1
|
Huang L, Huang H, Liang X, Su Q, Ye L, Zhai C, Huang E, Pang J, Zhong X, Shi M, Chen L. Skin locations inference and body fluid identification from skin microbial patterns for forensic applications. Forensic Sci Int 2024; 362:112152. [PMID: 39067177 DOI: 10.1016/j.forsciint.2024.112152] [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: 10/31/2023] [Revised: 03/15/2024] [Accepted: 07/15/2024] [Indexed: 07/30/2024]
Abstract
Given that microbiological analysis can be an alternative method that overcomes the shortcomings of traditional forensic technology, and skin samples may be the most common source of cases, the analysis of skin microbiome was investigated in this study. High-throughput sequencing targeting the V3-V4 region of 16S rRNA gene was performed to reveal the skin microbiome of healthy individuals in Guangdong Han. The bacterial diversity of the palm, navel, groin and plantar of the same individual was analyzed. The overall classification based on 16S rRNA gene amplicons revealed that the microbial composition of skin samples from different anatomical parts was different, and the dominant bacterial genus of the navel, plantar, groin and palm skin were dominated by Cutibacterium, Staphylococcus, Corynebacterium and Staphylococcus, respectively. PCoA analysis showed that the skin at these four anatomical locations could only be grouped into three clusters. A predictive model based on random forest algorithm showed the potential to accurately distinguish these four anatomical locations, which indicated that specific bacteria with low abundance were the key taxa. In addition, the skin microbiome in this study is significantly different from the dominant microbiome in saliva and vaginal secretions identified in our previous study, and can be distinguished from these two tissue fluids. In conclusion, the present findings on the community and microbial structure details of the human skin may reveal its potential application value in assessing the location of skin samples and the type of body fluids in forensic medicine.
Collapse
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
| | - Hongyan 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
| | - Qin Su
- 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
| | - Chuangyan Zhai
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Enping Huang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Junjie Pang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - XingYu Zhong
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Meisen Shi
- Criminal Justice College of China University of Political Science and Law, Beijing 100088, China.
| | - Ling Chen
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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.
| |
Collapse
|
4
|
Mir TUG, Manhas S, Khurshid Wani A, Akhtar N, Shukla S, Prakash A. Alterations in microbiome of COVID-19 patients and its impact on forensic investigations. Sci Justice 2024; 64:81-94. [PMID: 38182316 DOI: 10.1016/j.scijus.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 11/12/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024]
Abstract
The human microbiome is vital for maintaining human health and has garnered substantial attention in recent years, particularly in the context of the coronavirus disease 2019 (COVID-19) outbreak. Studies have underscored significant alterations in the microbiome of COVID-19 patients across various body niches, including the gut, respiratory tract, oral cavity, skin, and vagina. These changes manifest as shifts in microbiota composition, characterized by an increase in opportunistic pathogens and a decrease in beneficial commensal bacteria. Such microbiome transformations may play a pivotal role in influencing the course and severity of COVID-19, potentially contributing to the inflammatory response. This ongoing relationship between COVID-19 and the human microbiome serves as a compelling subject of research, underscoring the necessity for further investigations into the underlying mechanisms and their implications for patient health. Additionally, these alterations in the microbiome may have significant ramifications for forensic investigations, given the microbiome's potential in establishing individual characteristics. Consequently, changes in the microbiome could introduce a level of complexity into forensic determinations. As research progresses, a more profound understanding of the human microbiome within the context of COVID-19 may offer valuable insights into disease prevention, treatment strategies, and its potential applications in forensic science. Consequently, this paper aims to provide an overarching review of microbiome alterations due to COVID-19 and the associated impact on forensic applications, bridging the gap between the altered microbiome of COVID-19 patients and the challenges forensic investigations may encounter when analyzing this microbiome as a forensic biomarker.
Collapse
Affiliation(s)
- Tahir Ul Gani Mir
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144411, Punjab, India; State Forensic Science Laboratory, Srinagar, Jammu and Kashmir 190001, India.
| | - Sakshi Manhas
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Atif Khurshid Wani
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Nahid Akhtar
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Saurabh Shukla
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144411, Punjab, India.
| | - Ajit Prakash
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, NC 27599, USA
| |
Collapse
|
5
|
Liao L, Sun Y, Huang L, Ye L, Chen L, Shen M. A novel approach for exploring the regional features of vaginal fluids based on microbial relative abundance and alpha diversity. J Forensic Leg Med 2023; 100:102615. [PMID: 37995431 DOI: 10.1016/j.jflm.2023.102615] [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: 04/29/2023] [Revised: 09/14/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023]
Abstract
Vaginal fluids are one of the most common biological samples in forensic sexual assault cases, and their characterization is vital to narrow the scope of investigation. Presently, approaches for identifying vaginal fluids in different regions are not only rare but also have certain limitations. However, the microbiome has shown the potential to identify the source of body fluids and reveal the characteristics of individuals. In this study, 16S rRNA gene high-throughput sequencing was used to characterize the vaginal microbial community from three regions, Sichuan, Hainan and Hunan. In addition, data on relative abundance and alpha diversity were used to construct a random forest model. The results revealed that the dominant genera in the three regions were Lactobacillus, followed by Gardnerella. In addition, Ureaplasma, Nitrospira, Nocardiodes, Veillonella and g-norank-f-Vicinamibacteraceae were significantly enriched genera in Sichuan, llumatobacter was enriched in Hainan, and Pseudomonas was enriched in Hunan. The random forest classifier based on combined data on relative abundance and alpha diversity had a good ability to distinguish vaginal fluids with similar dominant microbial compositions in the three regions. The study suggests that combining high-throughput sequencing data with machine learning models has good potential for application in the biogeographic inference of vaginal fluids.
