16S rRNA sequencing data on human skin microbiome samples collected before and after swimming in the ocean. This dataset contains raw sequencing data contained in fasta and qual files produced from an Ion Torrent PGM sequencer. There were 2 sampling occurrences (041218 and 092718) and each occurrence has an associated fasta and qual file. This dataset contains the 041218 sampling data only due to storage restrictions. The other dataset is published separately. Our research has shown that the human skin microbiome is altered after swimming in the ocean. Normal commensals were washed off and simultaneously, exogenous bacteria were deposited on the skin. QIIME was used for initial analysis and indicated that the abundance and diversity of microbial communities on the skin increased after swimming and these changes persisted for more than 24 hours. Downstream analysis using PICRUSt to predict functional metagenomics indicated that there was an increase in antibiotic resistance genes, antibiotic biosynthesis genes, and virulence factor genes on the skin after ocean water exposure.

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. Methods Human research ethics approval: 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). Sample collection: 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. DNA extraction and amplicon sequencing: 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 (e.g., 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). Data processing: 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 P* = 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 in silico 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. Statistical analyses: 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 >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 < 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.

Dataset overview

This dataset provides:
a non-redundant high-quality catalog of 2.9 million genes
392 Metagenomic Species Pangenomes (MSPs)
This dataset can be used to analyze shotgun sequencing data of the human skin microbiota.

How to use this dataset

Create a gene abundance table by aligning reads from each sample against the catalog. For this purpose, you can use Meteor or NGLess. Then, normalize raw counts by gene length.
Taxonomic profiling: the abundance of each species can be estimated as the average abundance of its 100 first core genes. To reduce the false positive rate, only consider that a species is present if at least 10/100 marker genes are detected.

Methods

Data sources

This dataset was built using the following data sources:
118 isolate-derived genomes from the HMRGD
246 isolate-derived genomes from the Skin Microbial Genome Collection (SMGC)
1,407 skin metagenome assemblies from the Skin Microbial Genome Collection (SMGC)

Non-redundant gene catalog

After filtering out short contigs (<1500 bp), genes were predicted with Prodigal on genomes (mode: single) and metagenome assemblies (mode: meta). Complete genes (partial=00) were pooled and clustered with cd-hit-est (parameters -c 0.95 -aS 0.90 -G 0 -d 0 -M 0 -T 0) by choosing those from the longest contigs as representatives.

Functional annotation

KOs assignments were obtained with KofamScan using the KEGG 107 database.

MSPs recovery

Reads from the 1,120 skin metagenomes available in the bioproject PRJNA46333 were aligned against the non-redundant gene catalog with the Meteor software suite to produce a raw gene abundance table (2.9M genes quantified in 1,120samples). Then, co-abundant genes were binned in 392 Metagenomic Species Pan-genomes (MSPs, i.e. clusters of co-abundant genes that likely belong to the same microbial species) using MSPminer.

MSPs taxonomic annotation

Taxonomic annotation was performed by alignment of all core and accessory genes against representative genomes of the GTDB database (release r214) using blastn (version 2.7.1, task = megablast, word_size = 16). A species-level assignment was given if > 50% of the genes matched the representative genome of a given species, with a mean nucleotide identity ≥ 95% and mean gene length coverage ≥ 90%. The remaining MSPs were assigned to a higher taxonomic level (genus to superkingdom), if more than 50% of their genes had the same annotation.

Construction of the phylogenetic tree

39 universal phylogenetic markers genes were extracted from the MSPs (or the corresponding genome if available) with fetchMGs. Then, the markers were separately aligned with MUSCLE. The 40 alignments were merged and trimmed with trimAl (parameters: -automated1). Finally, the phylogenetic tree was computed with FastTreeMP (parameters: -gamma -pseudo -spr -mlacc 3 -slownni).

Raw sequencing data. This experiment was carried out in two sequencing runs (Run1 and Run2).

