To solve the elusive medical mystery of why many adults have both high blood pressure and depression, University of Florida Health researchers took a long, in-depth look at one suspected culprit: gut bacteria.
The gut microbiome affects physiology and molecular events throughout the body, including parts of the brain that control blood pressure and depression, newly published findings show.
The gut’s role in the two prevalent, chronic conditions was first explained by a trio of UF Health researchers in September 2019. Now, using a branch of artificial intelligence known as machine learning, the researchers have zeroed in on the specific bacteria suspected of causing depression coupled with high blood pressure.
It’s a crucial step toward the long-term goal of improving health management and developing novel treatments based on the analysis and manipulation of gut bacteria, the researchers said. The two conditions are sometimes so intertwined it has also led them to coin a new phrase: depressive hypertension. The findings were published recently in the American Heart Journal.
High blood pressure and depression are interrelated in many people, yet unlinked in others. Cardiologists and psychiatrists don’t know why. That can make diagnosis and treatment challenging, said Bruce R. Stevens, Ph.D., a professor of physiology and functional genomics in the UF College of Medicine, and the study’s lead author.
Stevens and his colleagues used a novel machine learning approach to develop a much more sophisticated DNA analysis of gut bacteria. Mohan K. Raizada, Ph.D., a co-author of the study and a distinguished professor emeritus of physiology and functional genomics in the UF College of Medicine, suspected that people with depression and hypertension would have unique gut microbiomes. To establish their findings, the researchers focused on four groups of people—those with high blood pressure and depression, those who only had high blood pressure, those who only had depression, and healthy people.
Stevens deployed machine learning to make sense of myriad data points about the patients and their gut bacteria. The computer analysis filtered out statistically irrelevant “noise” and distilled the data into a coherent picture. Without machine learning, Stevens said the mind-boggling mountain of raw data would have been incomprehensible to the human brain.
“Because the patients have unique gut microbes, we knew we could use machine learning to identify them. The computer could distinguish the patients’ health conditions based on their personal microbiome,” Stevens said.
What emerged was a clear view of the unique gut bacteria in different patients: A trio of dominant bacteria were found in people with depression. Five other bacteria were prevalent in those with high blood pressure and five different dominant strains were noted in people with depression and hypertension. The healthy patients in the study had yet another combination of four dominant bacteria.
Raizada and Carl J. Pepine, M.D., a co-author of the study and a professor in the UF College of Medicine’s department of medicine, said gut microbe analysis and manipulation holds significant promise for treating depression, hypertension or both. For cardiologists and psychiatrists, analyzing gut bacteria may prove to be a reliable shortcut to finding the most effective therapies or recommendations for improving lifestyles.
A gut bacteria analysis could someday be used to quickly predict which patients will respond to particular medications in the manner of personalized medicine, Raizada said. Pepine added, “It would certainly take us forward from the approach where we try a drug to lower blood pressure or treat depression and then wait weeks or months for a possible response.”
The overwhelming prevalence of hypertension and depression makes the search for new diagnoses and treatments an imperative, Pepine said. High blood pressure is the most modifiable risk factor for cardiovascular disease and a host of other disorders. Collectively, hypertension and depression impact more than half of adults in most industrialized countries, according to Pepine.
“We’re targeting and addressing a huge problem,” he said.
Next, the team is studying the potential benefits of anti-inflammatory drugs that can freely cross the highly selective blood-brain barrier. Their aim is to counteract depressive-hypertension effects of certain undesirable gut bacteria species. They have shown some effectiveness in preclinical models and Pepine would like to see these tested in humans in the future.
While much more research is needed to develop new gut-based therapies, Stevens says this much holds true: The human body is actually a “meta-organism”—a complex, intertwined system of trillions of human and bacterial cells. Likewise, the gastrointestinal tract is a novel target for preventing, diagnosing and treating hypertension, depression or both.
“This is not just about the gut, the heart or the brain alone,” Stevens said. “Depressive-hypertension is a three-dimensional symphony of these three organs.”
