Available via license: CC BY
Content may be subject to copyright.
Submitted 12 June 2018
Accepted 13 November 2018
Published 9 January 2019
Corresponding author
Thomas J. Sharpton,
thomas.sharpton@oregonstate.edu
Academic editor
Angelo Piato
Additional Information and
Declarations can be found on
page 12
DOI 10.7717/peerj.6103
Copyright
2019 Kirchoff et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
The gut microbiome correlates with
conspecific aggression in a small
population of rescued dogs (Canis
familiaris)
Nicole S. Kirchoff1, Monique A.R. Udell2and Thomas J. Sharpton1,3
1Department of Microbiology, Oregon State University, Corvallis, OR, United States of America
2Department of Animal and Rangeland Science, Oregon State University, Corvallis, OR,
United States of America
3Department of Statistics, Oregon State University, Corvallis, OR, United States of America
ABSTRACT
Aggression is a serious behavioral disorder in domestic dogs that endangers both dogs
and humans. The underlying causes of canine aggression are poorly resolved and
require illumination to ensure effective therapy. Recent research links the compositional
diversity of the gut microbiome to behavioral and psychological regulation in other
mammals, such as mice and humans. Given these observations, we hypothesized
that the composition of the canine gut microbiome could associate with aggression.
We analyzed fecal microbiome samples collected from a small population of pit bull
type dogs seized from a dogfighting organization. This population included 21 dogs
that displayed conspecific aggressive behaviors and 10 that did not. Beta-diversity
analyses support an association between gut microbiome structure and dog aggression.
Additionally, we used a phylogenetic approach to resolve specific clades of gut bacteria
that stratify aggressive and non-aggressive dogs, including clades within Lactobacillus,
Dorea,Blautia,Turicibacter, and Bacteroides. Several of these taxa have been implicated
in modulating mammalian behavior as well as gastrointestinal disease states. Although
sample size limits this study, our findings indicate that gut microorganisms are linked to
dog aggression and point to an aggression-associated physiological state that interacts
with the gut microbiome. These results also indicate that the gut microbiome may be
useful for diagnosing aggressive behaviors prior to their manifestation and potentially
discerning cryptic etiologies of aggression.
Subjects Animal Behavior, Bioinformatics, Microbiology
Keywords Gut microbiome, Aggression, Fecal microbiota, Dog, Gut-brain axis
INTRODUCTION
Domestic dogs (Canis familiaris) have coexisted with humans for over 14 thousand years
(Nobis, 1979), and remain among the most popular companion animals, especially in the
Western world where millions can be found living in human homes (American Pet Products
Association, 2014). Even larger populations of free-roaming and village dogs can be found
living among humans around the world (Coppinger & Coppinger, 2001). In recent years,
dogs have been studied for their capacity to form strong bonds with humans and other
How to cite this article Kirchoff NS, Udell MAR, Sharpton TJ. 2019. The gut microbiome correlates with conspecific aggression in a
small population of rescued dogs (Canis familiaris).PeerJ 7:e6103 http://doi.org/10.7717/peerj.6103
species, resulting in a range of prosocial, cooperative, and communicative behaviors (Udell
& Wynne, 2008). However, dog aggression towards humans, other dogs, or other animals
remains a common behavioral problem (Bamberger & Houpt, 2006) that can pose serious
risks to animals, owners, and other humans including neighbors, friends, or veterinary
staff. Aggressive interactions, especially those involving bites, may lead to physical harm,
psychological trauma, disease transmission, or even fatality in bitten humans and other
dogs (Overall & Love, 2001;Hampson et al., 2009;Brooks, Moxon & England, 2010;Ji et al.,
2010). Aggressive behavior also poses risks to the aggressor dog, as aggression is a common
reason for relinquishment to animal shelters, where poor progress on mitigation of the
behavior, assuming the shelter is even equipped to intervene, often leads to euthanasia
(Salman et al., 2000). Consequently, understanding the factors and mechanisms responsible
for dog behaviors that are incompatible with success in anthropogenic environments has
much potential to benefit both species.
