measurements over time, associated with clothed and bare decomposing remains. As part of three field investigations, a total of 9 still-born piglets (n = 3, per trial) were used as human analogues and were submerged bare or clothed in either natural cotton or synthetic nylon. Changes in the bacterial community composition of the water surrounding the submerged remains were assessed at 4 discrete time points post submersion (7, 14, 21 and 28 days) by 16 S rRNA gene Next Generation Sequencing analysis and compared to coinciding water parameter measurements (i.e. conductivity, total dissolved solids (TDS), salinity, pH, and dissolved oxygen (DO)). Bacterial diversity was found to change over time and relative to clothing type, where significant variation was observed between synthetic nylon samples and bare/cotton samples. Seasonality was a major driver of bacterial diversity, where substantial variation was found between samples collected in early winter to those collected in mid - late winter. Water parameter measures of pH, salinity and DO were identified to best explain the global bacterial community composition and their corresponding dynamic trajectory patterns overtime. Further investigation into bacterial community dynamics in accordance with varying environmental conditions could potentially lead to the determination of influential extrinsic factors that may drive bacterial activity in aquatic decomposition. Together with the identification of potential bacterial markers that complement the different stages of decomposition, this may provide a future approach to PMSI estimations.
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Affiliation(s)
- Madison S Bone
- School of Life and Environmental Sciences, Deakin University, Geelong, VIC 3216, Australia.
| | | | - Michelle L Harvey
- School of Life and Environmental Sciences, Deakin University, Geelong, VIC 3216, Australia
| | | | - Andrew P A Oxley
- School of Life and Environmental Sciences, Deakin University, Geelong, VIC 3216, Australia
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Wang X, Le C, Jin X, Feng Y, Chen L, Huang X, Tian S, Wang Q, Ji J, Liu Y, Zhang H, Huang J, Ren Z. Estimating postmortem interval based on oral microbial community succession in rat cadavers. Heliyon 2024; 10:e31897. [PMID: 38882314 PMCID: PMC11177140 DOI: 10.1016/j.heliyon.2024.e31897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/18/2024] Open
Abstract
The accurate estimation of the postmortem interval has been one of the crucial issues to be solved in forensic research, and it is influenced by various factors in the process of decay. With the development of high-throughput sequencing technology, forensic microbiology has become the major hot topic in forensic science, which provides new research options for postmortem interval estimation. The oral microbial community is one of the most diverse of microbiomes, ranking as the second most abundant microbiota following the gastrointestinal tract. It is remarkable that oral microorganisms have a significant function in the decay process of cadavers. Therefore, we collected outdoor soil to simulate the death environment and focused on the relationship between oral microbial community succession and PMI in rats above the soil. In addition, linear regression models and random forest regression models were developed for the relationship between the relative abundance of oral microbes and PMI. We also identified a number of microorganisms that may be important to estimate PMI, including: Ignatzschineria, Morganella, Proteus, Lysinibacillus, Pseudomonas, Globicatella, Corynebacterium, Streptococcus, Rothia, Aerococcus, Staphylococcus, and so on.
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Affiliation(s)
- Xiaoxue Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Cuiyun Le
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Xiaoye Jin
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Yuhang Feng
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Li Chen
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Xiaolan Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Shunyi Tian
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Qiyan Wang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Jingyan Ji
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Yubo Liu
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Hongling Zhang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Jiang Huang
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Zheng Ren
- Department of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
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Yuan H, Wang Z, Wang Z, Zhang F, Guan D, Zhao R. Trends in forensic microbiology: From classical methods to deep learning. Front Microbiol 2023; 14:1163741. [PMID: 37065115 PMCID: PMC10098119 DOI: 10.3389/fmicb.2023.1163741] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/08/2023] [Indexed: 04/18/2023] Open
Abstract
Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology.
