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Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways.

ABSTRACT: Obesity is associated with altered gut microbiome composition but data across different populations remain inconsistent. We meta-analyzed publicly available 16S-rRNA sequence datasets from 18 different studies and identified differentially abundant taxa and functional pathways of the obese gut microbiome. Most differentially abundant genera (Odoribacter, Oscillospira, Akkermansia, Alistipes, and Bacteroides) were depleted in obesity, indicating a deficiency of commensal microbes in the obese gut microbiome. From microbiome functional pathways, elevated lipid biosynthesis and depleted carbohydrate and protein degradation suggested metabolic adaptation to high-fat, low-carbohydrate, and low-protein diets in obese individuals. Machine learning models trained on the 18 studies were modest in predicting obesity with a median AUC of 0.608 using 10-fold cross-validation. The median AUC increased to 0.771 when models were trained in eight studies designed for investigating obesity-microbiome association. By meta-analyzing obesity-associated microbiota signatures, we identified obesity-associated depleted taxa that may be exploited to mitigate obesity and related metabolic diseases.

SUBMITTER: Lin Y

PROVIDER: S-EPMC10122048 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways.

Lin Yu Y Xu Zhilu Z Yeoh Yun Kit YK Tun Hein Min HM Huang Wenli W Jiang Wei W Chan Francis Ka Leung FKL Ng Siew Chien SC

iScience 20230322 4

Obesity is associated with altered gut microbiome composition but data across different populations remain inconsistent. We meta-analyzed publicly available 16S-rRNA sequence datasets from 18 different studies and identified differentially abundant taxa and functional pathways of the obese gut microbiome. Most differentially abundant genera (Odoribacter, Oscillospira, Akkermansia, Alistipes, and Bacteroides) were depleted in obesity, indicating a deficiency of commens ...[more]

PMID: 37096041

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Project description:BackgroundThe gut microbiota (GM) constitutes a critical factor in the maintenance of physiological homeostasis. Numerous studies have empirically demonstrated that the GM is closely associated with the onset and progression of osteoporosis (OP). Nevertheless, the characteristics of the GM and its metabolites related to different forms of OP are poorly understood. In the present study, we examined the changes in the GM and its metabolites associated with various types of OP as well as the correlations among them.MethodsWe simultaneously established rat postmenopausal, disuse-induced, and glucocorticoid-induced OP models. We used micro-CT and histological analyses to observe bone microstructure, three-point bending tests to measure bone strength, and enzyme-linked immunosorbent assay (ELISA) to evaluate the biochemical markers of bone turnover in the three rat OP models and the control. We applied 16s rDNA to analyze GM abundance and employed untargeted metabolomics to identify fecal metabolites in all four treatment groups. We implemented multi-omics methods to explore the relationships among OP, the GM, and its metabolites.ResultsThe 16S rDNA sequencing revealed that both the abundance and alterations of the GM significantly differed among the OP groups. In the postmenopausal OP model, the bacterial genera g__Bacteroidetes_unclassified, g__Firmicutes_unclassified, and g__Eggerthella had changed. In the disuse-induced and glucocorticoid-induced OP models, g__Akkermansia and g__Rothia changed, respectively. Untargeted metabolomics disclosed that the GM-derived metabolites significantly differed among the OP types. However, a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that it was mainly metabolites implicated in lipid and amino acid metabolism that were altered in all cases. An association analysis indicated that the histidine metabolism intermediate 4-(β-acetylaminoethyl) imidazole was common to all OP forms and was strongly correlated with all bone metabolism-related bacterial genera. Hence, 4-(β-acetylaminoethyl) imidazole might play a vital role in OP onset and progression.ConclusionsThe present work revealed the alterations in the GM and its metabolites that are associated with OP. It also disclosed the changes in the GM that are characteristic of each type of OP. Future research should endeavor to determine the causal and regulatory effects of the GM and the metabolites typical of each form of OP.

