Don't Neglect Their Microbiomes: Understanding Animal Microbiomes

Slides by Jonathan Eisen for BIS2C at UC Davis Spring 2014 1
Don’t Neglect Their Microbiomes
Jonathan A. Eisen
@phylogenomics
November 17, 2014
Talk for Nonhumans Meeting

Microbes and Non Humans 1: Bad Germs
!20
• Animals get and transmit
many pathogens
• But … can lead to excess
germophobia

Microbes and Nonhumans 2: Mutualisms
!21
Sharpshooter:
Cuerna sayi
bacteriomes
Sharpshooters harbor two obligate
symbionts in their bacteriomes
D Takiya
Copyright © National Academy of Sciences. All rights reserved.
al Biology of Microbial Communities: Workshop Summary
WORKSHOP OVERVIEW 9
et al., 2012). This simple model of persistent colonization of animal epithelia
by Gram-negative bacteria provides a “valuable complement to studies of both
beneficial and pathogenic consortial interactions, such as in the mammalian in-
testine, and chronic disease that involve persistent colonization by Gram-negative
bacteria, such as cystic fibrosis” (Nyholm and McFall-Ngai, 2004).
Plant roots and their partners Plants establish associations with several micro-
organisms in a relationship somewhat analogous to that of mammals with their
gastrointestinal microbiota. The roots of most higher plant species form mycor-
rhizae, an association with specific fungal species that significantly improves
the plant’s ability to acquire phosphorous, nitrogen, and water from the soil.12
A few plant families, including legumes, associate with nitrogen-fixing bacteria.
They colonize the plant’s roots and form specialized nodules, where the bacteria
12 See http://agronomy.wisc.edu/symbiosis.
DC
Figure WO-3
A B
FIGURE WO-3 The bacterium and the squid. A persistent, symbiotic association be-
tween the squid Euprymna scolopes (A) and its luminous bacterial symbiont Vibrio fischeri
(B) forms within the squid’s light organ (C and D). After colonization of the host’s light
organ tissue, V. fischeri induces a series of irreversible developmental changes that trans-
form these tissues into a mature, functional light organ (Nyholm and McFall-Ngai, 2004).
SOURCE: (A) Images taken by C. Frazee, provided by M. McFall-Ngai and E. G. Ruby;
(B) Image provided courtesy of Marianne Engel; (C and D). Reprinted by permission from
Macmillan Publishers Ltd: Nature, Dusheck (2002), copyright 2002.
The Social Biology of Microbial Communities: Workshop Summary
148 THE SOCIAL BIOLOGY OF MICROBIAL COMMUNITIES
Figure A5-3.eps
bitmap
FIGURE A4-3 Cooperation and conflict within the fungus-growing ant microbe symbio-
sis. A) Fungus-growing ants forage for substrate to nourish their cultivated fungus, which
they also groom to help remove garden parasites. B) In return, the fungus serves as the
primary food source for the ants; with some species producing nutrient-rich hyphal swell-

Microbes and Nonhumans 3: The Microbiome
!22

0
1000
2000
3000
4000
00 01 02 03 04 05 06 07 08 09 10 11 12 13
Pubmed “Microbiome” Hits
The Rise of the Microbiome

• Animals are covered in a cloud of microbes
!26

• This “microbiome” likely is involved in
many important animal phenotypes
!27

• This “microbiome” LIKELY is involved in
!28

• This “microbiome” LIKELY is INVOLVED in
!29

Why Now I: Growing Appreciation of Microbial Diversity
!31

!31
Diversity of Form

!31
Diversity of Form
Phylogenetic Diversity

!31
Functional Diversity
Diversity of Form

!31
Functional Diversity
Diversity of Form
MICROBES
RUN THE
PLANET

Why Now II: Post Genome Blues
!32

!32
Overselling the Human Genome?

!32
Transcriptome

!32
Transcriptome
Epigenome

!32
Transcriptome
VariomeEpigenome

!32
The Microbiome
Transcriptome
VariomeEpigenome

!33
Why Now III: Advances in Culture-Independent Work

!33
Observation

!33
Culturing Observation

!33
CountCount

!33
<<<<
CountCount

!33
<<<<
CountCount
http://www.google.com/url?
sa=i&rct=j&q=&esrc=s&source=images&
cd=&docid=rLu5sL207WlE1M&tbnid=CR
LQYP7d9d_TcM:&ved=0CAUQjRw&url=h
ttp%3A%2F%2Fwww.biol.unt.edu
%2F~jajohnson
%2FDNA_sequencing_process&ei=hFu7
U_TyCtOqsQSu9YGwBg&psig=AFQjCN
G-8EBdEljE7-
yHFG2KPuBZt8kIPw&ust=140487395121
1424

!33
<<<<
CountCount
http://www.google.com/url?
sa=i&rct=j&q=&esrc=s&source=images&
cd=&docid=rLu5sL207WlE1M&tbnid=CR
LQYP7d9d_TcM:&ved=0CAUQjRw&url=h
ttp%3A%2F%2Fwww.biol.unt.edu
%2F~jajohnson
%2FDNA_sequencing_process&ei=hFu7
U_TyCtOqsQSu9YGwBg&psig=AFQjCN
G-8EBdEljE7-
yHFG2KPuBZt8kIPw&ust=140487395121
1424
DNA

!34
Why Now IV: Sequencing Has Gone Crazy
!34

!3535
Approaching to NGS
Discovery of DNA structure
(Cold Spring Harb. Symp. Quant. Biol. 1953;18:123-31)
1953
Sanger sequencing method by F. Sanger
(PNAS ,1977, 74: 560-564)
1977
PCR by K. Mullis
(Cold Spring Harb Symp Quant Biol. 1986;51 Pt 1:263-73)
1983
Development of pyrosequencing
(Anal. Biochem., 1993, 208: 171-175; Science ,1998, 281: 363-365)
1993
1980
1990
2000
2010
Single molecule emulsion PCR 1998
Human Genome Project
(Nature , 2001, 409: 860–92; Science, 2001, 291: 1304–1351)
Founded 454 Life Science 2000
454 GS20 sequencer
(First NGS sequencer)
2005
Founded Solexa 1998
Solexa Genome Analyzer
(First short-read NGS sequencer)
2006
GS FLX sequencer
(NGS with 400-500 bp read lenght)
2008
Hi-Seq2000
(200Gbp per Flow Cell)
2010
Illumina acquires Solexa
(Illumina enters the NGS business)
2006
ABI SOLiD
(Short-read sequencer based upon ligation)
2007
Roche acquires 454 Life Sciences
(Roche enters the NGS business)
2007
NGS Human Genome sequencing
(First Human Genome sequencing based upon NGS technology)
2008
From Slideshare presentation of Cosentino Cristian
http://www.slideshare.net/cosentia/high-throughput-equencing
Miseq
Roche Jr
Ion Torrent
PacBio
Oxford
Sequencing Has Gone Crazy

Sequencing Revolution
!36
•More genes and genomes
•Deeper sequencing
• The rare biosphere
• Relative abundance estimates
•More samples (with barcoding)
• Times series
• Spatially diverse sampling
• Fine scale sampling

