Significant changes in the skin microbiome mediated by the sport of roller derby

Article (PDF Available)inPeerJ 1(1):e53 · August 2013with24 Reads
DOI: 10.7717/peerj.53 · Source: PubMed
Abstract
Diverse bacterial communities live on and in human skin. These complex communities vary by skin location on the body, over time, between individuals, and between geographic regions. Culture-based studies have shown that human to human and human to surface contact mediates the dispersal of pathogens, yet little is currently known about the drivers of bacterial community assembly patterns on human skin. We hypothesized that participation in a sport involving skin to skin contact would result in detectable shifts in skin bacterial community composition. We conducted a study during a flat track roller derby tournament, and found that teammates shared distinct skin microbial communities before and after playing against another team, but that opposing teams' bacterial communities converged during the course of a roller derby bout. Our results are consistent with the hypothesis that the human skin microbiome shifts in composition during activities involving human to human contact, and that contact sports provide an ideal setting in which to evaluate dispersal of microorganisms between people.
Submitted 14 November 2012
Accepted 28 February 2013
Published 12 March 2013
Corresponding author
James F. Meadow,
jfmeadow@gmail.com
Academic editor
Frederick Cohan
Additional Information and
Declarations can be found on
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DOI 10.7717/peerj.53
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Significant changes in the skin
microbiome mediated by the sport of
roller derby
James F. Meadow
1
, Ashley C. Bateman
1
, Keith M. Herkert
1,2
,
Timothy K. O’Connor
1,3
and Jessica L. Green
1,4
1
Biology and the Built Environment Center, Institute of Ecology and Evolution, University of
Oregon, Eugene, OR, USA
2
Oregon Health & Science University, Portland, OR, USA
3
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
4
Santa Fe Institute, Santa Fe, NM, USA
ABSTRACT
Diverse bacterial communities live on and in human skin. These complex communi-
ties vary by skin location on the body, over time, between individuals, and between
geographic regions. Culture-based studies have shown that human to human and
human to surface contact mediates the dispersal of pathogens, yet little is currently
known about the drivers of bacterial community assembly patterns on human skin.
We hypothesized that participation in a sport involving skin to skin contact would
result in detectable shifts in skin bacterial community composition. We conducted a
study during a flat track roller derby tournament, and found that teammates shared
distinct skin microbial communities before and after playing against another team,
but that opposing teams’ bacterial communities converged during the course of a
roller derby bout. Our results are consistent with the hypothesis that the human
skin microbiome shifts in composition during activities involving human to human
contact, and that contact sports provide an ideal setting in which to evaluate dispersal
of microorganisms between people.
Subjects Biodiversity, Biogeography, Ecology, Microbiology, Dermatology
Keywords Microbial biogeography, Contact sport, Human microbiome, Microbial ecology,
Skin microbiology, Microbial dispersal
INTRODUCTION
Microbial communities living on and in the human skin are diverse and complex. These
communities, which vary greatly both within and among people, play an important role in
human health and well-being. Skin microbial communities have been shown to mediate
skin disorders, provide protection from pathogens, and regulate our immune system
(Costello et al., 2009; Grice & Segre, 2011; Human Microbiome Project Consortium, 2012).
Despite the importance of our skin microbiota, we still know very little about what shapes
the distribution and diversity of the skin microbiome.
As for any other ecosystem, the composition of the skin microbiome is determined
by some combination of two simultaneous ecological processes: the selection of certain
microbial species by the skin environment and the dispersal of microbes from a pool of
How to cite this article Meadow et al. (2013), Significant changes in the skin microbiome mediated by the sport of roller derby. PeerJ
1:e53; DOI 10.7717/peerj.53
available species. Skin moisture, temperature, pH and exposure to ultraviolet light are all
well documented environmental factors that aect skin microbial communities (Grice
& Segre, 2011). The microbial species available for dispersal onto the skin of any given
individual likely stem from many sources including inanimate surfaces, people, pets,
cosmetics, air and water (Capone et al., 2011; Costello et al., 2009; Dominguez-Bello et
al., 2010; Fierer et al., 2008; Fujimura et al., 2010; Grice & Segre, 2011 ; Hospodsky et al.,
2012; Human Microbiome Project Consortium, 2012; Kembel et al., 2012). Our current
understanding of the relative contributions from these potential sources is nascent.
Human to surface and human to human contact have long been acknowledged as strong
vectors for microbial dispersal in the medical literature, which has been largely focused
on culture-based detection of single-species pathogen transmissions (Boyce et al., 1997;
Casewell & Phillips, 1977; Hamburger, 1947; Noble et al., 1976; Pessoa-Silva et al., 2004;
Pittet et al., 2006). In these culture-based studies, handshaking, as well as hand-contact
with other parts of the body and room surfaces, have been identified as strong vectors
of health care service infections, such as with methicillin-resistant Staphylococcus aureus
(MRSA) and Klebsiella spp. (Casewell & Phillips, 1977; Davis et al., 2012; Pittet et al., 2006).
