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 Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 2  |  Issue : 2  |  Page : 74-77

Analysis of Microbiome DNA on Frequently Touched Items and from Palm Prints


1 School of Criminal Science and Technology, People's Public Security University of China, Beijing, China
2 State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China

Date of Web Publication16-Jun-2016

Correspondence Address:
Yaping Luo
Muxidi South No. 1, School of Criminal Science and Technology, People's Public Security University of China, Beijing
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2349-5014.184190

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  Abstract 

Limited by the sensitivity of laboratory techniques, conventional human DNA analysis of touch DNA on frequently used items and prints does not always provide satisfactory results. In this study, microbiome DNA on personal computers, cell phones, and palm prints was analyzed and compared. After sample collection, DNA extraction, polymerase chain reaction amplification, library preparation, and sequencing, data were analyzed using the QIIME 1.8.0 software. Weighted unifrac distance between the right palm skin and the right side of a keyboard, the right palm skin and the mouse, and the left side of the keyboard and the left palm skin was 0.258850, 0.265474, and 0.214098, respectively. Even after palm prints were left for 1 week, microbial community structures were still quite similar to those of samples collected from the palm skin on the day they were left (weighted unifrac distance was 0.270885).

Keywords: Cell phone, keyboard, metagenomics, palm prints


How to cite this article:
Yao X, Liu W, Han J, Pei G, Tong Y, Luo Y. Analysis of Microbiome DNA on Frequently Touched Items and from Palm Prints. J Forensic Sci Med 2016;2:74-7

How to cite this URL:
Yao X, Liu W, Han J, Pei G, Tong Y, Luo Y. Analysis of Microbiome DNA on Frequently Touched Items and from Palm Prints. J Forensic Sci Med [serial online] 2016 [cited 2019 May 22];2:74-7. Available from: http://www.jfsmonline.com/text.asp?2016/2/2/74/184190




  Introduction Top


The human skin microbiome refers to the entire population of microbes that colonize the human skin including bacteria, fungi, and viruses. Bacteria density on the human skin may be as high as 107 cells/cm 2[1] and can be readily transferred to surfaces upon touching.[2] Next-generation sequencing technology has revealed high diversity in skin-associated bacterial communities.[3],[4],[5],[6],[7] This method may be useful for evaluating the residual skin microbiome left on objects for forensic identification. By calculation of distances from samples and their donators, it will be possible to estimate whether items or palm prints belong to one specific person or not.

Traditional human DNA analysis of touch DNA does not always provide satisfactory results because of the limited content and easy degradation of human DNA. The microbiome DNA is abundant in touch DNA, and these organisms are much more stable because of complicated cell wall structures. In this study, microbiome DNA on personal computers, cell phones, and palm prints was analyzed and compared with those collected from the palm skin. Microbial community structures of different samples were compared to evaluate the similarities between these frequently used items, palm prints, and palm skin.


  Materials and Methods Top


Sample collection and DNA extraction

In the first part of this study, samples were collected from a personal computer and cell phone used by a 29-year-old healthy female, which she used with both palms. In the second part of this study, samples from the palm skin and palm prints were collected. Two pairs of palm prints were provided by the same female. After washing hands, plastic gloves were worn for at least 1 h, and then palm prints were made on sterile plastic sheets. One pair of palm prints was collected and DNA was extracted within 1 h. The other pair was placed in a spare room devoid of human contact for 1 week. A note was posted to inform that anyone set foot in the room not to touch experiment materials. In addition, clean plastic sheets were placed to collect the microbiome from the air. After 1 week, samples from the subject's palm skin were also collected.

During sample collection, sharp cotton swabs were dipped with 0.15 M NaCl and 0.1% Tween-20 solution and then swabbed repeatedly to obtain as much DNA as possible. Next, the swabs were cut and placed into a 1.5-mL centrifuge tube containing 600 µL 0.15 M NaCl and 0.1% Tween-20 solution. After vortexing for 2 min, swabs were discarded and the solution was ultrasonicated at 18% power (ultrasonication for 2 s, pause for 3 s) for 5 min (JY88-II DN Ultrasonic signal generator; Nanjing Xinchen Biotechnology Co., Ltd., Nanjing, Jiangsu, China). After pretreatment, DNA was extracted using a Roche High Pure PCR Template Preparation Kit (Roche Diagnostics GMbH, Risch-Rotkreuz, Switzerland). Finally, DNA was dissolved in 100 µL ddH2O and concentration was measured using Qubit ® 2.0 Fluorometer (Life Technologies Corporation, Carlsbad, CA, America).