Project description:BACKGROUND: Analyses of DNA sequences from cultivated microorganisms have revealed genome-wide, taxa-specific nucleotide compositional characteristics, referred to as genome signatures. These signatures have far-reaching implications for understanding genome evolution and potential application in classification of metagenomic sequence fragments. However, little is known regarding the distribution of genome signatures in natural microbial communities or the extent to which environmental factors shape them. RESULTS: We analyzed metagenomic sequence data from two acidophilic biofilm communities, including composite genomes reconstructed for nine archaea, three bacteria, and numerous associated viruses, as well as thousands of unassigned fragments from strain variants and low-abundance organisms. Genome signatures, in the form of tetranucleotide frequencies analyzed by emergent self-organizing maps, segregated sequences from all known populations sharing < 50 to 60% average amino acid identity and revealed previously unknown genomic clusters corresponding to low-abundance organisms and a putative plasmid. Signatures were pervasive genome-wide. Clusters were resolved because intra-genome differences resulting from translational selection or protein adaptation to the intracellular (pH approximately 5) versus extracellular (pH approximately 1) environment were small relative to inter-genome differences. We found that these genome signatures stem from multiple influences but are primarily manifested through codon composition, which we propose is the result of genome-specific mutational biases. CONCLUSIONS: An important conclusion is that shared environmental pressures and interactions among coevolving organisms do not obscure genome signatures in acid mine drainage communities. Thus, genome signatures can be used to assign sequence fragments to populations, an essential prerequisite if metagenomics is to provide ecological and biochemical insights into the functioning of microbial communities.

Project description:BackgroundGrowth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection.ResultsWe implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell's neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model.ConclusionsWe present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis.

Project description:BackgroundBoth obesity (OB) and periodontitis (PD) are chronic non-communicable diseases, and numerous epidemiological studies have demonstrated the association between these two diseases. However, the molecular mechanisms that could explain the association between OB and PD are largely unclear. This study aims to investigate the common gene signatures and biological pathways in OB and PD through bioinformatics analysis of publicly available transcriptome datasets.MethodsThe RNA expression profile datasets of OB (GSE104815) and PD (GSE106090) were used as training data, and GSE152991 and GSE16134 as validation data. After screening for differentially expressed genes (DEGs) shared by OB and PD, gene enrichment analysis, protein-protein interaction (PPI) network construction, GeneMANIA analysis, immune infiltration analysis and gene set enrichment analysis (GSEA) were performed. In addition, receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of the hub gene. Finally, we constructed the hub gene-associated TF-miRNA-mRNA regulatory network.ResultsWe identified a total of 147 DEGs shared by OB and PD (38 down-regulated and 109 up-regulated). Functional analysis showed that these genes were mainly enriched in immune-related pathways such as B cell receptor signalling, leukocyte migration and cellular defence responses. 14 hub genes (FGR, MNDA, NCF2, FYB1, EVI2B, LY86, IGSF6, CTSS, CXCR4, LCK, FCN1, CXCL2, P2RY13, MMP7) showed high sensitivity and specificity in the ROC curve analysis. The results of immune infiltration analysis showed that immune cells such as macrophages, activated CD4 T cells and immune B cells were present at high infiltration levels in both OB and PD samples.The results of GeneMANIA analysis and GSEA analysis suggested that five key genes (FGR, LCK, FYB1, LY86 and P2RY13) may be strongly associated with macrophages. Finally, we constructed a TF-miRNA-mRNA regulatory network consisting of 233 transcription factors (TFs), 8 miRNAs and 14 mRNAs based on the validated information obtained from the database.ConclusionsFive key genes (FGR, LCK, FYB1, LY86, P2RY13) may be important biomarkers of OB and PD. These genes may play an important role in the pathogenesis of OB and PD by affecting macrophage activity and participating in immune regulation and inflammatory responses.

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Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways.

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Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways.

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