Project description:Evidence is accumulating that the establishment of the gut microbiome in early life influences the development of atopic eczema. In this longitudinal study, we used integrated multi-omics analyses to infer functional mechanisms by which the microbiome modulates atopic eczema risk. We measured the functionality of the gut microbiome and metabolome of 63 infants between ages 3 weeks and 12 months with well-defined eczema cases and controls in a sub-cohort from the Growing Up in Singapore Toward healthy Outcomes (GUSTO) mother-offspring cohort. At 3 weeks, the microbiome and metabolome of allergen-sensitized atopic eczema infants were characterized by an enrichment of Escherichia coli and Klebsiella pneumoniae, associated with increased stool D-glucose concentration and increased gene expression of associated virulence factors. A delayed colonization by beneficial Bacteroides fragilis and subsequent delayed accumulation of butyrate and propionate producers after 3 months was also observed. Here, we describe an aberrant developmental trajectory of the gut microbiome and stool metabolome in allergen sensitized atopic eczema infants. The infographic describes an impaired developmental trajectory of the gut microbiome and metabolome in allergen-sensitized atopic eczema (AE) infants and infer its contribution in modulating allergy risk in the Singaporean mother-offspring GUSTO cohort. The key microbial signature of AE is characterized by (1) an enrichment of Escherichia coli and Klebsiella pneumoniae which are associated with accumulation of pre-glycolysis intermediates (D-glucose) via the trehalose metabolic pathway, increased gene expression of associated virulence factors (invasin, adhesin, flagellin and lipopolysaccharides) by utilizing ATP from oxidative phosphorylation and delayed production of butyrate and propionate, (2) depletion of Bacteroides fragilis which resulted in lower expression of immunostimulatory bacterial cell envelope structure and folate (vitamin B9) biosynthesis pathway, and (3) accompanied depletion of bacterial groups with the ability to derive butyrate and propionate through direct or indirect pathways which collectively resulted in reduced glycolysis, butyrate and propionate biosynthesis.
Project description:Coal workers' pneumoconiosis (CWP) is a severe occupational disease resulting from prolonged exposure to coal dust. However, its pathogenesis remains elusive, compounded by a lack of early detection markers and effective treatments. Although the impact of gut microbiota on lung diseases is acknowledged, its specific role in CWP is unclear. This study aims to explore changes in the gut microbiome and metabolome in CWP, while also assessing the correlation between gut microbes and alterations in lung function. Fecal specimens from 43 CWP patients and 48 dust-exposed workers (DEW) were examined using 16S rRNA gene sequencing for microbiota and liquid chromatography-mass spectrometry for metabolite profiling. We observed similar gut microbial α-diversity but significant differences in flora composition (β-diversity) between patients with CWP and the DEW group. After adjusting for age using multifactorial linear regression analysis (MaAsLin2), the distinct gut microbiome profile in CWP patients revealed an increased presence of pro-inflammatory microorganisms such as Klebsiella and Haemophilus. Furthermore, in CWP patients, alterations in gut microbiota-particularly reduced α-diversity and changes in microbial composition-were significantly correlated with impaired pulmonary function, a relationship not observed in DEW. This underscores the specific impact of gut microbiota on pulmonary health in individuals with CWP. Metabolomic analysis of fecal samples from CWP patients and DEW identified 218 differential metabolites between the two groups, with a predominant increase in metabolites in CWP patients, suggesting enhanced metabolic activity in CWP. Key altered metabolites included various lipids, amino acids, and organic compounds, with silibinin emerging as a potential biomarker. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis linked these metabolites to pathways relevant to the development of pulmonary fibrosis. Additionally, studies on the interaction between microbiota and metabolites showed positive correlations between certain bacteria and increased metabolites in CWP, further elucidating the complex interplay in this disease state. Our findings suggest a potential contributory role of gut microbiota in CWP pathogenesis through metabolic regulation, with implications for diagnostic biomarkers and understanding disease mechanisms, warranting further molecular investigation.ImportanceThe findings have significant implications for the early diagnosis and treatment of coal workers' pneumoconiosis, highlighting the potential of gut microbiota as diagnostic biomarkers. They pave the way for new research into gut microbiota-based therapeutic strategies, potentially focusing on modifying gut microbiota to mitigate disease progression.
