Details Matter: Designing Skin Microbiome Studies
2016; Elsevier BV; Volume: 136; Issue: 5 Linguagem: Inglês
10.1016/j.jid.2016.03.004
ISSN1523-1747
Autores Tópico(s)Complementary and Alternative Medicine Studies
ResumoThe use of genomic sequencing to investigate microbes has expanded, yet it has also raised questions regarding optimal approaches to studying the skin microbiome. Meisel et al. show that while whole genome shotgun metagenomic sequences were most similar to expected microbial profiles, sequencing of the hypervariable regions V1–V3 of the 16S ribosomal RNA gene had greater accuracy than sequencing of the hypervariable region V4 in determining genus and species level classifications of prominent skin bacteria. The use of genomic sequencing to investigate microbes has expanded, yet it has also raised questions regarding optimal approaches to studying the skin microbiome. Meisel et al. show that while whole genome shotgun metagenomic sequences were most similar to expected microbial profiles, sequencing of the hypervariable regions V1–V3 of the 16S ribosomal RNA gene had greater accuracy than sequencing of the hypervariable region V4 in determining genus and species level classifications of prominent skin bacteria. Clinical Implications•Microbiome sequencing provides a more comprehensive determination of skin microbial communities compared with traditional cultivation methods.•Of more affordable methods, V1–V3 sequencing of the bacterial 16S rRNA gene outperformed V4 sequencing significantly in classifying the genus and species level of prominent skin bacteria.•Whole genome shotgun metagenomic sequencing produced the most representative profiles using a standardized mock community, emphasizing that results from this expensive method reflect the composition and functional profiles of the skin microbiome most accurately. •Microbiome sequencing provides a more comprehensive determination of skin microbial communities compared with traditional cultivation methods.•Of more affordable methods, V1–V3 sequencing of the bacterial 16S rRNA gene outperformed V4 sequencing significantly in classifying the genus and species level of prominent skin bacteria.•Whole genome shotgun metagenomic sequencing produced the most representative profiles using a standardized mock community, emphasizing that results from this expensive method reflect the composition and functional profiles of the skin microbiome most accurately. Advances in genomic sequencing have accelerated investigation into the microbial worlds of humans and the environment. The commonly used method of sequencing the 16S ribosomal RNA (rRNA) gene enables determination of relative compositions of bacterial communities in a sample even if difficult-to-cultivate microbes are present (Jo et al., 2016Jo J.-H. Kennedy E.A. Kong H.H. Research techniques made simple: bacterial 16S ribosomal RNA gene sequencing in cutaneous research.J Invest Dermatol. 2016; 136: e23-e27Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar). Whole genome shotgun (WGS) metagenomic sequencing is more involved and expensive, but it permits taxonomic classification and exploration of the full genomic complement, or functional potential, of mixed microbial communities, including bacteria, fungi, and viruses (Hannigan et al., 2015Hannigan G.D. Meisel J.S. Tyldsley A.S. Zheng Q. Hodkinson B.P. SanMiguel A.J. et al.The human skin double-stranded DNA virome: topographical and temporal diversity, genetic enrichment, and dynamic associations with the host microbiome.mBio. 2015; 6 (e01578–15)Google Scholar, Human Microbiome Project C, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6984) Google Scholar, Oh et al., 2014Oh J. Byrd A.L. Deming C. Conlan S. NISC Comparative Sequencing Program Kong H.H. et al.Biogeography and individuality shape function in the human skin metagenome.Nature. 2014; 514: 59-64Crossref PubMed Scopus (635) Google Scholar). Given the growing interest in understanding complex host-microbial interaction (Cogen et al., 2010Cogen A.L. Yamasaki K. Sanchez K.M. Dorschner R.A. Lai Y. MacLeod D.T. et al.Selective antimicrobial action is provided by phenol-soluble modulins derived from Staphylococcus epidermidis, a normal resident of the skin.J Invest Dermatol. 