Microbiome analysis: An emerging forensic investigative tool

Characterization of challenging forensic DNA traces using advanced molecular technologies

The majority of crime scenes contain DNA that is either present in small amounts or degraded, making it difficult to obtain usable DNA profiles using conventional technologies. The current standard for analyzing casework samples is the specific amplification of short tandem repeats (STR), which is limited by DNA quality and quantity. Since the goal of forensic science is to identify a suspect or victim regardless of trace quality, we evaluated three technological approaches to better characterize and exploit these traces: (i) ultra-sensitive pulse-field electrophoresis on a Femto Pulse System (FPS) to visualize DNA content, (ii) real-time quantitative PCR based on Alu repeats to quantify human DNA and analyze its integrity, and (iii) 16S ribosomal RNA gene (16S rRNA) amplicon sequencing to identify microbiota. We optimized FPS analysis using DNA from model traces (blood, saliva, semen, touch DNA, and vaginal swabs) and applied the protocol to 100 casework samples. We found differences between the FPS profiles of model and casework samples, showing a variation in fragment size and distribution, suggesting the presence of non-human DNA. Using Alu-qPCR and 16S rRNA amplicon sequencing, we determined the amount and proportion of human and non-human DNA. Human DNA was detected in 84% of traces with an average of 70 pg per trace, while 16S rRNA revealed microbial DNA as the most abundant DNA in traces. These analyses provide new insights into forensic trace composition, allowing better sorting and profiling of traces.

Microbial genomics: a potential toolkit for forensic investigations

Microbial forensics is a new discipline of science that analyzes evidence related to biological crime through the uniqueness and abundance of microorganisms and their toxins. Microorganisms remain alive longer than any other trace of biological evidence, such as DNA, fingerprints, and fibers, because of the protective cell membrane or capsules. Microbiological research has opened up various possibilities for forensic investigations of microbial flora. Current molecular technologies, including DNA sequencing, whole-genome sequencing, metagenomics, DNA fingerprinting, and molecular phylogeny, provide valid results for forensic investigations. Recent advancements in genome sequencing technologies, genetic data generation, and bioinformatic tools have significantly improved microbial sampling methods and forensic analyses. In this review, we discuss the applications of microbial genomic tools and technologies in forensic investigations, including human identification, geolocation, and causes of death.

Insights into Women's health: Exploring the vaginal microbiome, quorum sensing dynamics, and therapeutic potential of quorum sensing quenchers

The vaginal microbiome is an important aspect of women's health that changes dynamically with various stages of the woman's life. Just like the gut microbiome, the vaginal microbiome can also be affected by pathologies that dramatically change the typical composition of native vaginal microorganisms. However, the mechanism as to how both vaginal endemic and gut endemic opportunistic microbes can express pathogenicity in vaginal polymicrobial biofilms is poorly understood. Quorum sensing is the cellular density-dependent bacterial and fungal communication process in which chemical signaling molecules, known as autoinducers, activate expression for genes responsible for virulence and pathogenicity, such as biofilm formation and virulence factor production. Quorum sensing inhibition, or quorum quenching, has been explored as a potential therapeutic route for both bacterial and fungal infections. By applying these quorum quenchers, one can reduce biofilm formation of opportunistic vaginal microbes and combine them with antibiotics for a synergistic effect. This review aims to display the relationship between the vaginal and gut microbiome, the role of quorum sensing in polymicrobial biofilm formation which cause pathology in the vaginal microbiome, and how quorum quenchers can be utilized to attenuate the severity of bacterial and fungal infections.

Effects of forages on the microbiota of crossed sheep on cold Plateau

Diet is an important component to influence microbiota, there are less data available about the microbiome of Suffolk cross with Tibetan (SCT) animals with different fodders. The current study was conducted for comparing the fungi microbiota in SCT sheep fed with different forages. Sequencing of ileum samples from sheep groups of AH (alfalfa and oat grass), BH (mixture of grass and concentrated feeds), CH (concentrated feed I), DH (concentrated feed II) and EH (concentrated feed III) achieved 3,171,271 raw and 2,719,649 filtered sequences. Concentrated feeds changed fungi microbiota in SCT sheep with three phyla and 47 genera significantly different among the groups. Genera include positive genus of Scytalidium and negative fungi of Sarocladium, Kazachstania, Gibberella, Scytalidium, Candida, Wickerhamomyces. The findings of our study will contribute to efficient feeding of SCT sheep at cold plateau areas.

