Comparing DADA2 and OTU clustering approaches in studying the bacterial communities of atopic dermatitis Open Access

Abstract

The pathogenesis of atopic dermatitis (AD) is not yet fully understood, but the bacterial composition of AD patients’ skin has been shown to have an increased abundance of . More recently, coagulase-negative (CoNS) species were shown to be able to inhibit , but further studies are required to determine the effects of community variation in AD.

Here we investigated whether analysing metabarcoding data with the more recently developed DADA2 approach improves metabarcoding analyses compared to the previously used operational taxonomic unit (OTU) clustering, and can be used to study community dynamics.

The bacterial 16S rRNA region from tape strip samples of the stratum corneum of AD patients (non-lesional skin) and non-AD controls was metabarcoded. We processed metabarcoding data with two different bioinformatic pipelines (an OTU clustering method and DADA2), which were analysed with and without technical replication (sampling strategy).

We found that OTU clustering and DADA2 performed well for community-level studies, as demonstrated by the identification of significant differences in the skin bacterial communities associated with AD. However, the OTU clustering approach inflated bacterial richness, which was worsened by not having technical replication. Data processed with DADA2 likely handled sequencing errors more effectively and thereby did not inflate molecular richness.

We believe that DADA2 represents an improvement over an OTU clustering approach, and that biological replication rather than technical replication is a more effective use of resources. However, neither OTU clustering nor DADA2 gave insights into community dynamics, and caution should remain in not overinterpreting the taxonomic assignments at lower taxonomic ranks.

Funding
This study was supported by the:
  • LEO Fondet (Award LF-OC-19-000036)
    • Principle Award Recipient: Christopher James Barnes
  • LEO Fondet (DK) (Award 1110001001)
    • Principle Award Recipient: Anders Johannes Hansen
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/content/journal/jmm/10.1099/jmm.0.001256
2020-09-23
2024-03-28
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References

