Gut Microbiota Changes in Patients with Bipolar Depression

Reproducible gut microbial signatures in bipolar and schizophrenia spectrum disorders: A metagenome-wide study

BACKGROUND

Numerous studies report gut microbiome variations in bipolar disorder (BD) and schizophrenia spectrum disorders (SSD) compared to healthy individuals, though, there is limited consensus on which specific bacteria are associated with these disorders.

METHODS

In this study, we performed a comprehensive metagenomic shotgun sequencing analysis in 103 Dutch patients with BD/SSD and 128 healthy controls matched for age, sex, body mass index and income, while accounting for diet quality, transit time and technical confounders. To assess the replicability of the findings, we used two validation cohorts (total n = 203), including participants from a distinct population with a different metagenomic isolation protocol.

RESULTS

The gut microbiome of the patients had a significantly different β-diversity, but not α-diversity nor neuroactive potential compared to healthy controls. Initially, twenty-six bacterial taxa were identified as differentially abundant in patients. Among these, the previously reported genera Lachnoclostridium and Eggerthella were replicated in the validation cohorts. Employing the CoDaCoRe learning algorithm, we identified two bacterial balances specific to BD/SSD, which demonstrated an area under the receiver operating characteristic curve (AUC) of 0.77 in the test dataset. These balances were replicated in the validation cohorts and showed a positive association with the severity of psychiatric symptoms and antipsychotic use. Last, we showed a positive association between the relative abundance of Klebsiella and Klebsiella pneumoniae with antipsychotic use and between the Anaeromassilibacillus and lithium use.

CONCLUSIONS

Our findings suggest that microbial balances could be a reproducible method for identifying BD/SSD-specific microbial signatures, with potential diagnostic and prognostic applications. Notably, Lachnoclostridium and Eggerthella emerge as frequently occurring bacteria in BD/SSD. Last, our study reaffirms the previously established link between Klebsiella and antipsychotic medication use and identifies a novel association between Anaeromassilibacillus and lithium use.

Prophylactic effects of Tibetan goat kefir on depression-like behaviors in chronic unpredictable stress model through the gut-brain axis

BACKGROUND

Depression is a common psychological disorder, and traditional therapeutic drugs often result in side effects such as emesis, dry mouth, headache, dysentery and constipation. Probiotics and goat milk have garnered widespread attention for their ability to modulate immune function and regulate the endocrine system, and for their anti-inflammatory effects. In this work, the effects of Tibetan goat kefir on the behavior, immune status, neuroendocrine response and gut microbiological composition of chronic unpredictable mild stress (CUMS) mouse models were evaluated.

RESULTS

The results indicated that Tibetan kefir goat milk significantly alleviated behavioral despair in mice. Furthermore, the results demonstrated that Tibetan kefir goat milk mitigated the inflammatory response in the mice and moderated the hyperactivity of the hypothalamic-pituitary-adrenal axis and the expression of brain-derived neurotrophic factor. Meanwhile, chronic stress-induced gut microbial abnormalities were restored. In addition, the correlation between gut microbiota and nervous system was evaluated.

CONCLUSION

These results explained the potential mechanism of Tibetan kefir in the antidepressant effect on the CUMS model and enriched diets for depressed patients. © 2024 Society of Chemical Industry.

Gut microbiota dysbiosis contributes to depression-like behaviors via hippocampal NLRP3-mediated neuroinflammation in a postpartum depression mouse model

Postpartum depression (PPD) is a severe mental disorder that affects approximately 10---20% of women after childbirth. The precise mechanism underlying PPD pathogenesis remains elusive, thus limiting the development of therapeutics. Gut microbiota dysbiosis is considered to contribute to major depressive disorder. However, the associations between gut microbiota and PPD remain unanswered. Here, we established a mouse PPD model by sudden ovarian steroid withdrawal after hormone-simulated pseudopregnancy-human (HSP-H) in ovariectomy (OVX) mouse. Ovarian hormone withdrawal induced depression-like and anxiety-like behaviors and an altered gut microbiota composition. Fecal microbiota transplantation (FMT) from PPD mice to antibiotic cocktail-treated mice induced depression-like and anxiety-like behaviors and neuropathological changes in the hippocampus of the recipient mice. FMT from healthy mice to PPD mice attenuated the depression-like and anxiety-like behaviors as well as the inflammation mediated by the NOD-like receptor protein (NLRP)-3/caspase-1 signaling pathway both in the gut and the hippocampus, increased fecal short-chain fatty acids (SCFAs) levels and alleviated gut dysbiosis with increased SCFA-producing bacteria and reduced Akkermansia in the PPD mice. Also, downregulation of NLRP3 in the hippocampus mitigated depression-like behaviors in PPD mice and overexpression of NLRP3 in the hippocampal dentate gyrus induced depression-like behaviors in naïve female mice. Intriguingly, FMT from healthy mice failed to alleviate depression-like behaviors in PPD mice with NLRP3 overexpression in the hippocampus. Our results highlighted the NLRP3 inflammasome as a key component within the microbiota-gut-brain axis, suggesting that targeting the gut microbiota may be a therapeutic strategy for PPD.

Effects of antipsychotics on the gastrointestinal microbiota: A systematic review

Antipsychotics (APs) have been increasingly prescribed for psychiatric disorders from schizophrenia to disruptive behavioral conditions. These drugs have been associated with considerable side effects, such as weight gain, and increasing evidence has also indicated that its use impacts gut microbiota (GM), although this connection is still little understood. To assess APs effects on the GM of patients starting or ongoing treatment, a systematic review was carried out in PubMed and Scopus databases. Twelve articles were considered eligible for the review, which investigated the effects of risperidone (5 studies), quetiapine (3), amilsupride (1), olanzapine (1), and unspecified atypical drugs (2). Eleven reported changes in GM in response to APs, and associations between the abundance of bacterial groups and different metabolic parameters were described by most of them. However, the studies were noticeably heterogeneous considering design, methods, and results. In this way, the effects of APs on GM composition and diversity were inconclusive. Despite the uncertain interactions, a more comprehensive understanding on how microbiota is affected by APs may help to optimize treatment, potentially minimizing side effects and improving adherence to treatment.

Gut microbial GABAergic signaling improves stress-associated innate immunity to respiratory viral infection

INTRODUCTION

Epidemiological evidences reveal that populations with psychological stress have an increased likelihood of respiratory viral infection involving influenza A virus (IAV) and SARS-CoV-2.

OBJECTIVES

This study aims to explore the potential correlation between psychological stress and increased susceptibility to respiratory viral infections and how this may contribute to a more severe disease progression.

METHODS

A chronic restraint stress (CRS) mouse model was used to infect IAV and estimate lung inflammation. Alveolar macrophages (AMs) were observed in the numbers, function and metabolic-epigenetic properties. To confirm the central importance of the gut microbiome in stress-exacerbated viral pneumonia, mice were conducted through microbiome depletion and gut microbiome transplantation.

RESULTS

Stress exposure induced a decline in Lactobacillaceae abundance and hence γ-aminobutyric acid (GABA) level in mice. Microbial-derived GABA was released in the peripheral and sensed by AMs via GABAAR, leading to enhanced mitochondrial metabolism and α-ketoglutarate (αKG) generation. The metabolic intermediator in turn served as the cofactor for the epigenetic regulator Tet2 to catalyze DNA hydroxymethylation and promoted the PPARγ-centered gene program underpinning survival, self-renewing, and immunoregulation of AMs. Thus, we uncover an unappreciated GABA/Tet2/PPARγ regulatory circuitry initiated by the gut microbiome to instruct distant immune cells through a metabolic-epigenetic program. Accordingly, reconstitution with GABA-producing probiotics, adoptive transferring of GABA-conditioned AMs, or resumption of pulmonary αKG level remarkably improved AMs homeostasis and alleviated severe pneumonia in stressed mice.