!37
Turnbaugh et al Nature. 2006 444(7122):1027-31.
Why Now V: Microbiome Functions

IBD vs. normal
•
•
•
•
•
• •
• •
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
• •
•
•
•
•
•
•
•
•
•
•
•
•
Healthy
Crohn’s disease
Ulcerative colitis
P value: 0.031
PC2
PC1
Figure 4 | Bacterial species abundance differentiates IBD patients and
healthy individuals. Principal component analysis with health status as
ARTICLES
!38

Microbiomes and Plant Health
!40

Model Animal Microbiomes
!4141
Both natural surveys and laboratory experiments indicate that
host diet plays a major role in shaping the Drosophila bacterial
microbiome.
Laboratory strains provide only a limited model of natural host–
microbe interactions

Asthma, Dust, Dogs and Microbiomes
!42

Nice Counter to Germophobia but …
!43

Public Service Reminder
Correlation ≠ causation
!44

Woese: Classification of Cultured Taxa by rRNA
!47

!47
rRNA rRNArRNA

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
Eukaryotes

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria ?????

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria ?????Archaebacteria

!47
rRNA rRNArRNA
ACUGC
ACCUAU
CGUUCG
ACUCC
AGCUAU
CGAUCG
ACCCC
AGCUCU
CGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
R ACUCCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
F ACUCCAGGUAUCGAUCG
C ACCCCAGCUCUCGCUCG
W ACCCCAGCUCUGGCUCG
Taxa Characters
S ACUGCACCUAUCGUUCG
E ACUCCAGCUAUCGAUCG
C ACCCCAGCUCUCGCUCG
EukaryotesBacteria ?????ArchaebacteriaArchaea

Culture Independent rRNA PCR: One Taxon
• v
DNA
ACTGC
ACCTAT
CGTTCG
ACTGC
ACCTAT
CGTTCG
ACTGC
ACCTAT
CGTTCG
Taxa Characters
B1 ACTGCACCTATCGTTCG
B2 ACTCCACCTATCGTTCG
E1 ACTCCAGCTATCGATCG
E2 ACTCCAGGTATCGATCG
A1 ACCCCAGCTCTCGCTCG
A2 ACCCCAGCTCTGGCTCG
New1 ACTGCACCTATCGTTCG
EukaryotesBacteria Archaea
!48
Many
sequences
from one
sample all
point to the
same branch
on the tree

DNA
ACTGC
ACCTAT
CGTTCG
ACTGC
ACCTAT
CGTTCG
ACCCC
AGCTCT
CGCTCG
Taxa Characters
New1 ACCCCAGCTCTGCCTCG
!49
One can
estimate cell
counts from
the number of
times each
sequence is
seen.
Culture Independent rRNA PCR: Two Taxa

DNA
Taxa Characters
New2 AGGGGAGCTCTGCCTCG
New3 ACTCCAGCTATCGATCG
!50
ACTGC
ACCTAT
CGTTCG
ACTCC
AGCTAT
CGATCG
ACCCC
AGCTCT
CGCTCG
AGGGG
AGCTCT
CGCTCG
AGGGG
AGCTCT
CGCTCG
ACTGC
ACCTAT
CGTTCG
Even with
more taxa it
still works
Culture Independent rRNA PCR: Four Taxa

Culture Independent rRNA PCR: Communities
DNA DNADNA
ACTGC
ACCTAT
CGTTCG
ACTCC
AGCTAT
CGATCG
ACCCC
AGCTCT
CGCTCG
Taxa Characters
New2 ACGGCAGCTCTGCCTCG
!51

Culture Independent rRNA PCR: Communities
DNA DNADNA
ACTGC
ACCTAT
CGTTCG
ACTCC
AGCTAT
CGATCG
ACCCC
AGCTCT
CGCTCG
Taxa Characters
New2 ACGGCAGCTCTGCCTCG
!52

Culture Independent “Metagenomics”
DNA DNADNA
!53
Taxa Characters
RecA RecARecA
http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7
Genome Biology 2008, 9:R151
sequences are not conserved at the nucleotide level [29]. As a
result, the nr database does not actually contain many more
protein marker sequences that can be used as references than
those available from complete genome sequences.
Comparison of phylogeny-based and similarity-based phylotyping
Although our phylogeny-based phylotyping is fully auto-
mated, it still requires many more steps than, and is slower
than, similarity based phylotyping methods such as a
MEGAN [30]. Is it worth the trouble? Similarity based phylo-
typing works by searching a query sequence against a refer-
ence database such as NCBI nr and deriving taxonomic
information from the best matches or 'hits'. When species
that are closely related to the query sequence exist in the ref-
erence database, similarity-based phylotyping can work well.
However, if the reference database is a biased sample or if it
contains no closely related species to the query, then the top
hits returned could be misleading [31]. Furthermore, similar-
ity-based methods require an arbitrary similarity cut-off
value to define the top hits. Because individual bacterial
genomes and proteins can evolve at very different rates, a uni-
versal cut-off that works under all conditions does not exist.
As a result, the final results can be very subjective.
In contrast, our tree-based bracketing algorithm places the
query sequence within the context of a phylogenetic tree and
only assigns it to a taxonomic level if that level has adequate
sampling (see Materials and methods [below] for details of
the algorithm). With the well sampled species Prochlorococ-
cus marinus, for example, our method can distinguish closely
related organisms and make taxonomic identifications at the
species level. Our reanalysis of the Sargasso Sea data placed
672 sequences (3.6% of the total) within a P. marinus clade.
On the other hand, for sparsely sampled clades such as
Aquifex, assignments will be made only at the phylum level.
Thus, our phylogeny-based analysis is less susceptible to data
sampling bias than a similarity based approach, and it makes
Major phylotypes identified in Sargasso Sea metagenomic dataFigure 3
Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using
AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The
breakdown of the phylotyping assignments by markers and major taxonomic groups is listed in Additional data file 5.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Alphaproteobacteria
Betaproteobacteria
G
am
m
aproteobacteria
D
eltaproteobacteria
Epsilonproteobacteria
U
nclassified
proteobacteria
Bacteroidetes
C
hlam
ydiae
C
yanobacteria
Acidobacteria
Therm
otogae
Fusobacteria
ActinobacteriaAquificae
Planctom
ycetes
Spirochaetes
Firm
icutes
C
hloroflexiC
hlorobi
U
nclassified
bacteria
dnaG
frr
infC
nusA
pgk
pyrG
rplA
rplB
rplC
rplD
rplE
rplF
rplK
rplL
rplM
rplN
rplP
rplS
rplT
rpmA
rpoB
rpsB
rpsC
rpsE
rpsI
rpsJ
rpsK
rpsM
rpsS
smpB
tsf
Relativeabundance
RpoB RpoBRpoB
Rpl4 Rpl4Rpl4 rRNA rRNArRNA
Hsp70 Hsp70Hsp70
EFTu EFTuEFTu
Many other genes
better than rRNA

