DOI: 10.5061/dryad.gqnk98svk
title: Data for: Community assembly of the human piercing microbiome

description: Predicting how biological communities respond to disturbance requires
 understanding the forces that govern their assembly. We propose using
 human skin piercings as a model system for studying community assembly
 after rapid environmental change. Local skin sterilization provides a
 ‘clean slate’ within the novel ecological niche created by the piercing.
 Stochastic assembly processes can dominate skin microbiomes due to the
 influence of environmental exposure on local dispersal, but deterministic
 processes might play a greater role within occluded skin piercings if
 piercing habitats impose strong selection pressures on colonizing species.
 Here we explore the human ear-piercing microbiome and demonstrate that
 community assembly is predominantly stochastic but becomes significantly
 more deterministic with time, producing increasingly diverse and
 ecologically complex communities. We also observed changes in two dominant
 and medically relevant antagonists (Cutibacterium acnes and Staphylococcus
 epidermidis), consistent with competitive exclusion induced by a
 transition from sebaceous to moist environments. By exploiting this common
 yet uniquely human practice, we show that skin piercings are not just
 culturally significant but also represent ecosystem engineering on the
 human body. The novel habitats and communities that skin piercing produce
 may provide general insights into biological responses to environmental
 disturbance with implications for both ecosystem and human health.
description: <strong>Human research ethics approval:
 </strong>Protocols for study participant recruitment, data security,
 sample collection, and associated procedures were approved by the McGill
 University Research Ethics Board Office (REB-1 #70-0617).
 <strong>Sample collection: </strong>From October 2019
 to March 2020, we recruited 28 individuals who were receiving earlobe
 piercings at Tattoo Lounge in Montreal, Quebec, Canada and received their
 written, informed consent to participate in the study. Following standard
 ear-piercing protocols, we sterilized the ear lobe skin area to be pierced
 with a benzalkonium chloride antiseptic towelette (Jedmon Products)
 immediately before piercing. We pierced earlobes using a sterilized
 beveled hollow needle (Ruthless/Precision) and then inserted a 5/16”
 surgical steel grade (316L) piercing labret stud composed of chromium,
 nickel, and molybdenum. Both needle and stud were dipped in a water-based
 lubricant jelly (Personelle, Jean Coutu) to minimize friction and cleaned
 off after using a cotton-tipped swab. We collected skin swab samples using
 the DNA/RNA Shield Collection Tube w/Swab – DX (Zymo Research), which was
 used to preserve nucleic acids within samples at ambient temperatures. The
 piercer collected samples from the earlobe to be pierced and an adjacent
 unsterilized part of the ear farther up the ear but still part of the
 earlobe skin to serve as a temporal control. Samples were collected both
 before and after the piercing event (defined as a three-part process that
 includes A) skin sterilization followed by B) skin piercing and then C)
 insertion of the metal stud). Study participants were then instructed to
 self-sample both the piercing and the adjacent skin control while wearing
 gloves over the following 2 weeks at specified timepoints: 12 hours, 1
 day, 3 days, 1 week, and 2 weeks. Additionally, environmental controls
 were collected by the piercer before the piercing and by the participant
 at the 1- and 2-week timepoints by waving a swab in the air for 30
 seconds. In total, we collected 17 samples from each
 participant. <strong>DNA extraction and amplicon
 sequencing: </strong>We extracted DNA from swabs using the DNeasy
 PowerSoil kit (QIAGEN) and then purified using the OneStep PCR Inhibitor
 Removal kit (Zymo Research). Skin swab samples and environmental controls
 were processed with a DNA extraction negative control included within each
 batch of 24 extractions. This work was carried out in a laboratory
 facility designed to handle low-copy and highly degraded environmental DNA
 samples through mitigation of contamination factors
 (<em>e.g.</em>, no exposure to PCR products, regular deep
 cleaning, and strict usage protocols to limited trained personnel). The
 V1-V3 region of the 16S rRNA gene was PCR amplified using the primers 27F
 (5'-AGAGTTTGATCCTGGCTCAG-3') and 518R
 (5'-ATTACCGCGGCTGCTGG-3'). Library preparation, quality control,
 and high throughput sequencing with Illumina MiSeq v2/v3 were conducted at
 Génome Québec and the McGill Genome Centre (Montreal, Quebec,
 Canada). <strong>Data
 processing:</strong> Raw sequences were processed using the QIIME2
 bioinformatics pipeline. Primer sequences were trimmed using cutadapt
 before ASVs were generated using DADA2. Contaminant ASVs were identified
 using environmental and DNA extraction negative controls for each
 sequencing batch with the prevalence-based method at a classification
 threshold of <em>P*</em> = 0.5 within decontam. The unpierced
 control of each individual is only experimentally valid if it exhibits no
 significant differences from the microbiome of the skin to be pierced
 prior to piercing. Thus, statistical outlier individuals were defined as
 having an absolute difference in ASV richness between sample and control
 prior to piercing that was greater than 1.5 times the interquartile range
 across all individuals. A total of 1,047 contaminant ASVs and two
 statistical outlier individuals were removed resulting in 10,915 ASVs
 across 392 samples with a mean sequencing depth of 27,817 reads per
 sample. ASVs were aligned using MAFFT and phylogenetic trees were built
 using FastTree 2 based on Jukes-Cantor distances. For taxonomic
 assignment, the 27F/518R 16S rRNA primers were used to <em>in silico
 </em>extract the target V1-V3 amplicon from the SILVA 132 database.