2010; 30: 192-200Abstract Full Text Full Text PDF Scopus (283) Google Scholar), the amount of microbiome research activity has increased greatly (Human Microbiome Project C, 2012Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6984) Google Scholar, Waldor et al., 2015Waldor M.K. Tyson G. Borenstein E. Ochman H. Moeller A. Finlay B.B. et al.Where next for microbiome research?.PLoS Biol. 2015; 13: e1002050Crossref PubMed Scopus (96) Google Scholar). The quality and outcome of microbiome studies are influenced substantially by many aspects of study design (Goodrich et al., 2014Goodrich J.K. Di Rienzi S.C. Poole A.C. Koren O. Walters W.A. Caporaso J.G. et al.Conducting a microbiome study.Cell. 2014; 158: 250-262Abstract Full Text Full Text PDF PubMed Scopus (450) Google Scholar). The microbial communities in different epithelial sites (gut, oral mucosa, vaginal mucosa, nares, and skin) are distinct; therefore, identifying factors unique to skin-specific investigation is important in advancing the field (Costello et al., 2009Costello E.K. Lauber C.L. Hamady M. Fierer N. Gordon J.I. Knight R. Bacterial community variation in human body habitats across space and time.Science. 2009; 326: 1694-1697Crossref PubMed Scopus (2189) Google Scholar, Grice et al., 2009Grice E.A. Kong H.H. Conlan S. Deming C.B. Davis J. Young A.C. et al.Topographical and temporal diversity of the human skin microbiome.Science. 2009; 324: 1190-1192Crossref PubMed Scopus (1847) Google Scholar). Primer selection for 16S rRNA gene sequencing is a critical factor in skin microbiome studies. Of the nine hypervariable regions in the 16S rRNA gene, targeted V4 sequencing is the most frequently used method, because it helps to differentiate fecal bacteria in the gut microbiome. In contrast, earlier studies have underscored the importance of targeting the V1–V3 regions of the 16S rRNA gene to optimally identify important bacterial species common to skin, particularly Staphylococcus species (Chakravorty et al., 2007Chakravorty S. Helb D. Burday M. Connell N. Alland D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria.J Microbiol Methods. 2007; 69: 330-339Crossref PubMed Scopus (741) Google Scholar, Conlan et al., 2012Conlan S. Kong H.H. Segre J.A. Species-level analysis of DNA sequence data from the NIH Human Microbiome Project.PLoS One. 2012; 7: e47075Crossref PubMed Scopus (119) Google Scholar, Jonasson et al., 2002Jonasson J. Olofsson M. Monstein H.J. Classification, identification and subtyping of bacteria based on pyrosequencing and signature matching of 16S rDNA fragments.APMIS. 2002; 110: 263-272Crossref PubMed Scopus (124) Google Scholar, Jumpstart Consortium Human Microbiome Project Data Generation Working Group, 2012Jumpstart Consortium Human Microbiome Project Data Generation Working GroupEvaluation of 16S rDNA-based community profiling for human microbiome research.PLoS One. 2012; 7: e39315Crossref PubMed Scopus (214) Google Scholar). Meisel et al., 2016Meisel J.S. Hannigan G.D. Tyldsley A.S. SanMiguel A.J. Hodkinson B.P. Zheng Q. et al.Skin microbiome surveys are strongly influenced by experimental design.J Invest Dermatol. 2016; 136: 947-956Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar systematically compared the ability of current sequencing strategies to taxonomically classify skin bacteria, and they have confirmed prior analyses recommending V1–V3 targeted sequencing for skin microbiome studies. Using V1–V3, V4, and WGS sequencing methods, Meisel et al., 2016Meisel J.S. Hannigan G.D. Tyldsley A.S. SanMiguel A.J. Hodkinson B.P. Zheng Q. et al.Skin microbiome surveys are strongly influenced by experimental design.J Invest Dermatol. 2016; 136: 947-956Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar analyzed the bacterial communities identified in skin samples obtained from topographically distinct skin sites and from a standardized mock community with known bacterial DNA content, which was developed as a resource of the National Institutes of Health Common Fund's Human Microbiome Project (Jumpstart Consortium Human Microbiome Project Data Generation Working Group, 2012Jumpstart Consortium Human Microbiome Project Data Generation Working GroupEvaluation of 16S rDNA-based community profiling for human microbiome research.PLoS One. 2012; 7: e39315Crossref PubMed Scopus (214) Google Scholar, Meisel et al., 2016Meisel J.S. Hannigan G.D. Tyldsley A.S. SanMiguel A.J. Hodkinson B.P. Zheng Q. et al.Skin microbiome surveys are strongly influenced by experimental design.J Invest Dermatol. 