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Skin Microbiome Surveys Are Strongly Influenced by Experimental Design

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Skin Microbiome Surveys Are Strongly Influenced by Experimental Design See related commentary on pg 900 ORIGINAL ARTICLE Skin Microbiome Surveys Are Strongly Influenced by Experimental Design Jacquelyn S. Meisel1, Geoffrey D. Hannigan1, Amanda S. Tyldsley1, Adam J. SanMiguel1, Brendan P. Hodkinson1, Qi Zheng1 and Elizabeth A. Grice1 Culture-independent studies to characterize skin microbiota are increasingly common, due in part to afford- able 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. Here we establish a resource for the cutaneous research community to guide experimental design in characterizing skin microbiota. We compare two widely sequenced regions of the 16S rRNA gene to WMS sequencing for recapitulating skin microbiome community composition, diversity, and genetic functional enrichment. We show that WMS sequencing most accurately recapitulates microbial com- munities, but sequencing of hypervariable regions 1e3 of the 16S rRNA gene provides highly similar results. Sequencing of hypervariable region 4 poorly captures skin commensal microbiota, especially Propionibacte- rium. WMS sequencing, which is resource and cost intensive, provides evidence of a community’s functional potential; however, metagenome predictions based on 16S rRNA sequence tags closely approximate WMS genetic functional profiles. This study highlights the importance of experimental design for downstream results in skin microbiome surveys. Journal of Investigative Dermatology (2016) 136, 947e956; doi:10.1016/j.jid.2016.01.016 INTRODUCTION (Hannigan and Grice, 2013). Initial studies used full-length Research devoted to the skin microbiome has surged in the 16S rRNA gene sequences (w1500 kb) generated by Sanger past decade, due in large part to accessible, affordable high- sequencing methods. Next-generation sequencing platforms throughput DNA sequencing technology and the realization that allow for vastly increased sequencing depth at a fraction that the microbiome may modulate the pathogenesis of many of the cost generate shorter read lengths, making it imprac- cutaneous disorders. The majority of protocols for charac- tical to sequence the full-length gene. Therefore, one or terizing microbial communities were initially developed and more hypervariable regions, or 16S tags, are selected for optimized to survey the gastrointestinal tract or the environ- sequencing as a proxy for the full-length gene. No single ment, niches that harbor distinct sets of microbiota compared hypervariable region is able to distinguish among all bacteria, to the skin. A standardized methodology for skin microbiome and primer biases may differentially affect amplification studies is lacking, although these protocols are often pivotal efficiency of different types of bacteria. However, specific to their outcome (Albertsen et al., 2015; Guo et al., 2013; regions may be optimal for capturing the diversity and Nelson et al., 2014). composition of different ecosystems. A common approach to characterize cutaneous microbial More recently, whole metagenomic shotgun (WMS) communities relies on amplification, sequencing, and anal- sequencing has been used for both taxonomic and functional ysis of the prokaryotic 16S ribosomal RNA (rRNA) gene. This annotation of skin microbial communities (Hannigan et al., approach has been used in multiple studies of skin bacterial 2015; Human Microbiome Project Consortium, 2012b; Oh communities and their association with health and disease et al., 2014). This approach reduces amplification bias, captures multikingdom communities, and allows for strain- level analysis. WMS datasets, although information rich, 1Department of Dermatology, University of Pennsylvania Perelman School are more expensive to generate and require greater compu- of Medicine, Philadelphia, Pennsylvania, USA tational knowledge and resources to store, process, and Correspondence: Elizabeth A. Grice, 421 Curie Boulevard, 1007 BRB II/III, analyze. Although gene content can be extracted from WMS Philadelphia, Pennsylvania 19104, USA. E-mail: [email protected] data to provide insight into functional processes of the mi- Abbreviations: HUMAnN, HMP unified metabolic analysis network; KEGG, crobial community, bioinformatic tools now exist to predict Kyoto Encyclopedia of Genes and Genomes; MCC, mock community control; OTU, operational taxonomic unit; PICRUSt, Phylotypic Investigation of functional content from 16S tag sequences (Langille et al., Communities by Reconstruction of Unobserved States; QIIME, Quantitative 2013) and in some cases may