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Diet-induced obesity contributes to the development of type 2 diabetes, insulin resistance, metabolic inflammation, oxidative and endoplasmic reticulum (ER) stress. Overall, obesity is associated with deviations in the composition and functionality of the gut microbiota. There are many divergent findings regarding the link between the excessive intake of certain dietary components (i.e., fat and sugar) and obesity development. We therefore investigated the effect of specific diets, with a different content of sugar and fat, in promoting obesity and related comorbidities as well as their impact on microbial load and gut microbiota composition/diversity. C57BL/6J mice were fed either a low-sugar, low-fat control diet (CT), a high-sugar diet (HS), a high-fat, high-sugar diet (HF/HS), or a high-fat diet (HF) for 8 wk. The impact of the different diets on obesity, glucose metabolism, inflammation, and oxidative and ER stress was determined. Diet-induced changes in the gut microbiota composition and density were also analyzed. HF diet-fed mice showed the highest body weight and fat mass gains and displayed the most impaired glucose and insulin profiles. HS, HF/HS, and HF diets differently affected hepatic cholesterol content and mRNA expression of several markers associated with immune cells, inflammation, oxidative and ER stress in several organs/tissues. In addition, HF diet feeding resulted in a decreased microbial load at the end of the experiment. When analyzing the gut microbiota composition, we found that HS, HF/HS, and HF diets induced specific changes in the abundance of certain bacterial taxa. This was not associated with a specific change in systemic inflammatory markers, but HS mice exhibited higher FGF21 plasma levels compared with HF diet-fed mice. Taken together, our results highlight that dietary intake of different macronutrients distinctively impacts the development of an obese/diabetic state and the regulation of metabolic inflammation in specific organs. We propose that these differences are not only obesity-driven but that changes in the gut microbiota composition may play a key role in this context. [ABSTRACT FROM AUTHOR]

Virome and 16/18S analyses were performed on 304 longitudinal fecal samples of eight infants. The gut virota--the collection of all viruses present in the gut--was dominated by bacteriophages, which were nearly absent at birth and emerged rapidly within the first weeks after birth. Over 85% of phage reads correspond to 305 near-complete genomes, most of which (70.5%) were individual infant-specific, including two crAs-sphages, whereas 7.8% of phages were present in at least 50% of infants. Bacterial hosts could be predicted for 80% of phages, mainly infecting Firmicutes. Strong temporal correlations between phages and their predicted bacterial hosts were identified for >40% of our phages, and together with the observation of a decreasing fraction of phages with a temperate lifestyle further suggest that phages are induced from early-colonizing bacteria. The vast majority (>86%) of identified eukaryotic viruses, known to cause gastroenteritis, occurred without clinical signs, and an increase in the rate of infection occurred after day-care entrance. On average, 112 genomic contigs of distinct anelloviruses could be identified per infant, some of which were shed at > 1 y. The identified plant viruses reflected the infant diet. Finally, the sporadic identification of fungi and parasites argues against the presence of such stable communities in the study population. Overall, this work provides a very high temporal resolution on how the different members of the infant gut microbiota, and especially the virome, develop over time in the gut of healthy infants, and might serve as valuable baseline knowledge for further studies investigating the effect of perturbations in the infant gut microbiota. [ABSTRACT FROM AUTHOR]

