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Review Article
The Gut Microbiome and Mental Health:
Implications for Anxiety- and Trauma-Related Disorders
Stefanie Malan-Muller,
1
Mireia Valles-Colomer,
2,3
Jeroen Raes,
2,3
Christopher A. Lowry,
4–8
Soraya Seedat,
1
and Sian M.J. Hemmings
1
Abstract
Biological psychiatry research has long focused on the brain in elucidating the neurobiological mechanisms of
anxiety- and trauma-related disorders. This review challenges this assumption and suggests that the gut mi-
crobiome and its interactome also deserve attention to understand brain disorders and develop innovative
treatments and diagnostics in the 21st century. The recent, in-depth characterization of the human microbiome
spurred a paradigm shift in human health and disease. Animal models strongly suggest a role for the gut
microbiome in anxiety- and trauma-related disorders. The microbiota–gut–brain (MGB) axis sits at the epi-
center of this new approach to mental health. The microbiome plays an important role in the programming of
the hypothalamic–pituitary–adrenal (HPA) axis early in life, and stress reactivity over the life span. In this
review, we highlight emerging findings of microbiome research in psychiatric disorders, focusing on anxiety-
and trauma-related disorders specifically, and discuss the gut microbiome as a potential therapeutic target. 16S
rRNA sequencing has enabled researchers to investigate and compare microbial composition between indi-
viduals. The functional microbiome can be studied using methods involving metagenomics, metatran-
scriptomics, metaproteomics, and metabolomics, as discussed in the present review. Other factors that shape the
gut microbiome should be considered to obtain a holistic view of the factors at play in the complex interactome
linked to the MGB. In all, we underscore the importance of microbiome science, and gut microbiota in
particular, as emerging critical players in mental illness and maintenance of mental health. This new frontier of
biological psychiatry and postgenomic medicine should be embraced by the mental health community as it
plays an ever-increasing transformative role in integrative and holistic health research in the next decade.
Keywords: microbiome, anxiety, microbiota–gut–brain axis, interactome, mental health, stress-related disorders
Introduction
The global prevalence of anxiety disorders, as re-
ported in 2012, was estimated at 7.3%, ranging from
5.3% in African cultures to 10.4% in Euro/Anglo cultures
(Baxter et al., 2013). According to the DSM-5 (APA, 2013),
anxiety disorders include those that share features of exces-
sive fear and anxiety and related behavioral disturbances,
such as specific phobia, social anxiety disorder (social
phobia), panic disorder, and generalized anxiety disorder.
Posttraumatic stress disorder (PTSD), although previously
classified as an anxiety disorder, is currently classified as a
trauma- and stress-related disorder (APA, 2013). Anxiety-
and trauma-related disorders are complex and multifactorial,
and their differentiation and management are complicated by
phenotypic heterogeneity. An intricate interplay between the
genome, epigenome, and environment is thought to contrib-
ute to the development of these disorders (Nugent et al.,
2011). More recently, the etiological focus of complex
psychiatric and neurological diseases has shifted to the
1
Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa.
2
Department of Microbiology and Immunology, Rega Institute, KU Leuven–University of Leuven, Leuven, Belgium.
3
VIB, Center for Microbiology, Leuven, Belgium.
4
Department of Integrative Physiology and Center for Neuroscience, University of Colorado Boulder, Boulder, Colorado.
5
Military and Veteran Microbiome: Consortium for Research and Education ( MVM-Core), Aurora, Colorado.
6
Department of Psychiatry, Neurology & Physical Medicine and Rehabilitation, Anschutz School of Medicine, University of Colorado,
Aurora, Colorado.
7
VA Rocky Mountain Mental Illness Research, Education, and Clinical Center (MIRECC), Denver, Colorado.
8
Center for Neuroscience, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
OMICS A Journal of Integrative Biology
Volume 21, Number 0, 2017
ªMary Ann Liebert, Inc.
DOI: 10.1089/omi.2017.0077
1
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microbiota–gut–brain (MGB) axis, which requires an un-
derstanding of the holobiont in all its complexity.
The Human Gut Microbiome
‘‘Human microbiota’’ is the term used to describe all the
microorganisms (bacteria, eukaryotes, archaea, and viruses)
within the human body, while the microbiome is defined as
the complete catalog of these microbes and their genes (Dave
et al., 2012). The numbers of bacterial and human cells are
estimated to be close to equal, with about 3.9 ·10
13
bacterial
cells and 3.0 ·10
13
human cells (Sender et al., 2016). Re-
search has shown that the composition of the microbiota
changes across the life span (Douglas-Escobar et al., 2013).
It was originally believed that the intestines were sterile in
utero, however, recent evidence suggests that there is a degree
of maternal–fetal bacterial transmission via the amniotic fluid
and/or umbilical cord blood (Al-Asmakh et al., 2012; Jime
´nez
et al., 2005; Satokari et al., 2009; Wagner et al., 2008) and
bacterial species have been detected in the meconium of
healthy neonates ( Jime
´nez et al., 2008). During and immedi-
ately following delivery, the newborn is exposed to the mi-
crobiota of the mother as well as the environment to acquire a
range of commensal intestinal bacteria (Hooper et al., 2012).
During the perinatal period, the functional development of
the mammalian brain is susceptible to both internal and ex-
ternal environmental cues. Epidemiological studies have
found an association between microbial pathogen infections
during this period and common neurodevelopmental disor-
ders, such as autism and schizophrenia (Finegold et al., 2002;
Mittal et al., 2008). Similarly, exposure to microbial patho-
gens in rodents during developmental periods results in,
among others, anxiety-like behaviors and impaired cognitive
function (Bilbo et al., 2005; Goehler et al., 2008; Sullivan
et al., 2006). Desbonnet et al. (2010) showed that the com-
mensal bacterium, Bifidobacterium infantis, has the ability to
modulate tryptophan metabolism, suggesting that the gut
microbiota can influence the precursor pool for serotonin and
various other bioactive tryptophan metabolites.
These results underscore the importance of the gut mi-
crobiome in very early-life stages and its effects on neuro-
development and mental health. It could also provide insights
into the observed associations between early-life trauma and
susceptibility to the development of anxiety- and trauma-
related disorders later in life (Famularo et al., 1992; Felitti
et al., 1998; McCauley et al., 1997).
Over the past few years, a new research field has emerged
that investigates the human microbiome with the goal of de-
termining how the composition of the gut microbiome influ-
ences health and disease. This has given rise to large
international collaborative projects, such as the Human Mi-
crobiome Project (HMP) (Human Microbiome Project Con-
sortium, 2012) and MetaHIT (Qin et al., 2010). A study
conducted by the HMP detected marked interindividual dif-
ferences in the microbiota of healthy controls. Metabolic
pathways were, however, stable among individuals, despite
variation in community structure. Furthermore, ethnic/racial
background was one of the strongest associations of both mi-
crobes and pathways with clinical metadata (Human Micro-
biome Project Consortium, 2012).
Two large-scale studies of thousands of healthy individuals,
the Belgian Flemish Gut Flora Project and the Dutch LifeLines-
DEEP study, found that gut microbiome composition correlated
with several factors, including stool consistency, diet, use of
medication, red blood cell counts, and fecal chromogranin A
(Falony et al., 2016; Zhernakova et al., 2016). No associations
with microbiota composition variation and mode of delivery,
infant feeding, and residence type were found. The authors
noted that the lack of signal in the data was unexpected, and that
their results do not imply that early-life events do not affect
microbiota assembly during infancy, but only indicate that these
events are not significantly associated with microbiome com-
position in adulthood in those cohorts [see review by Tamburini
et al. (2016) for discussion of factors that influence microbial
homeostasis during early life that were associated with the
development or protection against disease during childhood].
The authors also made recommendations for sample size
(power) determination, emphasizing the importance of large-
scale microbiome studies and the inclusion of known covariates
to detect shifts in microbial composition (Falony et al., 2016).
The MGB Axis
The bidirectional communication between the brain andgut
microbiota has been termed the MGB axis, and preclinical
studies indicate that dysbiosis (dysregulation of the micro-
biota) influences anxiety and stress behaviors (Foster and
McVey Neufeld, 2013), suggesting that the MGB could in-
fluence the risk of disease, including anxiety and mood dis-
orders. The bidirectional interactions between the gut
microbiota and critical parts of the central nervous system
(CNS) and immune systems are maintained through direct and
indirect pathways, which include endocrine (hypothalamic–
pituitary–adrenal [HPA] axis) (Sudo et al., 2004), immune
(chemokines, cytokines) (Macpherson and Harris, 2004), and
metabolic pathways (Diaz-Anzaldua et al., 2011), the limbic
system (Carabotti et al., 2015), as well as the efferent (Rao and
Gershon, 2016; Liu et al., 2009), afferent (Wood, 2008), and
sympathetic afferent systems (Mayer, 2011).
