Evaluations and comparisons of microbial diversities in four types of body fluids based on two 16S rRNA gene sequencing methods

Administration of Alistipes indistinctus prevented the progression from nonalcoholic fatty liver disease to nonalcoholic steatohepatitis by enhancing the gut barrier and increasing Lactobacillus spp

Metabolic-associated fatty liver disease (MAFLD) is an important public health problem, and the gut microbiota has become a new treatment target for MAFLD. Previously, A. indistinctus, a core gut bacterium, was shown to potentially contribute to the prevention of MAFLD. However, the effect and mechanism of A. indistinctus on MAFLD are still unclear and need to be investigated. This study primarily evaluated whether A. indistinctus can improve gut microbiota disorders and prevent the progression from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) in mice fed a high-fat diet (HFD). First, we observed that A. indistinctus significantly improved lipid metabolism disorders and reduced hepatic inflammation induced by HFD consumption in mice. We found that A. indistinctus improved gut barrier function and inhibited the LPS/TLR4/NF-κB pathway, thereby reducing hepatic inflammation. Moreover, 16S rRNA V3-V4 analyses revealed that A. indistinctus could significantly change the structure of the gut microbiota and increase the abundance of L. johnsonii by promoting its growth. Finally, we showed that L. johnsonii administration significantly improved lipid metabolism disorders and reduced hepatic lipid accumulation induced by HFD consumption in mice. In summary, A. indistinctus administration significantly reduces hepatic inflammation by improving gut barrier function and improves lipid metabolism disorders by promoting the growth of L. johnsonii. Our research improves the understanding of the gut microbiota and provides a basis for future therapeutic use of A. indistinctus.

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference

The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. However, it is influenced by various factors and may vary among individuals residing in distinct geographic regions. In this study, 220 samples, consisting of sterile swabs from palmar skin and oral and nasal cavities were collected from Chinese Han individuals living in Shanghai, Chifeng, Kunming, and Urumqi, representing the geographic regions of east, northeast, southwest, and northwest China. The full-length 16S rRNA gene of the microbiota in each sample was sequenced using the PacBio single-molecule real-time sequencing platform, followed by clustering the sequences into operational taxonomic units (OTUs). The analysis revealed significant differences in microbial communities among the four regions. Cutibacterium was the most abundant bacterium in palmar samples from Shanghai and Kunming, Psychrobacter in Chifeng samples, and Psychrobacillus in Urumqi samples. Additionally, Streptococcus and Staphylococcus were the dominant bacteria in the oral and nasal cavities. Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. The prediction accuracy using hypervariable regions (V3-V4 and V4-V5) was comparable with that of using the entire 16S rRNA. Overall, our study highlights the distinctiveness of the human microbiome in individuals living in these four regions. Furthermore, the microbiome can serve as a biomarker for geographic origin inference, which has immense application value in forensic science.IMPORTANCEMicrobial communities in human hosts play a significant role in health and disease, varying in species, quantity, and composition due to factors such as gender, ethnicity, health status, lifestyle, and living environment. The characteristics of microbial composition at various body sites of individuals from different regions remain largely unexplored. This study utilized single-molecule real-time sequencing technology to detect the entire 16S rRNA gene of bacteria residing in the palmar skin, oral, and nasal cavities of Han individuals from four regions in China. The composition and structure of the bacteria at these three body sites were well characterized and found to differ regionally. The results elucidate the differences in bacterial communities colonizing these body sites across different regions and reveal the influence of geographical factors on human bacteria. These findings not only contribute to a deeper understanding of the diversity and geographical distribution of human bacteria but also enrich the microbiome data of the Asian population for further studies.

