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Microbial Genomics logo Microbial Genomics

Volume 9, Issue 7

An efficient method for high molecular weight bacterial DNA extraction suitable for shotgun metagenomics from skin swabs Open Access

Abstract

The human skin microbiome represents a variety of complex microbial ecosystems that play a key role in host health. Molecular methods to study these communities have been developed but have been largely limited to low-throughput quantification and short amplicon-based sequencing, providing limited functional information about the communities present. Shotgun metagenomic sequencing has emerged as a preferred method for microbiome studies as it provides more comprehensive information about the species/strains present in a niche and the genes they encode. However, the relatively low bacterial biomass of skin, in comparison to other areas such as the gut microbiome, makes obtaining sufficient DNA for shotgun metagenomic sequencing challenging. Here we describe an optimised high-throughput method for extraction of high molecular weight DNA suitable for shotgun metagenomic sequencing. We validated the performance of the extraction method, and analysis pipeline on skin swabs collected from both adults and babies. The pipeline effectively characterised the bacterial skin microbiota with a cost and throughput suitable for larger longitudinal sets of samples. Application of this method will allow greater insights into community compositions and functional capabilities of the skin microbiome.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): Long and short read sequencing , Microbial abundance and Skin microbiome
Funding
This study was supported by the:
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10349)
    • Principle Award Recipient: MarkA. Webber
  • Biotechnology and Biological Sciences Research Council (Award BB/R012504/1)
    • Principle Award Recipient: MarkA. Webber
  • Biotechnology and Biological Sciences Research Council (Award BB/T014644/1)
    • Principle Award Recipient: MarkA. Webber
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10356)
    • Principle Award Recipient: LindsayJ Hall
  • Biotechnology and Biological Sciences Research Council (Award BBS/E/F/000PR10353)
    • Principle Award Recipient: LindsayJ Hall
  • Biotechnology and Biological Sciences Research Council (Award BB/R012490/1)
    • Principle Award Recipient: LindsayJ Hall
  • Wellcome Trust (Award 220876/Z/20/Z)
    • Principle Award Recipient: LindsayJ Hall
  • Wellcome Trust (Award 100974/C/13/Z)
    • Principle Award Recipient: LindsayJ Hall
  • Biotechnology and Biological Sciences Research Council (Award BB/T508974/1)
    • Principle Award Recipient: IlianaRosa Serghiou
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-07-10
2025-05-15
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References

  1. National Academies of Sciences, Engineering and Medicine (NASEM) Environmental Chemicals, the Human Microbiome, and Health Risk: A Research Strategy Washington, DC, USA: National Academies Press; 2018 [View Article]
     [Google Scholar]
  2. Costello EK, Lauber CL, Hamady M, Fierer N, Gordon JI et al. Bacterial community variation in human body habitats across space and time. Science 2009; 326:1694–1697 [View Article] [PubMed]
     [Google Scholar]
  3. Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet 2012; 13:260–270 [View Article] [PubMed]
     [Google Scholar]
  4. Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature 2012; 486:207–214 [View Article] [PubMed]
     [Google Scholar]
  5. Kong HH. Skin microbiome: genomics-based insights into the diversity and role of skin microbes. Trends Mol Med 2011; 17:320–328 [View Article] [PubMed]
     [Google Scholar]
  6. Jo JH, Kennedy EA, Kong HH. Research techniques made simple: bacterial 16S ribosomal RNA gene sequencing in cutaneous research. J Invest Dermatol 2016; 136:e23–e27 [View Article] [PubMed]
     [Google Scholar]
  7. Allaband C, McDonald D, Vázquez-Baeza Y, Minich JJ, Tripathi A et al. Microbiome 101: studying, analyzing, and interpreting gut microbiome data for clinicians. Clin Gastroenterol Hepatol 2019; 17:218–230 [View Article] [PubMed]
