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Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions

Nature Microbiology volume 7, pages 169–179 (2022)Cite this article

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Abstract

Human skin functions as a physical barrier to foreign pathogen invasion and houses numerous commensals. Shifts in the human skin microbiome have been associated with conditions ranging from acne to atopic dermatitis. Previous metagenomic investigations into the role of the skin microbiome in health or disease have found that much of the sequenced data do not match reference genomes, making it difficult to interpret metagenomic datasets. We combined bacterial cultivation and metagenomic sequencing to assemble the Skin Microbial Genome Collection (SMGC), which comprises 622 prokaryotic species derived from 7,535 metagenome-assembled genomes and 251 isolate genomes. The metagenomic datasets that we generated were combined with publicly available skin metagenomic datasets to identify members and functions of the human skin microbiome. The SMGC collection includes 174 newly identified bacterial species and 12 newly identified bacterial genera, including the abundant genus ‘Candidatus Pellibacterium’, which has been newly associated with the skin. The SMGC increases the characterized set of known skin bacteria by 26%. We validated the SMGC metagenome-assembled genomes by comparing them with sequenced isolates obtained from the same samples. We also recovered 12 eukaryotic species and assembled thousands of viral sequences, including newly identified clades of jumbo phages. The SMGC enables classification of a median of 85% of skin metagenomic sequences and provides a comprehensive view of skin microbiome diversity, derived primarily from samples obtained in North America.

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Fig. 1: Metagenome assembly strategies for the recovery of skin microbial genomes.
Fig. 2: A comprehensive collection of skin microbial genomes uncovers abundant and prevalent bacterial diversity.
Fig. 3: Expanded fungal diversity associated with human skin.
Fig. 4: Jumbo phages found on the human skin are shared between individuals and body sites.

Data availability

Metagenome sequence data are publicly available in SRA (study accession SRP002480). The SBCC is maintained and stored at the National Human Genome Research Institute. Some isolates will be available through public repositories. Strains of novel species not otherwise available will be made available upon request by researchers. The sequenced genomes of the isolates have been submitted to NCBI under the accession numbers in Supplementary Table 1. Metagenome assemblies, isolate and metagenome-assembled genomes from the SMGC and genome annotations are available in: http://ftp.ebi.ac.uk/pub/databases/metagenomics/genome_sets/skin_microbiome. Source data are provided with this paper.

Code availability

The SMGCv1.0 pipeline is available at https://github.com/skinmicrobiome/skin_MAGs/releases/tag/v1.0. The VIRify v0.2.0 is available at https://github.com/EBI-Metagenomics/emg-viral-pipeline/releases/tag/v0.2.0.

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Acknowledgements

We thank S. Nurk and S. Conlan for their invaluable feedback regarding this work. S.S.K. is a graduate student supported by the NIH-Oxford-Cambridge Scholars Program. A.A. and R.D.F. are funded by EMBL core funds. This study utilized the computational resources of the NIH HPC Biowulf Cluster (http://hpc.nih.gov), and was supported by the Intramural Research Programs of the National Institutes of Health (NIH) National Institute of Arthritis and Musculoskeletal and Skin Diseases and National Human Genome Research Institute.

Author information

Author notes

  1. These authors jointly supervised this work: Julia A. Segre, Alexandre Almeida, Robert D. Finn.

Authors and Affiliations

  1. Microbial Genomics Section, Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Sara Saheb Kashaf, Diana M. Proctor, Clay Deming & Julia A. Segre

  2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK

    Sara Saheb Kashaf, Paul Saary, Martin Hölzer, Alexandre Almeida & Robert D. Finn

  3. Methodology and Research Infrastructure, MF1 Bioinformatics, Robert Koch Institute, Berlin, Germany

    Martin Hölzer

  4. Dermatology Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA

    Monica E. Taylor & Heidi H. Kong

  5. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Alexandre Almeida

  6. NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Jim Mullikin, Jim Thomas, Alice Young, Gerry Bouffard, Betty Barnabas, Shelise Brooks, Joel Han, Shi-ling Ho, Juyun Kim, Richelle Legaspi, Quino Maduro, Holly Marfani, Casandra Montemayor, Nancy Riebow, Karen Schandler, Brian Schmidt, Christina Sison, Mal Stantripop, Sean Black, Mila Dekhtyar, Cathy Masiello, Jenny McDowell, Morgan Park, Pam Thomas & Meg Vemulapalli

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  1. Sara Saheb Kashaf
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Consortia