Exploration and application of microorganisms related to the inference of the time since deposition (TsD) in semen and blood stains

Determining the time since deposition (TsD) of body fluid stains can provide crucial criminal information to forensic researchers. Although there are studies on inferring residual time through DNA and RNA markers, this requires high sample quality, and microorganisms, as a new type of marker with individual and tissue identification capabilities, have the potential for body fluid recognition and TsD inference. Blood and semen are the most common types of bodily fluid stains at crime scenes, but research on the inference of the TsD of these two types of stains through microorganisms still needs to be explored. Thus, this study collected samples of body fluid stains exposed indoors for up to 56 days and selected several microorganisms that were both liquid specific and related to residual time inference in blood (Methylobacterium and Sphingomonas) and semen (Gardnerella) stains via 16 S rRNA high-throughput sequencing. Furthermore, the microorganisms' ability to infer TsD was verified using qPCR in validation group samples stored under the same conditions, and two multiple logistic regression models were constructed. The average absolute deviation of differences between the predicted and actual retention times of the three types of body fluids in the test set using two estimation methods was 2.15 and 2.06 days, respectively. In conclusion, this study has discovered four novel microorganisms related to the retention time of blood and semen and has preliminarily constructed the TsD prediction models, providing a new direction for future forensic research on the inference of TsD in blood and semen stains.

Construction of the time since deposition (TsD) model in saliva stains with 16S rRNA full-length sequencing technology and microbial markers

Determining the time since deposition (TsD) and sex of saliva stains is crucial for revealing the time of the crime's occurrence and clarifying the nature of the crime. This process not only shortens the time required to solve the case but also helps narrow down the scope of investigation, thereby enhancing the efficiency of case resolution. Currently, the forensic study of the microbial composition in long-term saliva stains remains a relatively underexplored field. The purpose of this study was to explore the succession pattern of long-placed human saliva stains microbial communities and identify relevant microbial markers for estimating TsD and identifying the sex of the donor, in order to be an effective alternative tool for solving practical forensic cases. Therefore, in this study, saliva stains exposed to indoor environmental conditions for up to 140 days were collected and 16S rRNA full-length sequencing was performed using single-molecule real-time sequencing technology based on the PacBio sequencing platform. The study reveals that after 140 days of placement, the relative abundance of Firmicutes significantly decreased (p = 0.00304). At the genus level, the relative abundances of Streptococcus (p = 0.0008), Rothia (p = 0.0448), Gemella (p = 0.016), and Veillonella (p = 0.0208) also significantly decreased. Additionally, significant differences were found in the microbial communities between saliva stains from males and females (p = 0.00013). Then, we constructed a TsD estimating model for microbial community markers based on random forest, and the results showed that the mean absolute error was 9.59 days, and the accuracy of sex classification model based on stepwise logistic regression model and 4 bacterial markers was 84.21%. This indicates that saliva stains that have been in place for a long time still retain significant forensic value, and microbial markers can be used to determine the time since deposition (TsD) of dried saliva stains as well as to identify the sex of the donor.

Revealing microbial community characteristics in healthy human, cat and canine salivas and looking for species-specific microbes

As two kinds of increasingly popular pets, the saliva of cat or canine is most likely to be left at the crime scene compared with the common types of body fluids in forensics. Accurately identifying the species of saliva samples found at the crime scene involving pets will help the investigators find available testing materials, reduce the consumption of reagents and save the investigative time of the case. Therefore, it is necessary to explore the characteristics and differences of saliva microbiomes of cat, canine and human. In this study, 16S rRNA gene amplicon sequencing technology was used to reveal microbial communities of saliva samples of healthy human, cat, and canine. Alpha diversity analyses indicated that canine saliva demonstrated the highest microbial diversity, followed by cat saliva, whereas human saliva microbial diversity was the lowest. The saliva samples of the three species all had their own unique microbial community compositions, and the dominant phyla of canine and cat salivas were Proteobacteria and Bacteroidete, while the dominant phyla of human saliva were Firmicutes and Proteobacteria. There was no significant statistical difference in the salivary microbiota obtained by the two collection methods (cotton swab and liquid saliva). The gender of cats and canines might have no effect on the salivary microbiota, but the different breeds had an impact on their saliva microbiomes. Principal coordinates analysis, non-metric multidimensional scaling analysis and random forest analysis all indicated significant differences in microbial community structures among the three species, allowing inference on the species sources of saliva samples by microbiome method. Differential microbial biomarkers for the salivas of three species were screened out using a variety of bioinformatics analyses, and the results demonstrated that Prevotella melaninogenica, Veillonella parvula, and Haemophilus parainfluenzae could be used as species-specific microbial biomarkers of human saliva. The detections of human species-specific microbes provide a potential method for determining human saliva.