DNA DNADNA
!54
Taxa Characters
inputs of fixed carbon or nitrogen from external sources. As with
Leptospirillum group I, both Leptospirillum group II and III have the
genes needed to fix carbon by means of the Calvin–Benson–
Bassham cycle (using type II ribulose 1,5-bisphosphate carboxy-
lase–oxygenase). All genomes recovered from the AMD system
contain formate hydrogenlyase complexes. These, in combination
with carbon monoxide dehydrogenase, may be used for carbon
fixation via the reductive acetyl coenzyme A (acetyl-CoA) pathway
by some, or all, organisms. Given the large number of ABC-type
sugar and amino acid transporters encoded in the Ferroplasma type
Figure 4 Cell metabolic cartoons constructed from the annotation of 2,180 ORFs
identified in the Leptospirillum group II genome (63% with putative assigned function) and
1,931 ORFs in the Ferroplasma type II genome (58% with assigned function). The cell
cartoons are shown within a biofilm that is attached to the surface of an acid mine
drainage stream (viewed in cross-section). Tight coupling between ferrous iron oxidation,
pyrite dissolution and acid generation is indicated. Rubisco, ribulose 1,5-bisphosphate
carboxylase–oxygenase. THF, tetrahydrofolate.
articles
NATURE | doi:10.1038/nature02340 | www.nature.com/nature 5©2004 NaturePublishing Group

DNA DNADNA
!55
Taxa Characters

Animal Microbiomes as Ecosystems
!56

Biogeography
!57
a broader range of Proteobacteria, but yielded similar results
(Fig. S1 and Tables S2 and S3).
Across all samples, we identified 4,931 quality Nitrosomadales
sequences, which grouped into 176 OTUs (operational taxo-
nomic units) using an arbitrary 99% sequence similarity cutoff.
This cutoff retained a high amount of sequence diversity, but
minimized the chance of including diversity because of se-
quencing or PCR errors. Most (95%) of the sequences appear
closely related either to the marine Nitrosospira-like clade,
known to be abundant in estuarine sediments (e.g., ref. 19) or to
marine bacterium C-17, classified as Nitrosomonas (20) (Fig. S2).
Pairwise community similarity between the samples was calcu-
lated based on the presence or absence of each OTU using
a rarefied Sørensen’s index (4). Community similarity using this
incidence index was highly correlated with the abundance-based
Sørensen index (Mantel test: ρ = 0.9239; P = 0.0001) (21).
A plot of community similarity versus geographic distance for
each pairwise set of samples revealed that the Nitrosomonadales
display a significant, negative distance-decay curve (slope = −0.08,
P < 0.0001) (Fig. 2). Furthermore, the slope of this curve varied
significantly among the three spatial scales. The distance-decay
slope within marshes was significantly shallower than the overall
slope (slope = −0.04; P < 0.0334) and steeper across marshes within
a region than the overall slope (slope = −0.27, P < 0.0007) (Fig. 2).
In contrast, at the continental scale, the distance-decay curve did
not differ from zero (P = 0.0953). Thus, there is no evidence that
somonadales community similarity. Geographic distance con-
tributed the largest partial regression coefficient (b = 0.40,
P < 0.0001), with sediment moisture, nitrate concentration, plant
cover, salinity, and air and water temperature contributing to
smaller, but significant, partial regression coefficients (b = 0.09–
0.17, P < 0.05) (Table 1). Because salt marsh bacteria may be
dispersing through ocean currents, we also used a global ocean
circulation model (23), as applied previously (24), to estimate
relative dispersal times of hypothetical microbial cells between
each sampling location. Dispersal times between sampling points
did not explain more variability in bacterial community similarity
(ln dispersal time: b = 0.06, P = −0.0799; with dispersal R2
= 0.47
vs. without 0.46). Therefore, in the remaining analyses we use
geographic distance rather than dispersal time.
As hypothesized, the relative importance of environmental
factors versus geographic distance to Nitrosomadales community
similarity differed across the three spatial scales. Contrary to our
expectations, however, geographic distance had a strong effect
Fig. 1. The 13 marshes sampled (see Table S1 for details). Marshes com-
pared with one another within regions are circled. (Inset) The arrangement
of sampling points within marshes. Six points were sampled along a 100-m
transect, and a seventh point was sampled ∼1 km away. Two marshes in the
Northeast United States (outlined stars) were sampled more intensively,
along four 100-m transects in a grid pattern.
Fig. 2. Distance-decay curves for the Nitrosomadales communities. The
dashed, blue line denotes the least-squares linear regression across all spatial
scales. The solid lines denote separate regressions within each of the three
spatial scales: within marshes, regional (across marshes within regions circled in
Fig. 1), and continental (across regions). The slopes of all lines (except the solid
light blue line) are significantly less than zero. The slopes of the solid red lines
are significantly different from the slope of the all scale (blue dashed) line.
ECOLOGY
ults
ales
xo-
off.
but
se-
om-
ent
0-m
the
ely,
Fig. 2. Distance-decay curves for the Nitrosomadales communities. The
dashed, blue line denotes the least-squares linear regression across all spatial
scales. The solid lines denote separate regressions within each of the three
spatial scales: within marshes, regional (across marshes within regions circled in
Fig. 1), and continental (across regions). The slopes of all lines (except the solid
light blue line) are significantly less than zero. The slopes of the solid red lines
are significantly different from the slope of the all scale (blue dashed) line.

!58Huttenhower et al. 2012.
Population Variability
!58Morgan et al. Genome Biology 2012, 13:R79
MJ Blaser et al. ISMEJ 2012
US Amerindian
Actinobacteria
(Propionibacteria)
Firmicutes
(Staphylococcus)
Relativeabundance
Actinobacteria dominates in the US
Boulder NY Platanillal A Platanillal B
Proteobacteria
Between Countries
Age
Vaginal Microbiome
Corn
at
Different
Locations
Individuals

Community Assembly
From Mom
Other People

Community Assembly
From Mom
From Pets
Other People

Community Assembly
From Mom
From Food
From Pets
Other People

Community Assembly
From Mom
From Food
From Pets
From Built
Environment
Other People