Dog aggression is often divided into categories, including dominance aggression, fear
aggression, food or possessive aggression, and territorial aggression (Blackshaw, 1991;
Houpt, 2006;Lockwood, 2016) based on the form of the behavior and the identified or
presumed context or consequences associated with specific aggressive acts. However, the
factors that predict aggression in one dog, but not in another, under similar conditions
(for example, in a standard behavior evaluation) are less well understood. Current research
suggests that environmental, experiential, and owner specific variables are important
predictors of aggression in dogs (Roll & Unshelm, 1997;Hsu & Sun, 2010). However,
underlying biological correlates including genetics, sex, hormone levels, neuter status,
nutrition, and neurological health have also been identified (Sherman et al., 1996;DeNapoli
et al., 2000;Duffy, Hsu & Serpell, 2008;Rosado et al., 2010). While behavior modification or
environmental change can significantly reduce aggressive behavior in at least some contexts
(Sherman et al., 1996;Mohan-Gibbons, Weiss & Slater, 2012), underlying physiological
mechanisms including pain, elevated stress levels, reduced thresholds for aggression, or
impulsivity could impede behavioral treatment or lead to resumption of the behavior if left
unidentified. Therefore, further elucidating the physiological underpinnings of aggression
in dogs may be critical to mitigating aggressive behavior, especially for situations where
other treatment and training options are ineffective on their own. The limited research
in this area shows that aggression associates with high levels of cortisol and low levels
of serotonin (5HT) (Rosado et al., 2010;León et al., 2012;Roth et al., 2016). Stress in
dogs is often detected by measuring cortisol levels and is thought to be a component
associated with behavioral problems such as anxiety as well as aggression (Rooney, Clark &
Casey, 2016). Accordingly, many dogs diagnosed with aggression are also diagnosed with
anxiety (Bamberger & Houpt, 2006). Behaviors associated with anxieties in dogs include
increased heart rate, trembling, increased salivation, pacing, circling, transient anorexia,
inappropriate elimination, excessive vocalization, destructiveness, and restlessness (Stelow,
2018). Dogs with anxiety may also present with aggressive behaviors such as lunging
(Stelow, 2018). There remains much to learn about the underlying causes of aggressive
behavior, which limits the development of new preventative strategies, diagnostics, and
therapeutic interventions.
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 2/16
Emerging evidence suggests that the gut microbiome may interact with mammalian
physiology to influence behavior (Cryan & O’Mahony, 2011;Mayer et al., 2014;Foster et
al., 2016). These interactions include aspects of physiology that are relevant to mammalian
aggression. For example, treatments with a broad-spectrum antibiotic disrupted the
gut microbiome and decreased aggressive behavior in Siberian hamsters (Sylvia et al.,
2017). Additionally, germ-free and specific-pathogen free mice exhibit different anxiety
levels (Heijtz et al., 2011;Neufeld et al., 2011). Other studies have found that specific
strains of bacteria (i.e., probiotics) improve the health of the host by modulating anxiety
phenotypes and stress hormones such as glucocorticoids. For example, administration of
Lactobacillus rhamnosus (JB-1) reduced anxiety related behavior in mice, and Bacteroides
fragilis NCTC 9343 improves anxiety-like behavior in a mouse model known to exhibit
anxiety-like behaviors (Bravo et al., 2011;Hsiao et al., 2013). Moreover, gut bacteria can
produce neuroactive substances, such as precursors of monoamine neurotransmitters
that act on the gut-brain axis to potentially impact behavior, including anxiety (Heijtz
et al., 2011;Evrensel & Ceylan, 2015;Carabotti et al., 2015;O’Mahony et al., 2015). For
example, the gut microbiome produces tryptophan, which impacts host serotonin levels
and behaviors linked to serotonergic neurotransmission (O’Mahony et al., 2015;Yano et
al., 2015). Several studies show an inverse relationship between the serotonin metabolite,
5-hydroxyindoleacetic acid (5-HIAA), and aggressive behaviors (Coccaro et al., 2015).
Collectively, these observations indicate that the gut microbiome and aggressive behavior
may be linked in mammals.
To date, no studies have investigated the association between the gut microbiome and
aggression in dogs, which is a first necessary step towards ultimately ascertaining whether
the gut microbiome mediates aggression. Prior work points to a potential interaction
between the microbiome and canine aggression. For example, diet is a strong modulator
of gut microbial composition in many animals (David et al., 2013) and specific dietary
components are associated with aggression including diets that reduce aggressive behaviors
in dogs (DeNapoli et al., 2000;Re, Zanoletti & Emanuele, 2008). Additionally, the canine
gut microbiome associates with other health conditions such as inflammatory bowel disease
and acute diarrhea (Suchodolski et al., 2012) leading to discomfort or pain that could also
contribute to irritability or aggression. Here, we conducted an exploratory analysis of fecal
samples originating from a small shelter-housed population of pit bull type dogs seized
from organized dogfighting to determine if canine aggression could be predicted based on
the composition of the gut microbiome.
