Assess The Diversity of Gut Microbiota Among Healthy Adults For Forensic Application

doi:10.21203/rs.3.rs-1150105/v1

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Research Article

Assess The Diversity of Gut Microbiota Among Healthy Adults For Forensic Application

https://doi.org/10.21203/rs.3.rs-1150105/v1

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published 24 Mar, 2022

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Background: Human gut microbiota is individually unique that hints the microbiota in fecal traces left in the crime scene could act as a potential biomarker for forensic personal identification. Next-generation DNA sequencing and bioinformatic analysis of fecal samples are revolutionizing our insights into gut microbial communities. While the formation of the gut microbiota is known to be multifactorial, it is unclear whether these characteristics can be applied to forensic applications. Therefore, the gut microbiota of healthy adults with different traits in Chengdu was investigated in this study.

Results: Based on the STAMP analysis of each study group, the difference in gut microbiota composition in male and female subjects was observed. The male group was characterized by taxa in the phylum Proteobacteria, while the female group was described by Synergistetes phylum. The gut bacterial community assembly mechanism was mainly affected by the deterministic process. In addition, gut microbiota composition showed meaningful discrimination in each of the BMI groups. At the phylum level, in male subjects, increased representative phyla were Patescibacteria (p<0.05) in the underweight group and Bacteroidetes (p<0.05) in the normal-weight group, while in the female group, the significantly different phyla were Bacteroidetes, Firmicutes, and Actinobacteria. At the genus level, 44 unique genera were found to be significantly distinct across BMI study groups. By Fisher’s Linear Discriminant Analysis based on 38 of 44 unique genera, 94.4% of original BMI grouped subjects were correctly classified.

Conclusion: In conclusion, subjects with different individual characters have specific gut microbiota, and can be discriminated by bioinformatics methods, suggesting it is promising to apply gut microbiota to forensic personal identification.

Applied & Industrial Microbiology

gut microbiota

forensic science

microbiota diversity

16S rRNA

Identifying the personal characteristics of the trace material left at the crime scene is an important task of forensic science. Feces, as trace evidence of human activity, can be abandoned at the offense scene[1]and is not susceptible to incompletely removed outdoors. The feces sample contains host-microbial profiles that could provide insight into valuable individual information on digestion or diet habits as investigative clues and trial basis. In recent years, with the development of next-generation DNA sequencing technology and bioinformatic methods, the application of human microbiota to forensic personal identification has been studied in the saliva microbiome[2, 3], skin microbiome[4-7], hair microbiome[8], etc.. However, the studies about fecal material have focused on cell type identification to determine the cellular origin of samples/DNA profiles for forensic practice[9, 10], and there is little attention on gut microbiota for forensic personal identification. Recent studies reported the gut microbiota of subjects may be very specific[11, 12] that declared remarkable personal characters of gut microbiota across individuals. Despite it being known that human gut microbiota is individually unique, it is unclear whether gut microbiota can discriminate different subjects. With increased concern on forensic potential using the human microbiome, it is essential to investigate the gut microbiota of subjects with different traits.

This study was an attempt to explore the potential of individualized gut microbiota for forensic personal identification. The gut microbiota is acquired by the environment from birth and can act as a genetically determined property, shaped by and interacted with the host[13-15], and the gut microbiome is regarded to be shaped by human genetics[16]. Anyway, gut microbiota and host are interdependent, which is a consensual opinion. A series of factors affect the composition of the human microbiota, including external factors and internal factors, like food, pathogens, drugs, and endocrine ingredients[17]. Since the highly personalized gut microbiota is multifactorial, it is important to seek the elements associated with the composition of gut microbiota for forensic application. Recent studies have revealed a sex bias across microbiome-associated diseases, while another report has also shown sex differences in the gut microbiota of the Japanese population[17, 18]. In addition, the gut microbiota is an important role in human physiology, making it a significant factor in the development of obesity. A study of gut microbiota profiles of different weight individuals revealed distinctions of bacterial communities in Italian adults[19]. Therefore, this study was targeted to the gut microbiota of Chinese adults, and the subjects were sub-grouped based on the individual traits to 1) observe the differences of gut microbiota community composition in separate study groups, 2) use the variations of the gut microbiota composition to discriminate experimental subjects, and 3) explore the potential of individualized gut microbiota for using in forensic personal identification.