Communication between the visceral afferent, limbic, and
autonomic systems provides the neural connections that un-
derlie the link between behavior and gut function in health and
disease (Mayer, 2011). In addition, several other factors may
also impact the MGB, including the HPA axis, neurotrans-
mitters produced by the gut microbiota [such as tryptophan and
serotonin; reviewed by O’Mahony et al. (2015)], and the in-
tegrity of the brain/blood barrier (BBB) (Braniste et al., 2014)
and intestinal epithelial barrier (So
¨derholm et al., 2002).
The Gut Microbiome in Anxiety and Stress
The majority of earlier microbiome studies focused on
animal models, which are convenient model systems that
provide increased control over genetic and environmental
factors that influence the microbiome. The germ-free (GF)
animal model is a powerful tool to examine the effects of the
microbiota on behavior in an attempt to determine causation
and to study the effect of particular bacteria or a dietary
intervention on the MGB axis. GF animals exhibit major
alterations in gastrointestinal and immune system function-
ing, which could have important effects on the brain and
behavior (Crumeyrolle-Arias et al., 2014; Sudo et al., 2004).
However, it should be emphasized that this relationship is
also influenced by temporal, strain, sex, and species factors
(Mayer et al., 2014), all of which are not yet fully understood.
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To show that the microbiota can directly affect behavior,
researchers transplanted microbiota from adult GF BALB/c
mice (a high-anxiety mouse strain) into adult GF NIH Swiss
mice (a low-anxiety mouse strain), and the BALB/c mice
received the microbiota of the NIH Swiss mice. Following
fecal transplantation, the behavioral profile of the donor was
evident in the recipient animal (Bercik et al., 2011a). While it
was originally suggested that the critical window for re-
colonization to reverse the anxiolytic phenotype is during
early-life/adolescence (Clarke et al., 2013; Neufeld et al.,
2011; Stilling et al., 2014), this study and a few others (Collins
et al., 2012; Nishino et al., 2013) have illustrated that the
behavior of GF animals is susceptible to modification even
during adulthood. The behavior of GF animals is quite distinct
from that exhibited by control animals. Sudo et al. (2004)
discovered that GF mice exhibited an exaggerated HPA axis
response to restraint stress, which was reversed following
monocolonization with a particular Bifidobacterium species.
Similarly, when comparing GF and specific pathogen free
(SPF) stress-sensitive, F344 rats, GF rats showed exaggerated
neuroendocrine responses and increased anxiety-like be-
havior compared to SPF animals (Crumeyrolle-Arias et al.,
2014). Another study found that short-term colonization of
GF mice in adulthood is able to reduce anxiety-like behaviors
(Nishino et al., 2013). Moreover, variations in neurotrans-
mitter signaling have also been observed in specific brain
regions of GF mice (Diaz Heijtz et al., 2011) as well as altered
HPA axis functioning (Sudo et al., 2004).
The relationship between the microbiome and behavior is,
however, not unidirectional; stress and emotions can influ-
ence the gut microbial composition through the release of
stress hormones or sympathetic neurotransmitters that influ-
ence gut physiology and alter the habitat of the microbiota
(Montiel-Castro et al., 2013). Preclinical studies have shown
that psychological stress, including maternal separation and
restraint, heat, and acoustic stress, alters the composition of
the gut microbiota (Bailey et al., 2011; De Palma et al., 2014;
Moloney et al., 2014). Furthermore, stress has the ability to
increase intestinal permeability, probably through the in-
volvement of corticotrophin releasing factor (CRF) and its
receptors (CRFR1 and CRFR2), which play a key role in
stress-induced gut permeability dysfunction (Overman et al.,
2012; Rodin
˜o-Janeiro et al., 2015; Tache
´and Million, 2015).
Increased intestinal permeability provides bacteria an op-
portunity to translocate across the intestinal mucosa and di-
rectly access both the immune and neuronal cells of the
enteric nervous system (ENS) (Gareau et al., 2008; Tei-
telbaum et al., 2008). Stress also activates the autonomic
nervous system, which affects gastric acid, bile, and mucus
secretion, as well as gut motility (Beckh and Arnold, 1991;
Shigeshiro et al., 2012; So
¨derholm and Perdue, 2001). Gut
motility is of particular importance since it is strongly asso-
ciated with gut microbiota composition and richness, and it is
therefore important to capture this information in microbiota
studies (Falony et al., 2016; Vandeputte et al., 2016).
Preclinical microbiome data have been extrapolated to
humans, and although there has been an exponential increase
in the number of human microbiome studies in general, there
is still an underrepresentation of microbiome research in
psychiatric disorders. The majority of clinical studies focus
on key microorganisms as potential psychobiotics (micro-
organisms that exhibit positive effects on the CNS) in healthy
(Kelly et al., 2017) or affected individuals (refer to The Gut
Microbiome as a Therapeutic Target section).
Uncontrolled inflammatory responses are evident in pa-
tients with PTSD and have been shown to play a role in the
pathogenesis of the disorder. Altered regulatory T cells
(Tregs), cells that assist in maintaining optimum immune
regulation and protect against inappropriate inflammatory
responses, have been reported in individuals with PTSD
(Morath et al., 2014; Sommershof et al., 2009). In addition,
upregulated proinflammatory cytokine profiles, such as tu-
mor necrosis factor (TNF), interferon gamma (IFN-c), and
interleukin 1 beta (IL-1b), have been observed in PTSD
(Hoge et al., 2009; Lindqvist et al., 2014; Maes et al., 1999).
The origins of this altered immune regulation is a point of
discussion and one of the probable candidates is the human
microbiome, since it is an important determinant of
immunoregulation (Rook et al., 2014; Sefik et al., 2015).
In addition, the microbiota produce and utilize neuro- and
immune-active substances, such as c-aminobutyric acid,
melatonin, acetylcholine, catecholamines, histamine, and
serotonin, which can penetrate the gut mucosa, enter the
bloodstream, and subsequently cross the BBB, and ultimately
affect functioning within the CNS (Barrett et al., 2012;
Theoharides et al., 2004).
The aforementioned findings lay the ground for a recent
pilot study that investigated the gut microbiome in PTSD pa-
tients (Hemmings et al. in press). Although the authors did not
detect any differences in overall diversity measures between
the PTSD patients and trauma-exposed (TE) controls, random
forest analysis showed that decreased relative abundance of
Actinobacteria, Lentisphaerae, and Verrucomicrobia phyla in
PTSD subjects was able to distinguish PTSD from TE controls
with a high degree of accuracy. Their findings were consistent
with an animal model of PTSD that hypothesized that de-
creased exposure to Actinobacteria and other im-
munoregulatory/anti-inflammatory ‘‘Old Friends’’ could lead
to increased vulnerability to PTSD (Reber et al., 2016).
Furthermore, the authors did not detect any differences in
plasma C-reactive protein (CRP) concentrations between
PTSD and TE controls in this small pilot study, which con-
trasts with earlier findings. However, the mean time since
index trauma was 11 years, and other studies mostly inves-
tigated inflammatory markers at the time of trauma exposure
and used healthy as opposed to TE controls. This study also
had a limited sample size (18 PTSD patients and 12 TE
controls) and these findings await replication in considerably
larger samples that include healthy controls.
Several studies have investigated the fecal microbiota of
individuals with depression and they have yielded conflicting
results, both in abundances of specific taxa and in terms of
diversity indexes. One study found increased bacterial di-
versity in depressed individuals ( Jiang et al., 2015), while
Kelly et al. (2017) detected lower diversity in patients with
depression, and neither Naseribafrouei et al. (2014) nor
Zheng et al. (2016) found significant differences in bacterial
diversity between depressed individuals and healthy controls.
These findings highlight the importance of taking con-
founding factors (such as medication, diet, and stool con-
sistency) into account in microbiota studies (Falony et al.,
2016), as these factors could explain the conflicting results.
Furthermore, much larger and well-characterized cohorts
are required to establish the true relationship between the gut
THE GUT MICROBIOME AND MENTAL HEALTH 3
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microbiota and depression. Although these studies did not
investigate individuals with anxiety disorders, major de-
pression is highly comorbid with anxiety- and trauma-related
disorders (Elhai et al., 2008; Rytwinski et al., 2013), hence
studies investigating whether similar trends exist in anxiety-
and trauma-related disorders (with and without comorbid
depression) are needed.