be superior to WMS Insights into Microbial Ecology; rRNA, ribosomal RNA; V1eV3, hypervari- sequencing for microbial community classification (Xu et al., able regions 1 through 3 of the 16S rRNA gene; V4, hypervariable region 4 of 2014). the 16S rRNA gene; WMS, whole metagenomic shotgun Here we present a comparison of experimental strategies to Received 16 September 2015; revised 21 December 2015; accepted 11 January 2016; accepted manuscript published online 29 January 2016; identify optimal parameters for capturing the composition, corrected proof published online 11 March 2016 diversity, and genetic content of the cutaneous microbiome. ª 2016 The Authors. Published by Elsevier, Inc. on behalf of the Society for Investigative Dermatology. www.jidonline.org 947 JS Meisel et al. Skin Microbiome Sequencing Method Comparison We applied 16S rRNA tag sequencing to cutaneous swabs occiput, forehead), moist (toe web, umbilicus), and inter- and a publicly available mock community control of 20 mittently moist (antecubital fossa, palm) (Figure 1a). bacterial species in known concentration. We sequenced two Whole genomic DNA was extracted from the swab samples 16S tag regions commonly utilized in microbiome studies, and subjected to microbiome profiling using three different including hypervariable regions 1e3 (V1eV3) and region 4 approaches: (i) V1eV3 tag sequencing; (ii) V4 tag sequencing; (V4) (Caporaso et al., 2011) to compare their utility in and (iii) WMS sequencing (Figure 1b and c). V1eV3 tag accurately characterizing skin microbiota diversity and sequencing was used by the Human Microbiome Project composition. Additionally, we performed WMS sequencing (Aagaard et al., 2013; Human Microbiome Project on the same swab samples and controls to identify any Consortium 2012a, 2012b). The V4 region was used in the additional utility of WMS sequencing over 16S tag Earth Microbiome Project (Gilbert et al., 2014) and is widely sequencing for characterizing skin microbiota and identifying utilized to characterize microbiota of other body habitats. We genetic functional enrichment. did not include regions further 30 in the 16S rRNA gene, as these have been documented to generally perform less well RESULTS for a variety of analyses (Conlan et al., 2012; Jumpstart Sampling, sequencing, and quality control Consortium Human Microbiome Project Data Generation Sixty-two cutaneous skin swabs were collected from nine Working 2012; Wu et al., 2010) and/or are not widely used healthy volunteers (for cohort characteristics, see for characterization of microbial communities. All sequencing Supplementary Table S1 online). Sampled skin sites consisted was performed on either the Illumina MiSeq or HiSeq 2500 of diverse microenvironments with respect to moisture platforms. A publicly available mock community control (sweat) and sebum: sebaceous (retroauricular crease, (MCC) was sequenced in parallel with the skin samples. Figure 1. Study design for analyzing a cutaneous bacterial communities. (a) Seven skin sites were sampled from a BACK FRONT healthy human cohort of nine Retroauricular Forehead (Fh) volunteers. (b) DNA was isolated from crease (Ra) cutaneous swabs and sequenced for downstream bioinformatics analyses. (c) Schematic illustrating the primers Occiput (Oc) used for the two different targeted Antecubital fossa (Ac) hypervariable regions on the 16S ribosomal RNA (rRNA) gene. Ac, antecubital fossa; Fh, forehead; Oc, occiput; Pa, palm; Ra, retroauricular Palm (Pa) crease; Tw, toe web; Um, umbilicus; Umbilicus (Um) WMS, whole metagenome shotgun. Toe web space (Tw) Sebaceous Intermittently moist Moist b Cutaneous Swab Sequence Quality Control Whole DNA Isolation Bacterial Taxonomic ID 16S Whole Illumina MiSeq Metagenome Diversity Analysis Shotgun V4 region Sequencing Functional Prediction (16S) 150bp Paired End (WMS) Functional Profiling (WMS) V1-V3 region Illumina MiSeq/HiSeq Bioinformatics Analyses DNA Isolation & Sequencing DNA 300bp Paired End 150bp Paired End c V1-V3 region V4 region 27F 515F V1 V2 V3 V4 V5 V6 V7 V8 V9 16S r RNA Gene conserved region hyper-variable region 534R 806R 948 Journal of Investigative Dermatology (2016), Volume 136 JS Meisel et al. Skin Microbiome Sequencing Method Comparison The V1eV3 dataset contained 2,124,836 total high-quality concentration of the input DNA. We first mapped our se- sequence reads, with a median of 24,891 sequence reads per quences to the expected MCC species to identify community sample. The V4 dataset contained 5,328,215 total high- composition (Figure 2a). Hierarchical clustering of taxo- quality sequence reads, with a median of 77,928 sequence nomic profiles indicated that WMS provided
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