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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 microbiome 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 epicenter 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 individuals. The functional microbiome can be studied using methods involving metagenomics, metatranscriptomics, 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.
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Review Article
The Gut Microbiome and Mental Health:
Implications for Anxiety- and Trauma-Related Disorders
Stefanie Malan-Muller,
Mireia Valles-Colomer,
Jeroen Raes,
Christopher A. Lowry,
Soraya Seedat,
and Sian M.J. Hemmings
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
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
Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa.
Department of Microbiology and Immunology, Rega Institute, KU Leuven–University of Leuven, Leuven, Belgium.
VIB, Center for Microbiology, Leuven, Belgium.
Department of Integrative Physiology and Center for Neuroscience, University of Colorado Boulder, Boulder, Colorado.
Military and Veteran Microbiome: Consortium for Research and Education ( MVM-Core), Aurora, Colorado.
Department of Psychiatry, Neurology & Physical Medicine and Rehabilitation, Anschutz School of Medicine, University of Colorado,
Aurora, Colorado.
VA Rocky Mountain Mental Illness Research, Education, and Clinical Center (MIRECC), Denver, Colorado.
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
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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
cells and 3.0 ·10
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
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
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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
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
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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
¨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.
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.
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 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 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
H nuclear magnetic resonance (
H NMR) spectroscopy.
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,
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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
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
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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.
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
Abram F. (2015). Systems-based approaches to unravel multi-
species microbial community functioning. Comput Struct
Biotechnol J 13, 24–32.
Aguiar-Pulido V, Huang W, Suarez-Ulloa V, Cickovski T,
Mathee K, and Narasimhan G. (2016). Metagenomics, me-
tatranscriptomics, and metabolomics approaches for micro-
biome analysis. Evol Bioinforma Online 12, 5–16.
Ahmed FE, Jeffries CD, Vos PW, et al. (2009). Diagnostic
microRNA markers for screening sporadic human colon
cancer and active ulcerative colitis in stool and tissue. Cancer
Genomics Proteomics 6, 281–295.
Al-Asmakh M, Anuar F, Zadjali F, Rafter J, and Pettersson S.
(2012). Gut microbial communities modulating brain devel-
opment and function. Gut Microbes 3, 366–373.
American Psychiatric Association (2013). Diagnostic and sta-
tistical manual of mental disorders (5th ed.). Arlington, VA:
American Psychiatric Publishing.
Risperidone-induced weight gain is mediated through shifts
in the gut microbiome and suppression of energy expendi-
ture. EBioMedicine 2, 1725–1734.
Bailey MT, Dowd SE, Galley JD, Hufnagle AR, Allen RG, and
Lyte M. (2011). Exposure to a social stressor alters the struc-
ture of the intestinal microbiota: Implications for stressor-
induced immunomodulation. Brain Behav Immun 25, 397–407.
e M, and Huson DH. (2017). SILVA, RDP, Green-
genes, NCBI and OTT—How do these taxonomies compare?
BMC Genomics 18, 114.
Barrett E, Ross RP, O’Toole PW, Fitzgerald GF, and Stanton C.
(2012). c-Aminobutyric acid production by culturable bacte-
ria from the human intestine. J Appl Microbiol 113, 411–417.
Bartel DP. (2009). MicroRNAs: Target recognition and regu-
latory functions. Cell 136, 215–233.
Baxter AJ, Scott KM, Vos T, and Whiteford HA. (2013). Global
prevalence of anxiety disorders: A systematic review and
meta-regression. Psychol Med 43, 897–910.
Beckh K, and Arnold R. (1991). Regulation of bile secretion by
sympathetic nerves in perfused rat liver. Am J Physiol 261,
Belkaid Y, and Segre JA. (2014). Dialogue between skin mi-
crobiota and immunity. Science 346, 954–959.
Benjamini Y, and Hochberg Y. (1995). Controlling the false
discovery rate: A practical and powerful approach to multiple
testing. J R Stat Soc Ser B Methodol 57, 289–300.
Bercik P, Denou E, Collins J, et al. (2011a). The intestinal
microbiota affect central levels of brain-derived neurotropic
factor and behavior in mice. Gastroenterology 141, 599–609,
Bercik P, Park AJ, Sinclair D, et al. (2011b). The anxiolytic
effect of Bifidobacterium longum NCC3001 involves vagal
pathways for gut-brain communication. Neurogastroenterol
Motil Off J Eur Gastrointest Motil Soc 23, 1132–1139.
Downloaded by Univ Colorado Libraries from at 09/21/17. For personal use only.
Bercik P, Verdu EF, Foster JA, et al. (2010). Chronic gastro-
intestinal inflammation induces anxiety-like behavior and
alters central nervous system biochemistry in mice. Gastro-
enterology 139, 2102–2112.e1.
Bersani FS, Morley C, Lindqvist D, et al. (2016). Mitochondrial
DNA copy number is reduced in male combat veterans with
PTSD. Prog Neuropsychopharmacol Biol Psychiatry 64, 10–17.
Bilbo SD, Levkoff LH, Mahoney JH, Watkins LR, Rudy JW,
and Maier SF. (2005). Neonatal infection induces memory
impairments following an immune challenge in adulthood.
Behav Neurosci 119, 293–301.
Bjornsson HT, Fallin MD, and Feinberg AP. (2004). An inte-
grated epigenetic and genetic approach to common human
disease. Trends Genet TIG 20, 350–358.
Blekhman R, Goodrich JK, Huang K, et al. (2015). Host genetic
variation impacts microbiome composition across human
body sites. Genome Biol 16, 191.
Bonder MJ, Kurilshikov A, Tigchelaar EF, et al. (2016). The
effect of host genetics on the gut microbiome. Nat Genet 48,
Bongers G, Pacer ME, Geraldino TH, et al. (2014). Interplay
of host microbiota, genetic perturbations, and inflammation
promotes local development of intestinal neoplasms in mice.
J Exp Med 211, 457–472.
Braniste V, Al-Asmakh M, Kowal C, Anuar F, Abbaspour A,
´th M, Korecka A, Bakocevic N, Ng LG, Kundu P, Gulya
B, Halldin C, Huttenby K, Nilsson H, Hebert H, Volpe BT,
Diamond R, and Pettersson S (2014). The gut microbiota
influences blood-brain barrier permeability in mice. Sci
Transl Med 6(263): 263ral58.
Bravo JA, Forsythe P, Chew MV, et al. (2011). Ingestion of
Lactobacillus strain regulates emotional behavior and central
GABA receptor expression in a mouse via the vagus nerve.
Proc Natl Acad Sci U S A 108, 16050–16055.
Breitbart M, Haynes M, Kelley S, et al. (2008). Viral diversity
and dynamics in an infant gut. Res Microbiol 159, 367–373.
Breitbart M, Hewson I, Felts B, et al. (2003). Metagenomic
analyses of an uncultured viral community from human feces.
J Bacteriol 185, 6220–6223.
Brooks B, Mueller RS, Young JC, Morowitz MJ, Hettich RL,
and Banfield JF. (2015). Strain-resolved microbial commu-
nity proteomics reveals simultaneous aerobic and anaerobic
function during gastrointestinal tract colonization of a pre-
term infant. Front Microbiol 6:654.
¨ssow H, Canchaya C, and Hardt W-D. (2004). Phages and
the evolution of bacterial pathogens: From genomic re-
arrangements to lysogenic conversion. Microbiol Mol Biol
Rev MMBR 68, 560–602, table of contents.
Campbell JH, Foster CM, Vishnivetskaya T, et al. (2012). Host
genetic and environmental effects on mouse intestinal mi-
crobiota. ISME J 6, 2033–2044.
Cappi C, Diniz JB, Requena GL, et al. (2016). Epigenetic ev-
idence for involvement of the oxytocin receptor gene in
obsessive-compulsive disorder. BMC Neurosci 17, 79.
Carabotti M, Scirocco A, Maselli MA, and Severi C. (2015).
The gut-brain axis: Interactions between enteric microbiota,
central and enteric nervous systems. Ann Gastroenterol Q
Publ Hell Soc Gastroenterol 28, 203–209.
Carruthers VB, and Suzuki Y. (2007). Effects of Toxoplasma
gondii infection on the brain. Schizophr Bull 33, 745–751.
Castro-Nallar E, Bendall ML, Pe
´rez-Losada M, et al. (2015).
Composition, taxonomy and functional diversity of the oro-
pharynx microbiome in individuals with schizophrenia and
controls. PeerJ 3, e1140.
Chong CW, Ahmad AF, Lim YAL, et al. (2015). Effect of
ethnicity and socioeconomic variation to the gut microbiota
composition among pre-adolescent in Malaysia. Sci Rep 5,
Chu Y, Jin X, Parada I, et al. (2010). Enhanced synaptic con-
nectivity and epilepsy in C1q knockout mice. Proc Natl Acad
Sci U S A 107, 7975–7980.