     [Google Scholar]
  8. Kuczynski J, Lauber CL, Walters WA, Parfrey LW, Clemente JC et al. Experimental and analytical tools for studying the human microbiome. Nat Rev Genet 2012; 13:47–58 [View Article]
     [Google Scholar]
  9. Sfriso R, Egert M, Gempeler M, Voegeli R, Campiche R. Revealing the secret life of skin - with the microbiome you never walk alone. Int J Cosmet Sci 2020; 42:116–126 [View Article] [PubMed]
     [Google Scholar]
  10. Liu Y-X, Qin Y, Chen T, Lu M, Qian X et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell 2021; 12:315–330 [View Article] [PubMed]
     [Google Scholar]
  11. Amarasinghe SL, Su S, Dong X, Zappia L, Ritchie ME et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol 2020; 21:30 [View Article] [PubMed]
     [Google Scholar]
  12. Pearman WS, Freed NE, Silander OK. Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads. BMC Bioinformatics 2020; 21:220 [View Article] [PubMed]
     [Google Scholar]
  13. Singleton CM, Petriglieri F, Kristensen JM, Kirkegaard RH, Michaelsen TY et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nat Commun 2021; 12:2009 [View Article]
     [Google Scholar]
  14. Bjerre RD, Hugerth LW, Boulund F, Seifert M, Johansen JD et al. Effects of sampling strategy and DNA extraction on human skin microbiome investigations. Sci Rep 2019; 9:17287 [View Article] [PubMed]
     [Google Scholar]
  15. de Goffau MC, Lager S, Salter SJ, Wagner J, Kronbichler A et al. Recognizing the reagent microbiome. Nat Microbiol 2018; 3:851–853 [View Article] [PubMed]
     [Google Scholar]
  16. Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol 2021; 39:1348–1365 [View Article]
     [Google Scholar]
  17. Phillips S, Watt R, Atkinson T, Savva GM, Hayhoe A et al. The Pregnancy and EARly Life study (PEARL) - a longitudinal study to understand how gut microbes contribute to maintaining health during pregnancy and early life. BMC Pediatr 2021; 21:357 [View Article] [PubMed]
     [Google Scholar]
  18. Bey BS, Fichot EB, Dayama G, Decho AW, Norman RS. Extraction of high molecular weight DNA from microbial mats. Biotechniques 2010; 49:631–640 [View Article] [PubMed]
     [Google Scholar]
  19. Mandrekar PV, Ma Z, Krueger S, Cowan C. High-Concentration (>100ng/µl) Genomic DNA From Whole Blood Using the Maxwell® 16 Low Elution Volume Instrument. American Medical Association, Manual of Style, 10th edition. Promega Corporation Web site 2010 https://www.promega.co.uk/resources/pubhub/high-concentration-genomic-dna-from-whole-blood-using-the-maxwell-16-low-elution-volume-instrument accessed 1 November 2020
     [Google Scholar]
  20. Dunbar J, Gallegos-Graves LV, Gans J, Morse SA, Pillai S et al. Evaluation of DNA extraction methods to detect bacterial targets in aerosol samples. J Microbiol Methods 2018; 153:48–53 [View Article] [PubMed]
     [Google Scholar]
  21. Moeller JR, Moehn NR, Waller DM, Givnish TJ. Paramagnetic cellulose DNA isolation improves DNA yield and quality among diverse plant taxa. Appl Plant Sci 2014; 2:apps.1400048 [View Article] [PubMed]
     [Google Scholar]
  22. Promega Maxwell® RSC Blood DNA Kit. n.d https://www.promega.com/-/media/files/resources/protocols/technical-manuals/101/maxwell-rsc-blood-dna-kit-protocol.pdf?la=en accessed 1 November 2020
  23. Sui H-Y, Weil AA, Nuwagira E, Qadri F, Ryan ET et al. Impact of DNA extraction method on variation in human and built environment microbial community and functional profiles assessed by shotgun metagenomics sequencing. Front Microbiol 2020; 11:953 [View Article] [PubMed]
     [Google Scholar]
  24. Gill C, van de Wijgert J, Blow F, Darby AC, Larsen PE. Evaluation of lysis methods for the extraction of bacterial DNA for analysis of the vaginal microbiota. PLoS One 2016; 11:e0163148 [View Article] [PubMed]
     [Google Scholar]
  25. Martzy R, Bica-Schröder K, Pálvölgyi ÁM, Kolm C, Jakwerth S et al. Simple lysis of bacterial cells for DNA-based diagnostics using hydrophilic ionic liquids. Sci Rep 2019; 9:13994 [View Article] [PubMed]
