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Abstract

Gut microbiome diversity has been strongly associated with mood-relating behaviours, including major depressive disorder (MDD). This association stems from the recently characterised bi-directional communication system between the gut and the brain, mediated by neuroimmune, neuroendocrine and sensory neural pathways. While the link between gut microbiome and depression is well supported by research, a major question needing to be addressed is the causality in the connection between the two, which will support the understanding of the role that the gut microbiota play in depression. In this article, we address this question by examining a theoretical ‘chronology’, reviewing the evidence supporting two possible sequences of events. First, we discuss that alterations in the gut microbiota populations of specific species might contribute to depression, and secondly, that depressive states might induce modification of specific gut microbiota species and eventually contribute to more severe depression. The feasibility of both sequences is supported by pre-clinical trials. For instance, research in rodents has shown an onset of depressive behaviour following faecal transplantations from patients with MDD. On the other hand, mental induction of stress and depressive behaviour in rodents resulted in reduced gut microbiota richness and diversity. Synthesis of these chronology dynamics raises important research directions to further understand the role that gut microbiota play in mood-relating behaviours, which holds substantial potential clinical outcomes for persons who experience MDD or related depressive disorders.
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Gut microbiome and depression: what we know and what we need to know
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Gal Winter a*, Robert A Hart a, Richard P G Charlesworth a, Christopher Sharpley b
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a School of Science and Technology, the University of New England, Armidale, NSW, 2351,
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Australia
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b Brain-Behaviour Research Group, University of New England, Armidale, NSW, 2351,
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Australia
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* To whom correspondence should be addressed
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Dr Gal Winter
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School of Science & Technology,
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University of New England,
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Armidale, NSW, 2351, Australia
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Ph: 61 2 6773 2851. Email: gal.winter@une.edu.au
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Running title: Chronology of gut microbiome and depression
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Abstract
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Gut microbiome diversity has been strongly associated with mood-relating behaviours, including
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major depressive disorder (MDD). This association stems from the recently characterised bi-
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directional communication system between the gut and the brain, mediated by neuroimmune,
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neuroendocrine and sensory neural pathways. While the link between gut microbiome and
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depression is well supported by research, a major question needing to be addressed is the causality
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in the connection between the two, which will support understanding of the role that the gut
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microbiota play in depression. In this article we address this question by examining a theoretical
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‘chronology’, reviewing the evidence supporting two possible sequences of events. Firstly, we
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discuss that alterations in the gut microbiota populations of specific species might contribute to
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depression and secondly, that depressive states might induce modification of specific gut
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microbiota species and eventually contribute to more severe depression. The feasibility of both
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sequences is supported by pre-clinical trials. For instance, research in rodents has shown an onset
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of depressive behaviour following faecal transplantations from MDD patients. On the other hand,
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mental induction of stress and depressive behaviour in rodents resulted in reduced gut microbiota
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richness and diversity. Synthesis of these chronology dynamics raises important research
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directions to further understand the role that gut microbiota play in mood-relating behaviours,
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which holds substantial potential clinical outcomes for persons who experience MDD or related
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depressive disorders.
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Keywords
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Anxiety; depressive disorder; gut brain axis; gut microbiota; stress
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Introduction
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Depression is the leading cause of ill health and disability worldwide, with more than 300
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million people being depressed currently, an increase of more than 18% between 2005 and 2015
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(WHO, 2017). It also has greater adverse effects on personal health (Moussavi et al., 2007) and
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higher costs of care than other chronic diseases (Langa et al., 2004), and carries a similar risk for
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mortality from all causes as smoking does, even when related health factors such as blood pressure,
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alcohol intake, cholesterol and social status are taken into account (Mykletun et al., 2009). Recent
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meta-analytic data indicate that people with depression have a relative risk of mortality from all
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causes that is 1.86 times that for non-depressed individuals and that there are 2.74 million deaths
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annually from depression (Walker et al., 2015). It has been estimated that failing to recognise
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depression and provide access to treatment costs US$1 trillion globally each year from losses to
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households, employers and governments (WHO, 2017). However, despite these costs, standard
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pharmacological and psychological treatments for depression are effective in only about 74% of
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cases, even when combined (Rush et al., 2006).
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These data regarding prevalence, effects and treatment underscore the need to investigate
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models of depression that encompass a wider range of possible ‘causal’ factors than simply
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neurotransmitter depletion in an effort to identify more efficacious treatment approaches.
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Although depression is primarily a disease of the brain and effective treatment requires that
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neurological factors are understood (Ross et al., 2015), the brain does not exist in isolation, but is
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embedded within the overall physiology of the individual. In addition, depressive behaviour as
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defined by the standard symptomatology includes several somatic indicators, such as sleeping
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difficulties, weight loss/gain, and psychomotor agitation/retardation as well as the more easily-
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recognised ‘mental health’ factors of concentration difficulties, feeling sad, anhedonia, and
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thoughts of death (APA, 2013), supporting the case for considering organic factors in depression.
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Therefore, investigation of a multiplicity of physiological factors and pathways that might
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contribute to the state of the brain during depression has the potential to provide a more
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comprehensive (and perhaps efficacious) basis on which to mount treatment models that seek to
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describe associations between various body systems and mental states. Some of the possible
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physiological pathways that might contribute to changes in brain function that are associated with
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depression include the immune system (Dantzer, 2009), the Hypothalamus-Pituitary-Adrenal
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(HPA) axis (Dantzer, 2009), and the presence of preceding illness (Katon et al., 2007). Another
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potentially valuable pathway that has received some recent attention is the gut-brain axis (Alper
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and Ceylan, 2015), which is the focus of this review.
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The gut microbiota and depression
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A good deal of data have established that depression is associated with altered gut microbiota
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composition, generally in the form of reduced richness and diversity (Kelly et al., 2016; Zheng et
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al., 2016). The gut microbiota of adults is dominated primarily by members of the Bacteroidetes
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and Firmicutes phyla, representing approximately 90% of the adult microbiota (Tremaroli and
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Backhed, 2012). Comparison of the gut microbiota of patients diagnosed with major depressive
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disorder (MDD) and healthy individuals as well as studies regarding the gut microbiota of rodent
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models following stressor exposure, have revealed significant alterations in the abundance of
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different genera within Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria phyla.
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Interestingly, while there is a general consensus between the different studies, for some genera
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there are conflicting reports, suggesting that there may be confounding factors in some of those
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relationships. Figure 1 illustrates the changes that have been identified in microbial diversity
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between MDD patients and healthy individuals as well as changes to mice gut microbiota
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following stressor exposure. Detailed information of these modifications can be found in Table 1,
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describing the phylogenetic hierarchy of the different genera and whether the microbial population
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of the genus was increased or decreased in rodent models or MDD patients. Tables 2 and 3 provide
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more information regarding the nature of the clinical (Table 2) and preclinical (Table 3)
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experiments used to obtain these data.
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<Tables 1, 2 and 3 to be inserted here>
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How do gut microbiota affect mood state?
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Changes in the overall gut microbiota are relevant to mood state because gut microbiota
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interact with the brain via neuroimmune, neuroendocrine and neural pathways. While
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hypothalamic communication with the gut via the HPA axis is a significant component in the gut-
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brain axis, communication in this axis is hypothesized to be bidirectional, with the gut able to
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signal back to the brain (Collins et al., 2012; Cryan and Dinan, 2012; Mayer, 2011). The best
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evidence currently available shows that the primary conduit for this signalling is via the nervous
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system, in the form of the afferent vagus nerve. The vagus nerve is important in relaying signals
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from the brain to the viscera, becoming more active as the parasympathetic nervous system is
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activated, stimulating “rest-and-digest” functions. However, approximately 80% of vagus nerve
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fibres are afferent, relaying sensory information from the viscera, including the digestive tract, to
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the brain for integration and appropriate responses to maintain homeostasis (Berthoud and
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Neuhuber, 2000; Foley and DuBois, 1937). Detailed mechanisms facilitating communication in
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the gut-brain axis will be discussed in detail, as they fit with our proposed hypotheses of how this
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communication changes in depressive states.
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Gut-brain communication may also be indirect, mediated through different metabolites. For
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example- gut microbiota may have an influence upon brain states by the modulation of neuroactive
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substances such as serotonin, noradrenalin, dopamine and glutamate and gamma-aminobutyric
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acid (GABA), all of which (except GABA) are excitatory in their effects upon the post-synaptic
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neuron (GABA is inhibitory and, with glutamate, forms a ‘balance’ process for brain synaptic
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activity (Fendt et al., 2007). There is some evidence that decreased levels of serotonin (Reimold
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et al., 2008), noradrenalin (Delgardo and Morena, 2006), and dopamine (Willner, 1983), plus
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malfunction of the glutamate-GABA systems (Choudary et al., 2005), are associated with
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depression, based upon treatment studies in which the levels of these neurotransmitters have been
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either increased or decreased artificially, with some accompanying changes in depressive
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symptoms being observed (Foley and DuBois, 1937). Hence gut microbiota may potentially
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contribute to the levels of these neurotransmitters in the brain as well as in the gut (Mittal et al.,
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2017). These neurotransmitter-modulating biota might also be influenced by gut health, microbial
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diversity, and the relative activity of these organisms (Mittal et al., 2017). Moreover, gut
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microbiota can alter brain functioning in an indirect manner through changes in inflammatory
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states and immune status (Dinan and Cryan, 2013, 2016). Thus, a focus upon the gut-brain
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communication pathways is of interest and relevance when considering possible ‘causal’ pathways
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to depression.
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Chronology of depression
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One of the major questions needing to be addressed in this pathway from gut microbiota to
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depression is the causality in the connection between the two, which will elucidate the role that
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gut microbiota play in depression. This may be implied by the chronology of events connecting
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depression and gut microbiota changes. That is, there are three possible sequences of events that
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might occur between the gut microbiota and depression. First, reductions in the gut microbiota
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populations of specific species might precede reductions of neurotransmitter levels in the brain
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and hence contribute to depression. Second, depressive states might induce modification of
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specific gut microbiota species and eventually, contribute to more severe depression. Third, these
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changes to neurotransmitter levels in the brain and gut might occur simultaneously, and any
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relationship between them could be merely coincidental. Clearly, the first and second hypotheses
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have clinical implications for treatment of depression and/or gut microbiota, but the third does not.
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Therefore, this review will focus on pathways one and two.
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To gather experimental evidence supporting the connection between gut microbiome
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composition and depression, a review of the literature was undertaken. A Google Scholar search
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was conducted using the search terms: “gut”, “brain”, “communication”, “microbiota”,
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“microbiome” with at least one of the following words: “depression”, “anxiety”, “immune”,
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“neuroendocrine”. These search terms were to be included anywhere within the article. The first
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study to point out a direct connection between gut microbiota and the brain was published in 2004
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(Sudo et al., 2004), therefore our search criteria were limited to articles published between 2004
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and 04/2017. Considering the excellent reviews published on the topic of gut and brain bi-
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directional communication (e.g., (Dinan and Cryan, 2013; Dinan et al., 2015)), descriptions of
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those communication pathways was deemed unnecessarily repetitious here. Instead, this review
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focussed on the scientific evidence connecting depression and the gut microbiota vis-à-vis the
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chronology of changes in gut microbiota leading to changes in brain state versus changes in brain
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state leading to changes in gut microbiota. This focus was adopted in order to address the question
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of causality between these two hypothetically related physiological structures and their association
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with depression. Two directional hypotheses were therefore generated for testing on the basis of
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the literature. The first of these hypotheses is that changes in brain depressive state influence gut
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microbiota state.