CONCLUSION

Together, our study identifies microbiome-derived tonic signaling tuned by psychological stress to imprint resident immune cells and defensive response in the lungs. Further studies are warranted to translate these findings, basically from murine models, into the individuals with psychiatric stress during respiratory viral infection.

Chronically socially isolated mice exhibit depressive-like behavior regulated by the gut microbiota

Objectives

Chronic loneliness is a widespread issue, and the gut-brain axis is known to be crucial in facilitating communication between the gut and brain. However, the precise mechanism by which chronic loneliness affects the gut-brain axis remains uncertain.

Methods

Fourteen 55-week-old Balb/c mice were used in the experiment, with seven mice being randomly assigned to the chronic social isolation (CSI) group. The CSI group mice underwent 12 weeks of isolation to simulate the psychiatric state of a population in prolonged social isolation. The mental state of the CSI mice was assessed through animal behavior analysis, while plasma cytokines were measured using ELISA. Additionally, the composition of the gut microbiota was analyzed using 16S rRNA sequencing, and the metabolite composition of the intestinal contents was examined using nontargeted metabolomics. The Student-T test was used to determine significant mean differences.

Results

Mice that were exposed to the CSI exhibited increased immobility time lengths in forced swimming and hanging tail experiments, and decreased movement lengths and number of times traversing the intermediate region, compared to control mice. Additionally, CSI decreased the abundance of the probiotics Ruminococcaceae, Akkermansiaceae, and Christensenellaceae. Additionally, CSI reduced the production of the metabolites oleamide and tryptophan. Furthermore, IL-1β, IL-4, and IL-6 were significantly increased, while TNF-α was significantly decreased.

Conclusion

CSI induces a dysbiotic gut microbiota and the production of neurorelated metabolites, which in turn increase inflammatory responses and result in depressive behaviors in CSI mice. Therefore, these findings suggest that the gut microbiota may serve as a target for the treatment of long-term social isolation-induced mental disorders.

The emerging roles of microbiota-derived extracellular vesicles in psychiatric disorders

Major depressive disorder, schizophrenia, and bipolar disorder are three major psychiatric disorders that significantly impact the well-being and overall health of patients. Some researches indicate that abnormalities in the gut microbiota can trigger certain psychiatric diseases. Microbiota-derived extracellular vesicles have the ability to transfer bioactive compounds into host cells, altering signaling and biological processes, ultimately influencing the mental health and illness of the host. This review aims to investigate the emerging roles of microbiota-derived extracellular vesicles in these three major psychiatric disorders and discusses their roles as diagnostic biomarkers and therapies for these psychiatric disorders.

Bipolar disorder and the gut microbiota: a bibliometric analysis

Background

Previous studies have explored the relationship between bipolar disorder and gut microbiota. However, there has been no bibliometric analysis to summarize and analyze these publications. Our objective was to perform a bibliometric analysis to investigate the current status and frontiers of the publications in the field of the association between bipolar disorder and the gut microbiota.

Methods

We retrieved publications concerning the interplay between the gut microbiota and bipolar disorder from the Web of Science Core Collection (WoSCC). The analysis was executed using WoSCC's literature analysis tool and VOSviewer 1.6.16.

Results

In total, we identified 177 publications originating from 362 institutions across 39 countries/regions, and these articles were disseminated in 104 different journals. The most productive institutions, authors, countries/regions, and journals were Zhejiang University contributing 18 publications, Shaohua Hu authoring 12 publications, China with 53 publications, and Frontiers in Psychiatry with 11 publications. The first high-cited document was published in the Journal of Psychiatric Research in 2017, and authored by Evans. In this article, they found gut microbiome composition was associated with BD and its illness severity, and they concluded that targeting the gut microbiota may be helpful to develop the effective treatment for bipolar disorder. The top 5 keywords with the highest frequency except for bipolar disorder and gut microbiota were as follows: depression, inflammation, probiotic, gut-brain axis, and anxiety.

Conclusion

In conclusion, this is the first bibliometric analysis to explore the publications in the field of the association between bipolar disorder and the gut microbiota. The main research hotspots regarding this field were the characteristics, abundance, and diversity of gut microbiome in bipolar disorder, the role of treatment and gut microbiome in bipolar disorder, microbiome-brain connections in bipolar disorder, and interventions for bipolar disorder based on microbiota composition modification. The number of studies about the association between gut microbiota and bipolar disorder is relatively small, and more studies are needed to expand our understanding the association between gut microbiota and bipolar disorder.

Metagenomic Analysis Reveals Difference of Gut Microbiota in ADHD

OBJECTIVE

Although ADHD is highly heritable, some environmental factors contribute to its development. Given the growing evidence that gut microbiota was involved in psychiatric disorders, we aimed to identify the characteristic composition of the gut microbiota in ADHD.

METHODS

We recruited 47 medication-naive children and adolescents with ADHD, and 60 healthy controls (HCs). We used shotgun metagenomics to measure the structure of the gut microbiota and analyzed the difference in bacterial taxa between ADHD and HCs.

RESULTS

Significant differences were found between the ADHD and HC groups in both alpha diversity indices (Simpson index, p = .025 and Shannon index, p = .049) and beta diversity indices (Euclidean distance, Bray-Curtis distance, and JSD distance, p < 2.2e-16). Nine representative species best explain the difference.

CONCLUSION

Patients with ADHD showed significant differences in the composition of the gut microbiota compared with HCs. These results may help identify potential biomarkers of ADHD.

Gut Microbiota Dysbiosis and Inflammation Dysfunction in Late-Life Depression: An Observational Cross-Sectional Analysis

Purpose

There are some challenges to diagnosis in the context of similar diagnostic criteria for late-life depression (LLD) and adult depression due to cognitive impairment and other clinical manifestations. The association between gut microbiota and inflammation remains unclear in LLD. We analyzed gut microbiota characteristics and serum inflammatory cytokines in individuals with LLD to explore the combined role of these two factors in potential biomarkers of LLD.

Methods

This was an observational cross-sectional study. Fecal samples and peripheral blood from 29 patients and 33 sex- and age-matched healthy controls (HCs) were collected to detect gut microbiota and 12 inflammatory factors. We analyzed differences in diversity and composition of gut microbiota and evaluated relations among gut microbiota, inflammatory factors, and neuropsychological scales. We extracted potential biomarkers using receiver-operating characteristic curve analysis to predict LLD utilizing the combination of the microbiota and inflammatory cytokines.

Results

Elevated systemic inflammatory cytokine levels and gut microbiota dysbiosis were found in LLD patients. Relative abundance of Verrucomicrobia at the phylum level and Megamonas, Citrobacter, and Akkermansia at the genus level among LLD patients was lower than HCs. Abundance of Coprococcus, Lachnobacterium, Oscillospira, and Sutterella was higher in LLD patients. Notably, IL6, IFNγ, Verrucomicrobia, and Akkermansia levels were correlated with depression severity. Our study identified IL6, Akkermansia, and Sutterella as predictors of LLD, and their combination achieved an area under the curve of 0.962 in distinguishing LLD patients from HCs.

Conclusion

This research offers evidence of changes within gut microbiota and systemic inflammation in LLD. These findings possibly help elucidate functions of gut microbiota and systemic inflammation in LLD development and offer fresh ideas on biomarkers for clinical practise in the context of LLD.

