Dataset Information

Skin Microbiome Surveys Are Strongly Influenced by Experimental Design.

ABSTRACT: Culture-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. 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 communities, but sequencing of hypervariable regions 1-3 of the 16S rRNA gene provides highly similar results. Sequencing of hypervariable region 4 poorly captures skin commensal microbiota, especially Propionibacterium. 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.

SUBMITTER: Meisel JS

PROVIDER: S-EPMC4842136 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Skin Microbiome Surveys Are Strongly Influenced by Experimental Design.

Meisel Jacquelyn S JS Hannigan Geoffrey D GD Tyldsley Amanda S AS SanMiguel Adam J AJ Hodkinson Brendan P BP Zheng Qi Q Grice Elizabeth A EA

The Journal of investigative dermatology 20160129 5

Culture-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 sys ...[more]

PMID: 26829039

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Project description:High-throughput sequencing of small-subunit (SSU) rRNA genes has revolutionized understanding of microbial communities and facilitated investigations into ecological dynamics at unprecedented scales. Such extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain a substantial proportion of unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. Indeed, these novel taxonomic lineages are associated with so-called microbial "dark matter," which is the genomic potential of these lineages. Unfortunately, characterization beyond "unclassified" is challenging due to relatively short read lengths and large data set sizes. Here we demonstrate how mining of phylogenetically novel sequences from microbial ecosystems can be automated using SSUnique, a software pipeline that filters unclassified and/or rare operational taxonomic units (OTUs) from 16S rRNA gene sequence libraries by screening against consensus structural models for SSU rRNA. Phylogenetic position is inferred against a reference data set, and additional characterization of novel clades is also included, such as targeted probe/primer design and mining of assembled metagenomes for genomic context. We show how SSUnique reproduced a previous analysis of phylogenetic novelty from an Arctic tundra soil and demonstrate the recovery of highly novel clades from data sets associated with both the Earth Microbiome Project (EMP) and Human Microbiome Project (HMP). We anticipate that SSUnique will add to the expanding computational toolbox supporting high-throughput sequencing approaches for the study of microbial ecology and phylogeny. IMPORTANCE Extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain many unclassified sequences, many representing organisms with novel taxonomy (taxonomic "blind spots") and potentially unique ecology. This novelty is poorly explored in standard workflows, which narrows the breadth and discovery potential of such studies. Here we present the SSUnique analysis pipeline, which will promote the exploration of unclassified diversity in microbiome research and, importantly, enable the discovery of substantial novel taxonomic lineages through the analysis of a large variety of existing data sets.

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Skin Microbiome Surveys Are Strongly Influenced by Experimental Design.

Publications

Skin Microbiome Surveys Are Strongly Influenced by Experimental Design.

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