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Hypothesis 1: Depressive state modulates gut microbiota
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In 1998, Drossman (1998) published a description of the biopsychosocial model of mental
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states, showing a link between mental state and gastrointestinal diseases, and suggesting an
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integration between psychological and physiological information when diagnosing and treating
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depression. Therefore, this section of the review is focused on experimental findings relating to
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the role that a psychological state of depression or anxiety has upon the gut microbiome.
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Demonstration of these effects necessitates the use of animal models to establish cause and effect
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relationship, however this approach has two main limitations. First, findings achieved using rodent
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animal have limited ext rapolation capacity to humans (Shilov et al., 1971; Smirnov and Lizko,
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1987), although a comparison between human and mice gut microbiota composition has found the
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two to be quantitatively different yet qualitatively alike (Krych et al., 2013). Second, while the
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induction of depressive-like behaviour in animals using stressor exposure is well established
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(Golden et al., 2011; Hennessy et al., 2011), it is still unknown whether the behavioural changes
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are a cause or an effect of changes in gut microbiota diversity. Nonetheless, with the inclusions of
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experimental controls we can draw a cause and effect relation between stressor exposure and gut
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microbiota modulation.
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Physiological implications of Depressive-like behaviour in animal models
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One of the established experimental models used to induce depressive-like behaviour with
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comorbid anxiety in rodents is via surgical removal of the olfactory bulb (olfactory bulbectomy,
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OB), a practice shown to alter function of the prefrontal cortex (PFC) (Harkin et al., 2003; Song
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and Leonard, 2005), a change also observed in humans with depression in a region where negative
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emotions are thought to be generated (Koenigs and Grafman, 2009). OB mice demonstrate a
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significant reduction in the latency to the step down test, prolonged immobility in a tail suspension
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test, and hyperlocomotion and reduced exploratory activity that were consistent with a profile of
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depressive and anxiety-like behaviour (Harkin et al., 2003; Song and Leonard, 2005). Examination
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of OB mice revealed elevated corticotropin-releasing hormone (CRH) expression, indicative of
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increased HPA activity, which in turn increased colonic motility (Park et al., 2013) and altered
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colonic gut microbiota profile. As the PFC has projections into the hypothalamus (Koenigs and
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Grafman, 2009), there is likely to be a pathway activated from the PFC or the hypothalamus,
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increasing HPA axis activity during this stress. Surprisingly, the authors did not observe changes
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in cytokine expression in OB mice. It is well known that bi-directional communication exists
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between the HPA axis and the immune system (Otmishi et al., 2008). A number of peripheral
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cytokines are known to activate the HPA axis and in turn, the effects of the immune system can be
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altered through the secretion of one of the major HPA hormones, cortisol (Marques-Deak et al.,
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2005; Otmishi et al., 2008). Small elevations in cytokines and other inflammatory markers have
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been observed in patients with depression, and behavioural consequences of depression were noted
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following cytokine administration (Raedler, 2011; Raison and Miller, 2011). Considering the fact
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that cytokines levels were not changed in OB mice, the authors assume that the behavioural profile
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of OB mice is independent of peripheral inflammatory or immunological processes. Alternatively,
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the authors acknowledge the fact that cytokines secretion may have been impaired due to
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experimental conditions. Overall, the authors (Park et al., 2013) postulate that the observed
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changes in gut microbiome diversity were an effect of the changes in colon physiology rather than
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being the cause of those physiological changes. This supports the hypothesis that depressive state
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may induce changes to the gut microbiota through an increase of colonic activity.
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A different, non-surgical, approach used to induce depressive behaviour in mice is by
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applying uncontrollable social stress. Models using this approach include the sub-chronic mild
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social defeat stress model (sCSDS) (Goto et al., 2014) ; chronic social defeat stress model (CSDS)
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(Golden et al., 2011), and the social disruption model (SDR) (Bailey et al., 2011). These models
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are based on the resident intruder paradigm or inter-male aggression, where mice are repeatedly
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subjected to bouts of social defeat by a larger and more aggressive mouse (Berton et al., 2006;
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Golden et al., 2011). This social defeat results in the development of depressive-like symptoms,
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which have been alleviated with antidepressant medication (Berton et al., 2006; Golden et al.,
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2011). Mice exposed to 10 days of sCSDS displayed changes in microbial diversity as well as
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differences in caecal and faecal metabolites between sCSDS mice and a control group of mice that
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did not experience social defeat (Aoki-Yoshida et al., 2016). While the authors did not report
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significant changes in microbial richness between the two populations, operational taxonomy units
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(OTU) analysis revealed significant increases in OTUs belonging to families Desulfovibrionaceae,
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Rikenellaceae and Lachnospiraceae and a decrease in OTUs belonging to genera Allobaculum and
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Mucispirillum (Figure 1, Table 1). Correlational analysis of caecal OTUs and caecal metabolites
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identified a potential relationship between the two. For example, in the family Lachnospiraceae,
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five different OTUs that were increased in sCSDS mice were significantly correlated with
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metabolites that were more abundant in these mice. It has been reported that a noted metabolite
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that is significantly decreased in sCSDS mice is 5-aminovaleric acid (5-AV), a microbial-produced
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metabolite that is involved in the modulation of the GABA metabolic pathway and is implicated
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as a GABAb receptor antagonist (Dhaher et al., 2014). These findings suggest that suppressed
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intestinal concentration of 5-AV may have a negative effect on tissue homeostasis regulated by
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GABA receptors, thus linking sCSDS and brain activity.
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The most elevated metabolite in the caecum of sCSDS mice has been identified as cholic
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acid (Aoki-Yoshida et al., 2016), a principal bile acid produced by the liver; this infers that an
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intestinal ecosystem change is induced by sCSDS. This inference was supported by gene
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expression studies of the ileum that showed an increased expression of genes involved in bile acid
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absorption (Aoki-Yoshida et al., 2016). Transcriptomic analyses also revealed the downregulation
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of genes involved in immune responses such as response to other organisms, defence responses,
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and cytokine production (Aoki-Yoshida et al., 2016). While this study did not present temporal
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data documenting the changes in immune system regulation in relation to changes in gut
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microbiota diversity, the authors postulated that the downregulation of the immune system
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disturbed the balance of the gut microbiota, leading to the changes observed in the microbiome
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profile of sCSDS mice (Aoki-Yoshida et al., 2016) (Figure 1, Table 1).
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Similar observations were made by Bharwani et al. (2016), who observed an altered
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immunoregulatory response in CSDS mice that included an increase in dendritic cell activation
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and transiently elevated levels of IL-10+ CD4+ T regulatory cells. These alterations led to a general
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trend of reduced diversity and reduced microbial richness in CSDS mice (Figure 1, Table 1). In
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contrast to Aoki-Yoshida et al. (2016), Barwani et al. (2016) identified decreased abundance of
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OTUs belonging to the family Lachnospiraceae in CSDS mice as well as decreased OTUs
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belonging to the genus Oscillospira and increased abundance in genera Gelria and Lactobacillus.
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In silico metabolite prediction, based on OTUs genetic composition, predicted the metabolomic
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profile of CSDS mice gut microbiota to exhibit lower prevalence of pathways involved in the
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synthesis and metabolism of neurotransmitter precursors and short-chain fatty acids (Aoki-
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Yoshida et al., 2016). However, further research is needed to verify this prediction. The role of the
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immune system in modulating gut microbiome diversity is seen also in SDR mice that display
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enhanced innate immune activity and increased peripheral cytokines (Bailey et al., 2011). This rise
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in immune activity has been shown to reduce the population of genera from the Bacteriodetes and
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Firmicutes phyla following only two hours of SDR exposure (Galley et al., 2014) (Figure 1, Table
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1), although it has not been established that this effect is long lasting.
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There is a substantial body of studies using different stress models that predict the same
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paradigm of stress modulation of immunoregulation and gut microbial diversity. These include
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stressors induced by maternal separation (Bailey and Co, 1999; O'Mahony et al., 2009), prolonged
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restraint stressors (Bailey et al., 2010), and grid floor induced stress (Bangsgaard et al., 2012). As
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stress, anxiety and depression are considered to be interrelated phenomena, the microbial shift
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observed in these studies is also considered as relevant for this review and is outlined in Figure 1
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and Table 1. In short, the microbial shift includes an increase in the population of bacteria
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belonging to the family Coriobacteriaceae of phyla Actinobacteria (Bangsgaard et al., 2012), an
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increase in the population of genera Alistipes and Odoribacter of phyla Bacteriodetes (Bangsgaard
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et al., 2012), and a decrease in the population of identified genera classified to the same phyla
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(Bailey et al., 2010).Taken together, these studies demonstrate a shift in gut microbiota in response
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to stress, albeit there is some variability depending on the model used and the experimental
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conditions under which the phenomenon is observed.
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Analysis of gut microbiota diversity
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Figure 1 provides a visual representation of the changes observed in microbial population
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in response to stress. It is important to recognise the limitations of the technologies used to obtain
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these data, predominately using 16S rRNA sequencing, with the main limitation being the ability
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to classify bacteria mostly down to the genus level and not the species or strain level. It is well
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known that there could be major differences between even two strains of the same species, let
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alone two species of the same genus. For example, both bacterial strains K-12 and EDL933 belong
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to the species Eschericia coli, however the former is a non-pathogenic, commonly used laboratory
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strain and the latter is a pathogen (Conway and Cohen, 2015). New technologies for metagenomics
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offer a full genome sequencing which may provide a better characterisation of microbial identity
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and metabolic activity.
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Depressive state modulates gut microbiota diversity
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While its extent is yet to be fully described, it is clear from the above that induction of
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stress causes a significant shift in the gut microbiota diversity. There is general agreement that
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this diversity shift does not include the introduction of new genera or the complete elimination of
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certain bacterial genera (Aoki-Yoshida et al., 2016; Bangsgaard et al., 2012; Bharwani et al., 2016;
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Galley et al., 2014; Jiang et al., 2015; Kelly et al., 2016). Instead, the changes are limited to
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increases or decreases of pre-existing microbial population. It is also important to note that the
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alterations in microbial diversity across the bacterial phylogenetic hierarchy are not limited to a
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specific genus or even a specific phylum.
