Integrative metagenomic analysis reveals distinct gut microbial signatures related to obesity
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ABSTRACT:
SUBMITTER: Hu X
PROVIDER: S-EPMC10996249 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Emerging evidence indicates maternal microbiota as one major reservoir for pioneering microbes in infants. However, the global distinct and identical features of mother-infant gut microbiota at various taxonomic resolutions and metabolic functions across cohorts and potential of infant microbial prediction based on their paired mother's gut microbiota remain unclear. Here, we analyzed 376 mother-infant dyads (468 mother and 1024 infant samples) of eight studies from six countries and observed higher diversity at species and strain levels in maternal gut microbiota but not their metabolic functions. A number of 290 species were shared in at least one mother-infant dyad, with 26 species (five at strain level) observed across cohorts. The profile of mother-infant shared species and strains was further influenced by delivery mode and feeding regimen. The mother-sourced species in infants exhibited similar strain heterogeneity but more metabolic functions compared to other-sourced species, suggesting the comparable stability and fitness of shared and non-shared species and the potential role of shared species in the early gut microbial community, respectively. Predictive models showed moderate performance accuracy for shared species and strains occurrences in infants. These generalized mother-infant shared species and strains may be considered as the primary targets for future work toward infant microbiome development and probiotics exploration.
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Project description:PurposeAlterations in the gut microbiota have been associated with age-related macular degeneration (AMD). However, the dysbiosis shared by different ethnicity and geographic groups, which may associate with the disease pathogenesis, remain underexplored. Here, we characterized dysbiosis of the gut microbiota in patients with AMD from Chinese and Swiss cohorts and identified cross-cohort signatures associated with AMD.MethodsShotgun metagenomic sequencing was performed on fecal samples from 30 patients with AMD and 30 healthy subjects. Published datasets with 138 samples from Swiss patients with AMD and healthy subjects were re-analyzed. Comprehensive taxonomic profiling was conducted by matching to the RefSeq genome database, metagenome-assembled genome (MAG) database, and Gut Virome Database (GVD). Functional profiling was performed by reconstruction of the MetaCyc pathways.ResultsThe α-diversity of the gut microbiota was decreased in patients with AMD according to taxonomic profiles generated using MAG but not RefSeq database as reference. The Firmicutes/Bacteroidetes ratio was also decreased in patients with AMD. Among AMD-associated bacteria shared between Chinese and Swiss cohorts, Ruminococcus callidus, Lactobacillus gasseri, and Prevotellaceae (f) uSGB 2135 were enriched in patients with AMD, whereas Bacteroidaceae (f) uSGB 1825 was depleted in patients with AMD and was negatively associated with hemorrhage size. Bacteroidaceae was one of the major hosts of phages associated with AMD. Three degradation pathways were reduced in AMD.ConclusionsThese results demonstrated that dysbiosis of the gut microbiota was associated with AMD. We identified cross-cohort gut microbial signatures involving bacteria, viruses, and metabolic pathways, which potentially serve as promising targets for the prevention or treatment of AMD.
| S-EPMC10202153 | biostudies-literature
Project description:Overweight and obesity are growing health problems in domestic cats, increasing the risks of insulin resistance, lipid dyscrasias, neoplasia, cardiovascular disease, and decreasing longevity. The signature of obesity in the feline gut microbiota has not been studied at the whole-genome metagenomic level. We performed whole-genome shotgun metagenomic sequencing in the fecal samples of eight overweight/obese and eight normal cats housed in the same research environment. We obtained 271 Gbp of sequences and generated a 961-Mbp de novo reference contig assembly, with 1.14 million annotated microbial genes. In the obese cat microbiome, we discovered a significant reduction in microbial diversity (P < 0.01) and Firmicutes abundance (P = 0.005), as well as decreased Firmicutes/Bacteroidetes ratios (P = 0.02), which is the inverse of obese human/mouse microbiota. Linear discriminant analysis and quantitative PCR (qPCR) validation revealed significant increases of Bifidobacterium sp., Olsenella provencensis, Dialister sp.CAG:486, and Campylobacter upsaliensis as the hallmark of obese microbiota among 400 enriched species, whereas 1,525 bacterial species have decreased abundance in the obese microbiome. Phascolarctobacterium succinatutens and an uncharacterized Erysipelotrichaceae bacterium are highly abundant (>0.05%) in the normal gut with over 400-fold depletion in the obese microbiome. Fatty acid synthesis-related pathways are significantly overrepresented in the obese compared with the normal cat microbiome. In conclusion, we discovered dramatically decreased microbial diversity in obese cat gut microbiota, suggesting potential dysbiosis. A panel of seven significantly altered, highly abundant species can serve as a microbiome indicator of obesity. Our findings in the obese cat microbiome composition, abundance, and functional capacities provide new insights into feline obesity. IMPORTANCE Obesity affects around 45% of domestic cats, and licensed drugs for treating feline obesity are lacking. Physical exercise and calorie restrictions are commonly used for weight loss but with limited efficacy. Through comprehensive analyses of normal and obese cat gut bacteria flora, we identified dramatic shifts in the obese gut microbiome, including four bacterial species significantly enriched and two species depleted in the obese cats. The key bacterial community and functional capacity alterations discovered from this study will inform new weight management strategies for obese cats, such as evaluations of specific diet formulas that alter the microbiome composition, and the development of prebiotics and probiotics that promote the increase of beneficial species and the depletion of obesity-associated species. Interestingly, these bacteria identified in our study were also reported to affect the weight loss success in human patients, suggesting translational potential in human obesity.