Clarke G, Grenham S, Scully P, et al. (2013). The microbiome-
gut-brain axis during early life regulates the hippocampal
serotonergic system in a sex-dependent manner. Mol Psy-
chiatry 18, 666–673.
Collado MC, Meriluoto J, and Salminen S. (2008). Adhesion
and aggregation properties of probiotic and pathogen strains.
Eur Food Res Technol 226, 1065–1073.
Collins SM, Surette M, and Bercik P. (2012). The interplay
between the intestinal microbiota and the brain. Nat Rev
Microbiol 10, 735–742.
Corr SC, Li Y, Riedel CU, O’Toole PW, Hill C, and Gahan
CGM. (2007). Bacteriocin production as a mechanism for the
antiinfective activity of Lactobacillus salivarius UCC118.
Proc Natl Acad Sci U S A 104, 7617–7621.
Crumeyrolle-Arias M, Jaglin M, Bruneau A, et al. (2014).
Absence of the gut microbiota enhances anxiety-like behavior
and neuroendocrine response to acute stress in rats. Psycho-
neuroendocrinology 42, 207–217.
Dave M, Higgins PD, Middha S, and Rioux KP. (2012). The
human gut microbiome: Current knowledge, challenges, and
future directions. Transl Res J Lab Clin Med 160, 246–257.
Davenport ER. (2016). Elucidating the role of the host genome in
shaping microbiome composition. Gut Microbes 7, 178–184.
Davis DJ, Doerr HM, Grzelak AK, et al. (2016). Lactobacillus
plantarum attenuates anxiety-related behavior and protects against
stress-induced dysbiosis in adult zebrafish. Sci Rep 6, 33726.
Davis LMG, Martı
´nez I, Walter J, Goin C, and Hutkins RW.
(2011). Barcoded pyrosequencing reveals that consumption
of galactooligosaccharides results in a highly specific bifi-
dogenic response in humans. PLoS One 6, e25200.
De Palma G, Collins SM, Bercik P, and Verdu EF. (2014). The
microbiota-gut-brain axis in gastrointestinal disorders: Stres-
sed bugs, stressed brain or both? J Physiol 592, 2989–2997.
Desbonnet L, Garrett L, Clarke G, Kiely B, Cryan JF, and Dinan
TG. (2010). Effects of the probiotic Bifidobacterium infantis in
the maternal separation model of depression. Neuroscience
170, 1179–1188.
Diaz-Anzaldua A, Diaz-Martinez A, and Rosa Diaz-Martinez L.
(2011). The complex interplay of genetics, epigenetics, and
environment in the predisposition to alcohol dependence.
Salud Ment 34, 157–166.
Diaz Heijtz R, Wang S, Anuar F, Qiun Y, Bjo
¨rkholm B, Sa-
muelsson A, Hibberd ML, Forssberg H, and Pettersson S
(2011). Normal gut microbiota modulated brain development
and behavior. Proc Natl Acad Sci USA 108, 3047–3052.
Dimitrov D, Thiele I, and Ferguson LR. (2016). Editorial: The
human gutome: Nutrigenomics of host-microbiome interac-
tions. Front Genet 7, 158.
Dinan TG, Stanton C, and Cryan JF. (2013). Psychobiotics: A
novel class of psychotropic. Biol Psychiatry 74, 720–726.
Diop L, Guillou S, and Durand H. (2008). Probiotic food sup-
plement reduces stress-induced gastrointestinal symptoms in
volunteers: A double-blind, placebo-controlled, randomized
trial. Nutr Res N Y N 28, 1–5.
Douglas-Escobar M, Elliott E, and Neu J. (2013). Effect of
intestinal microbial ecology on the developing brain. JAMA
Pediatr 167, 374–379.
Downloaded by Univ Colorado Libraries from at 09/21/17. For personal use only.
Elhai JD, Grubaugh AL, Kashdan TB, and Frueh BC. (2008).
Empirical examination of a proposed refinement to DSM-IV
posttraumatic stress disorder symptom criteria using the Na-
tional Comorbidity Survey Replication data. J Clin Psychiatry
69, 597–602.
Erickson AR, Cantarel BL, Lamendella R, et al. (2012). In-
tegrated metagenomics/metaproteomics reveals human host-
microbiota signatures of Crohn’s disease. PLoS One 7, e49138.
Fabian MR, Sonenberg N, and Filipowicz W. (2010). Regula-
tion of mRNA translation and stability by microRNAs. Annu
Rev Biochem 79, 351–379.
Falony G, Joossens M, Vieira-Silva S, et al. (2016). Population-level
analysis of gut microbiome variation. Science 352, 560–564.
Famularo R, Kinscherff R, and Fenton T. (1992). Psychiatric
diagnoses of maltreated children: Preliminary findings. J Am
Acad Child Adolesc Psychiatry 31, 863–867.
Felitti VJ, Anda RF, Nordenberg D, et al. (1998). Relationship
of childhood abuse and household dysfunction to many of the
leading causes of death in adults. The Adverse Childhood
Experiences (ACE) Study. Am J Prev Med 14, 245–258.
Ferguson JF, Allayee H, Gerszten RE, et al. (2016). Nu-
trigenomics, the microbiome, and gene-environment interac-
tions: New directions in cardiovascular disease research,
prevention, and treatment: A scientific statement from the
American Heart Association. Circ Cardiovasc Genet 9, 291–
Ferrer M, Ruiz A, Lanza F, et al. (2013). Microbiota from the
distal guts of lean and obese adolescents exhibit partial
functional redundancy besides clear differences in commu-
nity structure. Environ Microbiol 15, 211–226.
Finegold SM, Molitoris D, Song Y, et al. (2002). Gastro-
intestinal microflora studies in late-onset autism. Clin Infect
Dis Off Publ Infect Dis Soc Am 35, S6–S16.
Folseraas T, Melum E, Rausch P, et al. (2012). Extended
analysis of a genome-wide association study in primary
sclerosing cholangitis detects multiple novel risk loci. J He-
patol 57, 366–375.
Forslund K, Hildebrand F, Nielsen T, et al. (2015). Disen-
tangling type 2 diabetes and metformin treatment signatures
in the human gut microbiota. Nature 528, 262–266.
Forster SC, Browne HP, Kumar N, et al. (2016). HPMCD: The
database of human microbial communities from metagenomic
datasets and microbial reference genomes. Nucleic Acids Res
44, D604–D609.
Foster JA, and McVey Neufeld K-A. (2013). Gut-brain axis: How
the microbiome influences anxiety and depression. Trends
Neurosci 36, 305–312.
Fuhrman JA. (1999). Marine viruses and their biogeochemical
and ecological effects. Nature 399, 541–548.
Fung WLA, McEvilly R, Fong J, Silversides C, Chow E, and
Bassett A. (2010). Elevated prevalence of generalized anxiety
disorder in adults with 22q11.2 deletion syndrome. Am J
Psychiatry 167, 998.
Gareau MG, Silva MA, and Perdue MH (2008). Pathophysio-
logical mechanisms of stress-induced intestinal damage. Curr
Mol Med 8, 274–281.
Gareau MG, Wine E, Rodrigues DM, et al. (2011). Bacterial
infection causes stress-induced memory dysfunction in mice.
Gut 60, 307–317.
Generoso SV, Viana M, Santos R, et al. (2010). Saccharomyces
cerevisiae strain UFMG 905 protects against bacterial trans-
location, preserves gut barrier integrity and stimulates the
immune system in a murine intestinal obstruction model.
Arch Microbiol 192, 477–484.
Goehler LE, Park SM, Opitz N, Lyte M, and Gaykema RPA.
(2008). Campylobacter jejuni infection increases anxiety-like
behavior in the holeboard: Possible anatomical substrates for
viscerosensory modulation of exploratory behavior. Brain
Behav Immun 22, 354–366.
Gomez A, Mve-Obiang A, Vray B, et al. (2001). Detection of
phospholipase C in nontuberculous Mycobacteria and its pos-
sible role in hemolytic activity. J Clin Microbiol 39, 1396–1401.
Goodrich JK, Davenport ER, Beaumont M, et al. (2016). Ge-
netic determinants of the gut microbiome in UK twins. Cell
Host Microbe 19, 731–743.
Goodrich JK, Waters JL, Poole AC, et al. (2014). Human ge-
netics shape the gut microbiome. Cell 159, 789–799.
Gosalbes MJ, Durba
´n A, Pignatelli M, et al. (2011). Meta-
transcriptomic approach to analyze the functional human gut
microbiota. PLoS One 6, e17447.
Hale MW, Rook GAW, and Lowry CA. (2012). Pathways un-
derlying afferent signaling of bronchopulmonary immune
activation to the central nervous system. Chem Immunol
Allergy 98, 118–141.
Handelsman J, Rondon MR, Brady SF, Clardy J, and Goodman
RM. (1998). Molecular biological access to the chemistry of
unknown soil microbes: A new frontier for natural products.
Chem Biol 5, R245–R249.