Microbes in the gut are of great importance to the human body. The composition of one’s gut microbiota is individually specific and is highly influenced by genetics, growth and development, and location . With an estimated 1018 microorganisms, mostly made up of anaerobic bacteria, the gut microbiome is responsible for multiple functions in bowel movement, digestion of food, and absorption of nutrients .
With the brain and the gut working in a bi-directional manner, they could affect each other’s functions and significantly impact stress, anxiety, depression, and cognition .
Depression is a serious mental illness caused by multiple factors . It is described as low emotional disposition, loss of confidence, and apathy . Depression is suggested to result from complex interactions of an individual’s genetics and their environment.
Major depressive disorder (MDD) tops the spot in contributing to the worldwide disease burden, as claimed by the World Health Organization (WHO) . Based on the WHO reports, there are approximately 350 million people affected by depression .
Research findings showed that healthy gut microflora transmits brain signals through the pathways involved in neurogenesis, neural transmission, microglial activation, and behavioral control under stable or stressful conditions. This process led several studies to recognize the importance of microbiomes in managing mental health issues .
In depression, there is also dysregulation of the neuroendocrine and neuroimmune pathways [8,9]. More than 20% of Inflammatory Bowel Disease (IBD) patients have sleep disturbances and depressed behaviors. By acknowledging that inflammation affects the brain and how one thinks, treatments addressing this phenomenon have grown its popularity in IBD patients and healthy populations .
The study of gut microbiota affecting mental health is a relatively new research topic that has gained popularity these past years. There are still parts that need to be delved deeper and to be understood. A comprehensive evaluation of the gut microbiome leading to depression reveals flaws in the research design and how it was performed, suggesting that results may be subpar compared to other research studies .
Furthermore, more studies are needed to ascertain the benefits of using probiotic interventions in promoting stable brain processing . For example, the laboratory findings in rodent studies have not yet been clear on the effects of these gut microbiota in modulating psychiatric illnesses .
The degree of changes in function and composition of gastrointestinal microflora leading to depression  and the causal connection of both the bacterial commensals and depression have to be fully understood to establish the role of the gut microbiome in depression .
The environmental factors contributing to MDD are also still unclear . With all the findings noted in the research of gut microbiome and depression, this literature review aims to establish an association between gut microbiota and depression and how these gut microbes affect mental health.
reference link :https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510518/
Depression: a multifaceted mood disorder
The history of Western medicine’s understanding of depression dates back as far as ancient Greek physician Hippocrates, whose humoral theory of pathology identified black bile as the cause of what we today refer to as depression (Hippocrates, 1931). The Greek term for black bile μέλαινα χολή, transliterates to melaina chole, the origin of the words melancholy and melancholia. In 1621, renaissance scholar Robert Burton published “The Anatomy of Melancholy,” an encyclopedic tome that was one of the first published works to combine history, cause, and treatment in a single volume. While Burton’s treatments varied widely, including practices such as blood-letting, Burton did suggest changes in diet as being connected with melancholia, changes that are pertinent to the current review (Burton, 1621). By the late 19th century, British Psychiatrist Charles Mercier is credited as one of the first to propose the idea that melancholia was a brain disorder, a concept that would go on to shape the field of mental health for the next century (Lawlor, 2012).
In the tradition of viewing depressed mood as a brain disorder, psychiatrist Adolph Meyer (1866–1950) pushed for a transition from using the term “melancholia” to “depression” in medicine, partially to promote moving away from previous treatment practices in favor of developing what he termed “somatic therapies,” including early psychopharmacology and shock treatment. This new, medical perspective on depression would go on to influence the formation of the first Diagnostic and Statistical Manual for Mental Disorders (DSM-I) in 1952. While the DSM-I and its successor, the DSM-II, were still strongly grounded in the psychoanalytic approach, pioneered by Freud in the early 20th century, the DSM-III in 1980 would transform the United States’ (and the global perspective, to a large extent) perspective on mental health. The DSM-III was the first modern diagnostic guide to organize around observable symptoms, rather than theoretical constructs, creating a shared language for health care professionals. The DSM-IV (1994), and now the current DSM-5 (Association American Psychiatric, 2013) would go on to clarify MDD as requiring a single major depressive episode with at least five depression-related symptoms (Lawlor, 2012). This common language has fueled an explosion of research that has made cross-study comparisons standardized and much simpler to carry out. The biotechnology revolution beginning in the 1980s and the development of functional magnetic resonance imaging (fMRI) as a technique in psychology research in the 1990s catapulted our understanding of depression etiology in the domain of the brain (Faro and Mohamed, 2010; Ogawa et al., 1992).