Sample collection and DNA extraction

54 unrelated participants, aged 20 to 30, who self-claimed that they had no intestinal diseases and had not used antibiotics in the past three months were enrolled. After obtaining the informed consent, a total of 54 fecal samples and data which involves age, body weight, body height, and habitation were collected. The fecal collection was strictly under the following requirements: (1) avoid miscellaneous bacteria pollution in urine and toilet; (2) use 5ml sterilized disposable stool sample collection tubes to collect the middle section of the feces isolated from the air and then promptly stored at -80°C pending DNA extraction. Total Genomic DNA was isolated using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s instructions. Extracted DNA was quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA was stored at -20 °C prior to further analysis.

PCR amplification and sequencing

The amplification targeted at the V3-V4 region of the 16S rRNA gene was performed using the forward primer 338F (ACTCCTACGGGAGGCAGCA) and the reverse primer 806R (GGACTACHVGGGTWTCTAAT). Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. The components of PCR (25μl) were as follows: 5μL of reaction buffer (5×), 5μL of GC buffer (5×), 2μL(2.5mM) of dNTPs, 1μL(10uM) of each forward primer and reverse primer, 2μL of DNA Template, 8.75μL of ddH₂O, and 0.25μL of Q5 DNA Polymerase. The thermal cycling conditions for the PCR amplification consisted of an initial denaturation at 98℃ for 2min, followed by 30 cycles of denaturation at 98℃ for 15s, annealing at 55℃ for 30s, extension at 72℃ for 30s, with a final extension at 72℃ for 5min. To remove any remaining contaminants and PCR artifacts, the purification of amplicons products was performed with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) according to the manufacture’s recommendations. The quality and quantity of amplicons were confirmed by 1.2% agarose gel electrophoresis and Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Purified amplicons were pooled in equal amounts, and pair-end 2´250 bp sequencing was performed using the Illumina NovaSeq platform with NovaSeq 6000 SP Reagent Kit (500 cycles) at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China).

Sequence, bioinformatics analysis, and visualization

The Illumina Novaseq platform is used for paired-end sequencing of community DNA fragments. Raw sequencing data was performed DADA2[20] sequence denoising with QIIME2[21] to demultiplex, quality filter, denoise, splice, merge and chimera remove according to the official tutorials(https://docs.qiime2.org/2019.4/tutorials/). Taxonomy was assigned to ASVs using the classify-sklearn naïve Bayes taxonomy classifier in the feature-classifier plugin[22]against the SILVA Release 132 Database (https://www.arb-silva.de/). The metrics of α diversity and β diversity were estimated using the diversity plugin. The α-diversity (microbial diversity within a sample): Chao1, observed_Species, Shannon was plotted as violin boxplot with Wlicox test using the R script. The β-diversity (microbial diversity between samples) was assessed using Bray-Curtis distances and visualized via principal coordinate analysis (PCoA). The Fisher’s Linear Discriminant Analysis and correlation analysis were performed in R. The comparison of gut bacterial community composition in each study group was analyzed with STAMP[23]software. The neural community model (NCM)was constructed with an R script[24]. The normalized stochasticity ratio (NST) was calculated to evaluate the relative importance of stochastic and deterministic processes in gut bacterial community assembly[25].

The overview of sequencing results and study group

5,726,427 raw reads were obtained from 54 fecal samples. After removing low-quality sequence reads, chimeras, and singletons, 3,265,754 clean reads were obtained. The length of clean reads ranged from 403bp to 431bp for each sample, with the average length of clean reads being 417bp. The subjects contained 32 females and 22 males in our study. The Body Mass Index (BMI) of participants was calculated to assess the difference of gut bacteria in various obesity groups. BMI is calculated as weight (kg) divided by height squared (m²), expressed in kg/m². The subjects were sub-grouped into male/female group, and underweight/normal-weight/overweight group according to the sex and BMI value.