Development of a multiplex PCR assay and quantification of microbial markers by ddPCR for identification of saliva and vaginal fluid

The identification of biological fluids at crime scenes contributes to crime scene reconstruction and provides investigative leads. Traditional methods for body fluid identification are limited in terms of sensitivity and are mostly presumptive. Emerging methods based on mRNA and DNA methylation require high quality template source. An exploitable characteristic of body fluids is their distinct microbial profiles allowing for the discrimination of body fluids based on microbiome content. Microbial DNA is highly abundant within the body, robust and stable and can persist in the environment long after human DNA has degraded. 16S rRNA sequencing is the gold standard for microbial analysis; however, NGS is costly, and requires intricate workflows and interpretation. Also, species level resolution is not always achievable. Based on the current challenges, the first objective of this study was to develop a multiplex conventional PCR assay to identify vaginal fluid and saliva by targeting species-specific 16S rRNA microbial markers. The second objective was to employ droplet digital PCR (ddPCR) as a novel approach to quantify bacterial species alone and in a mixture of body fluids. Lactobacillus crispatus and Streptococcus salivarius were selected because of high abundance within vaginal fluid and saliva respectively. While Fusobacterium nucleatum and Gardnerella vaginalis, though present in healthy humans, are also frequently found in oral and vaginal infections, respectively. The multiplex PCR assay detected L. crispatus and G. vaginalis in vaginal fluid while F. nucleatum and S. salivarius was detected in saliva. Multiplex PCR detected F. nucleatum, S. salivarius and L. crispatus in mixed body fluid samples while, G. vaginalis was undetected in mixtures containing vaginal fluid. For samples exposed at room temperature for 65 days, L. crispatus and G. vaginalis were detected in vaginal swabs while only S. salivarius was detected in saliva swabs. The limit of detection was 0.06 copies/µl for F. nucleatum (2.5 ×10-9 ng/µl) and S. salivarius (2.5 ×10-6 ng/µl). L. crispatus and G. vaginalis had detection limits of 0.16 copies/µl (2.5 ×10-4 ng/µl) and 0.48 copies/µl (2.5 ×10-7 ng/µl). All 4 bacterial species were detected in mixtures and aged samples by ddPCR. No significant differences were observed in quantity of bacterial markers in saliva and vaginal fluid. The present research reports for the first time the combination of the above four bacterial markers for the detection of saliva and vaginal fluid and highlights the sensitivity of ddPCR for bacterial quantification in pure and mixed body fluids.

Skin locations inference and body fluid identification from skin microbial patterns for forensic applications

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.

Utilization of the microbiome in personalized medicine

Inter-individual human variability, driven by various genetic and environmental factors, complicates the ability to develop effective population-based early disease detection, treatment and prognostic assessment. The microbiome, consisting of diverse microorganism communities including viruses, bacteria, fungi and eukaryotes colonizing human body surfaces, has recently been identified as a contributor to inter-individual variation, through its person-specific signatures. As such, the microbiome may modulate disease manifestations, even among individuals with similar genetic disease susceptibility risks. Information stored within microbiomes may therefore enable early detection and prognostic assessment of disease in at-risk populations, whereas microbiome modulation may constitute an effective and safe treatment tailored to the individual. In this Review, we explore recent advances in the application of microbiome data in precision medicine across a growing number of human diseases. We also discuss the challenges, limitations and prospects of analysing microbiome data for personalized patient care.