The Gut Microbiome as a Therapeutic Target
In light of the vital role of the MGB axis in CNS func-
tioning, strategies aimed at modulating the MGB axis offer an
attractive means of improving mental health outcomes. Pro-
biotics (live, beneficial microorganisms) (Shah, 2007), pre-
biotics (nondigestible food substances) (Parvez et al., 2006),
and synbiotics (combination of probiotics and prebiotics)
(Underwood et al., 2009; Vlieger et al., 2009) have been used
in attempts to modulate the gut microbiome content. The
mechanisms through which probiotics potentially mediate
health benefits are extensive and include several inter-
connected networks, including pathogen displacement (Col-
lado et al., 2008), competition with hostile bacteria for
metabolic interactions (Martin et al., 2010), production of
bacteriocins (Corr et al., 2007), inhibition of bacterial trans-
location (Generoso et al., 2010), enhancement of mucosal
barrier function (Liu et al., 2011), effects on calcium-
dependent potassium channels in intestinal sensory neurons
(Kunze et al., 2009), induction of cannabinoid and opioid
receptors in intestinal epithelial cells (IEC) (Rousseaux et al.,
2007), and modulation of the immune system (Sanders, 2011).
Preclinical investigations
Several probiotic therapies have been studied in animal
models, with mostly Bifidobacterium and Lactobacillus
genera eliciting beneficial effects on anxiety- and depression-
like behaviors (Barrett et al., 2012; Schousboe and Waage-
petersen, 2007), however, only certain strains have shown
positive effects (Dinan et al., 2013). Chronic Bifidobacterium
infantis treatment reduced immune alterations, depressive-
like behavior, and restored noradrenaline concentrations in
the brainstem in a model of early-life stress (Desbonnet et al.,
2010). Lactobacillus helveticus ROO52 improved anxiety-
like behavior and memory dysfunction in naive mice and
mice on a western-style diet (fat 33%, refined carbohydrate
49%) (Ohland et al., 2013). Lactobacillus rhamnosus JB-1
reduced anxiety- and depressive-like behaviors and induced
region-specific alterations in GABA
B1b
mRNA in the brain
(Bravo et al., 2011) and Bifidobacterium longum effectively
normalized anxiety-like behavior in a colitis model (Bercik
et al., 2011b).
In addition, a strain of B. longum, but not L. rhamnosus,
normalized anxiety-like behavior and levels of hippocampal
brain-derived neurotrophic factor (BDNF) induced by Tri-
churis muris infection (nematode thatcauses inflammation and
the appearance of anxiety-like behaviors) (Bercik et al., 2010).
A combined treatment of L. rhamnosus and L. helveticus
reversed stress-induced memory dysfunction in mice infected
with Citrobacter rodentium (Gareau et al., 2011). Another
study found that L. plantarum treatment significantly reduced
anxiety-related behavior and altered serotonergic and
GABAergic neural signaling in an adult zebrafish model.
Serum cortisol and leukocyte patterning revealed that
supplementation with L. plantarum protected against stress-
induced dysbiosis (Davis et al., 2016).
The aforementioned studies illustrate the efficacy of pro-
biotics in mediating changes in the CNS and behavior, and
underscore the strain-specific effects of probiotics. There is,
however, a paucity of preclinical and clinical studies inves-
tigating the effects of prebiotics in anxiety- and stress-related
behaviors. One study investigated the effects of a 5-week
treatment of the prebiotic compounds fructooligosaccharide
(FOS) and galactooligosaccharide (GOS) (shown to increase
relative abundance of microorganisms that are thought to be
beneficial) (Davis et al., 2011; Thompson et al., 2017) on
the levels of BDNF and N-methyl-D-aspartate receptors
(NMDARs) in rat brain (Savignac et al., 2013). Prebiotic
treatment resulted in increased BDNF and NR1 subunit ex-
pression in the hippocampus. GOS treatment resulted in in-
creased hippocampal NR2A subunits, frontal cortex NR1, and
d-serine, as well as plasma d-alanine.
The authors concluded that prebiotic treatment probably
altered the gut microbiota composition, facilitating increased
BDNF expression in the brain, possibly through the in-
volvement of gut hormones. GOS also elicited a stronger
effect on central NMDAR signaling than FOS, suggesting a
stronger proliferative potency of GOS on the microbiota
(Savignac et al., 2013).
Tarr et al. investigated whether prebiotic oligosaccharides
(naturally found in human milk) can inhibit stress-induced
changes in gut microbial composition and attenuate stress-
induced anxiety-like behavior. Exposure to the stressor
resulted in anxiety-like behavior, reduction in immature
neurons in the dentate gyrus, and altered colonic mucosa-
associated microbiota in mice on a standard laboratory diet.
None of these effects was noted in animals that were fed milk
oligosaccharides. These results support the potential role of
prebiotics, potentially through effects on the MGB axis, to
support normal microbial functions and regulate behavioral
responses in the context of a stressor (Tarr et al., 2015).
Another way to alter the composition of the gut micro-
biota is through dietary changes. One study showed that
including 50% lean beef into normal chow significantly af-
fected fecal bacteria composition, compared to mice on a
regular chow diet (Li et al., 2009). Furthermore, this altered
diet and the associated change in microbiota resulted in
improved cognitive parameters and reduced anxiety-like
behaviors (Li et al., 2009), suggesting that dietary inter-
ventions have the ability to alter intestinal microbiota, which
could promote beneficial changes to cognitive abilities.
The field of nutrigenomics has yielded valuable information
on correlations between an individual’s genetic composition
(host genetics) and dietary intake and how nutrition influences
gene expression of the host (Pavlidis et al., 2015, 2016) and in
the era of metagenomics, nutri-metagenomic approaches can
be applied to unravel the interaction between the microbiota,
nutrition, and host in the context of disease and as a therapeutic
target (Dimitrov et al., 2016; Ferguson et al., 2016).
Organisms present in the environment can also alter ho-
meostatic function and behavior in the host. Environmental
bacteria are nonpathogenic microorganisms that inhabit our
surroundings (Rook and Brunet, 2005). Some of these bac-
teria also form part of the ‘‘old-friends’’ or hygiene hypoth-
esis, originally described by Rook et al. (2003), who
proposed that reduced exposure to these ‘‘old friends’’ could
4 MALAN-MULLER ET AL.
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contribute to increased immunoregulatory disorders in indi-
viduals with a suboptimum regulation of Tregs (see the En-
vironmental Microbiome section). One such environmental
bacterium is Mycobacterium vaccae, a nonpathogenic aerobic
soil bacterium found in temperate environments and regarded
as transient commensal (i.e., cannot colonize the digestive
tract) (Gomez et al., 2001). Administration of heat-killed
M. vaccae effectively downregulates symptoms of allergic
inflammation through increased production of IL-10 and IFN-c
by mesenteric lymph node cells and splenocytes (Hunt et al.,
2005). Lowry et al. (2007) showed administration of heat-
killed M. vaccae antigen in mice activated serotonergic neu-
rons in the dorsal raphe nucleus (DR) of the brainstem.
Serotonin metabolism in the ventromedial prefrontal cor-
tex was increased and stress-related emotional behavior re-
duced in the forced swim test. Another study showed that
administration of live M. vaccae reduced anxiety-like be-
haviors and improved performance in the Hebb–Williams
complex maze. It was subsequently hypothesized that the
antigens elicited an effect on the immune system, through
which serotonin pathways were changed following ingestion
of these bacteria (through the Th1 and Treg pathways), re-
sulting in an anxiolytic response and improved performance
in a land maze (Matthews and Jenks, 2013).
More recent research found that M. vaccae-pretreated mice
responded to a larger, more aggressive animal with a more
proactive coping strategy. Furthermore, M. vaccae elicited an
anxiolytic response (Reber et al., 2016).
Although mouse models are very popular for in vivo im-
munological experimentation, there are significant differences
between the two species in immune system development, ac-
tivation, and response to a challenge, in the innate as well as
the adaptive arms (Mestas and Hughes, 2004). It will, there-
fore, be prudent to establish whether a similar immunological
and subsequent anxiolytic effect is present in humans treated
with M. vaccae.