Hayes KS, Bancroft AJ, Goldrick M, Portsmouth C, Roberts IS, and
Grencis RK. (2010). Exploitation of the intestinal microflora by
the parasitic nematode Trichuris muris. Science 328, 1391–1394.
Hesson LB. (2013). Gut microbiota and obesity-related gas-
trointestinal cancer: A focus on epigenetics. Transl Gastro-
intest Cancer 2, 204–210.
Hildebrand F, Nguyen TLA, Brinkman B, et al. (2013).
Inflammation-associated enterotypes, host genotype, cage
and inter-individual effects drive gut microbiota variation in
common laboratory mice. Genome Biol 14, R4.
Hoge EA, Brandstetter K, Moshier S, Pollack MH, Wong KK,
and Simon NM. (2009). Broad spectrum of cytokine abnor-
malities in panic disorder and posttraumatic stress disorder.
Depress Anxiety 26, 447–455.
Hoisington AJ, Brenner LA, Kinney KA, Postolache TT, and
Lowry CA. (2015). The microbiome of the built environment
and mental health. Microbiome 3, 60.
Holmes E, Li JV, Athanasiou T, Ashrafian H, and Nicholson
JK. (2011). Understanding the role of gut microbiome-host
metabolic signal disruption in health and disease. Trends
Microbiol 19, 349–359.
Hooper LV, Littman DR, and Macpherson AJ. (2012). Inter-
actions between the microbiota and the immune system.
Science 336, 1268–1273.
Human Microbiome Project Consortium. (2012). Structure,
function and diversity of the healthy human microbiome.
Nature 486, 207–214.
Hunt JRF, Martinelli R, Adams VC, Rook GA, and Brunet LR.
(2005). Intragastric administration of Mycobacterium vaccae in-
hibits severe pulmonary allergic inflammation in a mouse model.
Clin Exp Allergy J Br Soc Allergy Clin Immunol 35, 685–690.
Jiang H, Ling Z, Zhang Y, et al. (2015). Altered fecal micro-
biota composition in patients with major depressive disorder.
Brain Behav Immun 48, 186–194.
´nez E, Ferna
´ndez L, Marı
´n ML, et al. (2005). Isolation of
commensal bacteria from umbilical cord blood of healthy
neonates born by cesarean section. Curr Microbiol 51, 270–
´nez E, Marı
´n ML, Martı
´n R, et al. (2008). Is meconium from
healthy newborns actually sterile? Res Microbiol 159, 187–193.
Downloaded by Univ Colorado Libraries from at 09/21/17. For personal use only.
Juste C, Kreil DP, Beauvallet C, et al. (2014). Bacterial protein
signals are associated with Crohn’s disease. Gut 63, 1566–
Kahl KG, Schweiger U, Correll C, et al. (2015). Depression,
anxiety disorders, and metabolic syndrome in a population at
risk for type 2 diabetes mellitus. Brain Behav 5, e00306.
Kato-Kataoka A, Nishida K, Takada M, et al. (2016). Fer-
mented milk containing Lactobacillus casei strain Shirota
prevents the onset of physical symptoms in medical students
under academic examination stress. Benef Microbes 7, 153–
Kawamura Y, Otowa T, Koike A, et al. (2011). A genome-wide
CNV association study on panic disorder in a Japanese pop-
ulation. J Hum Genet 56, 852–856.
Kelly BJ, Gross R, Bittinger K, et al. (2015). Power and sample-
size estimation for microbiome studies using pairwise dis-
tances and PERMANOVA. Bioinformatics 31, 2461–2468.
Kelly JR, Allen AP, Temko A, et al. (2017). Lost in translation?
The potential psychobiotic Lactobacillus rhamnosus ( JB-1)
fails to modulate stress or cognitive performance in healthy
male subjects. Brain Behav Immun 61, 50–59.
Kim TY, Kim SJ, Chung HG, Choi JH, Kim SH, and Kang JI.
(2017). Epigenetic alterations of the BDNF gene in combat-
related post-traumatic stress disorder. Acta Psychiatr Scand
135, 170–179.
Knights D, Silverberg MS, Weersma RK, et al. (2014). Com-
plex host genetics influence the microbiome in inflammatory
bowel disease. Genome Med 6, 107.
Kolmeder CA, de Been M, Nikkila
¨J, et al. (2012). Comparative
metaproteomics and diversity analysis of human intestinal
microbiota testifies for its temporal stability and expression of
core functions. PLoS One 7, e29913.
Koskella B, and Brockhurst MA. (2014). Bacteria–phage co-
evolution as a driver of ecological and evolutionary processes
in microbial communities. Fems Microbiol Rev 38, 916–931.
Kunze WA, Mao Y-K, Wang B, et al. (2009). Lactobacillus
reuteri enhances excitability of colonic AH neurons by in-
hibiting calcium-dependent potassium channel opening. J
Cell Mol Med 13, 2261–2270.
Kurdyukov S, and Bullock M. (2016). DNA methylation anal-
ysis: Choosing the right method. Biology 5, pii: E3.
Li J, Jia H, Cai X, et al. (2014). An integrated catalog of ref-
erence genes in the human gut microbiome. Nat Biotechnol
32, 834–841.
Li W, Dowd SE, Scurlock B, Acosta-Martinez V, and Lyte M.
(2009). Memory and learning behavior in mice is temporally
associated with diet-induced alterations in gut bacteria. Phy-
siol Behav 96, 557–567.
Lindqvist D, Wolkowitz OM, Mellon S, et al. (2014). Proin-
flammatory milieu in combat-related PTSD is independent of
depression and early life stress. Brain Behav Immun 42, 81–88.
Link A, Becker V, Goel A, Wex T, and Malfertheiner P. (2012).
Feasibility of fecal microRNAs as novel biomarkers for
pancreatic cancer. PLoS One 7, e42933.
Liu M-T, Kuan Y-H, Wang J, Hen R, and Gershon MD. (2009).
5-HT4 receptor-mediated neuroprotection and neurogenesis
in the enteric nervous system of adult mice. J Neurosci Off J
Soc Neurosci 29, 9683–9699.
Liu S, da Cunha AP, Rezende RM, et al. (2016a). The host
shapes the gut microbiota via fecal MicroRNA. Cell Host
Microbe 19, 32–43.
Liu W, Zhang J, Wu C, et al. (2016b). Unique features of ethnic
mongolian gut microbiome revealed by metagenomic analy-
sis. Sci Rep 6, 34826.
Liu Z, Ma Y, and Qin H. (2011). Potential prevention and
treatment of intestinal barrier dysfunction using active com-
ponents of lactobacillus. Ann Surg 254, 832–833.
Lowry CA, Hollis JH, de Vries A, et al. (2007). Identification
of an immune-responsive mesolimbocortical serotonergic
system: Potential role in regulation of emotional behavior.
Neuroscience 146, 756–772.
Lowry CA, Smith DG, Siebler PH, Schmidt D, and Stamper CE.
(2016). The microbiota, immunoregulation, and mental
health: Implications for public health. Curr Environ Health
Rep 3, 270–286.
Luongo D, Miyamoto J, Bergamo P, et al. (2013). Differential
modulation of innate immunity in vitro by probiotic strains of
Lactobacillus gasseri. BMC Microbiol 13, 298.
Macpherson AJ, and Harris NL. (2004). Interactions between
commensal intestinal bacteria and the immune system. Nat
Rev Immunol 4, 478–485.
Maes M, Lin AH, Delmeire L, et al. (1999). Elevated serum
interleukin-6 (IL-6) and IL-6 receptor concentrations in
posttraumatic stress disorder following accidental man-made
traumatic events. Biol Psychiatry 45, 833–839.
Martin F-PJ, Sprenger N, Montoliu I, Rezzi S, Kochhar S, and Ni-
cholson JK. (2010). Dietary modulation of gut functional ecology
studied by fecal metabonomics. J Proteome Res 9, 5284–5295.
Matthews DM, and Jenks SM. (2013). Ingestion of Myco-
bacterium vaccae decreases anxiety-related behavior and
improves learning in mice. Behav Processes 96, 27–35.
Mayer EA. (2011). Gut feelings: The emerging biology of gut-
brain communication. Nat Rev Neurosci 12, 453–466.
Mayer EA, Knight R, Mazmanian SK, Cryan JF, and Tillisch K.
(2014). Gut microbes and the brain: Paradigm shift in neu-
roscience. J Neurosci 34, 15490–15496.
McCauley J, Kern DE, Kolodner K, et al. (1997). Clinical
characteristics of women with a history of childhood abuse:
Unhealed wounds. JAMA 277, 1362–1368.
McNulty NP, Yatsunenko T, Hsiao A, et al. (2011). The impact
of a consortium of fermented milk strains on the gut micro-
biome of gnotobiotic mice and monozygotic twins. Sci Transl
Med 3, 106ra106.
Messaoudi M, Violle N, Bisson J-F, Desor D, Javelot H, and
Rougeot C. (2011). Beneficial psychological effects of a
probiotic formulation (Lactobacillus helveticus R0052 and
Bifidobacterium longum R0175) in healthy human volunteers.