Response of salivary microbiome to temporal, environmental, and surface characteristics under in vitro exposure

The microbiome of saliva stains deposited at crime scenes and in everyday settings is valuable for forensic investigations and environmental ecology. However, the dynamics and applications of microbial communities in these saliva stains have not been fully explored. In this study, we analyzed saliva samples that were exposed to indoor conditions for up to 1 year and to different carriers (cotton, sterile absorbent cotton swab, woolen, dacron) in both indoor and outdoor environments for 1 month using high-throughput sequencing. The analysis of microbial composition and Mfuzz clustering showed that the salivary flora, specifically Streptococcus (cluster7), which was associated with microbial contamination, remained stable over short periods of time. However, prolonged exposure led to significant differences due to the invasion of environmental bacteria such as Pseudomonas and Achromobacter. The growth and colonization of environmental flora were promoted by humidity. The neutral model predictions indicated that the assembly of salivary microbial communities in outdoor environments was significantly influenced by stochastic processes, with environmental characteristics having a greater impact on community change compared to surface characteristics. By incorporating data from previous studies on fecal and vaginal secretion microbiology, we developed RF and XGBoost classification models that achieved high accuracy (>98 %) and AUC (>0.8). Additionally, a RF regression model was created to determine the time since deposition (TsD) of the stains. Time inference models yielded a mean absolute error (MAE) of 7.1 days for stains exposed for 1 year and 14.2 h for stains exposed for 14 days. These findings enhance our understanding of the changes in the microbiome of saliva stains over time, in different environments, and on different surfaces. They also have potential applications in assessing potential microbial contamination, identifying body fluids, and inferring the time of deposition.

Focus on studying the effects of different exposure durations on the microbial structures and characteristics of three types of body fluids

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.

Research progress on the application of 16S rRNA gene sequencing and machine learning in forensic microbiome individual identification

Forensic microbiome research is a field with a wide range of applications and a number of protocols have been developed for its use in this area of research. As individuals host radically different microbiota, the human microbiome is expected to become a new biomarker for forensic identification. To achieve an effective use of this procedure an understanding of factors which can alter the human microbiome and determinations of stable and changing elements will be critical in selecting appropriate targets for investigation. The 16S rRNA gene, which is notable for its conservation and specificity, represents a potentially ideal marker for forensic microbiome identification. Gene sequencing involving 16S rRNA is currently the method of choice for use in investigating microbiomes. While the sequencing involved with microbiome determinations can generate large multi-dimensional datasets that can be difficult to analyze and interpret, machine learning methods can be useful in surmounting this analytical challenge. In this review, we describe the research methods and related sequencing technologies currently available for application of 16S rRNA gene sequencing and machine learning in the field of forensic identification. In addition, we assess the potential value of 16S rRNA and machine learning in forensic microbiome science.

Application of omics techniques in forensic entomology research

With the advent of the post-genome era, omics technologies have developed rapidly and are widely used, including in genomics, transcriptomics, proteomics, metabolomics, and microbiome research. These omics techniques are often based on comprehensive and systematic analysis of biological samples using high-throughput analysis methods and bioinformatics, to provide new insights into biological phenomena. Currently, omics techniques are gradually being applied to forensic entomology research and are useful in species identification, phylogenetics, screening for developmentally relevant differentially expressed genes, and the interpretation of behavioral characteristics of forensic-related species at the genetic level. These all provide valuable information for estimating the postmortem interval (PMI). This review mainly discusses the available omics techniques, summarizes the application of omics techniques in forensic entomology, and their future in the field.

MTSv: rapid alignment-based taxonomic classification and high-confidence metagenomic analysis

As the size of reference sequence databases and high-throughput sequencing datasets continue to grow, it is becoming computationally infeasible to use traditional alignment to large genome databases for taxonomic classification of metagenomic reads. Exact matching approaches can rapidly assign taxonomy and summarize the composition of microbial communities, but they sacrifice accuracy and can lead to false positives. Full alignment tools provide higher confidence assignments and can assign sequences from genomes that diverge from reference sequences; however, full alignment tools are computationally intensive. To address this, we designed MTSv specifically for alignment-based taxonomic assignment in metagenomic analysis. This tool implements an FM-index assisted q-gram filter and SIMD accelerated Smith-Waterman algorithm to find alignments. However, unlike traditional aligners, MTSv will not attempt to make additional alignments to a TaxID once an alignment of sufficient quality has been found. This improves efficiency when many reference sequences are available per taxon. MTSv was designed to be flexible and can be modified to run on either memory or processor constrained systems. Although MTSv cannot compete with the speeds of exact k-mer matching approaches, it is reasonably fast and has higher precision than popular exact matching approaches. Because MTSv performs a full alignment it can classify reads even when the genomes share low similarity with reference sequences and provides a tool for high confidence pathogen detection with low off-target assignments to near neighbor species.