The microbiome and treatment response
The microbiome is not only an attractive therapeutic target
in the treatment of psychiatric disorders but it could also be
involved in treatment response and adverse drug reactions,
which often occur in psychiatric patients. A preclinical study,
using wild-type female C57BL/6J mice, investigated whether
risperidone treatment (known to induce metabolic side ef-
fects) altered the gut microbiome profile and whether this
shift was involved in the metabolic side effects of the drug
(Bahr et al., 2015). As expected, the risperidone-treated mice
exhibited significant weight gain, attributed to reduced en-
ergy expenditure, which was correlated with an altered gut
microbiome. Fecal transplant, as well as transplantation of
only the phage fraction, from risperidone-treated mice to
naive recipients, resulted in a reduction in total resting met-
abolic rate and weight gain in the recipients, as a result of
suppression of nonaerobic metabolism.
This study revealed the role of the gut microbiome in
risperidone-induced weight gain, associated with altered
nonaerobic resting metabolism (Bahr et al., 2015). In addi-
tion, it underscores the importance of taking treatment into
account in case–control studies. Research efforts should be
aimed at determining the role of the microbiome in the re-
sponse to treatment in anxiety- and trauma-related disorders.
Clinical research frontiers
The effects of probiotics on the structure and function of the
human gut microbiota have only been studied with a few spe-
cific bacterial strains; the effects of these treatments on clinical
symptoms remain to be fully elucidated (Sanders et al., 2013).
Tillisch et al. (2013) investigated the effects of probiotics on
brain function in healthy female participants on a 4-week,
chronic probiotic treatment (containing Bifidobacterium ani-
malis subspecies Lactis,Streptococcus thermophiles,Lacto-
bacillus bulgaricus,andLactococcus lactis subspecies Lactis).
Reduced brain response to the emotional faces attention task,
particularly in sensory and interoceptive regions, was evident in
participants who ingested the probiotic (4-week treatment).
Probiotic ingestion was also associated with changes in mid-
brain connectivity, however, no differences in mood were ob-
served between the treatment groups (Tillisch et al., 2013).
This study illustrated that treatment with this particular
probiotic affected activity of brain regions that control central
emotional and sensory processing.
In a randomized, double-blind, placebo-controlled trial, a
Lactobacillus-containing probiotic, decreased anxiety but not
depression symptoms in patients with chronic fatigue syndrome.
Increased relative abundance of Bifidobacterium and Lactoba-
cillus was also detected in stool samples of the treatment group
(Rao et al., 2009). However, this study used culture techniques
to determine the microbial composition of stool samples,
thereby limiting the findings to only culturable microbes. An-
other study investigated the effects of a Lactobacillius-and
Bifidobacterium-containing probiotic on mood and cognition in
healthy individuals and found that the percentage decrease in
the total Hospital Anxiety and Depression Scale score was
greater in the probiotic-treated group, but that there was no
difference in the subscale scores (Messaoudi et al., 2011).
A study by Diop et al. (2008) investigated the effects of a
probiotic preparation (L. acidophilus and B. longum)on
stress-induced symptoms in individuals affected by chronic
stress. Abdominal pain and nausea/vomiting symptoms were
significantly reduced in the probiotic group, however, phys-
ical and psychological symptoms were unaffected (Diop
et al., 2008). A 2-week, controlled trial of Clostridium bu-
tyricum treatment resulted in significantly decreased Ha-
milton Anxiety Scale scores, as well as lower serum CRH
levels before laryngeal cancer surgery compared to the
placebo-treated sample (Yang et al., 2016).
A double-blind, placebo-controlled pilot study investigated
the effects of an 8-week treatment of Lactobacillus casei strain
Shirota (LcS) on psychological, physiological, and physical
stress responses in medical students undertaking an authorized
nationwide examination for promotion. One day before the
examination, salivary cortisol and plasma L-tryptophan levels
were significantly increased in the placebo group only, which
was associated with a significant increase in anxiety. The
probiotic group had significantly higher fecal serotonin levels
compared to the placebo group, 2 weeks after the examination.
Moreover, the subjects receiving probiotics experienced
significantly fewer physical symptoms compared to the
placebo group during the pre-examination period and the
intervention period. These results suggest that daily con-
sumption of fermented milk containing LcS by healthy sub-
jects during stressful time periods may decrease the onset of
physical symptoms (Kato-Kataoka et al., 2016).
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A recent study applied a double-blind design to investigate
whether an 8-week-long treatment with a probiotic prepara-
tion, containing Lactobacillus helveticus and B. longum,hadan
effect on mood, stress, and anxiety in an antidepressant-naive
sample of 79 individuals, selected for low mood (based on self-
report data) (Romijn et al., 2017). The study found no evidence
that the probiotic formulationhad a positive effect on mood, or
in moderating the levels of inflammatory and other biomarkers.
The authors hypothesized that the severity, chronicity, or
treatment resistance of their sample may have contributed to
the lack of effect on mood symptoms (Romijn et al., 2017).
Another double-blinded, randomized, placebo-controlled
clinical trial evaluated the effect of a 12-week treatment of
Lactobacillus reuteri on digestive health and well-being in
290 older adults (>65 years). L. reuteri elicited no persistent
significant effects on the primary or secondary outcomes of
the study and the RCT failed to show a consistent improve-
ment in digestive health, well-being, stress, or anxiety fol-
lowing a 12-week daily probiotic supplementation containing
L. reuteri (O
¨stlund-Lagerstro
¨m et al., 2016).
These studies illustrate there is some potential for pro-
biotics to influence CNS functioning and behavior. Further-
more, these results also underscore probiotic strain-specific
effects. Probiotic trials require careful design as several
factors may influence the outcome of such interventions,
including confounding factors and matching of patients and
controls. Comparing the results of these studies is compli-
cated by the between-study differences, such as differences in
probiotic strains, treatment duration, outcome measures, as
well as gender and age distribution. In addition, samples sizes
are relatively small and larger cohorts would be required to
verify these findings. Well-designed trials in cohorts with
anxiety- and trauma-related disorders will shed more light on
the potential for pre- and probiotic treatments for the relief of
symptoms in these patients.
Approaches to Studying the Functional Microbiome
While the above review aimed to examine the ways in
which the gut microbiome might play a transformative role
for mental health pathogenesis and mental health mainte-
nance, we think that the following methodologies to study the
microbiome are in order for the interested reader who wishes
to take on this new line of research in biological psychiatry
and integrative holistic medicine.
The majority of studies discussed thus far used 16S rRNA
sequencing to understand the taxonomic distribution and
diversity of enteric microbial communities in health and
disease. However, to progress from mere phylotyping to
functional network analyses, and to identify proteins and
metabolites produced by the microbial communities, several
meta-omics approaches can be utilized.
Metagenomics
Shotgun metagenomics involves sequencing the collection
of genomes present in an ecosystem (Handelsman et al.,
1998) and allows characterization not only of the taxonomic
composition but also of the functional metabolic potential of
the microbiota and reconstruction of microbial metabolic
pathways. Although sequencing full genomes is more costly
than 16S sequencing, metagenomics provides a wealth of
information about the gut microbiota and its functions.
Metagenomic analyses in healthy individuals from large
population-based studies paved the way for future studies in a
clinical context. The MetaHIT and HMP projects revealed
similarities in functional gene profiles among individuals
despite significant variation in taxonomic composition (Hu-
man Microbiome Project Consortium, 2012; Qin et al., 2010).
This indicates the presence of a functional core microbiome
with conserved molecular activities, more than a taxonomic
core microbiota, which would consist of a conserved group of
phylotypes. An updated catalog of the genes in the human gut
microbiome containing data from all major large-scale me-
tagenomic projects has recently been released, containing
*10 million genes (Li et al., 2014).
Zheng et al. (2016) transplanted the gut microbiota from
major depression disorder (MDD) patients and healthy con-
trols to germ-free mice, and after observing behavioral dif-
ferences in the mice (including higher anxiety-like behavior
in the open field test in mice receiving microbiota from MDD
patients), performed metagenomic sequencing on cecum
samples. Most of the discriminating genes were related to
carbohydrate and amino acid metabolism; mice with de-
pression microbiota had, for example, increased starch,
sucrose, and glutamate metabolic potential but reduced
tryptophan and tyrosine synthesis potential. This highlights
the importance of examining the functional potential of the
microbiota to obtain insights into how the gut microbiome
influences disease.
Metatranscriptomics
In metatranscriptomics, the RNA transcript pool expressed
by a microbial community is analyzed using RNAseq. The
presence of a gene in a metagenome does not guarantee its
expression, and therefore, metatranscriptomics complements
metagenomic data by identifying the genes expressed by the
microbial community. It provides information on the active
microbial processes at a given time point and allows changes
in microbial gene expression over time and in response to
perturbations, such as antibiotic usage, to be monitored. The
main limitation of metatranscriptomics is that, as the mRNA
transcript pool changes rapidly, it is uncertain how well the
recovered RNA from stools represents the processes that
were active in the ileum and colon, and not due to sampling-
induced stress conditions. An interesting approach is dual
RNAseq, where the host and microbiota transcriptomes are
analyzed together.