Gut Microbes 2, 256–261.
Mestas J, and Hughes CCW. (2004). Of mice and not men:
Differences between mouse and human immunology. J Im-
munol 172, 2731–2738.
Meurs M, Roest AM, Wolffenbuttel BHR, Stolk RP, de Jonge
P, and Rosmalen JGM. (2016). Association of depressive and
anxiety disorders with diagnosed versus undiagnosed diabe-
tes: An epidemiological study of 90,686 participants. Psy-
chosom Med 78, 233–241.
Minot S, Sinha R, Chen J, et al. (2011). The human gut virome:
Inter-individual variation and dynamic response to diet.
Genome Res 21, 1616–1625.
Mittal VA, Ellman LM, and Cannon TD. (2008). Gene-
environment interaction and covariation in schizophrenia: The
role of obstetric complications. Schizophr Bull 34, 1083–1094.
Mokili JL, Rohwer F, and Dutilh BE. (2012). Metagenomics
and future perspectives in virus discovery. Curr Opin Virol 2,
Molloy MJ, Grainger JR, Bouladoux N, et al. (2013). Intraluminal
containment of commensal outgrowth in the gut during
infection-induced dysbiosis. Cell Host Microbe 14, 318–328.
Downloaded by Univ Colorado Libraries from at 09/21/17. For personal use only.
Moloney RD, Desbonnet L, Clarke G, Dinan TG, and Cryan JF.
(2014). The microbiome: Stress, health and disease. Mamm
Genome Off J Int Mamm Genome Soc 25, 49–74.
Montiel-Castro AJ, Gonza
´lez-Cervantes RM, Bravo-Ruiseco G,
and Pacheco-Lo
´pez G. (2013). The microbiota-gut-brain axis:
Neurobehavioral correlates, health and sociality. Front Integr
Neurosci 7, 70.
Morath J, Gola H, Sommershof A, et al. (2014). The effect of
trauma-focused therapy on the altered T cell distribution in
individuals with PTSD: Evidence from a randomized con-
trolled trial. J Psychiatr Res 54, 1–10.
Naseribafrouei A, Hestad K, Avershina E, et al. (2014). Cor-
relation between the human fecal microbiota and depression.
Neurogastroenterol Motil Off J Eur Gastrointest Motil Soc
26, 1155–1162.
Neufeld K-AM, Kang N, Bienenstock J, and Foster JA. (2011).
Effects of intestinal microbiota on anxiety-like behavior.
Commun Integr Biol 4, 492–494.
Nishino R, Mikami K, Takahashi H, et al. (2013). Com-
mensal microbiota modulate murine behaviors in a strictly
contamination-free environment confirmed by culture-
based methods. Neurogastroenterol Motil Off J Eur Gas-
trointest Motil Soc 25, 521–528.
Norman JM, Handley SA, Baldridge MT, et al. (2015). Disease-
specific alterations in the enteric virome in inflammatory
bowel disease. Cell 160, 447–460.
Nugent NR, Tyrka AR, Carpenter LL, and Price LH. (2011).
Gene–environment interactions: Early life stress and risk for
depressive and anxiety disorders. Psychopharmacology (Berl)
214, 175–196.
Ogilvie LA, and Jones BV. (2015). The human gut virome: A
multifaceted majority. Front Microbiol 6, 918.
Ohland CL, Kish L, Bell H, et al. (2013). Effects of Lactoba-
cillus helveticus on murine behavior are dependent on diet
and genotype and correlate with alterations in the gut mi-
crobiome. Psychoneuroendocrinology 38, 1738–1747.
O’Mahony SM, Clarke G, Borre YE, Dinan TG, and Cryan JF.
(2015). Serotonin, tryptophan metabolism and the brain-gut-
microbiome axis. Behav Brain Res 277, 32–48.
¨m L, Kihlgren A, Repsilber D, Bjo
Brummer RJ, and Schoultz I. (2016). Probiotic administration
among free-living older adults: A double blinded, random-
ized, placebo-controlled clinical trial. Nutr J 15, 80.
Overman EL, Rivier JE, and Moeser AJ. (2012). CRF induces
intestinal epithelial barrier injury via the release of mast cell
proteases and TNF-a. PLoS One 7, e39935.
Ozsolak F, Song JS, Liu XS, and Fisher DE. (2007). High-
throughput mapping of the chromatin structure of human
promoters. Nat Biotechnol 25, 244–248.
Parvez S, Malik KA, Ah Kang S, and Kim H-Y. (2006). Pro-
biotics and their fermented food products are beneficial for
health. J Appl Microbiol 100, 1171–1185.
Paterson S, Vogwill T, Buckling A, et al. (2010). Antagonistic co-
evolution accelerates molecular evolution. Nature 464, 275–278.
Paul B, Barnes S, Demark-Wahnefried W, et al. (2015). Influ-
ences of diet and the gut microbiome on epigenetic modulation
in cancer and other diseases. Clin Epigenetics 7, 112.
Pavlidis C, Lanara Z, Balasopoulou A, Nebel J-C, Katsila T,
and Patrinos GP. (2015). Meta-analysis of genes in com-
mercially available nutrigenomic tests denotes lack of asso-
ciation with dietary intake and nutrient-related pathologies.
OMICS 19, 512–520.
Pavlidis C, Nebel J-C, Katsila T, and Patrinos GP. (2016). Nu-
trigenomics 2.0: The need for ongoing and independent evalu-
ation and synthesis of commercial nutrigenomics tests’ scientific
knowledge base for responsible innovation. OMICS 20, 65–68.
Qin J, Li R, Raes J, et al. (2010). A human gut microbial gene catalog
established by metagenomic sequencing. Nature 464, 59–65.
Quinn RA, Navas-Molina JA, Hyde ER, et al. (2016). From sample
to multi-omics conclusions in under 48 hours. mSystems 1:
Rao AV, Bested AC, Beaulne TM, et al. (2009). A randomized,
double-blind, placebo-controlled pilot study of a probiotic in emo-
tional symptoms of chronic fatigue syndrome. Gut Pathog 1, 6.
Rao M, and Gershon MD (2016). The bowel and beyond: The
enteric nervous system in neurological disorders. Nat Rev
Gastroenterol Hepatol 13, 517–528.
Reber SO, Siebler PH, Donner NC, et al. (2016). Immunization
with a heat-killed preparation of the environmental bacterium
Mycobacterium vaccae promotes stress resilience in mice.
Proc Natl Acad Sci U S A 113, E3130–E3139.
Ren B, Robert F, Wyrick JJ, et al. (2000). Genome-wide lo-
cation and function of DNA binding proteins. Science 290,
Reyes A, Haynes M, Hanson N, et al. (2010). Viruses in the
faecal microbiota of monozygotic twins and their mothers.
Nature 466, 334–338.
Robyr D, and Grunstein M. (2003). Genomewide histone
acetylation microarrays. Methods San Diego Calif 31, 83–89.
˜o-Janeiro BK, Alonso-Cotoner C, Pigrau M, Lobo B,
Vicario M, and Santos J. (2015). Role of corticotropin-
releasing factor in gastrointestinal permeability. J Neurogas-
troenterol Motil 21, 33–50.
Romijn AR, Rucklidge JJ, Kuijer RG, and Frampton C. (2017).
A double-blind, randomized, placebo-controlled trial of
Lactobacillus helveticus and Bifidobacterium longum for the
symptoms of depression. Aust N Z J Psychiatry [Epub ahead
of print]; DOI: 10.1177/0004867416686694.
Rook GAW, and Brunet LR. (2005). Microbes, immunoregu-
lation, and the gut. Gut 54, 317–320.
Rook GAW, Martinelli R, and Brunet LR. (2003). Innate im-
mune responses to mycobacteria and the downregulation of
atopic responses. Curr Opin Allergy Clin Immunol 3, 337–342.
Rook GAW, Raison CL, and Lowry CA. (2014). Microbial
‘‘old friends,’’ immunoregulation and socioeconomic status.
Clin Exp Immunol 177, 1–12.
Roth SY, Denu JM, and Allis CD. (2001). Histone acetyl-
transferases. Annu Rev Biochem 70, 81–120.
Rousseaux C, Thuru X, Gelot A, et al. (2007). Lactobacillus
acidophilus modulates intestinal pain and induces opioid and
cannabinoid receptors. Nat Med 13, 35–37.
Rytwinski NK, Scur MD, Feeny NC, and Youngstrom EA.
(2013). The co-occurrence of major depressive disorder among
individuals with posttraumatic stress disorder: A meta-analysis.
J Trauma Stress 26, 299–309.
Sabino J, Vieira-Silva S, Machiels K, et al. (2016). Primary
sclerosing cholangitis is characterised by intestinal dysbiosis
independent from IBD. Gut 65, 1681–1689.
Sanders ME. (2011). Impact of probiotics on colonizing mi-
crobiota of the gut. J Clin Gastroenterol 45, S115–S119.