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Project description:BackgroundThe prevalence of obese children in China is increasing, which poses a great challenge to public health. Gut microbes play an important role in human gut health, and changes in gut status are closely related to obesity. However, how gut microbes contribute to obesity in children remains unclear. In our study, we performed shotgun metagenomic sequencing of feces from 23 obese children, 8 overweight children and 22 control children in Chengdu, Sichuan, China.ResultsWe observed a distinct difference in the gut microbiome of obese children and that of controls. Compared with the controls, bacterial pathogen Campylobacter rectus was significantly more abundant in obese children. In addition, functional annotation of microbial genes revealed that there might be gut inflammation in obese children. The guts of overweight children might belong to the transition state between obese and control children due to a gradient in relative abundance of differentially abundant species. Finally, we compared the gut metagenomes of obese Chinese children and obese Mexican children and found that Trichuris trichiura was significantly more abundant in the guts of obese Mexican children.ConclusionsOur results contribute to understanding the changes in the species and function of intestinal microbes in obese Chinese children.
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Project description:Common intestinal diseases such as Crohn's disease (CD), ulcerative colitis (UC), and colorectal cancer (CRC) share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multidisease models. Here, we performed meta-analyses on 13 fecal metagenome data sets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple data sets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and disease-common clusters and revealed their distinct phylogenetic relationships; species in the CD-specific cluster were phylogenetically related, while those in the CRC-specific cluster were more distant. Strikingly, UC-specific species were phylogenetically closer to CRC, likely because UC patients have higher risk of CRC. Consistent with their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. A portion of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human-derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection on gut microbes. With the marker species and pathways, we constructed four high-performance (including multidisease) models with an area under the receiver operating characteristic curve (AUROC) of 0.87 and true-positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multidisease diagnosis.IMPORTANCE Gut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis, and Crohn's disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multidisease classifications. To the best of our knowledge, this is the first study to construct multiclass models for these common intestinal diseases.
| S-EPMC8269207 | biostudies-literature
Project description:Microbiome studies have revealed gut microbiota's potential impact on complex diseases. However, many studies often focus on one disease per cohort. We developed a meta-analysis workflow for gut microbiome profiles and analyzed shotgun metagenomic data covering 11 diseases. Using interpretable machine learning and differential abundance analysis, our findings reinforce the generalization of binary classifiers for Crohn's disease (CD) and colorectal cancer (CRC) to hold-out cohorts and highlight the key microbes driving these classifications. We identified high microbial similarity in disease pairs like CD vs ulcerative colitis (UC), CD vs CRC, Parkinson's disease vs type 2 diabetes (T2D), and schizophrenia vs T2D. We also found strong inverse correlations in Alzheimer's disease vs CD and UC. These findings, detected by our pipeline, provide valuable insights into these diseases.ImportanceAssessing disease similarity is an essential initial step preceding a disease-based approach for drug repositioning. Our study provides a modest first step in underscoring the potential of integrating microbiome insights into the disease similarity assessment. Recent microbiome research has predominantly focused on analyzing individual diseases to understand their unique characteristics, which by design excludes comorbidities in individuals. We analyzed shotgun metagenomic data from existing studies and identified previously unknown similarities between diseases. Our research represents a pioneering effort that utilizes both interpretable machine learning and differential abundance analysis to assess microbial similarity between diseases.
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Project description:While recent research indicates that human health is affected by the gut microbiome, the functional mechanisms that underlie host-microbiome interactions remain poorly resolved. Metagenomic clinical studies can address this problem by revealing specific microbial functions that stratify healthy and diseased individuals. To improve our understanding of the relationship between the gut microbiome and health, we conducted the first integrative functional analysis of nearly 2,000 publicly available fecal metagenomic samples obtained from eight clinical studies. We identified characteristics of the gut microbiome that associate generally with disease, including functional alpha-diversity, beta-diversity, and beta-dispersion. Using regression modeling, we identified specific microbial functions that robustly stratify diseased individuals from healthy controls. Many of these functions overlapped multiple diseases, suggesting a general role in host health, while others were specific to a single disease and may indicate disease-specific etiologies. Our results clarify potential microbiome-mediated mechanisms of disease and reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics. IMPORTANCE The composition of the gut microbiome associates with a wide range of human diseases, but the mechanisms underpinning these associations are not well understood. To shift toward a mechanistic understanding, we integrated distinct metagenomic data sets to identify functions encoded in the gut microbiome that associate with multiple diseases, which may be important to human health. Additionally, we identified functions that associate with specific diseases, which may elucidate disease-specific etiologies. We demonstrated that the functions encoded in the microbiome can be used to classify disease status, but the inclusion of additional patient covariates may be necessary to obtain sufficient accuracy. Ultimately, this analysis advances our understanding of the gut microbiome functions that constitute a healthy microbiome and identifies potential targets for microbiome-based diagnostics and therapeutics.