Collapse
Affiliation(s)
- Lili Liao
- Department of Hygiene Inspection & Quarantine Science, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China; Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Yunxia Sun
- Department of Hygiene Inspection & Quarantine Science, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Litao Huang
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Linying Ye
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Ling Chen
- Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, China.
| | - Mei Shen
- Department of Hygiene Inspection & Quarantine Science, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
| |
Collapse
|
6
|
Mishra A, Khan S, Das A, Das BC. Evolution of Diagnostic and Forensic Microbiology in the Era of Artificial Intelligence. Cureus 2023; 15:e45738. [PMID: 37872929 PMCID: PMC10590455 DOI: 10.7759/cureus.45738] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2023] [Indexed: 10/25/2023] Open
Abstract
Diagnostic microbiology plays a vital role in managing infectious diseases, combating antimicrobial resistance, and containment of outbreaks. During the fourth industrial revolution, when artificial intelligence (AI) became an essential part of our day-to-day lives, its integration into healthcare would further revolutionize our knowledge and potential. Although in the budding stage, AI with machine learning is being increasingly utilized in various aspects of diagnostic microbiology. It can handle large datasets that are difficult to analyze manually. Researchers have developed and demonstrated several machine-learning algorithms for interpreting bacterial cultures, conducting image analysis for microbial detection, and predicting antimicrobial susceptibility patterns. Thus, AI may most likely be the ultimate solution to the ever-increasing demand for improved results with shorter turnaround times. AI can also assist forensic microbiologists in crime scene investigations, as it can guide individual identification, cause and time since death, and manner of death. This review summarizes the application of AI in diagnostic microbiology for performing diverse sets of microbial investigations and is an essential aid in forensic microbiology.
Collapse
Affiliation(s)
- Anwita Mishra
- Department of Microbiology, Mahamana Pandit Madan Mohan Malviya Cancer Centre and Homi Bhabha Cancer Hospital, Varanasi, IND
| | - Salman Khan
- Department of Microbiology, National Cancer Institute, Jhajjar, IND
| | - Arghya Das
- Department of Microbiology, All India Institute of Medical Sciences, Madurai, IND
| | - Bharat C Das
- Department of Microbiology, All India Institute of Medical Sciences, New Delhi, IND
| |
Collapse
|
7
|
Wohlfahrt D, Tan-Torres AL, Green R, Brim K, Bradley N, Brand A, Abshier E, Nogales F, Babcock K, Brooks J, Seashols-Williams S, Singh B. A bacterial signature-based method for the identification of seven forensically relevant human body fluids. Forensic Sci Int Genet 2023; 65:102865. [PMID: 37004371 DOI: 10.1016/j.fsigen.2023.102865] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
Detection and identification of body fluids plays a crucial role in criminal investigation, as it provides information on the source of the DNA as well as corroborative evidence regarding the crime committed, scene, and/or association with persons of interest. Historically, forensic serological methods have been chemical, immunological, catalytic, spectroscopic, and/or microscopic in nature. However, most of these methods are presumptive, with few robust confirmatory exceptions. In recent years several new molecular methods (mRNA, miRNA, DNA methylation, etc.) have been proposed; although promising, these methods require high quality human DNA or RNA. Additional steps are required in RNA based methods. Additionally, RNA based methods cannot be used for old cases where only DNA extracts remain to sample from. In this study, a novel non-human DNA (microbiome) based method was developed for the identification of the majority of forensically relevant human biological samples. Eight hundred and twelve (n = 812) biological samples (semen, vaginal fluid, menstrual blood, saliva, feces, urine, and blood) were collected and preserved using methods commonly used in forensic laboratories for evidence collection. Variable region four (V4) of 16 S ribosomal DNA (16 S rDNA) was amplified using a dual-indexing strategy and then sequenced on the MiSeq FGx sequencing platform using the MiSeq Reagent Kit v2 (500 cycles) and following the manufacturer's protocol. Machine learning prediction models were used to assess the classification accuracy of the newly developed method. As there was no significant difference in bacterial communities between vaginal fluid, menstrual blood, and female urine, these were combined as female intimate samples. Except in urine, the bacterial structures associated with male and female body fluid samples were not significantly different from one another. The newly developed method accurately identified human body fluid samples with an overall accuracy of more than 88%. This newly developed bacterial signature-based method is fast (no additional steps are needed as the same DNA can be used for both body fluid identification and STR typing), efficient (consume less sample as a single test can identify all major body fluids), sensitive (needs only 5 pg of bacterial DNA), accurate, and can be easily added into a forensic high throughput sequencing (HTS) panel.
Collapse
|
8
|
Abstract
Recent advances in next-generation sequencing technologies (NGS) coupled with machine learning have demonstrated the potential of microbiome-based analyses in applied areas such as clinical diagnostics and forensic sciences. Particularly in forensics, microbial markers in biological stains left at a crime scene can provide valuable information for the reconstruction of crime scene cases, as they contain information on bodily origin, the time since deposition, and donor(s) of the stain. Importantly, microbiome-based analyses provide a complementary or an alternative approach to current methods when these are limited or not feasible. Despite the promising results from recent research, microbiome-based stain analyses are not yet employed in routine casework. In this review, we highlight the two main gaps that need to be addressed before we can successfully integrate microbiome-based analyses in applied areas with a special focus on forensic casework: one is a comprehensive assessment of the method's strengths and limitations, and the other is the establishment of a standard operating procedure. For the latter, we provide a roadmap highlighting key decision steps and offering laboratory and bioinformatic workflow recommendations, while also delineating those aspects that require further testing. Our goal is to ultimately facilitate the streamlining of microbiome-based analyses within the existing forensic framework to provide alternate lines of evidence, thereby improving the quality of investigations.
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
He Q, Niu X, Qi RQ, Liu M. Advances in microbial metagenomics and artificial intelligence analysis in forensic identification. Front Microbiol 2022; 13:1046733. [PMID: 36458190 PMCID: PMC9705755 DOI: 10.3389/fmicb.2022.1046733] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 10/25/2023] Open
Abstract
Microorganisms, which are widely distributed in nature and human body, show unique application value in forensic identification. Recent advances in high-throughput sequencing technology and significant reductions in analysis costs have markedly promoted the development of forensic microbiology and metagenomics. The rapid progression of artificial intelligence (AI) methods and computational approaches has shown their unique application value in forensics and their potential to address relevant forensic questions. Here, we summarize the current status of microbial metagenomics and AI analysis in forensic microbiology, including postmortem interval inference, individual identification, geolocation, and tissue/fluid identification.