2016; 136: 947-956Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar). The results describing the compositions of skin bacterial communities varied based on sequencing strategy. For the mock community, WGS sequencing results were similar to the expected results. V1–V3 sequencing results more closely resembled expected 16S rRNA gene profiles than the V4 sequencing results, which showed lower relative abundances of Staphylococcus epidermidis (expected 5%; observed <0.1% for V4, 7.0% for V1–V3) and Propionibacterium acnes (expected 5%; observed <0.1% for V4, 4.5% for V1–V3) and higher relative abundance of Staphylococcus aureus (expected 5%; observed 13.6% for V4, 7.8% for V1–V3). Although targeting the V4 region of the 16S rRNA gene more effectively determines gut-associated bacteria, the V4 dataset resulted in less accurate assessment of the relative abundances of prominent bacteria of human skin. These notable differences, based on comparing results with a mock community standard, reiterate the importance of selecting V1–V3 primers for investigating these skin bacteria to achieve an outcome that more closely resembles the true microbial composition. The ability to distinguish between different species of bacteria has important clinical and biological implications. For example, the genus Staphylococcus includes known commensal (S. epidermidis) and potentially pathogenic (S. aureus) species, and distinguishing between S. epidermidis and S. aureus is feasible (Conlan et al., 2012Conlan S. Kong H.H. Segre J.A. Species-level analysis of DNA sequence data from the NIH Human Microbiome Project.PLoS One. 2012; 7: e47075Crossref PubMed Scopus (119) Google Scholar, Kong et al., 2012Kong H.H. Oh J. Deming C. Conlan S. Grice E.A. Beatson M.A. et al.Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis.Genome Res. 2012; 22: 850-859Crossref PubMed Scopus (1105) Google Scholar). Using pplacer, Meisel et al., 2016Meisel J.S. Hannigan G.D. Tyldsley A.S. SanMiguel A.J. Hodkinson B.P. Zheng Q. et al.Skin microbiome surveys are strongly influenced by experimental design.J Invest Dermatol. 2016; 136: 947-956Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar optimally classified staphylococcal sequences with the V1–V3 and WGS sequencing datasets but classified less than 1% of staphylococcal sequences from the V4 dataset (Matsen et al., 2010Matsen F.A. Kodner R.B. Armbrust E.V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree.BMC Bioinformatics. 2010; 11: 538Crossref PubMed Scopus (604) Google Scholar). Thus, the ability to draw clinically meaningful conclusions regarding specific genera, including Staphylococcus, strongly endorses V1–V3 sequencing for skin-associated bacteria compared with V4 sequencing. Extending beyond the species level, there is better recognition of variability among strains from the same bacterial species (Fitz-Gibbon et al., 2013Fitz-Gibbon S. Tomida S. Chiu B.H. Nguyen L. Du C. Liu M. et al.Propionibacterium acnes strain populations in the human skin microbiome associated with acne.J Invest Dermatol. 2013; 133: 2152-2160Abstract Full Text Full Text PDF PubMed Scopus (433) Google Scholar, Oh et al., 2014Oh J. Byrd A.L. Deming C. Conlan S. NISC Comparative Sequencing Program Kong H.H. et al.Biogeography and individuality shape function in the human skin metagenome.Nature. 2014; 514: 59-64Crossref PubMed Scopus (635) Google Scholar). Identification of strains and analyses of the functional potential of a given microbial community are possible with WGS sequencing data. However, the cost and complex bioinformatics of WGS sequencing continue to be relatively high in contrast to 16S rRNA gene sequencing. One of the computational tools developed for microbiome analyses, PICRUSt, uses the metagenomic data from available bacterial reference genomes to predict functional gene profiles from 16S rRNA gene datasets (Langille et al., 2013Langille M.G. Zaneveld J. Caporaso J.G. McDonald D. Knights D. Reyes J.A. et al.Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.Nat Biotechnol. 