Disturbance
!60
Switch to
solid foods

Captivity and Conservation
!61
Research article
Captivity results in disparate loss of gut microbial
diversity in closely related hosts
Kevin D. Kohl1*, Michele M. Skopec2 and M. Denise Dearing1
2
*Corresponding author: +
The gastrointestinal tracts of animals contain diverse communities of microbes that provide a number of services to their
hosts. There is recent concern that these communities may be lost as animals enter captive breeding programmes, due to
changes in diet and/or exposure to environmental sources. However, empirical evidence documenting the effects of captivity
and captive birth on gut communities is lacking. We conducted three studies to advance our knowledge in this area. First, we
compared changes in microbial diversity of the gut communities of two species of woodrats (Neotoma albigula, a dietary gen-
eralist, and Neotoma stephensi, which specializes on juniper) before and after 6–9 months in captivity. Second, we investi-
gated whether reintroduction of the natural diet of N. stephensi could restore microbial diversity. Third, we compared the
microbial communities between offspring born in captivity and their mothers. We found that the dietary specialist, N. ste-
phensi, lost a greater proportion of its native gut microbiota and overall diversity in response to captivity compared with N.
albigula. Addition of the natural diet increased the proportion of the original microbiota but did not restore overall diversity
in N. stephensi. Offspring of N. albigula more closely resembled their mothers compared with offspring–mother pairs of
N. stephensi.Thisresearchsuggeststhatthemicrobiotaofdietaryspecialistsmaybemoresusceptibletocaptivity.Furthermore,
this work highlights the need for further studies investigating the mechanisms underlying how loss of microbial diversity may
vary between hosts and what an acceptable level of diversity loss may be to a host.This knowledge will aid conservation biolo-
gists in designing captive breeding programmes effective at maintaining microbial diversity.
Sequence Accession Numbers: NCBI’s Sequence Read Archive (SRA) – SRP033616
Key words: Neotoma
Editor:
Cite as: Conserv
Physiol
Introduction
The gut microbial communities of animals are hyperdiverse
and inﬂuence many aspects of their physiology, such as nutri-
tion, immune development and even behaviour (Amato,
2013). The preservation of the microbial diversity present in
resulting in microbial communities that are more susceptible
to invasion or by altering host immune function (Blaser and
Falkow, 2009). Additionally, gut microbes serve as sources of
novel gene products, such as enzymes for biomass degradation
(Hess et al., 2011) or bioremediation (Verma et al., 2006).
byguestonNovember16,2014http://conphys.oxfordjournals.org/Downloadedfrom
Zoos and Shelters

!62
Antimicrobials are in Everything

Restoration
!63
Animal TransfaunationProbiotics

Restoration
!63
Animal Transfaunation
Ileal
Transplant
Probiotics

Restoration
!63
Fecal Transplants
Animal Transfaunation
Ileal
Transplant
Probiotics

History Important Too
Genera that cross the divide. Another way to visualize
the vertebrate gut–environment dichotomy is by using a
network diagram that displays, in addition to the clus-
tering of hosts with similar microbiotas, the bacterial
genera they share. In this representation of the data, the
vertebrate gut samples are more connected to one another
than to the environmental samples (FIG. 4a,b). As in the
UniFrac-based analysis, the non-gut human samples also
occupy an intermediate position between the free-living
andthegutcommunities. FIGURE 5 showsthephylogenetic
classification of operational taxonomic units (OTUs) that
are shared between samples: among humans, an over-
whelming number of these are from the Firmicutes, with
a smaller number from the Bacteroidetes. By contrast, the
free-living communities share OTUs from a wider range
of phyla. Samples from the guts of obese humans cluster
away from the samples of healthy subjects, and most of
theirsharedOTUsarefoundintheFirmicutes.Thisobser-
vation is consistent with the finding that samples from
obese individuals have a higher number of OTUs
family of the gammaproteobacteria class. This fam-
ily contained OTUs from both the vertebrate gut and
free-living communities in saline and non-saline
habitats. Members of the Enterobacteriales order (also
from the gammaproteobacteria) were detected in the
vertebrate gut, termite gut and other invertebrates, as
well as in a surface soil sample and anoxic saline water.
Staphylococcaceae family members (from the phylum
Firmicutes and class Bacilli) were common in the ver-
tebrate gut samples, but were also detected in soil and
cultures derived from freshwater and saline habitats.
Finally, members of the Fusobacterium genus were
detected in salt-water sediments, in addition to the
vertebrate gut. The cosmopolitan distribution of these
organisms might have made them particularly impor-
tant for introducing novel functions during evolution of
the gut microbiota, as they could bring new useful genes
from the global microbiome into the gut microbiome
through horizontal gene transfer. However, it should
be noted that some OTUs that are common in humans
Nature Reviews | Microbiology
16SribosomalRNAsequences(%)
0
20
40
60
80
100
Bacteroidetes (red)
Firmicutes (blue)
Vertebrate gut
Termite gut
Salt-water surface
Salt water
Subsurface, anoxic or sediment
Other human
Non-saline cultured
Insects or earthworms
Soils or freshwater sediments
Mixed water
Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample
thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown.
ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody
habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)).
SS
Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.