MATERIALS AND METHODS
Sample collection
A single fecal sample was collected from the kennel of each of 31 pit bull type dogs
residing at a temporary shelter while in protective custody. The inner core of the feces
was sampled in order to minimize potential bacterial contamination given that the feces
were in contact with the kennel floor. This population served as the focus of this pilot
study because it enabled control over as many factors as possible, including breed type,
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 3/16
environment, diet, and medical care, while providing access to a population with a relatively
more frequent aggressive phenotype compared to typical populations. Upon intake into
the shelter and prior to the initiation of this study, an animal welfare agency catalogued
various parameters of each individual, which were used in this study’s analysis as covariate
data (Table S1). Animal welfare employees collected feces using aseptic technique within
an hour of defecation and immediately froze them at −18 ◦C to −20 ◦C to fix bacterial
growth and preserve the DNA content. Fecal samples were shipped to Oregon State
University and stored at −20 ◦C. Thirty of the dogs were on a diet of Iams Proactive Health
minichunks adult kibble (chicken-based formula) and one dog was on a diet of Iams
Puppy. Fourteen males and 17 females received a behavior evaluation conducted by the
animal welfare agency shortly after intake that categorized these dogs as intraspecifically
aggressive (n=21) or non-aggressive (n=10). Aggressive dogs displayed aggression during
one of three scenarios: an introduction to a life-size dog plush, introduction to a dog of
the same sex behind a barrier, and introduction to a dog of the same sex without a barrier.
Aggressive displays toward the life-size dog plush included growling, snarling, biting, biting
and holding, biting and shaking combined with tense behavior inconsistent with object
play, and aggressive displays toward the same sex dogs included growling and lunging,
lunging and snarling, climbing on withers and growling, attempting to bite, and biting.
Non-aggressive dogs did not display aggression towards the dog plush or another dog
(Text S1). Data from these evaluations were sent to Oregon State University along with
the stool samples for analysis. With the exception of the collection and processing of fecal
material, this study did not involve any manipulation of, measurement of, or contact with
dogs that had not already occurred.
Ethical statement
No animal subjects, animal handling, or study specific animal interactions were required
for the purpose of this study. Dog fecal samples were collected from shelter kennels after
natural deposit. Previously collected behavioral data from the animal welfare agency’s
records were used in analysis. Therefore, this study was determined to be exempt from
institutional animal care and use review by Oregon State University’s ethical review board.
Fecal DNA extraction and 16S sequencing
DNA was extracted from fecal samples using the QIAGEN DNeasy R
PowerSoil R
DNA
isolation kit (QIAGEN, Germantown, MD USA) as per manufacturer instruction with
the exception of an additional heat incubation of 10 min at 65 ◦C immediately before
the bead beating step. The 16S rRNA gene was amplified from the extracted DNA with
PCR and primers designed to target the V4 region (Caporaso et al., 2012). Amplicons were
subsequently quantified using the Qubit R
HS kit (Thermo Fisher, Waltham, MA USA) and
then pooled and cleaned using the UltraClean R
PCR clean-up kit (MO BIO, Carlsbad, CA
USA). These cleaned amplicons were then sequenced on an Illumina MiSeq (v3 chemistry)
instrument. This sequencing generated 3.31 million 150 bp single end reads (median reads
per sample =78,272).
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 4/16
Bioinformatic and statistical analyses
The QIIME (v1.8.0) bioinformatics pipeline was used to quality control raw sequences
as well as quantify the diversity of microorganisms isolated from the fecal samples. The
Illumina-generated sequences were demultiplexed and quality filtered (i.e., sequences
with a Phred quality score less than 20 were removed) with the QIIME script
split_libraries_fastq.py. The pick_open_reference_otus.py script then assigned sequences
to Operational Taxonomic Units (OTUs) based on the alignment of sequences to the
Greengenes (v13_8) reference database using a 97% similarity threshold with the UCLUST
algorithm (v1.2.22). With the core_diversity_analysis.py script, samples were subject to
rarefaction through random sub-sampling of sequences at a depth of 40,000 reads, which
corresponded to the lowest sequencing depth obtained across samples. The BIOM table
generated from the core_diversity_analysis.py script was imported into R and potentially
spurious OTUs were filtered by removing those that (1) were found in fewer than three
samples and (2) were observed fewer than 20 times across all samples from all subsequent
analyses. The resulting OTU matrix was subsequently processed using the beta_diversity.py
script to calculate the weighted and unweighted UniFrac distances between all pairs of
samples (Lozupone & Knight, 2005). Alpha diversity was calculated in R (v3.2.3) using the
diversity function in the vegan package (v2.3-3).