The application of metatranscriptomics to study micro-
biota gene expression in health and disease is still rather
limited. The importance of taking the metatranscriptome into
account when performing microbiota studies was illustrated
in a study of the human gut microbiota in 10 healthy indi-
viduals. They showed that transcripts for carbohydrate me-
tabolism, energy production, and synthesis of cellular
components were overrepresented compared to what would
be expected based on their gene copy number in the meta-
genome, while activities such as lipid transport metabolism
were underrepresented in the metatranscriptome compared to
the metagenome (Gosalbes et al., 2011).
Metaproteomics
Metaproteomics is a high-throughput approach to identify
the entire protein pool within complex, microbial habitats. It
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provides information on the metabolic processes that are
active, and reveals how they are affected by perturbations,
such as inflammation or disease conditions. Metaproteomics,
therefore, provides a more direct insight into the functional
composition of the microbiota compared to metatran-
scriptomics, as mRNAs are subject to posttranscriptional
modification. In addition, the metaproteome is more stable
than the metatranscriptome and thus less prone to sampling-
induced alterations.
Metaproteomics involves cellular lysis and enzymatic di-
gestion of all accessible proteins in a particular sample to
produce peptide fragments that are separated by liquid
chromatography and subjected to tandem mass spectrometry
(LC-MS/MS). The mass and spectra of the peptides are
subsequently quantified and compared to reference protein
databases (predicted from genomic sequence information).
Although metaproteomics is a powerful tool to characterize
the function of complex microbial communities, some factors
need to be taken into account, such as host-specific biases and
choice of sequence databases for protein identification [the
reader is referred to a review by Tanca et al. (2016) regarding
investigation of variables concerning database construction
and annotation and how it impacts metaproteomic results].
Metaproteomic analyses from fecal samples retrieve an
important proportion of human proteins, but filtering strate-
gies have been put forward to fractionate microbial cells from
human cells and enhance microbial protein identification
(Xiong et al., 2015). Tanca et al. (2016) provide guidelines on
how to design gut microbiota studies to perform metapro-
teomic data analysis. They encourage the use of multiple
databases and annotation tools.
Metaproteomic investigations of gut microbial communi-
ties are currently relatively limited; only a few studies have
investigated the metaproteome in humans, including inves-
tigations in a healthy adult, monozygotic twin pair (Ver-
berkmoes et al., 2009), a longitudinal investigation in healthy
female participants (Kolmeder et al., 2012), Crohn’s disease
patients (Erickson et al., 2012; Juste et al., 2014), obese in-
dividuals (Ferrer et al., 2013), the infant gut (Xiong et al.,
2015), and the preterm infant gut (Brooks et al., 2015; Young
et al., 2015).
By using clusters of orthologous groups (COGs) to catalog
identified proteins (Tatusov et al., 2000), Verberkmoes et al.
(2009) found an uneven distribution of relative abundances of
each COG in the metaproteome relative to metagenome. The
metaproteome was enriched in proteins involved in transla-
tion, energy production, and carbohydrate metabolism, while
proteins involved in cell division, inorganic ion metabolism,
cell wall and membrane biogenesis, and secondary metabo-
lite biosynthesis were less present than in the metagenome.
Similar to what was observed using metatranscriptomics
(Gosalbes et al., 2011), the findings highlight the fact that in
situ functional activities (as measured by metaproteomics)
can be distinct from predictions from metagenome informa-
tion alone (Kolmeder et al., 2012; Verberkmoes et al., 2009).
Metabolomics
Metabolomics involves the metabolic profiling of biolog-
ical fluids, such as serum, urine, or fecal water, using spec-
troscopic techniques to enable either global metabolite
analysis (untargeted approach) or the measure of a selected
metabolite (targeted approach). MS-based techniques, often
preceded by separation techniques such as gas chromatog-
raphy or high-performance/ultra-performance liquid chro-
matography, facilitate the discrimination of metabolites
based on their mass to charge (m/z) ratio. Existing databases
of m/z values, such as METLIN, are interrogated to identify
metabolites (Smith et al., 2005). Another available method
that is particularly popular for high-throughput studies is that
of
1
H nuclear magnetic resonance (
1
H NMR) spectroscopy.
1
H NMR uses chemical shift (i.e., the resonant frequencies of
atomic nuclei relative to a reference standard) after per-
turbation with radiofrequency pulses to identify chemical
structures (Holmes et al., 2011).
The potential of metabolomics was illustrated in a gnoto-
biotic mouse study that colonized mice with a 15-species
model human gut microbiota. Following introduction of a
fermented milk product (containing five sequenced bacterial
strains), no significant changes in the metagenome were ob-
served, however, metatranscriptomics of fecal samples and
MS of urinary metabolites indicated the effects of the milk
product on the expression of microbial enzymes involved in
carbohydrate metabolism (McNulty et al., 2011).
In light of the observed discrepancies between effects on
microbial community structure relative to effects on gene
expression and metabolism, the need for complementary
approaches beyond 16S-based phylotyping is emphasized.
Although bioinformatically challenging, multivariate compu-
tational modeling allows for the integration of metagenomic,
metatranscriptomic, and metaproteomic/metabolomic profiles
to provide insight into microbial functionality. The application
of such approaches is quite challenging and would require the
concerted efforts of experienced bioinformatic specialists,
biostatisticians, mathematicians, clinicians, and molecular bi-
ologists to unravel the role of the functional microbiome in
disease. In future, these promising strategies can be used to
obtain a holistic overview of the role of the functional micro-
biome in anxiety- and trauma-related disorders.
Interactors of the Gut Microbiome
Other factors that shape the gut microbiome should also be
considered when interpreting microbiome data, especially in
the context of complex disorders. These include, but are not
limited to, host factors, including host genome (Davenport,
2016), host epigenome (Liu et al., 2016a), external factors such
as environmental microbiome, and constituents of the gut
microbiota, such as the gut virome (Ogilvie and Jones, 2015)
and parasitic gut infections (Molloy et al., 2013) (Fig. 1).
The host genome
Results from murine models showed that host genotypes
play a role in shaping microbiota composition (Bongers et al.,
2014; Campbell et al., 2012; Hildebrand et al., 2013). Turn-
baugh et al. (2009) and Yatsunenko et al. (2012) investigated
whether this was also the case in humans. They investigated
the heritability of the gut microbiome using monozygotic and
dizygotic twins, while controlling for environmental factors.
Both studies concluded that there were no statistically signif-
icant differences between monozygotic and dizygotic twins,
however, both studies were underpowered (small sample si-
zes). Larger follow-up studies of these twin data sets revealed
that host genetic variation did have an influence on microbial
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composition. They found Christensenellaceae to be the most
heritable taxon, while Bacteroidetes was more susceptible to
environmental influences (Goodrich et al., 2014, 2016). They
also showed that highly heritable taxa were associated with
higher levels of temporal stability, emphasizing the impor-
tance of these taxa to the host (Goodrich et al., 2016).
Folseraas et al. (2012) examined the role of host genetic loci
associated with primary sclerosing cholangitis (PSC) and the
effect on the biliary microbial community composition in PSC
patients. They showed that secretor status and genotype of the
Fucosyltransferase 2 (FUT2) gene (rs601338) significantly
influenced biliary microbial community composition, as it was
associated with a significant increase in the abundance of
Firmicutes and significant decrease of Proteobacteria. Fur-
thermore, decreased alpha diversity was noted in the hetero-
zygous state compared to both homozygous genotypes.
Knights et al. (2014) showed that host genetics influence the
microbiome in inflammatory bowel disease (IBD) when they
detected a significant association between nucleotide binding
oligomerization domain containing 2 (NOD2) risk allele count
and increased relative abundance of Enterobacteriaceae.
This finding was confirmed in two additional cohorts.
These two studies emphasized the impact of the genotypes of
these two genes and their associated bacterial taxa alterations
as risk factors for PSC and IBD. These results suggest
complex interactions between host genetics, subsequent al-
tered functional pathways, and the composition of the mi-
crobiome. These studies were able to identify genome–
microbiome associations in diseased cohorts using a candi-
date gene approach; however, recent studies have identified
genome-wide, statistically significant genetic loci that influ-
ence gut microbiota composition (Blekhman et al., 2015;
Bonder et al., 2016; Davenport, 2016; Goodrich et al., 2016;
Wang et al., 2016).