However, a decade into the 21st century, a completely brain-based understanding of depression was in question. The popular “chemical imbalance” theory of depression, stating that depression was related to an imbalance of specific neurotransmitters, was proving less effective both in explaining the etiology of depression and in developing novel treatments (Malenka, 2012). The current understanding of MDD has come to encompass not just changes in neurotransmitters, but shifts in neural circuits, as well as alterations in both immune and endocrine functioning (Irwin and Miller, 2007; see Fig. 1). This broadened scope is now beginning to inform a vast array of new, personalized treatments that are beginning to show great promise in a new holistic approach to depression (Henter et al., 2017).
Depression and the central nervous system
Moving from a focus on neurotransmitters to one of the entire brain has allowed for the identification of brain regions and circuits associated with depression. The following section surveys several representative highlights. Imaging techniques have identified structural changes in the brains of individuals with a diagnosis of MDD, including decreased volume in the prefrontal cortex (PFC), the anterior cingulate cortex (ACC), the basal ganglia, thalamus, and hippocampus (Drevets, 2007; Dusi et al., 2015).
Depression has also been associated with a number of resting state connectivity differences (Gong and He, 2015). Recent works have found distinct changes in prefrontal-limbic circuitry, such as altered connectivity between the amygdala and medial PFC in individuals who experienced early life adversity (Park et al., 2018). Others have found increased connectivity in the default mode network (DMN) connectivity in depressed adults, a brain network associated with self-referential thought and rumination, in depressed adults (Bessette et al., 2018; Korgaonkar et al., 2014).
Increased connectivity has also been found within the dorsolateral PFC, a brain region involved in executive functioning and cognitive flexibility (Murrough et al., 2016; Singh et al., 2013). In contrast, decreased connectivity has been described between areas of the salience network, a network that monitors the environment for events of personal relevance (Kaiser et al., 2015).
Functional changes in response to experimental stimuli relevant to depression have also been identified. For example, depression is associated with increased amygdala activity in response to threat and decreased PFC activity during cognitive tasks (Kerestes et al., 2014). Individuals with MDD show hyperactivation to negatively valanced emotional stimuli and hypoactivation to positively valanced emotional stimuli in brain regions that process emotions, including the amygdala, striatum, hippocampus, and ACC (Groenewold et al., 2013).
Faulty reward processing has also been observed in MDD (Whitton et al., 2015), which has been elucidated, in part, through experimental paradigms that study brain activity in the context of monetary wins and losses (Pizzagalli et al., 2009), studies of genes related to the neurotransmission of dopamine (Bogdan et al., 2013), and large-scale circuitry studies (Peters et al., 2016).
This larger perspective on brain function has also enhanced the field’s understanding of depression treatment. For example, increased functional connectivity between frontal and limbic regions has been observed in response to pharmacotherapy (Dichter et al., 2015). Antidepressant treatment is associated with a normalization of limbic, ACC, and PFC activity in response to aversive stimuli (Wessa and Lois, 2015). Cognitive behavioral therapy (CBT) has been connected to changes in ACC activity, altered dynamics in the PFC, and shifts in activity in the amygdala and hippocampus (Anthes, 2014; Franklin et al., 2016).
Depression and the endocrine system
While the understanding that stress is related to depression goes back at least as far as Burton’s “Anatomy of Melancholy” (Burton, 1621), a modern understanding that the relationship between stress and depression is grounded in body systems and molecular biology is more recent.