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Overall, the evidence reviewed here shows similar trends in microbial population shift
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across studies, including an increase in genera from the Actinobacteria and Proteobacteria phyla
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and a mixed trend in the phyla Bacteroidetes and Firmicutes across the different families in both
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phyla. The information displayed in Figure 1 also shows some conflicting evidence where some
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studies reported an increase of a specific microbial group while others reported a decrease in the
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same group population in response to stress. Interestingly, the observations made using rodent
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models showed similarity to the observations made in humans diagnosed with MDD (Aizawa et
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al., 2016; Jiang et al., 2015; Kelly et al., 2016; Naseribafrouei et al., 2014; Zheng et al., 2016).
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Table 1 displays the microbial shift characterised in the gut microbiota of MDD patients in
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comparison to control individuals. While the observations are quite different at the genera level,
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they display the same trend at the phyla and class level.
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In conclusion, the data reviewed here demonstrate that stress and depression may precede
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gut microbiota alterations. These studies suggest that depressive state may precede modulation of
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HPA axis and cortisol secretion, which alters cytokine production and immune activity. These then
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cause changes to the gut microbiota habitat, which in turn lead to altered microbial population
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(Figure 2). Further research is needed to understand the exact nature of the microbial population
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shift as well as the metabolic and physiological implications of this change, but it is clear that
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depressive brain states can influence gut microbiota states.
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Hypothesis 2: Gut microbiota modulate depressive state
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This hypothesis contends that the gut microbiome is capable of affecting brain function.
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Primarily, this is hypothesised to occur via activation of vagal afferent fibres or by production of
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humoral chemicals that ultimately enter the brain. Through the production of the agents stimulating
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these pathways, the gut microbiome may thus affect mental health.
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Gut microbial transplantation
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Perhaps the most conclusive evidence for gut induction of depressive state would be the
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transplantation of microbial population from depressed subjects into healthy subjects so that the
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latter become depressed themselves. This finding was reported by Zheng et al. (2016) and Kelly
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et al. (2016) who performed faecal microbiota transplantation from depressed patients into a germ-
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free mouse or a microbiota-depleted rat model, respectively. Both showed behavioural changes
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that correlated with human depression and anxiety (as measured by maze exploration and open
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field tests, sucrose preference testing, etc.), and physiological features that are characteristic of
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depression following this procedure. Interestingly, the transplanted rats, which were treated for 12
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weeks (with twice weekly “top-up” doses) also showed an increase in plasma kynurenine and in
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kynurenine/tryptophan ratio, a change reported in the depressed donor group (although total
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tryptophan was not significantly altered) as well as an increase in acetate and total short chain fatty
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acids, as measured in their faeces (Kelly et al., 2016). This use of animal models and faecal
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transplant provide conceptual support for the hypothesis that changes in the gut microbiome can
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induce depression through a gut-brain communication route. As far as manipulating the microbiota
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as a treatment, there are currently limited data available, but faecal microbiota transplant shows
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promise based on its use in treating recurrent Clostridium difficile infection (Bakken et al., 2011;
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Brandt et al., 2012; Youngster et al., 2014). The central effects of such treatment remain to be
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investigated.
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Vagal nerve activation
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The vagus innervates a large proportion of the digestive tract and is known to be responsive
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to a number of endogenous chemicals in the digestive tract. Vagal afferent fibres project into the
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nucleus tractus solitarius, brain stem and forebrain structures, including the hypothalamus
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(Berthoud et al., 2011), which may allow regulation of stress responses of the HPA axis. Hormones
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and neurotransmitters known to activate vagal afferents include cholecystokinin, leptin (Peters et
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al., 2006), peptide YY3-36 (Koda et al., 2005), glucagon-like peptide-1 (Abbott et al., 2005), ghrelin
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(Date et al., 2002), adrenalin (Miyashita and Williams, 2006), glutamate (Uneyama et al., 2006)
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and serotonin (Hillsley and Grundy, 1998). Serotonin is of particular interest because it has
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regulatory roles for gut motility and secretion, but is also an important neurotransmitter in affective
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disorders such as depression (O'Mahony et al., 2015). In depression, both human and animal
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subjects have been found to have altered composition of the gut microbiota in comparison with
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control subjects (Dinan and Cryan, 2013; O'Mahony et al., 2015; Park et al., 2013). These
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alterations in the composition of the gut microbiota may result in altered vagal activation, which
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may contribute to the symptoms seen in depression.
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Interaction between microbiota and vagal nerve function
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The importance of the microbiota secretions activating vagal afferents that then signal to
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the central nervous system and brain regions, such as the hypothalamus, has been shown in animal
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models, particularly rodent models of anxiety. Some mouse models of anxiety are induced by oral
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agent administration (Hassan et al., 2014; Painsipp et al., 2011), or there may be a genetic
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predisposition in some rodent strains (Carola et al., 2004), allowing the use of behavioural testing
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to assess the efficacy of subsequent treatments. The application of specific bacteria into the
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digestive tract can abrogate these anxiety-like behaviours, with measurable changes in enteric
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neuron excitability (Bercik et al., 2011) and GABA receptor expression in the brain (Bravo et al.,
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2011). These normalising effects are lost following vagotomy (Bercik et al., 2011; Bravo et al.,
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2011). Thus, it has been hypothesised that vagal activation by the gut microbiome is necessary for
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regulation of normal mental health (Dinan and Cryan, 2013). It has been also suggested that
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pathologic microbes may modulate the same mechanism to potentiate depressive and sickness
355
behaviours (Maes et al., 2012). Therefore, it is reasonable to hypothesise that pathogenic microbes
356
may modulate activation of the vagal afferents, causing subsequent pathologic changes in the
357
17
central nervous system, which may then propagate systemic changes such as disease symptoms
358
(Figure 2).
359
Gut microbiota and humoral communication
360
The use of germ-free mice has indicated the probable use of humoral agents from the gut
361
microbiota to communicate with the host (Figure 2). For example, germ-free mouse models lack
362
a normal anxiety response if normal gut microbiota do not colonise the gastrointestinal tract early
363
in life (Clarke et al., 2013; Neufeld et al., 2011). In this model, there is a higher hippocampal
364
concentration of serotonin and, in males, there is a higher plasma concentration of its precursor,
365
tryptophan (Clarke et al., 2013). Serotonin and other tryptophan metabolites are produced by a
366
range of microbes in the digestive tract and enter the circulation (Morris et al., 2017; O'Mahony et
367
al., 2015). Thus, it is hypothesised that these products produced by the gut microbiota act as
368
humoral modulators of the central nervous system (Clarke et al., 2013). This is consistent with a
369
previous report showing that serotonin activation of the dorsal raphe nucleus promotes secretion
370
of CRH (Marcinkiewcz et al., 2016), and the higher circulating concentrations of corticosterone
371
(the rodent equivalent of cortisol) in the germ-free mice (Clarke et al., 2013; Neufeld et al., 2011).
372
However, the reduced anxiety response is inconsistent, and warrants exploration of serotonin
373
receptor expression in the brains of germ-free mice, which may provide some explanation for the
374
apparent selective sensitivity to serotonin. While the use of these animal models has some
375
limitations when findings are applied to human physiology, they do appear to be providing
376
important mechanistic information about how the gut microbiota affect the brain by clearly
377
showing that products of the gut microbiota affect the central nervous system and produce
378
measurable behaviours such as reducing anxiety-like responses (Clarke et al., 2013). This is
379
significant as it shows that the gut microbiome is capable of inducing changes in the central
380
18
nervous system and consequent behavioural responses to environmental stimuli. Therefore, by
381
modulating the availability of neurotransmitters (or their precursors) and receptors in the brain, the
382
gut microbiome has the potential to regulate mental health status.
383
As well as the gut microbiota producing neurotransmitters, they also produce hormone
384
analogues and other biologically active products. Among these biologically active products are
385
tyrosine derivatives, such as dopamine and adrenalin (Asano et al., 2012), as well as various short
386
chain fatty acids (Nankova et al., 2014). While dopamine and adrenalin are capable of acting
387
locally to affect gut functions such as motility and secretion (Asano et al., 2012), the short chain
388
fatty acids are capable of entering the systemic circulation if absorbed via the large intestine
389
(Nankova et al., 2014). Once in circulation, they are distributed throughout the body and are
390
preferentially taken up by various tissues affecting their function and the individual’s health,
391
(Koves et al., 2008). For example, altered fatty acid concentrations may contribute to insulin
392
resistance, resulting in a chronic condition, type 2 diabetes mellitus (Koves et al., 2008). This may
393
be one pathway by which the gut microbiota may contribute to an individual’s well-being.
394
Of note, short chain fatty acids that remain in circulation, avoiding uptake and metabolism
395
by the peripheral tissues, are capable of entering the central nervous system. Short chain fatty
396
acids, including propionic acid, are transported across the blood-brain-barrier, directly entering the
397
brain via a saturable transport mechanism (Conn et al., 1983). These products have been shown to
398
modulate the behaviour in animals in ways that mimic anti-depressive and anxiolytic effects
399
following peripheral and central (intracerebroventricular) administration (Nankova et al., 2014).
400
Importantly, altered concentrations of these short chain fatty acids in the brain change the
401
expression of neuromodulatory genes, such as CREB, which have been implicated in the
402
development of autism spectrum disorders (Nankova et al., 2014). While has not been conclusively
403
19
shown that gut derived SCFAs reach the brain, this appears to be a promising area for future
404
research.
405
Synthesis of findings and directions for future research
406
Each of the two hypotheses posited in the Introduction to this paper has been shown to
407
have substantial evidence to support at least the pathways that may be activated to achieve the
408
outcomes (i.e., changes in gut microbiota, or changes in mood state). Thus, it is most unlikely that,
409
at this stage, either one of these two pathways can be accepted as validated over the other. This
410
leaves both hypotheses as worthy of consideration. While this might appear to support the third
411
position (i.e., that the gut microbiota and depression occur co-temporaneously), that is unlikely
412
due to the evidence presented above that each can precede the other. With each of those pathways
413
receiving some research support, coincidental occurrence of the end points of each of those
414
pathways does not currently have a strong basis.
415
Alternatively, it may be that each pathway operates, but under different circumstances.
416
That is, some particular events/stressors may occur that trigger mood changes, and those mood
417
changes subsequently change gut microbiota. The reverse pathway might occur under different
418
conditions, so that digestive or infection-based challenges instigate gut microbiota changes which
419
later contribute to depressive behaviour. The role of stress per se in depression needs to be
420
controlled for in studies of these pathways to avoid a confound of ‘causal’ factors. However, at
421
present, the nature of each of those particular sets of environmental stressors/events in humans is
422
unknown, and represents a potential focus for future research into naturalistically-occurring
423
depression among human populations. Longitudinal data could assist in further deciphering these
424
20
issues and (potentially) providing more detailed stress/eventsdepression/gut microbiota
425
equations.