Sanders ME, Guarner F, Guerrant R, et al. (2013). An update on
the use and investigation of probiotics in health and disease.
Gut 62, 787–796.
Satokari R, Gro
¨nroos T, Laitinen K, Salminen S, and Isolauri E.
(2009). Bifidobacterium and Lactobacillus DNA in the human
placenta. Lett Appl Microbiol 48, 8–12.
Savignac HM, Corona G, Mills H, et al. (2013). Prebiotic
feeding elevates central brain derived neurotrophic factor,
Downloaded by Univ Colorado Libraries from at 09/21/17. For personal use only.
N-methyl-d-aspartate receptor subunits and d-serine. Neu-
rochem Int 63, 756–764.
Schousboe A, and Waagepetersen HS. (2007). GABA: Homeo-
static and pharmacological aspects. Prog Brain Res 160, 9–19.
Sefik E, Geva-Zatorsky N, Oh S, et al. (2015). Individual in-
testinal symbionts induce a distinct population of RORc+
regulatory T cells. Science 349, 993–997.
Selle K, and Klaenhammer TR. (2013). Genomic and pheno-
typic evidence for probiotic influences of Lactobacillus gas-
seri on human health. FEMS Microbiol Rev 37, 915–935.
Sender R, Fuchs S, and Milo R. (2016). Revised estimates for the
number of human and bacteria cells in the body. PLoS Biol 14,
Shah NP. (2007). Functional cultures and health benefits. Int
Dairy J 17, 1262–1277.
Shigeshiro M, Tanabe S, and Suzuki T. (2012). Repeated ex-
posure to water immersion stress reduces the Muc2 gene level
in the rat colon via two distinct mechanisms. Brain Behav
Immun 26, 1061–1065.
Smith CA, O’Maille G, Want EJ, et al. (2005). METLIN: A me-
tabolite mass spectral database. Ther Drug Monit 27, 747–751.
¨derholm JD, and Perdue MH. (2001). Stress and gastroin-
testinal tract. II. Stress and intestinal barrier function. Am J
Physiol Gastrointest Liver Physiol 280, G7–G13.
¨derholm JD, Yates DA, Gareau MG, Yang P-C, MacQueen
G, and Perdue MH. (2002). Neonatal maternal separation
predisposes adult rats to colonic barrier dysfunction in re-
sponse to mild stress. Am J Physiol Gastrointest Liver Physiol
283, G1257–G1263.
Sommershof A, Aichinger H, Engler H, et al. (2009). Sub-
stantial reduction of naı
¨ve and regulatory T cells following
traumatic stress. Brain Behav Immun 23, 1117–1124.
Stamper CE, Hoisington AJ, Gomez OM, et al. (2016). The
microbiome of the built environment and human behavior:
Implications for emotional health and well-being in post-
modern western societies. Int Rev Neurobiol 131, 289–323.
Stewart AM, Roy S, Wong K, Gaikwad S, Chung KM, and
Kalueff AV. (2015). Cytokine and endocrine parameters in
mouse chronic social defeat: Implications for translational
‘‘cross-domain’’ modeling of stress-related brain disorders.
Behav Brain Res 276, 84–91.
Stilling RM, Dinan TG, and Cryan JF. (2014). Microbial genes,
brain & behaviour—epigenetic regulation of the gut-brain
axis. Genes Brain Behav 13, 69–86.
Sudo N, Chida Y, Aiba Y, et al. (2004). Postnatal microbial
colonization programs the hypothalamic-pituitary-adrenal
system for stress response in mice. J Physiol 558, 263–275.
Sullivan R, Wilson DA, Feldon J, et al. (2006). The Inter-
national Society for Developmental Psychobiology annual
meeting symposium: Impact of early life experiences on brain
and behavioral development. Dev Psychobiol 48, 583–602.
Suttle CA. (2007). Marine viruses—major players in the global
ecosystem. Nat Rev Microbiol 5, 801–812.
´Y, and Million M. (2015). Role of corticotropin-releasing
factor signaling in stress-related alterations of colonic mo-
tility and hyperalgesia. J Neurogastroenterol Motil 21, 8–24.
Tamburini S, Shen N, Wu HC, and Clemente JC. (2016). The
microbiome in early life: Implications for health outcomes.
Nat Med 22, 713–722.
Tanca A, Palomba A, Fraumene C, et al. (2016). The impact of
sequence database choice on metaproteomic results in gut
microbiota studies. Microbiome 4, 51.
Tarr AJ, Galley JD, Fisher S, Chichlowski M, Berg BM, Bailey
MT. (2015). The prebiotics 3¢Sialyllactose and 6¢Sialyllactose
diminish stressor-induced anxiety-like behavior and colonic
microbiota alterations: Evidence for effects on the gut-brain
axis. Brain Behav Immun 50, 166–177.
Tatusov RL, Galperin MY, Natale DA, and Koonin EV. (2000).
The COG database: A tool for genome-scale analysis of protein
functions and evolution. Nucleic Acids Res 28, 33–36.
Teitelbaum AA, Gareau MG, Jury J, Yang PC, and Perdue MH.
(2008). Chronic peripheral administration of corticotropin-
releasing factor causes colonic barrier dysfunction similar to
psychological stress. Am J Physiol Gastrointest Liver Physiol
295, G452–G459.
de Theije CGM, Wopereis H, Ramadan M, et al. (2014). Al-
tered gut microbiota and activity in a murine model of autism
spectrum disorders. Brain Behav Immun 37, 197–206.
Theoharides TC, Weinkauf C, and Conti P. (2004). Brain cyto-
kines and neuropsychiatric disorders. J Clin Psychopharmacol
24, 577–581.
Thompson RS, Roller R, Mika A, et al. (2017). Dietary prebi-
otics and bioactive milk fractions improve NREM sleep,
enhance REM sleep rebound and attenuate the stress-induced
decrease in diurnal temperature and gut microbial alpha di-
versity. Front Behav Neurosci 10, 240.
Tillisch K, Labus J, Kilpatrick L, et al. (2013). Consumption of
fermented milk product with probiotic modulates brain ac-
tivity. Gastroenterology 144, 1394–1401, 1401.e1–4.
Torrey EF, Bartko JJ, Lun Z-R, and Yolken RH. (2007). An-
tibodies to Toxoplasma gondii in patients with schizophrenia:
A meta-analysis. Schizophr Bull 33, 729–736.
Torrey EF, Bartko JJ, and Yolken RH. (2012). Toxoplasma
gondii and other risk factors for schizophrenia: An update.
Schizophr Bull 38, 642–647.
Tremblay J, Singh K, Fern A, et al. (2015). Primer and platform
effects on 16S rRNA tag sequencing. Front Microbiol 6, 771.
Turnbaugh PJ, Hamady M, Yatsunenko T, et al.(2009). A core gut
microbiome in obese and lean twins. Nature 457, 480–484.
Underwood MA, Salzman NH, Bennett SH, et al. (2009). A
randomized placebo-controlled comparison of 2 prebiotic/
probiotic combinations in preterm infants: Impact on weight
gain, intestinal microbiota, and fecal short-chain fatty acids. J
Pediatr Gastroenterol Nutr 48, 216–225.
Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M,
and Raes J. (2016). Stool consistency is strongly associated
with gut microbiota richness and composition, enterotypes
and bacterial growth rates. Gut 65, 57–62.
Verberkmoes NC, Russell AL, Shah M, et al. (2009). Shotgun
metaproteomics of the human distal gut microbiota. ISME J 3,
Virgin HW. (2014). The virome in mammalian physiology and
disease. Cell 157, 142–150.
Vlieger AM, Robroch A, van Buuren S, et al. (2009). Tolerance
and safety of Lactobacillus paracasei ssp. paracasei in
combination with Bifidobacterium animalis ssp. lactis in a
prebiotic-containing infant formula: A randomised controlled
trial. Br J Nutr 102, 869–875.
Vogtmann E, Chen J, Amir A, et al. (2017). Comparison of
collection methods for fecal samples in microbiome studies.
Am J Epidemiol 185, 115–123.
Wagner CL, Taylor SN, and Johnson D. (2008). Host factors in
amniotic fluid and breast milk that contribute to gut matura-
tion. Clin Rev Allergy Immunol 34, 191–204.
Wang J, Thingholm LB, Skiecevic
e J, et al. (2016). Genome-
wide association analysis identifies variation in vitamin D
receptor and other host factors influencing the gut microbiota.
Nat Genet 48, 1396–1406.
Downloaded by Univ Colorado Libraries from at 09/21/17. For personal use only.
Weber JA, Baxter DH, Zhang S, et al. (2010). The microRNA
spectrum in 12 body fluids. Clin Chem 56, 1733–1741.
Weckwerth W, and Morgenthal K. (2005). Metabolomics: From
pattern recognition to biological interpretation. Drug Discov
Today 10, 1551–1558.
Weiss S, Amir A, Hyde ER, Metcalf JL, Song SJ, and Knight R.