Collapse
Affiliation(s)
- Qing He
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Xueli Niu
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Rui-Qun Qi
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Key Laboratory of Immunodermatology, Ministry of Education and NHC, National Joint Engineering Research Center for Theranostics of Immunological Skin Diseases, Shenyang, China
| | - Min Liu
- Department of Dermatology, The First Hospital of China Medical University, Shenyang, China
- Institute of Respiratory Disease, China Medical University, Shenyang, China
| |
Collapse
|
11
|
Arora N, Matias Rodrigues JF, Swayambhu M, Witlox P. The Microbiome Forensics Database UZH. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2022. [DOI: 10.1016/j.fsigss.2022.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
12
|
Carratto TMT, Moraes VMS, Recalde TSF, Oliveira MLGD, Teixeira Mendes-Junior C. Applications of massively parallel sequencing in forensic genetics. Genet Mol Biol 2022; 45:e20220077. [PMID: 36121926 PMCID: PMC9514793 DOI: 10.1590/1678-4685-gmb-2022-0077] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/15/2022] [Indexed: 11/22/2022] Open
Abstract
Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.
Collapse
Affiliation(s)
- Thássia Mayra Telles Carratto
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| | - Vitor Matheus Soares Moraes
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| | | | | | - Celso Teixeira Mendes-Junior
- Universidade de São Paulo, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Departamento de Química, Laboratório de Pesquisas Forenses e Genômicas, Ribeirão Preto, SP, Brazil
| |
Collapse
|
13
|
Kumari P, Prakash P, Yadav S, Saran V. Microbiome analysis: An emerging forensic investigative tool. Forensic Sci Int 2022; 340:111462. [PMID: 36155349 DOI: 10.1016/j.forsciint.2022.111462] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/15/2022] [Accepted: 09/08/2022] [Indexed: 12/30/2022]
Abstract
Microbial diversity's potential has been investigated in medical and therapeutic studies throughout the last few decades. However, its usage in forensics is increasing due to its effectiveness in circumstances when traditional approaches fail to provide a decisive opinion or are insufficient in forming a concrete opinion. The application of human microbiome may serve in detecting the type of stains of saliva and vaginal fluid, as well as in attributing the stains to the individual. Similarly, the microbiome makeup of a soil sample may be utilised to establish geographic origin or to associate humans, animals, or things with a specific area, additionally microorganisms influence the decay process which may be used in depicting the Time Since death. Further in detecting the traces of the amount and concentration of alcohol, narcotics, and other forensically relevant compounds in human body or visceral tissues as they also affect the microbial community within human body. Beside these, there is much more scope of microbiomes to be explored in terms of forensic investigation, this review focuses on multidimensional approaches to human microbiomes from a forensic standpoint, implying the potential of microbiomes as an emerging tool for forensic investigations such as individual variability via skin microbiomes, reconstructing crime scene, and linking evidence to individual.
Collapse
Affiliation(s)
- Pallavi Kumari
- Department of Forensic Science, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India.
| | - Poonam Prakash
- Department of Forensic Science, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India
| | - Shubham Yadav
- Department of Forensic Science, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India
| | - Vaibhav Saran
- Department of Forensic Science, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India
| |
Collapse
|
14
|
Zhang J, Liu W, Simayijiang H, Hu P, Yan J. Application of Microbiome in Forensics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00096-1. [PMID: 36031058 PMCID: PMC10372919 DOI: 10.1016/j.gpb.2022.07.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/29/2022] [Indexed: 06/04/2023]
Abstract
Recent advances in next-generation sequencing technology and improvements in bioinformatics have expanded the scope of microbiome analysis as a forensic tool. Microbiome research is concerned with the study of the compositional profile and diversity of microbial flora as well as the interactions between microbes, hosts, and the environment. It has opened up many new possibilities for forensic analysis. In this review, we discuss various applications of microbiomes in forensics, including identification of individuals, geolocation inference, post-mortem interval (PMI) estimation, and others.
Collapse
Affiliation(s)
- Jun Zhang
- School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Wenli Liu
- Beijing Center for Physical and Chemical Analysis, Beijing 100089, China
| | | | - Ping Hu
- Key Laboratory of Environment and Health (HUST), Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Jiangwei Yan
- School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China.
| |
Collapse
|
15
|
Díez López C, Montiel González D, Vidaki A, Kayser M. Prediction of Smoking Habits From Class-Imbalanced Saliva Microbiome Data Using Data Augmentation and Machine Learning. Front Microbiol 2022; 13:886201. [PMID: 35928158 PMCID: PMC9343866 DOI: 10.3389/fmicb.2022.886201] [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: 02/28/2022] [Accepted: 06/21/2022] [Indexed: 11/24/2022] Open
Abstract
Human microbiome research is moving from characterization and association studies to translational applications in medical research, clinical diagnostics, and others. One of these applications is the prediction of human traits, where machine learning (ML) methods are often employed, but face practical challenges. Class imbalance in available microbiome data is one of the major problems, which, if unaccounted for, leads to spurious prediction accuracies and limits the classifier's generalization. Here, we investigated the predictability of smoking habits from class-imbalanced saliva microbiome data by combining data augmentation techniques to account for class imbalance with ML methods for prediction. We collected publicly available saliva 16S rRNA gene sequencing data and smoking habit metadata demonstrating a serious class imbalance problem, i.e., 175 current vs. 1,070 non-current smokers. Three data augmentation techniques (synthetic minority over-sampling technique, adaptive synthetic, and tree-based associative data augmentation) were applied together with seven ML methods: logistic regression, k-nearest neighbors, support vector machine with linear and radial kernels, decision trees, random forest, and extreme gradient boosting. K-fold nested cross-validation was used with the different augmented data types and baseline non-augmented data to validate the prediction outcome. Combining data augmentation with ML generally outperformed baseline methods in our dataset. The final prediction model combined tree-based associative data augmentation and support vector machine with linear kernel, and achieved a classification performance expressed as Matthews correlation coefficient of 0.36 and AUC of 0.81. Our method successfully addresses the problem of class imbalance in microbiome data for reliable prediction of smoking habits.