Polymerase chain reaction amplification, sequencing, and analysis

Primers targeted to the V1–V2 region of 16S ribosomal RNA, F27 and R338, were used in this study, with 4-bp tags connected to the 5' ends to differentiate between samples. The polymerase chain reaction (PCR) system contained 1 µL (10 pM) of each forward and reverse primer, 25 µL of Q5 High-Fidelity 2X Master Mix (New England Biolabs, Ipswich, MA, USA), and ddH2O containing DNA to 50 µL (approximately 3 ng DNA in each PCR reaction). A negative control with no DNA template was used to maintain the sensitivity of the analysis. Samples were initially denatured at 95°C for 5 min and then amplified for 30 cycles at 95°C for 30 s, 55°C for 30 s, and 72°C for 30 s. A final extension for 7 min at 72°C was conducted at the end of the program to ensure complete amplification of the target region. Next, the libraries were prepared and sequenced on an Ion Torrent Personal Genome Machine (Life Technologies Corporation, Carlsbad, CA, USA). Sequencing results were analyzed using QIIME 1.8.0.[8] Only those sequences >200 bp in length with an average quality score of at least 20 and no ambiguous characters were included in the analysis. Similar sequences were clustered into operational taxonomic units (OTUs) with a minimum identity of 97%.


  Results and Analysis Top


Microbial community structures of frequently touched items

Overall conditions of sequencing results

The total number of sequences after filtering was 74,815; average OTU number per sample was 1005, and average species observed per sample was 415.

Alpha diversity of different samples

The diversity indices of each sample are calculated and the relative abundance of different samples at the genus level is shown in [Figure 1]. In [Figure 1], different colors represent different genus and lengths of bars represent percentage of different genus. Microbial community structures on the downside of the cell phone are quite different from that in other samples.
Figure 1: Relative abundance at genus level in the first part of the experiment. LP: Left palm skin, RP: Right palm skin, UP: Upside of phone, DP: Downside of phone, LK: Left side of keyboard, RK: Right side of keyboard, M: Mouse

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Beta diversity of different samples

Weighted unifrac distances between samples were calculated; the smallest the value of distances between samples indicates the most similar microbial structure. The most similar samples and their distances are listed in [Table 1].
Table 1: Most similar samples' weighted unifrac distance in the first part of experiment

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Weighted unifrac distance between the right side of the keyboard and right-hand skin was 0.258850. Weighted unifrac distance between the mouse and right-hand skin was 0.265474. Microbial community structures between the left side of the keyboard and her left palm skin were also similar (weighted unifrac distance was 0.214098). However, microbial community structures from the downside of the phone were quite different between both hands (weighted unifrac distances were 0.414164 and 0.455530). An unweighted pair group method with arithmetic mean (UPGMA) clustering tree based on weighted unifrac distances is shown in [Figure 2].
Figure 2: Unweighted pair group method with arithmetic mean clustering tree based on weighted unifrac distance in the first part of experiment

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Microbial community structures of palm prints

Overall conditions of sequencing results

The total number of sequences after filtering was 270,312; average OTU number per sample was 1071, and average species observed per sample was 367. Among all samples sequenced, samples collected from the blank plastic sheet to collect the microbiome in the air recovered only 98 OTUs, which is significantly fewer than for other samples.