Intersample similarity was visualized using principal coordinates analysis (PCoA) based
on the Bray–Curtis dissimilarity index using the vegan (v2.3-3) package in R (v3.2.3).
The association between sample covariates, including dog aggression, and intersample
similarity was quantified with the envfit function in the vegan package. Kruskal–Wallis
tests, as implemented by the coin package (version 1.1-2), were used to identify OTUs
and phylotypes that stratify samples by covariate factors. Phylogenetic clades that associate
with aggression were identified by assembling a reference-guided 16S sequence phylogeny
via FastTree as previously described (O’Dwyer, Kembel & Sharpton, 2015), using Claatu
to resolve monophyletic clades that are conserved in aggressive or non-aggressive dogs
(FDR < 0.05) (Gaulke et al., 2018), and Kruskal–Wallis tests to ascertain if these conserved
clades are differentially abundant across these populations. The taxonomy of these clades
was determined by identifying the most resolved taxonomy label that is shared among
all members of the clade. Multiple tests were corrected using the qvalue package (version
2.2.2). Phylotypes or clades with a p-value less than 0.05 and a q-value less than 0.2 were
designated as those that stratify samples.
RESULTS
To determine possible differences in gut microbial composition between aggressive and
non-aggressive dogs, we compared stool microbiomes that were sampled from 21 aggressive
dogs and 10 non-aggressive dogs. A Principal Coordinates Analysis (PCoA) using the
weighted UniFrac metric shows separation of the aggressive and non-aggressive samples
based on 95% confidence interval ellipses (Fig. 1;Figs. S1;S2). The separation between
aggressive and non-aggressive samples in the PCoA plot was confirmed by environmental
fit (p=0.0250, R2=0.1297) and PERMANOVA (p=0.0346, R2=0.0349) analyses.
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 5/16
−0.4 −0.2 0.0 0.2 0.4
−0.3 −0.2 −0.1 0.0 0.1
PC1 (46%)
PC2 (12%)
Aggressive
Not Aggressive
Figure 1 Aggressive and non-aggressive dogs differ in beta-diversity using the weighted UniFrac
metric. Visualization of the phylogenetic differences in fecal microbiota of aggressive (green) and non-
aggressive (purple) dogs using principal coordinates analysis (PCoA) of OTU abundances and weighted
UniFrac distance. The separation between aggressive and non-aggressive samples in the PCoA plot was
confirmed with an environmental fit analysis (p=0.0250, R2=0.1297), which supports aggression status
as being a variable that is separating the microbial composition of the samples. The gut microbiome
structure of aggressive and non-aggressive dogs is also significantly different with the weighted UniFrac
metric using PERMANOVA (p=0.0346, R2=0.0349). Ellipses are based on 95% confidence intervals and
standard error.
Full-size DOI: 10.7717/peerj.6103/fig-1
Alternative measures of beta-diversity marginally support these results. For example,
using a Bray–Curtis dissimilarity metric finds a similar trend (PERMANOVA, p=0.0957,
R2=0.0573). Other study covariates were tested for their association with the fecal
microbial composition. Dog age did not associate with microbial composition (weighted
UniFrac, PERMANOVA, p=0.1763, R2=0.0652). Conversely, the sex of the dogs did
associate with microbial composition when using unweighted UniFrac (PERMANOVA,
p=0.0400, R2=0.0652), but not when using weighted UniFrac (PERMANOVA,
p=0.1424, R2=0.0582). Unlike the differences in beta-diversity between aggressive
and non-aggressive dogs, no significant differences were detected in alpha diversity based
on the Shannon index when comparing behavioral groups (p=0.5258).
The bacterial phylotypes that were observed across the dog fecal samples were compared
between behavioral groups to resolve those phylotypes that vary in association with
aggression (Fig. 2). Firmicutes, Fusobacteria, Bacteroidetes, and Proteobacteria were the
dominant phyla in all fecal samples. The relative abundances of these predominant phyla
also significantly differed across aggressive and non-aggressive dogs (p< 0.05, q< 0.1).