     [Google Scholar]
  26. Ogai K, Nagase S, Mukai K, Iuchi T, Mori Y et al. A comparison of techniques for collecting skin microbiome samples: swabbing versus tape-stripping. Front Microbiol 2018; 9:2812 [View Article] [PubMed]
     [Google Scholar]
  27. ATCC Skin Microbiome Whole Cell Mix. n.d https://www.atcc.org/products/msa-2005 accessed 1 December 2022
  28. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018; 34:i884–i890 [View Article] [PubMed]
     [Google Scholar]
  29. Matthews TC, Bristow FR, Griffiths EJ, Petkau A, Adam J et al. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform. bioRxiv 2018 [View Article]
     [Google Scholar]
  30. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 2018; 46:W537–W544 [View Article] [PubMed]
     [Google Scholar]
  31. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  32. Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci 2017; 3:e104 [View Article]
     [Google Scholar]
  33. The Huttenhower Lab. Kneaddata. n.d https://huttenhower.sph.harvard.edu/kneaddata/ accessed 1 January 2023
  34. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015; 31:1674–1676 [View Article]
     [Google Scholar]
  35. Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018; 6:158 [View Article] [PubMed]
     [Google Scholar]
  36. Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015; 3:e1165 [View Article]
     [Google Scholar]
  37. Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016; 32:605–607 [View Article] [PubMed]
     [Google Scholar]
  38. Alneberg J, Bjarnason BS, Bruijn I, Schirmer M, Quick J et al. CONCOCT: clustering contigs on coverage and composition. arXiv 20131312.4038 [View Article]
     [Google Scholar]
  39. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015; 25:1043–1055 [View Article] [PubMed]
     [Google Scholar]
  40. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH, Hancock J. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics 2019; 36:1925–1927 [View Article] [PubMed]
     [Google Scholar]
  41. RStudio Team RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA; 2021 http://www.rstudio.com/
  42. Wickham H. ggplot2. In Elegant Graphics for Data Analysis, 1st Edition. New York, NY: Springer-Verlag; 2009 [View Article]
     [Google Scholar]
  43. GraphPad Software GraphPad Prism 5.04. San Diego California; 2010 https://www.graphpad.com/company accessed 1 January 2023
  44. Toscano M, De Grandi R, Grossi E, Drago L. Role of the human breast milk-associated microbiota on the newborns’ immune system: a mini review. Front Microbiol 2017; 8:2100 [View Article] [PubMed]
     [Google Scholar]
  45. Yan W, Luo B, Zhang X, Ni Y, Tian F. Association and occurrence of bifidobacterial phylotypes between breast milk and fecal microbiomes in mother–Iinfant dyads during the first 2 years of life. Front Microbiol 2021; 12:669442 [View Article]
     [Google Scholar]
  46. Banning N, Toze S, Mee BJ. Escherichia coli survival in groundwater and effluent measured using a combination of propidium iodide and the green fluorescent protein. J Appl Microbiol 2002; 93:69–76 [View Article] [PubMed]
     [Google Scholar]
  47. Downey AS, Da Silva SM, Olson ND, Filliben JJ, Morrow JB. Impact of processing method on recovery of bacteria from wipes used in biological surface sampling. Appl Environ Microbiol 2012; 78:5872–5881 [View Article] [PubMed]
     [Google Scholar]
  48. Liao C-H, Shollenberger LM. Survivability and long-term preservation of bacteria in water and in phosphate-buffered saline*. Lett Appl Microbiol 2003; 37:45–50 [View Article]
     [Google Scholar]
  49. Albertsen M, Karst SM, Ziegler AS, Kirkegaard RH, Nielsen PH. Back to basics--the influence of DNA extraction and primer choice on phylogenetic analysis of activated sludge communities. PLoS One 2015; 10:e0132783 [View Article] [PubMed]
     [Google Scholar]
  50. Schindler CA, Schuhardt VT. Lysostaphin: a new bacteriolytic agent for the Staphylococcus. Proc Natl Acad Sci 1964; 51:414–421 [View Article] [PubMed]
     [Google Scholar]
  51. Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ et al. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One 2012; 7:e33865 [View Article]