426
It is also relevant to question the value of continuing to undertake this research by way of
427
a unitary definition of depression. That is, when the nine major diagnostic criteria for MDD as set
428
out in the Diagnostic and Statistical Manual of Mental Disorders (5th revision) (DSM-5) are added
429
to the extra features described on pp. 162-165 of the DSM-5, it has been noted by Ostergaard et
430
al. (2011) that the possible number of combinations of those criteria that fulfil a diagnosis of MDD
431
is nearly 1,500. Because (as mentioned above) the current major treatments for MDD are only
432
effective about 74% of the time (Rush et al., 2006), the need to consider depression from a multi-
433
faceted model of symptom-clusters presents a potential research agenda. That approach has been
434
urged upon researchers and clinicians alike for several years (Insel, 2013), and there have been
435
some interesting reports of the nature of different ‘subtypes’ of depression (Parker, 2005; Parker
436
et al., 2002), focussing upon ‘atypical’ depression, ‘melancholic’ depression, and four subtypes
437
based upon the clinical content of the particular symptoms present (Sharpley and Bitsika, 2014).
438
Other models of depression subtypes might include those endophenotypes based upon genetic
439
factors, HPA-axis responsivity, immunological factors, and treatment outcomes. Matching of such
440
depression subtypes to specific stressors/events and tracing the prevalent gut microbiota-
441
depression/depression-gut microbiota pathway from those stressors/events by way of depression
442
subtypes represents a potentially.
443
Evolving from the points made in the preceding paragraph, although MDD is the most
444
common form of depression used in clinical and research settings (Hasin et al., 2005), it was
445
established two decades ago that patients with fewer than the five symptoms required for MDD
446
suffer from a disease-related burden that is undifferentiated from patients with the full MDD
447
21
diagnosis (Judd et al., 1996; Judd et al., 1997). Referred to as Subsyndromal Depression (SSD)
448
(Judd et al., 1994), SSD requires any two MDD criteria, whereas MDD requires at least one of the
449
two key symptoms of depressed mood or anhedonia to be present. SSD patients with just two of
450
the MDD diagnostic criteria were found to have no large consistent differences in impairment
451
compared with patients who fulfilled the complete criteria for MDD across eight domains of
452
functioning (Judd et al., 1996); both depressive groups suffering significantly more than
453
participants with no symptoms of MDD (Judd et al., 1998). The incidence and effects of SSD are
454
particularly relevant in older persons who have SSD because they also have a 5.5-fold chance of
455
developing MDD within one year compared to people who have none of the symptoms of MDD
456
at all (Lyness et al., 2006), and show significantly greater levels of psychological disability,
457
hopelessness and death ideation (Chopra et al., 2005). Other data suggest that elderly SSD patients
458
“are as ill as those with minor or major depression (in terms of ) medical burden” (Lyness et al.,
459
2007) but it is prevalent, underdiagnosed, and undertreated (Goldney et al., 2004; Vanitallie,
460
2005). It may be that some symptoms of MDD (enough to qualify the individuals for a diagnosis
461
of SSD but not MDD) may be associated with one of the pathways and other symptoms are
462
associated with the alternative pathway. That model could produce the combined result of both
463
pathways being present in some (highly symptomatic) patients, potentially clarifying the relative
464
roles of the two hypotheses described above.
465
It may also be of value to investigate the interaction between different treatment regimes
466
and the gut microbiota-depression/depression-gut microbiota pathways. That is, does medication
467
or psychotherapy (or any other therapy such as exercise or transcranial stimulation, etc.) work
468
more effectively when gut microbiota changes precede depression or when depression precedes
469
gut microbiota changes? It may be that, like most MDD patients, a series or combination of
470
22
treatments is most effective, but the question of which order is most efficacious for which of these
471
two pathways remains unanswered.
472
In conclusion, the gut microbiota affect very many aspects of human functioning, and it is
473
not unexpected that mood and mood-related behaviour should be included among those aspects.
474
If, as has been argued for some time, depression is characterised by an adaptive behavioural
475
withdrawal from a noxious and uncontrollable stimulus (Ferster, 1973; Kanter et al., 2008), and
476
that depressive behaviour can bring some advantage to the depressed person (Gilbert, 2005), then
477
the involvement of such a fundamental component of the individual’s physiology as gut microbiota
478
in this process is not to be unexpected. Notwithstanding that logical argument, the tracing of the
479
pathways by which the gut microbiota are involved in mood-related behaviours remains a major
480
challenge for researchers, and holds substantial potential clinical outcomes for persons who
481
experience MDD or related depressive disorders.
482
483
23
Acknowledgement
484
The authors are grateful to Dr Cedric Gondro for his help in the preparation of Figure 1.
485
486
24
References
487
Abbott, C.R., Monteiro, M., Small, C.J., Sajedi, A., Smith, K.L., Parkinson, J.R.C., Ghatei, M.A. and Bloom,
488
S.R. (2005). The inhibitory effects of peripheral administration of peptide YY336 and glucagon-
489
like peptide-1 on food intake are attenuated by ablation of the vagalbrainstemhypothalamic
490
pathway. Brain Res. 1044, 127-131.
491
Aizawa, E., Tsuji, H., Asahara, T., Takahashi, T., Teraishi, T., Yoshida, S., Ota, M., Koga, N., Hattori, K. and
492
Kunugi, H. (2016). Possible association of Bifidobacterium and Lactobacillus in the gut
493
microbiota of patients with major depressive disorder. J. Affect. Disord. 202, 254-257.
494
Alper, E. and Ceylan, M.E. (2015). The gut-brain axis: the missing link in depression. Clin.
495
Psychopharmacol. Neurosci. 13, 239-244.
496
Aoki-Yoshida, A., Aoki, R., Moriya, N., Goto, T., Kubota, Y., Toyoda, A., Takayama, Y. and Suzuki, C.
497
(2016). Omics studies of the murine intestinal ecosystem exposed to subchronic and mild social
498
defeat stress. J. Proteome. Res. 15, 3126-3138.
499
APA (2013).Diagnostic and statistical manual of mental disorders-5 (Washington, DC: American
500
Psychiatric Association).
501
Asano, Y., Hiramoto, T., Nishino, R., Aiba, Y., Kimura, T., Yoshihara, K., Koga, Y. and Sudo, N. (2012).
502
Critical role of gut microbiota in the production of biologically active, free catecholamines in the
503
gut lumen of mice. Am. J. Physiol. Gastrointest. Liver Physiol. 303, G1288-G1295.
504
Bailey, M.T. and Co, C.L. (1999). Maternal separation disrupts the integrity of the intestinal microflora in
505
infant rhesus monkeys. Dev. Psychobiol. 35, 146-155.
506
Bailey, M.T., Dowd, S.E., Galley, J.D., Hufnagle, A.R., Allen, R.G. and Lyte, M. (2011). Exposure to a social
507
stressor alters the structure of the intestinal microbiota: implications for stressor-induced
508
immunomodulation. Brain. Behav. Immun. 25, 397-407.
509
Bailey, M.T., Dowd, S.E., Parry, N.M., Galley, J.D., Schauer, D.B. and Lyte, M. (2010). Stressor exposure
510
disrupts commensal microbial populations in the intestines and leads to increased colonization
511
by Citrobacter rodentium. Infect. Immun. 78, 1509-1519.
512
Bakken, J.S., Borody, T., Brandt, L.J., Brill, J.V., Demarco, D.C., Franzos, M.A., Kelly, C., Khoruts, A., Louie,
513
T., Martinelli, L.P., Moore, T.A., Russell, G. and Surawicz, C. (2011). Treating Clostridium difficile
514
infection with fecal microbiota transplantation. Clin. Gastroenterol. Hepatol. 9, 1044-1049.
515
Bangsgaard, B., K. M., Krych, L., Sorensen, D.B., Pang, W., Nielsen, D.S., Josefsen, K., Hansen, L.H.,
516
Sorensen, S.J. and Hansen, A.K. (2012). Gut microbiota composition is correlated to grid floor
517
induced stress and behavior in the BALB/c mouse. PLoS One 7, e46231.
518
Bercik, P., Park, A.J., Sinclair, D., Khoshdel, A., Lu, J., Huang, X., Deng, Y., Blennerhassett, P.A.,
519
Fahnestock, M., Moine, D., Berger, B., Huizinga, J.D., Kunze, W., McLean, P.G., Bergonzelli, G.E.,
520
Collins, S.M. and Verdu, E.F. (2011). The anxiolytic effect of Bifidobacterium longum NCC3001
521
involves vagal pathways for gutbrain communication. Neurogastroenterol. Motil. 23, 1132-
522
1139.
523
Berthoud, H.-R. and Neuhuber, W.L. (2000). Functional and chemical anatomy of the afferent vagal
524
system. Auton. Neurosci. 85, 1-17.
525
Berthoud, H.-R., Shin, A.C. and Zheng, H. (2011). Obesity surgery and gutbrain communication. Physiol.
526
Behav. 105, 106-119.
527
Berton, O., McClung, C.A., DiLeone, R.J., Krishnan, V., Renthal, W., Russo, S.J., Graham, D., Tsankova,
528
N.M., Bolanos, C.A., Rios, M., Monteggia, L.M., Self, D.W. and Nestler, E.J. (2006). Essential role
529
of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 311, 864-868.
530
25
Bharwani, A., Mian, M.F., Foster, J.A., Surette, M.G., Bienenstock, J. and Forsythe, P. (2016). Structural
531
& functional consequences of chronic psychosocial stress on the microbiome & host.
532
Psychoneuroendocrinology 63, 217-227.
533
Brandt, L.J., Aroniadis, O.C., Mellow, M., Kanatzar, A., Kelly, C., Park, T., Stollman, N., Rohlke, F. and
534
Surawicz, C. (2012). Long-term follow-up of colonoscopic fecal microbiota transplant for
535
recurrent Clostridium difficile infection. Am. J. Gastroenterol. 107, 1079-1087.
536
Bravo, J.A., Forsytheb, P., Chew, M.V., Escaravage, E., Savignac, H.M., Dinan, T.G., Bienenstock, J. and
537
Cryan, J.F. (2011). Ingestion of Lactobacillus strain regulates emotional behavior and central
538
GABA receptor expression in a mouse via the vagus nerve. Proc. Natl. Acad. Sci. U S A 108,
539
16050-16055.
540
Carola, V., D’Olimpio, F., Brunamonti, E., Bevilacqua, A., Renzi, P. and Mangia, F. (2004). Anxiety-related
541
behaviour in C57BL/6↔BALB/c chimeric mice. Behav. Brain Res. 150, 25-32.
542
Chopra, M.P., Zubritsky, C., Knott, K., Have, T.T., Hadley, T., Coyne, J.C. and Oslin, D.W. (2005).
543
Importance of subsyndromal symptoms of depression in elderly patients. Am. J. Geriatr.
544
Psychiatry 13, 597-606.
545
Choudary, P.V., Molnar, M., Evans, S.J., Tomita, H., Li, J.Z., Vawter, M.P., Myers, R.M., Bunney, W.E., Akil,
546
H., Watson, S.J. and Jones, E.G. (2005). Altered cortical glutamatergic and GABAergic signal
547
transmission with glial involvement in depression. Proc. Natl. Acad. Sci. U S A 102, 15653-
548
15658.
549
Clarke, G., Grenham, S., Scully, P., Fitzgerald, P., Moloney, R.D., Shanahan, F., Dinan, T.G. and Cryan, J.F.