Given that human contact with surfaces, and especially the skin surfaces of others, has
been shown to transfer individual microbial taxa, activities which involve human to human
contact could be hypothesized to result in the sharing of skin microbial communities.
Here we explore how activities involving human to human contact influence the skin
microbiome. We use a contact sport, flat track roller derby, as a model study system. Flat
track roller derby is an organized team sport, played worldwide, that involves individuals
roller-skating in close proximity and making frequent contact with other players. Roller
derby teams frequently engage in tournaments, where teams from dierent geographical
locations come together to play, or ‘bout’ against one another for several days at a time. Flat
track roller derby tournaments present an ideal setting in which to study the transmission
of skin microbial communities during a contact sport for two main reasons. First, they
provide an opportunity to assess if the skin microbiome from athletes that frequently come
into contact with one another – members of the same team – have similar microbiomes.
Second, they provide an opportunity to assess if skin microbiomes of athletes on opposing
teams become more similar after competing against one another. Specifically, we addressed
the following questions in our study: (1) Were players’ skin microbiomes predicted by team
membership; (2) Were team-specific skin microbiomes altered during a bout; and (3) Did
opposing teams’ skin microbiomes become more similar, or converge, after competing in a
bout?
MATERIALS AND METHODS
Flat-track roller derby
For a full explanation of approved Womens Flat Track Derby Association rules, refer to
www.wftda.com. Briefly, a bout consists of two 30-min periods, where two competing
teams, each composed of up to 4 blockers” and 1 “jammer”, circle a track with the goal of
facilitating their own jammer in accumulating points. Players, both blockers and jammers,
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 2/17
periodically rotate with players on the bench, so that few or no players actually play for the
entire 60 min bout. Points are accrued when one teams jammer makes her second, and
subsequent, pass through the pack of blockers, in eect lapping the pack. Activity occurs
in intervals called “jams, and a single jam lasts for a maximum of 2 min. Flat track roller
derby is a contact sport; blockers are allowed to initiate contact with another player to
compete for track position using any of the following body parts: upper arm (shoulder
to elbow), torso, hips, “booty” (ocial WFTDA nomenclature), and mid to upper thigh.
Roller derby tournaments often involve multiple pairwise bouts in a single day between
several teams, one home team and multiple visiting teams from dierent geographical
locations. Players within a team practice together on a regular basis, and thus come into
frequent physical contact, and live in or near the same city. Teams involved in this study
were from Eugene, OR (Emerald City Roller Girls); Washington, DC (DC Roller Girls) and
San Jose, CA (Silicon Valley Roller Girls).
Ethics statement
Written consent forms were signed and collected from all participating subjects. The
Institutional Review Board Initial Application Form for the study was reviewed and
approved by the University of Oregon IRB with the Oce for Protection of Human
Subjects in January 2012 (protocol #10262011.038). The Willamalane Park and Recreation
District Human Resources oce granted written permission for the study to take place in
their recreation facility. Written permission was acquired from the three teams’ coaches
and administrators.
Sample collection
Microbial communities inhabiting skin vary greatly across the human body (Grice et al.,
2008; Grice et al., 2009; Human Microbiome Project Consortium, 2012). We chose the upper
arm (approximately the distal end of the lateral deltoid) as our focal skin sample site. The
upper arm is the one skin region on roller derby skaters that is nearly universally exposed
and frequently contacted during a bout. All players sampled had this area exposed during
the entire bout. All samples were collected Feb. 10, 2012, at the “Big O” Tournament in
Eugene, OR, USA, and all biological samples were taken by trained technicians using
sterile technique. The two bouts that were sampled took place at 12:00pm (Emerald City
vs. Silicon Valley) and 6:00pm (Emerald City vs. DC). DC had already played in one bout
the same day at 10:00am (against Santa Cruz, not considered here), but Emerald City and
Silicon Valley had not played that day prior to bout 1. For the purposes of this study, players
were not monitored between bouts. Samples were collected by swabbing individual’s
upper arms in a c. 4 cm by 5 cm area of skin with nylon-flocked swabs (COPAN Flock
Technologies, Brescia, Italy) moistened with sterile buer solution (0.15M NaCl, 0.1M
Tween20). Both arms were swabbed on each player at each sampling point, and all samples
were taken within 30 min of the beginning and end of each bout. Samples were stored at
20
C until DNA extraction. Total number of jams was recorded for each player, and
multiplied by 2 min (maximum jam length) to approximate total time played per person.
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 3/17
Four swab samples were also taken from the floor of the facility (track) following the
tournament using the same swabbing method and surface area as the arm samples.