New insights into the interaction between polycystic ovary syndrome and psychiatric disorders: A narrative review

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disease characterized by hyperandrogenism, ovulatory dysfunction, and ovarian polycystic changes, which combines with reproductive problems, metabolic disorders, and psychological disorders to exhibit a far-reaching impact on the physical and mental health of women. We reviewed previous research and discovered that psychiatric disorders are more common in PCOS patients and their children, potentially exacerbating the condition and creating a vicious loop. To understand the reasons, relevant articles were collected following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines from PubMed, Web of Science, and Cochrane Library, through December 2022. Evidence suggested that PCOS-related clinical manifestations, hyperandrogenism, insulin resistance, obesity, gut dysbiosis, and other variables may increase the risk of psychiatric disorders in patients. In turn, psychiatric disorders may aggravate the pathologic process of PCOS and increase the difficulty of the treatment. We systematically reported the mechanisms underlying the psychiatric disorders-PCOS interactions, intending to provide potential ways to break the vicious cycle and lay the groundwork for future research. However, research on PCOS and psychiatric disorders were still in initial stages, which limited the scope of this review. More studies are needed to further verify our findings.

Microbiota-Induced Epigenetic Alterations in Depressive Disorders Are Targets for Nutritional and Probiotic Therapies

Major depressive disorder (MDD) is a complex disorder and a leading cause of disability in 280 million people worldwide. Many environmental factors, such as microbes, drugs, and diet, are involved in the pathogenesis of depressive disorders. However, the underlying mechanisms of depression are complex and include the interaction of genetics with epigenetics and the host immune system. Modifications of the gut microbiome and its metabolites influence stress-related responses and social behavior in patients with depressive disorders by modulating the maturation of immune cells and neurogenesis in the brain mediated by epigenetic modifications. Here, we discuss the potential roles of a leaky gut in the development of depressive disorders via changes in gut microbiota-derived metabolites with epigenetic effects. Next, we will deliberate how altering the gut microbiome composition contributes to the development of depressive disorders via epigenetic alterations. In particular, we focus on how microbiota-derived metabolites such as butyrate as an epigenetic modifier, probiotics, maternal diet, polyphenols, drugs (e.g., antipsychotics, antidepressants, and antibiotics), and fecal microbiota transplantation could positively alleviate depressive-like behaviors by modulating the epigenetic landscape. Finally, we will discuss challenges associated with recent therapeutic approaches for depressive disorders via microbiome-related epigenetic shifts, as well as opportunities to tackle such problems.

Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning

In the last few years, investigation of the gut-brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.

Methodological recommendations for human microbiota-gut-brain axis research

Observational studies have determined numerous correlations between sequence-based gut microbiota data and human mental traits. However, these associations are often inconsistent across studies. This inconsistency is one of the reasons that mechanistic validation studies of the observed correlations are lagging, making it difficult to establish causal associations. The absence of consistent study findings may partially be due to the lack of clear guidelines for identifying confounders of relations between complex microbial communities and mental conditions. Gut microbial complexity also impedes deciphering microbiota-host relations by using a single analytical approach. The aim of the current review is to help solve these problems by providing methodological recommendations for future human microbiota-gut-brain axis research on the selection of confounders, the use of integrative biostatistical methods, and the steps needed to translate correlative findings into causal conclusions.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Characterization of gut microbiota profile in Iranian patients with bipolar disorder compared to healthy controls

Introduction

The human gut microbiota plays a crucial role in mental health through the gut-brain axis, impacting central nervous system functions, behavior, mood, and anxiety. Consequently, it is implicated in the development of neuropsychiatric disorders. This study aimed to assess and compare the gut microbiota profiles and populations of individuals with bipolar disorder and healthy individuals in Iran.

Methods

Fecal samples were collected from 60 participants, including 30 bipolar patients (BPs) and 30 healthy controls (HCs), following rigorous entry criteria. Real-time quantitative PCR was utilized to evaluate the abundance of 10 bacterial genera/species and five bacterial phyla.

Results

Notably, Actinobacteria and Lactobacillus exhibited the greatest fold change in BPs compared to HCs at the phylum and genus level, respectively, among the bacteria with significant population differences. Ruminococcus emerged as the most abundant genus in both groups, while Proteobacteria and Bacteroidetes showed the highest abundance in BPs and HCs, respectively, at the phylum level. Importantly, our investigation revealed a lower Firmicutes/Bacteroidetes ratio, potentially serving as a health indicator, in HCs compared to BPs.

Conclusion

This study marks the first examination of an Iranian population and provides compelling evidence of significant differences in gut microbiota composition between BPs and HCs, suggesting a potential link between brain functions and the gut microbial profile and population.

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

Background

Methamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.

Method

Fecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.

Results

MUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.

Conclusion

In conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings.

Can we manage gut microbiome imbalances in patients with bipolar disorder with pharmacotherapy?

INTRODUCTION

A novel new area of exploration in the treatment of bipolar disorder is the gut brain axis. Studies have shown significant differences between the gut microbiome in those with bipolar disorder and those without the illness, as well as documented microbiome changes associated with the effects of bipolar pharmacotherapy and targeted microbial interventions. Although we have evidence suggesting the bi-directional relationship between the gut microbiome and psychiatric disorders, we are still unable to utilize this understanding clinically.

AREAS COVERED

We need to better understand the factors that impact the microbiome in this illness and vice versa.

EXPERT OPINION

Additionally, changes in gut microbiome in bipolar disorder might be used for biomarker identification with a potential to help in diagnosis and monitoring of the condition. It is an important area for further research and may provide improved therapeutic outcomes.

Prebiotics for depression: how does the gut microbiota play a role?

Depression, a mood disorder characterized by persistent feelings of sadness and aversion to activity that can interfere with daily life, is a condition of great concern. Prebiotics, which are non-digestible substances selectively utilized by host microorganisms for health benefits, have gained attention for their potential to improve overall wellness and alleviate various disorders including depression. This study aims to review clinical trials utilizing carbohydrate-type prebiotics such as inulin-type fructans, galactooligosaccharides (GOS), human milk oligosaccharides, resistant starch, prebiotic phytochemicals including epigallocatechin gallate (EGCG), chlorogenic acids, resveratrol, and prebiotic lipids (n-3 polysaturated fatty acids) to determine their effects on depression. Our findings suggest that GOS at a daily dosage of 5 g and eicosapentaenoic acid at or less than 1 g can effectively mitigate depressive symptoms. While EGCG exhibits potential antidepressant properties, a higher dosage of 3 g/d may be necessary to elicit significant effects. The plausible mechanisms underlying the impact of prebiotics on depression include the synthesis of neurotransmitters, production of short-chain fatty acids, and regulation of inflammation.

Microbiota-gut-brain axis mechanisms in the complex network of bipolar disorders: potential clinical implications and translational opportunities

Bipolar disorders (BD) represent a severe leading disabling mental condition worldwide characterized by episodic and often progressive mood fluctuations with manic and depressive stages. The biological mechanisms underlying the pathophysiology of BD remain incompletely understood, but it seems that there is a complex picture of genetic and environmental factors implicated. Nowadays, gut microbiota is in the spotlight of new research related to this kind of psychiatric disorder, as it can be consistently related to several pathophysiological events observed in BD. In the context of the so-called microbiota-gut-brain (MGB) axis, it is shown to have a strong influence on host neuromodulation and endocrine functions (i.e., controlling the synthesis of neurotransmitters like serotonin or mediating the activation of the hypothalamic-pituitary-adrenal axis), as well as in modulation of host immune responses, critically regulating intestinal, systemic and brain inflammation (neuroinflammation). The present review aims to elucidate pathophysiological mechanisms derived from the MGB axis disruption and possible therapeutic approaches mainly focusing on gut microbiota in the complex network of BD. Understanding the mechanisms of gut microbiota and its bidirectional communication with the immune and other systems can shed light on the discovery of new therapies for improving the clinical management of these patients. Besides, the effect of psychiatric drugs on gut microbiota currently used in BD patients, together with new therapeutical approaches targeting this ecosystem (dietary patterns, probiotics, prebiotics, and other novelties) will also be contemplated.

