Highlights
- •Salivary time-dependent microbial markers were identified with HTS technique.
- •A generalized HTS model was established to estimate the TsD of saliva stains.
- •A simplified qPCR-TsD model was established to estimate the TsD of saliva stains.
- •Salivary microbial biomarkers could be invoked as a “clock” for TsD estimating.
Abstract
Determining the time since deposition (TsD) of traces could be helpful in the investigation
of criminal offenses. However, there are no reliable markers and models available
for the inference of short-term TsD. The goal of this study was to investigate the
potential of the succession pattern of human salivary microbial communities to serve
as an efficiency TsD prediction tool in the resolution of the forensic cases. Saliva
stains exposed to indoor conditions up to 20 days were collected and analyzed by 16S
rRNA profiling using high-throughput sequencing technique. Noticeable differences
in microbial composition were observed between different time points, and the indoor
exposure time of saliva stains were inversely correlated with alpha diversity estimates
across the measured time period. The sequencing results were used to identify TsD-dependent
bacterial indicators to regress a generalized random forest model, resulting in a
mean absolute deviation (MAD) of 1.41 days. Furthermore, a simplified TsD predictive
model was also developed utilizing Enhydrobacter, Paenisporosarcina, and Janthinobacterium
by quantitative PCR (qPCR) with a MAD of 1.32 days, and then forensic practice assessment
were also performed by using mock samples with a MAD of 3.53 days. In conclusion,
this study revealed significant changes in salivary microbial abundance as the prolongation
of TsD. It demonstrated that the microbial biomarkers could be invoked as a “clock”
for TsD estimation in human dried saliva stains.
Keywords
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References
- Microbiome-based body fluid identification of samples exposed to indoor conditions.Forensic Sci. Int. Genet. 2019; 40: 105-113https://doi.org/10.1016/j.fsigen.2019.02.010
- A 17-month time course study of human RNA and DNA degradation in body fluids under dry and humid environmental conditions.Int. J. Leg. Med. 2016; 130: 1431-1438https://doi.org/10.1007/s00414-016-1373-9
- Salivary signature in forensic profiling: a scoping review.J. Forensic Dent. Sci. 2018; 10: 123-127https://doi.org/10.4103/jfo.jfds_30_18
- A method for determining the age of a bloodstain.Forensic Sci. Int. 2005; 148: 37-45https://doi.org/10.1016/j.forsciint.2004.04.071
- Quantification of RNA degradation by semi-quantitative duplex and competitive RT-PCR: a possible indicator of the age of bloodstains?.Forensic Sci. Int. 2003; 138: 94-103https://doi.org/10.1016/j.forsciint.2003.09.008
- Estimation of bloodstain age by rapid determinations of oxyhemoglobin by use of oxygen electrode and total hemoglobin.Biol. Pharm. Bull. 1995; 18: 1031-1035https://doi.org/10.1248/bpb.18.1031
- A blue spectral shift of the hemoglobin soret band correlates with the age (time since deposition) of dried bloodstains.PLoS One. 2010; 5e12830https://doi.org/10.1371/journal.pone.0012830
- A Raman “spectroscopic clock” for bloodstain age determination: the first week after deposition.Anal. Bioanal. Chem. 2016; 408: 3993-4001https://doi.org/10.1007/s00216-016-9486-z
- Determination of the age of bloodstains using immunoelectrophoresis.J. Forensic Sci. 1977; 22: 159-164
- Multivariate analysis for estimating the age of a bloodstain.J. Forensic Sci. 2011; 56: 186-193https://doi.org/10.1111/j.1556-4029.2010.01551.x
- Estimating trace deposition time with circadian biomarkers: a prospective and versatile tool for crime scene reconstruction.Int. J. Leg. Med. 2010; 124: 387-395https://doi.org/10.1007/s00414-010-0457-1
- Evaluating the use of hypoxia sensitive markers for body fluid stain age prediction.Sci. Justice. 2020; 60: 547-554https://doi.org/10.1016/j.scijus.2020.09.001
- Estimating time since deposition using quantification of RNA degradation in body fluid-specific markers.Forensic Sci. Int. 2019; 298: 58-63https://doi.org/10.1016/j.forsciint.2019.02.046
- Estimating the time since deposition of saliva stains with a targeted bacterial DNA approach: a proof-of-principle study.Front. Microbiol. 2021; 12647933https://doi.org/10.3389/fmicb.2021.647933
- Transcription and microbial profiling of body fluids using a massively parallel sequencing approach.Forensic Sci. Int. Genet. 2019; 43102149https://doi.org/10.1016/j.fsigen.2019.102149
- Assessing time dependent changes in microbial composition of biological crime scene traces using microbial RNA markers.Forensic Sci. Int. Genet. 2021; 53102537https://doi.org/10.1016/j.fsigen.2021.102537