Several of these studies found that specific bacterial taxa,
such as Bifidobacterium, are inheritable and correlate with
specific host genotypes (Blekhman et al., 2015; Davenport,
2016; Goodrich et al., 2016). Goodrich et al. (2016) linked the
lactase (LCT) gene locus, which encodes the enzyme lactase
that hydrolyzes lactose, to Bifidobacteria, which metabolizes
lactose in the gut. They discovered that lactase ‘‘non-
persisters’’ (inactive lactase enzyme) harbored lower levels of
FIG. 1. The human interactome encompasses the gut microbiome; its genes, proteins, and metabolites; and host factors
and external environmental factors that concomitantly shape the microbiome and influence health and disease. The MGB
axis contains pathways through which the microbiota influences the CNS, cognition, and mood. Furthermore, microbially
produced proteins and metabolites can influence the host stress response system, CNS functioning, and the host epigenome
and transcriptome. Traumatic experiences and stress can also alter the gut microbiota via HPA axis dysregulation and
subsequent release of stress hormones or neurotransmitters that influence gut physiology, microbiota habitat, and
composition and bacterial gene expression. CNS, central nervous system; HPA, hypothalamic–pituitary–adrenal; MGB,
microbiota–gut–brain.
8 MALAN-MULLER ET AL.
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Bifidobacterium compared to lactase ‘‘persisters’’ (active
lactase enzyme) possibly due to higher lactose levels in the gut
of lactase nonpersisters.
Similarly, Bonder et al. (2016) found an association be-
tween a functional LCT SNP and the Bifidobacterium genus.
They also found nine genetic loci associated with microbial
taxonomies and 33 loci with microbial pathways, and re-
ported on associations between bacterial taxa and metabolic
loci, suggesting that the gut microbiota could be a mediating
factor in the link between host genetics and immunological
and metabolic phenotypes (Bonder et al., 2016). Blekhman
et al. (2015) and Davenport (2016) found that host genetic
variation in immunity-related pathways, such as IL-2, is
correlated with microbiome composition, and host genes and
variants that are correlated with microbiome composition are
enriched in genes associated with complex diseases that have
been linked to the microbiome (such as irritable bowel syn-
drome (IBS) and obesity-related diseases).
A recent study found genome-wide significant associations
between gut microbial characteristics and several host genetic
factors, including the vitamin D receptor (VDR) gene. They
calculated that, in total, the host genetic loci contributed to
10.43% of bdiversity and nongenetic factors (including age,
sex, BMI, smoking status, and dietary patterns) explained 8.87%
of the variation in the gut microbiome. They were also able to
replicate genetic associations reported in previous studies (such
as FUT2,NOD2,andLCT), but found that they contributed less
to the overall microbial variation (Wang et al., 2016).
Future studies could investigate combined host genome–
microbiome data in anxiety- and trauma-related disorders, using
candidate gene and, given sufficient sample numbers, genome-
wide association study (GWAS) approaches to shed more light
on this complex link between host genome and microbiome.
The host epigenome
Epigenetics literally translates to ‘‘outside conventional
genetics’’ and it investigates the stable alterations in gene
expression not attributable to changes in DNA sequence
(Bjornsson et al., 2004). Epigenetic processes include DNA
methylation, posttranslational modification of histone pro-
teins, genomic imprinting, and noncoding RNAs (including
microRNA [miRNA], small interfering RNA, and long
noncoding RNA).
miRNAs are a class of small, noncoding RNAs that epi-
genetically modulate gene expression. A recent study dis-
covered that host fecal miRNAs, produced by the gut
epithelial and Hopx
+
cells, regulate bacterial gene expression
and growth (Liu et al., 2016a). miRNAs are usually synthe-
sized in the nucleus and processed in the cytoplasm where
they perform their function.
However, there is evidence that miRNAs exist in extra-
cellular compartments and circulate in body fluids (Weber
et al., 2010). Abundant levels of miRNAs have been detected
in mouse and human fecal samples (Ahmed et al., 2009; Link
et al., 2012; Liu et al., 2016a). Liu et al. (2016a) showed that
extracellular fecal miRNAs are mainly produced by the IEC
and Hopx
+
cells and that fecal miRNAs can enter bacteria and
regulate bacterial gene transcripts and directly affect gut
bacterial growth. In addition, they illustrated that deficiency
of IEC miRNAs increases the dissimilarity of the gut mi-
crobiota and alters intestinal barrier integrity. Transplanting
wild-type fecal miRNAs in IEC miRNA-deficient mice re-
stored the fecal microbes and rescued dextran sulfate sodium-
induced colitis in a colitis animal model.
The authors explained that this miRNA-mediated bacterial
regulation was different from traditional miRNA regulation
in eukaryotic cells that mainly result in posttranscriptional re-
pression (including mRNA cleavage, destabilization, and a re-
duced translation efficiency) (Bartel, 2009; Fabian et al., 2010).
In this case, the regulation of bacterial targets by host miRNAs
extended to rRNA (16S rRNA) and ribozyme (RNaseP), and
the effect included a decrease, as well as enhancement of the
transcripts. The mechanism through which miRNAs regulate
gene expression and affect bacterial growth probably depends
on the function of the target gene (Liu et al., 2016a).
Their results highlight the role of host fecal miRNAs in
targeting and regulating the gut microbiota and the possibility
of using miRNAs as a tool to manipulate the microbiome to
benefit the host. Such investigations can easily be performed
in human stool samples to determine how fecal miRNAs
regulate bacterial gene expression and growth, and should be
performed to determine whether particular miRNA profiles
can be associated with a dysregulated microbial composition
in the context of psychiatric disorders.
The microbiome also has the ability to alter certain host
epigenetic processes (Paul et al., 2015). Bacteria can pro-
duce epigenetically active metabolites such as folate, buty-
rate, and acetate, and therefore have the ability to influence
host DNA methylation patterns. For instance, folate (pro-
duced by Bifidobacterium spp., among other bacterial
genera) is a methyl donor and is crucial for the production of
S-adenosylmethionine, which in turn is a methyl-donating
substrate for DNA methyltransferases (Hesson, 2013).
Histone acetylation entails the transfer of an acetyl group
from acetyl coenzyme A (acetyl-CoA) to lysine residues,
with the subsequent production of CoA (Roth et al., 2001).
The histone acetylation process is regulated by the tricar-
boxylic acid (TCA) cycle and acetylation is primarily asso-
ciated with transcriptional activation, due to increased
accessibility of nucleosomal DNA to transcription factors.
Histone deacetylases (HDACs) remove acetyl groups from
lysine residues. The gut microbiome produces short-chain
fatty acids that are used in the production of ATP via the TCA
cycle. In addition, some of the metabolites produced by the
gut microbiome, such as butyrate and propionate, can inhibit
HDACs, thereby influencing the histone acetylation process
and ultimately transcription (Paul et al., 2015).
Methods such as DNA methylation arrays, bisulfite se-
quencing (Kurdyukov and Bullock, 2016), and chromatin im-
munoprecipitation (ChIP) microarrays (ChIP-chip) (Ren et al.,
2000) map DNA methylation, functional status of DNA-binding
proteins, histone modifications (Robyr and Grunstein, 2003),
and nucleosome distribution (Ozsolak et al., 2007) on a global
scale. These techniques are routinely used and can be incorpo-
rated into microbiome studies to investigate whether the abun-
dance of certain metabolite-producing bacteria is associated
with, for instance, altered DNA methylation or histone profiles,
which, in turn, could influence host gene expression profiles.
Despite these indications, it is evident that more research is
warranted into the intricate relationship between the host
epigenome and gut microbiome, how they influence each
other, and their impact on the evolution and course of disease
(Fig. 1).
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Environmental microbiome
Although consideration of a role for the environmental
microbiome, either the microbiome of outdoor environments
or the microbiome of the built environment (MoBE), is in its
infancy, there is growing evidence that the environmental
microbiome may play a role in determining mental health
outcomes. Although outside the scope of this review, this
emerging field has been extensively reviewed (Hoisington
et al., 2015; Lowry et al., 2016; Stamper et al., 2016) and can
be included in future microbiome-interactome models to
better understand health and disease (Fig. 1).
Constituents of the gut microbiota
The gut virome. The gut virome consists of viruses, or
virus-like particles, that coexist with bacteria in the gut.