A systematic review on gut-brain axis aberrations in bipolar disorder and methods of balancing the gut microbiota

BACKGROUND

Bipolar disorder (BD) is a mood disorder that affects millions worldwide. Up to half of the diagnosed patients are reported to not receive adequate treatment. This study aims to assess the relationship between the gut-brain axis and BD and to discuss and compare the efficacy of varying methods of balancing gut microbiotas in BD.

METHODS

Using PubMed, Embase, and Google Scholar from November 2021 to February 2022, we found 5310 studies on gut microbiota and its relation to BD. Using our inclusion criteria, 5283 studies were excluded. A total of 27 full-text articles were assessed for eligibility. Also, 12 articles that met our criteria and eligibility criteria reported on 613 BD patients.

RESULTS

Most studies analyzed found an overall difference in gut microbiota composition in bipolar patients compared to healthy controls, though the alterations found were not consistent. Differences in Lactobacillus, Faecalibacterium, and Ruminococcus abundance in BD compared to controls were found to be the most consistent across a few of the studies, but their effects on the gut-brain axis conflicted. Probiotic supplementation was found to lower patient rehospitalizations and significantly improve depressive symptoms and cognitive impairments among patients with BD.

CONCLUSIONS

Multiple studies included in this review point toward a possible link between BD and the gut microbiota. Probiotic supplements and other gut-balancing therapies could serve as effective adjunctive methods for the treatment of BD. Notable limitations of the studies included for analysis were small sample sizes and majority observational study designs. Furthermore, the microbiota aberrations found in patients with BD were not consistent across multiple studies. Despite these limitations, our findings demonstrate the need for further research regarding the relationship between aberrant gut microbiota profiles and BD, as well as the effectiveness of gut balancing methods as adjunctive treatments.

Characteristics of the gut microbiota in bipolar depressive disorder patients with distinct weight

BACKGROUND

Preliminary studies have indicated metabolic dysfunction and gut dysbiosis in patients with bipolar disorder (BD). In this study, we aimed to clarify the impact of the gut microbial composition and function on metabolic dysfunction in BD patients with an acute depressive episode.

METHODS

Fresh fecal samples were provided from 58 patients with BD depression, including 29 with normal weight (NW) and 29 with overweight/obesity (OW), and 31 healthy controls (HCs). The hypervariable region of 16 S rRNA gene (V3-V4) sequencing was performed using IonS5TMXL platform to evaluate the bacterial communities. Differences of microbial community and correlation to clinical parameters across different groups were analyzed.

RESULTS

Compared to NW and HCs, the OW group showed a decreased tendency in alpha diversity index. Beta diversity was markedly different among these groups (PERMANOVA: R2  = 0.034, p = 0.01) and was higher in patients versus HCs. A total number of 24 taxa displayed significantly different abundance among OW, NW, and HCs. At the family level, the abundance of three taxa was remarkably increased in NW, one in OW, and one in HCs. At the genus level, five taxa were enriched in OW, eight in NW, and two in HCs. The relative abundance of the genera Megamonas was positively associated with BMI, while Eggerthella was negatively correlated with BMI. Functional prediction analysis revealed the metabolism of cofactors and vitamins and amino acid were highly enriched in OW compared to HCs. In addition, microbial functions involved in "lipid metabolism" were depleted while the "fructose and mannose metabolism" was enriched in OW compared to NW group.

CONCLUSIONS

Specific bacterial taxa involved in pathways regulating the lipid, energy, and amino acid metabolisms may underlie the weight concerns in depressed BD patients. Potential targeting gut microbial therapy is provided for overweight/obesity patients with BD, which still need further studies in the future.

BBIBP-CorV Vaccination against the SARS-CoV-2 Virus Affects the Gut Microbiome

Several observational studies have confirmed that the severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) might substantially affect the gastrointestinal (GI) system by replicating in human small intestine enterocytes. Yet, so far, no study has reported the effects of inactivated SARS-CoV-2 virus vaccines on gut microbiota alterations. In this study, we examined the effects of the BBIBP-CorV vaccine (ChiCTR2000032459, sponsored by the Beijing Institute of Biological Products/Sinopharm), on gut microbiota. Fecal samples were collected from individuals whoreceived two doses of intramuscular injection of BBIBP-CorV and matched unvaccinated controls. DNA extracted from fecal samples was subjected to 16S ribosomal RNA sequencing analysis. The composition and biological functions of the microbiota between vaccinated and unvaccinated individuals were compared. Compared with unvaccinated controls, vaccinated subjects exhibited significantly reduced bacterial diversity, elevated firmicutes/bacteroidetes (F/B) ratios, a tendency towards Faecalibacterium-predominant enterotypes, and altered gut microbial compositions and functional potentials. Specifically, the intestinal microbiota in vaccine recipients was enriched with Faecalibacterium and Mollicutes and with a lower abundance of Prevotella, Enterococcus, Leuconostocaceae, and Weissella. Microbial function prediction by phylogenetic investigation of communities using reconstruction of unobserved states (PICRUSt) analysis further indicated that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in carbohydrate metabolism and transcription were positively associated with vaccine inoculation, whereas capacities in neurodegenerative diseases, cardiovascular diseases, and cancers were negatively affected by vaccines. Vaccine inoculation was particularly associated with gut microbiota alterations, as was demonstrated by the improved composition and functional capacities of gut microbiota.

Leveraging the microbiome to understand clinical heterogeneity in depression: findings from the T-RAD study

Alterations in the gut microbiome have been linked to a variety of mental illnesses including anxiety and depression. This study utilized advanced bioinformatics tools that integrated both the compositional and community nature of gut microbiota to investigate how gut microbiota influence clinical symptoms in a sample of participants with depression. Gut microbiota of 179 participants with major depressive disorder (MDD) in the Texas Resilience Against Depression (T-RAD) study were analyzed by 16S rRNA gene sequencing of stool samples. Severity of anxiety, depression, and anhedonia symptoms were assessed with General Anxiety Disorder - 7 item scale, Patient Health 9-item Questionnaire, and Dimensional Anhedonia Rating Scale, respectively. Using weighted correlation network analysis, a data-driven approach, three co-occurrence networks of bacterial taxa were identified. One of these co-occurrence networks was significantly associated with clinical features including depression and anxiety. The hub taxa associated with this co-occurrence module -one Ruminococcaceae family taxon, one Clostridiales vadinBB60 group family taxon, and one Christencenellaceae family taxon- were connected to several additional butyrate-producing bacteria suggesting that deficits in butyrate production may contribute to clinical symptoms. Therefore, by considering the community nature of the gut microbiome in a real world clinical sample, this study identified a gut microbial co-occurrence network that was significantly associated with clinical anxiety in a cohort of depressed individuals.