Environmental microbiota from substrate may interfere with microbiome-based identification of forensically relevant body fluids: A pilot study

The microbiome is a promising tool for identifying body fluids which can be deposited on various substrates at a crime scene. Body fluids collected from crime scenes are not entirely free from substrate microbes whose effects on the microbiome-based identification of body fluids are not well understood. In this study, five body fluids (peripheral blood, menstrual blood, nasal secretions, saliva, and semen) were deposited on sterile swabs, bedspreads, and floors under indoor exposure conditions for 7 days. The microbial communities in the samples were characterized using amplicon sequencing targeted V4 region of 16S rRNA gene. The results showed that the microbial communities of fresh samples deposited on sterile swabs clustered together according to the type of body fluid. The microbial composition of the body fluids deposited on the bedspread and floor is significant different from those deposited on sterile swabs. The microbial communities of mock body fluids were a mixture of microbes from pure body fluids and environmental microbes. FEAST analysis showed that the microbes of mock saliva samples were mainly from pure body fluids (51.53 % and 63.04 % on the bedspread and floor, respectively), but not from substrates (25.70 % and 18.92 % on the bedspread and floor, respectively). Contrary results were observed in peripheral blood, mock nasal secretion, and semen samples. All samples were mainly clustered based on the substrate, but not on the type of body fluid in the PCoA visualization. PERMANOVA results showed that the substrate accounted for more of the variance (R2 = 0.211, P < 0.001) than the type of body fluid (R2 = 0.152, P < 0.001). MicroDecon was used to remove contamination by microbes from the substrate of mock body fluid samples. PCoA and PERMANOVA were performed using decontaminated data. The results showed that samples were no longer clustered based on the substrate, and the type of body fluid (R2 = 0.240, P < 0.001) accounted for more of the variance in the microbial communities of samples than the substrate (R2 = 0.108, P < 0.001). Our results suggest that environmental microbiota from substrates may interfere with the microbiome-based identification of forensically relevant body fluids. To some extent, decontamination could decrease the effects of the substrate on the microbial communities of the samples and enhance the ability to distinguish between the types of body fluids. This pilot study will be valuable in promoting the application of microbiome-based stain analysis in forensics.

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.

Body fluids should be identified before estimating the time since deposition (TsD) in microbiome-based stain analyses for forensics

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.

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.

Metatranscriptomic characterization of six types of forensic samples and its potential application to body fluid/tissue identification: A pilot study

Microorganisms are potential markers for identifying body fluids (venous and menstrual blood, semen, saliva, and vaginal secretion) and skin tissue in forensic genetics. Existing published studies have mainly focused on investigating microbial DNA by 16 S rRNA gene sequencing or metagenome shotgun sequencing. We rarely find microbial RNA level investigations on common forensic body fluid/tissue. Therefore, the use of metatranscriptomics to characterize common forensic body fluids/tissue has not been explored in detail, and the potential application of metatranscriptomics in forensic science remains unknown. Here, we performed 30 metatranscriptome analyses on six types of common forensic sample from healthy volunteers by massively parallel sequencing. After quality control and host RNA filtering, a total of 345,300 unigenes were assembled from clean reads. Four kingdoms, 137 phyla, 267 classes, 488 orders, 985 families, 2052 genera, and 4690 species were annotated across all samples. Alpha- and beta-diversity and differential analysis were also performed. As a result, the saliva and skin groups demonstrated high alpha diversity (Simpson index), while the venous blood group exhibited the lowest diversity despite a high Chao1 index. Specifically, we discussed potential microorganism contamination and the "core microbiome," which may be of special interest to forensic researchers. In addition, we implemented and evaluated artificial neural network (ANN), random forest (RF), and support vector machine (SVM) models for forensic body fluid/tissue identification (BFID) using genus- and species-level metatranscriptome profiles. The ANN and RF prediction models discriminated six forensic body fluids/tissue, demonstrating that the microbial RNA-based method could be applied to BFID. Unlike metagenomic research, metatranscriptomic analysis can provide information about active microbial communities; thus, it may have greater potential to become a powerful tool in forensic science for microbial-based individual identification. This study represents the first attempt to explore the application potential of metatranscriptome profiles in forensic science. Our findings help deepen our understanding of the microorganism community structure at the RNA level and are beneficial for other forensic applications of metatranscriptomics.