While the link between the gut microbiome and host behaviour is well established, how the microbiomes of other organs correlate with behaviour remains unclear. Additionally, behaviour–microbiome correlations are likely sex-specific because of sex differences in behaviour and physiology, but this is rarely tested. Here, we tested whether the skin microbiome of the Trinidadian guppy, Poecilia reticulata , predicts fish activity level and shoaling tendency in a sex-specific manner. High-throughput sequencing revealed that the bacterial community richness on the skin (Faith's phylogenetic diversity) was correlated with both behaviours differently between males and females. Females with richer skin-associated bacterial communities spent less time actively swimming. Activity level was significantly correlated with community membership (unweighted UniFrac), with the relative abundances of 16 bacterial taxa significantly negatively correlated with activity level. We found no association between..., These methods are modified from the manuscript: a) Fish origin and maintenance We used laboratory-bred descendants of guppies collected from the Caura River, Trinidad. Guppies were housed at densities of 1–2 fish per litre in 4.5-litre tanks in a recirculating system at 24 ± 1°C, on a 12 L:12 D lighting schedule and fed daily on flake and Artemia. All conditions were kept identical for the experiment duration, conducted in five experimental batches between August and November 2017. b) Behavioural experiment and microbiome sampling To assay behaviours, we used a central glass aquarium ('test aquarium'), flanked on both short sides by smaller aquaria ('stimulus aquaria'), all with 5cm water depth. The stimulus aquaria were lit from above by 32cm LED strip lights (350lm, 5W, 4000k, MeRox® Technics). The test aquarium had a black Perspex lid, to which was fastened a GoPro camera (Hero4 Black Edition; GoPro Inc. San Mateo, CA). We placed a test fish (of either sex: n=18 females; 19 males) i..., We have included .csv files containing the data, and a .pdf README file including the R code and output we used to analyse the data, plus more details on the variables in the datasheets.

Genome collection of skin microbiota derived from the study 'Geographic Variation and Host Genetics Shape the Human Skin Microbiome'https://www.biorxiv.org/content/10.1101/2025.05.01.651599v1

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 provide 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 work highlights the importance of experimental design for downstream results in skin microbiome surveys. This project folder includes intermediate files generated during the analysis of the dataset and the corresponding analysis scripts used to generate the figures in the paper.

Skin microbes play a role in human body odour, health and disease. Compared to gut microbes we know comparatively little about the changes in the composition of skin microbes in response to evolutionary changes in hosts, or more recent behavioral and cultural changes in humans. No studies have used sequence-based approaches to consider the skin microbe communities of gorillas and chimpanzees, for example. Comparison of the microbial associates of non-human primates with those of humans offers unique insights into both the ancient and modern features of our skin associated microbes. Here we describe the microbes found on the skin of humans, chimpanzees, gorillas, rhesus macaques and baboons. We focus on the bacterial and Archaeal residents in the axilla using high throughput sequencing of the 16S rRNA gene. We find that human skin microbial communities are unique relative to those of other primates, both in terms of their diversity and composition. These differences appear to reflect bo...

Data for NCBI BioProject PRJNA966929: Total Shark microbiome 16S

We here analyzed bacterial communities associated with different microenvironments associated with wild-caught black-tip reef sharks (Carcharhinus melanopterus), specifically the body surface, oral/buccal cavity, and the cloaca in comparison to the water column from the Amirante Islands, Seychelles, using 16S rRNA gene amplicon sequencing.

Description:Supp. Table S2. 16S rRNA gene sequence counts distributed over amplicon sequence variants (ASVs) and samples (shark and seawater samples), including taxonomic annotation and representative sequences; low abundant sequences (< 10 sequences in total across all samples) and contaminants were removed.

16S skin microbiome dataset V3/4 variable region

MCRO-D-21-00316

AFSmicrobiome_SI_OTUprocessingPipelineScripts_DatasetS1Collection of scripts for the OTU processing pipelineAFSmicrobiome_SI_OTUprocessingPipelineStatistics_DatasetS2Summary statistics for the OTU processing pipelineAFSmicrobiome_SI_Rmarkdown_DatasetS3_htmlR Markdown file in .html format containing all the R code used for the analyses of the Antarctic fur seal microbiomeAFSmicrobiome_SI_Rmarkdown_DatasetS3_Final_revision.htmlAFSmicrobiome_SI_Rmarkdown_DatasetS3_RmdR Markdown file in Rmd format containing all the R code used for the analyses of the Antarctic fur seal microbiomeAFSmicrobiome_SI_Rmarkdown_DatasetS3_Final_revision.RmdAFSmicrobiome_SI_SequencingStatsFile_Rinput_DatasetS4R markdown input file containing sequencing statisticsAFSmicrobiome_SI_MicrosatelliteGenotypes50_P22removed_colnames_Rinput_DatasetS5R markdown input file containing genotypes at 50 microsatellite loci for 95 fur seal individuals (pup P22 was removed due to large number of missing genotypes). Contains table h...