NISC Comparative Sequencing Program

  • Jim Mullikin
  • , Jim Thomas
  • , Alice Young
  • , Gerry Bouffard
  • , Betty Barnabas
  • , Shelise Brooks
  • , Joel Han
  • , Shi-ling Ho
  • , Juyun Kim
  • , Richelle Legaspi
  • , Quino Maduro
  • , Holly Marfani
  • , Casandra Montemayor
  • , Nancy Riebow
  • , Karen Schandler
  • , Brian Schmidt
  • , Christina Sison
  • , Mal Stantripop
  • , Sean Black
  • , Mila Dekhtyar
  • , Cathy Masiello
  • , Jenny McDowell
  • , Morgan Park
  • , Pam Thomas
  •  & Meg Vemulapalli

Contributions

S.S.K., H.H.K., J.A.S., A.A. and R.D.F. conceived the study. S.S.K. and A.A. performed the analyses. M.H. contributed to the evaluation and Nextflow implementation of the VIRify pipeline, and provided guidance on the viral analyses. P.S. developed the EukCC tool and provided intellectual input on the eukaryotic analyses. D.M.P. provided intellectual input and contributed to the interpretation of the results. C.D., H.H.K. and M.E.T. performed the sample collection and culturing. J.A.S., A.A. and R.D.F. supervised the work. H.H.K., J.A.S. and R.D.F. provided funding. S.S.K., J.A.S., A.A. and R.D.F. wrote the manuscript. All authors read, edited and approved the manuscript.

Corresponding authors

Correspondence to Julia A. Segre, Alexandre Almeida or Robert D. Finn.

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The authors declare no competing interests.

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Nature Microbiology thanks David Moyes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Genome statistics of the prokaryotic skin MAGs.

a, The completeness and b, contamination estimates for genomes (Single Run, n = 2,389; Per Sample, n = 1,206; Pool Time, n = 973; Pool Site, n = 1,171; Pool HV, n = 1,054, Other datasets, n = 1,099) recovered from different metagenomic samples as determined by CheckM. ‘Other datasets’ refers to skin metagenomes excluding the healthy volunteer dataset SRP002480. c, N50 of these MAGs as determined through BBMap. Significance for a-c was determined using the two tailed t-test relative to Per Sample, with ns representing not significant. d, The mean proportion of these genomes classified as taxonomically mismatched by comparing the annotation of the bin to the annotation of each contig via the contig annotation tool (CAT). ‘No support’ indicates that no taxonomic annotation was available at the respective rank. In panels a, b and c, box lengths represent the IQR of the data, with whiskers depicting the lowest and highest values within 1.5 times the IQR of the first and third quartiles, respectively.

Source data

Extended Data Fig. 2 Comparison of MAG and SBCC isolate genomes.

a, Misassembled fraction as a proportion of the total genome length, estimated by QUAST. b, Single-nucleotide mismatches between MAGs and isolates per 100 kbp. c, percent MAG aligned, and d, percent isolate aligned for all pairwise MAG-isolate matches sharing > =99% average nucleotide identity across different pooling strategies (Single Run, n = 124; Per Sample, n = 91; Pool Time, n = 116; Pool Site, n = 134; Pool HV, n = 115). e, CheckM completeness relative to percent isolate aligned for these MAGs, coloured by pooling strategies. The majority of the points fall below the dashed identity line, indicating that CheckM frequently overestimates genome completeness f, Dot plot of a novel Corynebacterium MAG obtained through Pool HV and the matching isolate, cultured from the same healthy volunteer. In panels a, b, c and d, box lengths represent the IQR of the data, with whiskers depicting the lowest and highest values within 1.5 times the IQR of the first and third quartiles, respectively.

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Extended Data Fig. 3 Comparison of the number of species recovered by each sampling strategy.

Venn diagram of the number of species recovered by single run/per sample and pooled approaches (Pool Time, Pool HV, Pool Site) as part of the study accession SRP002480 or by a per sample investigation of other publicly available metagenomic datasets (other studies).

Extended Data Fig. 4 The metabolisms of the prokaryotic SMGC MAGs and isolates.

Annotation of the prokaryotic SMGC using DRAM shows that clades largely represented by uncultured species (outlined in black) are depleted in pathways involved in aerobic respiration, suggesting that the standard skin culture conditions are not able to capture the full diversity of microbes found on human skin.