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Project description:The use of metaproteomics for studying the human gut microbiota can shed light on the taxonomic profile and the functional role of the microbial community. Nevertheless, methods for extracting proteins from stool samples continue to evolve, in the pursuit of optimal protocols for moistening and dispersing the stool sample and for disrupting microbial cells, which are two critical steps for ensuring good protein recovery. Here, we evaluated different stool sample processing (SSP) and microbial cell disruption methods (CDMs). The combination of a longer disintegration period of the stool sample in a tube rotator with sonication increased the overall number of identified peptides and proteins. Proteobacteria, Bacteroidetes, Planctomycetes, and Euryarchaeota identification was favored by mechanical cell disruption with glass beads. In contrast, the relative abundance of Firmicutes, Actinobacteria, and Fusobacteria was improved when sonication was performed before bead beating. Tenericutes and Apicomplexa identification was enhanced by moistening the stool samples during processing and by disrupting cells with medium-sized glass beads combined with or without sonication. Human protein identifications were affected by sonication. To test the reproducibility of these gut metaproteomic analyses, we examined samples from six healthy individuals using a protocol that had shown a good taxonomic diversity and identification of proteins from Proteobacteria and humans. We also detected proteins involved in microbial functions relevant to the host and related mostly to specific taxa, such as B12 biosynthesis and short chain fatty acid (SCFA) production carried out mainly by members in the Prevotella genus and the Firmicutes phylum, respectively. The taxonomic and functional profiles obtained with the different protocols described in this work provides the researcher with valuable information when choosing the most adequate protocol for the study of certain pathologies under suspicion of being related to a specific taxon from the gut microbiota.
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Project description:Chronic kidney disease (CKD) is a serious healthcare dilemma, associated with specific changes in gut microbiota and circulating metabolome. Yet, the functional capacity of CKD microbiome and its intricate relationship with the host metabolism at different stages of disease are less understood.MethodsHere, shotgun sequencing of fecal samples and targeted metabolomics profiling of serum bile acids, short- and medium-chain fatty acids, and uremic solutes were performed in a cohort of CKD patients with different severities and non-CKD controls.ResultsWe identified that levels of 13 microbial species and 6 circulating metabolites were significantly altered across early to advanced stages or only in particular stage(s). Among these, Prevotella sp. 885 (decreased) was associated with urea excretion, while caproic acid (decreased) and p-cresyl sulfate (elevated) were positively and negatively correlated with the glomerular filtration rate, respectively. In addition, we identified gut microbial species linked to changes in circulating metabolites. Microbial genes related to secondary bile acid biosynthesis were differentially abundant at the early stage, while pathway modules related to lipid metabolism and lipopolysaccharide biosynthesis were enriched in the CKD microbiome at the advanced stage, suggesting that changes in microbial metabolism and host inflammation may contribute to renal health. Further, we identified metagenomic and metabolomic markers to discriminate cases of different severities from the controls, among which Bacteroides eggerthii individually was of particular value in early diagnosis.ConclusionsOur dual-omics data reveal the connections between intestinal microbes and circulating metabolites perturbed in CKD, which may be of etiological and diagnostic importance.
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Project description:The aim of this exploratory study was to evaluate the gut microbial signatures of distinct trimethylamine N-oxide (TMAO) responses following raspberry consumption. Investigations were carried out in 24 subjects at risk of developing metabolic syndrome who received 280 g/day of frozen raspberries for 8 weeks. Blood and stool samples were collected at weeks 0 and 8. Inter-individual variability in plasma TMAO levels was analyzed, 7 subjects were excluded due to noninformative signals and 17 subjects were kept for analysis and further stratified according to their TMAO response. Whole-metagenome shotgun sequencing analysis was used to determine the impact of raspberry consumption on gut microbial composition. Before the intervention, the relative abundance of Actinobacteriota was significantly higher in participants whose TMAO levels increased after the intervention (p = 0.03). The delta TMAO (absolute differences of baseline and week 8 levels) was positively associated with the abundance of gut bacteria such as Bilophila wadsworthia (p = 0.02; r2 = 0.37), from the genus Granulicatella (p = 0.03; r2 = 0.48) or the Erysipelotrichia class (p = 0.03; r2 = 0.45). Changes in the gut microbial ecology induced by raspberry consumption over an 8-week period presumably impacted quaternary amines-utilizing activity and thus plasma TMAO levels.
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