Collapse
Affiliation(s)
| | | | | | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| |
Collapse
|
16
|
Liang X, Han X, Liu C, Du W, Zhong P, Huang L, Huang M, Fu L, Liu C, Chen L. Integrating the salivary microbiome in the forensic toolkit by 16S rRNA gene: potential application in body fluid identification and biogeographic inference. Int J Legal Med 2022; 136:975-985. [PMID: 35536322 DOI: 10.1007/s00414-022-02831-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/21/2022] [Indexed: 11/30/2022]
Abstract
Saliva is a common body fluid with significant forensic value used to investigate criminal cases such as murder and assault. In the past, saliva identification often relied on the α-amylase test; however, this method has low specificity and is prone to false positives. Accordingly, forensic researchers have been working to find new specific molecular markers to refine the current saliva identification approach. At present, research on immunological methods, mRNA, microRNA, circRNA, and DNA methylation is still in the exploratory stage, and the application of these markers still has various limitations. It has been established that salivary microorganisms exhibit good specificity and stability. In this study, 16S rDNA sequencing technology was used to sequence the V3-V4 hypervariable regions in saliva samples from five regions to reveal the role of regional location on the heterogeneity in microbial profile information in saliva. Although the relative abundance of salivary flora was affected to a certain extent by geographical factors, the salivary flora of each sample was still dominated by Streptococcus, Neisseria, and Rothia. In addition, the microbial community in the saliva samples in this study was significantly different from that in the vaginal secretions, semen, and skin samples reported in our previous studies. Accordingly, saliva can be distinguished from the other three body fluids and tissues. Moreover, we established a prediction model based on the random forest algorithm that could distinguish saliva between different regions at the genus level even though the model has a certain probability of misjudgment which needs more in-depth research. Overall, the microbial community information in saliva stains might have prospects for potential application in body fluid identification and biogeographic inference.
Collapse
Affiliation(s)
- Xiaomin Liang
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Xiaolong Han
- Guangzhou Forensic Science Institute, Guangzhou, 510030, People's Republic of China
| | - Changhui Liu
- Guangzhou Forensic Science Institute, Guangzhou, 510030, People's Republic of China
| | - Weian Du
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Peiwen Zhong
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Litao Huang
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Manling Huang
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Linhe Fu
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Chao Liu
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
- Guangzhou Forensic Science Institute, Guangzhou, 510030, People's Republic of China.
| | - Ling Chen
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
| |
Collapse
|
17
|
Unlocking the potential of forensic traces: Analytical approaches to generate investigative leads. Sci Justice 2022; 62:310-326. [PMID: 35598924 DOI: 10.1016/j.scijus.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 11/21/2022]
Abstract
Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.
Collapse
|
18
|
Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa. PLoS Comput Biol 2022; 18:e1010066. [PMID: 35446845 PMCID: PMC9064115 DOI: 10.1371/journal.pcbi.1010066] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/03/2022] [Accepted: 03/29/2022] [Indexed: 12/14/2022] Open
Abstract
Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies. The composition of the human microbiome has been linked to a large number of different diseases. In this context, classification methodologies based on machine learning approaches have represented a promising tool for diagnostic purposes from metagenomics data. The link between microbial population composition and host phenotypes has been usually performed by considering taxonomic profiles represented by relative abundances of microbial species. In this study, we show that it is more the presence rather than the relative abundance of microbial taxa to be relevant to maximize classification accuracy. This is accomplished by conducting a meta-analysis on more than 4,000 shotgun metagenomes coming from 25 case-control studies and in which original relative abundance data are degraded to presence/absence profiles. Findings are also extended to 16S rRNA data and advance the research field in building prediction models directly from human microbiome data.
Collapse
|
19
|
Gouello A, Dunyach-Remy C, Siatka C, Lavigne JP. Analysis of Microbial Communities: An Emerging Tool in Forensic Sciences. Diagnostics (Basel) 2021; 12:diagnostics12010001. [PMID: 35054168 PMCID: PMC8774847 DOI: 10.3390/diagnostics12010001] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 01/16/2023] Open
Abstract
The objective of forensic sciences is to find clues in a crime scene in order to reconstruct the scenario. Classical samples include DNA or fingerprints, but both have inherent limitations and can be uninformative. Another type of sample has emerged recently in the form of the microbiome. Supported by the Human Microbiome Project, the characteristics of the microbial communities provide real potential in forensics. They are highly specific and can be used to differentiate and classify the originating body site of a human biological trace. Skin microbiota is also highly specific and different between individuals, leading to its possibility as an identification tool. By extension, the possibilities of the microbial communities to be deposited on everyday objects has also been explored. Other uses include the determination of the post-mortem interval or the analysis of soil communities. One challenge is that the microbiome changes over time and can be influenced by many environmental and lifestyle factors. This review offers an overview of the main methods and applications to demonstrate the benefit of the microbiome to provide forensically relevant information.
Collapse
Affiliation(s)
- Audrey Gouello
- Institut de Recherche Criminelle de la Gendarmerie Nationale, 95037 Cergy-Pontoise, France;
- Bacterial Infection and Chronic Infection, INSERM U1047, Department of Microbiology and Hospital Infection, University Hospital Nîmes, Université de Montpellier, 30908 Nimes, France;
| | - Catherine Dunyach-Remy
- Bacterial Infection and Chronic Infection, INSERM U1047, Department of Microbiology and Hospital Infection, University Hospital Nîmes, Université de Montpellier, 30908 Nimes, France;
| | | | - Jean-Philippe Lavigne
- Bacterial Infection and Chronic Infection, INSERM U1047, Department of Microbiology and Hospital Infection, University Hospital Nîmes, Université de Montpellier, 30908 Nimes, France;
- Correspondence: ; Tel.: +33-466683202
| |
Collapse
|
20
|
Mei S, Zhao M, Liu Y, Zhao C, Xu H, Fang Y, Zhu B. Evaluations and comparisons of microbial diversities in four types of body fluids based on two 16S rRNA gene sequencing methods. Forensic Sci Int 2021; 331:111128. [PMID: 34959019 DOI: 10.1016/j.forsciint.2021.111128] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND Body fluids are one of the common biological traces at crime scenes. Understanding the types of these biological traces could provide key clues for the investigations of the forensic cases. In recent years, partial hypervariable regions of 16S rRNA gene sequencing and full-length 16S rRNA gene sequencing have attracted the interests of researchers and we intend to explore which method can be better applied to forensic researches. METHODS In this study, the 16S rRNA gene V3-V4 (short-read) sequencing based on next-generation sequencing and the full-length 16S rRNA gene sequencing based on single molecule real-time sequencing were used to classify microbes in saliva, peripheral blood, vaginal secretion and menstrual blood samples. RESULTS Alpha diversity metrics in short-read sequencing were larger than those of full-length sequencing. Phylum-level bacteria in four kinds of body fluids obtained from the two platforms were similar, while their abundances were different. The results of principal coordinates analysis and analysis of molecular variance indicated the microbial compositions of vaginal secretion and menstrual blood samples were similar, and the microbial compositions among saliva, peripheral blood, vaginal secretion or menstrual blood samples were significantly different. The linear discriminant analysis effect size showed the differential bacteria screened among the four kinds of body fluids were variant in two sequencing results. CONCLUSION Both sequencing methods could be used to detect bacterial diversities in four different types of body fluids and provide potential tools for microbes to identify the four kinds of body fluids in forensic investigation, in which full-length sequencing could provide more accurate taxonomy.