DNA Extraction, amplification and sequencing
Whole genomic DNA was extracted using the MO BIO PowerWater DNA Isolation Kit
(MO BIO Laboratories, Carlsbad, CA) according to manufacturers instructions with the
following modifications: swab tips were incubated with Solution PW1 in a 65
C water
bath for 15 min prior to bead beating; bead beating length was extended to 10 min since
swab tips were included; and samples were eluted in 50 µL Solution PW6. Dual-arm
samples from each player were combined for DNA extraction.
A fragment of the 16S rRNA gene including the V4 region was amplified using
a modified F515/R806 primer combination (5
0
-GTGCCAGCMGCCGCGGTAA-3
0
,
5
0
-TACNVGGGTATCTAATCC-3
0
) (Caporaso et al., 2011b; Claesson et al., 2010). Amplifi-
cation proceeded in two steps using a custom Illumina preparation protocol, where PCR1
was performed with forward primers that contained partial unique barcodes and partial
Illumina adapters. The remaining ends of the Illumina adapters were attached during
PCR2, and barcodes were recombined in silico using paired-end reads. Adapter sequences
are detailed in Supplemental Data. All extracted samples were amplified in triplicate for
PCR1 and triplicates were pooled before PCR2. PCR1 (25 µL total volume per reaction)
consisted of the following ingredients: 5 µL GC buer (Thermo Fisher Scientific, U.S.A.),
0.5 µL dNTPs (10 mM, Invitrogen), 0.25 µL Phusion Hotstart II polymerase (Thermo
Fisher Scientific, U.S.A.), 13.25 µL certified nucleic-acid free water, 0.5 µL forward primer,
0.5 µL reverse primer, and 5 µL template DNA. The PCR1 conditions were as follows:
initial denaturation for 2 min at 98
C; 22 cycles of 20 s at 98
C, 30 s at 50
C and 20 s at
72
C; and 72
C for 2 min for final extension. After PCR1, the triplicate reactions were
pooled and cleaned with the QIAGEN Minelute PCR Purification Kit according to the
manufacturers protocol (QIAGEN, Germantown, MD). Ten µL of 3M NaOAc (pH 5.2)
was added to decrease the pH of the pooled reactions and facilitate ecient binding to the
spin column during cleanup. Samples were eluted in 11.5 µL of Buer EB. For PCR2, a
single primer pair was used to add the remaining Illumina adaptor segments to the ends
of the concentrated amplicons of PCR1. The PCR2 (25 µL volume per reaction) consisted
of the same combination of reagents that was used in PCR1, along with 5 µL concentrated
PCR1 product as template. The PCR 2 conditions were as follows: 2 min denaturation at
98
C; 12 cycles of 20 s at 98
C, 30 s at 66
C and 20 s at 72
C; and 2 min at 72
C for
final extension. Amplicons were size-selected by gel electrophoresis: gel bands at c. 440bp
were extracted and concentrated, using the ZR-96 Zymoclean Gel DNA Recovery Kit
(ZYMO Research, Irvine, CA), following manufacturer’s instructions, quantified using a
Qubit Fluoromoeter (Invitrogen, NY), and pooled in equimolar concentrations for library
preparation for sequencing. Samples were sent to the Georgia Genomics Facility at the
University of Georgia (Athens, GA; www.dna.uga.edu), and sequenced on the Illumina
MiSeq platform as paired-end reads.
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 4/17
Table 1 Description of the two roller derby bouts considered in analyses. Two dierent bouts were
sampled; bout 2 occurred approximately 5 h after bout 1. Emerald City Roller Girls played in both bouts.
Neither team in bout 1 had played a bout previously in the day, but both teams in bout 2 had done so.
Total skin samples considered in analysis = 82. Colored points correspond to those used in all figures.
Team n Players Bout 1st Bout
of the day
Before After
Emerald City 7 7 1 yes
Silicon Valley 10 4 1 yes
Emerald City 14 14 2 no
DC 13 13 2 no
Sequence processing
Raw sequences were processed using the FastX Toolkit (http://hannonlab.cshl.edu/fastx
toolkit) and the QIIME pipeline (Caporaso et al., 2010). Barcodes were recombined from
paired-end reads, and forward reads were used for downstream analysis. All sequences
were trimmed to 112 bp, including a 12 bp barcode, and low quality sequences were
removed. Quality filtering settings were as follows: minimum 30 quality score over at
least 75% of the sequence read; no ambiguous bases allowed; 1 primer mismatch allowed.
