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Integrating the salivary microbiome in the forensic toolkit by 16S rRNA gene: potential application in body fluid identification and biogeographic inference

International Journal of Legal Medicine volume 136, pages 975–985 (2022)Cite this article

Abstract

Saliva is a common body fluid with significant forensic value used to investigate criminal cases such as murder and assault. In the past, saliva identification often relied on the α-amylase test; however, this method has low specificity and is prone to false positives. Accordingly, forensic researchers have been working to find new specific molecular markers to refine the current saliva identification approach. At present, research on immunological methods, mRNA, microRNA, circRNA, and DNA methylation is still in the exploratory stage, and the application of these markers still has various limitations. It has been established that salivary microorganisms exhibit good specificity and stability. In this study, 16S rDNA sequencing technology was used to sequence the V3-V4 hypervariable regions in saliva samples from five regions to reveal the role of regional location on the heterogeneity in microbial profile information in saliva. Although the relative abundance of salivary flora was affected to a certain extent by geographical factors, the salivary flora of each sample was still dominated by Streptococcus, Neisseria, and Rothia. In addition, the microbial community in the saliva samples in this study was significantly different from that in the vaginal secretions, semen, and skin samples reported in our previous studies. Accordingly, saliva can be distinguished from the other three body fluids and tissues. Moreover, we established a prediction model based on the random forest algorithm that could distinguish saliva between different regions at the genus level even though the model has a certain probability of misjudgment which needs more in-depth research. Overall, the microbial community information in saliva stains might have prospects for potential application in body fluid identification and biogeographic inference.

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References

  1. Bradbury C, Kottgen A, Staubach F (2019) Off-target phenotypes in forensic DNA phenotyping and biogeographic ancestry inference: a resource[J]. Forensic Sci Int Genet 38:93–104

    Article  CAS  Google Scholar 

  2. Kapoor P, Chowdhry A (2018) Salivary signature in forensic profiling: a scoping review[J]. J Forensic Dent Sci 10(3):123–127

    Article  PubMed Central  Google Scholar 

  3. Anzai-Kanto E, Hirata MH, Hirata RD et al (2005) DNA extraction from human saliva deposited on skin and its use in forensic identification procedures[J]. Braz Oral Res 19(3):216–222

    Article  Google Scholar 

  4. Tsai LC, Su CW, Lee JC et al (2018) The detection and identification of saliva in forensic samples by RT-LAMP[J]. Forensic Sci Med Pathol 14(4):469–477

    Article  CAS  Google Scholar 

  5. Martin NC, Clayson NJ, Scrimger DG (2006) The sensitivity and specificity of red-starch paper for the detection of saliva[J]. Sci Justice 46(2):97–105

    Article  CAS  Google Scholar 

  6. Wornes DJ, Speers SJ, Murakami JA (2018) The evaluation and validation of Phadebas((R)) paper as a presumptive screening tool for saliva on forensic exhibits[J]. Forensic Sci Int 288:81–88

    Article  CAS  Google Scholar 

  7. Plomp R, de Haan N, Bondt A et al (2018) Comparative glycomics of immunoglobulin A and G from saliva and plasma reveals biomarker potential[J]. Front Immunol 9:2436

    Article  PubMed Central  Google Scholar 

  8. Sakurada K, Ikegaya H, Fukushima H et al (2009) Evaluation of mRNA-based approach for identification of saliva and semen[J]. Leg Med (Tokyo) 11(3):125–128

    Article  CAS  Google Scholar 

  9. Ohta J, Sakurada K (2019) Oral gram-positive bacterial DNA-based identification of saliva from highly degraded samples[J]. Forensic Sci Int Genet 42:103–112

    Article  CAS  Google Scholar 

  10. Diez LC, Vidaki A, Ralf A et al (2019) Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials[J]. Forensic Sci Int Genet 41:72–82

    Article  Google Scholar 

  11. Hao Y, Tang C, Du Q et al (2021) Comparative analysis of oral microbiome from Zang and Han populations living at different altitudes[J]. Arch Oral Biol 121:104986

    Article  CAS  Google Scholar 

  12. Astasov-Frauenhoffer M, Kulik EM (2021) Cariogenic biofilms and caries from birth to old age[J]. Monogr Oral Sci 29:53–64

    Article  Google Scholar 

  13. Li J, Quinque D, Horz HP et al (2014) Comparative analysis of the human saliva microbiome from different climate zones: Alaska, Germany, and Africa[J]. BMC Microbiol 14:316

    Article  CAS  PubMed Central  Google Scholar 

  14. Nasidze I, Li J, Quinque D et al (2009) Global diversity in the human salivary microbiome[J]. Genome Res 19(4):636–643

    Article  CAS  PubMed Central  Google Scholar 

  15. Huang H, Yao T, Wu W et al (2019) Specific microbes of saliva and vaginal fluid of Guangdong Han females based on 16S rDNA high-throughput sequencing[J]. Int J Legal Med 133(3):699–710

    Article  Google Scholar 

  16. Yao T, Han X, Guan T et al (2021) Exploration of the microbiome community for saliva, skin, and a mixture of both from a population living in Guangdong[J]. Int J Legal Med 135(1):53–62

    Article  Google Scholar 

  17. Chen S, Zhou Y, Chen Y et al (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor[J]. Bioinformatics 34(17):i884–i890