Project description:Stress negatively impacts gut and brain health. Individual differences in response to stress have been linked to genetic and environmental factors and more recently, a role for the gut microbiota in the regulation of stress-related changes has been demonstrated. However, the mechanisms by which these factors influence each other are poorly understood, and there are currently no established robust biomarkers of stress susceptibility. To determine the metabolic and microbial signatures underpinning physiological stress responses, we compared stress-sensitive Wistar Kyoto (WKY) rats to the normo-anxious Sprague Dawley (SD) strain. Here we report that acute stress-induced strain-specific changes in brain lipid metabolites were a prominent feature in WKY rats. The relative abundance of Lactococcus correlated with the relative proportions of many brain lipids. In contrast, plasma lipids were significantly elevated in response to stress in SD rats, but not in WKY rats. Supporting these findings, we found that the greatest difference between the SD and WKY microbiomes were the predicted relative abundance of microbial genes involved in lipid and energy metabolism. Our results provide potential insights for developing novel biomarkers of stress vulnerability, some of which appear genotype specific.
Project description:BackgroundThe effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.ResultsWe propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).ConclusionThese results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
Project description:BackgroundMyopia is one of the most common eye diseases leading to blurred distance vision. Inflammatory diseases could trigger or exacerbate myopic changes. Although gut microbiota bacteria are associated with various inflammatory diseases, little is known about its role in myopia.Materials and methodsThe mice were randomly divided into control and model groups, with the model group being attached-30D lens onto the eyes for 3 weeks. Then, mouse cecal contents and plasma were collected to analyze their intestinal microbiota and plasma metabolome.ResultsWe identified that the microbial composition differed considerably between the myopic and non-myopic mice, with the relative abundance of Firmicutes phylum decreased obviously while that of Actinobacteria phylum was increased in myopia. Furthermore, Actinobacteria and Bifidobacterium were positively correlated with axial lengths (ALs) of eyeballs while negatively correlated with refractive diopters. Untargeted metabolomic analysis identified 141 differentially expressed metabolites, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed considerable enrichment mainly in amino acid metabolism pathways. Notably, pathways involved glutamate metabolism including "Glutamine and D-glutamate metabolism" and "Alanine, aspartate and glutamate metabolism" was changed dramatically, which presented as the concentrations of L-Glutamate and L-Glutamine decreased obviously in myopia. Interestingly, microbiome dysbiosis and metabolites alternations in myopia have a disrupting gut barrier feature. We further demonstrated that the gut barrier function was impaired in myopic mice manifesting in decreased expression of Occludin, ZO-1 and increased permeation of FITC-dextran.DiscussionMyopic mice had obviously altered gut microbiome and metabolites profiles compared to non-myopic mice. The dysbiosis and plasma metabolomics shift in myopia had an interrupting gut barrier feature. Our study provides new insights into the possible role of the gut microbiota in myopia and reinforces the potential feasibility of microbiome-based therapies in myopia.
Project description:BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.