After quality control and barcode assignment, the remaining 1,368,938 sequences were
binned into operational taxonomic units (OTUs) at a 97% sequence similarity cuto
using uclust (Edgar, 2010). The highest-quality sequences from each OTU cluster were
taxonomically identified using reference sequences from Greengenes (DeSantis et al.,
2006). Plant-chloroplast and mitochondrial OTUs were removed. Not all samples returned
the same number of sequences. Following rarefaction precedents (e.g., Human Microbiome
Project Consortium, 2012; Kuczynski et al., 2010) we rarefied all samples to 500 sequences
per sample. Samples with fewer than 500 sequences were not used in subsequent analyses
(Table 1); since some low-yield samples were removed from all team groups, players
were not considered as paired samples before vs. after a bout. Samples analyzed for this
study were compiled from two separate MiSeq runs, and additional aspects of this study
were included in the MiSeq runs but are not considered here, so the returned sequence
count does not reflect the full volume of the runs. Three of the track samples contained
enough sequences to be considered in analysis, and were processed exactly as the rest of the
samples, but were not used in any ordination analysis. Sequence files and metadata for all
samples used in this study have been deposited in MG-RAST (ID 4506457.3–4506498.3).
Statistical analysis
All statistical analyses were performed in R. Community variation among samples, or
β-diversity, was calculated using the quantitative, taxonomy-based Canberra distance,
implemented in the vegan package (Oksanen et al., 2011). Non-metric multidimen-
sional scaling (NMDS) was performed using the bestnmds function in the labdsv
package (Roberts, 2010), using 20 random starts. Discriminant analysis of within-group
similarity was conducted using permutational MANOVA with the adonis function
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 5/17
in vegan. To determine whether skin microbial communities became more similar to
one another after playing in a bout, we used a β-dispersion test with the betadisper
function in vegan. This test is a multivariate analog of Levenes test for homogeneity of
variances (Anderson, Ellingsen & McArdle, 2006), and it tests for a significant dierence in
sample heterogeneity between groups (i.e. the spread of data points in ordination space).
Indicator analysis (Dufrene & Legendre, 1997), using indval in labdsv, was conducted
on each team group, and all players combined before vs. after respective bouts, to identify
OTUs responsible for observed β-diversity results. P-values for significant indicators were
adjusted for multiple comparisons using Holms correction (Holm, 1979). The relationship
between time played and change in community composition was assessed with Pearsons
correlation test by comparing individual players pairwise community distances with their
estimated cumulative times during a bout.
RESULTS
Illumina sequencing of the V4 region of the 16S rRNA genes produced 1,368,938 barcoded
sequences. After quality filtering and rarefaction, 82 samples were considered during
analysis, taken from 3 teams and two bouts (Table 1). Emerald City Roller Girls (EC)
played in both bouts and thus were included twice in the analyses as two dierent team
groups; Silicon Valley Roller Girls (SI) and DC Roller Girls (DC) played in the first and
second bouts, respectively, against EC. Including EC players twice in the study allowed
us to evaluate the change in community composition in a single team after playing
successive bouts. Some EC players were sampled in both bouts, but were not analyzed
on a paired-sample basis. Rarefaction to 500 sequences per sample left 1034 bacterial
OTUs, with the most abundant OTU (Corynebacterium sp.) representing c. 34% of
total sequences, and c. 55% of all OTUs represented by singletons (i.e. a single sequence
represents a single OTU). The bacterial taxa identified in our skin samples were consistent
with what has been reported in other skin microbiome studies (e.g. Caporaso et al., 2011a;
Costello et al., 2009; Grice & Segre, 2011; Kong, 2011) When considered at the taxonomic
class level, the majority of sequences were Actinobacteria (57.9%), followed by Bacilli
(23.4%), Gammaproteobacteria (7.4%), Betaproteobacteria (3.7%), Alphaproteobacteria
(2.7%), and Clostridia (1.3%). All skin samples were dominated by skin-associated genera
(especially Corynebacterium, Micrococcus, Staphylococcus, and Acinetobacter), and by
oral-associated genera (including Neisseria and Rothia), both before and after bouting.
Post-bout samples, however, did contain higher relative abundances of a few soil- and
plant-associated genera, especially Arthrobacter and Xanthomonas. Normalized OTU
richness, at the 500 sequences per sample level (mean richness = 67.6 OTUs), was not
significantly dierent after a bout (t = 0.007; p = 0.9; from a Welch two-sample t-test).
Were players’ skin microbiomes predicted by team membership?
Bacterial communities detected on players’ upper arms from dierent teams were signif-
icantly dierent before playing a bout, as well as after playing a bout (Table 2). In other
words, the skin microbiome of an individual player was predicted by team membership.
Teams clustered together in ordination space using a non-metric multidimensional
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 6/17
Figure 1 Variation in skin microbial community composition is significantly explained by team iden-
tity. Ordination diagrams (axes 1 and 2 from separate 3-dimensional NMDS ordinations) summarizing
similarity of skin bacterial community composition of all players. (A) Points represent players before bout
1 (EC vs. SI ) and before bout 2 (EC vs. DC ). Corresponding-colored ellipses show standard
deviations around community variances from each team. The skin bacterial communities of the four
team groups were significantly dierent before playing a bout (p < 0.001; from permutational MANOVA
on Canberra taxonomic distances). (B) The four team groups are also significantly dierent after playing
bouts (p < 0.001), though more overlap is observed between teams after bout 1 (EC vs. SI ) and
after bout 2 (EC vs. DC ). NMDS 3-dimensional stress = 19.66 (A) & 17.55 (B).
