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Huang L, Liang X, Xiao G, Du J, Ye L, Su Q, Liu C, Chen L. Response of salivary microbiome to temporal, environmental, and surface characteristics under in vitro exposure. Forensic Sci Int Genet 2024; 70:103020. [PMID: 38286081 DOI: 10.1016/j.fsigen.2024.103020] [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: 10/17/2023] [Revised: 12/22/2023] [Accepted: 01/21/2024] [Indexed: 01/31/2024]
Abstract
The microbiome of saliva stains deposited at crime scenes and in everyday settings is valuable for forensic investigations and environmental ecology. However, the dynamics and applications of microbial communities in these saliva stains have not been fully explored. In this study, we analyzed saliva samples that were exposed to indoor conditions for up to 1 year and to different carriers (cotton, sterile absorbent cotton swab, woolen, dacron) in both indoor and outdoor environments for 1 month using high-throughput sequencing. The analysis of microbial composition and Mfuzz clustering showed that the salivary flora, specifically Streptococcus (cluster7), which was associated with microbial contamination, remained stable over short periods of time. However, prolonged exposure led to significant differences due to the invasion of environmental bacteria such as Pseudomonas and Achromobacter. The growth and colonization of environmental flora were promoted by humidity. The neutral model predictions indicated that the assembly of salivary microbial communities in outdoor environments was significantly influenced by stochastic processes, with environmental characteristics having a greater impact on community change compared to surface characteristics. By incorporating data from previous studies on fecal and vaginal secretion microbiology, we developed RF and XGBoost classification models that achieved high accuracy (>98 %) and AUC (>0.8). Additionally, a RF regression model was created to determine the time since deposition (TsD) of the stains. Time inference models yielded a mean absolute error (MAE) of 7.1 days for stains exposed for 1 year and 14.2 h for stains exposed for 14 days. These findings enhance our understanding of the changes in the microbiome of saliva stains over time, in different environments, and on different surfaces. They also have potential applications in assessing potential microbial contamination, identifying body fluids, and inferring the time of deposition.
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Affiliation(s)
- Litao Huang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Xiaomin Liang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Guichao Xiao
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Jieyu Du
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Linying Ye
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Qin Su
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Chao Liu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou, China.
| | - Ling Chen
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China.
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Zhang J, Yu D, Wang T, Gao N, Shi L, Wang Y, Huo Y, Ji Z, Li J, Zhang X, Zhang L, Yan J. Body fluids should be identified before estimating the time since deposition (TsD) in microbiome-based stain analyses for forensics. Microbiol Spectr 2024; 12:e0248023. [PMID: 38470485 PMCID: PMC10986545 DOI: 10.1128/spectrum.02480-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
Identification and the time since deposition (TsD) estimation of body fluid stains from a crime scene could provide valuable information for solving the cases and are always difficult for forensics. Microbial characteristics were considered as a promising biomarker to address the issues. However, changes in the microbiota may damage the specific characteristics of body fluids. Correspondingly, incorrect body fluid identification may result in inaccurate TsD estimation. The mutual influence is not well understood and limited the codetection. In the current study, saliva, semen, vaginal secretion, and menstrual blood samples were exposed to indoor conditions and collected at eight time points (from fresh to 30 days). High-throughput sequencing based on the 16S rRNA gene was performed to characterize the microbial communities. The results showed that a longer TsD could decrease the discrimination of different body fluid stains. However, the accuracies of identification still reached a quite high value even without knowing the TsD. Correspondingly, the mean absolute error (MAE) of TsD estimation significantly increased without distinguishing the types of body fluids. The predictive TsD of menstrual blood reached a quite low MAE (1.54 ± 0.39 d). In comparison, those of saliva (6.57 ± 1.17 d), semen (6.48 ± 1.33 d), and vaginal secretion (5.35 ± 1.11 d) needed to be further improved. The great effect of individual differences on these stains limited the TsD estimation accuracy. Overall, microbial characteristics allow for codetection of body fluid identification and TsD estimation, and body fluids should be identified before estimating TsD in microbiome-based stain analyses.IMPORTANCEEmerged evidences suggest microbial characteristics could be considered a promising tool for identification and time since deposition (TsD) estimation of body fluid stains. However, the two issues should be studied together due to a potential mutual influence. The current study provides the first evidence to understand the mutual influence and determines an optimal process for codetection of identification and TsD estimation for unknown stains for forensics. In addition, we involved aged stains into our study for identification of body fluid stains, rather than only using fresh stains like previous studies. This increased the predictive accuracy. We have preliminary verified that individual differences in microbiotas limited the predictive accuracy of TsD estimation for saliva, semen, and vaginal secretion. Microbial characteristics could provide an accurate TsD estimation for menstrual blood. Our study benefits the comprehensive understanding of microbiome-based stain analyses as an essential addition to previous studies.