Collapse
Affiliation(s)
- Shuyan Mei
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, P. R. China
| | - Ming Zhao
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, P. R. China
| | - Yanfang Liu
- School of Nursing, Guangdong Medical University, Dongguan 523808, P. R. China
| | - Congying Zhao
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, P. R. China
| | - Hui Xu
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, P. R. China
| | - Yating Fang
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, P. R. China
| | - Bofeng Zhu
- Multi-Omics Innovative Research Center of Forensic Identification; Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou 510515, P. R. China.
| |
Collapse
|
21
|
Barone M, D'Amico F, Fabbrini M, Rampelli S, Brigidi P, Turroni S. Over-feeding the gut microbiome: A scoping review on health implications and therapeutic perspectives. World J Gastroenterol 2021; 27:7041-7064. [PMID: 34887627 PMCID: PMC8613651 DOI: 10.3748/wjg.v27.i41.7041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/02/2021] [Accepted: 10/14/2021] [Indexed: 02/06/2023] Open
Abstract
The human gut microbiome has gained increasing attention over the past two decades. Several findings have shown that this complex and dynamic microbial ecosystem can contribute to the maintenance of host health or, when subject to imbalances, to the pathogenesis of various enteric and non-enteric diseases. This scoping review summarizes the current knowledge on how the gut microbiota and microbially-derived compounds affect host metabolism, especially in the context of obesity and related disorders. Examples of microbiome-based targeted intervention strategies that aim to restore and maintain an eubiotic layout are then discussed. Adjuvant therapeutic interventions to alleviate obesity and associated comorbidities are traditionally based on diet modulation and the supplementation of prebiotics, probiotics and synbiotics. However, these approaches have shown only moderate ability to induce sustained changes in the gut microbial ecosystem, making the development of innovative and tailored microbiome-based intervention strategies of utmost importance in clinical practice. In this regard, the administration of next-generation probiotics and engineered microbiomes has shown promising results, together with more radical intervention strategies based on the replacement of the dysbiotic ecosystem by means of fecal microbiota transplantation from healthy donors or with the introduction of synthetic communities specifically designed to achieve the desired therapeutic outcome. Finally, we provide a perspective for future translational investigations through the implementation of bioinformatics approaches, including machine and deep learning, to predict health risks and therapeutic outcomes.
Collapse
Affiliation(s)
- Monica Barone
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Federica D'Amico
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Marco Fabbrini
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Simone Rampelli
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| | - Patrizia Brigidi
- Microbiomics Unit, Department of Medical and Surgical Sciences, University of Bologna, Bologna 40138, Italy
| | - Silvia Turroni
- Unit of Microbiome Science and Biotechnology, Department of Pharmacy and Biotechnology, University of Bologna, Bologna 40126, Italy
| |
Collapse
|
22
|
Integrating the human microbiome in the forensic toolkit: Current bottlenecks and future solutions. Forensic Sci Int Genet 2021; 56:102627. [PMID: 34742094 DOI: 10.1016/j.fsigen.2021.102627] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 10/12/2021] [Accepted: 10/27/2021] [Indexed: 12/13/2022]
Abstract
Over the last few years, advances in massively parallel sequencing technologies (also referred to next generation sequencing) and bioinformatics analysis tools have boosted our knowledge on the human microbiome. Such insights have brought new perspectives and possibilities to apply human microbiome analysis in many areas, particularly in medicine. In the forensic field, the use of microbial DNA obtained from human materials is still in its infancy but has been suggested as a potential alternative in situations when other human (non-microbial) approaches present limitations. More specifically, DNA analysis of a wide variety of microorganisms that live in and on the human body offers promises to answer various forensically relevant questions, such as post-mortem interval estimation, individual identification, and tissue/body fluid identification, among others. However, human microbiome analysis currently faces significant challenges that need to be considered and overcome via future forensically oriented human microbiome research to provide the necessary solutions. In this perspective article, we discuss the most relevant biological, technical and data-related issues and propose future solutions that will pave the way towards the integration of human microbiome analysis in the forensic toolkit.
Collapse
|
23
|
Sijen T, Harbison S. On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime. Genes (Basel) 2021; 12:1728. [PMID: 34828334 PMCID: PMC8617621 DOI: 10.3390/genes12111728] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 10/26/2021] [Accepted: 10/26/2021] [Indexed: 12/13/2022] Open
Abstract
Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.
Collapse
Affiliation(s)
- Titia Sijen
- Division Human Biological Traces, Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB The Hague, The Netherlands
- Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - SallyAnn Harbison
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand;
- Department of Statistics, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
| |
Collapse
|
24
|
Tan-Torres AL, Brooks JP, Singh B, Seashols-Williams S. Machine learning clustering and classification of human microbiome source body sites. Forensic Sci Int 2021; 328:111008. [PMID: 34656848 DOI: 10.1016/j.forsciint.2021.111008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/07/2021] [Accepted: 09/15/2021] [Indexed: 12/12/2022]
Abstract
Distinct microbial signatures associated with specific human body sites can play a role in the identification of biological materials recovered from the crime scene, but at present, methods that have capability to predict origin of biological materials based on such signatures are limited. Metagenomic sequencing and machine learning (ML) offer a promising enhancement to current identification protocols. We use ML for forensic source body site identification using shotgun metagenomic sequenced data to verify the presence of microbiomic signatures capable of discriminating between source body sites and then show that accurate prediction is possible. The consistency between cluster membership and actual source body site (purity) exceeded 99% at the genus taxonomy using off-the-shelf ML clustering algorithms. Similar results were obtained at the family level. Accurate predictions were observed for genus, family, and order taxonomies, as well as with a core set of 51 genera. The accurate outcomes from our replicable process should encourage forensic scientists to seriously consider integrating ML predictors into their source body site identification protocols.