Specifically, Proteobacteria and Fusobacteria manifested higher relative abundance in
non-aggressive dogs, while Firmicutes was relatively more abundant in aggressive dogs.
These trends were driven by variation in a small number of more granular phylotypes
(Fig. 3). The family Lactobacillaceae was more abundant in aggressive dogs, while the
family Fusobacteriaceae was more abundant in non-aggressive dogs (p< 0.05, q< 0.2).
Consistently, the genus Lactobacillus was more abundant in aggressive dogs, while the genus
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 6/16
k__Bacteria
p__Actinobacteria
c__Actinobacteria
o__Bifidobacteriales
f__Bifidobacteriaceae g__Bifidobacterium
c__Coriobacteriia
o__Coriobacteriales
f__Coriobacteriaceae
g__Adlercreutzia
g__Collinsella
s__stercoris
g__Slackia
p__Bacteroidetes
c__Bacteroidia
o__Bacteroidales
f__[Paraprevotellaceae]
g__[Prevotella]
f__Bacteroidaceae
g__5−7N15
g__Bacteroides
s__caccae
s__coprophilus
s__plebeius
f__Porphyromonadaceae
g__Parabacteroides
f__Prevotellaceae
g__Prevotella
s__copri
f__S24−7
p__Firmicutes
c__Bacilli
o__Lactobacillales
f__Enterococcaceae
g__Enterococcus
f__Lactobacillaceae
g__Lactobacillus
s__reuteri
s__ruminis
f__Streptococcaceae
g__Streptococcus
s__luteciae
o__Turicibacterales
f__Turicibacteraceae
g__Turicibacter
c__Clostridia
o__Clostridiales
f__
f__[Mogibacteriaceae]
f__Clostridiaceae
g__CandidatusArthromitus
g__Clostridium
s__hiranonis
s__perfringens
g__Sarcina
f__Lachnospiraceae
g__[Ruminococcus]
s__gnavus
s__torques
g__Blautia
s__producta
g__Coprococcus
g__Dorea
f__Peptococcaceae
g__Peptococcus
f__Peptostreptococcaceae
f__Ruminococcaceae
g__Faecalibacterium
s__prausnitzii
g__Oscillospira
g__Ruminococcus
f__Veillonellaceae
g__Megamonas
g__Phascolarctobacterium
g__Veillonella
c__Erysipelotrichi
o__Erysipelotrichales
f__Erysipelotrichaceae
g__[Eubacterium]
s__biforme
s__dolichum
g__Allobaculum
g__Catenibacterium
g__Clostridium
g__Coprobacillus
p__Fusobacteria
c__Fusobacteriia
o__Fusobacteriales
f__Fusobacteriaceae
g__Cetobacterium
s__somerae
g__Fusobacterium
p__Proteobacteria
c__Betaproteobacteria
o__Burkholderiales
f__Alcaligenaceae
g__Sutterella
g__Limnohabitans
f__Oxalobacteraceae
g__Cupriavidus
c__Epsilonproteobacteria
o__Campylobacterales
f__Campylobacteraceae
g__Campylobacter
f__Helicobacteraceae
g__Helicobacter
c__Gammaproteobacteria
o__Aeromonadales
f__Aeromonadaceae
f__Succinivibrionaceae
g__Anaerobiospirillum
o__Alteromonadales
f__Shewanellaceae
g__Shewanella
o__Enterobacteriales
f__Enterobacteriaceae
g__Plesiomonas
s__shigelloides
o__Pseudomonadales
o__Xanthomonadales
f__Xanthomonadaceae
p__Tenericutes
c__CK−1C4−19
c__Mollicutes
o__Anaeroplasmatales
f__Anaeroplasmataceae
1
217
864
1940
3450
5400
7770
−4.00
−2.67
−1.33
0.00
1.33
2.67
4.00
Log 2 ratio of median proportions
Number of OTUs
Nodes
Figure 2 Many of the most relatively abundant phylotypes in our dog fecal samples are significantly different across aggressive and non-
aggressive dogs. A metacoder (Foster, Sharpton & Grünwald, 2017) heattree illustrates the variation in microbiome phylotypes between the
aggressive and non-aggressive dog populations. Nodes in the heattree correspond to phylotypes, as indicated by node labels, while edges link
phylotypes in accordance to the taxonomic hierarchy. Node sizes correspond to the number of OTUs observed within a given phylotype. Colors
represent the log fold difference of a given phylotype’s median relative abundance in the aggressive dogs as compared to the non-aggressive dogs.