550
(2013). The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic
551
system in a sex-dependent manner. Mol. Psychiatry 18, 666-673.
552
Collins, S.M., Surette, M. and Bercik, P. (2012). The interplay between the intestinal microbiota and the
553
brain. Nat. Rev. Micro. 10, 735-742.
554
Conn, A.R., Fell, D.I. and Steele, R.D. (1983). Characterization of alpha-keto acid transport across blood-
555
brain barrier in rats. Am. J. Physiol. Endocrinol. Metabol. 245, E253-E260.
556
Conway, T. and Cohen, P.S. (2015). Commensal and pathogenic Escherichia coli metabolism in the gut.
557
Microbiol. Spectr. 3.
558
Cryan, J.F. and Dinan, T.G. (2012). Mind-altering microorganisms: the impact of the gut microbiota on
559
brain and behaviour. Nat. Rev. Neurosci. 13, 701.
560
Dantzer, R. (2009). Cytokine, sickness behavior, and depression. Immunology and allergy clinics of
561
North America 29, 247-264.
562
Date, Y., Murakami, N., Toshinai, K., Matsukura, S., Niijima, A., Matsuo, H., Kangawa, K. and Nakazato,
563
M. (2002). The role of the gastric afferent vagal nerve in ghrelin-induced feeding and growth
564
hormone secretion in rats. Gastroenterology 123, 1120-1128.
565
Delgardo, P. and Morena, F. (2006). Neurochemistry of mood disorders. In: The textbook of mood
566
disorders (Washington DC: Stein, D.K., Kupfer, D.J., Schatzberg, A.F. American Psychiatric
567
Publishing Inc.), pp. 101-116.
568
Dhaher, R., Damisah, E.C., Wang, H., Gruenbaum, S.E., Ong, C., Zaveri, H.P., Gruenbaum, B.F. and Eid, T.
569
(2014). 5-Aminovaleric acid suppresses the development of severe seizures in the methionine
570
sulfoximine model of mesial temporal lobe epilepsy. Neurobiol. Dis. 67, 18-23.
571
Dinan, T.G. and Cryan, J.F. (2013). Melancholic microbes: a link between gut microbiota and
572
depression? Neurogastroenterol. Motil. 25, 713-719.
573
Dinan, T.G. and Cryan, J.F. (2016). Mood by microbe: towards clinical translation. Genome. Med. 8, 36.
574
Dinan, T.G., Stilling, R.M., Stanton, C. and Cryan, J.F. (2015). Collective unconscious: how gut microbes
575
shape human behavior. J. Psychiatr. Res. 63, 1-9.
576
Drossman, D.A. (1998). Presidential Address: Gastrointestinal Illness and the Biopsychosocial Model.
577
Psychosom. Med. 60, 258-267.
578
26
Farshim, P., Walton, G., Chakrabarti, B., Givens, I., Saddy, D., Kitchen, I., J, R.S. and Bailey, A. (2016).
579
Maternal weaning modulates emotional behavior and regulates the gut-brain axis. Sci. Rep. 6,
580
21958.
581
Fendt, M., Schmid, S., Thakker, D.R., Jacobson, L.H., Yamamoto, R., Mitsukawa, K., Maier, R., Natt, F.,
582
Husken, D., Kelly, P.H., McAllister, K.H., Hoyer, D., van der Putten, H., Cryan, J.F. and Flor, P.J.
583
(2007). mGluR7 facilitates extinction of aversive memories and controls amygdala plasticity.
584
Mol. Psychiatry 13, 970-979.
585
Ferster, C.B. (1973). A functional analysis of depression. Am. Psychol. 28, 857-870.
586
Foley, J.O. and DuBois, F.S. (1937). Quantitative studies of the vagus nerve in the cat. I. The ratio of
587
sensory to motor fibers. J. Comparat. Neurol. 67, 49-67.
588
Galley, J.D., Nelson, M., Yu, Z., Dowd, S.E., Walter, J., Kumar, P.S., Lyte, L. and Bailey, M.T. (2014).
589
Exposure to a social stressor disrupts the community structure of the colonic mucosa-associated
590
microbiota. BMC Microbiol. 14.
591
Gilbert, P. (2005). Evolution and depression: issues and implications. Psychol. Med. 36, 287-297.
592
Golden, S.A., Covington, H.E., Berton, O. and Russo, S.J. (2011). A standardized protocol for repeated
593
social defeat stress in mice. Nature protocols 6, 1183-1191.
594
Golden, S.A., Covington, H.E., Berton, O. and Russo, S.J. (2011). A standardized protocol for repeated
595
social defeat stress in mice. Nat. Protocols 6, 1183-1191.
596
Goldney, R.D., Fisher, L.J., Dal Grande, E. and Taylor, A.W. (2004). Subsyndromal depression:
597
prevalence,use of health services and quality of life in an Australian population. Soc. Psychiatry
598
Psychiatr. Epidemiol. 39, 293-298.
599
Goto, T., Kubota, Y., Tanaka, Y., Iio, W., Moriya, N. and Toyoda, A. (2014). Subchronic and mild social
600
defeat stress accelerates food intake and body weight gain with polydipsia-like features in mice.
601
Behav. Brain. Res. 270, 339-348.
602
Harkin, A., Kelly, J.P. and Leonard, B.E. (2003). A review of the relevance and validity of olfactory
603
bulbectomy as a model of depression. Clin. Neurosc. Res. 3, 253-262.
604
Hasin, D.S., Goodwin, R.D., Stinson, F.S. and Grant, B.F. (2005). Epidemiology of major depressive
605
disorder: Results from the national epidemiologic survey on alcoholism and related conditions.
606
Arch. Gen. Psychiatry 62, 1097-1106.
607
Hassan, A.M., Jain, P., Reichmann, F., Mayerhofer, R., Farzi, A., Schuligoi, R. and Holzer, P. (2014).
608
Repeated predictable stress causes resilience against colitis-induced behavioral changes in mice.
609
Front. Behav. Neurosci. 8.
610
Hennessy, M.B., Paik, K.D., Caraway, J.D., Schiml, P.A. and Deak, T. (2011). Proinflammatory activity and
611
the sensitization of depressive-like behavior during maternal separation. Behav Neurosci 125.
612
Hillsley, K. and Grundy, D. (1998). Serotonin and cholecystokinin activate different populations of rat
613
mesenteric vagal afferents. Neurosci. Lett. 255, 63-66.
614
Insel, T. (2013). Transforming diagnosis. USA: National Institute of Mental Health.
615
www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml
616
Jiang, H., Ling, Z., Zhang, Y., Mao, H., Ma, Z., Yin, Y., Wang, W., Tang, W., Tan, Z., Shi, J., Li, L. and Ruan, B.
617
(2015). Altered fecal microbiota composition in patients with major depressive disorder. Brain.
618
Behav. Immun. 48, 186-194.
619
Judd, L., M. , Paulus, K.W. and Rapaport, M.P. (1996). Socioeconomic burden of subsyndromal
620
depressive symptoms and major depression in a sample of the general population. Am. J.
621
Psychiatry 153, 1411-1417.
622
Judd, L., M. , Rapaport, M.P. and Brown, J. (1994). Subsyndromal symptomatic depression: A new mood
623
disorder? J. Clin. Psychiatry 54, 18-28.
624
27
Judd, L.L., Akiskal, H.S., Maser, J.D. and et al. (1998). A prospective 12-year study of subsyndromal and
625
syndromal depressive symptoms in unipolar major depressive disorders. Arch. Gen. Psychiatry
626
55, 694-700.
627
Judd, L.L., Akiskal, H.S. and Paulus, M.P. (1997). The role and clinical significance of subsyndromal
628
depressive symptoms (SSD) in unipolar major depressive disorder. J. Affect. Disord. 45, 5-18.
629
Kanter, J.W., Busch, A.M., Weeks, C.E. and Landes, S.J. (2008). The nature of clinical depression:
630
symptoms, syndromes, and behavior analysis. Behav. Anal. 31, 1-21.
631
Katon, W., Lin, E.H.B. and Kroenke, K. (2007). The association of depression and anxiety with medical
632
symptom burden in patients with chronic medical illness. Gen. Hosp. Psychiatry 29, 147-155.
633
Kelly, J.R., Borre, Y., O' Brien, C., Patterson, E., El Aidy, S., Deane, J., Kennedy, P.J., Beers, S., Scott, K.,
634
Moloney, G., Hoban, A.E., Scott, L., Fitzgerald, P., Ross, P., Stanton, C., Clarke, G., Cryan, J.F. and
635
Dinan, T.G. (2016). Transferring the blues: Depression-associated gut microbiota induces
636
neurobehavioural changes in the rat. J. Psychiatr. Res. 82, 109-118.
637
Koda, S., Date, Y., Murakami, N., Shimbara, T., Hanada, T., Toshinai, K., Niijima, A., Furuya, M., Inomata,
638
N., Osuye, K. and Nakazato, M. (2005). The role of the vagal nerve in peripheral PYY336-
639
induced feeding reduction in rats. Endocrinology 146, 2369-2375.
640
Koenigs, M. and Grafman, J. (2009). The functional neuroanatomy of depression: Distinct roles for
641
ventromedial and dorsolateral prefrontal cortex. Behav. Brain. Res. 201, 239-243.
642
Koves, T.R., Ussher, J.R., Noland, R.C., Slentz, D., Mosedale, M., Ilkayeva, O., Bain, J., Stevens, R., Dyck,
643
J.R.B., Newgard, C.B., Lopaschuk, G.D. and Muoio, D.M. (2008). Mitochondrial overload and
644
incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 7,
645
45-56.
646
Krych, L., Hansen, C.H., Hansen, A.K., van den Berg, F.W. and Nielsen, D.S. (2013). Quantitatively
647
different, yet qualitatively alike: a meta-analysis of the mouse core gut microbiome with a view
648
towards the human gut microbiome. PLoS One 8, e62578.
649
Langa, K.M., Valenstein, M.A., Fendrick, A.M., Kabeto, M.U. and Vijan, S. (2004). Extent and cost of
650
informal caregiving for older americans with symptoms of depression. Am. J. Psychiat. 161,
651
857-863.
652
Lyness, J.M., Heo, M., Datto, C.J. and et al. (2006). Outcomes of minor and subsyndromal depression
653
among elderly patients in primary care settings. Ann. Int. Med. 144, 496-504.
654
Lyness, J.M., Kim, J., Tu, X., Conwell, Y., King, D.A. and Caine, E.D. (2007). The clinical significance of
655
subsyndromal depression in older primary care patients. Am. J. Geriatr. Psychiatry 15, 214-223.
656
Maes, M., Berk, M., Goehler, L., Song, C., Anderson, G., Gałecki, P. and Leonard, B. (2012). Depression
657
and sickness behavior are Janus-faced responses to shared inflammatory pathways. BMC Med.
658
10, 66.