Collapse
Affiliation(s)
- Antonio L Tan-Torres
- Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA, USA.
| | - J Paul Brooks
- Department of Supply Chain Management and Analytics, Virginia Commonwealth University, Richmond, VA, USA
| | - Baneshwar Singh
- Department of Forensic Science, Virginia Commonwealth University, Richmond, VA, USA
| | | |
Collapse
|
25
|
Karadayı S, Arasoglu T, Akmayan İ, Karadayı B. Assessment of the exclusion potential of suspects by using microbial signature in sexual assault cases: A scenario-based experimental study. Forensic Sci Int 2021; 325:110886. [PMID: 34192646 DOI: 10.1016/j.forsciint.2021.110886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 06/18/2021] [Indexed: 02/08/2023]
Abstract
Sexual assault offences are one of the most serious crimes committed against a person, typically rank only second to homicide, and represent one of the major challenges in forensic sciences. In some cases of sexual assault, there may be more than one suspect and the analysis of the biological evidence with currently available methods such as human DNA analysis may not yield results. In this study using the designed experimental model (with different experimental scenarios that can be designed), it was aimed to investigate the effectiveness of the microbiome profile for the identification of the offender by comparing the microbiome structures of the suspects' saliva samples with the mixed samples on the victim (saliva transmitted on breast skin) within the first 48 h after a sexual assault. For this purpose, a total of 44 samples was collected from four healthy females and four healthy males aged 20-50 years. Microbiome profiles of 44 samples in four groups containing saliva, breast skin and mixed samples were determined with the IIlumina HiSeq platform. Differentiation between samples were calculated by beta-diversity analysis methods by using QIIME software (v1.80). To compare the differentiation among samples and groups, unweighted UniFrac distance values were applied. Eight dominant microbial genera accounted for 86.15% of the total bacterial population in male saliva samples and were composed of Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, Rothia, Streptococcus and Veillonella. These genera constituted 76.72% of the bacterial population in mixed samples, whereas they constituted 34.40% of the bacterial population in the breast skin samples. Results of this study show that bacterial DNA in saliva can be recovered from saliva transmitted breast skin within at least 48 h. In conclusion, it has been found that examination of the microbiota of the saliva transmitted to breast skin of a sexual assault victim as a forensic tool may have the potential to determine the offender of the incident among the suspects or to reduce the number of suspects. Supporting the results of this study with further studies using parameters such as different case models, different body regions, larger time periods and a higher number of participants will be beneficial to draw accurate conclusion of the judicial case.
Collapse
Affiliation(s)
- Sukriye Karadayı
- Vocational School of Health Services, Altınbaş University, Istanbul, Turkey.
| | - Tulin Arasoglu
- Department of Molecular Biology and Genetics, Faculty of Arts and Science, Yıldız Technical University, İstanbul, Turkey.
| | - İlkgül Akmayan
- Department of Molecular Biology and Genetics, Faculty of Arts and Science, Yıldız Technical University, İstanbul, Turkey.
| | - Beytullah Karadayı
- Department of Forensic Sciences, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpaşa, Istanbul, Turkey.
| |
Collapse
|
26
|
Díez López C, Kayser M, Vidaki A. Estimating the Time Since Deposition of Saliva Stains With a Targeted Bacterial DNA Approach: A Proof-of-Principle Study. Front Microbiol 2021; 12:647933. [PMID: 34149638 PMCID: PMC8206545 DOI: 10.3389/fmicb.2021.647933] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 04/14/2021] [Indexed: 01/04/2023] Open
Abstract
Information on the time when a stain was deposited at a crime scene can be valuable in forensic investigations. It can link a DNA-identified stain donor with a crime or provide a post-mortem interval estimation in cases with cadavers. The available methods for estimating stain deposition time have limitations of different types and magnitudes. In this proof-of-principle study we investigated for the first time the use of microbial DNA for this purpose in human saliva stains. First, we identified the most abundant and frequent bacterial species in saliva using publicly available 16S rRNA gene next generation sequencing (NGS) data from 1,848 samples. Next, we assessed time-dependent changes in 15 identified species using de-novo 16S rRNA gene NGS in the saliva stains of two individuals exposed to indoor conditions for up to 1 year. We selected four bacterial species, i.e., Fusobacterium periodonticum, Haemophilus parainfluenzae, Veillonella dispar, and Veillonella parvula showing significant time-dependent changes and developed a 4-plex qPCR assay for their targeted analysis. Then, we analyzed the saliva stains of 15 individuals exposed to indoor conditions for up to 1 month. Bacterial counts generally increased with time and explained 54.9% of the variation (p = <2.2E–16). Time since deposition explained ≥86.5% and ≥88.9% of the variation in each individual and species, respectively (p = <2.2E–16). Finally, based on sample duplicates we built and tested multiple linear regression models for predicting the stain deposition time at an individual level, resulting in an average mean absolute error (MAE) of 5 days (ranging 3.3–7.8 days). Overall, the deposition time of 181 (81.5%) stains was correctly predicted within 1 week. Prediction models were also assessed in stains exposed to similar conditions up to 1 month 7 months later, resulting in an average MAE of 8.8 days (ranging 3.9–16.9 days). Our proof-of-principle study suggests the potential of the DNA profiling of human commensal bacteria as a method of estimating saliva stains time since deposition in the forensic scenario, which may be expanded to other forensically relevant tissues. The study considers practical applications of this novel approach, but various forensic developmental validation and implementation criteria will need to be met in more dedicated studies in the future.