Example: Behavior
!65
PERSPECTIVES
H
uman bodies house trillions of sym-
biotic microorganisms. The genes
in this human microbiome outnum-
ber human genes by 100 to 1, and their study
is providing profound insights into human
health. But humans are not the only ani-
mals with microbiomes, and microbiomes
do not just impact health. Recent research is
revealing surprising roles for microbiomes
in shaping behaviors across many animal
taxa—shedding light on how behaviors from
diet to social interactions affect the compo-
sition of host-associated microbial commu-
nities (1, 2), and how microbes in turn influ-
ence host behavior in dramatic ways (2–6).
Our understanding of interactions
between host behavior and microbes stems
largely from studies of pathogens. Animal
social and mating activities have profound
effects on pathogen transmission, and many
animals use behavioral strategies to avoid
or remove pathogens (7). Pathogens can
also manipulate host behavior in overt or
covert ways. However, given the diversity of
microbes in nature, it is important to expand
the view of behavior-microbe interactions to
include nonpathogens.
For diverse animals, including iguanas,
squids, and many insects, behavior plays a
central role in the establishment and regula-
tion of microbial associations (see the first
figure). For example, the Kudzu bug (Mega-
copta cribraria), an agricultural pest, is
born without any symbionts. After birth it
acquires a specific symbiont from bacterial
capsules left by its mother. If these capsules
fit of social living in many species may be
the transmission of beneficial microbes (9).
Koch and Schmid-Hempel have shown that
in the case of bumble bees (Bombus terres-
tris), either direct contact with nest mates or
feeding on feces of nest mates was neces-
sary for establishing the normal gut micro-
biota. Bees never exposed to feces had an
altered gut microbiota and were more sus-
ceptible to the parasite Crithidia bombi (1).
Once host-microbe associations are
established, microbes can influence host
behavior in ways that have far-reaching
implications for host ecology and evolution
(see the second figure). Sharon et al. recently
found that fruit flies (Drosophila melano-
gaster) strongly prefer to mate with individ-
uals reared on the same diet on which they
were reared. Antibiotic treatment abolished
the mating preference, and inoculation of
Animal Behavior and
the Microbiome
MICROBIOLOGY
Vanessa O. Ezenwa1
, Nicole M. Gerardo2
, David W. Inouye3,4
, Mónica Medina5
, Joao B. Xavier6
Feedbacks between microbiomes and their
hosts affect a range of animal behaviors.
Gut microbiota
Behaviorsimpactmicrobiomes
Juvenile iguanas eat soil
or feces to tailor the
microbiota to their current
diet
Animals may adjust
the microbiota at
different life-history
stages
Ishikawaella
capsulata
When born, bugs feed on
capsules of symbionts; if no
capsules are present, nymphs
wander in search of microbes
Behaviors shape
symbiont acquisition
Vibrio fischeri Squids eject bioluminescent
bacteria daily
Suggests animals
can actively control
their symbiont
populations
Green iguana
(Iguana iguana)
Bobtail squid
(Euprymna scolopes)
Kudzu bug
(Megacopta cribraria)
Animal Implication
Microbial species
or consortium
Interaction with
behavior
Behaviors alter microbiomes. In Kudzu bugs (8), green iguanas (15), and bobtail squid (16), host behaviors
alter microbial acquisition and maintenance.
EUNIVERSITY,NORTHRIDGE;(SQUID)W.ORMEROD,COURTESYOFM.MCFALL-NGAI/UNIVERSITYOFWISCONSIN;(BUG)N.GERARDO/EMORYUNIVERSITY
onNovember21,2012www.sciencemag.orgDownloadedfrom
S
m-
es
m-
dy
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is
es
mal
om
o-
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ns
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id
an
or
of
nd
to
as,
s a
la-
rst
a-
is
it
fit of social living in many species may be
the transmission of beneficial microbes (9).
Koch and Schmid-Hempel have shown that
in the case of bumble bees (Bombus terres-
tris), either direct contact with nest mates or
feeding on feces of nest mates was neces-
sary for establishing the normal gut micro-
biota. Bees never exposed to feces had an
Once host-microbe associations are
established, microbes can influence host
behavior in ways that have far-reaching
implications for host ecology and evolution
(see the second figure). Sharon et al. recently
found that fruit flies (Drosophila melano-
gaster) strongly prefer to mate with individ-
uals reared on the same diet on which they
r and
avid W. Inouye3,4
, Mónica Medina5
, Joao B. Xavier6
Feedbacks between microbiomes and their
hosts affect a range of animal behaviors.
Gut microbiota
Behaviorsimpactmicrobiomes
Juvenile iguanas eat soil
or feces to tailor the
microbiota to their current
diet
Animals may adjust
the microbiota at
different life-history
stages
Ishikawaella
capsulata
When born, bugs feed on
capsules of symbionts; if no
capsules are present, nymphs
wander in search of microbes
Behaviors shape
symbiont acquisition
Vibrio fischeri Squids eject bioluminescent
bacteria daily
Suggests animals
can actively control
their symbiont
populations
Green iguana
(Iguana iguana)
Bobtail squid
(Euprymna scolopes)
Kudzu bug
(Megacopta cribraria)
Animal Implication
Microbial species
or consortium
Interaction with
behavior
Behaviors alter microbiomes. In Kudzu bugs (8), green iguanas (15), and bobtail squid (16), host behaviors
alter microbial acquisition and maintenance.
SITY,NORTHRIDGE;(SQUID)W.ORMEROD,COURTESYOFM.MCFALL-NGAI/UNIVERSITYOFWISCONSIN;(BUG)N.GERARDO/EMORYUNIVERSITY
onNovember21,2012www.sciencemag.orgDownloadedfrom
Microbial effects on animal chemistry
also recently have been linked to changes
in predator-prey interactions (11) and feed-
ing behavior (12). Females of the African
malaria mosquito, Anopheles gambiae, use
chemical cues released from human skin
to locate hosts. By analyzing skin emana-
tions from 48 subjects, Verhulst et al. (12)
found that humans with higher microbial
diversity on their skin were less attractive
to these mosquitoes. High abundances of
Pseudomonas spp. and Variovorax spp.
were also associated with poor attractive-
ness to A. gambiae. These bacteria may pro-
duce chemicals that repel mosquitoes or
mask attractive volatiles emanating from
human skin. Given the importance of chem-
marine tubeworm Hydroides elegans. Bac-
terial biofilms play a key role in the settle-
ment behavior of many marine inverte-
brates, from corals to sea urchins. To study
the H. elegans system, the authors used
transposon mutagenesis to knock out a num-
ber of genes from the bacterium Pseudoal-
teromonas luteoviolacea, which is required
for larval settlement. Mutagenesis of four
genes related to cell adhesion and secretion
generated bacterial strains that altered worm
settlement behavior and metamorphosis (4).
It remains to be shown whether similar bac-
terial phenotypes drive this important life-
history transition across metazoans.
Some animal behaviors will be linked
to single microbial species, but many will
can
(5,
bac
swi
sho
acid
of t
betw
otic
mit
(5).
mo
bra
mo
beh
the
man
stan
man
by s
enh
tion
alte
the
olo
und
mu
nut
hid
1.
2.
3.
4.
5.
6.
7.
8.
Animal
Microbiomesimpactbehaviors
Implication
Microbial species
or consortium
Interaction with
behavior
Human skin
microbiota
Skin microbes of humans
influence attraction to
mosquitoes
Differential attraction
could impact disease
spread
Lactobacillus
rhamnosus
The probiotic L. rhamnosus
decreases anxiety in mice
Suggests bacteria
can alter mood
Gut microbiota Diet-specific microbiota
influence mating
preferences
Microbes could drive
speciation
Mosquito
(Anopheles gambiae)
Mouse
(Mus musculus)
Fruit fly
(Drosophila melanogaster)
Microbiomes alter behaviors. In fruit flies (2), mosquitoes (12), and mice (5, 6), microbes alter mating,
feeding, and anxiety levels.
EASECONTROL;(MOUSE)G.SHUKLIN/WIKIMEDIACOMMONS;(FLIES)T.CHAPMAN/UNIVERSITYOFEASTANGLIA
www.sciencemag.org SCIENCE VOL 338 12 OCTOBER 2012
allowing characterization of microbiomes
beyond the few cultivable microbes (10, 13,
14). However, determining which animal
behaviors influence and are influenced by
microbial symbionts, and the mechanisms
underlying these interactions, will require
a combination of molecular and experimen-
tal approaches. For example, Huang et al.
have studied the settlement behavior in the
two.This requires manipulative experiments
and will be facilitated by studying the under-
lying mechanisms by which signals are sent
between hosts and microbes.
Recent experiments with mice, showing
that the gut microbiome can influence stress,
anxiety, and depression-related behavior via
effects on the host’s neuroendrocrine sys-
tem, provide insight into how information
Physiol. A Neuroethol. Sens. Neura
65 (1996).
Acknowledgments: This perspective
thanks to NSF meeting grant IOS 12294
Future of Research in Animal Behavior.”
A. Laughton, B. Wehrle, C. Fontaine, and
discussion and comments.
PHOTOCREDITSSECONDFIGURE:(M
10.1
Published by AAAS