The predominant system associated with endocrine responses to stress is the hypothalamic-pituitary-adrenal (HPA) axis, which relies on a cascade of hormones that ultimately prepare the body for adaptive responses to stress. Information about threat encoded in other areas of the brain signal the hypothalamus to release corticotropin-releasing hormone (CRH), which signals the pituitary gland to release adrenocorticotropic hormone (ACTH). ACTH then travels through the bloodstream to the adrenal cortex of the adrenal glands, which are then prompted to manufacture and release the stress hormone cortisol in humans (Tafet and Nemeroff, 2016).
Early study of this system in relation to depression made use of the dexamethasone suppression test (DST). In the DST, administration of the synthetic glucocorticoid dexamethasone typically creates negative feedback for the HPA axis and will reduce cortisol output the following day. Research found that depressed individuals often had a blunted response to the DST, functionally resulting in a less robust decrease in cortisol secretion. (Lesch and Rupprecht, 1989; Rupprecht and Lesch, 1989). This response often returned to normal, following successful depression treatment (Murphy, 1991).
Subsequently, the 1990s would see an explosion of research looking at the dynamics of psychosocial stress, in part due to the development of the Trier Social Stress Test (TSST), a public speaking-based stressor (Kirschbaum et al., 1993). Psychosocial stress was linked to activity of the HPA axis, and in some cases with depression as well (Foley and Kirschbaum, 2010).
A recent study contrasting the cortisol response to the TSST between women with depression, panic disorder, posttraumatic stress disorder (PTSD), and typical controls found that depression showed a much higher cortisol response than the other diagnoses, but lower than controls (Wichmann et al., 2017). However, recent work has demonstrated that there may be gender differences in depression-induced changes in HPA axis activity.
An experiment of heterosexual couples discussing an unresolved relationship conflict with each other in a laboratory setting showed distinct gender differences related to current depressive symptoms (Powers et al., 2016). Women experienced hypoactivation of the cortisol response to stress, including attenuated cortisol levels overall along with decreased reactivity and a flatter recovery curve. Men, on the other hand, presented with hyperactivation, including elevated levels of HPA axis activity, during the laboratory conflict.
Differences in depression-related HPA axis activity may even occur in response to different subtypes of depression, such as typical melancholic depression, anxious depression(defined as also having subsyndromal anxiety or a diagnosed anxiety disorder), and atypical depression, which is often characterized by increased mood reactivity (Fischer et al., 2017; Ionescu et al., 2013; ten Have et al., 2016). A recent examination of the depression subtype literature speaks to the high level of heterogeneity when it comes to measures of the HPA axis in depression (Juruena et al., 2018).
Perhaps the most salient divide lies in response to the DST, with most patients with melancholic depression showing elevated non-suppression and patients with atypical depression showing a profile more consistent with suppression. Other measures, such as basal levels of cortisol or ACTH, showed a great deal of variability from individual to individual.
This is consistent with the literature, which often finds sustained, elevated HPA axis activity via higher basal plasma cortisol concentration at both circadian trough and peak, increased amplitude of cortisol pulses in the context of circadian fluctuations, elevated 24-hour urinary free cortisol, and even increased adrenal size; however, studies often show heightened variation and high numbers of individuals with very different patterns of HPA axis activity (Jacobson, 2014).
This heterogeneity may also be due to the interaction of depression with stress and trauma. While increased stress typically leads to elevated cortisol secretion, ongoing stress and severe trauma are typically associated with the opposite, hypocortisolism (Heim et al., 2000). This is typical with stress-related disorders, such as PTSD, which frequently presents decreased circulating cortisol and hypersuppression in response to the DST and has even been incorporated into the development of mouse models of PTSD (Reber et al., 2016a; Yehuda and Seckl, 2011; Yehuda et al., 1993).
Interestingly, depression in the context of a trauma history, though not necessarily a diagnosis of PTSD, is associated with a hypersuppression response to the DST (Savic et al., 2012; Yehuda et al., 2004). Approximately half of people with a PTSD diagnosis also meet criteria for major depression, which may explain some of the variation in HPA axis activity among depressed individuals (Flory and Yehuda, 2015).