The sex-related gut microbiome

Initially, the overall gut microbial richness of male and female subjects was reflected by α diversity indices. The two sex groups did differ significantly in Chao1 index, the number of observed species (richness estimation) and Shannon index (evenness estimation) (Wlicox test, p=0.029, p=0.028, and p=0.036, respectively), which were shown in Figure S1. Subsequently, the β diversity was assessed using Bray-Curtis distance and visualized by the principal coordinate analysis (PCoA). It showed the overall structure of the gut bacterial community was different between the male and female groups (Figure 1). Then the difference in bacterial community composition was analyzed. At the phylum level, the predominant phyla were Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria in the gut bacterial community (Figure S2). It was observed only Proteobacteria had remarkable differences in the two sex groups (Figure 2). The difference of taxon in the gut bacterial community was also assessed at the genus level. In total, the genera with relatively high abundance were Faecalibacterium, Bacteroides, Subdoligranulum, Escherichia-Shigella, Blautia, Prevotella_9, Agathobacter, Bifidobacterium, Roseburia, and Dialister (Figure S2). The change in the gut bacterial community between the male and female group were manifested in Figure 2, which showed a significant difference of 2 genera in male subjects and 11 genera in female subjects. The Linear discriminant analysis effect size (LEfSe) result showed 13 nested taxa in the female group and 5 in the male group were regarded to explain the difference between the two groups (Figure 3). The male group was characterized by proteobacteria phylum, while the female group was characterized by Synergistetes phylum.

Ecological assembly of bacterial community

The gut microbial community assembly mechanism was analyzed. The normalized stochasticity ratio (NST) was performed to assess the role of stochastic and deterministic processes in bacterial community assembly (Figure 4B). It was observed that the distribution of NST values was below the 50% threshold line for both the female and male groups of microbial communities, indicating the dominance of deterministic processes in both groups of communities. Moreover, there was no significant difference in the NST values between the female (an average of 42.77%) and male (an average of 45.87%) groups (Wilcox Test P>0.05). Additionally, the neutral community model (NCM) was used to show the relative importance of the neutral process (Figure 4A). The R² represents the overall goodness of fit of the neutral community model. A low R² (0.28) was overserved in this study that indicating it is not close to the neutral model, and the community construction was more likely influenced by the deterministic process and rarely influenced by the stochastic process.

The BMI-related gut microbiome

A relative abundance analysis for each bacterial phylum and bacterial genus in different BMI groups was performed. The subjects in our study were divided into three BMI groups according the Chinese reference standard: underweight (L): BMI < 18.5 kg/m², normal weight (N):18.5 kg/m² ≤ BMI < 24.0 kg/m²), overweight(H): BMI ≥ 24.0 kg/m². Considering the sex-related gut microbial differences, male and female individuals were analyzed separately, and the subjects were divided into six groups: L-F, N-F, H-F, L-M, N-M, and H-M. The various α diversity indices were calculated to show the gut bacterial diversity in different groups. However, no significant difference in α diversity was observed in each BMI group (p>0.05). The relationship between BMI and gut bacterial diversity was investigated with Pearson correlation. A slightly negative correlation was observed between BMI and gut bacterial α diversity, though not reaching significant difference in each group (Observed_species (R=-0.12, p=0.39), Shannon index (R=-0.17, p=0.23) and Simpson index (R=-0.15, p=0.28)) (Figure S3). The difference in gut bacterial taxon was analyzed at bacterial phylum and genus level in each BMI group. The results of male and female subjects were shown separately.

In male subjects, the taxonomic difference in the gut bacterial community at phylum and genus level were manifested in Figure 5 which shows there were remarkable differences in each BMI group. In detail, at the phylum level, increased representative phyla were Patescibacteria (p<0.05) in the underweight group and Bacteroidetes (p<0.05) in the normal-weight group (Figure 5a-b). At the genus level, comparing the microbial changes between overweight and underweight groups, it was found that the abundance of 3 genera in the underweight group increased significantly (Figure 5c), and the distinction between the overweight and normal-weight group showed a great increase in the abundance of 8 genera in normal-weight subjects (Figure 5d), while the collation of underweight and normal-weight subjects revealed a remarkable increase in the abundance of 13 genera in normal-weight subjects and 3 genera in underweight subjects (Figure 5e).

Within the female group, the taxonomic distinction in the gut bacterial community at phylum and genus level were also revealed notable differences across BMI groups. Specifically, at the phylum level, the significantly different phyla were Bacteroidetes, Firmicutes, and Actinobacteria (Figure 5f-h). At the genus level, comparing the microbial changes between overweight and underweight groups, the abundance of 1 genus in the overweight group increased significantly was found (Figure 5i), and the collation of the overweight and the normal-weight group showed a remarkable increase in the abundance of 15 genera in normal-weight subjects (Figure 5j), while the distinction between underweight and normal-weight subjects revealed a great increase in the abundance of 10 genera in normal-weight subjects (Figure 5k).