BackgroundChanges in the microbial populations on the skin of animals have traditionally been evaluated using conventional microbiology techniques. The sequencing of bacterial 16S rRNA genes has revealed that the human skin is inhabited by a highly diverse and variable microbiome that had previously not been demonstrated by culture-based methods. The goals of this study were to describe the microbiome inhabiting different areas of the canine skin, and to compare the skin microbiome of healthy and allergic dogs.Methodology/Principal FindingsDNA extracted from superficial skin swabs from healthy (n = 12) and allergic dogs (n = 6) from different regions of haired skin and mucosal surfaces were used for 454-pyrosequencing of the 16S rRNA gene. Principal coordinates analysis revealed clustering for the different skin sites across all dogs, with some mucosal sites and the perianal regions clustering separately from the haired skin sites. The rarefaction analysis revealed high individual variability between samples collected from healthy dogs and between the different skin sites. Higher species richness and microbial diversity were observed in the samples from haired skin when compared to mucosal surfaces or mucocutaneous junctions. In all examined regions, the most abundant phylum and family identified in the different regions of skin and mucosal surfaces were Proteobacteria and Oxalobacteriaceae. The skin of allergic dogs had lower species richness when compared to the healthy dogs. The allergic dogs had lower proportions of the Betaproteobacteria Ralstonia spp. when compared to the healthy dogs.Conclusions/SignificanceThe study demonstrates that the skin of dogs is inhabited by much more rich and diverse microbial communities than previously thought using culture-based methods. Our sequence data reveal high individual variability between samples collected from different patients. Differences in species richness was also seen between healthy and allergic dogs, with allergic dogs having lower species richness when compared to healthy dogs.

The NIH Intramural Skin Microbiome Consortium (NISMC) is a collaboration of investigators with primary expertise in genomics, bioinformatics, large-scale DNA sequencing, dermatology, immunology, allergy, infectious disease, and clinical microbiology. Atopic dermatitis (AD, "eczema") is a chronic relapsing skin disorder that affects ~15% of U.S. children and is associated with $1 billion of medical costs annually. AD is characterized by dry, itchy skin, infiltrated with immune cells. Colonization by Staphylococcus aureus (S. aureus) is ten-fold more common in AD patients and is associated with disease flares. We hypothesize that, in addition to S. aureus, AD may also be associated with additional novel microbes and/or selective shifts of commensal microbes that are relevant to disease progression. The NISMC seeks to define the microbiota that resides in and on the skin and nares of three patient groups, all of whom have eczematous lesions and are currently seen at the NIH Clinical Center: (1) AD patients; (2) Wiskott-Aldrich syndrome (WAS) patients; and (3) Hyper IgE syndrome (HIES) syndrome patients. Examination of the microbiome of patients with WAS or HIES syndromes, both rare immunodeficiencies, will advance our understanding of how an individual's immune system shapes their cutaneous microbial community. We are performing a prospective longitudinal study that follows these groups of patient thorough the cycles of eczema flares, ascertaining clinical data and samples at each stage.

Variation in the composition of skin-associated microbiomes has been attributed to host species, geographic location, and habitat, but the role of intraspecific phenotypic variation among host individuals remains elusive. We explored if and how host environment and different phenotypic traits were associated with microbiome composition. We conducted repeated sampling of dorsal and ventral skin microbiomes of carp individuals (Cyprinus carpio) before and after translocation from laboratory conditions to a semi-natural environment. Both alpha and beta diversity of skin-associated microbiomes increased substantially within and among individuals following translocation, particularly on dorsal body sites. The variation in microbiome composition among hosts was significantly associated with body site, sun-basking, habitat switch, and growth, but not temperature gain while basking, sex, personality, or colour morph. We suggest that the overall increase in the alpha and beta diversity estimates among hosts were induced by individuals expressing greater variation in behaviours and thus exposure to potential colonizers in the pond environment compared to the laboratory. Our results exemplify how biological diversity at one level of organization (phenotypic variation among and within fish host individuals) together with the external environment impacts biological diversity at a higher hierarchical level of organisation (richness and composition of fish-associated microbial communities). Methods Estimation of alpha and beta diversity All statistical analyses were performed in Rstudio v1.3.1093 (53) with R v3.6.0. We included three alpha diversity estimates. Observed number of ASVs (100% identical ‘amplicon sequence variants’) was used to illustrate the partitioning of ASVs according to sample type and environment. Statistical analysis of richness was based on estimates generated with default settings in the breakaway function (package breakaway v4.6.11). To incorporate an alpha diversity measurement that takes abundance and evenness into account we used Shannon-Weaver diversity index estimated from data subsampled to the smallest sample size (3775 reads per sample) using the diversity function in the vegan package (v2.5-6). Apart from the subsampling prior to the Shannon index, raw data was used throughout the analyses. However, to explore the contribution of rare ASVs, we conducted a filtering step for comparison with the results based on raw data. To this end, we followed the method described in Bokulich, Subramanian: ASVs with a total count <10 within each sample and total abundance <0.01% across all samples were considered “rare”. This step decreased the total number of ASVs found in fish skin microbiomes from 16,881 to 1883 representing 11% of the total number of ASVs. The results based on data subsets are reported in Table S2 and S3. For beta diversity, the data was transformed by centred log ratio (clr) allowing it to be used as input for linear regressions. Distances to group centroid for samples from each of the two environments were estimated from Euclidean distance matrix on clr-values, using the function betadisper (type = centroid) in the vegan package.

