Evaluation of reverse transcription yield of RNA standards and forensic samples based on droplet digital PCR.

RNA analysis has shown great value in forensic science, such as body fluids and tissue identification, postmortem interval estimation, biological age prediction, etc. Currently, most RNA follow-up experiments involve reverse transcription (RT) procedures. It has been shown that the RT step is variable and has a greater impact on subsequent data analysis, especially for forensic trace samples. However, the pattern of variation between different RNA template inputs and complementary DNA (cDNA) yield is unclear. In this study, a series of 2-fold gradient dilutions of RNA standards (1 µg/µL - 0.24 ng/µL) and forensic samples (including blood samples, saliva samples, bloodstains, and saliva stains) were reverse-transcribed using EasyQuick RT MasterMix. The obtained cDNA was quantified by droplet digital PCR (ddPCR) to assess the RT yield of the ACTB gene. The results showed that the 125 ng RNA template had the highest RT yield in a 10 µL RT reaction system with the selected kit. For all stain samples, the RT yield improved as the amount of RNA template input increased since RNA quantities were below 125 ng. As many commercialized reverse transcription kits using different kinds of enzymes are available for forensic RNA research, we recommend that systematic experiments should be performed in advance to determine the amount of RNA input at the optimum RT yield when using any kit for reverse transcription experiments.

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.

Changes in Gut Microbiota and Metabolites in Papillary Thyroid Carcinoma Patients Following Radioactive Iodine Therapy.

Purpose: Radioactive iodine therapy is administered through oral route, which is accumulated and absorbed in the intestine. However, its effects on the intestine remain unclear. In this study, we investigated the changes in the gut microbiota and metabolites following radioactive iodine therapy. Patients and Methods: A total of 76 stool samples from the same 38 patients were collected at the start of radioactive iodine therapy and three days following the therapy. Stool microbiota and metabolites were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry. Results: Enterobacteriales, Enterobacteriaceae and Escherichia-Shigella were elevated in most patients (27/38) following the therapy. The levels of 2-hydroxyundec-7-enoylcarnitine were significantly lower, whereas those of 5-dehydroavenasterol, butylisopropylamine, and salsoline-1-carboxylate were higher following the therapy. The relative abundance of Escherichia-Shigella was negatively correlated with 2-hydroxyundec-7-enoylcarnitine level (r2 = -0.661, P = 0.009). Functional pathways were predicted to be involved in amino acid and lipid metabolism following the therapy. Particularly, phenylalanine, linoleic acid, sphingolipid, purine, and alpha-linolenic acid metabolism were the main metabolic pathways. Conclusion: Gut microbiota was disturbed following radioactive iodine therapy, with increased Escherichia-Shigella. Processes associated with energy production seems to be impacted following the therapy, with significantly decreased 2-hydroxyundec-7-enoylcarnitine level. Meanwhile, some metabolites and functional pathways may have a positive effect on intestinal homeostasis, and may be related to the repair and promotion of gut recovery following the therapy. This study provides a basic foundation to explore how radioactive iodine affects gut microbiota and metabolites, and how gut function is regulated in response to radioactive iodine therapy.

Estimation of bloodstain deposition time within a 24-h day-night cycle with rhythmic mRNA based on a machine learning algorithm.

Estimating the time that bloodstains are left at a crime scene can provide invaluable evidence for law enforcement investigations, including determining the time of the crime, linking the perpetrator to the crime scene, narrowing the pool of possible suspects, and verifying witness statements. There have been some attempts to estimate the time since deposition of bloodstains, i.e., how much time has passed since the bloodstain was left at a crime scene. However, most studies focus on the time interval of days. As far as we know, previous study have been conducted to estimate the deposition time of blood within a 24-h day-night cycle. To date, there is a lack of studies on whether rhythmic mRNA of blood is suitable for bloodstain samples. In this study, we estimated the bloodstain deposition time within a 24-h day-night cycle based on the expression of messenger RNAs (mRNAs) by real-time quantitative polymerase chain reaction. Bloodstain samples were prepared from eight individuals at eight time points under real and uncontrolled conditions. Four mRNAs expressed rhythmically and were used to construct a regression model using the k-nearest neighbor (KNN) algorithm, resulting in a mean absolute error of 3.92 h. Overall, using the rhythmic mRNAs, a machine learning model was developed which has allowed us to predict the deposition time of bloodstains within the 24-h day-night cycle in East Asian populations. This study demonstrates that mRNA biomarkers can be used to estimate the bloodstain deposition time within a 24-h period. Furthermore, rhythmic mRNA biomarkers provide a potential method and perspective for estimating the deposition time of forensic traces in forensic investigation. Case samples in forensic analysis are usually limited or degraded, so the stability and sensitivity of rhythmic biomarkers need to be further investigated.