Rapid and visual detection of specific bacteria for saliva and vaginal fluid identification with the lateral flow dipstick strategy

Identification of body fluids is critical for crime scene reconstruction, and a source of investigation source of investigative leads. In recent years, microbial DNA analysis using sequencing and quantitative real-time polymerase chain reaction have been used to identify body fluids. However, these techniques are time-consuming, expensive, and require complex workflows. In this study, a new method for simultaneous detection of Streptococcus salivarius and Lactobacillus crispatus using polymerase chain reaction (PCR) in combination with a lateral flow dipstick (LFD) was developed to identify saliva and vaginal fluid in forensic samples. LFD results can be observed with the naked eye within 3 min with a sensitivity of 0.001 ng/µL DNA. The PCR-LFD assay was successfully used to detect S. salivarius and L. crispatus in saliva and vaginal fluid respectively, and showed negative results in blood, semen, nasal fluid, and skin. Moreover, saliva and vaginal fluid were detectable even at an extremely high mixing ratio of sample DNA (1:999). Saliva and vaginal fluid were identified in various mock forensic samples. These results indicate that saliva and vaginal fluid can be effectively detected by identifying S. salivarius and L. crispatus, respectively. Furthermore, we have shown that DNA samples used to identify saliva and vaginal fluid can also provide a complete short tandem repeat (STR) profile when used as source material for forensic STR profiling. In summary, our results suggest that PCR-LFD is a promising assay for rapid, simple, reliable, and efficient identification of body fluids.

Development and application of a multiplex PCR system for forensic salivary identification

In forensics, accurate identification of the origin of body fluids is essential for reconstructing a crime scene or presenting strong evidence in court. Microorganisms have demonstrated great potential in body fluid identification. We developed a multiplex PCR system for forensic salivary identification, which contains five types of bacteria:Streptococcus salivarius, Neisseria subflava, Streptococcus. mutans, Bacteroides thetaiotaomicron, and Bacteroides. uniformis. And the validated studies were carried out following the validation guidelines for DNA analysis methods developed by the Scientific Working Group on DNA Analysis Methods (SWGDAM), which included tests for sensitivity, species specificity, repeatability, stability, and mixed samples, trace samples, case samples, and a population study. Our result depicted that the lowest detection limit of the system was 0.01 ng template DNA. Moreover, the corresponding bacteria can still be detected when the amount of saliva input is low to 0.1 μL for DNA extraction. In addition, the target bacteria were not detected in the DNA of human, seven common animals, and seven bacteria DNA and in nine other body fluid samples (skin, semen, blood, menstrual blood, nasal mucus, sweat, tears, urine, and vaginal secretions). Six common inhibitors such as indigo, EDTA, hemoglobin, calcium ions, alcohol and humic acid were well tolerated by the system. What is more, the salivary identification system recognized the saliva component in all mixed samples and simulated case samples. Among 400 unrelated individuals from the Chinese Han population analyzed by this novel system, the detection rates of N. subflava, S. salivarius, and S. mutans were 97.75%, 70.75%, and 19.75%, respectively, with 100% identification of saliva. In conclusion, the salivary identification system has good sensitivity, specificity, stability, and accuracy, which can be a new effective tool for saliva identification.