Estimation Of Sex And Body Mass Index Based On Population Salivary Microbiome In Northern Han Chinese

Objective: It is an important task of forensic research to infer the phenotype characteristics of the donor with the stain left at the crime scene.In particular,when the DNA of biological samples is degraded or at the low detection level which cannot be compared with the database,the phenotypic characteristics of the donor of these biological traces,such as body size,will be helpful to narrow the scope of investigation and solve the case.Saliva stains are common biological stains in forensic practical cases,often left at crime scenes,such as water cups,cigarette butts,nipple hickey and bite marks of victims in sex crimes.Studies have shown that there are significant differences in saliva microbial composition among individuals of different body types.But whether saliva microbiome characteristics can be used to distinguish between individuals of different body types is not clear.The study explores the potential application value of saliva microbiota in forensic body type inference by analyzing the differences in the composition of saliva microbiota among Han people of different body types in northern China,which provides new ideas and strategies for providing forensic evidence.Methods: A total of 72 healthy subjects,including 35 males and 37 females,aged from 18 to 29 years old,were included in this study.2m L saliva sample was taken from each subject in the same environment.The DNA of the saliva sample was extracted using the MN Nucleo Spin 96 Soi DNA extraction kit(Tiangen Biotech(Beijing)).High throughput sequencing and bioinformatics analysis of 16 S r DNA.In this study,Illumina Nova Seq 6000 sequencing platform was used to amplify the V3-V4 region of 16 S rRNA gene of saliva community,and double-terminal sequencing method was used to construct community DNA fragments for sequencing.The sequence obtained after the original data was spliced was filtered to remove the chimeras.The sequences were clustered at the 97% similarity level,and 0.005% of all sequences were used as the threshold to filter OTUs.The Silva database was used as the reference sequence database for comparison,and the species annotation was performed using the pre-trained naive Bayes classifier in QIIME2 software with default parameters.Wilcoxon rank sum test was used to evaluate the difference of α diversity index among different groups.The Bray Curtis algorithm was used to calculate the distance between samples to obtain the beta value.Random forest algorithm was used to construct the model on the platform of R software(version 4.1.2).Results:1.Analysis of microbial composition in salivaAmong all the 72 saliva samples tested,the top 5 bacteria groups in terms of relative abundance were Firmicutes,Proteobacteria,Bacteroidota,Actinobacteriota,Fusobacteriota,overall accounted for more than 95% of oral saliva microbes;The top 10 bacteria genera are Streptococcus,Neisseria,Veillonella,Rothia,Haemophilus,Actinomyces,Gemella,Fusobacterium,Porphyromonas,as a whole,occupy more than 73% of oral saliva microbiota.2.Influence of sex on salivary microbiomeThere were significant differences between male and female groups in saliva microbial Simpson index,and the average Simpson index of female was significantly lower than that of male.Beta diversity analysis also revealed significant differences in the overall microbiome between male and female subjects.According to the relative abundance of OTUs,the prediction accuracy of the random forest sex inference model was 100% in the training set and 94.12% in the test set.3.Influence of body size on salivary microbiomeNo significant differences were found between each of the four BMI groups(N,U,OV,Ob)of all subjects.Stratified analysis by sex showed statistically significant differences in saliva microbiota between women with normal BMI and obese women,but no significant differences between other groups.The prediction accuracy of BMI model of random forest model was77.68% for male and 80.52% for female.In the BMI random forest regression model constructed regardless of gender,the inferred error of BMI is about 3.6,and the R squared is 0.61.Conclusions: In this study,we analyzed the saliva microbiome composition of 72 students aged 18 to 29 with different BMI,and found that there were significant sex differences in saliva microbiome composition between normal BMI and obese BMI in females.Based on this,the random forest prediction model was constructed in this study.Salivary microbiome was used to predict gender and BMI,and the prediction accuracy of gender was more than 93%,that of male BMI was 77.68%,and that of female BMI was 80.51%,providing a new idea and strategy for the characterization of phenotypic characteristics of forensic biological samples.