2013; 31: 814-821Crossref PubMed Scopus (5789) Google Scholar). In comparing PICRUSt's ability to predict functional pathways reflective of WGS data, Meisel et al., 2016Meisel J.S. Hannigan G.D. Tyldsley A.S. SanMiguel A.J. Hodkinson B.P. Zheng Q. et al.Skin microbiome surveys are strongly influenced by experimental design.J Invest Dermatol. 2016; 136: 947-956Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar identified discrepancies between the WGS data and the predicted metagenomes, based on V1–V3 and V4 data. Reverting to less-specific Kyoto Encyclopedia of Genes and Genomes pathways was required to identify correlation trends between predicted functional profiles and WGS data in this study. Predictions of functional profiles based on 16S rRNA gene sequencing data are approximations that rely on a finite number of reference genomes and require validation studies. Recent investigations of bacterial strain-level differences highlight the limitations of 16S rRNA gene-based predictions (Greenblum et al., 2015Greenblum S. Carr R. Borenstein E. Extensive strain-level copy-number variation across human gut microbiome species.Cell. 2015; 160: 583-594Abstract Full Text Full Text PDF PubMed Scopus (153) Google Scholar, Zhu et al., 2015Zhu A. Sunagawa S. Mende D.R. Bork P. Inter-individual differences in the gene content of human gut bacterial species.Genome Biol. 2015; 16: 82Crossref PubMed Scopus (109) Google Scholar). Improving investigations of functional differences will require significant expansion of biological pathway annotations and of publicly available reference genomes and WGS datasets. Through comparisons of sequencing strategies, the work of Meisel et al., 2016Meisel J.S. Hannigan G.D. Tyldsley A.S. SanMiguel A.J. Hodkinson B.P. Zheng Q. et al.Skin microbiome surveys are strongly influenced by experimental design.J Invest Dermatol. 2016; 136: 947-956Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar emphasizes the importance of the elements of skin microbiome study design in achieving relevant results. In addition to examining primer selection for skin microbiome studies, the authors incorporated other crucial study design elements, including consistency in sampling skin sites based on known topographical heterogeneity of skin microbial communities, parallel sequencing of DNA from the same clinical samples to enable direct comparisons of sequencing methods, incorporation of a standardized mock community to provide a benchmark for assessing the quality and comparability of sequencing runs, and use of negative control swabs to monitor for contamination of low-biomass skin samples. The findings that targeting the V1–V3 region differentiates particular skin bacterial sequences at the genus and species level better than targeting the V4 region underscores the need for study design standardization based on the scientific goals of each investigation. The limitations of predictions of functional profiles also highlight the need for further research into biological pathways and microbial genomes to improve database annotations and to improve our understanding of complex host-microbial interactions. As the microbiome field continues to expand, adoption of standardized methodologies that have been systematically studied will improve our ability to develop a shared language to advance scientific breakthroughs. The author states no conflict of interest. This work was supported by the Intramural Research Program of the Center for Cancer Research, National Cancer Institute, National Institutes of Health. The contents are solely the responsibility of the author and do not necessarily represent the official views of the National Institute of Health. Skin Microbiome Surveys Are Strongly Influenced by Experimental DesignJournal of Investigative DermatologyVol. 136Issue 5PreviewCulture-independent studies to characterize skin microbiota are increasingly common, due in part to affordable and accessible sequencing and analysis platforms. Compared to culture-based techniques, DNA sequencing of the bacterial 16S ribosomal RNA (rRNA) gene or whole metagenome shotgun (WMS) sequencing provides more precise microbial community characterizations. Most widely used protocols were developed to characterize microbiota of other habitats (i.e., gastrointestinal) and have not been systematically compared for their utility in skin microbiome surveys. Full-Text PDF Open Archive
Referência(s)