Neuroscience and genetics studies support these systems-wide findings, and particularly tie together the central nervous and endocrine systems. Gene polymorphisms in the HPA axis are associated with increased amygdala reactivity, which is a proposed link between early-life adversity, stress reactivity, and depression (Iorio et al., 2017). One study found that functioning of the HPA axis acts as a mediator between certain gene variants of the serotonin transporter and developing MDD, linking neurotransmission with endocrine activity (Ancelin et al., 2017).
Recent work has proposed that elevated cortisol levels in MDD decrease hippocampal volume by interfering with neurogenesis, and that these changes may be etiological for depressive symptoms (Boku et al., 2018). Finally, epigenetic changes in response to extended stress have been connected to depression. Specifically, heightened stress, particularly in response to early-life adversity, leads to epigenetic changes that alter glucocorticoid receptor (GR) expression and function, resulting in the prolonged and dysregulated HPA axis activity often associated with depression (Farrell and O’Keane, 2016).
Overall, an astounding 40% to 60% of depressed individuals have been found to have a dysregulated HPA axis to some degree, which can also be accompanied by abnormalities in other branches of the endocrine system, including the hypothalamic-pituitary-thyroid (HPT), and hypothalamic-pituitary-gonadal (HPG) axes (Howland, 2010). This makes the endocrine system a tantalizing target for pharmacological intervention in the context of depression.
While showing theoretical promise, research beginning in the 1990s that explored antagonism of CRH receptor 1 has largely been scaled back in response to poor safety and efficacy of initial candidates, along with the unanticipated interaction of antagonists with other receptors, including CRH receptor 2 (Spierling and Zorrilla, 2017). Alternatively, glucocorticoid receptor antagonists, primarily mifepristone, have also been explored in the context of depression (Howland, 2013).
Perhaps the most work has been done in Cushing’s syndrome, a disorder that results in heightened circulating cortisol that has a comorbidity with anxiety and depression that may be as high as 81% (Pivonello et al., 2015). Mifepristone is typically prescribed to manage Cushing’s syndrome (Nieman et al., 1985); however, it does not typically alleviate depressive symptoms, which often persist long after treatment (Pivonello et al., 2015).
When mifepristone has been used in the context of mood disorders in non-Cushing’s syndrome individuals, studies have found cognitive improvements but typically no related shifts in mood (Roat-Shumway et al., 2018; Young et al., 2004). However, while direct pharmacological manipulation of the endocrine system has not revealed promising treatments, many depression treatments help to regulate endocrine function indirectly, from psychopharmaceuticals (Manthey et al., 2011) to meditation (Cahn et al., 2017).
Depression and the immune system
In parallel to, and often in concert with, the central nervous and endocrine systems, the immune system has increasingly been found to play a large role in depression as well. The seminal Maier and Watkins paper, “Cytokines for psychologists,” (Maier and Watkins, 1998) was an early integration of inflammation and mood that presciently set the course of the field for the past two decades. The authors outlined the bidirectional lines of communication between the immune system and the brain, mediated by a variety of inflammatory molecules, known as cytokines, released by immune cells both centrally and peripherally.
As evidence, they cited the expression of proinflammatory cytokines throughout the central nervous system, and the powerful effects sickness has on mood in general (Maier and Watkins, 1998). However, in order to reach signaling receptors on neurons and glial cells, cytokines would need to cross the blood-brain barrier (BBB), the highly selective, semipermeable barrier that separates the vascular system from the central nervous system.
Due to the size of cytokines, it was initially assumed that transport across the BBB would be rare, and thus, early research focused on other paths, identifying the vagus nerve as a means of relaying signals of peripheral inflammation to the central nervous system (Konsman et al., 2000). However, recent research has identified mechanisms through which cytokines can traverse the BBB. Distinct transport molecules present at certain locations along the BBB can actively shuttle key immune-modulating cytokines such as interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF) across this selectively permeable barrier (Banks, 2005; Banks et al., 1995). Additionally, inflammation can disrupt the BBB, making it more permeable to circulating immune molecules (Varatharaj and Galea, 2016).