Where You Reside / Spend Time Important
!66
ORIGINAL ARTICLE
Architectural design influences the diversity and
structure of the built environment microbiome
Steven W Kembel1
, Evan Jones1
, Jeff Kline1,2
, Dale Northcutt1,2
, Jason Stenson1,2
,
Ann M Womack1
, Brendan JM Bohannan1
, G Z Brown1,2
and Jessica L Green1,3
1
Biology and the Built Environment Center, Institute of Ecology and Evolution, Department of
Biology, University of Oregon, Eugene, OR, USA; 2
Energy Studies in Buildings Laboratory,
Department of Architecture, University of Oregon, Eugene, OR, USA and 3
Santa Fe Institute,
Santa Fe, NM, USA
Buildings are complex ecosystems that house trillions of microorganisms interacting with each
other, with humans and with their environment. Understanding the ecological and evolutionary
processes that determine the diversity and composition of the built environment microbiome—the
community of microorganisms that live indoors—is important for understanding the relationship
between building design, biodiversity and human health. In this study, we used high-throughput
sequencing of the bacterial 16S rRNA gene to quantify relationships between building attributes and
airborne bacterial communities at a health-care facility. We quantified airborne bacterial community
structure and environmental conditions in patient rooms exposed to mechanical or window
ventilation and in outdoor air. The phylogenetic diversity of airborne bacterial communities was
lower indoors than outdoors, and mechanically ventilated rooms contained less diverse microbial
communities than did window-ventilated rooms. Bacterial communities in indoor environments
contained many taxa that are absent or rare outdoors, including taxa closely related to potential
human pathogens. Building attributes, specifically the source of ventilation air, airflow rates, relative
humidity and temperature, were correlated with the diversity and composition of indoor bacterial
communities. The relative abundance of bacteria closely related to human pathogens was higher
indoors than outdoors, and higher in rooms with lower airflow rates and lower relative humidity.
The observed relationship between building design and airborne bacterial diversity suggests that
we can manage indoor environments, altering through building design and operation the community
of microbial species that potentially colonize the human microbiome during our time indoors.
The ISME Journal advance online publication, 26 January 2012; doi:10.1038/ismej.2011.211
Subject Category: microbial population and community ecology
Keywords: aeromicrobiology; bacteria; built environment microbiome; community ecology; dispersal;
environmental filtering
Introduction microbiome—includes human pathogens and com-
mensals interacting with each other and with their
The ISME Journal (2012), 1–11
& 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12
www.nature.com/ismej
Microbial Biogeography of Public Restroom Surfaces
Gilberto E. Flores1
, Scott T. Bates1
, Dan Knights2
, Christian L. Lauber1
, Jesse Stombaugh3
, Rob Knight3,4
,
Noah Fierer1,5
*
1 Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, Colorado, United States of America, 2 Department of Computer Science,
University of Colorado, Boulder, Colorado, United States of America, 3 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, United
States of America, 4 Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, United States of America, 5 Department of Ecology and Evolutionary
Biology, University of Colorado, Boulder, Colorado, United States of America
Abstract
We spend the majority of our lives indoors where we are constantly exposed to bacteria residing on surfaces. However, the
diversity of these surface-associated communities is largely unknown. We explored the biogeographical patterns exhibited
by bacteria across ten surfaces within each of twelve public restrooms. Using high-throughput barcoded pyrosequencing of
the 16 S rRNA gene, we identified 19 bacterial phyla across all surfaces. Most sequences belonged to four phyla:
Actinobacteria, Bacteriodetes, Firmicutes and Proteobacteria. The communities clustered into three general categories: those
found on surfaces associated with toilets, those on the restroom floor, and those found on surfaces routinely touched with
hands. On toilet surfaces, gut-associated taxa were more prevalent, suggesting fecal contamination of these surfaces. Floor
surfaces were the most diverse of all communities and contained several taxa commonly found in soils. Skin-associated
bacteria, especially the Propionibacteriaceae, dominated surfaces routinely touched with our hands. Certain taxa were more
common in female than in male restrooms as vagina-associated Lactobacillaceae were widely distributed in female
restrooms, likely from urine contamination. Use of the SourceTracker algorithm confirmed many of our taxonomic
observations as human skin was the primary source of bacteria on restroom surfaces. Overall, these results demonstrate that
restroom surfaces host relatively diverse microbial communities dominated by human-associated bacteria with clear
linkages between communities on or in different body sites and those communities found on restroom surfaces. More
generally, this work is relevant to the public health field as we show that human-associated microbes are commonly found
on restroom surfaces suggesting that bacterial pathogens could readily be transmitted between individuals by the touching
of surfaces. Furthermore, we demonstrate that we can use high-throughput analyses of bacterial communities to determine
sources of bacteria on indoor surfaces, an approach which could be used to track pathogen transmission and test the
efficacy of hygiene practices.
Citation: Flores GE, Bates ST, Knights D, Lauber CL, Stombaugh J, et al. (2011) Microbial Biogeography of Public Restroom Surfaces. PLoS ONE 6(11): e28132.
doi:10.1371/journal.pone.0028132
Editor: Mark R. Liles, Auburn University, United States of America
Received September 12, 2011; Accepted November 1, 2011; Published November 23, 2011
Copyright: ß 2011 Flores et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported with funding from the Alfred P. Sloan Foundation and their Indoor Environment program, and in part by the National
Institutes of Health and the Howard Hughes Medical Institute. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: noah.fierer@colorado.edu
Introduction
More than ever, individuals across the globe spend a large
portion of their lives indoors, yet relatively little is known about the
microbial diversity of indoor environments. Of the studies that
have examined microorganisms associated with indoor environ-
ments, most have relied upon cultivation-based techniques to
detect organisms residing on a variety of household surfaces [1–5].
Not surprisingly, these studies have identified surfaces in kitchens
and restrooms as being hot spots of bacterial contamination.
Because several pathogenic bacteria are known to survive on
surfaces for extended periods of time [6–8], these studies are of
obvious importance in preventing the spread of human disease.
However, it is now widely recognized that the majority of
communities and revealed a greater diversity of bacteria on
indoor surfaces than captured using cultivation-based techniques
[10–13]. Most of the organisms identified in these studies are
related to human commensals suggesting that the organisms are
not actively growing on the surfaces but rather were deposited
directly (i.e. touching) or indirectly (e.g. shedding of skin cells) by
humans. Despite these efforts, we still have an incomplete
understanding of bacterial communities associated with indoor
environments because limitations of traditional 16 S rRNA gene
cloning and sequencing techniques have made replicate sampling
and in-depth characterizations of the communities prohibitive.
With the advent of high-throughput sequencing techniques, we
can now investigate indoor microbial communities at an
unprecedented depth and begin to understand the relationship
the stall in), they were likely dispersed manually after women used
the toilet. Coupling these observations with those of the
distribution of gut-associated bacteria indicate that routine use of
toilets results in the dispersal of urine- and fecal-associated bacteria
throughout the restroom. While these results are not unexpected,
they do highlight the importance of hand-hygiene when using
public restrooms since these surfaces could also be potential
vehicles for the transmission of human pathogens. Unfortunately,
previous studies have documented that college students (who are
likely the most frequent users of the studied restrooms) are not
always the most diligent of hand-washers [42,43].
Results of SourceTracker analysis support the taxonomic
patterns highlighted above, indicating that human skin was the
primary source of bacteria on all public restroom surfaces
examined, while the human gut was an important source on or
around the toilet, and urine was an important source in women’s
restrooms (Figure 4, Table S4). Contrary to expectations (see
above), soil was not identified by the SourceTracker algorithm as
being a major source of bacteria on any of the surfaces, including
floors (Figure 4). Although the floor samples contained family-level
taxa that are common in soil, the SourceTracker algorithm
probably underestimates the relative importance of sources, like
Figure 3. Cartoon illustrations of the relative abundance of discriminating taxa on public restroom surfaces. Light blue indicates low
abundance while dark blue indicates high abundance of taxa. (A) Although skin-associated taxa (Propionibacteriaceae, Corynebacteriaceae,
Staphylococcaceae and Streptococcaceae) were abundant on all surfaces, they were relatively more abundant on surfaces routinely touched with
hands. (B) Gut-associated taxa (Clostridiales, Clostridiales group XI, Ruminococcaceae, Lachnospiraceae, Prevotellaceae and Bacteroidaceae) were most
abundant on toilet surfaces. (C) Although soil-associated taxa (Rhodobacteraceae, Rhizobiales, Microbacteriaceae and Nocardioidaceae) were in low
abundance on all restroom surfaces, they were relatively more abundant on the floor of the restrooms we surveyed. Figure not drawn to scale.
doi:10.1371/journal.pone.0028132.g003
Bacteria of Public Restrooms
high diversity of floor communities is likely due to the frequency of
contact with the bottom of shoes, which would track in a diversity
of microorganisms from a variety of sources including soil, which is
known to be a highly-diverse microbial habitat [27,39]. Indeed,
bacteria commonly associated with soil (e.g. Rhodobacteraceae,
Rhizobiales, Microbacteriaceae and Nocardioidaceae) were, on average,
related differences in the relative abundances of s
some surfaces (Figure 1B, Table S2). Most notably
were clearly more abundant on certain surfaces
restrooms than male restrooms (Figure 1B). Some
family are the most common, and often most abun
found in the vagina of healthy reproductive age w
Figure 2. Relationship between bacterial communities associated with ten public restroom surfaces. Communities were
PCoA of the unweighted UniFrac distance matrix. Each point represents a single sample. Note that the floor (triangles) and toilet (as
form clusters distinct from surfaces touched with hands.
doi:10.1371/journal.pone.0028132.g002
Bacteria of P
time, the
un to take
of outside
om plants
ours after
ere shut
ortion of
e human
ck to pre-
which
26 Janu-
Journal,
hanically
had lower
y than ones with open win-
ility of fresh air translated
tions of microbes associ-
an body, and consequently,
pathogens. Although this
hat having natural airflow
Green says answering that
clinical data; she’s hoping
they move around. But to quantify those con-
tributions, Peccia’s team has had to develop
new methods to collect airborne bacteria and
extract their DNA, as the microbes are much
less abundant in air than on surfaces.
In one recent study, they used air filters
to sample airborne particles and microbes
in a classroom during 4 days during which
pant in indoor microbial
ecology research, Peccia
thinks that the field has
yet to gel. And the Sloan
Foundation’s Olsiewski
shares some of his con-
cern. “Everybody’s gen-
erating vast amounts of
data,” she says, but looking across data sets
can be difficult because groups choose dif-
ferent analytical tools. With Sloan support,
though, a data archive and integrated analyt-
ical tools are in the works.
To foster collaborations between micro-
biologists, architects, and building scientists,
the foundation also sponsored a symposium
100
80
60
40
20
0
Averagecontribution(%)
DoorinDoorout
StallinStallout
Faucethandles
SoapdispenserToiletseat
ToiletflushhandleToiletfloorSinkfloor
SOURCES
Soil
Water
Mouth
Urine
Gut
Skin
Bathroom biogeography. By
swabbing different surfaces in
public restrooms, researchers
determinedthatmicrobesvaryin
where they come from depend-
ing on the surface (chart).
February9,2012