659
Marcinkiewcz, C.A., Mazzone, C.M., D’Agostino, G., Halladay, L.R., Hardaway, J.A., DiBerto, J.F., Navarro,
660
M., Burnham, N., Cristiano, C., Dorrier, C.E., Tipton, G.J., Ramakrishnan, C., Kozicz, T., Deisseroth,
661
K., Thiele, T.E., McElligott, Z.A., Holmes, A., Heisler, L.K. and Kash, T.L. (2016). Serotonin
662
engages an anxiety and fear-promoting circuit in the extended amygdala. Nature 537, 97-101.
663
Marques-Deak, A., Cizza, G. and Sternberg, E. (2005). Brain-immune interactions and disease
664
susceptibility. Mol. Psychiatry 10, 239-250.
665
Mayer, E.A. (2011). Gut feelings: the emerging biology of gut-brain communication. Nat. Rev. Neurosci.
666
12, 453-466.
667
Mittal, R., Debs, L.H., Patel, A.P., Nguyen, D., Patel, K., O'Connor, G., Grati, M.h., Mittal, J., Yan, D.,
668
Eshraghi, A.A., Deo, S.K., Daunert, S. and Liu, X.Z. (2017). Neurotransmitters: the critical
669
modulators regulating gutbrain axis. J. Cell. Physiol. 232, 2359-2237.
670
28
Miyashita, T. and Williams, C.L. (2006). Epinephrine administration increases neural impulses
671
propagated along the vagus nerve: Role of peripheral β-adrenergic receptors. Neurobiol. Learn.
672
Mem. 85, 116-124.
673
Morris, G., Berk, M., Carvalho, A., Caso, J.R., Sanz, Y., Walder, K. and Maes, M. (2017). The role of the
674
microbial metabolites including tryptophan catabolites and short chain fatty acids in the
675
pathophysiology of immune-inflammatory and neuroimmune disease. Mol. Neurobiol. 54,
676
4432-4451.
677
Moussavi, S., Chatterji, S., Verdes, E., Tandon, A., Patel, V. and Ustun, B. (2007). Depression, chronic
678
diseases, and decrements in health: results from the World Health Surveys. Lancet 370, 851-
679
858.
680
Mykletun, A., Bjerkeset, O., Øverland, S., Prince, M., Dewey, M. and Stewart, R. (2009). Levels of anxiety
681
and depression as predictors of mortality: the HUNT study. Br. J. Psychiatry 195, 118-125.
682
Nankova, B.B., Agarwal, R., MacFabe, D.F. and La Gamma, E.F. (2014). Enteric bacterial metabolites
683
propionic and butyric acid modulate gene expression, including CREB-dependent
684
catecholaminergic neurotransmission, in PC12 Cells - possible relevance to autism spectrum
685
disorders. PLoS ONE 9, e103740.
686
Naseribafrouei, A., Hestad, K., Avershina, E., Sekelja, M., Linlokken, A., Wilson, R. and Rudi, K. (2014).
687
Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil.
688
26, 1155-1162.
689
Neufeld, K.M., Kang, N., Bienenstock, J. and Foster, J.A. (2011). Reduced anxiety-like behavior and
690
central neurochemical change in germ-free mice. Neurogastroenterol. Motil. 23, 255-264,
691
e119.
692
O'Mahony, S.M., Clarke, G., Borre, Y.E., Dinan, T.G. and Cryan, J.F. (2015). Serotonin, tryptophan
693
metabolism and the brain-gut-microbiome axis. Behav Brain Res 277, 32-48.
694
O'Mahony, S.M., Marchesi, J.R., Scully, P., Codling, C., Ceolho, A.M., Quigley, E.M., Cryan, J.F. and Dinan,
695
T.G. (2009). Early life stress alters behavior, immunity, and microbiota in rats: implications for
696
irritable bowel syndrome and psychiatric illnesses. Biol. Psychiatry 65, 263-267.
697
Østergaard, S.D., Jensen, S.O.W. and Bech, P. (2011). The heterogeneity of the depressive syndrome:
698
when numbers get serious. Acta Psychiatr. Scand. 124, 495-496.
699
Otmishi, P., Gordon, J., El-Oshar, S., Li, H., Guardiola, J., Saad, M., Proctor, M. and Yu, J. (2008).
700
Neuroimmune interaction in inflammatory diseases. Clin. Med. Circ. Resp. Pulm. Med. 2, 35-44.
701
Painsipp, E., Herzog, H., Sperk, G. and Holzer, P. (2011). Sex-dependent control of murine emotional-
702
affective behaviour in health and colitis by peptide YY and neuropeptide Y. B. J. Pharmacol. 163,
703
1302-1314.
704
Park, A.J., Collins, J., Blennerhassett, P.A., Ghia, J.E., Verdu, E.F., Bercik, P. and Collins, S.M. (2013).
705
Altered colonic function and microbiota profile in a mouse model of chronic depression.
706
Neurogastroenterol. Motil. 25, 733-e575.
707
Parker, G. (2005). Beyond major depression. Psychol. Med. 41, 467-474.
708
Parker, G., Roy, K., Mitchell, P., Wilhelm, K., Malhi, G. and Hadzi-Pavlovic, D. (2002). Atypical
709
depression: a reappraisal. Am. J. Psychiatry 159, 1470-1479.
710
Peters, J.H., Ritter, R.C. and Simasko, S.M. (2006). Leptin and CCK selectively activate vagal afferent
711
neurons innervating the stomach and duodenum. Am. J. Physiol. Regul. Integr. Comp. Physiol.
712
290, R1544-R1549.
713
Raedler, T.J. (2011). Inflammatory mechanisms in major depressive disorder. Curr. Opin. Psychiatry 24,
714
519-525.
715
Raison, C.L. and Miller, A.H. (2011). Is depression an inflammatory disorder? Curr. Psychiatry Rep. 13,
716
467-475.
717
29
Reimold, M., Batra, A., Knobel, A., Smolka, M.N., Zimmer, A., Mann, K., Solbach, C., Reischl, G.,
718
Schwarzler, F., Grunder, G., Machulla, H.J., Bares, R. and Heinz, A. (2008). Anxiety is associated
719
with reduced central serotonin transporter availability in unmedicated patients with unipolar
720
major depression: a [11C]DASB PET study. Mol. Psychiatry 13, 606-613.
721
Ross, D.A., Travis, M.J. and Arbuckle, M.R. (2015). The future of psychiatry as clinical neuroscience: Why
722
not now? JAMA Psychiatry 72, 413-414.
723
Rush, A.J., Trivedi, M.H., Wisniewski, S.R., Nierenberg, A.A., Stewart, J.W., Warden, D., Niederehe, G.,
724
Thase, M.E., Lavori, P.W., Lebowitz, B.D., McGrath, P.J., Rosenbaum, J.F., Sackeim, H.A., Kupfer,
725
D.J., Luther, J. and Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients
726
requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry 163, 1905-1917.
727
Sharpley, C.F. and Bitsika, V. (2014). Validity, reliability and prevalence of four ‘clinical content’
728
subtypes of depression. Behav. Brain Res. 259, 9-15.
729
Shilov, V., Lizko, N., Borisova, O. and Prokhorov, V. (1971). Changes in the microflora of man during
730
long-term confinement. Life Sci. Space Res. 9, 43-49.
731
Smirnov, K.V. and Lizko, N.N. (1987). Problems of space gastroenterology and microenvironment.
732
Nahrung 31, 563-566.
733
Song, C. and Leonard, B.E. (2005). The olfactory bulbectomised rat as a model of depression. Neurosci.
734
Biobehav. Rev. 29.
735
Sudo, N., Chida, Y., Aiba, Y., Sonoda, J., Oyama, N., Yu, X.N., Kubo, C. and Koga, Y. (2004). Postnatal
736
microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response
737
in mice. J Physiol 558, 263-275.
738
Tremaroli, V. and Backhed, F. (2012). Functional interactions between the gut microbiota and host
739
metabolism. Nature 489, 242-249.
740
Uneyama, H., Niijima, A., San Gabriel, A. and Torii, K. (2006). Luminal amino acid sensing in the rat
741
gastric mucosa. Am. J. Physiol. Gastrointest. Liver Physiol. 291, G1163-G1170.
742
Vanitallie, T. (2005). Subsyndromal depression in the elderly: Underdiagnosed and undertreated.
743
Metabolism 54, 39-44.
744
Walker, E., McGee, R.E. and Druss, B.G. (2015). Mortality in mental disorders and global disease burden
745
implications: A systematic review and meta-analysis. JAMA Psychiatry 72, 334-341.
746
Willner, P. (1983). Dopamine and depression: a review of recent evidence. I. Empirical studies. Brain.
747
Res. Rev. 6, 211-224.
748
Youngster, I., Sauk, J., Pindar, C., Wilson, R.G., Kaplan, J.L., Smith, M.B., Alm, E.J., Gevers, D., Russell, G.H.
749
and Hohmann, E.L. (2014). Fecal microbiota transplant for relapsing Clostridium difficile
750
infection using a frozen inoculum from unrelated donors: a randomized, open-label, controlled
751
pilot study. Clin. Infect. Dis. 58, 1515-1522.
752
Zheng, P., Zeng, B., Zhou, C., Liu, M., Fang, Z., Xu, X., Zeng, L., Chen, J., Fan, S., Du, X., Zhang, X., Yang, D.,
753
Yang, Y., Meng, H., Li, W., Melgiri, N.D., Licinio, J., Wei, H. and Xie, P. (2016). Gut microbiome
754
remodeling induces depressive-like behaviors through a pathway mediated by the host's
755
metabolism. Mol. Psychiatry 21, 786-796.