These cytokine signals have been connected to a pattern of activity collectively known as “sickness behavior.” They include depressed mood, lethargy, decreased appetite, heightened sensitivity to pain, difficulty concentrating, and malaise: all hallmarks of MDD. These behavioral changes, along with fever and several other physiological changes, are adaptive strategies that evolved to fight infection, and have been directly linked to cytokine signaling (Dantzer, 2001; Konsman et al., 2002). Interestingly, cytokine signals associated with a proinflammatory response, and ensuing sickness behaviors, can be triggered by stress, including psychosocial stressors (Miller et al., 2002).
From an evolutionary perspective, stress has typically been associated with the risk of physical injury and infection. Mounting a preemptive immune response, including behaviors that increase the chance of healing and recovery, has maximized the survival of the human race (Raison et al., 2006).
In modern life, however, these past evolutionary advantages have created a very real present problem (Miller and Raison, 2016). Depressive symptoms align closely with immune-mediated sickness behaviors, and hypotheses have been generated to unify our understanding of the two. For example, the Pathogen Host Defense (PATHOS-D) hypothesis presented by Raison and Miller outlines that symptoms such as hyperthermia, conservation/withdrawal behavior, hypervigilance, and anorexia, all associated with depression, also played a role in the survival of our ancestors during pathogen defense (Raison and Miller, 2013).
When measuring circulating cytokines, individuals with depression often have elevated circulating IL-1β IL-6, TNF, IL-10, IL-12, with decreased levels of interferon gamma (IFNγ) and IL-4 (Goldsmith et al., 2016). Laboratory psychosocial stressors tend to elicit elevated levels of IL-6 and CRP in response to stress as compared to individuals without depression (Irwin and Miller, 2007). However, as with endocrine profiles in depression, there is a great deal of variation within measured samples.
Supporting the relationship between inflammation and depression, many current treatments for MDD have anti-inflammatory properties. Some antidepressants reduce endogenous production of proinflammatory cytokines, and can even modify immune reactivity in the central nervous system (Capuron et al., 2002; Gałecki et al., 2018; Nazimek et al., 2017). A recent study found that depressed individuals treated either pharmacologically or with psychotherapy for four weeks both experienced a reduction in proinflammatory cytokines; however, individuals that were treatment-resistant maintained elevated cytokine levels, suggesting that treatment-resistant depression may be related to altered responsivity to inflammatory signals (Syed et al., 2018).
Viewing depression through the lens of inflammation has opened up the possibility of using a new generation of anti-inflammatory compounds to augment current therapies, including cytokine inhibitors, non-steroidal anti-inflammatory drugs (NSAIDS), statins, and even anti-epileptics (Andrade, 2014; Cowen, 2017; Raison and Miller, 2011; Shariq et al., 2018). However, while the future of novel immune therapeutics for MDD holds great promise, the complex nature of immune communication and the heterogeneous presentation of depression itself have warranted caution, particularly since anti-inflammatory treatments can have no effect or even exacerbate depressive symptoms in some individuals (Köhler et al., 2014; Raison et al., 2013b).
Advancing modern understanding and treatment of depression necessitates a holistic view of the individual to uniquely identify and target treatments that take into account the many body systems involved. In the past 15 years, a new player has entered the field of depression. It interacts with the human body’s endocrine, immune, and central nervous systems, influencing mood and behavior from a position within the body, but uniquely separate from it. This new edition to our understanding of depression is the human gut microbiome.
Defining the microbiome
A microbiome, in general, refers to a collective population of commensal microbes living symbiotically with a multicellular organism (Turnbaugh et al., 2007). While the initial usage of the term “microbiome” referred to the collective genomes of these microbes, and the initial usage of the term “microbiota” referred to the actual microorganisms themselves, the two words have since become fairly interchangeable, with microbiome often being used as a catchall for both (Ursell et al., 2012).
For clarity, this paper will generally follow the original convention, referring to the microbes as microbiota, with the exception of describing the microbiome at an organism level or in integrated systems, such as immune-microbiome interactions, or the microbiome-gut-brain axis.