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.

Tracing recent outdoor geolocation by analyzing microbiota from shoe soles and shoeprints even after indoor walking.

The microbial communities on shoe soles and shoeprints could carry microbial information about where someone walked. This is possible evidence to link a suspect in a crime case to a geographic location. A previous study had shown that the microbiota found on shoe soles depend on the microbiota of the soil on which people walk. However, there is a turnover of microbial communities on shoe soles during walking. The impact of microbial community turnover on tracing recent geolocation from shoe soles has not been adequately studied. In addition, it is still unclear whether the microbiota of shoeprints can be used to determine recent geolocation. In this preliminary study, we investigated whether the microbial characteristics of shoe soles and shoeprints can be used to trace geolocation and whether this information can be destroyed by walking on indoor floors. In this study, participants were asked to walk outdoors on exposed soil, then walk indoors on a hard wood floor. High-throughput sequencing of the 16S rRNA gene was performed to characterize the microbial communities of shoe soles, shoeprints, indoor dust, and outdoor soil. Samples of shoe soles and shoeprints were collected at steps 5, 20, and 50 while walking indoors. The PCoA result showed that the samples were clustered by geographic origin. The shoeprint showed a more rapid turnover of microbial community than the shoe sole during indoor walking. The result of FEAST showed that the microbial communities of shoe sole and shoeprint were mainly (shoe sole, 86.21∼92.34 %; shoeprint, 61.66∼90.41 %) from the soil of the outdoor ground where the individual recently walked, and a small portion (shoe sole, 0.68∼3.33 %; shoeprint, 1.43∼27.14 %) from the indoor dust. Based on the matching of microbial communities between geolocation and shoe sole or shoeprint, we were able to infer the recent geolocation of the individual with relatively high accuracy using the random forest prediction model (shoe sole: 100.00 %, shoeprint: 93.33∼100.00 %). Overall, we are able to accurately infer the geolocation of an individual's most recent outdoor walk based on the microbiota of shoe sole and shoeprint, even though these microbiotas show a turnover when walking indoor floor. The pilot study was expected to provide a potential method for tracing recent geolocation of suspects.

The Effects of Drug Addiction and Detoxification on the Human Oral Microbiota.

Drug addiction can powerfully and chronically damage human health. Detoxification contributes to health recovery of the body. It is well established that drug abuse is associated with poor oral health in terms of dental caries and periodontal diseases. We supposed that drug addiction and detoxification might have significant effects on the oral microbiota. To test the hypothesis, we assessed the effects of drug (heroin and methylamphetamine) addiction/detoxification on the oral microbiota based on 16S rRNA gene sequencing by an observational investigation, including 495 saliva samples from participants. The oral microbial compositions differed between non-users, current and former drug users. Lower alpha diversities were observed in current drug users, with no significant differences between non-users and former drug users. Heroin and METH addiction can cause consistent variations in several specific phyla, such as the enrichment of Acidobacteria and depletion of Proteobacteria and Tenericutes. Current drug users had significantly lower relative abundances of Neisseria subflava and Haemophilus parainfluenzae compared to non-users and former drug users. The result of random forest prediction model suggested that the oral microbiota has a powerful classification potential for distinguishing current drug users from non-users and former drug users. A cooccurrence network analysis showed that current drug users had more complex oral microbial networks and lower functional modularity. Overall, our study suggested that drug addiction may damage the balance of the oral microbiota. These results may have benefits for further understanding the effects of addiction-related oral microbiota on the health of drug users and promoting the microbiota to serve as a potential tool for accurate forensic identification. IMPORTANCE Drug addiction has serious negative consequences for human health and public security. The evidence indicates that drug abuse can cause poor oral health. In the current study, we observed that drug addiction caused oral microbial dysbiosis. Detoxication have positive effects on the recovery of oral microbial community structures to some extent. Understanding the effects of drug addiction and detoxification on oral microbial communities will promote a more rational approach for recovering the oral function and health of drug users. Furthermore, specific microbial species might be considered biomarkers that could provide information regarding drug abuse status for saliva left at crime scenes. To the best of our knowledge, this is the first report on the role of the oral microbiota in drug addiction and detoxification. Our findings give new clues to understand the association between drug addiction and oral health.