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Affiliation(s)
- Jun Zhang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Daijing Yu
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Tian Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Niu Gao
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Linyu Shi
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Yaya Wang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Yumei Huo
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Zhimin Ji
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Junli Li
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Xiaomeng Zhang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Liwei Zhang
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Jinzhong, Shanxi, China
- Shanxi Key Laboratory of Forensic Medicine, Jinzhong, Shanxi, China
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Zhang J, Yan M, Ji A, Sun Q, Ying W. Mass spectrometry-based proteomic analysis of biological stains identifies body fluids specific markers. Forensic Sci Int 2024; 357:112008. [PMID: 38522320 DOI: 10.1016/j.forsciint.2024.112008] [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: 01/08/2024] [Revised: 03/04/2024] [Accepted: 03/20/2024] [Indexed: 03/26/2024]
Abstract
The identification of biological stains and their tissue resource is an important part of forensic research. Current methods suffer from several limitations including poor sensitivity and specificity, trace samples, and sample destruction. In this study, we profiled the proteomes of menstrual blood, peripheral blood, saliva, semen, and vaginal fluid with mass spectrometry technology. Tissue-enhanced and tissue-specific proteins of each group have been proposed as potential biomarkers. These candidate proteins were further annotated and screened through the combination with the Human Protein Atlas database. Our data not only validates the protein biomarkers reported in previous studies but also identifies novel candidate biomarkers for human body fluids. These candidates lay the foundation for the development of rapid and specific forensic examination methods.
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Affiliation(s)
- Jian Zhang
- MPS's Key Laboratory of Forensic Genetics, National Engineering Laboratory for Crime Scene Evidence Investigation and Examination, Institute of Forensic Science, Ministry of Public Security (MPS), Beijing 100038, China; State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Meng Yan
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Anquan Ji
- MPS's Key Laboratory of Forensic Genetics, National Engineering Laboratory for Crime Scene Evidence Investigation and Examination, Institute of Forensic Science, Ministry of Public Security (MPS), Beijing 100038, China
| | - Qifan Sun
- MPS's Key Laboratory of Forensic Genetics, National Engineering Laboratory for Crime Scene Evidence Investigation and Examination, Institute of Forensic Science, Ministry of Public Security (MPS), Beijing 100038, China.
| | - Wantao Ying
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
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Mei S, Wang X, Lei F, Lan Q, Cai M, Zhu B. Focus on studying the effects of different exposure durations on the microbial structures and characteristics of three types of body fluids. Forensic Sci Int 2024; 356:111949. [PMID: 38368751 DOI: 10.1016/j.forsciint.2024.111949] [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: 03/02/2023] [Revised: 01/16/2024] [Accepted: 01/24/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND Body fluid traceability inferences can provide important clues to the investigation of forensic cases. Microbiome has been proven to be well applied in forensic body fluid traceability studies. Most of the specimens at crime scenes are often exposed to the external environment when collected, so it is extremely important to exploring the structure characteristics of microbial communities of body fluid samples under different exposure durations for tracing the origin of body fluids based on microorganisms. METHODS Full-length 16S rRNA sequencing technology and multiple data analysis methods were used to explore the microbial changes in three types of body fluid samples at five different exposure time points. RESULTS With increasing exposure time, the Proteobacteria abundance gradually increased in the negative control and body fluid samples, and the Bacteroidetes and Firmicutes abundance decreased gradually, but the relative abundance of dominant genera in each body fluid remained dynamically stable. The microbial community structures of those samples from the same individual at different exposure durations were similar, and there were no significant differences in the microbial community structures among the different exposure time points. LEfSe and random forest analyses were applied to screen stable and differential microbial markers among body fluids, such as Streptococcus thermophilus, Streptococcus pneumoniae and Haemophilus parainfluenzae in saliva; Lactobacillus iners and Streptococcus agalactiae in vaginal fluid. CONCLUSIONS There were no significant differences in microbial community structures of the three types of body fluid samples exposed to the environment for various time periods, although the relative abundance of some microbes in these samples would change. The exposed samples could still be traced back to their source of the body fluid samples using the microbial community structures.