Microbiomes have existed on planet earth for over a billion years, and are present at all levels of multicellular life, from plants to invertebrates and vertebrates (Berg et al., 2016; Ley et al., 2008). Strains of stomach-associated Helicobacter pylori can even be used to trace migration patterns and human evolution (Ley et al., 2008).
Interest in the human gut microbiome and its relationship to health can be dated as far back as the early 1900s, when authorities advocated for the ingestion of certain lactic acid producing bacteria as a cure for “autointoxication,” or the process by which intestinally derived toxins negatively affect systemic health (Bested et al., 2013b).
However, interest in the microbiome and its relationship to the brain largely waned, and although a theory of gastrointestinal-related depression was developed almost 90 years ago in the early 20th century, it was “swept into the dustbin of history” (Bowe and Logan, 2011; Kligman, 2002; Stokes and Pillsbury, 1930). A wave of identification of potential uses for probiotics therapeutically helped to bring the microbiome and its connection to the brain back into focus, perhaps culminating in the initiation of the Human Microbiome Project (HMP) (Bested et al., 2013a; Turnbaugh et al., 2007).
The mission of the HMP was to characterize the nature of the human microbiome, understanding its distribution and evolution in ways that would benefit our understanding of human health and disease (Turnbaugh et al., 2007). While research efforts have predominantly focused on the bacterial inhabitants of the gastrointestinal tract, partly due to the relative ease in studying bacteria, the human microbiome is also made up of fungal flora (the mycobiome) and viruses (the virome or phageome for bacteria-infecting viruses), both of which are receiving increasing attention (Enaud et al., 2018; Mukhopadhya et al., 2019). Alterations in gut fungal balance have been associated with gastrointestinal disorders, changes in cognition, and altered immune and endocrine functioning (Enaud et al., 2018).
Viruses that make up the human virome have been linked to chronic fatigue syndrome, type 2 diabetes, and, potentially, mood disorders (Ma et al., 2018; Newberry et al., 2018; Prusty et al., 2018). Specific viral microbiota, particularly phages, have even been linked to positive clinical outcomes in the treatment of Clostridium difficile infections via fecal-microbiota transplantation, a technique discussed further below (Broecker et al., 2016; Zuo et al., 2018).
The prospects of greater incorporation of the mycobiome and virome into modern study of the microbiome holds great promise. However, as there are few depression-related studies in these domains, bacterial residents of the human gut microbiome will be the ensuing focus.
Study of the gut microbiome in humans typically involves fecal analysis, although this is not without limitations, as fecal samples are often more representative of microbiota inhabiting specific segments of the colon lumen and do not reflect the complex diversity of the gut mucosa and other segments of the intestines (Parthasarathy et al., 2016; Sartor, 2015).
Early efforts at characterization of microbiome composition were culture-based, but these methods lacked specificity, as many microbiota strains do not grow in culture (Turnbaugh et al., 2007), and gave way to genomic techniques that have allowed for more specific identification of larger numbers of gut microbiota species than ever before (Knight et al., 2018).
Recent First-wave genomic methods have focused on the 16S ribosomal RNA (rRNA) gene sequence, a gene less susceptible to horizontal transfer, or transfer of genetic information between a microbe and other microbes, or between a microbe and its multicellular host. Sequencing of 16S rRNA gene allowed for the identification of divergence between microbiota species, and thus detailed phylogenetic mapping of the gut microbiome (Zaneveld et al., 2010).
More recently, massively parallel shotgun sequencing techniques have allowed for sequencing of broad regions of the microbiota genome, not just the 16S rRNA gene component. This allows not just for greater specificity in strain identification, but for the mapping of particular genes of interest as well (Ranjan et al., 2016). Patterns of variation within the microbiome are typically measured via alpha and beta diversity.
Measurements of alpha diversity are within a single individual or sample and include species richness (i.e., how many species are present?), e.g., measured by the number of different operational taxonomic units (OTUs) using Observed OTUs, as well as species evenness (i.e., how evenly represented are the different species?), using measures including the Shannon index and Faith’s phylogenetic diversity.