Specifically, darker green represents higher relative abundance of aggressive OTUs and darker brown represents higher relative abundance of
non-aggressive OTUs.
Full-size DOI: 10.7717/peerj.6103/fig-2
Fusobacteria was more abundant in non-aggressive dogs (p< 0.05, q< 0.2). Additional
separation between aggressive and non-aggressive dogs was observed at the OTU level.
Specifically, seven OTUs significantly differed between aggressive and non-aggressive dogs
(p<0.05, q<0.1), including four OTUs from the genus Dorea, two OTUs from the
genus Lactobacillus, and one OTU from Turicibacter. All of the phylotypes and OTUs that
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 7/16
549
540
539
521
509
132
131
130
126
119
117
113
107
103
523
518
517
512
508
124
114
510
115
112
532
528
527
131.1
129
105
101
−2
−1
0
1
Bacteroides (Node 874)
Log10
Relative
Abundance
549
540
539
521
509
132
131
130
126
119
117
113
107
103
523
518
517
512
508
124
114
510
115
112
532
528
527
131.1
129
105
101
−2
−1
0
1
2
3
Lactobacillus (Node 3489)
Log10
Relative
Abundance
A
B
Aggressive Non-Aggressive
Aggressive Non-Aggressive
Figure 3 The abundance of monophyletic clades within phylotypes stratify aggressive and non-
aggressive dogs. (A) illustrates a subtree within the Bacteroides phylotype containing node 874 (red
branches), which is a monophyletic clade that is both common to and relatively more abundant amongst
the non-aggressive individuals than the aggressive individuals. The heat map adjacent to this subtree
illustrates the log10 relative abundance of each lineage in this subtree across the individuals subject to our
investigation. The red rectangle highlights the relative abundance of the lineages within node 874. The
vertical blue line separates aggressive and non-aggressive individuals. (B) illustrates a similar subtree, but
in this case, it has been extracted from within the Lactobacillus phylotypes and highlights a monophyletic
clade (node 3489) that is common to and relatively more abundant amongst the aggressive dogs.
Full-size DOI: 10.7717/peerj.6103/fig-3
significantly associated with aggression are included in Table S2 (phylotypes) and Table S3
(OTUs).
To better resolve taxa that stratify aggressive and nonaggressive dogs, we used a
phylogenetic approach that defines taxa as monophyletic clades of bacteria that are
prevalently observed across members of the aggressive or nonaggressive populations. These
clades represent evolutionary groupings of bacteria that often correspond to intermediate
levels of taxonomy (e.g., between species and genus) that are defined by the shared ancestry
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 8/16
and ecology of clade members. Moreover, by focusing on prevalent clades, which are those
that are observed in more individuals within a population than expected by chance (Gaulke
et al., 2018), we are able to resolve bacterial taxa that are especially common to at least
one population. This property of a high prevalence of behavior-stratifying gut microbes
may be a desirable characteristic when searching for potentially diagnostic indicators of
aggression status.
Of the 578 clades that are prevalent in either aggressive or non-aggressive dogs, 96
significantly differ in abundance between the two populations (q<0.2). Of these clades,
39 have a mean relative abundance that is significantly higher in the gut microbiomes
of aggressive dogs, while 57 have a higher relative abundance in non-aggressive dog
microbiomes. A complete list of clades that associate with behavior can be found in
Table S4. Of particular note is our finding that nine clades with the genus Bacteroides
are elevated in the gut microbiomes of non-aggressive dogs compared to aggressive dogs.
This finding indicates that the relative abundance of these lineages within Bacteroides may
predict aggression status and that their depletion may contribute to aggression. We also
find that the genus Lactobacillus contains 25 clades that are relatively abundant in aggressive
canines. Similar patterns are observed for clades within the family Paraprevotellaceae. These
observations indicate that aggression may be associated with an increase in specific lineages
within Lactobacillus and Paraprevotellaceae and they may express traits that interact with
aggression-associated aspects of canine physiology. Moreover, we find that the genus
Turicibacter contains both aggression-elevated and aggression-depleted clades (Fig. S3),
indicating that descendants of this genus may have recently evolved traits that contribute
to their differential association with canine behavior.