(2014). Tracking down the sources of experimental contam-
ination in microbiome studies. Genome Biol 15, 564.
Wommack KE, and Colwell RR. (2000). Virioplankton: Viruses
in aquatic ecosystems. Microbiol Mol Biol Rev 64, 69–114.
Wood JD. (2008). Enteric nervous system: Reflexes, pattern
generators and motility. Curr Opin Gastroenterol 24, 149–158.
Xiong W, Giannone RJ, Morowitz MJ, Banfield JF, and Hettich
RL. (2015). Development of an enhanced metaproteomic
approach for deepening the microbiome characterization of
the human infant gut. J Proteome Res 14, 133–141.
Yang H, Zhao X, Tang S, et al. (2016). Probiotics reduce
psychological stress in patients before laryngeal cancer sur-
gery. Asia Pac J Clin Oncol 12, e92–e96.
Yatsunenko T, Rey FE, Manary MJ, et al. (2012). Human gut
microbiome viewed across age and geography. Nature 486,
Yolken RH, Jones-Brando L, Dunigan DD, et al. (2014). Chlor-
ovirus ATCV-1 is part of the human oropharyngeal virome and
is associated with changes in cognitive functions in humans and
mice. Proc Natl Acad Sci U S A 111, 16106–16111.
Yolken RH, Severance EG, Sabunciyan S, et al. (2015). Me-
tagenomic sequencing indicates that the oropharyngeal pha-
geome of individuals with schizophrenia differs from that of
controls. Schizophr Bull 41, 1153–1161.
Young JC, Pan C, Adams RM, et al. (2015). Metaproteomics reveals
functional shifts in microbial and human proteins during a preterm
infant gut colonization case. Proteomics 15, 3463–3473.
Zass LJ, Hart SA, Seedat S, Hemmings SMJ, and Malan-Mu
S. (2017). Neuroinflammatory genes associated with post-
traumatic stress disorder: Implications for comorbidity. Psy-
chiatr Genet 27, 1–16.
Zheng P, Zeng B, Zhou C, et al. (2016). Gut microbiome remodeling
induces depressive-like behaviors through a pathway mediated by
the host’s metabolism. Mol Psychiatry 21, 786–796.
Zhernakova A, Kurilshikov A, Bonder MJ, et al. (2016).
Population-based metagenomicsanalysis revealsmarkers for gut
microbiome composition and diversity. Science 352, 565–569.
Address correspondence to:
Stefanie Malan-Muller, PhD
Department of Psychiatry
Faculty of Medicine and Health Sciences
Stellenbosch University
Tygerberg 7600
South Africa
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
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
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
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... In contrast, in Deferribacteraceae, genes for cofactors and vitamin metabolism and amino acid metabolism were upregulated. The major disadvantage of metatranscriptomics is that due to the fast alterations in the mRNA transcript pool, it's uncertain if RNA extracted from faeces may properly represent gastrointestinal activity processes and is not a result of challenging sampling conditions [64]. ...
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Anastomotic leak (AL) is a life-threatening postoperative complication following colorectal surgery, which has not decreased over time. Until now, no specific risk factors or surgical technique could be targeted to improve anastomotic healing. In the past decade, gut microbiota dysbiosis has been recognized to contribute to AL, but the exact effects are still vague. In this context, interpretation of the mechanisms underlying how the gut microbiota contributes to AL is significant for improving patients’ outcomes. This review concentrates on novel findings to explain how the gut microbiota of patients with AL are altered, how the AL-specific pathogen colonizes and is enriched on the anastomosis site, and how these pathogens conduct their tissue breakdown effects. We build up a framework between the gut microbiota and AL on three levels. Firstly, factors that shape the gut microbiota profiles in patients who developed AL after colorectal surgery include preoperative intervention and surgical factors. Secondly, AL-specific pathogenic or collagenase bacteria adhere to the intestinal mucosa and defend against host clearance, including the interaction between bacterial adhesion and host extracellular matrix (ECM), the biofilm formation, and the weakened host commercial bacterial resistance. Thirdly, we interpret the potential mechanisms of pathogen-induced poor anastomotic healing.
... In addition, recent investigations on the linkage between the gut microbiota and brain function have suggested that the gut microbiota may play a role in the etiology of ADHD [8]. The bidirectional communication between the gut and the brain, also known as the "gut-brain axis", has been proposed to be involved in some neuropsychiatric disorders, including depression, anxiety, and schizophrenia [9][10][11]. Furthermore, the gut microbiota has also been reported to affect host development and physiology, which is linked to autism and ADHD [12,13]. ...
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Previous studies have explored the role of the microbiome in attention-deficit/hyperactivity disorder (ADHD). However, whether the microbiome is correlated with emotional–behavioral disturbances, the most common comorbid symptom of ADHD, remains unclear. We established a cross-sectional study in which 6- to 18-year-old children with ADHD who were receiving no medication and a healthy control group of children without ADHD were recruited to analyze their microbiome composition. Microbiota of fecal samples were collected and analyzed using a 16s rRNA gene sequencing approach. In comparison with the healthy control group, the gut microbiota in children with ADHD exhibited significantly lower beta diversity. The abundance of the phylum Proteobacteria and the genera Agathobacter, Phascolarctobacterium, Prevotella_2, Acidaminococcus, Roseburia, and Ruminococcus gnavus group was increased in the ADHD group compared with the healthy group. Linear discriminant effect size (LEfSe) analysis was used to highlight specific bacteria phylotypes that were differentially altered between the ADHD and control groups. A regression analysis was performed to investigate the association between microbiota and emotional–behavioral symptoms in children with ADHD. A significant association was noted between withdrawal and depression symptoms and Agathobacter (p = 0.044), and between rule-breaking behavior and the Ruminococcus gnavus group (p = 0.046) after adjusting for sex, age, and the ADHD core symptoms score. This study advances the knowledge of how gut microbiota composition may contribute to emotional–behavioral symptoms in children with ADHD. The detailed mechanisms underlying the role of the gut microbiota in ADHD pathophysiology still require further investigation.
... Recently, there has been increasing emphasis on the importance of microbiome science in the field of human medicine, especially the intestinal microbiome, which is increasingly being recognized as an organ in its own right or even as a "second brain". Their role in the physiological function and metabolism of other organs and tissues has been the subject of a great deal of research (3,5). Accompanied by the specific process of intestinal microbiome colonization, the microbiota-intestinal-brain axis or the microbiota-intestinalliver axis established in an individual during this period can potentially represent the main determinants of the life-long metabolic pattern. ...
... Although not well investigated, the gut microbiome develops before birth, and it differs between fetal twins based on the placental microbiome [27][28][29]. The gut microbiome has been reported to influence brain development [30,31]. Familial studies also demonstrate an 18-fold increase in autism risk among siblings of an autistic child, compared to the general population [32]. ...
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Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders (NDD) characterized by deficits in three domains: impairments in social interactions, language, and communication, and increased stereotyped restrictive/repetitive behaviors and interests. The exact etiology of ASD remains unknown. Genetics, gestational exposure to inflammation, and environmental stressors, which combine to affect mitochondrial dysfunction and metabolism, are implicated yet poorly understood contributors and incompletely delineated pathways toward the relative risk of ASD. Many studies have shown a clear male bias in the incidence of ASD and other NDD. In other words, being male is a significant yet poorly understood risk factor for the development of NDD. This review discusses the link between these factors by looking at the current body of evidence. Understanding the link between the multiplicity of hits—from genes to environmental stressors and possible sexual determinants, contributing to autism susceptibility is critical to developing targeted interventions to mitigate these risks.
... LPS is a component of the outer membrane of gram-negative bacteria, while LBP is induced by prolonged elevation of LPS and is considered a biological marker of "leaky gut," a condition where bacteria and other microorganisms within the lumen of the gut can translocate across the gut mucosa into the body and systemic circulation. Together, these data are consistent with the hypothesis that PTSD is associated with a dysregulated microbiome-gut-brain axis (Hemmings et al. 2017;Loupy and Lowry 2019;Malan-Muller et al. 2018 and that comprehensive therapy would benefit from stabilizing the dysregulated microbiome and gut mucosal barrier. ...
The prevalence of inflammatory disease conditions, including allergies, asthma, and autoimmune disorders, increased during the latter half of the twentieth century, as societies transitioned from rural to urban lifestyles. A number of hypotheses have been put forward to explain the increasing prevalence of inflammatory disease in modern urban societies, including the hygiene hypothesis and the "Old Friends" hypothesis. In 2008, Rook and Lowry proposed, based on the evidence that increased inflammation was a risk factor for stress-related psychiatric disorders, that the hygiene hypothesis or "Old Friends" hypothesis may be relevant to psychiatric disorders. Since then, it has become more clear that chronic low-grade inflammation is a risk factor for stress-related psychiatric disorders, including anxiety disorders, mood disorders, and trauma- and stressor-related disorders, such as posttraumatic stress disorder (PTSD). Evidence now indicates that persons raised in modern urban environments without daily contact with pets, relative to persons raised in rural environments in proximity to farm animals, respond with greater systemic inflammation to psychosocial stress. Here we consider the possibility that increased inflammation in persons living in modern urban environments is due to a failure of immunoregulation, i.e., a balanced expression of regulatory and effector T cells, which is known to be dependent on microbial signals. We highlight evidence that microbial signals that can drive immunoregulation arise from phylogenetically diverse taxa but are strain specific. Finally, we highlight Mycobacterium vaccae NCTC 11659, a soil-derived bacterium with anti-inflammatory and immunoregulatory properties, as a case study of how single strains of bacteria might be used in a psychoneuroimmunologic approach for prevention and treatment of stress-related psychiatric disorders.