Discrimination of different BMI groups

According to the comparison of gut bacterial community composition in each BMI group at the genus level, 44 genera were found to be unique (Figure 5). A Fisher’s Linear Discriminant Analysis (LDA) based on the 44 specific genera was performed to define a model to discriminate the subjects in six different BMI groups. Some bacteria were excluded and finally, 38 bacteria were left for LDA analysis. The goodness-of-fit of the discriminant model was measured using the resubstitutaion method. By Fisher’s Linear Discriminant analysis, 94.4% of original BMI grouped subjects were correctly classified. According to the model’s goodness-of-fit, we found 3 subjects were incorrectly predicted (Figure 6b). The linear discriminant plot was shown in Figure 6a, which revealed a clear separation across six BMI groups. In detail, it illustrated an obvious classification among L-M, N-M, L-F, and N-F groups, with the majority of samples within each group clustered together with a clear distinction. However, there were two overlapping points in the H-F and H-M groups. One sample in the L-F group and one in the N-M group overlapped with the H-F group. These overlaps also explained why the model incorrectly predicted three samples.

Microbial typing of fecal samples can play a vital role in forensic practice, but the research on its application in forensic personal identification is still lacking. In this study, the gut microbiota composition of unrelated subjects was examined to explore whether individual characteristics of the gut microbiota could be applied to forensic practice. Various factors that affect the gut microbiota. Sex maturation and sex hormones perform critical roles after puberty, and estrogen, testosterone, and androgen are proven to be related to the gut microbiome have been reported[26]. Moreover, increased reports have revealed the relationship between gut microbiota and obesity[27-29]. The indicator of obesity is body mass index, which is closely related to body type. It seems sex and body mass index could be suitable traits to apply gut microbiota to forensic investigation. So, the subjects were grouped according to the two characteristics in the gut microbiota composition analysis. Two variables, age and region, were controlled when collecting the samples. The subjects were all 20-30 years old and local college students in this study.

In the analysis of gut microbiota and sex, significant differences had been revealed between female and male gut microbiota in α diversity and bacterial community composition. From the results of LEfSe, Proteobacteria and Synergistetes were the biomarkers with significant differences in males and females, respectively. At the genus level, there were five biomarker genera (Anaerofusis, Intestinibacter, etc.) of gut microbiota in females and three in males (Escherichia_shigella, Rodentibacter, Caproiciproducens). These results indicated the gut microbiota composition had a sex-related variation. Previous studies have also shown a differential gut microbial community composition between males and females, but the unique bacterial genera are diverse. The previous study based on the human microbiome project had shown community type D was characterized in males gut microbiota with Prevotella in a high relative abundance, while community type C microbial genera contained Ruminococcaceae, Alistipes, Faecalibacterium were reparented in females[12]. Besides, in the gut microbial studies of Dutch, Spanish, and Japanese populations, the results as well as showed a differential gut microbial composition between males and females, but the unique bacterial genera of the differences were also not completely consistent with each other that was shown in Table 1[18, 30, 31]. A higher α diversity in the female gut microbiota than in the male was revealed in this study (p > 0.05), which was consistent with a study of 1135 subjects in which females showed greater gut microbial diversity than males[31], while other studies showed no statistically significant difference[18, 30](Table 1). These inconsistent phenomena may be caused by the complexity of factors included region and age that affect the gut microbiota. A series of changes of gut community composition have been revealed associated with age[18]. The variable of age was controlled in our study, while the subjects were distributed between 20 and 89 years of age in other studies. In addition, there is a study has shown differences in the gut microbiota in diverse US populations that may be caused by dietary acculturation[32]. These could also explain the inconsistent results of sex-related gut microbial genera and sex differences in gut microbial diversity in various studies. On the one hand, sex differences in gut microbiota may be hormonally related. Estrogen and testosterone, as a manifestation of sex discrimination, have been shown to directly affect the gut microbiota[26, 33]. This sex-related gut microbial difference is reflected not only in the age group with high sex hormone secretion but also in all age groups[30, 34]. On the other hand, the various immune system and functions in different sex subjects might affect the human gut microbial community composition[35]. The human immune system and the physiological activities of hormones are all regulated by genes. It’s wise to regard the gut microbiome as shaped by human genetics[16]. Thus, this difference in gut composition caused by human genes facilitates the application of gut microbiota to individual portraits in forensic practice.