DISCUSSION
Accumulating evidence indicates that the gut microbiome acts as an agent of the nervous
system and influences affective disorders such as anxiety and depression (Clapp et al., 2017).
However, it is unknown if the gut microbiome similarly relates to animal aggression.
Our exploratory analysis of a population of rescued, sheltered-housed dogs links the
composition of the gut microbiome to conspecific aggression in canines. While this
associative study cannot disentangle cause and effect, it holds important implications for
clinical practices surrounding canines, as its results indicate that: (a) the gut microbiome
may contribute to aggression or its severity, and that manipulation of the microbiome (e.g.,
by probiotic administration) may alleviate the behavior; (b) the physiology of aggressive
dogs results in different gut microbiome compositions, which indicates that the microbiome
may facilitate predictive diagnosis of aggressive behavior and preventative intervention; or
(c) aggression and the gut microbiome are similarly associated with a cryptic physiological
or environmental covariate, such as inflammation or cortisol levels, which may help discern
the physiological underpinnings of canine aggression. Future studies should build upon this
exploratory investigation to discern the mechanisms underlying the relationship between
canine aggression and the gut microbiome.
Our investigation finds that the composition of the gut microbiome differs between
aggressive and non-aggressive dogs in the population that we studied. The rescued,
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 9/16
shelter-housed dogs included in this investigation proved useful for this study because
they included aggressive and non-aggressive individuals and were taken into the shelter
at the same time, maintained in the same facility, mostly exposed to the same diet, and
generally of consistent breed type. Despite our attempt to homogenize the sources of
variation amongst these dogs, we observed extensive variation in the composition of the
gut microbiome within each behavioral cohort. This intra-cohort variation indicates that
the stool samples we studied are subject to cryptic factors that associate with microbiome
composition (e.g., early life history (Rodríguez et al., 2015)). This is unsurprising given
that individuals living outside of a laboratory setting (including pet and shelter dogs,
as well as humans) are subject to genetic and environmental diversity that cannot fully
be controlled for. That said, the identification of significant differences between these
populations under naturalistic conditions heightens the applied value of these findings. We
were able to measure other factors that may influence the gut microbial composition of
these dogs besides aggression and found that dog sex partially explains the inter-individual
variation in the unweighted UniFrac dissimilarity of gut microbiome samples. Considering
this result alongside the finding that aggression status links to the weighted UniFrac
dissimilarity of the same samples indicates that, of the covariates measured in this study,
sex explains the types of microbes that are present in the gut of these dogs while aggression
status explains which of the microbes dominates their gut community. These observations
suggest that dog sex, aggression, and gut microbiome composition are intertwined, and
align with prior work that observed sex-dependent effects on how disruption of the gut
microbiome affects animal aggression (Sylvia et al., 2017). Also, because prior experiences
can impact the gut microbiome and because we do not know the prior experiences of
these individuals, future studies may find that aggression is not linked to the microbiome
in other dogs. Such a finding would indicate that there are contextual dependencies
underlying the aggression-microbiome connection observed in this study. Additionally,
researchers should observe if there is a consistent connection between canine aggression
and microbial composition while correcting for possible confounding variables such as age
and diet, measuring additional forms of aggression, such as aggression towards humans,
and studying greater numbers of pit bull breed type dogs or including different dog breeds
in study populations. Future efforts should consider larger populations and measure
more diverse covariates per individual to disentangle the properties that influence the gut
microbiome’s apparent relationship with aggression.
Several taxa found in our study were also observed in other canine gut microbiome
studies. For example, the most abundant phyla, Firmicutes, Fusobacteria, Bacteroidetes, and
Proteobacteria, were also dominant in fecal samples from previous canine gut microbiome
studies (Deng & Swanson, 2015). Additionally, several taxa significantly differ in their
relative abundance between aggressive and nonaggressive dogs. For instance, we find
that that lineages within the genus Bacteroides are elevated in non-aggressive dogs, which
might be expected given that species within this genus, such as Bacteroides fragilis, have
been shown to modulate mammalian behavior in prior investigations (Hsiao et al., 2013).