     [Google Scholar]
  52. Maghini DG, Moss EL, Vance SE, Bhatt AS. Improved high-molecular-weight DNA extraction, nanopore sequencing and metagenomic assembly from the human gut microbiome. Nat Protoc 2021; 16:458–471 [View Article] [PubMed]
     [Google Scholar]
  53. Chiou YB, Blume-Peytavi U. Stratum corneum maturation. A review of neonatal skin function. Skin Pharmacol Physiol 2004; 17:57–66 [View Article] [PubMed]
     [Google Scholar]
  54. Narendran V, Visscher MO, Abril I, Hendrix SW, Hoath SB. Biomarkers of epidermal innate immunity in premature and full-term infants. Pediatr Res 2010; 67:382–386 [View Article] [PubMed]
     [Google Scholar]
  55. Byrd AL, Belkaid Y, Segre JA. The human skin microbiome. Nat Rev Microbiol 2018; 16:143–155 [View Article] [PubMed]
     [Google Scholar]
  56. Grice EA, Kong HH, Conlan S, Deming CB, Davis J et al. Topographical and temporal diversity of the human skin microbiome. Science 2009; 324:1190–1192 [View Article] [PubMed]
     [Google Scholar]
  57. Capone KA, Dowd SE, Stamatas GN, Nikolovski J. Diversity of the human skin microbiome early in life. J Invest Dermatol 2011; 131:2026–2032 [View Article] [PubMed]
     [Google Scholar]
  58. Zhu T, Liu X, Kong F-Q, Duan Y-Y, Yee AL et al. Age and mothers: potent influences of children’s skin microbiota. J Invest Dermatol 2019; 139:2497–2505 [View Article] [PubMed]
     [Google Scholar]
  59. Marquet M, Zöllkau J, Pastuschek J, Viehweger A, Schleußner E et al. Evaluation of microbiome enrichment and host DNA depletion in human vaginal samples using Oxford Nanopore’s adaptive sequencing. Sci Rep 2022; 12:4000 [View Article] [PubMed]
     [Google Scholar]
  60. Hammond M, Homa F, Andersson-Svahn H, Ettema TJG, Joensson HN. Picodroplet partitioned whole genome amplification of low biomass samples preserves genomic diversity for metagenomic analysis. Microbiome 2016; 4:52 [View Article] [PubMed]
     [Google Scholar]
  61. Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol 2019; 27:105–117 [View Article] [PubMed]
     [Google Scholar]
  62. Lou YC, Hoff J, Olm MR, West-Roberts J, Diamond S et al. Using strain-resolved analysis to identify contamination in metagenomics data. BioRxiv 2022 [View Article]
     [Google Scholar]
  63. Olomu IN, Pena-Cortes LC, Long RA, Vyas A, Krichevskiy O et al. Elimination of “kitome” and “splashome” contamination results in lack of detection of a unique placental microbiome. BMC Microbiol 2020; 20:157 [View Article] [PubMed]
     [Google Scholar]
  64. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018; 6:226 [View Article] [PubMed]
     [Google Scholar]
  65. Zhou Q, Su X, Ning K. Assessment of quality control approaches for metagenomic data analysis. Sci Rep 2014; 4:6957 [View Article]
     [Google Scholar]
  66. Díaz S, Escobar JS, Avila FW, Zhu L. Identification and removal of potential contaminants in 16S rRNA gene sequence data sets from low-microbial-biomass samples: an example from mosquito tissues. ASM 2021; 6:e0050621 [View Article] [PubMed]
     [Google Scholar]
  67. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 2017; 35:725–731 [View Article] [PubMed]
     [Google Scholar]
  68. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat Methods 2017; 14:1063–1071 [View Article] [PubMed]
     [Google Scholar]
  69. DeMaere MZ, Darling AE. bin3C: exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes. Genome Biol 2019; 20:46 [View Article] [PubMed]
     [Google Scholar]
  70. Gweon HS, Shaw LP, Swann J, De Maio N, AbuOun M et al. The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples. Environ Microbiome 2019; 14:7 [View Article] [PubMed]
     [Google Scholar]
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An efficient method for high molecular weight bacterial DNA extraction suitable for shotgun metagenomics from skin swabs
M Gen 9, 001058 (2023); https://doi.org/10.1099/mgen.0.001058
/content/journal/mgen/10.1099/mgen.0.001058
/content/journal/mgen/10.1099/mgen.0.001058
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