756
757
758
30
Table 1 Changes in gut microbial diversity observed in depressed patients and animal models following stressor exposure
759
Phylum
Order
Family
Genus
Model
organism
Population shift
Actinobacteria
Coriobacteriales
Coriobacteriaceae
Eggerthella
Human
Increase (Kelly et al., 2016)
Actinobacteria
Coriobacteriales
Coriobacteriaceae
unidentified genera
Mice
Increase (Bangsgaard et al.,
2012)
Proteobacteria
Desulfovibrionales
Desulfovibrionaceae
Desulfovibrio
Mice
Increase (Aoki-Yoshida et al.,
2016)
Proteobacteria
Rhodobacterales
Hyphomonadaceae
Ponticaulis
Mice
Increase (Galley et al., 2014)
Proteobacteria
Enterobacteriales
Enterobacteriaceae
unidentified genera
Human
Increase (Jiang et al., 2015)
Bacteroidetes
Bacteroidales
Rikenellaceae
unidentified genera
Mice
Increase (Aoki-Yoshida et al.,
2016)
Decrease (Bharwani et al.,
2016)
Bacteroidetes
Bacteroidales
Rikenellaceae
Alistipes
Mice, Human
Increase (Bangsgaard et al.,
2012; Jiang et al., 2015)
Bacteroidetes
Bacteroidales
Porphyromonoadaceae
unidentified genera
Human
Increase (Jiang et al., 2015)
Mice
Decrease (Bailey et al., 2010;
Galley et al., 2014)
Bacteroidetes
Bacteroidales
Porphyromonoadaceae
Odoribacter
Mice
Increase (Bangsgaard et al.,
2012)
Bacteroidetes
Bacteroidales
Porphyromonoadaceae
Parabacteroides
Human
Increase (Jiang et al., 2015)
Mice
Decrease (Bailey et al., 2011;
Galley et al., 2014)
Bacteroidetes
Bacteroidales
Bacteroidaceae
unidentified genera
Human
Decrease (Jiang et al., 2015)
Bacteroidetes
Bacteroidales
Bacteroidaceae
Bacteroides
Human
Decrease (Jiang et al., 2015)
Bacteroidetes
Bacteroidales
Prevotellaceae
unidentified genera
Human
Decrease (Jiang et al., 2015;
Kelly et al., 2016)
Bacteroidetes
Bacteroidales
Prevotellaceae
Paraprevotella
Human
Increase (Kelly et al., 2016)
Bacteroidetes
Bacteroidales
Prevotellaceae
Prevotella
Human
Decrease (Jiang et al., 2015;
Kelly et al., 2016)
Firmicutes
Clostridiales
Lachnospiraceae
unidentified genera
Mice
Increase (Aoki-Yoshida et al.,
2016)
Human, Mice
Decrease (Bharwani et al.,
2016; Jiang et al., 2015;
Naseribafrouei et al., 2014)
Firmicutes
Clostridiales
Lachnospiraceae
Pseudobutyrivibrio
Mice
Decrease (Bailey et al., 2011)
Firmicutes
Clostridiales
Lachnospiraceae
Coprococcus
Mice
Decrease (Bailey et al., 2011)
31
Firmicutes
Clostridiales
Lachnospiraceae
Roseburia
Mice
Increase (Bailey et al., 2011)
Firmicutes
Clostridiales
Lachnospiraceae
Dorea
Mice
Decrease (Bailey et al., 2011)
Firmicutes
Clostridiales
Lachnospiraceae
Anaerofilum
Human
Increase (Kelly et al., 2016)
Firmicutes
Clostridiales
Lachnospiraceae
Blautia
Human
Increase (Jiang et al., 2015)
Firmicutes
Clostridiales
Peptostreptococcaceae
Clostridium
Mice
Increase (Bailey et al., 2011)
Firmicutes
Clostridiales
Ruminococcaceae
Ruminococcus
Human
Decrease (Jiang et al., 2015)
Firmicutes
Clostridiales
Ruminococcaceae
Oscillospira
Mice
Decrease (Bharwani et al.,
2016)
Firmicutes
Clostridiales
Clostridiaceae
Faecalibacterium
Human
Decrease (Jiang et al., 2015)
Firmicutes
Thermoanaerobacterales
Thermoanaerobacteraceae
Gelria
Human
Increase (Kelly et al., 2016)
Firmicutes
Lactobacillales
Enterococcaceae
Enterococcus
Mice
Increase (Bharwani et al.,
2016)
Decrease (Farshim et al.,
2016)
Firmicutes
Lactobacillales
Lactobacillaceae
unidentified genera
Mice
Decrease (Galley et al., 2014)
Firmicutes
Lactobacillales
Lactobacillaceae
Lactobacillus
Mice
Increase (Bharwani et al.,
2016)
Decrease (Bailey et al., 2011;
Galley et al., 2014)
Firmicutes
Erysipelotrichales
Erysiopelotrichaceae
unidentified genera
Human
Decrease (Jiang et al., 2015)
Firmicutes
Erysipelotrichales
Erysiopelotrichaceae
Allobaculum
Mice
Decrease (Aoki-Yoshida et
al., 2016)
Firmicutes
Erysipelotrichales
Erysiopelotrichaceae
Turicibacter
Human
Increase (Kelly et al., 2016)
Firmicutes
Erysipelotrichales
Erysipelotrichidae
Holdemania
Human
Increase (Kelly et al., 2016)
Firmicutes
Selenomonadales
Acidaminococcaceae
unidentified genera
Human
Increase (Jiang et al., 2015)
Firmicutes
Veillonellales
Veillonellaceae
unidentified genera
Human
Decrease (Jiang et al., 2015)
Firmicutes
Veillonellales
Veillonellaceae
Dialister
Human
Decrease (Jiang et al., 2015;
Kelly et al., 2016)
Firmicutes
Veillonellales
Veillonellaceae
Megamonas
Human
Increase (Jiang et al., 2015)
Deferribacteres
Deferribacterales
Deferribacteraceae
Mucispirillum
Mice
Decrease (Aoki-Yoshida et
al., 2016)
Fusobacteria
Fusobacteriaceae
Fusobacterium
unidentified genera
Human
Increase (Jiang et al., 2015)
760
32
Table 2 Clinical studies comparing gut microbiota of depressed and healthy subjects
761
762
763
764
Study
Definition of depression
Diagnostic criteria
Sample size
(Kelly et al., 2016)
MDD
Clinical diagnosis based on DSM-
IV criteria for MDD, using MINI,
plus HAM-D score > 17.
34 MDD patients
33 healthy controls
Aged between 18 and
65 years;
Matched for gender,
age, ethnicity
(Jiang et al., 2015)
Active MDD and
responded MDD
Clinical diagnosis based on DSM-
IV using SCID)
46 MDD patients
30 Healthy controls
(Naseribafrouei et
al., 2014)
Clinical diagnosis based on ICD-10,
plus “mild to severe depression”
using Montgomery-Asberg
Depression Rating Scale (MADRS)
37 depressed
18 control
33
Table 3 - Preclinical studies comparing gut microbiota of mice following stressor exposure
765
Study
Type of stressor
Behaviour analysis
Sample size
(Bangsgaard et al., 2012)
Grid floor induced stress
Tripletest (Elevated Plus
Maze,
Light/Dark Box, and
Open Field combined)
Tail Suspension Test
Burrowing
n = 14 female BALB/c
mice per group
(Aoki-Yoshida et al.,
2016)
Subchronic and mild
social defeat stress
Social interaction test
E levated-plus maze test
n = 6 male
C57BL/6JJmsSlc mice
per group
(Galley et al., 2014)
Social disruption (2 hours
exposure)
none
n = 5 male C57BL/6
mice per group
(Bharwani et al., 2016)
Chronic social defeat
Three-chambered
sociability test
Aggressor interaction test
n = 9 male C57BL/6
mice per group
(Bailey et al., 2010)
Prolonged restraint stress
none
Male CD-1 mice,
n = 3 for treatment, n = 8
control
(Bailey et al., 2011)
Social disruption (6 daily
2 hours cycles of stressor
exposure)
n = 5 male CD-1 mice
per group
766
767
34
Figure legends
768
769
Figure 1
770
Alterations in microbial diversity observed in depressed patients and animal models
771
following stressor exposure. Illustration of microbial diversity shift induced by external stressors,
772
based on data presented in Supplementary Table 1. Phylogenetic structure representation is
773
outlined in the figure, including phyla names and genera names.
774
775
Figure 2
776
Hypotheses of major routes of communication between the gut microbiome and the brain in
777
depressive states. In hypothesis 1 (orange arrows), a depressed brain state induces changes in the
778
microbiome through the HPA axis and immune system. This may then lead to gut symptomology
779
which could further exacerbate stress. In hy pothesis 2 (blue arrows), alterations in the gut
780
microbiome produce different molecules which signal through the vagal afferents to induce
781
behavioural changes in the brain. HPA- hypothalamus-pituitary-adrenaline, ENS Enteric
782
Nervous system, NTS-nucleus tractus solitarius
783
... Therefore, the study of subthreshold depression will benefit early provision of clinical therapy and better treatment efficacy when the adolescents have not developed full-blown depression (Regeer et al., 2006;Vulser et al., 2018). It has been found that gut microbiota played an important role in the pathologies of MDD in animals as well as human adults and adolescents (Winter et al., 2018;Ng et al., 2018). For instance, the gut microbiota in patients with MDD was significantly altered in the abundance of different genera within the phyla Bacteroidetes, Proteobacteria, Firmicutes and Actinobacteria (Jiang et al., 2015). ...
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... In this study, these harmful and pathogenic bacteria, including Collinsella, Fusobacterium, Odoribacter, Peptococcus, Tyzzerella, and Veillonella with low relative abundance, were identified and the association with enterotypes was investigated. Odoribacter was significantly enriched in EnB (Figure 2), which has previously been reported to be overrepresented in neurological-related cases, such as attention deficit hyperactivity disorder (ADHD) [91] and depression [92]. Collinsella was discovered in EnB (Figure 2), which was strongly correlated with rheumatoid arthritis [93]. ...
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Full-text available
Gut microbiota play vital roles in human health, utilizing indigestible nutrients, producing essential substances, regulating the immune system, and inhibiting pathogen growth. Gut microbial profiles are dependent on populations, geographical locations, and long-term dietary patterns resulting in individual uniqueness. Gut microbiota can be classified into enterotypes based on their patterns. Understanding gut enterotype enables us to interpret the capability in macronutrient digestion, essential substance production, and microbial co-occurrence. However, there is still no detailed characterization of gut microbiota enterotype in urban Thai people. In this study, we characterized the gut microbiota of urban Thai individuals by amplicon sequencing and classified their profiles into enterotypes, including Prevotella (EnP) and Bacteroides (EnB) enterotypes. Enterotypes were associated with lifestyle, dietary habits, bacterial diversity, differential taxa, and microbial pathways. Microbe–microbe interactions have been studied via co-occurrence networks. EnP had lower α-diversities than those in EnB. A correlation analysis revealed that the Prevotella genus, the predominant taxa of EnP, has a negative correlation with α-diversities. Microbial function enrichment analysis revealed that the biosynthesis pathways of B vitamins and fatty acids were significantly enriched in EnP and EnB, respectively. Interestingly, Ruminococcaceae, resistant starch degraders, were the hubs of both enterotypes, and strongly correlated with microbial diversity, suggesting that traditional Thai food, consisting of rice and vegetables, might be the important drivers contributing to the gut microbiota uniqueness in urban Thai individuals. Overall findings revealed the biological uniqueness of gut enterotype in urban Thai people, which will be advantageous for developing gut microbiome-based diagnostic tools.
... Ademais, atualmente a disfunção do eixo HPA tem sido descrita como um dos principais contribuintes para o desenvolvimento de depressão (5). A microbiota intestinal e seu eixo intestino-cérebro cada vez mais tem sido estudada por desempenhar um papel significativo na patogênese dessa doença (6). A microbiota intestinal, composta por aproximadamente 100 trilhões de células bacterianas, é definida como o conjunto de microrganismos presentes no intestino que realizam diversas funções, onde, além disso, apresenta uma importante associação com o cérebro através do eixo intestino-cérebro. ...