Some neotropical amphibians, including a few species in Costa Rica, were presumed to be "extinct" after dramatic population declines in the late 1980s but have been rediscovered in isolated populations. Such populations seem to have evolved a resistance/tolerance to Batrachochytrium dendrobatidis (Bd), a fungal pathogen that causes a deadly skin disease and is considered one of the main drivers of worldwide amphibian declines. The skin microbiome is an important component of the host´s innate immune system and is associated with Bd-resistance. However, the way that the bacterial diversity of the skin microbiome confers protection against Bd in surviving species remains unclear. We studied variation in the skin microbiome and the prevalence of putatively anti-Bd bacterial taxa in four co-habiting species in the highlands of the Juan Castro Blanco National Park in Costa Rica using 16S rRNA amplicon sequencing. Lithobates vibicarius, Craugastor escoces, and Isthomohyla rivularis have recently been rediscovered, whereas Isthmohyla pseudopuma has suffered population fluctuations but has never disappeared. To investigate the life stage at which the protective skin microbiome is shaped and when shifts occur in the diversity of putatively anti-Bd bacteria, we studied the skin microbiome of tadpoles, juveniles and adults of L. vibicarius. We show that the skin bacterial composition of sympatric species and hosts with distinct Bd-infection statuses differs at the phyla, family, and genus level. We detected 94 amplicon sequence variants (ASVs) with putative anti-Bd activity pertaining to distinct bacterial taxa, e.g., Pseudomonas spp., Acinetobacter johnsonii, and Stenotrophomonas maltophilia. Bd-uninfected L. vibicarius harbored 79% more putatively anti-Bd ASVs than Bd-infected individuals. Although microbiome composition and structure differed across life stages, the diversity of putative anti-Bd bacteria was similar between pre- and post-metamorphic stages of L. vibicarius. Despite low sample size, our results support the idea that the skin microbiome is dynamic and protects against ongoing Bd presence in endangered species persisting after their presumed extinction. Our study serves as a baseline to understand the microbial patterns in species of high conservation value. Identification of microbial signatures linked to variation in disease susceptibility might, therefore, inform mitigation strategies for combating the global decline of amphibians.

These data are associated with a study that explores variation in microbial communities on western tiger salamander skin (Ambystoma mavortium) through space, time, and across life history stages. Lake water and lake substrate microbiome samples were collected to observe microbial taxa which were disproportionately abundant between salamander skin and the environment. Microbiome samples were collected at two lakes during the summer and fall of 2018, and sampling occurred at each lake every other week. During each sampling event, water quality data were collected at four or five locations within the lake and are associated with microbiome samples collected in the lake's respective regions. For each sample, bacterial and fungal communities were examined through metabarcoding of the 16S and ITS metabarcoding regions, respectively, using Illumina next-generation sequencing. The dataset includes negative control samples to aid in detecting contamination, and the dataset includes mock community samples to aid in validating our bioinformatics methods. Each sample received spike-ins of cross-contamination oligos and synthetic genes to observe cross-contamination during library preparation and to allow for the estimation of absolute microbial abundances, respectively. This dataset includes spatiotemporal, ontogenetic, morphometric, and water quality metadata for microbiome samples along with essential information for processing the DNA sequence data. Methods Microbiome samples from western tiger salamander (Ambystoma mavortium) skin, lake water, and lake substrate were collected at two Rocky Mountain lakes throughout the summer and fall of 2018. Sampling at each lake occurred every other week, and negative control wet and dry swab samples were collected after sampling each lake. Distilled water samples were taken as negative water controls. Twelve negative control extraction blanks were included. Two positive control mock community samples were used. 16S rRNA V4 and ITS1 metabarcoding for bacteria and fungi, respectively, were performed for each microbiome sample. DNA was extracted using the Qiagen DNeasy PowerSoil Pro Kit. Prior to library preparation, each sample received spike-ins of cross-contamination oligos and synthetic genes. During library preparation, samples were normalized and each received two PCR replicates with unique dual index combinations. Magnetic bead cleanups were performed following PCR. DNA sequencing was performed on both Illumina MiSeq and NextSeq. Sample metadata and essential information for processing the DNA sequence data are included in this dataset.

