Gut Microbiome Composition Associated With Major Depressive Disorder and Sleep Quality
TL;DR: In this paper, the authors performed 16S rRNA sequencing of stool samples from 36 patients with major depressive disorder and 45 healthy controls (HC) and found that six microbiota targets were associated with the severity of depression, 11 with sleep quality, and 3 with sleep severity.
Abstract: The microbiota-gut-brain axis plays a critical role in the pathogenesis of major depressive disorder (MDD) and related subclinical symptoms. However, studies on the gut microbiota in MDD are inconsistent, and data on MDD's effects on sleep are lacking. This study aimed to analyze the gut microbiota composition and sleep quality of patients with MDD. We performed 16S rRNA sequencing of stool samples from 36 patients with MDD and 45 healthy controls (HC). Sleep quality was assessed using the Pittsburgh Sleep Quality Index, depressive severity with the Hamilton Depression Scale, and insomnia severity using the Insomnia Severity Index. Forty-eight microbiota targets showed significant differences between MDD and HC. In MDD, six microbiota targets were associated with the severity of depression, 11 with sleep quality, and 3 with sleep severity. At the genus level, Dorea was simultaneously related to depression and sleep quality, while Intestinibacter was more closely related to sleep problems. Coprococcus and Intestinibacter were associated with sleep quality independent of the severity of depression. In conclusion, the present findings enable a better understanding of the relationship between gut microbiota and MDD-related symptoms. Gut microbiota alterations may become potential biomarkers and/or treatment targets for sleep quality in MDD.
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TL;DR: In this paper , the authors synthesized the current literature investigating differences in gut microbiota composition in people with the major psychiatric disorders, major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), compared to healthy controls.
Abstract: Abstract The emerging understanding of gut microbiota as ‘metabolic machinery’ influencing many aspects of physiology has gained substantial attention in the field of psychiatry. This is largely due to the many overlapping pathophysiological mechanisms associated with both the potential functionality of the gut microbiota and the biological mechanisms thought to be underpinning mental disorders. In this systematic review, we synthesised the current literature investigating differences in gut microbiota composition in people with the major psychiatric disorders, major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ), compared to ‘healthy’ controls. We also explored gut microbiota composition across disorders in an attempt to elucidate potential commonalities in the microbial signatures associated with these mental disorders. Following the PRISMA guidelines, databases were searched from inception through to December 2021. We identified 44 studies (including a total of 2510 psychiatric cases and 2407 controls) that met inclusion criteria, of which 24 investigated gut microbiota composition in MDD, seven investigated gut microbiota composition in BD, and 15 investigated gut microbiota composition in SZ. Our syntheses provide no strong evidence for a difference in the number or distribution (α-diversity) of bacteria in those with a mental disorder compared to controls. However, studies were relatively consistent in reporting differences in overall community composition (β-diversity) in people with and without mental disorders. Our syntheses also identified specific bacterial taxa commonly associated with mental disorders, including lower levels of bacterial genera that produce short-chain fatty acids (e.g. butyrate), higher levels of lactic acid-producing bacteria, and higher levels of bacteria associated with glutamate and GABA metabolism. We also observed substantial heterogeneity across studies with regards to methodologies and reporting. Further prospective and experimental research using new tools and robust guidelines hold promise for improving our understanding of the role of the gut microbiota in mental and brain health and the development of interventions based on modification of gut microbiota.
104 citations
TL;DR: The main microbial metabolites (short-chain fatty acids -SCFAs-, bile acids, amino acids, tryptophan -trp- derivatives, and more), their signaling pathways and functions will be summarized to explain part of MDD pathophysiology.
Abstract: The gut microbiota is a complex and dynamic ecosystem essential for the proper functioning of the organism, affecting the health and disease status of the individuals. There is continuous and bidirectional communication between gut microbiota and the host, conforming to a unique entity known as “holobiont”. Among these crosstalk mechanisms, the gut microbiota synthesizes a broad spectrum of bioactive compounds or metabolites which exert pleiotropic effects on the human organism. Many of these microbial metabolites can cross the blood–brain barrier (BBB) or have significant effects on the brain, playing a key role in the so-called microbiota-gut-brain axis. An altered microbiota-gut-brain (MGB) axis is a major characteristic of many neuropsychiatric disorders, including major depressive disorder (MDD). Significative differences between gut eubiosis and dysbiosis in mental disorders like MDD with their different metabolite composition and concentrations are being discussed. In the present review, the main microbial metabolites (short-chain fatty acids -SCFAs-, bile acids, amino acids, tryptophan -trp- derivatives, and more), their signaling pathways and functions will be summarized to explain part of MDD pathophysiology. Conclusions from promising translational approaches related to microbial metabolome will be addressed in more depth to discuss their possible clinical value in the management of MDD patients.
37 citations
TL;DR: The GMB profiles of patients with MDD differ significantly from HC, but further studies are needed to elucidate the benefits of prebiotic, probiotic and synbiotic treatments relative to antidepressants and over longer follow-up before these therapies are implemented into clinical practice.
Abstract: An emerging body of literature demonstrates differences in the gut microbiome (GMB) of patients with major depressive disorder (MDD) compared to healthy controls (HC), as well as the potential benefits of prebiotic, probiotic, and synbiotic treatment. We conducted a systematic review of 24 observational studies (n = 2817), and 19 interventional trials (n = 1119). We assessed alpha diversity, beta diversity, and taxa abundance changes in patients with MDD relative to HC, as well as the effect of prebiotics, probiotics, and synbiotics on depressive symptoms in individuals with clinical or subclinical depression. We observed no significant differences in alpha diversity but a significant difference in beta diversity between patients with MDD and HC. There were fluctuations in the abundance of specific taxa in patients with MDD relative to HC. Probiotic and synbiotic, but not prebiotic, treatment showed a modest benefit in reducing depressive symptoms in patients with MDD over four to nine weeks. The GMB profiles of patients with MDD differ significantly from HC, but further studies are needed to elucidate the benefits of prebiotic, probiotic and synbiotic treatments relative to antidepressants and over longer follow-up before these therapies are implemented into clinical practice.
34 citations
TL;DR: A review of recent scientific elements that explore the communication between gut microbiota and the brain by focusing on the enteric nervous system (ENS) as an intermediate partner is presented in this article.
22 citations
TL;DR: An overarching model of the evolution of microbiome–CNS interaction is set out and how a growing knowledge of these complex systems can be used to determine disease susceptibility and reduce risk in a targeted manner is examined.
18 citations
References
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TL;DR: Timmomatic is developed as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data and is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Abstract: Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data.
Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic
Contact: ed.nehcaa-htwr.1oib@ledasu
Supplementary information: Supplementary data are available at Bioinformatics online.
39,291 citations
TL;DR: The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.
Abstract: Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.
23,155 citations
University of Massachusetts Amherst1, University of Michigan2, University of New Mexico3, University of British Columbia4, Texas A&M University5, University of Minnesota6, University of Warwick7, Dalhousie University8, Colorado School of Mines9, University of Ljubljana10, Graz University of Technology11, Louisiana State University12
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
17,350 citations
TL;DR: The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% correct bases commonly reported by other methods.
Abstract: Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.
11,329 citations
TL;DR: It is shown that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.
Abstract: DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.
6,840 citations