AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data

Shaffer, Michael J.; Thurimella, Kumar; Quinn, Kevin; Doenges, Katrina; Zhang, Xueyi; Bokatzian, Samantha S.; Reisdorph, Nichole; Lozupone, Catherine

doi:10.1186/s12859-019-3176-8

Cited by 42 publications

(46 citation statements)

References 54 publications

(64 reference statements)

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“…In order to identify compounds whose prevalence may be related to the gut microbiome we used two complimentary approaches. First, we predicted which of these compounds could have been produced by the microbiome using information in KEGG and the bioinformatics tool AMON (40). Second, we measured which compounds changed in relative abundance in germ-free versus mice colonized with feces from our study cohort.…”

Section: Discussionmentioning

confidence: 99%

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Systems analysis of gut microbiome influence on metabolic disease in HIV and high-risk populations

Armstrong

Quinn

et al. 2021

Preprint

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Poor metabolic health, characterized by insulin resistance and dyslipidemia, is higher in people living with HIV (PLWH) and has been linked with inflammation, anti-retroviral therapy (ART) drugs, and ART-associated lipodystrophy (LD). Metabolic disease is associated with gut microbiome composition outside the context of HIV but has not been deeply explored in HIV infection nor in high-risk men who have sex with men (HR-MSM), who have a highly altered gut microbiome composition. Furthermore, the contribution of increased bacterial translocation and associated systemic inflammation that has been described in HIV-positive and HR-MSM individuals has not been explored. We used a multi-omic approach to explore relationships between gut microbes, immune phenotypes, diet, and metabolic health across ART-treated PLWH with and without LD; untreated PLWH; and HR-MSM. For PLWH on ART, we further explored associations with the plasma metabolome. Sixty-nine measures of diet, gut microbes, inflammation, and demographics were associated with impaired metabolic health defined using fasting blood markers including lipids, glucose and hormones. We found microbiome-associated metabolites associated with metabolic disease including the microbially produced metabolites, dehydroalanine and bacteriohopane-32,33,34,35-tetrol. Our central result was that elevated plasma lipopolysaccharide binding protein (LBP) was the most important predictor of metabolic disease in PLWH and HR-MSM, with network analysis of predictors showing that LBP formed a hub joining correlated microbial and immune predictors of metabolic disease. Our results suggest the role of inflammatory processes linked with bacterial translocation (measured by LBP) and interaction with dietary components and the gut microbiome in metabolic disease among PLWH and HR-MSM.

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Section: Discussionmentioning

confidence: 99%

“…Second, we measured which compounds changed in relative abundance in germ-free versus mice colonized with feces from our study cohort. The AMON analysis allows us to specifically evaluate which compounds could have been directly produced by the gut microbiome but is limited by a lack of KEGG annotations for many compounds (40).…”

Section: Discussionmentioning

confidence: 99%

Systems analysis of gut microbiome influence on metabolic disease in HIV and high-risk populations

Armstrong

Quinn

et al. 2021

Preprint

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“…Even though previous research suggests that the fecal metabolome is a functional representation of the gut microbiome [16], metabolites are often resultant of both host and microbial processes [11,13]. This means for each metabolite, there are host-associated components that drive measured concentrations [32]. We hypothesize that metabolites that are more predictable are those which are associated with microbe-specific metabolism.…”

Section: The Microbiome Is Significantly Correlated But Weakly Predicmentioning

confidence: 94%

Multi-omic Characterization of the Taxa-function Relationship in Infant Gut Microbiomes

Nguyen¹,

Madan²,

Dade³

et al. 2020

Preprint

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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 relationships 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 = 168) 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 of sample distances (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. R2: -5.06% -- 6 weeks; -3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg. correlation: 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.

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“…Some of the anomalies can be partially solved using information from pathway databases such as KEGG; this approach has led to the development of several metabolome database visualization programs [88] , [101] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] ( Table 1 ). These programs typically annotate features by matching their computed molecular weights to those of the compounds in a pathway database.…”

Section: The Metabolome Network As a Metabolic Network Surrogatementioning

confidence: 99%

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Desmet

Brouckaert

Boerjan

et al. 2021

Computational and Structural Biotechnology Journal

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AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data

Cited by 42 publications

References 54 publications

Systems analysis of gut microbiome influence on metabolic disease in HIV and high-risk populations

Systems analysis of gut microbiome influence on metabolic disease in HIV and high-risk populations

Multi-omic Characterization of the Taxa-function Relationship in Infant Gut Microbiomes

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

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