Collapse
Affiliation(s)
- Celia Díez López
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Athina Vidaki
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands
| |
Collapse
|
27
|
Salzmann AP, Arora N, Russo G, Kreutzer S, Snipen L, Haas C. Assessing time dependent changes in microbial composition of biological crime scene traces using microbial RNA markers. Forensic Sci Int Genet 2021; 53:102537. [PMID: 34090061 DOI: 10.1016/j.fsigen.2021.102537] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 05/21/2021] [Accepted: 05/22/2021] [Indexed: 01/16/2023]
Abstract
Current body fluid identification methods do not reveal any information about the time since deposition (TsD) of biological traces, even though determining the age of traces could be crucial for the investigative process. To determine the utility of microbial RNA markers for TsD estimation, we examined RNA sequencing data from five forensically relevant body fluids (blood, menstrual blood, saliva, semen, and vaginal secretion) over seven time points, ranging from fresh to 1.5 years. One set of samples was stored indoors while another was exposed to outdoor conditions. In outdoor samples, we observed a consistent compositional shift, occurring after 4 weeks: this shift was characterized by an overall increase in non-human eukaryotic RNA and an overall decrease in prokaryotic RNA. In depth analyses showed a high fraction of tree, grass and fungal signatures, which are characteristic for the environment the samples were exposed to. When examining the prokaryotic fraction in more detail, three bacterial phyla were found to exhibit the largest changes in abundance, namely Actinobacteria, Proteobacteria and Firmicutes. More detailed analyses at the order level were done using a Lasso regression analysis to find a predictive subset of bacterial taxa. We found 26 bacterial orders to be indicative of sample age. Indoor samples did not reveal such a clear compositional change at the domain level: eukaryotic and prokaryotic abundance remained relatively stable across the assessed time period. Nonetheless, a Lasso regression analysis identified 32 bacterial orders exhibiting clear changes over time, enabling the prediction of TsD. For both indoor and outdoor samples, a larger number (around 60%) of the bacterial orders identified as indicative of TsD are part of the Actinobacteria, Proteobacteria and Firmicutes. In summary, we found that the observed changes across time are not primarily due to changes associated with body fluid specific bacteria but mostly due to accumulation of bacteria from the environment. Orders of these environmental bacteria could be evaluated for TsD prediction, considering the location and environment of the crime scene. However, further studies are needed to verify these findings, determine the applicability across samples, replicates, donors, and other variables, and also to further assess the effect of different seasons and locations on the samples.
Collapse
Affiliation(s)
| | - Natasha Arora
- Zurich Institute of Forensic Medicine, University of Zurich, Switzerland
| | - Giancarlo Russo
- Functional Genomics Centre Zurich (FGCZ), University of Zurich/ETH Zurich, Switzerland
| | - Susanne Kreutzer
- Functional Genomics Centre Zurich (FGCZ), University of Zurich/ETH Zurich, Switzerland
| | - Lars Snipen
- Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
| | - Cordula Haas
- Zurich Institute of Forensic Medicine, University of Zurich, Switzerland.
| |
Collapse
|
28
|
Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau ECD, D’Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol 2021; 12:635781. [PMID: 33692771 PMCID: PMC7937616 DOI: 10.3389/fmicb.2021.635781] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 12/23/2022] Open
Abstract
The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
Collapse
Affiliation(s)
- Isabel Moreno-Indias
- Instituto de Investigación Biomédica de Málaga (IBIMA), Unidad de Gestión Clìnica de Endocrinologìa y Nutrición, Hospital Clìnico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Miroslava Nedyalkova
- Human Genetics and Disease Mechanisms, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | | | - Muhamed Adilovic
- Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Onder Aydemir
- Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | | | - Domenica D’Elia
- Department for Biomedical Sciences, Institute for Biomedical Technologies, National Research Council, Bari, Italy
| | - Mahesh S. Desai
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Laurent Falquet
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Aycan Gundogdu
- Department of Microbiology and Clinical Microbiology, Faculty of Medicine, Erciyes University, Kayseri, Turkey
- Metagenomics Laboratory, Genome and Stem Cell Center (GenKök), Erciyes University, Kayseri, Turkey
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal
- Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Cláudia Marques
- CINTESIS, NOVA Medical School, NMS, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Michael Mason
- Computational Oncology, Sage Bionetworks, Seattle, WA, United States
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lejla Pašić
- Sarajevo Medical School, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Sándor Pongor
- Faculty of Information Tehnology and Bionics, Pázmány University, Budapest, Hungary
| | - Vasilis J. Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruñ, Poland
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Heidelberg, Germany
| | - Alexia Sampri
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Jozef Stefan Institute, Ljubljana, Slovenia
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Lab, Faculty of the Food Science and Technology, Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Ciprian-Octavian Truică
- Department of Computer Science and Engineering, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Ercument Yilmaz
- Department of Computer Technologies, Karadeniz Technical University, Trabzon, Turkey
| | - Georg Zeller
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Aldert L. Zomer
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - David Gómez-Cabrero
- Navarrabiomed, Complejo Hospitalario de Navarra (CHN), IdiSNA, Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Marcus J. Claesson
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
| |
Collapse
|
29
|
Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lopes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Microbiol 2021; 12:634511. [PMID: 33737920 PMCID: PMC7962872 DOI: 10.3389/fmicb.2021.634511] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/01/2021] [Indexed: 12/19/2022] Open
Abstract
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
Collapse
Affiliation(s)
- Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | | | | | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland
| | - Vladimir Trajkovik
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | - Oliver Aasmets
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Magali Berland
- Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Jasminka Hasic
- University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Mikhail Kolev
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal
- Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO)Barcelona, Spain
- Colorectal Cancer Group, Institut de Recerca Biomedica de Bellvitge (IDIBELL), Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Irina Naskinova
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Elin Org
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
| | - Inês Paciência
- EPIUnit – Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | | | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Group for Microbiology and Microbial Biotechnology, Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | | | | | - Marcus J. Claesson
- School of Microbiology & APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Isabel Moreno-Indias
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Clínico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| |
Collapse
|
30
|
Yao T, Wang Z, Liang X, Liu C, Yu Z, Han X, Liu R, Liu Y, Liu C, Chen L. Signatures of vaginal microbiota by 16S rRNA gene: potential bio-geographical application in Chinese Han from three regions of China. Int J Legal Med 2021; 135:1213-1224. [PMID: 33594458 DOI: 10.1007/s00414-021-02525-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/03/2021] [Indexed: 12/12/2022]
Abstract
The human microbiome is expected to be a new and promising tool for classification of human epithelial materials. Vaginal fluids are one of the most common biological samples in forensic sexual assault cases, and its identification is crucial to accurately determine the nature of the case. With the development of molecular biology technologies, the concept of vaginal microflora in different physiological states, ethnic groups, and geography is constantly improved. In this study, we conducted high-throughput sequencing of the V3-V4 hypervariable regions of the 16S rRNA gene in vaginal samples from Henan, Guangdong, and Xinjiang populations, in an attempt to reveal more information about the vaginal microflora in different regions. The results showed that the bio-geographical factors might affect the relative abundance of some vaginal microflora, but there was no significant difference in the composition of dominant bacteria in the vagina, which was mainly composed of Lactobacillus and Gardnerella. However, prediction models based on the random forest algorithm suggested that we might be able to distinguish vaginal fluids from populations of different regions according to the species-level OTUs in low abundance. It is promising that microbiome-based methods could provide more personal information when being attempted to trace the origin of body fluids.