... Conditions such as anxiety, obsessive-compulsive disorder, and major depression are common comorbidities of AN. Data from literature has shown a link between anxiety and the gut microbiota (21,167) . Germ-free mice show reduced anxiety-like behaviour (63) , although germ-free rats exhibit more anxiety-like behaviour compared to controls (168) . ...
Anorexia nervosa (AN) is characterised by the restriction of energy intake in relation to energy needs and a significantly lowered body weight than normally expected, coupled with an intense fear of gaining weight. Treatment of AN is currently based on psychological and refeeding approaches, but their efficacy remains limited, since 40% of patients after ten years of medical care, still present symptoms of AN. The intestine hosts a large community of microorganisms, called the “microbiota”, which live in symbiosis with the human host. The gut microbiota of a healthy human is dominated by bacteria from two phyla: Firmicutes and majorly Bacteroidetes . However, the proportion in their representation differs on an individual basis and depends on many external factors, such as medical treatment, geographical location, and hereditary, immunological and lifestyle factors. Drastic changes in dietary intake may profoundly impact the composition of the gut microbiota, and the resulting dysbiosis may play a part in the onset and/or maintenance of comorbidities associated with AN, such as gastrointestinal disorders, anxiety, and depression, as well as appetite dysregulation. Furthermore, studies have reported the presence of atypical intestinal microbial composition in patients with AN compared to healthy normal-weight controls. This review addresses the current knowledge about the role of the gut microbiota in the pathogenesis and treatment of AN. The review also focuses on the bidirectional interaction between the gastrointestinal tract and the central nervous system (microbiota-gut-brain axis), considering the potential use of the gut microbiota manipulation in the prevention and treatment of AN.
... It was discovered that the microbiome plays a crucial role in the development of the hypothalamic-pituitary-adrenal axis and stress reactivity in adulthood. 16S rRNA sequencing was employed to investigate and compare the microbial composition of different individuals (Malan-Muller et al., 2018). ...
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A new research field is emerging that combines nutrition and genetics at the molecular level, namely nutrigenomics. Several aspects of nutrigenomics are examined in this review, with a particular focus on psychological disorders. The origin of this field in the 20th century and its modern developments have been investigated. Various studies have reported the impact of genetic factors and diet on various chronic disorders, elucidating how the deficiency of several macronutrients results in significant ailments, including diabetes, cancer, cardiovascular disorders, and others. Furthermore, the application of nutrigenomics to diet and its impact on the global disease rate and quality of life have been discussed. The relationship between diet and gene expression can facilitate the classification of diet-gene interactions and the diagnosis of polymorphisms and anomalies. Numerous databases and research tools for the study of nutrigenomics are essential to the medical application of this field. The nutrition-gene interrelationships can be utilized to study brain development, impairment, and diseases, which could be a significant medical breakthrough. It has also been observed that psychological conditions are exacerbated by the interaction between gut microbes and the prevalence of malnutrition. This article focuses on the impact of nutrition on genes involved in various psychological disorders and the potential application of nutrigenomics as a revolutionary treatment method.
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Background Depression is one of the most serious mental illnesses worldwide that endangers the health of people. The pathogenesis of depression is complex and is associated with abnormal neurotransmitter levels, activation of the hypothalamic–pituitary–adrenal (HPA) axis, inflammation, and gut flora–related disorders. However, most of the current pharmacological therapies used to manage depression are inconsistent and are associated with side effects. Owing to their low toxicity and wide availability in nature, polysaccharides are gradually attracting attention and are being discovered to exert direct or indirect antidepressant effects. Purpose In this review, we have summarized the classification, dosage, and experimental models to study polysaccharides with antidepressant effects obtained from different sources. We have also reviewed the protective effects and underlying mechanisms of these polysaccharides in depression by modulating inflammation, the HPA axis, and intestinal flora. Methods We searched the PubMed, Web of Science, and Google scholar databases and included studies that reported the use of polysaccharides in treating depression. Results The unique benefits of natural polysaccharides as antidepressants lie in their potential to modulate inflammation, regulate the HPA axis, and regulate intestinal flora, giving full play to their antidepressant effects via multiple pathways and targets. Conclusion Natural polysaccharides may be a promising resource for use as adjuvant antidepressant therapy. Our study might therefore provide evidence for the development of polysaccharide resources as antidepressants.
Research conducted in the past couple of decades has showcased the importance of the gut microbiota in human health and well-being. While many studies have reported on the differences in community membership between a disease state and a healthy state, few have investigated the mechanisms through which an aberrant microbiota contributes to a disease phenotype. One of the primary reasons for this are the many technical and ethical barriers to conducting the necessary studies directly in human individuals. Human microbiota-associated (HMA) porcine models have the potential to become important research tools which can enable the testing of hypotheses regarding host-microbiota interactions in human health and disease without directly involving humans. However, relatively few microbiome studies have utilized porcine models in this capacity. Through multiple studies, we evaluated HMA porcine models in terms of their suitability for use in gut microbiota studies. Results demonstrated that (1) compared to an HMA C3H/HeN mouse model, a higher percentage of donor taxa from donors of different age groups were able to persistently colonize HMA piglets, (2) while a majority of donor taxa in infant donors were able to colonize HMA piglets, rare/low-abundance taxa found in the infant donors enriched once engrafted into the piglets, and (3) the potential for using HMA piglets for studying host-microbiota interactions related to obesity. We believe that further improvements to address some of the shortcoming and challenges associated with HMA piglets will facilitate more wide-spread use of this animal model in the field of gut microbiome research. Advisor: Samodha C. Fernando
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BackgroundA key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to compare analysis results that are based on different taxonomies. ResultsWe provide a method and software for mapping taxonomic entities from one taxonomy onto another. We use it to compare the four taxonomies and the Open Tree of life Taxonomy (OTT). Conclusions While we find that SILVA, RDP and Greengenes map well into NCBI, and all four map well into the OTT, mapping the two larger taxonomies on to the smaller ones is problematic.
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Objectives: This trial investigated whether probiotics improved mood, stress and anxiety in a sample selected for low mood. We also tested whether the presence or severity of irritable bowel syndrome symptoms, and levels of proinflammatory cytokines, brain-derived neurotrophic factor and other blood markers, would predict or impact treatment response. Method: Seventy-nine participants (10 dropouts) not currently taking psychotropic medications with at least moderate scores on self-report mood measures were randomly allocated to receive either a probiotic preparation (containing Lactobacillus helveticus and Bifidobacterium longum) or a matched placebo, in a double-blind trial for 8 weeks. Data were analysed as intent-to-treat. Results: No significant difference was found between the probiotic and placebo groups on any psychological outcome measure (Cohen's d range = 0.07-0.16) or any blood-based biomarker. At end-point, 9 (23%) of those in the probiotic group showed a ⩾60% change on the Montgomery-Åsberg Depression Rating Scale (responders), compared to 10 (26%) of those in the placebo group ([Formula: see text], p = ns). Baseline vitamin D level was found to moderate treatment effect on several outcome measures. Dry mouth and sleep disruption were reported more frequently in the placebo group. Conclusions: This study found no evidence that the probiotic formulation is effective in treating low mood, or in moderating the levels of inflammatory and other biomarkers. The lack of observed effect on mood symptoms may be due to the severity, chronicity or treatment resistance of the sample; recruiting an antidepressant-naive sample experiencing mild, acute symptoms of low mood, may well yield a different result. Future studies taking a preventative approach or using probiotics as an adjuvant treatment may also be more effective. Vitamin D levels should be monitored in future studies in the area. The results of this trial are preliminary; future studies in the area should not be discouraged.