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A ligação direta do eixo intestino-cérebro é cada vez mais debatida como um fator importante no desenvolvimento das doenças mentais, principalmente no transtorno depressivo maior. Há um eixo de comunicação bidirecional entre cérebro e intestino, onde anormalidades na microbiota intestinal afetam o cérebro e o comportamento. Neste sentido, as perturbações na microbiota intestinal, que favorecem a disbiose, são consideradas fator de risco chave para o transtorno depressivo, e a regulação desta é um método de terapia e prevenção da depressão. O presente estudo analisou o impacto do tratamento com probióticos no desenvolvimento do transtorno depressivo maior, por meio de uma revisão bibliográfica integrativa de estudos clínicos publicados nos últimos 5 anos. Ao total, foram selecionados 8 artigos, nos quais as bactérias estudadas pertencem aos gêneros Bifidobacterium, Lactobacillus e Lactococcus. Os artigos apontam que a intervenção com probióticos atua no transtorno depressivo maior por três mecanismos de ação. Referindo-se a produção de ácidos graxos de cadeia curta, reduzindo o pH intestinal e retardando o crescimento de bactérias patogênicas. Promovendo a síntese de IgA a qual protege a barreira intestinal, impedindo a passagem de LPS para a circulação, diminuindo a inflamação sistêmica. Por fim, através do aumento dos níveis plasmáticos de triptofano e a diminuição da concentração de quinurenina, consequentemente melhorando a produção de serotonina. Assim, o cuidado com a microbiota intestinal e o uso de probióticos pode auxiliar no tratamento de transtornos depressivos e também na prevenção, atuando como uma nova opção terapêutica.
... One such example is the negative genetic correlations reported for anorexia nervosa with metabolic (e.g., type 2 diabetes, insulin resistance, circulating leptin levels) and anthropometric measures (e.g., body mass index, overweight, obesity), reestablishing this deadly eating disorder as a metabo-psychiatric disorder as opposed to one having a purely psychiatric origin (12). Considering our improved understanding of the complex interplay between monoamines (13), cytokines, and the microbiome (14) influencing the risk of depression, Leone and colleagues' findings also point toward the need for a more comprehensive evaluation of the pathophysiology of depression. Importantly, this study is a crucial first step toward setting the path for future work establishing a causal relationship between endocrinemetabolic disorders and depression. ...
... The neurotransmitters also play a vital role in this two-way communication. The gut microbiota can affect the brain function by modulating neurotransmitters such as catecholamines (epinephrine, norepinephrine, dopamine), serotonin, glutamine, and γ-Aminobutyric acid (GABA) (Fendt et al., 2008;Winter et al., 2018). The gut microbiota can either produce these neurotransmitters by themselves or influence their synthesis/metabolism. ...
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What is the effect of our gut microbial flora on brain? Does the gut microbiome have any role in the causation of psychiatric and neurodegenerative diseases? Does the effect of gut microbiota traverse the gut-brain axis? Questions like these have captured the interest and imagination of the scientific community for quite some time now. Research in the quest for answers to these questions, to unravel the potential role of the microbiota inhabiting the gut in controlling brain functions, has progressed manifold over the last two decades. Although the possibility of microbiome as a key susceptibility factor for neurological disorders viz. Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and autism spectrum disorder has bolstered by an increase in the clinical and preclinical evidence, the field is still in its infancy. Given the fact that the diversity of the gut microbiota is affected by various factors including the diet and exercise, the interpretation of such data becomes all the more difficult. Also, such studies have been mostly conducted on animal models, so there is a need for randomized controlled trials in human subjects, corroborated by longitudinal studies, to establish if modulating the gut microbiota can unravel novel therapeutic interventions. Exploring the genomic, metagenomic and metabolomic data from clinical subjects with psychiatric and neurological diseases can prove to be a helpful guide in individual treatment selection.
... Although there was no significant difference in age and sex among individuals with MDD, individuals with SCZ and HCs included in this study, the characteristics of lipid composition under other age conditions and sex composition are not clear. Furthermore, other signatures that can discriminate individuals with MDD and SCZ from HCs and each other, such as gut microbiota and tryptophan metabolism, have been recently reported [68][69][70], and the interaction between plasma lipid metabolism and these signatures needs to be further explored. ...
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Background and Objectives: Lipidomics is a pivotal tool for investigating the pathogenesis of mental disorders. However, studies qualitatively and quantitatively analyzing peripheral lipids in adult patients with schizophrenia (SCZ) and major depressive disorder (MDD) are limited. Moreover, there are no studies comparing the lipid profiles in these patient populations. Materials and Method: Lipidomic data for plasma samples from sex- and age-matched patients with SCZ or MDD and healthy controls (HC) were obtained and analyzed by liquid chromatography-mass spectrometry (LC-MS). Results: We observed changes in lipid composition in patients with MDD and SCZ, with more significant alterations in those with SCZ. In addition, a potential diagnostic panel comprising 103 lipid species and another diagnostic panel comprising 111 lipid species could distinguish SCZ from HC (AUC = 0.953) or SCZ from MDD (AUC = 0.920) were identified, respectively. Conclusions: This study provides an increased understanding of dysfunctional lipid composition in the plasma of adult patients with SCZ or MDD, which may lay the foundation for identifying novel clinical diagnostic methods for these disorders.
... Neither of the existing studies included functional or metabolic information, and one examined a very limited sample of only 15 patients. Nevertheless, numerous studies have connected depressive syndromes in general with changes in the gut microbiome [16,17,19,62], and preliminary evidence even points to a causal contribution of the gut microbiota as suggested by the transferability of depressive behaviour ...
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Background Extraintestinal symptoms are common in inflammatory bowel diseases (IBD) and include depression and fatigue. These are highly prevalent especially in active disease, potentially due to inflammation-mediated changes in the microbiota-gut-brain axis. The aim of this study was to investigate the associations between structural and functional microbiota characteristics and severity of fatigue and depressive symptoms in patients with active IBD. Methods We included clinical data of 62 prospectively enrolled patients with IBD in an active disease state. Patients supplied stool samples and completed the questionnaires regarding depression and fatigue symptoms. Based on taxonomic and functional metagenomic profiles of faecal gut microbiota, we used Bayesian statistics to investigate the associative networks and triangle motifs between bacterial genera, functional modules and symptom severity of self-reported fatigue and depression. Results Associations with moderate to strong evidence were found for 3 genera (Odoribacter, Anaerotruncus and Alistipes) and 3 functional modules (pectin, glycosaminoglycan and central carbohydrate metabolism) with regard to depression and for 4 genera (Intestinimonas, Anaerotruncus, Eubacterium and Clostridiales g.i.s) and 2 functional modules implicating amino acid and central carbohydrate metabolism with regard to fatigue. Conclusions This study provides the first evidence of association triplets between microbiota composition, function and extraintestinal symptoms in active IBD. Depression and fatigue were associated with lower abundances of short-chain fatty acid producers and distinct pathways implicating glycan, carbohydrate and amino acid metabolism. Our results suggest that microbiota-directed therapeutic approaches may reduce fatigue and depression in IBD and should be investigated in future research.
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The National Institute of Mental Health (NIMH) is one of 27 Institutes and Centers of the National Institutes of Health (NIH). Formally established in 1949, it was one of the first four NIH Institutes. Research and research training at NIMH address a variety of mental disorders, including attention deficit hyperactivity disorder, autism spectrum disorders, anxiety disorders, bipolar disorder, borderline personality disorder, depression, eating disorders, post-traumatic stress disorder, and schizophrenia.
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Neurotransmitters including catecholamines and serotonin play a crucial role in maintaining homeostasis in the human body. Studies on these neurotransmitters mainly revolved around their role in the “fight or flight” response, transmitting signals across a chemical synapse and modulating blood flow throughout the body. However, recent research has demonstrated that neurotransmitters can play a significant role in the gastrointestinal (GI) physiology. Norepinephrine (NE), epinephrine (E), dopamine (DA), and serotonin have recently been a topic of interest because of their roles in the gut physiology and their potential roles in gastrointestinal and central nervous system pathophysiology. These neurotransmitters are able to regulate and control not only blood flow, but also affect gut motility, nutrient absorption, gastrointestinal innate immune system, and the microbiome. Furthermore, in pathological states such as inflammatory bowel disease (IBD) and Parkinson's disease, the levels of these neurotransmitters are dysregulated, therefore causing a variety of gastrointestinal symptoms. Research in this field has shown that exogenous manipulation of catecholamine serum concentrations can help in decreasing symptomology and/or disease progression. In this review article, we discuss the current state-of-the-art research and literature regarding the role of neurotransmitters in regulation of normal gastrointestinal physiology, their impact on several disease processes, and novel work focused on the use of exogenous hormones and/or psychotropic medications to improve disease symptomology. This article is protected by copyright. All rights reserved
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The microbiota-gut-brain axis plays an important role in the development of stress-induced mental disorders. We previously established the subchronic and mild social defeat stress (sCSDS) model, a murine experimental model of depression, and investigated the metabolomic profiles of plasma and liver. Here, we used omics approaches to identify stress-induced changes in the gastrointestinal tract. Mice exposed to sCSDS for 10 days showed the following changes: 1) elevation of cholic acid and reduction of 5-aminovaleric acid among cecal metabolites; 2) downregulation of genes involved in the immune response in the terminal ileum; 3) a shift in the diversity of the microbiota in cecal contents and feces; and 4) fluctuations in the concentrations of cecal metabolites produced by gut microbiota reflected in plasma and hepatic metabolites. Operational taxonomic units (OTUs) within the family Lachnospiraceae showed an inverse correlation with certain metabolites. The social interaction score correlated with cecal metabolites, IgA, and cecal and fecal microbiota, suggesting that sCSDS suppressed the ileal immune response, altering the balance of microbiota, which together with host cells and host enzymes resulted in a pattern of accumulated metabolites in the intestinal ecosystem distinct from that of control mice.
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Background: Bifidobacterium and Lactobacillus in the gut have been suggested to have a beneficial effect on stress response and depressive disorder. We examined whether these bacterial counts are reduced in patients with major depressive disorder (MDD) than in healthy controls. Method: Bifidobacterium and Lactobacillus counts in fecal samples were estimated in 43 patients and 57 controls using bacterial rRNA-targeted reverse transcription-quantitative polymerase chain reaction RESULTS: The patients had significantly lower Bifidobacterium counts (P=0.012) and tended to have lower Lactobacillus counts (P=0.067) than the controls. Individuals whose bacterial counts below the optimal cut-off point (9.53 and 6.49log10 cells/g for Bifidobacterium and Lactobacillus, respectively) were significantly more common in the patients than in the controls for both bacteria (Bifidobacterium: odds ratio 3.23, 95% confidence interval [CI] 1.38-7.54, P=0.010; Lactobacillus: 2.57, 95% CI 1.14-5.78, P=0.027). Using the same cut-off points, we observed an association between the bacterial counts and Irritable bowel syndrome. Frequency of fermented milk consumption was associated with higher Bifidobacterium counts in the patients. Limitations: The findings should be interpreted with caution since effects of gender and diet were not fully taken into account in the analysis. Conclusion: Our results provide direct evidence, for the first time, that individuals with lower Bifidobacterium and/or Lactobacillus counts are more common in patients with MDD compared to controls. Our findings provide new insight into the pathophysiology of MDD and will enhance future research on the use of pro- and prebiotics in the treatment of MDD.