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Affiliation(s)
- Huiya Yuan
- Department of Forensic Analytical Toxicology, China Medical University School of Forensic Medicine, Shenyang, China
- Liaoning Province Key Laboratory of Forensic Bio-Evidence Science, Shenyang, China
| | - Ziwei Wang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Zhi Wang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Fuyuan Zhang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Dawei Guan
- Liaoning Province Key Laboratory of Forensic Bio-Evidence Science, Shenyang, China
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
- *Correspondence: Dawei Guan
| | - Rui Zhao
- Liaoning Province Key Laboratory of Forensic Bio-Evidence Science, Shenyang, China
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
- Rui Zhao
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Wang L, Zhang F, Zeng K, Dong W, Yuan H, Wang Z, Liu J, Pan J, Zhao R, Guan D. Microbial communities in the liver and brain are informative for postmortem submersion interval estimation in the late phase of decomposition: A study in mouse cadavers recovered from freshwater. Front Microbiol 2022; 13:1052808. [PMID: 36458191 PMCID: PMC9705336 DOI: 10.3389/fmicb.2022.1052808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction Bodies recovered from water, especially in the late phase of decomposition, pose difficulties to the investigating authorities. Various methods have been proposed for postmortem submersion interval (PMSI) estimation and drowning identification, but some limitations remain. Many recent studies have proved the value of microbiota succession in viscera for postmortem interval estimation. Nevertheless, the visceral microbiota succession and its application for PMSI estimation and drowning identification require further investigation. Methods In the current study, mouse drowning and CO2 asphyxia models were developed, and cadavers were immersed in freshwater for 0 to 14 days. Microbial communities in the liver and brain were characterized via 16S rDNA high-throughput sequencing. Results Only livers and brains collected from 5 to 14 days postmortem were qualified for sequencing. There was significant variation between microbiota from liver and brain. Differences in microbiota between the cadavers of mice that had drowned and those only subjected to postmortem submersion decreased over the PMSI. Significant successions in microbial communities were observed among the different subgroups within the late phase of the PMSI in livers and brains. Eighteen taxa in the liver which were mainly related to Clostridium_sensu_stricto and Aeromonas, and 26 taxa in the brain which were mainly belonged to Clostridium_sensu_stricto, Acetobacteroides, and Limnochorda, were selected as potential biomarkers for PMSI estimation based on a random forest algorithm. The PMSI estimation models established yielded accurate prediction results with mean absolute errors ± the standard error of 1.282 ± 0.189 d for the liver and 0.989 ± 0.237 d for the brain. Conclusions The present study provides novel information on visceral postmortem microbiota succession in corpses submerged in freshwater which sheds new light on PMSI estimation based on the liver and brain in forensic practice.
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Affiliation(s)
- Linlin Wang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China,Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
| | - Fuyuan Zhang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Kuo Zeng
- Institute of Evidence Law and Forensic Science, China University of Political Science and Law, Beijing, China
| | - Wenwen Dong
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China,Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
| | - Huiya Yuan
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China,Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
| | - Ziwei Wang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Jin Liu
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Jiaqing Pan
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Rui Zhao
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China,Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China,*Correspondence: Rui Zhao,
| | - Dawei Guan
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China,Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China,Dawei Guan,
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Martlin BA, Anderson GS, Bell LS. A review of human decomposition in marine environments. CANADIAN SOCIETY OF FORENSIC SCIENCE JOURNAL 2022. [DOI: 10.1080/00085030.2022.2135741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Britny A. Martlin
- Centre for Forensic Research, School of Criminology, Simon Fraser University, Burnaby, BC, Canada
| | - Gail S. Anderson
- Centre for Forensic Research, School of Criminology, Simon Fraser University, Burnaby, BC, Canada
| | - Lynne S. Bell
- Centre for Forensic Research, School of Criminology, Simon Fraser University, Burnaby, BC, Canada
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Wang Z, Zhang F, Wang L, Yuan H, Guan D, Zhao R. Advances in artificial intelligence-based microbiome for PMI estimation. Front Microbiol 2022; 13:1034051. [PMID: 36267183 PMCID: PMC9577360 DOI: 10.3389/fmicb.2022.1034051] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Abstract
Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation.
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Affiliation(s)
- Ziwei Wang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Fuyuan Zhang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
| | - Linlin Wang
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
| | - Huiya Yuan
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
| | - Dawei Guan
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
- *Correspondence: Dawei Guan,
| | - Rui Zhao
- Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China
- Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China
- Rui Zhao,
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