Gut Microbiota Dysbiosis Correlates With Long COVID-19 at One-Year After Discharge

BACKGROUND

Long coronavirus disease 2019 (COVID-19) in recovered patients (RPs) is gradually recognized by more people. However, how long it will last and the underlining mechanism remains unclear.

METHODS

We conducted a prospective follow-up study to evaluate the long-term symptoms and clinical indices of RPs at one-year after discharge from Union Hospital, Wuhan, China between December 2020 to May 2021. We also performed the 16S rRNA sequencing of stool samples from RPs and healthy controls (HCs) and analyzed the correlation between the gut microbiota and long COVID-19.

RESULTS

In total, 187 RPs were enrolled, among them, 84 (44.9%) RPs reported long COVID-19 symptoms at one-year after discharge. The most common long-term symptoms were cardiopulmonary symptoms, including chest tightness after activity (39/187, 20.9%), palpitations on exercise (27/187, 14.4%), sputum (21/187, 11.2%), cough (15/187, 8.0%) and chest pain (13/187, 7.0%), followed by systemic symptoms including fatigue (34/187, 18.2%) and myalgia (20/187, 10.7%), and digestive symptoms including constipation (14/187, 7.5%), anorexia (13/187, 7.0%), and diarrhea (8/187, 4.3%). Sixty-six (35.9%) RPs presented either anxiety or depression (42/187 [22.8%] and 53/187 [28.8%] respectively), and the proportion of anxiety or depression in the long symptomatic group was significantly higher than that in the asymptomatic group (41/187 [50.6%] vs. 25/187 [24.3%]). Compared with the asymptomatic group, scores of all nine 36-Item Short Form General Health Survey domains were lower in the symptomatic group (all P < 0.05). One hundred thirty RPs and 32 HCs (non-severe acute respiratory syndrome coronavirus 2 infected subjects) performed fecal sample sequencing. Compared with HCs, symptomatic RPs had obvious gut microbiota dysbiosis including significantly reduced bacterial diversities and lower relative abundance of short-chain fatty acids (SCFAs)-producing salutary symbionts such as Eubacterium_hallii_group, Subdoligranulum, Ruminococcus, Dorea, Coprococcus, and Eubacterium_ventriosum_group. Meanwhile, the relative abundance of Eubacterium_hallii_group, Subdoligranulum, and Ruminococcus showed decreasing tendencies between HCs, the asymptomatic group, and the symptomatic group.

CONCLUSION

This study demonstrated the presence of long COVID-19 which correlates with gut microbiota dysbiosis in RPs at one-year after discharge, indicating gut microbiota may play an important role in long COVID-19.

Gut Microbiota in Anxiety and Depression: Unveiling the Relationships and Management Options

The gut microbiota is critical for maintaining human health and the immunological system. Several neuroscientific studies have shown the significance of microbiota in developing brain systems. The gut microbiota and the brain are interconnected in a bidirectional relationship, as research on the microbiome-gut-brain axis shows. Significant evidence links anxiety and depression disorders to the community of microbes that live in the gastrointestinal system. Modified diet, fish and omega-3 fatty acid intake, macro- and micro-nutrient intake, prebiotics, probiotics, synbiotics, postbiotics, fecal microbiota transplantation, and 5-HTP regulation may all be utilized to alter the gut microbiota as a treatment approach. There are few preclinical and clinical research studies on the effectiveness and reliability of various therapeutic approaches for depression and anxiety. This article highlights relevant research on the association of gut microbiota with depression and anxiety and the different therapeutic possibilities of gut microbiota modification.

A Systematic Review on the Association between Obesity and Mood Disorders and the Role of Gut Microbiota

Obesity is a complex health condition that increases the susceptibility to developing cardiovascular diseases, diabetes, and numerous other metabolic health issues. The effect of obesity is not just limited to the conditions mentioned above; it is also seen to have a profound impact on the patient’s mental state, leading to the onset of various mental disorders, particularly mood disorders. Therefore, it is necessary to understand the mechanism underlying the crosstalk between obesity and mental disorders. The gut microbiota is vital in regulating and maintaining host physiology, including metabolism and neuronal circuits. Because of this newly developed understanding of gut microbiota role, here we evaluated the published diverse information to summarize the achievement in the field. In this review, we gave an overview of the association between obesity, mental disorders, and the role of gut microbiota there. Further new guidelines and experimental tools are necessary to understand the microbial contribution to regulate a balanced healthy life.

Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases

Gut microbiota has been implicated in the pathogenesis of multiple gastrointestinal (GI) and systemic metabolic and inflammatory disorders where disrupted gut microbiota composition and function (dysbiosis) has been found in multiple studies. Thus, human microbiome data has a potential to be a great source of information for the diagnosis and disease characteristics (phenotypes, disease course, therapeutic response) of diseases with dysbiotic microbiota community. However, multiple attempts to leverage gut microbiota taxonomic data for diagnostic and disease characterization have failed due to significant inter-individual variability of microbiota community and overlap of disrupted microbiota communities among multiple diseases. One potential approach is to look at the microbiota community pattern and response to microbiota modifiers like dietary fiber in different disease states. This approach is now feasible by availability of machine learning that is able to identify hidden patterns in the human microbiome and predict diseases. Accordingly, the aim of our study was to test the hypothesis that application of machine learning algorithms can distinguish stool microbiota pattern and microbiota response to fiber between diseases where overlapping dysbiotic microbiota have been previously reported. Here, we have applied machine learning algorithms to distinguish between Parkinson's disease, Crohn's disease (CD), ulcerative colitis (UC), human immune deficiency virus (HIV), and healthy control (HC) subjects in the presence and absence of fiber treatments. We have shown that machine learning algorithms can classify diseases with accuracy as high as 95%. Furthermore, machine learning methods applied to the microbiome data to predict UC vs CD led to prediction accuracy as high as 90%.

Acupuncture treatment for post-stroke depression: Intestinal microbiota and its role

Stroke-induced depression is a common complication and an important risk factor for disability. Besides psychiatric symptoms, depressed patients may also exhibit a variety of gastrointestinal symptoms, and even take gastrointestinal symptoms as the primary reason for medical treatment. It is well documented that stress may disrupt the balance of the gut microbiome in patients suffering from post-stroke depression (PSD), and that disruption of the gut microbiome is closely related to the severity of the condition in depressed patients. Therefore, maintaining the balance of intestinal microbiota can be the focus of research on the mechanism of acupuncture in the treatment of PSD. Furthermore, stroke can be effectively treated with acupuncture at all stages and it may act as a special microecological regulator by regulating intestinal microbiota as well. In this article, we reviewed the studies on changing intestinal microbiota after acupuncture treatment and examined the existing problems and development prospects of acupuncture, microbiome, and poststroke depression, in order to provide new ideas for future acupuncture research.