Table 2 Results from Permutational MANOVA on Canberra distances among skin bacterial commu-
nities sampled from players before and after bouting. Each team was considered individually when
testing for intra-team before/after community shifts, while teams were considered together for the all
players” before/after test. Team identity was used as a grouping factor to test inter-team clustering (“all
teams”), both before and after bouts. Emerald City was considered to be two dierent teams (bout 1 and
bout 2) in analyses.
Comparison Team DF
resid
F-statistic p-value Bout
Before/After Emerald City 12 1.25 0.017
*
1
Silicon Valley 12 1.39 0.005
*
1
Emerald City 26 1.22 0.011
*
2
DC 24 1.35 <0.001
*
2
all players 80 1.96 <0.001
*
Before all teams 40 1.74 <0.001
*
After all teams 34 1.27 <0.001
*
Notes.
*
Significant at p < 0.05 level.
scaling representation of players skin microbiomes both before (Fig. 1A) and after
(Fig. 1B) playing a bout, based on Canberra taxonomic distances. Though team clustering
is significant in both cases (before and after a bout), there is a greater degree of overlap
between the teams following bouts. Emerald City was considered as two dierent teams in
two dierent bouts during analysis.
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 7/17
Figure 2 Home team (EC) players’ skin microbiomes were more similar to the microbial community
detected on the roller derby track than visiting teams. When each player’s pre-bout skin microbiomes
were compared to the microbial communities found on the track surface, Emerald City players’ skin
microbiomes were significantly more similar on average to the three track samples than were the skin
microbiomes of players from Silicon Valley or DC. The same is true when considering teams on a
per-bout basis (p = 0.001 & 0.007; for bouts 1 & 2, respectively).
The home teams pre-bout bacterial communities (EC) were more similar on average to
communities detected on the track than the two visiting teams when considered together
(p < 0.001; from a Welch two-sample t-test; Fig. 2), and when considered separately
(p = 0.001 & 0.007; for bouts 1 & 2, respectively). All players were more dissimilar on
average from track samples (mean Canberra distance = 0.89) than from all other players
(mean Canberra distance = 0.83; p < 0.001; from a Welch two-sample t-test). Player
bacterial communities did not become more similar to the track after a bout, and in fact
both bout 2 teams (EC & DC) became less similar to the track following a bout (p = 0.008
& 0.003, respectively; from Welch two-sample t-tests).
Were team-specific skin microbiomes different after playing a
bout?
When teams were considered separately, bacterial communities detected on players’ upper
arms before a bout were significantly dierent than those detected after the bout in all
cases (Table 2; Fig. 3). We also detected a signal of already having played in a bout. Two
teams, Emerald City and DC, had already played in a bout the morning of the tournament,
and that was a significant predictor of community composition before the second bout
(F-statistic = 2.16; p-value <0.001; EC & DC in Fig. 1A).
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 8/17
Figure 3 Team-specific micobiomes are significantly dierent after playing in a bout. NMDS ordi-
nation diagrams summarizing similarity of skin bacterial community composition when all players
are compared within their own teams before and after a bout. All ordinations are based on Canberra
taxonomic distances. (A) Emerald City before and after bout 1; (B) Silicon Valley before and
after bout 1; (C) Emerald City before and after bout 2; (D) DC before and after bout
2. Corresponding-colored ellipses are standard deviations on community variances for each group. All
teams showed significantly dierent microbial communities before vs. after a bout. NMDS 3-dimensional
stress: A = 8.1, B = 10.47, C = 16.2, D = 17.65.
Did opposing teams’ skin microbiomes become more similar after
competing in a bout?
All players skin microbiomes were more similar to one another after competing in a
bout. To test this we conducted a β-dispersion test, which compared all players before
and after bouting. A significant reduction in β-dispersion between groups (before
vs. after) confirmed that communities became more similar (based on Canberra distances;
F = 11.79; p < 0.001; Table 4; Fig. 4) when all players were considered together, and when
players were grouped by the bout in which they played (F = 12.41 & 4.11; p = 0.002
& 0.048; Table 4). A greater proportion of OTUs was shared by competing teams after
bouting compared to before (Table 5), but, interestingly, the opposite is true in 3 out of 4
cases when comparing non-competing teams. When each team was considered separately,
both teams in bout 1 experienced a significant β-dispersion reduction as did EC after
playing in bout 2, while DC did not (Table 4; Fig. 4). None of the four teams experienced
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 9/17
Figure 4 Bacterial community variance is reduced after playing in a bout for all players and for three
of the four teams individually. When all players were considered, regardless of team identity, bacterial
communities were significantly more similar to one another after a bout than they were before a bout
(p < 0.001). Both teams in bout 1 (EC and SI), as well as EC in bout 2, showed the same microbial
community convergence. Points are jittered around the x-axis to more clearly describe distributions. All
p-values are from β-dispersion tests; a lower mean community variance for the after-bout points means
that players’ skin micobiomes were more similar to one another after playing in a bout. Colored points
correspond to Table 1 and Figs. 1 and 3.