Example: Context
!67
K.R. Amato
with reduced resource availability [71]. Such a trend is likelyfrequencies of social interaction and contact are likely to have
Figure 1. Basic model of factors influencing host fitness, including predicted interactions between host and gut microbiota. Relationships and factors
represented by dashed lines indicate areas that are not well studied in wild animal populations.
Research Article • DOI: 10.2478/micsm-2013-0002 • MICSM • 2013 • 10-29
MicrobioMe Science and Medicine
Introduction
As sequencing technology makes data generation faster,
cheaper, and more comprehensive, studies of gut microbial
communities are multiplying at an astonishing rate. As a
result, our understanding of the host-gut microbe relationship
is constantly improving. Studies to date have demonstrated
that the gut microbiota contributes to host nutrition, health
and behavioral patterns by providing energy and nutrients,
improving immune function, and influencing the production of
neuroactive molecules [1-12]. Changes in the composition of
the gut microbial community are known to lead to changes in
its function, which can alter host nutrition, health and behavior
[6,13-23]. Environmental factors such as diet or social contact
are largely responsible for determining the composition of the
gut microbial community [24-31], but host genotype also affects
the abundances of some microbial genera [28,32,33].
Because host-gut microbe relationships are influenced to
some extent by host genotype, and gut microbial community
composition differs according to host phylogeny [34-36],
discussions of the co-evolution of host and gut microbiota are
common in the current literature [7,34-37]. Some researchers
argue that since microbes are found in animals as simple as
Co-evolution in context: The importance of studying gut
microbiomes in wild animals
1
Program in Ecology Evolution and
Conservation Biology, University of
Illinois at Urbana-Champaign,
Urbana, IL, USA, 61801
2
Department of Anthropology,
University of Illinois at Urbana-Champaign,
Urbana, IL, USA, 61801
Katherine R. Amato1,2
*
Received 05 August 2013
Accepted 29 September 2013
Abstract
Because the gut microbiota contributes to host nutrition, health and
behavior, and gut microbial community composition differs according
to host phylogeny, co-evolution is believed to have been an important
mechanism in the formation of the host-gut microbe relationship. However,
current research is not ideal for examining this theme. Most studies of
the gut microbiota are performed in controlled settings, but gut microbial
community composition is strongly influenced by environmental factors.
To truly explore the co-evolution of host and microbe, it is necessary to
have data describing host-microbe dynamics in natural environments with
variation in factors such as climate, food availability, disease prevalence,
and host behavior. In this review, I use current knowledge of host-gut
microbe dynamics to explore the potential interactions between host
and microbe in natural habitats. These interactions include the influence
of host habitat on gut microbial community composition as well as the
impacts of the gut microbiota on host fitness in a given habitat. Based on
what we currently know, the potential connections between host habitat,
the gut microbiota, and host fitness are great. Studies of wild animals will
be an essential next step to test these connections and to advance our
understanding of host-gut microbe co-evolution.
Keywords
Gut microbiota • host-microbe • co-evolution • habitat • ecology • fitness
occurring for more than 800 million years [38,39]. Additionally, the
increased complexity and stability of gut microbial communities
in vertebrates as well as the presence of fewer physical barriers
to bacteria has been used to suggest that the adaptive immune
system evolved in vertebrates to recognize gut bacteria and
improve host-gut microbe interactions [40]. Nevertheless, while
it seems likely that co-evolution is an important mechanism for
understanding host-gut microbe relationships, current research
is not ideal for examining the co-evolution of host and microbe.
Most studies of the gut microbiota are performed in
controlled laboratory settings or are focused solely on human
populations [9,16,25,41-49]. Therefore, despite an understanding
that environmental factors greatly influence the host-gut microbe
relationship [25,27-29,31], the effects of natural environmental
variation in factors such as food availability on the host-gut
microbe relationship have generally not been explored. Because
the host-gut microbe mutualism evolved in a natural environment
with complex patterns of climate, food availability, disease
prevalence, and host behavior, a comprehensive examination
of host-gut microbe dynamics must consider these factors.
Specifically, we must establish the ways in which a host’s habitat
influences the selective environment the host imposes upon its
gut microbiota, and in turn, how the gut microbiota influences
© 2013 Katherine R. Amato, licensee Versita Sp. z o. o.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license,
which means that the text may be used for non-commercial purposes, provided credit is given to
the author