    Article  PubMed Central  Google Scholar 

  18. Magoc T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies[J]. Bioinformatics 27(21):2957–2963

    Article  CAS  PubMed Central  Google Scholar 

  19. Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities[J]. Appl Environ Microbiol 75(23):7537–7541

    Article  CAS  PubMed Central  Google Scholar 

  20. Song J, Gao Y, Yin P et al (2021) The random forest model has the best accuracy among the four pressure ulcer prediction models using machine learning algorithms[J]. Risk Manag Healthc Policy 14:1175–1187

    Article  PubMed Central  Google Scholar 

  21. Breiman L (2001) Random forests[J]. Mach Learn 45(1):5–32

    Article  Google Scholar 

  22. Huang H, Liu X, Cheng J et al (2022) A novel multiplex assay system based on 10 methylation markers for forensic identification of body fluids[J]. J Forensic Sci 67(1):136–148

    Article  CAS  Google Scholar 

  23. Yao T, Wang Z, Liang X et al (2021) Signatures of vaginal microbiota by 16S rRNA gene: potential bio-geographical application in Chinese Han from three regions of China[J]. Int J Legal Med 135(4):1213–1224

    Article  Google Scholar 

  24. Yao T, Han X, Guan T et al (2020) Effect of indoor environmental exposure on seminal microbiota and its application in body fluid identification[J]. Forensic Sci Int 314:110417

    Article  CAS  Google Scholar 

  25. Krishnan K, Chen T, Paster BJ (2017) A practical guide to the oral microbiome and its relation to health and disease[J]. Oral Dis 23(3):276–286

    Article  CAS  Google Scholar 

  26. Verma D, Garg PK, Dubey AK (2018) Insights into the human oral microbiome[J]. Arch Microbiol 200(4):525–540

    Article  CAS  Google Scholar 

  27. Yamashita Y, Takeshita T (2017) The oral microbiome and human health[J]. J Oral Sci 59(2):201–206

    Article  CAS  Google Scholar 

  28. Mughini-Gras L, van Pelt W (2014) Salmonella source attribution based on microbial subtyping: does including data on food consumption matter?[J]. Int J Food Microbiol 191:109–115

    Article  Google Scholar 

  29. Lokmer A, Aflalo S, Amougou N et al (2020) Response of the human gut and saliva microbiome to urbanization in Cameroon[J]. Sci Rep 10(1):2856

    Article  CAS  PubMed Central  Google Scholar 

  30. Hansen TH, Kern T, Bak EG et al (2018) Impact of a vegan diet on the human salivary microbiota[J]. Sci Rep 8(1):5847

    Article  PubMed Central  Google Scholar 

  31. Song JX, Ren H, Gao YF et al (2017) Dietary capsaicin improves glucose homeostasis and alters the gut microbiota in obese diabetic ob/ob mice[J]. Front Physiol 8:602

    Article  PubMed Central  Google Scholar 

  32. Li X, Zhang J, Chen W et al (2022) Inter-patient automated arrhythmia classification: a new approach of weight capsule and sequence to sequence combination[J]. Comput Methods Programs Biomed 214:106533

    Article  Google Scholar 

  33. Marsh PD, Do T, Beighton D et al (2016) Influence of saliva on the oral microbiota[J]. Periodontol 2000 70(1):80–92

    Article  Google Scholar 

  34. Wang Q, Garrity GM, Tiedje JM et al (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy[J]. Appl Environ Microbiol 73(16):5261–5267

    Article  CAS  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to all volunteers who contributed samples for this study.

Funding

This study was supported by the Natural Science Foundation of Guangdong Province (Grant No. 2020A1515010938), the Science and Technology Program of Guangzhou, China (Grant No. 2019030016), and the Opening Fund of Shanghai Key Laboratory of Forensic Medicine (Institute of Forensic Science, Ministry of Justice, China) (Grant No. KF1914).

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Author notes

  1. Xiaomin Liang, Xiaolong Han, and Changhui Liu contributed equally to this work.

Authors and Affiliations

  1. Multi-Omics Innovative Research Center of Forensic Identification, Department of Forensic Genetics, School of Forensic Medicine, Southern Medical University, Guangzhou, 510515, People’s Republic of China

    Xiaomin Liang, Weian Du, Peiwen Zhong, Litao Huang, Manling Huang, Linhe Fu, Chao Liu & Ling Chen

  2. Guangzhou Forensic Science Institute, Guangzhou, 510030, People’s Republic of China

    Xiaolong Han, Changhui Liu & Chao Liu

Authors
  1. Xiaomin Liang
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  2. Xiaolong Han
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  3. Changhui Liu
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  4. Weian Du
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  7. Manling Huang
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  9. Chao Liu
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  10. Ling Chen
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Corresponding authors

Correspondence to Chao Liu or Ling Chen.

Ethics declarations

The project was approved by the Ethics Committee of Southern Medical University (No. 2019–0011), and it was carried out in strict accordance with the ethical research principle of Southern Medical University.

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The authors declare no competing interests.

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Liang, X., Han, X., Liu, C. et al. Integrating the salivary microbiome in the forensic toolkit by 16S rRNA gene: potential application in body fluid identification and biogeographic inference. Int J Legal Med 136, 975–985 (2022). https://doi.org/10.1007/s00414-022-02831-z

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  • DOI: https://doi.org/10.1007/s00414-022-02831-z

Keywords

  • Forensic medicine
  • Salivary microbiome
  • 16S rRNA gene sequencing
  • Body fluid identification
  • Biogeographic inference