  1. Park H-Y, Kim C-R, Huh I-S, Jung M-Y, Seo E-Y et al. Staphylococcus aureus colonization in acute and chronic skin lesions of patients with atopic dermatitis. Ann Dermatol 2013; 25:410–416 [View Article][PubMed]
    [Google Scholar]
  2. Shi B, Leung DYM, Taylor PA, Li H. MRSA colonization is associated with decreased skin commensal bacteria in atopic dermatitis. J Invest Dermatol 2018; 138:1668–1671
    [Google Scholar]
  3. Nakatsuji T, Chen TH, Narala S, Chun KA, Two AM et al. Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Sci Transl Med 2017; 9:eaah4680 [View Article]
    [Google Scholar]
  4. Simpson EL, Villarreal M, Jepson B, Rafaels N, David G et al. Patients with atopic dermatitis colonized with Staphylococcus aureus have a distinct phenotype and Endotype. J Invest Dermatol 2018; 138:2224–2233 [View Article][PubMed]
    [Google Scholar]
  5. Byrd AL, Deming C, Cassidy SKB, Harrison OJ, Ng W-I et al. Staphylococcus aureus and Staphylococcus epidermidis strain diversity underlying pediatric atopic dermatitis. Sci Transl Med 2017; 9:eaal4651 [View Article][PubMed]
    [Google Scholar]
  6. Kong HH. Details matter: designing skin microbiome studies. J Invest Dermatol 2016; 136:900–902 [View Article][PubMed]
    [Google Scholar]
  7. Bjerre RD, Bandier J, Skov L, Engstrand L, Johansen JD. The role of the skin microbiome in atopic dermatitis: a systematic review. Br J Dermatol 2017; 177:1272–1278 [View Article][PubMed]
    [Google Scholar]
  8. Ramadan M, Solyman S, Yones M, Abdallah Y, Halaby H et al. Skin microbiome differences in atopic dermatitis and healthy controls in Egyptian children and adults, and association with serum immunoglobulin E. OMICS 2019; 23:247–260 [View Article][PubMed]
    [Google Scholar]
  9. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13:581–583 [View Article][PubMed]
    [Google Scholar]
  10. Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT et al. Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2017; 2: [View Article]
    [Google Scholar]
  11. Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat Commun 2017; 8:1–11 [View Article]
    [Google Scholar]
  12. Tsuji S, Miya M, Ushio M, Sato H, Minamoto T et al. Evaluating intraspecific genetic diversity using environmental DNA and denoising approach: a case study using tank water. Environmental DNA 2020; 2:42–52 [View Article]
    [Google Scholar]
  13. Alberdi A, Aizpurua O, Gilbert MTP, Bohmann K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol Evol 2018; 9:134–147 [View Article]
    [Google Scholar]
  14. Williams HC, Burney PG, Hay RJ, Archer CB, Shipley MJ et al. The UK Working Party's diagnostic criteria for atopic dermatitis. I. derivation of a minimum set of discriminators for atopic dermatitis. Br J Dermatol 1994; 131:383–396 [View Article][PubMed]
    [Google Scholar]
  15. Clausen M-L, Slotved H-C, Krogfelt KA, Agner T. Measurements of AMPs in stratum corneum of atopic dermatitis and healthy skin–tape stripping technique. Sci Rep 2018; 8:1–8 [View Article]
    [Google Scholar]
  16. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011 May 2; 17:10–12 [View Article]
    [Google Scholar]
  17. Bay L, Barnes CJ, Fritz BG, Thorsen J, Restrup MEM et al. Universal dermal microbiome in human skin. mBio 2020 Feb 25; 11: [View Article]
    [Google Scholar]
  18. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 2013; 41:D590–D596 [View Article][PubMed]
    [Google Scholar]
  19. Thompson JD, Gibson TJ, Higgins DG. Multiple sequence alignment using ClustalW and ClustalX. Curr Protoc Bioinformatics 2003; 00:2.3.1–2.3.2 [View Article]
    [Google Scholar]
  20. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One 2010; 5:e9490 [View Article][PubMed]
    [Google Scholar]
  21. Wilcox TM, McKelvey KS, Young MK, Jane SF, Lowe WH et al. Robust detection of rare species using environmental DNA: the importance of primer specificity. PLoS One 2013; 8:e59520 [View Article][PubMed]
    [Google Scholar]
  22. Leung AD, Schiltz AM, Hall CF, Liu AH. Severe atopic dermatitis is associated with a high burden of environmental Staphylococcus aureus . Clin Exp Allergy 2008; 38:789–793 [View Article][PubMed]
    [Google Scholar]
  23. Wickham H. ggplot2: elegant graphics for data analysis. Springer 2016266 p
    [Google Scholar]
  24. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR et al. Package ‘vegan’. Community ecology package, version. 2 2013 pp 1–295
  25. Wang Y, Naumann U, Wright ST, Warton DI. mvabund - an R package for model-based analysis of multivariate abundance data. Methods Ecol Evol 2012; 3:471–474 [View Article]
    [Google Scholar]
  26. Wilcoxon F. Individual comparisons by ranking methods. In Kotz S, Johnson NL. (editors) Breakthroughs in Statistics: Methodology and Distribution [Internet] New York, NY: Springer Series in Statistics; 1992 pp 196–202
    [Google Scholar]
  27. Guevara MR, Hartmann D, Mendoza M. Diverse: an R package to analyze diversity in complex systems. R J 2016; 8:60 [View Article]
    [Google Scholar]
  28. Zar JH. Significance testing of the Spearman Rank correlation coefficient. J Am Stat Assoc 1972; 67:578–580 [View Article]
    [Google Scholar]
  29. Huse SM, Welch DM, Morrison HG, Sogin ML. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ Microbiol 2010; 12:1889–1898 [View Article][PubMed]
    [Google Scholar]
  30. Bukin YS, Galachyants YP, Morozov IV, Bukin SV, Zakharenko AS et al. The effect of 16S rRNA region choice on bacterial community metabarcoding results. Sci Data 2019; 6:1–14 [View Article]
    [Google Scholar]
  31. Oh J, Byrd AL, Park M, Kong HH, Segre JA. Temporal stability of the human skin microbiome. Cell 2016; 165:854–866 [View Article]
    [Google Scholar]
  32. Francuzik W, Franke K, Schumann RR, Heine G, Worm M. Propionibacterium Acnes Abundance Correlates Inversely with Staphylococcus aureus: Data from Atopic Dermatitis Skin Microbiome [Internet] Medical Journals Limited; 2018
    [Google Scholar]
  33. Ghebremedhin B, Layer F, König W, König B. Genetic classification and distinguishing of Staphylococcus species based on different partial gap, 16S rRNA, hsp60, rpoB, sodA, and tuf gene sequences. J Clin Microbiol 2008; 46:1019–1025 [View Article]
    [Google Scholar]
  34. Schloss PD. The effects of alignment quality, distance calculation method, sequence filtering, and region on the analysis of 16S rRNA gene-based studies. PLoS Comput Biol 2010 Jul 8; 6:e1000844 [View Article]
    [Google Scholar]
  35. Totté JEE, van der Feltz WT, Hennekam M, van Belkum A, van Zuuren EJ et al. Prevalence and odds of Staphylococcus aureus carriage in atopic dermatitis: a systematic review and meta-analysis. Br J Dermatol 2016; 175:687–695 [View Article][PubMed]
    [Google Scholar]
  36. Jukes L, Mikhail J, Bome-Mannathoko N, Hadfield SJ, Harris LG et al. Rapid differentiation of Staphylococcus aureus, Staphylococcus epidermidis and other coagulase-negative staphylococci and meticillin susceptibility testing directly from growth-positive blood cultures by multiplex real-time PCR. J Med Microbiol 2010; 59:1456–1461 [View Article]
    [Google Scholar]
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