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Hand bacteria as an identifier: a biometric evaluation | SpringerLink

Hand bacteria as an identifier: a biometric evaluation

Abstract

Molecular and soft bio-molecular biometrics are an advancing field that involves the analysis of a person’s unique biological markers at a molecular level to ascertain identity. Bacteria communities found on the skin of the human hand have shown to be highly diverse and to have a low percentage of similarity between individuals. The goal of this research effort is to see if a person’s demographics, primarily ethnicity, share a relationship with the bacteria communities that exist on their hand. A sample collection was carried out in which the left and right inner palms of 250 individuals were swabbed to obtain a total of 500 bacteria samples. Of these, 104 samples from 52 individuals (left and right hands) covering a range of age, gender, and ethnicity of the participants were sequenced using 150 paired-end multiplex reads on an Illumina MiSeq. The reads contained the third hypervariable region DNA of the microbial 16S rRNA gene commonly used for microbial identification. Sequences were analyzed using a combination of commercial and custom bioinformatics tools. Results indicated that women who participated in the sample collection had a 15.7 % higher diversity of bacteria at the genus level than men. Using a support vector machine with a 60 % train and 40 % test approach, ethnicities of individuals who provided samples could be classified with a range of 72–94 % accuracy depending on the method used. Principal coordinate plots generated using the unique fraction (UniFrac) algorithm devised by Lozupone et al. at University of Colorado at Boulder showed that similar clustering appeared with people of Turkish, Asian Indian, and Middle Eastern descent and less clustering with people of Caucasian and African American descent. Although focused on a small subset of the human population with no temporal variance in bacterial diversity explored, these results provide a basis for performing identification based on human bacteria that can be expanded upon using time varying sampling and other regions of the 16S rRNA gene.

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References

  1. Baker GC, Smith JJ, Cowan DA (2003) Review and re-analysis of domain-specific 16S primers. J Microbial Methods 55(3):541–555

    Article  Google Scholar 

  2. “BaseSpace™: Getting Started,” Illumina BaseSpace™ Developers, [Online]. http://developer.basespace.illumina.com/docs/content/documentation/getting-started/overview

  3. Bartram AK, Lynch MD, Stearns JC, Moreno-Hagelsieb G, Neufeld JD (2011) Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end illumina reads. Appl Environ Microbial 77(15):5569

    Article  Google Scholar 

  4. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336

    Article  Google Scholar 

  5. Chakravorty S, Helb D, Burday M, Connell N, Alland D (2007) A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods 69(2):330–339

    Article  Google Scholar 

  6. Consortium, The Human Microbiome Project (2012) Structure, function and diversity of the healthy human microbiome. Nature 486(7402):207–214

    Article  Google Scholar 

  7. Dantcheva A, Velardo C, D’Angelo A, Dugelay JL (2010) Bag of Soft Biometrics for Person Identification: new trends and challenges. Multimed Tools Appl 51(2):739–777

    Article  Google Scholar 

  8. Edge RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460–2461

    Article  Google Scholar 

  9. Fierer N, Hamady M, Lauber CL, Rob K (2008) The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc Natl Acad Sci USA 105(46):17994–17999

    Article  Google Scholar 

  10. Fierer N, Lauber CL, Zhou N, McDonald D, Costello EK, Knight R (2010) Forensic identification using skin bacterial communities. Proc Natl Acad Sci USA 107(14):6477–6481

    Article  Google Scholar 

  11. Fredricks DN (2001) Microbial ecology of human skin in health and disease. J Investig Dermatol Symp Proc 6:167–169

    Article  Google Scholar 

  12. Hamady M, Lozupone C, Knight R (2010) Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J 4(1):17–27

    Article  Google Scholar 

  13. Huse SM, Dethlefsen L, Huber JA, Welch DM, Relman DA, Sogin ML (2008) Exploring microbial diversity and taxonomy using SSU rRNA hypervariable tag sequencing. PLoS Genetics 4(11):e1000255 Eisen JA, ed

    Article  Google Scholar 

  14. IBI Scientific, “Gel/PCR DNA Fragments Extraction Kit,” IBI Scientific (2014) [Online]. https://www.ibisci.com/images/IB47010IB47020IB47030Protocol.pdf

  15. Jain AK, Ross AA, Nandakumar K (2011) Introduction to biometrics. Springer, Boston, pp 1–4

    Book  Google Scholar 

  16. Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71(12):8228–8235

    Article  Google Scholar 

  17. Lozupone C, Hamady M, Knight R (2006) Unifrac—an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinform 7:371

    Article  Google Scholar 

  18. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: an effective distance metric for microbial community comparison. ISME J 5(2):169–172