Table 1 The comparison of sex-related gut microbiota composition differences in various country.

Country	Males	Females	α diversity	Age of subjects	Reference
China	Escherichia_shigella,Rodentibacter, Caproiciproducens	Anaerofusis, Intestinibacter	F > M	20-30	This study
Japan	Prevotella, Megamonas, Fusobacterium, Megasphaera	Bifidobacterium, Ruminococcus, Akkermansia	NS^*	20-89	[18]
Netherlands	12 specific microbial species were significantly associated with sex, including Akkermansia muciniphila, Lachnospiraceae bacterium, Subdoligranulum, Eggerthella, Collinsella aerofaciens, Eubacterium eligens, Gordonibacter pamelaeae, Lachnospiraceae bacterium, Bifidobacterium longum, Alistipes, onderdonkii, Bilophila		F > M	18-81	[31]
Spanish	Clostridium, Coprococcus, Dorea, Lachnospira, Roseburia, Veillonella	Bacteroides, Barneciellaceae, Butyricimonas, Parabacteroides, Rikenellaceae	NS	20-75	[30]

NS represented no statistically significant differences.

Two methods were used to evaluate the gut bacterial community assembly mechanism. NST reflects the contribution of stochastic assembly relative to deterministic assembly, based on the magnitude of the difference between observed and null expectations, as a quantitative measure of stochasticity. The relative importance of deterministic and stochastic processes in different community constructions was quantified by comparing the numerical distribution of NST between sample pairs within different groups: the range of values of NST (0-1). If the NST of a group of communities is mainly distributed above 50% then the stochastic process is considered to dominate within that group of communities, conversely, if the NST value is below 50%, the deterministic process is considered to be dominant in that group of communities[25, 36]. NCM, a neutral-based process model, has been successfully applied to a wide range of ecological phenomena due to the effective ability to infer the stochastic processes acting on community assembly mechanisms[37, 38]. The results of the two approachs were both suggested that deterministic processes played a vital role in shaping the gut bacterial community assembly. The influence of deterministic processes on the two sex groups of communities was almost equal, which may explain why male and female subjects cannot be completely distinguished based on BC distance (Figure 1). Therefore, the other personal trait has been included in the research.