Collapse
Affiliation(s)
- Ting Yao
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Zhi Wang
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Xiaomin Liang
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Changhui Liu
- Guangzhou Forensic Science Institute, Guangzhou, 510030, People's Republic of China
| | - Zhonghao Yu
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Xiaolong Han
- Guangzhou Forensic Science Institute, Guangzhou, 510030, People's Republic of China
| | - Ruolan Liu
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Yinglin Liu
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China
| | - Chao Liu
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
| | - Ling Chen
- Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
| |
Collapse
|
31
|
Challenges in Human Skin Microbial Profiling for Forensic Science: A Review. Genes (Basel) 2020; 11:genes11091015. [PMID: 32872386 PMCID: PMC7564248 DOI: 10.3390/genes11091015] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/20/2020] [Accepted: 08/26/2020] [Indexed: 12/12/2022] Open
Abstract
The human microbiome is comprised of the microbes that live on and within an individual, as well as immediately surrounding them. Microbial profiling may have forensic utility in the identification or association of individuals with criminal activities, using microbial signatures derived from a personal microbiome. This review highlights some important aspects of recent studies, many of which have revealed issues involving the effect of contamination of microbial samples from both technical and environmental sources and their impacts on microbiome research and the potential forensic applications of microbial profiling. It is imperative that these challenges be discussed and evaluated within a forensic context to better understand the future directions and potential applications of microbial profiling for human identification. It is necessary that the limitations identified be resolved prior to the adoption of microbial profiling, or, at a minimum, acknowledged by those applying this new approach.
Collapse
|
32
|
Exploration of the microbiome community for saliva, skin, and a mixture of both from a population living in Guangdong. Int J Legal Med 2020; 135:53-62. [PMID: 32583081 DOI: 10.1007/s00414-020-02329-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 05/28/2020] [Indexed: 12/13/2022]
Abstract
The identification of biological traces provides vital evidence in forensic reconstruction at crime scenes, especially in sexual offences. Compared with traditional presumptive or confirmatory methods, the microbiome-based method has been proven to be of great value in body fluid identification. Mixture of body fluids or tissue is common in sexual assault cases; thus, it is essential to determine the sources of mixed samples. In this study, 60 samples consisting of skin, saliva, and a mixed model of saliva deposited on facial skin were collected from a population living in Guangdong. Through 16s rDNA high-throughput sequencing, we identified the predominant microbes in saliva samples, viz., Haemophilus parainfluenzae T3T1, Neisseria flava, Gemella haemolysans, Prevotella melaninogenica, and Actinomyces odontolyticus; in skin samples, Cutibacterium acnes and Corynebacterium tuberculostearicum were the predominant species. The microbial composition of the same body fluid or tissue is similar in different individuals. However, among different body fluids or tissue, the composition of microflora in saliva is more stable than that on skin. Additionally, the microbial community in the mixed model of saliva deposited on facial skin from the same and different individuals was clearly determined by the constituent fluids or tissue, apart from the differences among the donors. Overall, the microbiome-based method may have good potential as a tool for identifying single and mixed body fluid or tissue.
Collapse
|
33
|
Díez López C, Montiel González D, Haas C, Vidaki A, Kayser M. Microbiome-based body site of origin classification of forensically relevant blood traces. Forensic Sci Int Genet 2020; 47:102280. [PMID: 32244163 DOI: 10.1016/j.fsigen.2020.102280] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 03/18/2020] [Accepted: 03/18/2020] [Indexed: 12/12/2022]
Abstract
Human blood traces are amongst the most commonly encountered biological stains collected at crime scenes. Identifying the body site of origin of a forensic blood trace can provide crucial information in many cases, such as in sexual and violent assaults. However, means for reliably and accurately identifying from which body site a forensic blood trace originated are missing, but would be highly valuable in crime scene investigations. With this study, we introduce a taxonomy-independent deep neural network approach based on massively parallel microbiome sequencing, which delivers accurate body site of origin classification of forensically-relevant blood samples, such as menstrual, nasal, fingerprick, and venous blood. A total of 50 deep neural networks were trained using a large 16S rRNA gene sequencing dataset from 773 reference samples, including 220 female urogenital tract, 190 nasal cavity, 213 skin, and 150 venous blood samples. Validation was performed with de-novo generated 16S rRNA gene massively parallel sequencing (MPS) data from 94 blood test samples of four different body sites, and achieved high classification accuracy with AUC values at 0.992 for menstrual blood (N = 23), 0.978 for nasal blood (N = 16), 0.978 for fingerprick blood (N = 30), and 0.990 for venous blood (N = 25). The obtained highly accurate classification of menstrual blood was independent of the day of the menses, as established in additional 86 menstrual blood test samples. Accurate body site of origin classification was also revealed for 45 fresh and aged mock casework blood samples from all four body sites. Our novel microbiome approach works based on the assumption that a sample is from blood, as can be obtained in forensic practise from prior presumptive blood testing, and provides accurate information on the specific body source of blood, with high potentials for future forensic applications.
Collapse
Affiliation(s)
- Celia Díez López
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Diego Montiel González
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Cordula Haas
- Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland
| | - Athina Vidaki
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| |
Collapse
|