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Affiliation(s)
- Shuyan Mei
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515 China; School of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, Henan 471000 China
| | - Xi Wang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515 China
| | - Fanzhang Lei
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515 China
| | - Qiong Lan
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515 China; Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510282 China
| | - Meiming Cai
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515 China
| | - Bofeng Zhu
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515 China.
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Dou S, Ma G, Liang Y, Fu G, Shen J, Fu L, Wang Q, Li T, Cong B, Li S. Preliminary exploratory research on the application value of oral and intestinal meta-genomics in predicting subjects' occupations-A case study of the distinction between students and migrant workers. Front Microbiol 2024; 14:1330603. [PMID: 38390220 PMCID: PMC10883652 DOI: 10.3389/fmicb.2023.1330603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/26/2023] [Indexed: 02/24/2024] Open
Abstract
Background In the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual. Methods and results In this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, "ko04145" or Phagosome. Conclusion Although this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.
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Affiliation(s)
- Shujie Dou
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Guanju Ma
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Yu Liang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Guangping Fu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Jie Shen
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Lihong Fu
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Qian Wang
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
| | - Tao Li
- Institute of Intelligent Medical Research (IIMR), BGI Genomics, Shenzhen, China
| | - Bin Cong
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
- Hainan Tropical Forensic Medicine Academician Workstation, Haikou, China
| | - Shujin Li
- College of Forensic Medicine, Hebei Medical University, Hebei Key Laboratory of Forensic Medicine, Research Unit of Digestive Tract Microecosystem Pharmacology and Toxicology, Chinese Academy of Medical Sciences, Shijiazhuang, China
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Hänggi NV, Bleka Ø, Haas C, Fonneløp AE. Quantitative PCR analysis of bloodstains of different ages. Forensic Sci Int 2023; 350:111785. [PMID: 37527614 DOI: 10.1016/j.forsciint.2023.111785] [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: 04/28/2023] [Revised: 05/30/2023] [Accepted: 07/15/2023] [Indexed: 08/03/2023]
Abstract
An accurate method to estimate the age of a stain or the time since deposition (TsD) would represent an important tool in police investigations for evaluating the true relevance of a stain. In this study, two laboratories reproduced an mRNA-based method for TsD estimation published by another group. The qPCR-based assay includes four transcripts (B2M, LGALS2, CLC, and S100A12) and showed preferential degradation of the 5' end over the 3' end. In this study, the blood-specific marker ALAS2 was added to examine whether it would show the same degradation pattern. Based on our qPCR data several elastic net models with different penalty combinations were created, using training data from the two laboratories separately and combined. Each model was then used to estimate the age of bloodstains from two independent test sets each laboratory had prepared. The elastic net model built on both datasets with training samples up to 320 days old displayed the best prediction performance across all test samples (MAD=18.9 days). There was a substantial difference in the prediction performance for the two laboratories: Restricting TsD to up to 100 days for test data, one laboratory obtained an MAD of 2.0 days when trained on its own data, whereas the other laboratory obtained an MAD of 15 days.
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Affiliation(s)
| | - Øyvind Bleka
- Department of Forensic Sciences, Oslo University Hospital, Norway
| | - Cordula Haas
- Zurich Institute of Forensic Medicine, University of Zurich, Switzerland.
| | - Ane Elida Fonneløp
- Department of Forensic Sciences, Oslo University Hospital, Norway; Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Norway
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