The Role of Gut Dysbiosis in the Pathophysiology of Neuropsychiatric Disorders

Mounting evidence shows that the complex gut microbial ecosystem in the human gastrointestinal (GI) tract regulates the physiology of the central nervous system (CNS) via microbiota and the gut-brain (MGB) axis. The GI microbial ecosystem communicates with the brain through the neuroendocrine, immune, and autonomic nervous systems. Recent studies have bolstered the involvement of dysfunctional MGB axis signaling in the pathophysiology of several neurodegenerative, neurodevelopmental, and neuropsychiatric disorders (NPDs). Several investigations on the dynamic microbial system and genetic-environmental interactions with the gut microbiota (GM) have shown that changes in the composition, diversity and/or functions of gut microbes (termed "gut dysbiosis" (GD)) affect neuropsychiatric health by inducing alterations in the signaling pathways of the MGB axis. Interestingly, both preclinical and clinical evidence shows a positive correlation between GD and the pathogenesis and progression of NPDs. Long-term GD leads to overstimulation of hypothalamic-pituitary-adrenal (HPA) axis and the neuroimmune system, along with altered neurotransmitter levels, resulting in dysfunctional signal transduction, inflammation, increased oxidative stress (OS), mitochondrial dysfunction, and neuronal death. Further studies on the MGB axis have highlighted the significance of GM in the development of brain regions specific to stress-related behaviors, including depression and anxiety, and the immune system in the early life. GD-mediated deregulation of the MGB axis imbalances host homeostasis significantly by disrupting the integrity of the intestinal and blood-brain barrier (BBB), mucus secretion, and gut immune and brain immune functions. This review collates evidence on the potential interaction between GD and NPDs from preclinical and clinical data. Additionally, we summarize the use of non-therapeutic modulators such as pro-, pre-, syn- and post-biotics, and specific diets or fecal microbiota transplantation (FMT), which are promising targets for the management of NPDs.

Gut microbiome-wide association study of depressive symptoms

Depression is one of the most poorly understood diseases due to its elusive pathogenesis. There is an urgency to identify molecular and biological mechanisms underlying depression and the gut microbiome is a novel area of interest. Here we investigate the relation of fecal microbiome diversity and composition with depressive symptoms in 1,054 participants from the Rotterdam Study cohort and validate these findings in the Amsterdam HELIUS cohort in 1,539 subjects. We identify association of thirteen microbial taxa, including genera Eggerthella, Subdoligranulum, Coprococcus, Sellimonas, Lachnoclostridium, Hungatella, Ruminococcaceae (UCG002, UCG003 and UCG005), LachnospiraceaeUCG001, Eubacterium ventriosum and Ruminococcusgauvreauiigroup, and family Ruminococcaceae with depressive symptoms. These bacteria are known to be involved in the synthesis of glutamate, butyrate, serotonin and gamma amino butyric acid (GABA), which are key neurotransmitters for depression. Our study suggests that the gut microbiome composition may play a key role in depression.

Perturbed gut microbiota is gender-segregated in unipolar and bipolar depression

OBJECTIVE

This study aimed to explore the gender specificity of gut microbiome in patients with unipolar and bipolar depression disorder by analyzing the data of gut microbiome in this two mental disorders and healthy people.

METHODS

A case-control study using 16S ribosomal RNA gene sequencing from fecal samples of MDD (male set, n = 43; female set, n = 77) and BD (male set, n = 82; female set, n = 83) compared with HCs (male set, n = 71; female set, n = 100) was conducted. Linear discriminant analysis was used to identify microbial characteristics. Through cooccurrence analysis, the potential correlations of the differential gut microbiota in different genders was explored. Finally, the gender-specific distinguishing microorganisms were identified as biomaker, and the diagnostic performance was verified by five-fold cross validation.

RESULTS

A specific cluster was found enriched only in female MDD set, including 4 Bacteroideae OTUs. Similarly, 3 Lachnospiraceae OTUs was found significantly increased in female BD compared with other groups. In addition, the consistent enrichment of Pseudomonadacea in male and female may be the characteristic disease-related gut microbiota of BD. Besides, the diagnostic potential of gender specific biomarker panel in male (male validation AUC: 0.758-0.874, accurancy: 0.693-0.792; female validation AUC: 0.727-0.883, accurancy: 0.678-0.781) and female (male validation AUC: 0.787-0.883, accurancy: 0.719-0.784; female validation AUC: 0.795-0.898, accurancy: 0.689-0.838) has also been identified and confirmed.

CONCLUSIONS

The microbiological changes in both MDD and BD are sex specific, and gender specific biomarker panel has better diagnostic performance, which provide a certain reference in sex difference for future clinical differentiation and microbial intervention.

Revealing the Pathogenesis of Salt-Sensitive Hypertension in Dahl Salt-Sensitive Rats through Integrated Multi-Omics Analysis

Salt-induced renal metabolism dysfunction is an important mechanism of salt-sensitive hypertension. Given that the gut-liver axis is the first hit of a high-salt diet (HSD), we aimed to identify the extra-renal mechanism from hepatic metabolism and gut microbiota, and attempted to relieve the salt-induced metabolic dysfunctions by curcumin. Untargeted metabolomics analysis was performed to identify the changes in hepatic metabolic pathways, and integrated analysis was employed to reveal the relationship between hepatic metabolic dysfunction and gut microbial composition. HSD induced significant increase in fumaric acid, l-lactic acid, creatinine, l-alanine, glycine, and l-cysteine levels, and amino acids metabolism pathways associated with glycolysis were significantly altered, including alanine, aspartate, and glutamate metabolism; glycine, serine, and threonine metabolism, which were involved in the regulation of blood pressure. Integrated multi-omics analysis revealed that changes in Paraprevotella, Erysipelotrichaceae, and genera from Clostridiales are associated with metabolic disorders. Gene functional predication analysis based on 16S Ribosomal RNA sequences showed that the dysfunction in hepatic metabolism were correlated with enhanced lipopolysaccharide (LPS) biosynthesis and apoptosis in gut microbes. Curcumin (50 mg/kg/d) might reduce gut microbes-associated LPS biosynthesis and apoptosis, partially reverse metabolic dysfunction, ameliorate renal oxidative stress, and protect against salt-sensitive hypertension.

Microbiome in Precision Psychiatry: An Overview of the Ethical Challenges Regarding Microbiome Big Data and Microbiome-Based Interventions

There has been a spurt in both fundamental and translational research that examines the underlying mechanisms of the human microbiome in psychiatric disorders. The personalized and dynamic features of the human microbiome suggest the potential of its manipulation for precision psychiatry in ways to improve mental health and avoid disease. However, findings in the field of microbiome also raise philosophical and ethical questions. From a philosophical point of view, they may yet be another attempt at providing a biological cause for phenomena that ultimately cannot be so easily localized. From an ethical point of view, it is relevant that the human gut microbiome comprises data on the individual's lifestyle, disease history, previous medications, and mental health. Massive datasets of microbiome sequences are collected to facilitate comparative studies to identify specific links between the microbiome and mental health. Although this emerging research domain may show promise for psychiatric patients, it is surrounded by ethical challenges regarding patient privacy, health risks, effects on personal identity, and concerns about responsibility. This narrative overview displays the roles and advances of microbiome research in psychiatry and discusses the philosophical and ethical implications of microbiome big data and microbiome-based interventions for psychiatric patients. We also investigate whether these issues are really "new," or "old wine in new bottles."

The Microbiota-Gut-Brain Axis in Psychiatric Disorders

Modulating the gut microbiome and its influence on human health is the subject of intense research. The gut microbiota could be associated not only with gastroenterological diseases but also with psychiatric disorders. The importance of factors such as stress, mode of delivery, the role of probiotics, circadian clock system, diet, and occupational and environmental exposure in the relationship between the gut microbiota and brain function through bidirectional communication, described as "the microbiome-gut-brain axis", is especially underlined. In this review, we discuss the link between the intestinal microbiome and the brain and host response involving different pathways between the intestinal microbiota and the nervous system (e.g., neurotransmitters, endocrine system, immunological mechanisms, or bacterial metabolites). We review the microbiota alterations and their results in the development of psychiatric disorders, including major depressive disorder (MDD), schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), and attention-deficit hyperactivity disorder (ADHD).