P. Amplicon, P. Clean-Up, P. Index, 16s metagenomic sequencing library preparation, Illumina. com., 2013.
- Altered gut microbiota in a mouse model of Alzheimer’s disease.J. Alzheimers Dis. 2017; 60: 1241-1257https://doi.org/10.3233/jad-170020
- cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing.J. Comput. Biol. 2017; 24: 1138-1143https://doi.org/10.1089/cmb.2017.0096
- QIIME allows analysis of high-throughput community sequencing data.Nat. Methods. 2010; 7: 335-336https://doi.org/10.1038/nmeth.f.303
- Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.Nat. Protoc. 2020; 15: 799-821https://doi.org/10.1038/s41596-019-0264-1
- UCHIME improves sensitivity and speed of chimera detection.Bioinformatics. 2011; 27: 2194-2200https://doi.org/10.1093/bioinformatics/btr381
- Microbial community assembly and metabolic function during mammalian corpse decomposition.Science. 2016; 351: 158-162https://doi.org/10.1126/science.aad2646
- Search and clustering orders of magnitude faster than BLAST.Bioinformatics. 2010; 26: 2460-2461https://doi.org/10.1093/bioinformatics/btq461
- Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.Appl. Environ. Microbiol. 2006; 72: 5069-5072https://doi.org/10.1128/aem.03006-05
- Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice.PLoS Biol. 2018; 16e2003862https://doi.org/10.1371/journal.pbio.2003862
- APE: analyses of phylogenetics and evolution in R language.Bioinformatics. 2004; 20: 289-290https://doi.org/10.1093/bioinformatics/btg412
- Classification and regression by randomForest.R N. 2002; 2: 18-22
- Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median.J. Exp. Soc. Psychol. 2013; 49: 764-766
- A robust mean absolute deviation model for portfolio optimization.Comput. Oper. Res. 2011; 38: 1251-1258
- Predicting human age by detecting DNA methylation status in hair.Electrophoresis. 2021; 42: 1255-1261https://doi.org/10.1002/elps.202000349
- Is rotator cuff related shoulder pain a multidimensional disorder?.Explor. Study. 2020; 20: 297-305https://doi.org/10.1515/sjpain-2019-0108
- Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.Forensic Sci. Int. Genet. 2018; 35: 38-45https://doi.org/10.1016/j.fsigen.2018.03.009
- The real-time polymerase chain reaction.Mol. Asp. Med. 2006; 27: 95-125https://doi.org/10.1016/j.mam.2005.12.007
- Setup of quantitative PCR for oral Neisseria spp.Eval. Celiac Dis. Diagn. 2019; 10https://doi.org/10.3390/diagnostics10010012
- Relationship between Candida albicans and Streptococcus mutans in early childhood caries, evaluated by quantitative PCR.F1000Res. 2018; 7: 1645https://doi.org/10.12688/f1000research.16275.2
- Succession of oral microbiota community as a tool to estimate postmortem interval.Sci. Rep. 2019; 9: 13063https://doi.org/10.1038/s41598-019-49338-z
- Structure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214https://doi.org/10.1038/nature11234
- Characterization of the bacterial and fungal microbiome in indoor dust and outdoor air samples: a pilot study.Environ. Sci. Process Impacts. 2016; 18: 713-724https://doi.org/10.1039/c5em00639b
- Environmental changes affect the assembly of soil bacterial community primarily by mediating stochastic processes.Glob. Change Biol. 2016; 22: 198-207https://doi.org/10.1111/gcb.13080
- A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system.Elife. 2013; 2e01104https://doi.org/10.7554/eLife.01104
- Informativeness of NGS analysis for vaginal fluid identification.J. Forensic Sci. 2017; 62: 192-196https://doi.org/10.1111/1556-4029.13222
- Comparison of detection methods for vaginal lactobacilli.Benef. Microbes. 2015; 6: 747-751https://doi.org/10.3920/bm2014.0154
- Variable selection with stepwise and best subset approaches.Ann. Transl. Med. 2016; 4: 136https://doi.org/10.21037/atm.2016.03.35
- Detecting personal microbiota signatures at artificial crime scenes.Forensic Sci. Int. 2020; 313110351https://doi.org/10.1016/j.forsciint.2020.110351
- Temporal stability of the human skin microbiome.Cell. 2016; 165: 854-866https://doi.org/10.1016/j.cell.2016.04.008
- Cigarette smoking and oral microbiota in low-income and African-American populations.J. Epidemiol. Community Health. 2019; 73: 1108-1115https://doi.org/10.1136/jech-2019-212474
Article info
Publication history
Published online: July 14, 2022
Accepted:
July 14,
2022
Received in revised form:
June 7,
2022
Received:
September 12,
2021
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