 A naïve bayes classifier was trained using scikit-learn on the extracted
 database and then used to taxonomically assign ASVs from domain down to
 species. Assignments were accepted if classification confidence was at
 least 0.7. <strong>Statistical
 analyses:</strong> ASV counts were normalized via Total Sum Scaling
 (TSS), and biodiversity indices, PCoA, and PERMANOVA (999 permutations)
 were calculated using the R ‘phyloseq’ and ‘vegan’ packages implemented
 within MicrobiomeAnalyst 2.0. Data was not rarefied to maximize the amount
 of data analyzed and the number of participants included in the study.
 Alpha and beta diversities were measured using Chao1 and Bray-Curtis
 dissimilarity, respectively. Betadisper was calculated separately using
 the R ‘vegan’ package version 2.6-2 and ‘ggstatsplot’ version 0.10.0 was
 used for plotting within RStudio Desktop version 2022.12.0+353 and R
 version 4.2.2. ASV co-occurrence networks were built using Random Matrix
 Theory (RMT)-based Spearman’s rank correlation through the Molecular
 Ecological Network Analysis Pipeline (MENA) implemented within the
 Integrated Network Analysis Pipeline (iNAP). Data was first filtered by
 retaining only ASVs present in &gt;15% of samples and then log
 transformed before calculation of similarity matrices allowing a single
 timepoint lag for time-dependent interactions. Co-occurrence networks were
 visualized using Cytoscape version 3.9.1 keeping only nodes with valid
 genus-level taxonomic assignments and edges with a P-value &lt; 0.05.
 The ‘iCAMP’ R package version 1.5.12 was used to calculate pNST and infer
 community assembly mechanisms by phylogenetic bin-based null model
 analysis. Bootstrapping tests with a resampling size of 1000 were used to
 assess significant pairwise differences between time points. Core
 microbiome community taxa were classified based on a minimum of 5%
 relative abundance across at least 50% of all samples.
description: # Community assembly of the human piercing microbiome For further details
 on softwares/platforms used, please see: [QIIME2](https://qiime2.org/)
 *[decontam](https://benjjneb.github.io/decontam/vignettes/decontam_intro.html/)* [iCAMP](https://github.com/DaliangNing/iCAMP1/) [iNAP](https://inap.denglab.org.cn/root/login?redirect=%2F/) [Cytoscape](https://cytoscape.org/) [MicrobiomeAnalyst](https://www.microbiomeanalyst.ca/) ## Description of the data and file structure ### QIIME2 **QIIME2.bash:** QIIME2 commands for producing ASV table from raw sequencing data. **manifest1.csv/manifest2.csv:** QIIME2 manifest files of forward/reverse read identities for joining paired-end reads from sequencing batches 1 & 2. **metadata_full.tsv:** QIIME2 metadata. **NB_V1-V3_198bp_silva_classifier.qza:** QIIME2 data file containing Naïve Bayes classifier of amplicon (198 bp of V1-V3 16S rRNA) extracted from the SILVA 138 database. **relfq_pierced.qza/relfq_unpierced.qza:** QIIME2 data files containing relative frequencies of ASVs. ### R scripts **decontam.R:** Decontamination of ASV table (dada2_table.qza). **betadisper.R:** Dispersion analysis of beta diversities (ASV_pierced_B_2W.csv, ASV_unpierced_B_2W.csv). **network.R:** Z-tests for significance of time-dependency and positive-to-negative links (P/N) ratios. **NST_iCAMP.R:** Community assembly mechanisms by phylogenetic-bin-based null model analysis to assess stochasticity/determinism. **ggstatsplot.R:** Pairwise comparison of *S. epidermidis* and *C. acnes* alpha diversities (chao1_pierced.csv, chao1_unpierced.csv). ### Data **decontam_metadata.tsv:** Batch identities of samples for decontamination. **dada2_table_decontam.tsv.txt:** Decontaminated ASV table. **taxonomy.csv:** Taxonomic classifications of ASVs. **tree.nwk:** Phylogenetic tree of ASVs **ASV_pierced.csv/ASV_unpierced.csv:** ASV tables of pierced/unpierced samples across all time points. **ASV_pierced_B_2W.csv/ASV_unpierced_B_2W.csv:** ASV tables of pierced/unpierced samples before piercing and 2 weeks after. **chao1_pierced.csv/chao1_unpierced.csv:** Alpha diversity (Chao1) of pierced/unpierced samples across all time points. ### iNAP inputs **iNAP_pierced_feature-table.tabular/iNAP_unpierced_feature-table.tabular:** Unfiltered ASV tables of pierced/unpierced samples for iNAP analyses. **iNAP_pierced_filtered.tabular/iNAP_unpierced_filtered.tabular:** Filtered ASV tables of pierced/unpierced samples for iNAP analyses. **iNAP_RMT_timeseries.csv:** Sample IDs in time-series for random matrix theory (RMT)-based Spearman's rank correlation to construct ASV co-occurrence networks within iNAP ### Cytoscape sessions **network_pierced.cys/network_unpierced.cys:** Visualizations of pierced/unpierced networks. ## Sharing/Access information Access this dataset on: [Dryad](https://doi.org/10.5061/dryad.gqnk98svk) [Zenodo](https://zenodo.org/records/7832626) ## Funding Vanier Canada Graduate Scholarship Natural Sciences Engineering Research Council of Canada Canada Research Chair