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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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Ratiner K, Ciocan D, Abdeen SK, Elinav E. Utilization of the microbiome in personalized medicine. Nat Rev Microbiol 2024; 22:291-308. [PMID: 38110694 DOI: 10.1038/s41579-023-00998-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2023] [Indexed: 12/20/2023]
Abstract
Inter-individual human variability, driven by various genetic and environmental factors, complicates the ability to develop effective population-based early disease detection, treatment and prognostic assessment. The microbiome, consisting of diverse microorganism communities including viruses, bacteria, fungi and eukaryotes colonizing human body surfaces, has recently been identified as a contributor to inter-individual variation, through its person-specific signatures. As such, the microbiome may modulate disease manifestations, even among individuals with similar genetic disease susceptibility risks. Information stored within microbiomes may therefore enable early detection and prognostic assessment of disease in at-risk populations, whereas microbiome modulation may constitute an effective and safe treatment tailored to the individual. In this Review, we explore recent advances in the application of microbiome data in precision medicine across a growing number of human diseases. We also discuss the challenges, limitations and prospects of analysing microbiome data for personalized patient care.
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Affiliation(s)
- Karina Ratiner
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Dragos Ciocan
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Suhaib K Abdeen
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel.
| | - Eran Elinav
- Systems Immunology Department, Weizmann Institute of Science, Rehovot, Israel.
- Division of Cancer-Microbiome Research, DKFZ, Heidelberg, Germany.
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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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Yang MQ, Wang ZJ, Zhai CB, Chen LQ. Research progress on the application of 16S rRNA gene sequencing and machine learning in forensic microbiome individual identification. Front Microbiol 2024; 15:1360457. [PMID: 38371926 PMCID: PMC10869621 DOI: 10.3389/fmicb.2024.1360457] [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: 12/23/2023] [Accepted: 01/23/2024] [Indexed: 02/20/2024] Open
Abstract
Forensic microbiome research is a field with a wide range of applications and a number of protocols have been developed for its use in this area of research. As individuals host radically different microbiota, the human microbiome is expected to become a new biomarker for forensic identification. To achieve an effective use of this procedure an understanding of factors which can alter the human microbiome and determinations of stable and changing elements will be critical in selecting appropriate targets for investigation. The 16S rRNA gene, which is notable for its conservation and specificity, represents a potentially ideal marker for forensic microbiome identification. Gene sequencing involving 16S rRNA is currently the method of choice for use in investigating microbiomes. While the sequencing involved with microbiome determinations can generate large multi-dimensional datasets that can be difficult to analyze and interpret, machine learning methods can be useful in surmounting this analytical challenge. In this review, we describe the research methods and related sequencing technologies currently available for application of 16S rRNA gene sequencing and machine learning in the field of forensic identification. In addition, we assess the potential value of 16S rRNA and machine learning in forensic microbiome science.
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Affiliation(s)
- Mai-Qing Yang
- Department of Pathology, Weifang People's Hospital (First Affiliated Hospital of Shandong Second Medical University), Weifang, China
| | - Zheng-Jiang Wang
- Department of Pathology, Weifang People's Hospital (First Affiliated Hospital of Shandong Second Medical University), Weifang, China
| | - Chun-Bo Zhai
- Department of Second Ward of Thoracic Surgery, Weifang People's Hospital (First Affiliated Hospital of Shandong Second Medical University), Weifang, China
| | - Li-Qian Chen
- Department of Pathology, Weifang People's Hospital (First Affiliated Hospital of Shandong Second Medical University), Weifang, China
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Shao S, Yang L, Hu G, Li L, Wang Y, Tao L. Application of omics techniques in forensic entomology research. Acta Trop 2023; 246:106985. [PMID: 37473953 DOI: 10.1016/j.actatropica.2023.106985] [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: 06/01/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/22/2023]
Abstract
With the advent of the post-genome era, omics technologies have developed rapidly and are widely used, including in genomics, transcriptomics, proteomics, metabolomics, and microbiome research. These omics techniques are often based on comprehensive and systematic analysis of biological samples using high-throughput analysis methods and bioinformatics, to provide new insights into biological phenomena. Currently, omics techniques are gradually being applied to forensic entomology research and are useful in species identification, phylogenetics, screening for developmentally relevant differentially expressed genes, and the interpretation of behavioral characteristics of forensic-related species at the genetic level. These all provide valuable information for estimating the postmortem interval (PMI). This review mainly discusses the available omics techniques, summarizes the application of omics techniques in forensic entomology, and their future in the field.
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Affiliation(s)
- Shipeng Shao
- Department of Forensic Medicine, Soochow University, Ganjiang East Road, Suzhou, China
| | - Lijun Yang
- Criminal Police Branch, Suzhou Public Security Bureau, Renmin Road, Suzhou, China
| | - Gengwang Hu
- Department of Forensic Medicine, Soochow University, Ganjiang East Road, Suzhou, China
| | - Liangliang Li
- Department of Forensic Medicine, Soochow University, Ganjiang East Road, Suzhou, China
| | - Yu Wang
- Department of Forensic Medicine, Soochow University, Ganjiang East Road, Suzhou, China.
| | - Luyang Tao
- Department of Forensic Medicine, Soochow University, Ganjiang East Road, Suzhou, China
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Furstenau TN, Schneider T, Shaffer I, Vazquez AJ, Sahl J, Fofanov V. MTSv: rapid alignment-based taxonomic classification and high-confidence metagenomic analysis. PeerJ 2022; 10:e14292. [PMID: 36389404 PMCID: PMC9651046 DOI: 10.7717/peerj.14292] [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: 03/29/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022] Open
Abstract
As the size of reference sequence databases and high-throughput sequencing datasets continue to grow, it is becoming computationally infeasible to use traditional alignment to large genome databases for taxonomic classification of metagenomic reads. Exact matching approaches can rapidly assign taxonomy and summarize the composition of microbial communities, but they sacrifice accuracy and can lead to false positives. Full alignment tools provide higher confidence assignments and can assign sequences from genomes that diverge from reference sequences; however, full alignment tools are computationally intensive. To address this, we designed MTSv specifically for alignment-based taxonomic assignment in metagenomic analysis. This tool implements an FM-index assisted q-gram filter and SIMD accelerated Smith-Waterman algorithm to find alignments. However, unlike traditional aligners, MTSv will not attempt to make additional alignments to a TaxID once an alignment of sufficient quality has been found. This improves efficiency when many reference sequences are available per taxon. MTSv was designed to be flexible and can be modified to run on either memory or processor constrained systems. Although MTSv cannot compete with the speeds of exact k-mer matching approaches, it is reasonably fast and has higher precision than popular exact matching approaches. Because MTSv performs a full alignment it can classify reads even when the genomes share low similarity with reference sequences and provides a tool for high confidence pathogen detection with low off-target assignments to near neighbor species.
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Affiliation(s)
- Tara N. Furstenau
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States
| | - Tsosie Schneider
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States
| | - Isaac Shaffer
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States
| | - Adam J. Vazquez
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States
| | - Jason Sahl
- Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States
| | - Viacheslav Fofanov
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States,Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, United States
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