Project description:Background/aimsShifts in the gut microbiota and metabolites are interrelated with liver cirrhosis progression and complications. However, causal relationships have not been evaluated comprehensively. Here, we identified complication-dependent gut microbiota and metabolic signatures in patients with liver cirrhosis.MethodsMicrobiome taxonomic profiling was performed on 194 stool samples (52 controls and 142 cirrhosis patients) via V3-V4 16S rRNA sequencing. Next, 51 samples (17 controls and 34 cirrhosis patients) were selected for fecal metabolite profiling via gas chromatography mass spectrometry and liquid chromatography coupled to time-of-flight mass spectrometry. Correlation analyses were performed targeting the gut-microbiota, metabolites, clinical parameters, and presence of complications (varices, ascites, peritonitis, encephalopathy, hepatorenal syndrome, hepatocellular carcinoma, and deceased).ResultsVeillonella bacteria, Ruminococcus gnavus, and Streptococcus pneumoniae are cirrhosis-related microbiotas compared with control group. Bacteroides ovatus, Clostridium symbiosum, Emergencia timonensis, Fusobacterium varium, and Hungatella_uc were associated with complications in the cirrhosis group. The areas under the receiver operating characteristic curve (AUROCs) for the diagnosis of cirrhosis, encephalopathy, hepatorenal syndrome, and deceased were 0.863, 0.733, 0.71, and 0.69, respectively. The AUROCs of mixed microbial species for the diagnosis of cirrhosis and complication were 0.808 and 0.847, respectively. According to the metabolic profile, 5 increased fecal metabolites in patients with cirrhosis were biomarkers (AUROC >0.880) for the diagnosis of cirrhosis and complications. Clinical markers were significantly correlated with the gut microbiota and metabolites.ConclusionCirrhosis-dependent gut microbiota and metabolites present unique signatures that can be used as noninvasive biomarkers for the diagnosis of cirrhosis and its complications.
Project description:BackgroundHumans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome's interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome.ResultsUsing data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles.ConclusionsSpecific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome-host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.
Project description:The advent of high-throughput 'omics' technologies has improved our knowledge of gut microbiome in human health and disease, including Alzheimer's disease (AD), a neurodegenerative disorder. Frequent bidirectional communications and mutual regulation exist between the gastrointestinal tract and the central nervous system through the gut-brain axis. A large body of research has reported a close association between the gut microbiota and AD development, and restoring a healthy gut microbiota may curb or even improve AD symptoms and progression. Thus, modulation of the gut microbiota has become a novel paradigm for clinical management of AD, and emerging effort has focused on developing potential novel strategies for preventing and/or treating the disease. In this review, we provide an overview of the connection and causal relationship between gut dysbiosis and AD, the mechanisms of gut microbiota in driving AD progression, and the successes and challenges of implementing available gut microbiome-targeted therapies (including probiotics, prebiotics, synbiotics, postbiotics, and fecal microbiota transplantation) in preventive and/or therapeutic preclinical and clinical intervention studies of AD. Finally, we discuss the future directions in this field.
Project description:Obstructive sleep apnea (OSA), a common sleep disorder characterized by intermittent hypoxia and hypercapnia (IHC), increases atherosclerosis risk. However, the contribution of intermittent hypoxia (IH) or intermittent hypercapnia (IC) in promoting atherosclerosis remains unclear. Since gut microbiota and metabolites have been implicated in atherosclerosis, we examined whether IH or IC alters the microbiome and metabolome to induce a pro-atherosclerotic state. Apolipoprotein E deficient mice (ApoE-/- ), treated with IH or IC on a high-fat diet (HFD) for 10 weeks, were compared to Air controls. Atherosclerotic lesions were examined, gut microbiome was profiled using 16S rRNA gene amplicon sequencing and metabolome was assessed by untargeted mass spectrometry. In the aorta, IC-induced atherosclerosis was significantly greater than IH and Air controls (aorta, IC 11.1 ± 0.7% vs. IH 7.6 ± 0.4%, p < 0.05 vs. Air 8.1 ± 0.8%, p < 0.05). In the pulmonary artery (PA), however, IH, IC, and Air were significantly different from each other in atherosclerotic formation with the largest lesion observed under IH (PA, IH 40.9 ± 2.0% vs. IC 20.1 ± 2.6% vs. Air 12.2 ± 1.5%, p < 0.05). The most differentially abundant microbial families (p < 0.001) were Peptostreptococcaceae, Ruminococcaceae, and Erysipelotrichaceae. The most differentially abundant metabolites (p < 0.001) were tauro-β-muricholic acid, ursodeoxycholic acid, and lysophosphoethanolamine (18:0). We conclude that IH and IC (a) modulate atherosclerosis progression differently in distinct vascular beds with IC, unlike IH, facilitating atherosclerosis in both aorta and PA and (b) promote an atherosclerotic luminal gut environment that is more evident in IH than IC. We speculate that the resulting changes in the gut metabolome and microbiome interact differently with distinct vascular beds.