Gut metagenomic characteristics of ADHD reveal low Bacteroides ovatus-associated host cognitive impairment

Attention-deficit/hyperactivity disorder (ADHD) is a highly heterogeneous psychiatric disorder that can have three phenotypical presentations: inattentive (I-ADHD), hyperactive-impulsive (HI-ADHD), and combined (C-ADHD). Environmental factors correlated with the gut microbiota community have been implicated in the development of ADHD. However, whether different ADHD symptomatic presentations are associated with distinct microbiota compositions and whether patients could benefit from the correction of aberrant bacterial colonization are still largely unclear. We carried out metagenomic shotgun analysis with 207 human fecal samples to characterize the gut microbial profiles of patients with ADHD grouped according to their phenotypical presentation. Then, we transplanted the candidate low-abundance bacteria identified in patient subgroups into ADHD rats and evaluated ADHD-associated behaviors and neuronal activation in these rats. Patients with C-ADHD had a different gut microbial composition from that of healthy controls (HCs) (p = .02), but not from that of I-ADHD patients. Eight species became progressively attenuated or enriched when comparing the compositions of HCs to those of I-ADHD and C-ADHD; in particular, the abundance of Bacteroides ovatus was depleted in patients with C-ADHD. In turn, Bacteroides ovatus supplementation ameliorated spatial working memory deficits and reversed θ electroencephalogram rhythm alterations in ADHD rats. In addition, Bacteroides ovatus induced enhanced neuronal activation in the hippocampal CA1 subregion. These findings indicate that gut microbial characteristics that are unique to patients with C-ADHD may be masked when considering a more heterogeneous group of patients. We link the gut microbiota to brain function in an ADHD animal model, suggesting the relevance of testing a potential bacteria-based intervention for some aspects of ADHD.

Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis

INTRODUCTION

Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.

OBJECTIVES

This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.

METHODS

Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.

RESULTS

Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).

CONCLUSION

From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.

Machine Learning on Early Diagnosis of Depression

To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.

Abnormal gut microbiota and bile acids in patients with first-episode major depressive disorder and correlation analysis

AIM

Gut microbiota and its metabolite bile acids may play a significant role in the occurrence and development of major depressive disorder (MDD). Therefore, this study analyzes gut microbiota and bile acids, as well as their correlation in patients.

METHODS

Thirty-one patients with MDD and 29 healthy controls (HCs) were enrolled in this study. We collected their both blood and feces. Plasma bile acid content was determined by liquid chromatography-mass spectrometry and gut microbiota was detected by 16SrRNA gene sequencing and subsequently analyzed. We also analyzed the correlation between different gut microbiota, bile acids, and Hamilton Depression (HAMD) score.

RESULTS

The α-diversity analysis found that Simpson and Pielou evenness index was much higher in HCs than in the patients with MDD. The β-diversity of the two groups were differences by nonmetric multidimensional scaling analysis. Linear discriminant analysis effect size analysis identified 16 different strains. Bile acids detection showed that 23-nordeoxycholic acid in patients with MDD was significantly higher than in HCs, whereas taurolithocholic acid (TLCA), glycolithocholic acid (GLCA), and lithocholic acid 3-sulfate were significantly lower. Spearman correlation analysis showed that Turicibacteraceae, Turicibacterales, and Turicibacter were positively related with TLCA, GLCA, glycodeoxycholic acid (GDCA), and taurodeoxycholic acid, and were negatively correlated with HAMD score. At the same time, TLCA, GLCA, and GDCA were negatively correlated with HAMD score.

CONCLUSIONS

Gut microbiota and bile acids metabolism are disturbances in MDD, and there exists a correlation between gut microbiota and bile acids metabolism. Moreover, their interaction may be related to the pathophysiological mechanism of MDD.

Overlapping Mechanisms of Action of Brain-Active Bacteria and Bacterial Metabolites in the Pathogenesis of Common Brain Diseases

The involvement of the gut microbiota and the metabolites of colon-residing bacteria in brain disease pathogenesis has been covered in a growing number of studies, but comparative literature is scarce. To fill this gap, we explored the contribution of the microbiota-gut-brain axis to the pathophysiology of seven brain-related diseases (attention deficit hyperactivity disorder, autism spectrum disorder, schizophrenia, Alzheimer's disease, Parkinson's disease, major depressive disorder, and bipolar disorder). In this article, we discussed changes in bacterial abundance and the metabolic implications of these changes on disease development and progression. Our central findings indicate that, mechanistically, all seven diseases are associated with a leaky gut, neuroinflammation, and over-activated microglial cells, to which gut-residing bacteria and their metabolites are important contributors. Patients show a pro-inflammatory shift in their colon microbiota, harbouring more Gram-negative bacteria containing immune-triggering lipopolysaccharides (LPS) in their cell walls. In addition, bacteria with pro-inflammatory properties (Alistipes, Eggerthella, Flavonifractor) are found in higher abundances, whereas lower abundances of anti-inflammatory bacteria (Bifidobacterium, Coprococcus, Eucbacterium, Eubacterium rectale, Faecalibacterium, Faecalibacterium prasunitzii, Lactobacillus, Prevotella, Roseburia) are reported, when compared to healthy controls. On the metabolite level, aberrant levels of short-chain fatty acids (SCFAs) are involved in disease pathogenesis and are mostly found in lower quantities. Moreover, bacterial metabolites such as neurotransmitters (acetylcholine, dopamine, noradrenaline, GABA, glutamate, serotonin) or amino acids (phenylalanine, tryptophan) also play an important role. In the future, defined aberrations in the abundance of bacteria strains and altered bacterial metabolite levels could likely be possible markers for disease diagnostics and follow-ups. Moreover, they could help to identify novel treatment options, underlining the necessity for a deeper understanding of the microbiota-gut-brain axis.

Gut Microbial Dysbiosis and Cognitive Impairment in Bipolar Disorder: Current Evidence

Recent studies have reported that the gut microbiota influences mood and cognitive function through the gut-brain axis, which is involved in the pathophysiology of neurocognitive and mental disorders, including Parkinson’s disease, Alzheimer’s disease, and schizophrenia. These disorders have similar pathophysiology to that of cognitive dysfunction in bipolar disorder (BD), including neuroinflammation and dysregulation of various neurotransmitters (i.e., serotonin and dopamine). There is also emerging evidence of alterations in the gut microbial composition of patients with BD, suggesting that gut microbial dysbiosis contributes to disease progression and cognitive impairment in BD. Therefore, microbiota-centered treatment might be an effective adjuvant therapy for BD-related cognitive impairment. Given that studies focusing on connections between the gut microbiota and BD-related cognitive impairment are lagging behind those on other neurocognitive disorders, this review sought to explore the potential mechanisms of how gut microbial dysbiosis affects cognitive function in BD and identify potential microbiota-centered treatment.

Role of the gut microbiome in three major psychiatric disorders

Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia-spectrum disorders (SSD) are heterogeneous psychiatric disorders, which place significant burden on patient's well-being and global health. Disruptions in the gut-microbiome may play a role in these psychiatric disorders. This review presents current data on composition of the human gastrointestinal microbiota, and its interaction mechanisms in the gut-brain axis in MDD, BD and SSD. Diversity metrics and microbial relative abundance differed across studies. More studies reported inconsistent findings (n = 7) or no differences (n = 8) than studies who reported lower α-diversity in these psychiatric disorders (n = 5). The most consistent findings across studies were higher relative abundances of the genera Streptococcus, Lactobacillus, and Eggerthella and lower relative abundance of the butyrate producing Faecalibacterium in patients with psychiatric disorders. All three increased genera were associated with higher symptom severity. Confounders, such as medication use and life style have not been accounted for. So far, the results of probiotics trials have been inconsistent. Most traditional and widely used probiotics (consisting of Bifidobacterium spp. and Lactobacillus spp.) are safe, however, they do not correct potential microbiota disbalances in these disorders. Findings on prebiotics and faecal microbiota transplantation (FMT) are too limited to draw definitive conclusions. Disease-specific pro/prebiotic treatment or even FMT could be auspicious interventions for prevention and therapy for psychiatric disorders and should be investigated in future trials.