Beta diversity, on the other hand, compares individuals or samples to each other and measures how different they are from one another. Quantitative measures of beta diversity include Bray-Curtis dissimilarity and weighted UniFrac, while qualitative measures include the Jaccard distance and unweighted UniFrac (Knight et al., 2018).
Modern sequencing techniques have identified over 1000 unique bacterial species making up the human microbiome, mostly dominated by the two phyla Bacteriodetes and Firmicutes (Lloyd-Price et al., 2016). There is incredible variation in microbiome makeup not just between individuals, but even between body habitats on the same person (Ding and Schloss, 2014; Huttenhower et al., 2012).
This deeper understanding of microbiome variation between individuals, as well as within a single person across time, has challenged the idea of what it means to have a healthy microbiome as compared to one that is unhealthy and out of balance, historically referred to as “dysbiosis” (Falony et al., 2016; Lloyd-Price et al., 2016)
Efforts to identify distinct “enterotypes” that most microbiomes fit into has also proven to be more complex than initially assumed, prompting the idea that microbiome makeup may exist more on a continuum (Arumugam et al., 2011; Bäckhed et al., 2012). This has led to a view of dysbiosis grounded in individual health, not as a standard to compare all microbiomes against (Petersen and Round, 2014).
Additionally, a growing interest in functional readouts of microbiome activity, such as through metabolomics, has introduced the concept that a healthy microbiome may also depend on the active metabolic pathways in which the microbiota take part (Ursell et al., 2014). This nuanced understanding of microbiome makeup and health has allowed for more nuanced connections with human health and behavior, including connections between the microbiome and depression (Fond et al., 2015).
The microbiome-gut-brain axis and depression: a bidirectional highway
Modern approaches to understanding the relationship between the microbiome and mental health typically consider brain-gut communication as a bidirectional information highway referred to as a variant of the “microbiome-gut-brain axis” (several examples: Cryan and O’Mahony, 2011; Dinan and Cryan, 2017a; Kelly et al., 2016b; Petra et al., 2015). Emphasis is placed on the bidirectional nature of communication.
In one direction, the central nervous system sends signals to the gut environment, which modulate microbiota composition and function. In the other direction, microbiota either interface with components of the peripheral nervous system that directly relay signals to the central nervous system, such as the vagus nerve innervating the brainstem or afferent fibers traveling in sympathetic nerve bundles and innervating the spinal cord, or do so indirectly, such as via moderation by the enteric nervous system (ENS; the mesh-like network of neurons governing gastrointestinal functioning; Furness, 2006).
Evidence suggests that the microbiome can also signal to the central nervous system by way of neuroactive metabolites in the blood stream (Martin et al., 2018). However, when considering the psychiatric implications of the microbiome, neural components of the brain-gut axis are not the only bidirectional pathways involved. The mucosal immune system in particular (as well as both peripheral and central immune components) is in constant communication with gut microbiota, in both an inflammatory and immunoregulatory sense (Powell et al., 2017).
Finally, the endocrine system, too, communicates bidirectionally with the gut microbiota, primarily through the HPA axis (Farzi et al., 2018) but also through sex hormones, like androgens and estrogens (Vemuri et al., 2018), and other hormonal systems (Cussotto et al., 2018). In fact, these four systems (neural, immune, endocrine, and microbiome systems) are all highly interconnected in an intricate dance affecting not only depression, but behavior more broadly (see Fig. 1).
Research is only beginning to scratch the surface of these interrelationships. The following review explores each of the bidirectional pathways listed above, and the current state of the literature in both rodent and human microbiome research. Preclinical studies are listed in Table 1 for reference and displayed graphically in Fig. 2, while clinical studies are listed in Table 2 for reference and displayed graphically in Fig. 3.
reference link : https://www.sciencedirect.com/science/article/pii/S0969996119302463
More information: Bruce R. Stevens et al, Depressive-hypertension: A proposed human endotype of brain/gut microbiome dysbiosis, American Heart Journal (2021). DOI: 10.1016/j.ahj.2021.05.002