    Article  Google Scholar 

  19. Madeira S, Oliveria A (2004) Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 1(1):24–25

    Article  Google Scholar 

  20. Marathe N, Shetty S, Lanjekar V, Ranade D, Shouche Y (2012) Changes in human gut flora with age: an Indian familial study. BMC Microbiol 12(1):222

    Article  Google Scholar 

  21. Mason MR, Nagaraja HN, Camerlengo T, Joshi V, Kumar PS (2013) Deep sequencing identifies ethnicity-specific bacterial signatures in the oral microbiome. Plos One 8:e77287

    Article  Google Scholar 

  22. MO BIO Laboratories Inc “UltraClean® Plant DNA Isolation Kit, Instruction Manual,” MO BIO Laboratories, Inc, [Online]. http://www.mobio.com/images/custom/file/protocol/13000.pdf

  23. Muyzer G, De Waal EC, Uitterlinden AG (1993) Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol 59(3):695–700

    Google Scholar 

  24. Oksanen J, Blanchet FG, Kindt R, Legendre P, O’Hara RB, Simpson GL (2011) Package ‘vegan’ [Online]. http://CRAN.R-project.org/package=vegan

  25. Paulino LC, Tseng C-H, Strober BE, Blaser MJ (2006) Molecular analysis of fungal microbiota in samples from healthy human skin and psoriatic lesions. J Clin Micobiol 44(8):2933–2941

    Article  Google Scholar 

  26. Rodriguez-Lujan I, Bailador G, Sanchez-Avila C, Herrero A, Vidal-de-Miguel G (2013) Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics. Knowl Based Syst 52:279–289

    Article  Google Scholar 

  27. Roth RR, James WD (1988) Microbial ecology of the skin. Annu Rev Microbiol 42:441–464

    Article  Google Scholar 

  28. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann R, Hollister EB, Lesniewski RA, Oakley BB, Parks DH (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541

    Article  Google Scholar 

  29. Smith SM, Eng RH, Padberg FTJ (1996) Survival of nosocomial pathogenic bacteria at ambient temperature. J Med 27(5–6):293–302

    Google Scholar 

  30. Song SJ, Lauber C, Costello EK, Lozupone CA, Humphrey G, Berg-Lyons D, Caporaso JG, Knights D, Clemente JC, Nakielny S, Gordon JI, Fierer N, Knight R (2013) Cohabiting family members share microbiota with one another and with their dogs. eLife 2:e00458

    Google Scholar 

  31. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267

    Article  Google Scholar 

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Acknowledgments

This work was supported by DOJ contract no. 2010-DD-BX-0161 and the Center for Identification Technology Research (CITeR http://www.citer.wvu.edu), an NSF-funded I/UCRC. Holly Whitelam participated in this project as part of the summer 2013 CITeR Research Experience for Undergraduates (REU) Program. We would like to acknowledge the use of the West Virginia University Genomics Core Facility for sequencing and assistance in bioinformatics methods.

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Correspondence to Jeremy M. Dawson.

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The authors declare that they have no conflict of interest associated with the work presented herein.

Ethical statement

This manuscript has not been submitted to any other journal for review. This submission is an expansion of a conference paper presented at BIBE 2014, submitted to this journal upon request from Reda Alhajj, who outlined the conditions for submission as a journal via email; specifically, 40 % new material. In compliance with these conditions, it contains a significantly expanded introductory section, and an expanded data analysis performed on samples obtained from a larger cohort of subjects than what was considered in the conference paper. All work presented herein, none of which has been fabricated or falsified, is original to the authors listed above. All authors have contributed to the described research, and gave their consent to be listed. The samples used in this research were obtained from consenting participants under WVU IRB protocol H-23693.

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Holly Whitelam participated in the project as part of the Summer 2013 CITeR Research Experience for Undergraduates (REU) Program.

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Holbert, A.B., Whitelam, H.P., Sooter, L.J. et al. Hand bacteria as an identifier: a biometric evaluation. Netw Model Anal Health Inform Bioinforma 4, 22 (2015). https://doi.org/10.1007/s13721-015-0095-0

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Keywords

  • Ethnicity
  • Identification of persons
  • Bio-molecular biometrics
  • Next-generation sequencing
  • Skin bacteria