Alpha diversity of different samples

The diversity indices of each sample are calculated and the relative abundance of different samples at the genus level is shown in [Figure 3]. The figure shows that the relative abundance of samples collected from blank plastic sheets at the genus level is quite different from that of other samples.
Figure 3: Relative abundance at genus level in the second part of the experiment. YL0: Samples collected from left palm skin on the day when palm prints were printed, YR0: Samples collected from right palm skin on the day when palm prints were printed, YLP0: Samples collected from left palm print on the day when palm prints were printed, YRP0: Samples collected from right palm print on the day when palm prints were printed, YL1: Samples collected from left palm skin 1 week after palm prints were printed, YR1: Samples collected from right palm skin 1 week after palm prints were printed, YLP1: Samples collected from left palm print 1 week after palm prints were printed, YRR1: Samples collected from right palm print 1 week after palm prints were printed, AIR1: Samples collected from blank plastic sheet 1 week after palm prints were printed

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Beta diversity of different samples

Weighted unifrac distances between samples are calculated, and the most similar samples are listed in [Table 2]. The distance between samples collected from the blank plastic sheet and other samples was significantly higher (0.419592–0.780260). A UPGMA clustering tree based on weighted unifrac distance is shown in [Figure 4]. Even after palm prints were left for 1 week, microbial community structures remained quite similar to samples collected from the palm skin (YR0) on the day they were obtained.
Table 2: Most similar samples' weighted unifrac distance in the second part of experiment

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Figure 4: Unweighted pair group method with arithmetic mean clustering tree based on weighted unifrac distance

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  Discussion and Conclusion Top


In this study, microbial community structures on a keyboard and mouse successfully matched the corresponding palm skin. However, microbial community structures collected from the downside of the cell phone were quite different from the palm skin or computer keyboard. This may be because the microbial community was transferred from various locations onto the phone. Even after the palm prints were left for 1 week, the microbial community structures remained quite similar to samples collected from the palm skin on the day they were left, and the microbiome in the air did not significantly change the microbial community structures of palm prints over a 1-week period.

This study showed a potential method to compare microbial community structures between palm skin, items, and palm prints. After large-scale sample analysis, threshold values can be obtained for forensic application. For example, if samples from 100 persons' palm skin and cell phone were collected, statistical analysis of weighted unifrac distances can be made according to this study. If most (for example, 90%) of the distances between one's palm skin and his/her cell phone are below 0.35 (for instance), while distances between one's palm skin and others' cell phone are above 0.5 (for instance), then these values can be used as threshold. When a cell phone was discovered from a crime scene that may belong to one suspect and weighted unifrac distance between the cell phone and the suspect was 0.2, then chances are 90% of it belonged to this suspect.

Additional studies are required to determine how microbial community structures change over longer time periods, and a large number of subjects should be evaluated. Successful implementation of this technology is beneficial for promoting the key evidence usage ratio and improving the technical support capabilities in criminal investigations.

Financial support and sponsorship

This work has received funding from the State Key Laboratory of Pathogen and Biosecurity (xxhz201510), and Shanghai Research Institute of Criminal Science and Technology (2014XCWZK14).

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Fredricks DN. Microbial ecology of human skin in health and disease. J Investig Dermatol Symp Proc 2001;6:167-9.  Back to cited text no. 1
    
2.
Pittet D, Allegranzi B, Boyce J; World Health Organization World Alliance for Patient Safety First Global Patient Safety Challenge Core Group of Experts. The World Health Organization Guidelines on Hand Hygiene in Health Care and their consensus recommendations. Infect Control Hosp Epidemiol 2009;30:611-22.  Back to cited text no. 2
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3.
Fierer N, Hamady M, Lauber CL, Knight R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc Natl Acad Sci U S A 2008;105:17994-9.  Back to cited text no. 3
    
4.
Fierer N, Lauber CL, Zhou N, McDonald D, Costello EK, Knight R. Forensic identification using skin bacterial communities. Proc Natl Acad Sci U S A 2010;107:6477-81.  Back to cited text no. 4
    
5.
Grice EA, Segre JA. The skin microbiome. Nat Rev Microbiol 2011;9:244-53.  Back to cited text no. 5
    
6.
Caporaso JG, Lauber CL, Costello EK, Berg-Lyons D, Gonzalez A, Stombaugh J, et al. Moving pictures of the human microbiome. Genome Biol 2011;12:R50.  Back to cited text no. 6
    
7.
Lax S, Hampton-Marcell JT, Gibbons SM, Colares GB, Smith D, Eisen JA, et al. Forensic analysis of the microbiome of phones and shoes. Microbiome 2015;3:21.  Back to cited text no. 7
    
8.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010;7:335-6.  Back to cited text no. 8
[PUBMED]    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2]


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