!68
From Wu et al. 2009 Nature 462, 1056-1060
Challenge 1: Biological Dark Matter

Challenge 2: Function Prediction Difficult
Lateral Gene Transfer
Metagenomic Binning
Hypothetical
Proteins

Solution: Better Prediction Methods and Data
!70
Characterizing the niche-space distributions of components
Sites
North American East Coast_GS005_Embayment
North American East Coast_GS002_Coastal
North American East Coast_GS011_Estuary
Eastern Tropical Pacific_GS021_Coastal
North American East Coast_GS006_Estuary
Polynesia Archipelagos_GS051_Coral Reef Atoll
Galapagos Islands_GS036_Coastal
Indian Ocean_GS117a_Coastal sample
Galapagos Islands_GS031_Coastal upwelling
Galapagos Islands_GS030_Warm Seep
Sargasso Sea_GS001c_Open Ocean
Eastern Tropical Pacific_GS022_Open Ocean
Indian Ocean_GS149_Harbor
Indian Ocean_GS123_Open Ocean
Caribbean Sea_GS016_Coastal Sea
Indian Ocean_GS148_Fringing Reef
Indian Ocean_GS112a_Open Ocean
Caribbean Sea_GS017_Open Ocean
Caribbean Sea_GS018_Open Ocean
Indian Ocean_GS108a_Lagoon Reef
Eastern Tropical Pacific_GS023_Open Ocean
Caribbean Sea_GS019_Coastal
Caribbean Sea_GS015_Coastal
Galapagos Islands_GS026_Open Ocean
Polynesia Archipelagos_GS049_Coastal
Polynesia Archipelagos_GS048a_Coral Reef
Component 1
Component 2
Component 3
Component 4
Component 5
0.1 0.2 0.3 0.4 0.5 0.6 0.2 0.4 0.6 0.8 1.0
Salinity
SampleDepth
Chlorophyll
Temperature
Insolation
WaterDepth
General
High
Medium
Low
NA
High
Medium
Low
NA
Water depth
>4000m
2000!4000m
900!2000m
100!200m
20!100m
0!20m
>4000m
2000!4000m
900!2000m
100!200m
20!100m
0!20m
(a) (b) (c)
Figure 3: a) Niche-space distributions for our five components (HT
); b) the site-
similarity matrix ( ˆHT ˆH); c) environmental variables for the sites. The matrices are
aligned so that the same row corresponds to the same site in each matrix. Sites are
ordered by applying spectral reordering to the similarity matrix (see Materials and
Methods). Rows are aligned across the three matrices.
Figure 3a shows the estimated niche-space distribution for each of the five com-
ponents. Components 2 (Photosystem) and 4 (Unidentified) are broadly distributed;
Components 1 (Signalling) and 5 (Unidentified) are largely restricted to a handful of
sites; and component 3 shows an intermediate pattern. There is a great deal of overlap
between niche-space distributions for di erent components.
Figure 3b shows the pattern of filtered similarity between sites. We see clear pat-
terns of grouping, that do not emerge when we calculate functional distances without
filtering, or using PCA rather than NMF filtering (Figure 3 in Text S1). As with
the Pfams, we see clusters roughly associated with our components, but there is more
overlapping than with the Pfam clusters (Figure 2b).
Figure 3c shows the distribution of environmental variables measured at each site.
Inspection of Figure 3 reveals qualitative correspondence between environmental factors
Better Prediction Methods
More Reference Data
In Situ Function
A
B
C
Representative
Genomes
Extract
Protein
Annotation
All v. All
BLAST
Homology
Clustering
(MCL)
SFams
Align &
Build
HMMs
HMMs
Screen for
Homologs
New
Genomes
Extract
Protein
Annotation
Figure 1

Challenge 3: Systems are Complex
!71
• How distinguish
! Good vs. Bad
! Correlation
vs. Causation
• Solutions:
! More
controlled
ecological
studies
! Better
analysis tools
Good? Bad?

How define “bad” vs. “good” ecosystems
• Can (try to) define features that indicate whether an
ecosystem is good or bad
! Productivity
! Diversity
! Stability
! Resilience
• Key major challenge is predicting future “health” of
ecosystem
!72

• Can (try to) define features that indicate whether a
microbiome is good or bad
! Productivity
! Diversity
! Stability
! Resilience
• Key major challenge is predicting future “health” of a
microbiome
!73

• Can (try to) define features that indicate whether a
microbiome is good or bad
! Health of host
! Diversity
! Stability
! Resilience
• Key major challenge is predicting future “health” of a
microbiome
!74
How do these relate to health?

• Idea of a healthy community vs. a unhealthy community is
very complex
• The enormous variation between people and over time
makes it VERY difficult and very risky to try and say what
is “normal”
!75

Challenge 4: Need More Reference Data
Historical
Collections
Global
Automated
Sampling
Filling
in the  
Tree of Life

Acknowledgements
• GEBA:
• $$: DOE-JGI, DSMZ
• Eddy Rubin, Phil Hugenholtz, Hans-Peter Klenk, Nikos Kyrpides, Tanya Woyke, Dongying Wu, Aaron Darling,
Jenna Lang
• GEBA Cyanobacteria
• $$: DOE-JGI
• Cheryl Kerfeld, Dongying Wu, Patrick Shih
• Haloarchaea
• $$$ NSF
• Marc Facciotti, Aaron Darling, Erin Lynch,
• Phylosift
• $$$ DHS
• Aaron Darling, Erik Matsen, Holly Bik, Guillaume Jospin
• iSEEM:
• $$: GBMF
• Katie Pollard, Jessica Green, Martin Wu, Steven Kembel, Tom Sharpton, Morgan Langille, Guillaume Jospin,
Dongying Wu,
• aTOL
• $$: NSF
• Naomi Ward, Jonathan Badger, Frank Robb, Martin Wu, Dongying Wu
• Others (not mentioned in detail)
• $$: NSF, NIH, DOE, GBMF, DARPA, Sloan
• Frank Robb, Craig Venter, Doug Rusch, Shibu Yooseph, Nancy Moran, Colleen Cavanaugh, Josh Weitz
• EisenLab: Srijak Bhatnagar, Russell Neches, Lizzy Wilbanks, Holly Bik

Don't Neglect Their Microbiomes: Understanding Animal Microbiomes

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