Shared postulations between bipolar disorder and polycystic ovary syndrome pathologies

INTRODUCTION

Women with bipolar disorder (BD) present a high prevalence of polycystic ovary syndrome (PCOS) and other reproductive disorders even before diagnosis or treatment of the disease. Postulations on the potential molecular mechanisms of comorbid PCOS in women with BD remain limited to influence of medications and need further extension.

OBJECTIVES

This review focuses on evidence suggesting that common metabolic and immune disorders may play an important role in the development of BD and PCOS.

RESULTS

The literature covered in this review suggests that metabolic and immune disorders, including the dysfunction of the hypothalamic-pituitary-adrenal axis, chronic inflammatory state, gut microbial alterations, adipokine alterations and circadian rhythm disturbance, are observed in patients with BD and PCOS. Such disorders may be responsible for the increased prevalence of PCOS in the BD population and indicate a susceptibility gene overlap between the two diseases. Current evidence supports postulations of common metabolic and immune disorders as endophenotype in BD as well as in PCOS.

CONCLUSIONS

Metabolic and immune disorders may be responsible for the comorbid PCOS in the BD population. The identification of hallmark metabolic and immune features common to these two diseases will contribute to the clarification of the effect of BD on the reproductive endocrine function and development of symptomatic treatments targeting the biomarkers of the two diseases.

Short chain fatty acids: Microbial metabolites for gut-brain axis signalling

The role of the intestinal microbiota as a regulator of gut-brain axis signalling has risen to prominence in recent years. Understanding the relationship between the gut microbiota, the metabolites it produces, and the brain will be critical for the subsequent development of new therapeutic approaches, including the identification of novel psychobiotics. A key focus in this regard have been the short-chain fatty acids (SCFAs) produced by bacterial fermentation of dietary fibre, which include butyrate, acetate, and propionate. Ongoing research is focused on the entry of SCFAs into systemic circulation from the gut lumen, their migration to cerebral circulation and across the blood brain barrier, and their potential to exert acute and chronic effects on brain structure and function. This review aims to discuss our current mechanistic understanding of the direct and indirect influence that SCFAs have on brain function, behaviour and physiology, which will inform future microbiota-targeted interventions for brain disorders.

Gut Microbiota – A Potential Contributor in the Pathogenesis of Bipolar Disorder

Bipolar disorder (BD) is one of the major psychiatric disorders that is characterized by recurrent episodes of depression and mania (or hypomania), leading to seriously adverse outcomes with unclear pathogenesis. There is an underlying relationship between bacterial communities residing in the gut and brain function, which together form the gut-brain axis (GBA). Recent studies have shown that changes in the gut microbiota have been observed in a large number of BD patients, so the axis may play a role in the pathogenesis of BD. This review summarizes briefly the relationship between the GBA and brain function, the composition and changes of gut microbiota in patients with BD, and further explores the potential role of GBA-related pathway in the pathogenesis of BD as well as the limitations in this field at present in order to provide new ideas for the future etiology research and drug development.

Gut Microbiota Research in Bipolar Disorder and Possible Implications for Precision Psychiatry: A Systematic Review

Bipolar disorder (BD) is a highly disabling condition with a chronic and relapsing nature. Despite the substantial socioeconomic burden associated with BD, there are still significant research gaps in risk stratification, diagnostic accuracy, and treatment selection, all key components of precision psychiatry. One possible strategy to increase the validity of precision psychiatry approaches in BD is to increase our knowledge of disorder-associated gut microbiota perturbations. To this end, we systematically reviewed the evidence on gut microbiota alterations in relation to precision psychiatry approaches on BD. We performed a systematic review on PubMed/MEDLINE and Web of Science to identify original articles investigating the possible clinical applications of microbiota analyses for pragmatic precision psychiatry in BD. A pearl growing strategy was employed to enlarge the scope of this review. The primary search strategy yielded one paper and an additional one was identified through reference tracking. The included studies were observational, with one study of good quality. The identified results justify the efforts devolved in this area of research and underscore the need to expand these investigations through additional larger and properly designed studies.

Changes in gut viral and bacterial species correlate with altered 1,2-diacylglyceride levels and structure in the prefrontal cortex in a depression-like non-human primate model

Major depressive disorder (MDD) is a debilitating mental disease, but its underlying molecular mechanisms remain obscure. Our previously established model of naturally occurring depression-like (DL) behaviors in Macaca fascicularis, which is characterized by microbiota-gut-brain (MGB) axis disturbances, can be used to interrogate how a disturbed gut ecosystem may impact the molecular pathology of MDD. Here, gut metagenomics were used to characterize how gut virus and bacterial species, and associated metabolites, change in depression-like monkey model. We identified a panel of 33 gut virus and 14 bacterial species that could discriminate the depression-like from control macaques. In addition, using lipidomic analyses of central and peripheral samples obtained from these animals, we found that the DL macaque were characterized by alterations in the relative abundance, carbon-chain length, and unsaturation degree of 1,2-diacylglyceride (DG) in the prefrontal cortex (PFC), in a brain region-specific manner. In addition, lipid-reaction analysis identified more active and inactive lipid pathways in PFC than in amygdala or hippocampus, with DG being a key nodal player in these lipid pathways. Significantly, co-occurrence network analysis showed that the DG levels may be relevant to the onset of negative emotions behaviors in PFC. Together our findings suggest that altered DG levels and structure in the PFC are hallmarks of the DL macaque, thus providing a new framework for understanding the gut microbiome's role in depression.

New Evidence of Gut Microbiota Involvement in the Neuropathogenesis of Bipolar Depression by TRANK1 Modulation: Joint Clinical and Animal Data

Tetratricopeptide repeat and ankyrin repeat containing 1 (TRANK1) is a robust risk gene of bipolar disorder (BD). However, little is known on the role of TRANK1 in the pathogenesis of BD and whether the gut microbiota is capable of regulating TRANK1 expression. In this study, we first investigated the serum mRNA level of TRANK1 in medication-free patients with a depressive episode of BD, then a mice model was constructed by fecal microbiota transplantation (FMT) to explore the effects of gut microbiota on brain TRANK1 expression and neuroinflammation, which was further verified by in vitro Lipopolysaccharide (LPS) treatment in BV-2 microglial cells and neurons. 22 patients with a depressive episode and 28 healthy individuals were recruited. Serum level of TRANK1 mRNA was higher in depressed patients than that of healthy controls. Mice harboring 'BD microbiota' following FMT presented depression-like phenotype. mRNA levels of inflammatory cytokines and TRANK1 were elevated in mice hippocampus and prefrontal cortex. In vitro, LPS treatment activated the secretion of pro-inflammatory factors in BV-2 cells, which was capable of upregulating the neuronal expression of TRANK1 mRNA. Moreover, primary cortical neurons transfected with plasmid Cytomegalovirus DNA (pcDNA3.1(+)) vector encoding human TRANK1 showed decreased dendritic spine density. Together, these findings add new evidence to the microbiota-gut-brain regulation in BD, indicating that microbiota is possibly involved in the neuropathogenesis of BD by modulating the expression of TRANK1.