a significant shift in Shannon-Wiener OTU diversity or evenness after playing in a bout,
nor was there a dierence when all players were considered together (all p-values > 0.2).
Both teams in bout 2 had already played a bout previously in the day; neither team in bout
1 had played during the same day. Changes in bacterial communities before and after a
bout were not correlated with each players time spent in a bout (Pearsons correlation test;
ρ = 0.12; p = 0.45). Jammers and blockers play dierent roles on a team, and thus engage
in dierent amounts of contact and time played; however, since players are not limited
to a single position during a bout, we did not dierentiate between the positions during
analysis.
Indicator analysis
We conducted indicator analysis to identify OTUs responsible for the observed dierence
between teams and for the convergence of bacterial communities after playing. Forty-nine
OTUs were significant indicators for single pre-bout teams (p-value <0.05) before multiple
comparison adjustment, and 11 were significant after adjustment (Table 3); indicator
taxa are presented at the finest taxonomic resolution provided by Greengenes assignment.
Though the significant indicators include bacterial genera commonly associated with
soil and plants (e.g. Dietzia & Xanthomonas), notable human-associated groups also
stand out (e.g. Coprococcus, Streptococcus, Lachnospiraceae & Brevibacterium). This list
of indicators includes both rare OTUs that account for <1% of sequences, and relatively
common OTUs (Alicyclobacillus made up >25% of pre-bout SI sequences). We also
identified OTUs that were shared between competing teams in post-bout samples but
not in pre-bout samples. Six OTUs fit this description for both bouts, belonging to six
dierent bacterial genera: Streptococcus, Sphingomonas, Eubacterium, Porphyromonas,
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 10/17
Table 3 Indicator OTUs from each team prior to sampled bouts. All bacterial OTUs were tested as
indicator taxa for any of the four teams before playing in a bout, using Dufrene and Legendre’s (1997)
procedure. Only those significant at the p < 0.05 level after multiple comparison adjustments are shown.
Percentages are calculated as a share of all sequences in the dataset, and as a share of pre-bout sequences
from each team for whom the OTU is indicative. Six of the eight pre-bout indicator OTUs detected in
opposing teams’ samples increased (+) in mean abundance for opposing team members after playing in
a bout.
OTU Genus Team (
a
) Bout Indicator value Percent of
total dataset
Percent of
pre-bout
team sequences
Adjusted
p-value
Dietzia EC (+) 1 0.582 0.75 2.06 0.021
*
Coprococcus EC 1 0.410 0.03 0.31 0.039
*
Alcaligenes EC 1 0.381 0.01 0.11 0.030
*
Alicyclobacillus SI () 1 0.554 7.21 25.46 0.021
*
Xanthomonas SI (+) 1 0.551 0.14 0.84 0.044
*
Alcanivorax SI 1 0.500 0.03 0.26 0.021
*
Streptococcus EC (+) 2 0.552 2.18 3.66 0.021
*
Nesterenkonia EC () 2 0.532 0.39 0.67 0.039
*
Streptococcus EC (+) 2 0.499 4.02 4.41 0.030
*
Lachnospiraceae EC (+) 2 0.425 0.05 0.11 0.021
*
Brevibacterium DC (+) 2 0.680 1.38 2.58 0.021
*
Notes.
*
Significant at p < 0.05 level after Holms correction for multiple comparisons.
a
Indicates whether OTU increased (+) or decreased () in average abundance when detected in opposing team.
Table 4 Results from β-dispersion ANOVA on Canberra distances when comparing community vari-
ances from each team before and after, as well as all players regardless of team identity. The first four
tests describe β-dispersion tests (comparison of within-team bacterial community variance) when each
team is considered individually before and after a bout, and the fifth ignores team identity. Results
indicate that skin bacterial communities from Emerald City (bout 1) and Silicon Valley players both
became more similar following a bout, as did Emerald City from their 2nd bout. But this was not the case
for DC after playing in bout 2. Bacterial communities became more similar when players were grouped
by the bout in which they played, and when all players were considered in the same analysis.