Viruses are at least 10 times more abundant in the human
body than the microbes that form part of the bacterial mi-
crobiome (Mokili et al., 2012). The virome includes viruses
that infect host cells or other organisms (such as bacterio-
phages and plant viruses) as well as virus-derived elements in
our chromosomes. Bacteriophages are prokaryotic viruses
that infect bacteria and alter their metabolism and replication
(Breitbart et al., 2003, 2008; Minot et al., 2011; Reyes et al.,
2010). Bacteriophages have the ability to facilitate gene
transfer between strains and species (transduction) and
thereby influence community function.
Phages can also confer some of their crucial functional
attributes to their bacterial hosts, such as the production of
virulence factors and toxins as well as genes that provide
metabolic flexibility (Bru
¨ssow et al., 2004; Fuhrman, 1999;
Suttle, 2007; Wommack and Colwell, 2000). The phage–host
relationship is a dynamic coevolutionary interaction, which
forms an integral part in the evolution of the bacterial hosts
(Paterson et al., 2010). Phages are therefore considered as a
strong driving force of ecological function and evolutionary
change in prokaryotes (Koskella and Brockhurst, 2014). Al-
though a comprehensive discussion of the phage–host rela-
tionship is beyond the scope of this article, the reader is
referred to an insightful review by Ogilvie and Jones (2015).
Yolken et al. (2014) detected DNA homologous to the
chlorovirus, Acanthocystis turfacea chlorella virus 1 (ATCV-1),
in 43.5% of the oropharyngeal samples of their healthy co-
hort. Chloroviruses infect certain eukaryotic green algae but
have never been shown to infect humans or to be part of the
human virome. Individuals that harbored the ATCV-1 DNA
showed a significant decrease in the performance on cogni-
tive assessments of visual processing and visual motor speed.
Further investigations in a mouse model showed that in-
oculation of mice with ATCV-1 DNA resulted in decreased
performance in several cognitive domains. Exposure to
ATCV-1 DNA also induced altered gene expression profiles
in the hippocampus in pathways related to synaptic plasticity,
learning, memory, and immune response to viral exposure.
These results implicate immune response as a possible
mechanism underlying the cognitive deficits to ATCV -1.
The authors hypothesized that immune activation resulted
in proinflammatory cytokine secretion, subsequently affect-
ing neuronal functioning, which in turn resulted in behavioral
abnormalities. Shared and unique profiles of cytokine upre-
gulation have been shown for various microbial infections
(e.g., Borna virus vs. Toxoplasma), and therefore, unique
signatures of cytokine expression might help to explain dif-
ferential neurobehavioral outcomes of different microbial
infections (Stewart et al., 2015).
Investigations into the role of the virome in psychiatric
disorders are very limited. One study compared the bacte-
riophage genomes in the oral pharynx of individuals with
schizophrenia to those of control individuals (Yolken et al.,
2015) and found that Lactobacillus phage phiadh was sig-
nificantly enriched in individuals with schizophrenia com-
pared to controls. Phage phiadh was also associated with an
increased prevalence of comorbid immunological disorders
in individuals with schizophrenia.
In addition, individuals taking valproate had no detectable
levels of Lactobacillus phage phiadh. Interestingly, valproate
was previously shown to alter the gut microbiome and to change
levels of microbial metabolites in an animal model of autism (de
Theije et al., 2014). The mechanisms through which Lactoba-
cillus phage phiadh is associated with schizophrenia and co-
morbid immunological conditions are not clear; however, the
authors speculated that Lactobacillus phage phiadh probably
modifies the level of its host bacteria, Lactobacillus gasseri,with
subsequent effects on the host immune systems. Lactobacillus
gasseri modulates the immune system by modifying the func-
tion of enterocytes, dendritic cells, and components of innate
immunity (Luongo et al., 2013; Selle and Klaenhammer, 2013).
The authors concluded that the therapeutic altering of
bacteriophages could provide new means of treating
schizophrenia and some of its comorbid immunological
diseases (Yolken et al., 2015).
The relationship between changes in bacterial and viral
communities is a novel area of investigation. The micro-
biome and the virome are affected by similar environmental
stimuli, evidenced by covariation of the virome with the
bacterial microbiome in response to diet (Minot et al., 2011).
The virome also plays a crucial role in the regulation of in-
testinal immunity and homeostasis (Norman et al., 2015).
The virome can induce continuous, low-level immune re-
sponses without triggering any apparent symptoms. It is
therefore plausible that variations within the systemic and
local gut virome could influence the host gut microbiome as
well as host immunophenotype (Virgin, 2014) and ultimately
affect CNS functioning (Fig. 1).
Parasitic infections. The mammalian gut is not only
populated by microscopic members of the microbiota but
could also include larger organisms, such as parasitic nema-
todes or worms. A study of a murine model infected with T.
muris showed that these parasites compete for nutrients in the
intestines of infected animals and can directly interact with
bacterial members of the microbiota during the parasitic life
cycle to promote hatching of parasite eggs. These parasites
also influence immune functioning through factors such as
excretory–secretory products, which modulate cytokine pro-
duction, immune-cell recruitment, basophil degranulation, and
interfere with toll-like receptor signaling (Hayes et al., 2010).
Interestingly, one of the risk factors for the development of
schizophrenia and a contributor to dysbiosis and altered im-
mune reactivity is infection with the parasite Toxoplasma
gondii (Molloy et al., 2013; Torrey et al., 2007). Causal
mechanisms linking infection with disease risk are speculative,
but could, in part, be attributed to the tachyzoites forming cysts
in the brain and the establishment of a chronic infection
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(Carruthers and Suzuki, 2007). Two meta-analyses found el-
evated levels of T. gondii antibodies in patients with schizo-
phrenia compared to healthy controls (Torrey et al., 2007,
2012). Infection by T. gondii results in gastrointestinal in-
flammation, which subsequently triggers an innate immune
reaction, including activation of complement C1q; C1q in turn
plays a role in synaptic pruning (Chu et al., 2010).
Since pathogen proteins closely resemble those of humans,
the immune attack directed toward the pathogen may also
result in the development of pathogen-derived auto-
antibodies. In addition, this inflammation influences endo-
thelial barrier permeability and could therefore facilitate
translocation of gut bacteria into systemic circulation, re-
sulting in further dysbiosis and immune reactivity. T. gondii
also induces major perturbations on gut microbiota compo-
sition (Molloy et al., 2013). These findings suggest that in-
dividuals living in areas with higher exposure to such
parasites could have significantly different structural and
functional configurations of gut-associated immune systems
compared to individuals without these exposures. Such ef-
fects should be taken into account when interpreting micro-
biome findings from populations with higher exposure,
especially in psychiatric patients.
Future Perspectives
There have been major advances in the field of microbiome
research, including large-scale population-based studies,
such as the Human Microbiome Project, MetaHIT, Lifelines-
DEEP, and the Flemish Gut Flora Project, which have
identified factors that are linked to gut microbiota composi-
tion. Studies have reported that ethnicity and lifestyle could
influence gut microbial profiles (Chong et al., 2015; Liu et al.,
2016b), and therefore, future studies should include more
population-based studies in ethnically diverse groups to
clarify this association and to determine the composition of a
healthy gut microbiome in a particular population.
Furthermore, certain pitfalls should be taken into account
when performing microbiome analyses. These include ex-
perimental design, such as selection of 16S rRNA target re-
gion and sequencing platform (Tremblay et al., 2015), sample
collection, storage (Vogtmann et al., 2017) and extraction
methods, inclusion of positive and negative controls (Weiss
et al., 2014), taking cage effects into consideration in animal
models (Hildebrand et al., 2013), and the use of discovery and
validation cohorts (Forslund et al., 2015; Sabino et al., 2016)
as well as robust data analyses that incorporate power cal-
culations (Kelly et al., 2015), appropriate reference genome
databases (Balvoc
ˇi
ut_
e and Huson, 2017; Forster et al., 2016),
correction for multiple comparisons (Benjamini and Hoch-
berg, 1995), and confounders (Falony et al., 2016).
Meta-omic technologies (such as metatranscriptomics,
metaproteomics, and metabolomics, as discussed earlier) are
increasingly used in the laboratory and enable us to interro-
gate the taxonomic and functional composition of the mi-
crobiome, as well as protein and metabolite synthesis to
determine their role in health and disease.
However, these techniques are not without challenges,
which mirror those discussed for microbiome analyses (in-
cluding sample collection, storage, processing, data analysis
strategies, and use of appropriate databases and analysis
pipelines). In addition, there is a need for meta-information
(i.e., databases of information on sample origin, collection and
storage, and experimental and analytical conditions) (Weck-
werth and Morgenthal, 2005) and the integration of multiple
data sets arising from multi-omic outputs (Abram, 2015), for
example, byusing network-based approaches, such as the 48-h
multi-omic pipeline developed by Quinn et al. (2016) [also
refer to review by Aguiar-Pulido et al. (2016)].