Source data

Extended Data Fig. 5 Gene frequency and metabolic pathway distribution of species from abundant skin genera.

a, Number of genes in relation to the number of near-complete (≥90% completeness) conspecific genomes recovered for Staphylococcus epidermidis. Other species showcased in b, showed similar distributions. b, Genome accumulation curves of the number of genes detected as a function of the number of non-redundant genomes analysed. c, Venn diagram of the number of KEGG pathways shared by the two genera Staphylococcus and Corynebacterium. Barplot comparing the predominant KEGG pathways unique to the Staphylococcus or the Corynebacterium skin genomes only showing pathways present in at least 5% of the genomes.

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Extended Data Fig. 6 Quality and taxonomic classification of fungal and viral genomes.

a, Genome completeness and b contamination of the 499 eukaryotic MAGs estimated by EukCC. c N50 for these MAGs determined via BBMap. The number of bins were 81 for Single Run, 123 for Per Sample, 112 for Pool Time, 87 for Pool Site, 65 for Pool HV, and 31 for Other datasets. Significance was determined using the two tailed t-test relative to Per Sample, with ns representing not significant. ‘Other datasets’ refers to skin metagenomes excluding the healthy volunteer dataset, which is a part of the study SRP002480. d, Taxonomic classification of the viral genomes according to DemoVir. In panels a, b and c, box lengths represent the IQR of the data, with whiskers depicting the lowest and highest values within 1.5 times the IQR of the first and third quartiles, respectively.

Source data

Extended Data Fig. 7 The human skin harbours vast viral diversity, of which the sebaceous sites remain stable over time.

a, The number of viral genomes in the SMGC coloured by their assigned CheckV quality. Comparison of the putative viral genomes to IMG/VR and the Gut Phage Database reveals that only a small fraction of the virome has been previously identified. b, The number of viral sequences detected for each SMGC bacterial genus using CRISPR host analysis. c, The stability of the SMGC over time for different body sites as estimated by the theta dissimilarity metric, with a theta dissimilarity of zero indicating high similarity. When calculating the theta dissimilarity, comparisons were made between the same body site of the same healthy volunteer over time. Body sites (Ac, n = 39; Al, n = 36; Ba, n = 33; Ch, n = 35; Ea, n = 35; Fh, n = 34; Hp, n = 35; Ic, n = 34; Id, n = 32; Mb, n = 35; N, n = 42; Oc, n = 36; Pc, n = 35; Ph, n = 36; Ra, n = 41; Tn, n = 32; Tw, n = 35; Vf, n = 38) are defined in Fig. 1a. The Ax was excluded due to limited sampling. Box lengths represent the IQR of the data, with whiskers depicting the lowest and highest values within 1.5 times the IQR of the first and third quartiles, respectively.

Source data

Extended Data Fig. 8 Characterization of the cluster 5 jumbo phage genome.

Distribution of viral protein families (ViPhOGs) and the GC (%) content along the cluster 5 jumbo phage genome reveals that viral proteins are evenly distributed and GC (%) content is consistent.

Extended Data Fig. 9 The SMGC improves classification of the skin microbiome.

a, Percentage of sequencing reads from different body sites classified by the SMGC as compared to the standard Kraken 2 database and the Pasolli et al skin prokaryotic MAGs. Box lengths represent the IQR of the data, with whiskers depicting the lowest and highest values within 1.5 times the IQR of the first and third quartiles, respectively. b, The species in the SMGC present at different body sites. Novelty was determined by comparison to both the GTDB database and the Pasolli et al catalogue. Body sites (Ac, n = 39; Al, n = 36; Ba, n = 33; Ch, n = 35; Ea, n = 35; Fh, n = 34; Hp, n = 35; Ic, n = 34; Id, n = 32; Mb, n = 35; N, n = 42; Oc, n = 36; Pc, n = 35; Ph, n = 36; Ra, n = 41; Tn, n = 32; Tw, n = 35; Vf, n = 38) are defined in Fig. 1a. The Ax was excluded due to limited sampling.

Source data

Extended Data Fig. 10 A new multi-kingdom view of the healthy human skin microbiome.

a, Relative abundance of viruses and members from the top 6 most abundant skin genera across the healthy volunteers for the first time point. Body sites are defined in Fig. 1a. b, Mean relative abundance across time of the most abundant species found in the external auditory ear canal and the nares for each healthy volunteer.

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Saheb Kashaf, S., Proctor, D.M., Deming, C. et al. Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. Nat Microbiol 7, 169–179 (2022). https://doi.org/10.1038/s41564-021-01011-w

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