Team DF
resid
F-statistic p-value Bout
Emerald City 12 11.34 0.006
*
1
Silicon Valley 12 7.16 0.02
*
1
Emerald City 26 6.03 0.02
*
2
DC 24 0.05 0.82 2
bout 1 26 12.41 0.002
*
1
bout 2 52 4.11 0.048
*
2
all players 80 19.07 <0.001
*
Notes.
*
Significant at p < 0.05 level.
Aerococcus and Methylobacterium. It is worth noting that although these OTUs showed a
shared response after bouting, all six OTUs were relatively rare throughout the study; none
accounted for more than 1% of total sequences for any player.
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 11/17
Table 5 The number of OTUs shared by competing teams increased after a bout. When the proportion
of shared OTUs is compared for each combination of teams, both sets of competing teams saw an increase
() after playing in a bout, while 3 out of 4 possible combinations of non-competing teams saw a decrease
() in shared OTUs.
Teams (Bouts) Percent of OTUs shared
Before After
Competing teams EC(1) & SI(1) 28.2 32.7
EC(2) & DC(2) 27.3 29.9
Non-Competing Teams EC(1) & EC(2) 26.5 29.0
EC(1) & DC(2) 28.9 26.7
EC(2) & SI(1) 24.5 21.7
DC(2) & SI(1) 26.5 23.0
DISCUSSION
Bacteria are ubiquitous. Those inhabiting the human body have received increased
attention in recent years, owing to a greater appreciation of the interrelated nature
of humans and their microbiome, an improved understanding of microbial ecology,
and an unprecedented ability to detect fine-scale microbial community changes with
high-throughput sequencing technology (Human Microbiome Project Consortium, 2012).
The skin is the largest organ and an important barrier that regulates microbial entry
into the human body. Despite the importance of the skin ecosystem to human health
and well-being, we know very little about the forces that shape microbial structure
and composition in the skin environment. The present study was designed as a way
to understand how human to human contact influences the skin microbiome, since
contact has long been acknowledged as a major dispersal vector for skin bacterial
communities (Hamburger, 1947; Pittet et al., 2006).
We found that team membership was a strong predictor of skin microbial community
composition, and that dierences between teams were partly driven by the presence of
unique indicator taxa that are commonly associated with human skin, gut, mouth, and
respiratory tract. For example, Brevibacterim was the sole indicator taxon for DC. While
short reads are limited in their ability to identify bacterial taxa to the species level, this
genus contains well known human commensals that are ubiquitous on skin, and have even
been studied for their role in foot odor (Dixon, 1996). The strong microbial fingerprint
linked to each team could be because they were from three distinctly dierent geographic
locations within the United States (Eugene, OR; San Jose, CA; and Washington, DC), each
associated with a dierent climate, urban setting, and outdoor macrobiota. These cities
may also have very dierent environmental microbiota. Blaser et al. (2012) recently found
that human populations from dierent geographical locations share distinct skin microbial
communities. Consistent with the idea that humans carry a microbial fingerprint that
reflects where they live, we found that home team (EC) microbiomes were more similar
to their home track than either of the visiting teams prior to bouting. This is also the EC
practice track, so it is perhaps unsurprising that EC players share some of their microbiome
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 12/17
with the track surface since they shed skin cells and frequently come into direct contact
with the floor. While a variety of factors likely contribute to this geographic signature, it is
plausible that human contact plays a role.
Although each team retained their microbial fingerprint, we found that team microbial
communities became more similar to one another after players competed in a bout. This
was found when considering all players together, when players grouped by the two dierent
bouts, and when considering each team individually, though the latter was not the case
for DC, who played in the second bout. Several reasonable explanations arise given these
results: (1) all players were exercising, and exercise produces predictable changes in skin
habitat conditions that are likely to aect bacterial communities over time; (2) players were
acquiring microbial transients from the built environment; and (3) players were coming
into repeated physical contact with their teammates and those from opposing teams, often
using the sampled area of their upper arms, and potentially sharing portions of their
skin microbiomes. With regards to explanation (1), the current study was not set up to
conclusively rule out the potential for exercise-related bodily changes to alter skin bacterial
communities. It seems unlikely that 60 min of elevated skin temperature and perspiration
would be long enough for microbial growth dynamics to eect the magnitude of changes
observed, given that bacterial doubling times generally exceed 20 min even in optimal
conditions. It is possible that exercise results in migration from subcutaneous habitats to
the skin surface, but little is known about this potential mechanism. Additionally, both
bouts resulted in a greater proportion of shared OTUs between competing teams, but
not between non-competing teams except when considering only EC over the course
of their two bouts, arguing against an overall exercise eect. Finally, none of the teams
experienced a shift in Shannon-Wiener diversity or evenness, which would be expected
in an exercise-driven community shift, since metabolically active bacteria might come
to dominate the community with a change in pH, temperature and moisture at the skin
surface.