Sequences of 16S bacterial rRNA genes sequenced on the Illumina MiSeq platform. Raw data after exclusion of liker and barcoding sequences.

Rough-skinned newts (Taricha granulosa) use tetrodotoxin (TTX) to block voltage-gated sodium (Nav) channels as a chemical defense against predation. Interestingly, newts exhibit extreme population-level variation in toxicity attributed to a coevolutionary arms race with TTX-resistant predatory snakes, but the source of TTX in newts is unknown. Here, we investigated whether symbiotic bacteria isolated from toxic newts could produce TTX. We characterized the skin-associated microbiota from a toxic and non-toxic population of newts and established pure cultures of isolated bacterial symbionts from toxic newts. We then screened bacterial culture media for TTX using LC-MS/MS and identified TTX-producing bacterial strains from four genera, including Aeromonas, Pseudomonas, Shewanella, and Sphingopyxis. Additionally, we sequenced the Nav channel gene family in toxic newts and found that newts expressed Nav channels with modified TTX binding sites, conferring extreme physiological resistance to TTX. This study highlights the complex interactions among adaptive physiology, animal-bacterial symbiosis, and ecological context. Methods These data were generated from bacterial DNA samples collected from wild-caught rough-skinned newts (Taricha granulosa). The V4 hypervariable region of the 16S rRNA gene was amplified from each bacterial DNA sample, and resulting amplicons were sequenced on the Illumina MiSeq v3 platform with paired-end 300-bp protocol for 600 cycles. The resulting sequence data were assembled and processed using mothur (https://www.mothur.org) and the MiSeq SOP protocol (https://www.mothur.org/wiki/MiSeq_SOP). Mothur generates a "shared" file, which is an OTU abundance table (file: newts.OTU.shared). Mothur also generates a taxonomy file, with SILVA rRNA database taxonomic classifications for each OTU (file: newts.OTU.taxonomy). We manually generated a corresponding metadata file (file: newts.OTU.metadata). We also include a processed shared file that we used in our analyses, in which singletons and doubletons have been removed and each bacterial community sample has been subsampled to 5,000 seqs/sample.

Environmental metadata for 100 Plethodon salamanders of three species sampled in the Central Appalachian Mountains. We conducted a general descriptive analysis of the bacterial OTUs identified from the three host species (n = 100), including description of their core microbiome. Then, we examined microbiome structure in two subsets of the data: a co-occurring species dataset (n = 62) and an elevational dataset (n = 50). Twelve individuals were included in both analyses. We characterized skin microbiome structure (alpha-diversity, beta-diversity and bacterial operational taxonomic unit [OTU] abundances) using 16S rRNA gene sequencing for co-occurring Plethodon salamander species (35 P. cinereus, 17 P. glutinosus, 10 P. cylindraceus) at three localities to differentiate the effects of host species from environmental factors on the microbiome. We sampled the microbiome of P. cinereus along an elevational gradient (n = 50, 700 – 1000 masl) at one locality to determine whether elevation predicts microbiome structure. Finally, we quantified prevalence and abundance of putatively anti-Bd bacteria to determine if Bd-inhibitory bacteria are dominant microbiome members. Pyrosequencing runs have been deposited in the National Center for Biotechnology Information Sequence Read Archive (www.ncbi.nlm.nih.gov/sra) under accession number SUB2660574

The NIH-funded Human Microbiome Project (HMP) is a collaborative effort of over 300 scientists from more than 80 organizations to comprehensively characterize the microbial communities inhabiting the human body and elucidate their role in human health and disease. To accomplish this task, microbial community samples were isolated from a cohort of 300 healthy adult human subjects at 18 specific sites within five regions of the body (oral cavity, airways, urogenital track, skin, and gut). Targeted sequencing of the 16S bacterial marker gene and/or whole metagenome shotgun sequencing was performed for thousands of these samples. In addition, whole genome sequences were generated for isolate strains collected from human body sites to act as reference organisms for analysis. Finally, 16S marker and whole metagenome sequencing was also done on additional samples from people suffering from several disease conditions.