The gut microbiota has been linked to various neurological disorders via the gut–brain axis. Diet influences the composition of the gut microbiota. The ketogenic diet (KD) is a high-fat, adequate-protein, low-carbohydrate diet established for treatment of therapy-resistant epilepsy in children. Its efficacy in reducing seizures has been confirmed, but the mechanisms remain elusive. The diet has also shown positive effects in a wide range of other diseases, including Alzheimer’s, depression, autism, cancer, and type 2 diabetes. We collected fecal samples from 12 children with therapy-resistant epilepsy before starting KD and after 3 months on the diet. Parents did not start KD and served as diet controls. Applying shotgun metagenomic DNA sequencing, both taxonomic and functional profiles were established. Here we report that alpha diversity is not changed significantly during the diet, but differences in both taxonomic and functional composition are detected. Relative abundance of bifidobacteria as well as E. rectale and Dialister is significantly diminished during the intervention. An increase in relative abundance of E. coli is observed on KD. Functional analysis revealed changes in 29 SEED subsystems including the reduction of seven pathways involved in carbohydrate metabolism. Decomposition of these shifts indicates that bifidobacteria and Escherichia are important contributors to the observed functional shifts. As relative abundance of health-promoting, fiber-consuming bacteria becomes less abundant during KD, we raise concern about the effects of the diet on the gut microbiota and overall health. Further studies need to investigate whether these changes are necessary for the therapeutic effect of KD.
Multiple molecular assays now enable high-throughput profiling of the ecology, metabolic capacity, and activity of the human microbiome. However, to date, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms linking these various facets of the microbiome. Here, we introduce a comprehensive framework to systematically link variation in metabolomic data with community composition by utilizing taxonomic, genomic, and metabolic information. Specifically, we integrate available and inferred genomic data, metabolic network modeling, and a method for predicting community-wide metabolite turnover to estimate the biosynthetic and degradation potential of a given community. Our framework then compares variation in predicted metabolic potential with variation in measured metabolites’ abundances to evaluate whether community composition can explain observed shifts in the community metabolome, and to identify key taxa and genes contributing to the shifts. Focusing on two independent vaginal microbiome data sets, each pairing 16S community profiling with large-scale metabolomics, we demonstrate that our framework successfully recapitulates observed variation in 37% of metabolites. Well-predicted metabolite variation tends to result from disease-associated metabolism. We further identify several disease-enriched species that contribute significantly to these predictions. Interestingly, our analysis also detects metabolites for which the predicted variation negatively correlates with the measured variation, suggesting environmental control points of community metabolism. Applying this framework to gut microbiome data sets reveals similar trends, including prediction of bile acid metabolite shifts. This framework is an important first step toward a system-level multi-omic integration and an improved mechanistic understanding of the microbiome activity and dynamics in health and disease. IMPORTANCE Studies characterizing both the taxonomic composition and metabolic profile of various microbial communities are becoming increasingly common, yet new computational methods are needed to integrate and interpret these data in terms of known biological mechanisms. Here, we introduce an analytical framework to link species composition and metabolite measurements, using a simple model to predict the effects of community ecology on metabolite concentrations and evaluating whether these predictions agree with measured metabolomic profiles. We find that a surprisingly large proportion of metabolite variation in the vaginal microbiome can be predicted based on species composition (including dramatic shifts associated with disease), identify putative mechanisms underlying these predictions, and evaluate the roles of individual bacterial species and genes. Analysis of gut microbiome data using this framework recovers similar community metabolic trends. This framework lays the foundation for model-based multi-omic integrative studies, ultimately improving our und...
Most infants with cystic fibrosis (CF) have pancreatic exocrine insufficiency that results in nutrient malabsorption and requires oral pancreatic enzyme replacement. Newborn screening for CF has enabled earlier diagnosis, nutritional intervention, and enzyme replacement for these infants, allowing most infants with CF to achieve their weight goals by 12 months of age 1 . Nevertheless, most infants with CF continue to have poor linear growth during their first year of life 1 . Although this early linear growth failure is associated with worse long-term respiratory function and survival 2 , 3 , the determinants of stature in infants with CF have not been defined. Several characteristics of the CF gastrointestinal (GI) tract, including inflammation, maldigestion and malabsorption, could promote intestinal dysbiosis 4 , 5 . As GI microbiome activities are known to affect endocrine functions 6 , 7 , the intestinal microbiome of infants with CF might also impact growth. We identified an early, progressive fecal dysbiosis that distinguished infants with CF and low length from infants with CF and normal length. This dysbiosis included altered abundances of taxa that perform functions important for GI health, nutrient harvest, and growth hormone signaling, including decreased Bacteroidetes and increased Proteobacteria. Thus, the GI microbiota represent a potential therapeutic target to correct linear growth defects among infants with CF.
SUMMARYMetagenomic sequencing is a promising approach for identifying and characterizing organisms and their functional characteristics in complex, polymicrobial infections, such as airway infections in people with cystic fibrosis. These analyses are often hampered, however, by overwhelming quantities of human DNA, yielding only a small proportion of microbial reads for analysis. In addition, many abundant microbes in respiratory samples can produce large quantities of extracellular bacterial DNA originating either from biofilms or dead cells. We describe a method for simultaneously depleting DNA from intact human cells and extracellular DNA (human and bacterial) in sputum, using selective lysis of eukaryotic cells and endonuclease digestion. We show that this method increases microbial sequencing depth and, consequently, both the number of taxa detected and coverage of individual genes such as those involved in antibiotic resistance. This finding underscores the substantial impact of DNA from sources other than live bacteria in micro-biological analyses of complex, chronic infection specimens.
BackgroundThe species composition of a microbial community is rarely fixed and often experiences fluctuations of varying degrees and at varying frequencies. These perturbations to a community’s taxonomic profile naturally also alter the community’s functional profile–the aggregate set of genes encoded by community members–ultimately altering the community’s overall functional capacities. The magnitude of such functional changes and the specific shift that will occur in each function, however, are strongly dependent on how genes are distributed across community members’ genomes. This gene distribution, in turn, is determined by the taxonomic composition of the community and would markedly differ, for example, between communities composed of species with similar genomic content vs. communities composed of species whose genomes encode relatively distinct gene sets. Combined, these observations suggest that community functional robustness to taxonomic perturbations could vary widely across communities with different compositions, yet, to date, a systematic study of the inherent link between community composition and robustness is lacking.ResultsIn this study, we examined how a community’s taxonomic composition influences the robustness of that community’s functional profile to taxonomic perturbation (here termed taxa-function robustness) across a wide array of environments. Using a novel simulation-based computational model to quantify this taxa-function robustness in host-associated and non-host-associated communities, we find notable differences in robustness between communities inhabiting different body sites, including significantly higher robustness in gut communities compared to vaginal communities that cannot be attributed solely to differences in species richness. We additionally find between-site differences in the robustness of specific functions, some of which are potentially related to site-specific environmental conditions. These taxa-function robustness differences are most strongly associated with differences in overall functional redundancy, though other aspects of gene distribution also influence taxa-function robustness in certain body environments, and are sufficient to cluster communities by environment. Further analysis revealed a correspondence between our robustness estimates and taxonomic and functional shifts observed across human-associated communities.ConclusionsOur analysis approach revealed intriguing taxa-function robustness variation across environments and identified features of community and gene distribution that impact robustness. This approach could be further applied for estimating taxa-function robustness in novel communities and for informing the design of synthetic communities with specific robustness requirements.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0425-4) contains supplementary material, which is available to authorized users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.