Explanations (2) and (3) above both derive from dispersal, either from the built
environment or from other players. Dispersal from the built environment to skaters is
likely, since roller derby and spectator movements stirred up dust from the recreational
venue, and players also frequently fall on the floor. Although humans have been estimated
to contribute more than 10
6
airborne microbial cells per-hour (Qian et al., 2012),
culture-based disease transmission studies suggest that direct contact with humans and
other surfaces is a stronger bacterial dispersal vector than airborne particles (Casewell &
Phillips, 1977; Pessoa-Silva et al., 2004; Pittet et al., 2006). We found that human to track
surface contact did not seem to explain the observed shifts in community composition,
since none of the four team groups became more similar to the track samples after playing
in a bout. Given that the proportion of OTUs shared between competing teams increased
after both bouts, but not between non-competing teams, human to human contact is
the most parsimonious interpretation for the significant changes in skin microbiome we
observed. Future research - particularly over longer time scales - is needed to understand
the fate of dispersed microbiota and the dynamics of the human skin microbiome.
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 13/17
We know very little about how our social, family, and professional interactions shape
our microbial identities. Contact sports are an ideal setting in which to study how human
to human interactions influence our microbial ecosystems. As the rise of mega-cities and
population growth continues, humans may experience an increased rate of person to
person contact mediated by urban living and global travel. To predict the implications of
these changes will require, in part, understanding the ecological and evolutionary forces
that act on the skin microbiome. A thorough comprehension of the drivers of the skin
microbiome is still emerging; novel approaches to studying our skin ecosystems will likely
have lasting implications for health care, disease transmission, and our understanding of
urban environment microbiology.
ACKNOWLEDGEMENTS
We would like to thank the players and coaches who facilitated and participated in this
study; we are particularly grateful to Burnadeath, Katarina Van Rotten, Rex Havoc, Vexine,
Blue Ruin, and Agent Orange. Willamalane Park and Recreation District Human Resources
oce provided permission to conduct sampling in the recreation facility. H. Arnold,
R. Mueller, P. Pillai, J. Reichman, Z. Stephens, A. Womack, M. Naidoo, and Super Cake
helped with sampling and development of molecular protocols. We thank members of the
Bohannan and Green Labs for valuable input on this research, and also three reviewers for
their thoughtful suggestions on the manuscript.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
Research reported in this publication was supported by the Alfred P. Sloan Foundation
under award number 2010-5-22 IEC and the University of Oregon. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the
manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Alfred P. Sloan Foundation: award number 2010-5-22 IEC.
University of Oregon.
Competing Interests
JL Green is an Academic Editor for PeerJ.
Author Contributions
James F. Meadow analyzed the data, wrote the paper.
Ashley C. Bateman analyzed the data.
Keith M. Herkert conceived and designed the experiments, performed the experiments.
Timothy K. O’Connor conceived and designed the experiments, performed the
experiments, analyzed the data.
Meadow et al. (2013), PeerJ, DOI 10.7717/peerj.53 14/17
Jessica L. Green conceived and designed the experiments, performed the experiments,
contributed reagents/materials/analysis tools.
Human Ethics
The following information was supplied relating to ethical approvals (i.e. approving body
and any reference numbers):
University of Oregon IRB & Oce for Protection of Human Subjects. January 2012.
# 10262011.038
DNA Deposition
The following information was supplied regarding the deposition of DNA sequences:
MG-RAST # 4506457.3 4506498.3
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/
10.7717/peerj.53.
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    • "Skin physiologies have been shown to both differ by population group and affect the SM ([115, 116] and reviewed in [117]). In addition to host physiological properties, anthropogenic characteristics, such as gender, age, handedness, personal hygiene, and lifestyles, have all been shown to affect SM [96, 113,[118][119][120]. Our comparison of SMs between urban and rural populations reveals the expansion of a global cutaneous pan- microbiome [96]. "
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    Full-text · Article · Dec 2016
    • "DNA extraction, amplification and Illumina library preparation followed methods described previously [9,10]. DNA was extracted from swabs using a PowerWater DNA extraction kit (MoBio Laboratories, Inc., Carlsbad, CA, USA) with the following modifications: samples were frozen and thawed for two cycles; bead beating length was extended to 10 minutes; and samples were eluted in 50 μL Solution PW6. "
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    • "The V4 region of the 16S rRNA gene was amplified using F515/R806 primer combination (5′-GTGCCA- GCMGCCGCGG-3′, 5′-TACNVGGGTATCTAATC- C-3′) (Caporaso et al., 2012; Claesson et al., 2010). Amplification proceeded in two steps using a custom Illumina preparation protocol described in Meadow et al. (2013), where PCR1 was performed with forward primers that contained partial unique barcodes and partial Illumina adapters. The remaining ends of the Illumina adapters were attached during PCR2, and barcodes were recombined in silico using paired-end reads. "
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