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Severe, repeated or chronic stress produces negative health outcomes including disruptions of the sleep/wake cycle and gut microbial dysbiosis. Diets rich in prebiotics and glycoproteins impact the gut microbiota and may increase gut microbial species that reduce the impact of stress. This experiment tested the hypothesis that consumption of dietary prebiotics, lactoferrin (Lf) and milk fat globule membrane (MFGM) will reduce the negative physiological impacts of stress. Male F344 rats, postnatal day (PND) 24, received a diet with prebiotics, Lf and MFGM (test) or a calorically matched control diet. Fecal samples were collected on PND 35/70/91 for 16S rRNA sequencing to examine microbial composition and, in a subset of rats; Lactobacillus rhamnosus was measured using selective culture. On PND 59, biotelemetry devices were implanted to record sleep/wake electroencephalographic (EEG). Rats were exposed to an acute stressor (100, 1.5 mA, tail shocks) on PND 87 and recordings continued until PND 94. Test diet, compared to control diet, increased fecal Lactobacillus rhamnosus colony forming units (CFU), facilitated non-rapid eye movement (NREM) sleep consolidation (PND 71/72) and enhanced rapid eye movement (REM) sleep rebound after stressor exposure (PND 87). Rats fed control diet had stress-induced reductions in alpha diversity and diurnal amplitude of temperature, which were attenuated by the test diet (PND 91). Stepwise multiple regression analysis revealed a significant linear relationship between early-life Deferribacteres (PND 35) and longer NREM sleep episodes (PND 71/72). A diet containing prebiotics, Lf and MFGM enhanced sleep quality, which was related to changes in gut bacteria and modulated the impact of stress on sleep, diurnal rhythms and the gut microbiota.
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Background Obsessive–compulsive disorder (OCD) is a chronic neurodevelopmental disorder that affects up to 3% of the general population. Although epigenetic mechanisms play a role in neurodevelopment disorders, epigenetic pathways associated with OCD have rarely been investigated. Oxytocin is a neuropeptide involved in neurobehavioral functions. Oxytocin has been shown to be associated with the regulation of complex socio-cognitive processes such as attachment, social exploration, and social recognition, as well as anxiety and other stress-related behaviors. Oxytocin has also been linked to the pathophysiology of OCD, albeit inconsistently. The aim of this study was to investigate methylation in two targets sequences located in the exon III of the oxytocin receptor gene (OXTR), in OCD patients and healthy controls. We used bisulfite sequencing to quantify DNA methylation in peripheral blood samples collected from 42 OCD patients and 31 healthy controls. ResultsWe found that the level of methylation of the cytosine-phosphate-guanine sites in two targets sequences analyzed was greater in the OCD patients than in the controls. The higher methylation in the OCD patients correlated with OCD severity. We measured DNA methylation in the peripheral blood, which prevented us from drawing any conclusions about processes in the central nervous system. Conclusion To our knowledge, this is the first study investigating DNA methylation of the OXTR in OCD. Further studies are needed to evaluate the roles that DNA methylation and oxytocin play in OCD.
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Human gut microbiota is an important determinant for health and disease, and recent studies emphasize the numerous factors shaping its diversity. Here we performed a genome-wide association study (GWAS) of the gut microbiota using two cohorts from northern Germany totaling 1,812 individuals. Comprehensively controlling for diet and non-genetic parameters, we identify genome-wide significant associations for overall microbial variation and individual taxa at multiple genetic loci, including the VDR gene (encoding vitamin D receptor). We observe significant shifts in the microbiota of Vdr(-/-) mice relative to control mice and correlations between the microbiota and serum measurements of selected bile and fatty acids in humans, including known ligands and downstream metabolites of VDR. Genome-wide significant (P < 5 × 10(-8)) associations at multiple additional loci identify other important points of host-microbe intersection, notably several disease susceptibility genes and sterol metabolism pathway components. Non-genetic and genetic factors each account for approximately 10% of the variation in gut microbiota, whereby individual effects are relatively small.
Although we might shudder at the thought of billions of bacteria living in our lower intestine, we are colonized by these passengers shortly after birth. However, the relationship is mostly of mutual benefit, and they shape our immune system throughout life. Here, we describe our developing understanding of the far-reaching effects that the commensal flora have on mucosal and systemic immunity and their relevance to the effects of hygiene on human disease.
Prospective cohort studies are needed to assess the relationship between the fecal microbiome and human health and disease. To evaluate fecal collection methods, we determined technical reproducibility, stability at ambient temperature, and accuracy of 5 fecal collection methods (no additive, 95% ethanol, RNAlater Stabilization Solution, fecal occult blood test cards, and fecal immunochemical test tubes). Fifty-two healthy volunteers provided fecal samples at the Mayo Clinic in Rochester, Minnesota, in 2014. One set from each sample collection method was frozen immediately, and a second set was incubated at room temperature for 96 hours and then frozen. Intraclass correlation coefficients (ICCs) were calculated for the relative abundance of 3 phyla, 2 alpha diversity metrics, and 4 beta diversity metrics. Technical reproducibility was high, with ICCs for duplicate fecal samples between 0.64 and 1.00. Stability for most methods was generally high, although the ICCs were below 0.60 for 95% ethanol in metrics that were more sensitive to relative abundance. When compared with fecal samples that were frozen immediately, the ICCs were below 0.60 for the metrics that were sensitive to relative abundance; however, the remaining 2 alpha diversity and 3 beta diversity metrics were all relatively accurate, with ICCs above 0.60. In conclusion, all fecal sample collection methods appear relatively reproducible, stable, and accurate. Future studies could use these collection methods for microbiome analyses.
Objective: Brain-derived neurotrophic factor (BDNF) plays a crucial role in modulating resilience and vulnerability to stress. The aim of this study was to investigate whether epigenetic regulation of the BDNF gene is a biomarker of post-traumatic stress disorder (PTSD) development among veterans exposed to combat in the Vietnam War. Methods: Using the Clinician-Administered PTSD Scale, combat veterans were grouped into those with (n = 126) and without (n = 122) PTSD. DNA methylation levels at four CpG sites within the BDNF promoter I region were quantified in the peripheral blood using pyrosequencing. The effects of BDNF DNA methylation levels and clinical variables on the diagnosis of PTSD were tested using binary logistic regression analysis. Results: Subjects with PTSD showed a higher DNA methylation of four CpG sites at the BDNF promoter compared with those without PTSD. High methylation levels at the BDNF promoter CpG site, high combat exposure, and alcohol problems were significantly associated with PTSD diagnosis. Conclusions: This study demonstrated an association between higher DNA methylation of the BDNF promoter and PTSD diagnosis in combat-exposed individuals. Our findings suggest that altered BDNF methylation may be a valuable biomarker of PTSD after trauma exposure.
Background: Preclinical studies have identified certain probiotics as psychobiotics - live microorganisms with a potential mental health benefit. Lactobacillus rhamnosus (JB-1) has been shown to reduce stress-related behaviour, corticosterone release and alter central expression of GABA receptors in an anxious mouse strain. However, it is unclear if this single putative psychobiotic strain has psychotropic activity in humans. Consequently, we aimed to examine if these promising preclinical findings could be translated to healthy human volunteers. Objectives: To determine the impact of L. rhamnosus on stress-related behaviours, physiology, inflammatory response, cognitive performance and brain activity patterns in healthy male participants. Methods: An 8week, randomized, placebo-controlled, cross-over design was employed. Twenty-nine healthy male volunteers participated. Participants completed self-report stress measures, cognitive assessments and resting electroencephalography (EEG). Plasma IL10, IL1β, IL6, IL8 and TNFα levels and whole blood Toll-like 4 (TLR-4) agonist-induced cytokine release were determined by multiplex ELISA. Salivary cortisol was determined by ELISA and subjective stress measures were assessed before, during and after a socially evaluated cold pressor test (SECPT). Results: There was no overall effect of probiotic treatment on measures of mood, anxiety, stress or sleep quality and no significant effect of probiotic over placebo on subjective stress measures, or the HPA response to the SECPT. Visuospatial memory performance, attention switching, rapid visual information processing, emotion recognition and associated EEG measures did not show improvement over placebo. No significant anti-inflammatory effects were seen as assessed by basal and stimulated cytokine levels. Conclusions: L. rhamnosus was not superior to placebo in modifying stress-related measures, HPA response, inflammation or cognitive performance in healthy male participants. These findings highlight the challenges associated with moving promising preclinical studies, conducted in an anxious mouse strain, to healthy human participants. Future interventional studies investigating the effect of this psychobiotic in populations with stress-related disorders are required.
The gut microbiome is affected by multiple factors, including genetics. In this study, we assessed the influence of host genetics on microbial species, pathways and gene ontology categories, on the basis of metagenomic sequencing in 1,514 subjects. In a genome-wide analysis, we identified associations of 9 loci with microbial taxonomies and 33 loci with microbial pathways and gene ontology terms at P < 5 × 10⁻⁸. Additionally, in a targeted analysis of regions involved in complex diseases, innate and adaptive immunity, or food preferences, 32 loci were identified at the suggestive level of P < 5 × 10⁻⁶. Most of our reported associations are new, including genome-wide significance for the C-type lectin molecules CLEC4F–CD207 at 2p13.3 and CLEC4A–FAM90A1 at 12p13. We also identified association of a functional LCT SNP with the Bifidobacterium genus (P = 3.45 × 10⁻⁸) and provide evidence of a gene–diet interaction in the regulation of Bifidobacterium abundance. Our results demonstrate the importance of understanding host–microbe interactions to gain better insight into human health. © 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.