The body types, as one of the important individual features, could help police narrow down suspects in the practical application of forensic science. BMI was identified as a suitable proxy for determining the percentage of body fat in a population by Ancel Keys in 1972[39], and subsequently became a criterion for differentiating weight levels, closely related to body types. The BMI standard for Chinese adults are as follows: underweight (BMI<18.5kg/m²), normal weight (18.5kg/m²≤BMI<24.0kg/m²), overweight (24.0kg/m²≤BMI<28.0kg/m²) and obese (BMI≥28.0kg/m²). As an appearance data depicting offenders, BMI is also associated with the composition of the gut microbiota[19, 28]. Given that the sex differences in gut microbiota observed above, male and female subjects were compared separately in the study of BMI and gut microbiota. No significant differences were revealed in the comparison of α diversity. A negative correlation between α diversity and BMI was shown in a study exploring the gut microbiota and BMI in Chinese male college students[27], and a weak negative correlation was also shown between α diversity and BMI in our study, although it did not reach a significant difference. The sample distribution was uneven and the subjects were mainly concentrated in the normal-weight group in our study, which may have contributed to a p-value of the Pearson test greater than 0.05. In a comparison of bacterial composition at the phylum level, significant differences in the relative abundance of Firmicute in both male and female overweight groups have existed, and the underweight and normal-weight group of Bacteroidetes showed remarkably different relative abundances within the female group. However, unfortunately, a non-meaningful positive correlation had not been shown (p > 0.05, not shown) in the correlation analysis between F/B ratio and BMI, and it was also shown to be uncorrelated with BMI in the study by Tomohisa Takagi et al[18]. It conflicts with the correlation between the F/B ratio and BMI in various studies[27, 40], which may be due to the external factors contained habitat, dietary acculturation, psychical exercise, social pressure, etc. To apply the gut microbiota to forensic practice, forensic scientists are keen on whether different individuals/populations can be distinguished by the characteristics of gut microbiota to provide clues for investigation and evidence for trial. 94.4% of the subjects in six BMI groups were correctly classified by LDA analysis based on 38 unique genera, and only three samples were mismatched, which suggested that it was possible to distinguish various individuals using bacterial genera with high differences. In addition, there was an interesting observation. According to the linear discriminant plot, we found that male and female subjects in the normal weight and underweight group were clustered together separately showing a clear difference, which indicated that it was wise to separate male and female subjects for analysis. However, there were some overlaps in the overweight group. This may indicate that sex differences were not obvious in the overweight group, and the gut microbial differences were dominated by some bacterial genera associated with obesity. It seemed the LDA analysis model based on bacterial genera with high differences had a good fit and could classify most individuals correctly. Whereas, the small number of subjects in our study, especially in the overweight and underweight group, prevented us from dividing the data into validation and test sets to perform predication, which made the predictive effect of the model was not validated. In addition, some articles postulating a potential association between gut microbiota and obesity have conflicting results and have not been duplicated in clinical studies, and these divergent findings block the identification of consistent patterns of human gut microbiota associated with BMI[41-44], making it challenging to apply the relationship between BMI and gut microbiota to forensic personal identification. Only two factors (sex and BMI) were explored in this study, the analysis results of human portraits are more comprehensive and reasonable if more influencing factors are taken into consideration, which needs to be achieved based on a large number of population data of gut microbial sequencing, and combined with machine learning. The individual differentiation using gut microbiota needs further study.

In summary, a series of individual factors included sex, BMI, etc. affect the composition of gut microbiota. These factors make each individual have a unique gut microbiota. In our study, LDA analysis based on distinctive bacterial genera was able to correctly classify 94.4% of subjects into different BMI groups, which seems that it is possible to use the gut microbiota to make inferences about individual characteristics.

Availability of data and materials

The dataset supporting the conclusions of this article had been deposited in the NCBI Sequence Read Archive repository under the accession number PRJNA777123, and will be released in a certain time (https://dataview.ncbi.nlm.nih.gov/object/PRJNA777123?reviewer=k7r03vmmpm0h7os9a69kl0c61j)

Author information

Affiliations

Department of Forensic Genetics, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, China

Contributions

L-WB, L-HB conceptualized the work. W-SS, SF, L-WB, and L-HB designed the study. G-HY, S-ZL, J-LR, Z-YX, L-JN, and W-ZF collected the samples. G-HY, S-ZL, W-XW, and ZK extracted the genomic DNA. W-SS performed the sequencing and the bioinformatic data analysis. W-SS, G-HY and SF wrote and edited the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Haibo Luo &Weibo Liang.

Funding

This study was funded by the National Natural Science Foundation of China (NSFC, No. 81772030).

Ethics approval and consent to participate

The study was performed with the approval of the Ethics Committee at the Department of Forensic Genetics, Sichuan University(K2020033).

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

additionalfile.docx
Additional file: Figure S1. The α diversity of gut microbiota in male and female subjects. The sex group did differ significantly in Chao1 index, the number of observed species (richness estimation) and Shannon index (evenness estimation) (wlicox test, p=0.029, p=0.028 and p=0.036, respectively). Figure S2. The relative high abundance bacteria of male and female subjects at phylum and genus level. At phylum level, the predominant phyla in gut bacterial community were Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria. At genus level, the relative high abundance genera were Faecalibacterium, Bacteroides, Subdoligranulum, Escherichia-Shigella, Blautia, Prevotella_9, Agathobacter, Bifidobacterium, Roseburia, Dialister. Figure S3. The correlation between BMI with gut microbial α diversity: Observed_species(a), Shannon index (b) and Simpson index (c) derived by Pearson correlation. A slightly negative correlation was observed between BMI and gut bacterial α diversity, though not reaching significant difference in each group (Observed_species (R=-0.12, p=0.39), Shannon index (R=-0.17, p=0.23) and Simpson index (R=-0.15, p=0.28).

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Assess The Diversity of Gut Microbiota Among Healthy Adults For Forensic Application

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