Moreover, the genus Dorea elevates in non-aggressive dogs compared to aggressive dogs,
which is notable because Dorea manifests a reduced abundance in dogs afflicted with
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 10/16
inflammatory bowel disease (Jergens et al., 2010) and other enteropathies (Suchodolski,
2011), and because psychological disorders are frequently comorbid with gastrointestinal
inflammation (Bannaga & Selinger, 2015;Clapp et al., 2017). However, our observations
of which taxa stratify these cohorts are not always consistent with prior investigations
of microbial taxa that associate with mammalian behavior. As an example, we find that
members of Lactobacillus are more abundant in the gut microbiomes of aggressive dogs,
which might defy expectations given that prior research of specific strains of Lactobacillus
rhamnosus have been found to reduce stress-associated corticosterone levels and anxiety
related behavior in mice and is known to produce GABA neurotransmitters (Bravo et al.,
2011). Similarly, the genus Fusobacterium is typically thought to elicit pro-inflammatory
effects inside the gut (Bashir et al., 2016); here, we find that Fusobacterium is more abundant
in the stool of non-aggressive dogs. That said, it is challenging to determine the physiological
role of specific microbiota from 16S sequences given that an organism’s interaction with
its host may be context dependent (Schubert, Sinani & Schloss, 2015) and may rapidly
diversify (Conley et al., 2016). Indeed, our analysis of monophyletic clades of gut bacteria
that associate with aggression finds that closely related clades can manifest opposite
patterns of association with behavior, such as that of Turicibacter. Additionally, the limited
population size may challenge the discovery of taxa that statistically stratify cohorts. Despite
this, these taxa represent compositional distinctions between aggressive and non-aggressive
dogs in our population. Further study of their physiological role may help clarify whether
or how they influence canine aggression as well as their probiotic suitability and therapeutic
capacity towards alleviating aggression in dogs.
CONCLUSIONS
Our results indicate that there are statistical associations between aggression status and
the gut microbiome. For example, microbial composition differs based on aggressive and
non-aggressive evaluations. Additionally, the relative abundances of specific bacterial taxa
and lineages are different across aggressive and non-aggressive groups. These observations
are important because they indicate that either (a) aggressive dogs manifest physiological
conditions in the gut that influence the composition of the gut microbiome, (b) the
composition of the gut microbiome may influence aggressive behavior, or (c) that aggressive
dogs are subject to some biased covariate relative to non-aggressive dogs that also influences
the gut microbiome. Future studies should seek to confirm that these findings are consistent
in additional populations of dogs, and seek to discriminate between these possibilities.
Additionally, future studies should expand the size of the populations being studied,
measure a diverse array of physiological covariates to tease out aggression-specific effects
and discern mechanisms of interactions, identify and test specific bacterial strains as
probiotics that could alleviate aggression, and consider using metagenomic analyses to
deduce the potential functional role of the microbiome in these interactions.
Ultimately, our results indicate that the composition of the gut microbiome associates
with conspecific canine aggression in this group of dogs. These results pave the way for
future investigations to ascertain whether similar results are seen in other dog populations
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 11/16
and if the microbiome can be used to develop diagnostics, preventative strategies, and
therapeutics of aggression.
ACKNOWLEDGEMENTS
We thank Dr. Pamela Reid for her advice and discussions regarding dog behavior. We also
thank Dr. Christopher A. Gaulke and Dr. Yuan Jiang for their helpful comments.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The National Science Foundation (Grant 1557192) and institutional funds to Thomas J.
Sharpton supported this work. 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:
The National Science Foundation: Grant 1557192.
Institutional funds.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
•Nicole S. Kirchoff analyzed the data, prepared figures and/or tables, authored or reviewed
drafts of the paper, approved the final draft.
•Monique A.R. Udell conceived and designed the experiments, contributed
reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the
final draft.
•Thomas J. Sharpton conceived and designed the experiments, analyzed the data,
contributed reagents/materials/analysis tools, prepared figures and/or tables, authored
or reviewed drafts of the paper, approved the final draft.
DNA Deposition
The following information was supplied regarding the deposition of DNA sequences:
Oregon State University CGRB, Sharpton Lab Public Repository:
http://files.cgrb.oregonstate.edu/Sharpton_Lab/Papers/Kirchoff_PeerJ_2018/data/raw_
16S/.
Data Availability
The following information was supplied regarding data availability:
Sharpton Lab Repository:
http://files.cgrb.oregonstate.edu/Sharpton_Lab/Papers/Kirchoff_PeerJ_2018/.
Kirchoff et al. (2019), PeerJ, DOI 10.7717/peerj.6103 12/16
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.6103#supplemental-information.
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