Recent research has also indicated a role of the host in
shaping the gut microbiome content, including host genetic
variation and host epigenetic factors, as well as the host vir-
ome. Furthermore, the effect of exposure to environmental
microbes and parasitic gut infections also plays a role in
modulating the immune system as well as the gut microbiome.
The feasibility of host-genome-epigenome-microbiome in-
vestigations lie within our grasp, as evident in the literature
presented in this review; however, the combined investigation
in a single cohort is yet to be endeavored.
Challenges include the high cost of multi-omic investigations
in large, well-characterized cohorts as well the need for so-
phisticated bioinformatic pipelines and mathematical models
to integrate output from several omic data sets. Moreover, in
an attempt to attain a whole systems overview (including the
functional microbiome as well as host and environmental fac-
tors that influence and interact with the microbiome), we re-
quire mathematical models and statistical approaches to enable
the meaningful biological interpretation of multi-omic outputs.
Animal models provide strong evidence for the role of the gut
microbiome in regulating anxiety- and stress-related pheno-
types, and the potential to target the gut microbiome to alleviate
anxiety. However, few human studies of the microbiome in
anxiety- and stress-related disorders have been published. As
effective probiotic treatments in animal models have not trans-
lated well to humans, gut microbiome studies of human subjects
with anxiety disorders are warranted, before we can even at-
tempt to understand the complexities of the functions, genes,
pathways, proteins, and metabolites of the gut microbiome.
Furthermore, to show causation and to understand the
mechanisms through which dysbiosis influences disease, lon-
gitudinal studies are needed, preferably with birth cohorts or
pre- and postdeployment cohorts, to track disease progression
before onset. Such study designs would also enable the inves-
tigation of the role of the gut microbiome in treatment response.
Probiotic intervention studies in humans suggest that the
gut microbiome could be targeted to alleviate anxiety- and
stress- or trauma-related outcomes. However, interpretation
of these findings is impeded by several limitations, in-
cluding small sample sizes and confounders such as different
populations/ethnicities, gender bias, different probiotic strains
(or combinations of strains) at different doses and for different
treatment durations, and differences in outcome measure-
ments. More concerted efforts to recruit large cohorts and
adopt standardized approaches could yield more insights into
how the gut microbiome can be targeted to alleviate anxiety-
and stress- or trauma-related outcomes.
Future studies should also address aspects such as the host’s
baseline microbiota composition and whether it predicts re-
sponse to the probiotic treatment, possible effects of the pro-
biotic vehicle, dose/response effects, and the stability of the
treatment response. These studies should preferably use a lon-
gitudinal design, even beyond the standard treatment duration
in clinical trials, to fully assess the long-term effects of mi-
crobial manipulation on behavior. Once we understand how the
THE GUT MICROBIOME AND MENTAL HEALTH 11
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microbial composition is associated with disease, the afore-
mentioned recommendations can be used to design more tar-
geted microbial therapies in the near future. Once successful
therapies have been designed and proven to be useful, future
investigations could also utilize imaging data, such as fMRI
and spectroscopy, to measure functional brain changes pre- and
post-prebiotic, synbiotic, probiotic, or antibiotic interventions.
Another complicating factor in the investigation and under-
standing of anxiety- and trauma-related disorders is phenotypic
heterogeneity and the high prevalence of psychiatric and
medical comorbid disorders, including depression (Elhai et al.,
2008; Rytwinski et al., 2013) and metabolic diseases (Kahl
et al., 2015; Meurs et al., 2016). Anxiety- and trauma-related
disorders, and their common comorbidities, have been associ-
ated with increased inflammation (Zass et al., 2017), suggesting
that the gut microbiome could play a role in comorbidity.
Careful study designs, using large cohorts and inclusion of
appropriate controls, will be required to understand the un-
derpinnings of comorbidity. Furthermore, since a plethora of
environmental variables has been shown to alter the micro-
biome, the collection of metadata should be extensive and
thorough and variables should be tested for association with
microbial composition to correct for the effects of con-
founding variables during data analysis.
This review focused on the gut microbiome in the context
of the human interactome. However, it should be noted that
other microbial habitats include the mouth, skin, urogenital
tract, and vagina, and that these could also play an intricate
role in the human interactome. As an example, afferent sig-
naling of bronchopulmonary immune activation to the CNS
has been described (Hale et al., 2012; Lowry et al., 2016).
Immune signals in the bronchopulmonary system reach the
brain via the vagus nerve and sympathetic nerves, much like
the afferents from the gastrointestinal system, but the specific
targets of the bronchopulmonary afferents in the brain are
distinct from the specific targets of the gastrointestinal af-
ferents in the brain (Hale et al., 2012).
Thus, peripheral signals arising from the microbiome in
the bronchopulmonary system are not redundant with those
arising from the gastrointestinal system, and may have un-
ique cognitive and affective functions. Similar arguments
could be made for the skin (Belkaid and Segre, 2014) and oral
microbiomes (Castro-Nallar et al., 2015). This review,
however, focussed on the gut microbiome due to its estab-
lished involvement in the MGB and its implications for
anxiety- and stress-related disorders. Future studies could
further investigate the role of the microbiome in other body
sites in the context of anxiety- and stress-related disorders.
Great efforts are being made to discover the missing
heritability in complex disorders, such as anxiety- and trauma-
related disorders. Investigations of gene–gene and gene–
environment interactions (Nugent et al., 2011), copy number
variations (Bersani et al., 2016; Fung et al., 2010; Kawamura
et al., 2011), and epigenetic factors (Cappi et al., 2016; Kim
et al., 2017) have yielded some additional insights into the
molecular etiology of these disorders. However, renewed in-
terest and large-scale focus on microbial communities, in-
vestigation of the human interactome,which includes the (gut)
microbiome composition, its genes, proteins, and metabolites,
as well as host and environmental factors that shape the
microbiome, have the potential to unravel the etiology of
complex disorders and direct novel treatment strategies.
Acknowledgments
This work is based on research supported by the South
African Research Chairs Initiative of the Department of
Science and Technology and National Research Foundation
and the South African Medical Research Council.
Author Disclosure Statement
The authors declare that no conflicting financial interests
exist.
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Address correspondence to:
Stefanie Malan-Muller, PhD
Department of Psychiatry
Faculty of Medicine and Health Sciences
Stellenbosch University
Tygerberg 7600
South Africa
E-mail: smalan@sun.ac.za
Abbreviations Used
Acetyl-CoA ¼acetyl coenzyme A
ATCV-1 ¼Acanthocystis turfacea chlorella virus 1
BBB ¼brain/blood barrier
BDNF ¼brain-derived neurotrophic factor
ChIP ¼chromatin immunoprecipitation
ChIP-chip ¼chromatin immunoprecipitation
microarrays
CNS ¼central nervous system
COGs ¼clusters of orthologous groups
CRF ¼corticotrophin releasing factor
CRP ¼C-reactive protein
DR ¼dorsal raphe nucleus
ENS ¼enteric nervous system
FOS ¼fructooligosaccharide
FUT2 ¼fucosyltransferase 2
GF ¼germ free
GOS ¼galactooligosaccharide
HDACs ¼histone deacetylases
HMP ¼Human Microbiome Project
HPA ¼hypothalamic–pituitary–adrenal
IBD ¼inflammatory bowel disease
IBS ¼irritable bowel syndrome
IEC ¼intestinal epithelial cells
IFN-c¼interferon gamma
IL-1b¼interleukin 1 beta
IL-2 ¼interleukin-2
LC-MS/MS ¼liquid chromatography/mass
spectrometry
LcS ¼Lactobacillus casei strain Shirota
LCT ¼lactase
MDD ¼major depression disorder
MGB ¼microbiota–gut–brain
MoBE ¼microbiome of the built environment
mRNA ¼messenger RNA
MS ¼mass spectrometry
niRNAs ¼microRNAs
NMDARs ¼N-methyl-D-aspartate receptors
NMR ¼nuclear magnetic resonance
NOD2 ¼nucleotide binding oligomerization
domain containing 2
PSC ¼primary sclerosing cholangitis
PTSD ¼posttraumatic stress disorder
rRNA ¼ribosomal RNA
SCFAs ¼short-chain fatty acids
SPF ¼specific pathogen free
TCA ¼tricarboxylic acid
TE ¼trauma exposed
TNF ¼tumor necrosis factor
Tregs ¼regulatory T cells
VDR ¼vitamin D receptor
18 MALAN-MULLER ET AL.
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