Background: Leptin-deficient ob/ob mice and leptin receptor-deficient db/db mice are commonly used mice models mimicking the conditions of obesity and type 2 diabetes development. However, although ob/ob and db/db mice are similarly gaining weight and developing massive obesity, db/db mice are more diabetic than ob/ob mice. It remains still unclear why targeting the same pathway—leptin signaling—leads to the development of two different phenotypes. Given that gut microbes dialogue with the host via different metabolites (e.g., short-chain fatty acids) but also contribute to the regulation of bile acids metabolism, we investigated whether inflammatory markers, bacterial components, bile acids, short-chain fatty acids, and gut microbes could contribute to explain the specific phenotype discriminating the onset of an obese and/or a diabetic state in ob/ob and db/db mice. Results: Six-week-old ob/ob and db/db mice were followed for 7 weeks; they had comparable body weight, fat mass, and lean mass gain, confirming their severely obese status. However, as expected, the glucose metabolism and the glucose-induced insulin secretion were significantly different between ob/ob and db/db mice. Strikingly, the fat distribution was different, with db/db mice having more subcutaneous and ob/ob mice having more epididymal fat. In addition, liver steatosis was more pronounced in the ob/ob mice than in db/db mice. We also found very distinct inflammatory profiles between ob/ob and db/db mice, with a more pronounced inflammatory tone in the liver for ob/ob mice as compared to a higher inflammatory tone in the (subcutaneous) adipose tissue for db/db mice. When analyzing the gut microbiota composition, we found that the quantity of 19 microbial taxa was in some way affected by the genotype. Furthermore, we also show that serum LPS concentration, hepatic bile acid content, and cecal short-chain fatty acid profiles were differently affected by the two genotypes. Conclusion: Taken together, our results elucidate potential mechanisms implicated in the development of an obese or a diabetic state in two genetic models characterized by an altered leptin signaling. We propose that these differences could be linked to specific inflammatory tones, serum LPS concentration, bile acid metabolism, short-chain fatty acid profile, and gut microbiota composition. DUGQcxDcUnuc9Bgg7Th-A2 Video abstract. [ABSTRACT FROM AUTHOR]

Wheat bran fibers are considered beneficial to human health through their impact on gut microbiota composition and activity. Here, we assessed the prebiotic potential of selected bran fractions by performing a series of fecal slurry anaerobic fermentation experiments using aleurone as well as total, ultrafine, and soluble wheat bran (swb) as carbon sources. By combining amplicon-based community profiling with a fluorescent in situ hybridization (FISH) approach, we found that incubation conditions favor the growth of Proteobacteria such as Escherichia and Bilophila. These effects were countered in all but one [total wheat bran (twb)] fermentation experiments. Growth of Bifidobacterium species was stimulated after fermentation using ultrafine, soluble, and twb, in the latter two as part of a general increase in bacterial load. Both ultrafine and swb fermentation resulted in a trade-off between Bifidobacterium and Bilophila, as previously observed in human dietary supplementation studies looking at the effect of inulin-type fructans on the human gut microbiota. Aleurone selectively stimulated growth of Dorea and butyrateproducing Roseburia. All fermentation experiments induced enhanced gas production; increased butyrate concentrations were only observed following soluble bran incubation. Our results open perspectives for the development of aleurone as a complementary prebiotic selectively targeting colon butyrate producers. [ABSTRACT FROM AUTHOR]

First insights on the human gut microbiome have been gained from medium-sized, cross-sectional studies. However, given the modest portion of explained variance of currently identified covariates and the small effect size of gut microbiota modulation strategies, upscaling seems essential for further discovery and characterisation of the multiple influencing factors and their relative contribution. In order to guide future research projects and standardisation efforts, we here review currently applied collection and preservation methods for gut microbiome research. We discuss aspects such as sample quality, applicable omics techniques, user experience and time and cost efficiency. In addition, we evaluate the protocols of a large-scale microbiome cohort initiative, the Flemish Gut Flora Project, to give an idea of perspectives, and pitfalls of large-scale faecal sampling studies. Although cryopreservation can be regarded as the gold standard, freezing protocols generally require more resources due to cold chain management. However, here we show that much can be gained from an optimised transport chain and sample aliquoting before freezing. Other protocols can be useful as long as they preserve the microbial signature of a sample such that relevant conclusions can be drawn regarding the research question, and the obtained data are stable and reproducible over time. [ABSTRACT FROM AUTHOR]

Fecal microbiome variation in the average, healthy population has remained under-investigated. Here, we analyzed two independent, extensively phenotyped cohorts: the Belgian Flemish Gut Flora Project (FGFP; discovery cohort; N = 1106) and the Dutch Life Lines-DEEP study (LLDeep; replication; N = 1135). Integration with global data sets (N combined = 3948) revealed a 14-genera core microbiota, but the 664 identified genera still underexplore total gut diversity. Sixty-nine clinical and questionnaire-based covariates were found associated to microbiota compositional variation with a 92% replication rate. Stool consistency showed the largest effect size, whereas medication explained largest total variance and interacted with other covariate-microbiota associations. Early-life events such as birth mode were not reflected in adult microbiota composition. Finally, we found that proposed disease marker genera associated to host covariates, urging inclusion of the latter in study design. [ABSTRACT FROM AUTHOR]

Recent metagenomic studies have demonstrated that the overall functional potential of the intestinal microbiome is rather conserved between healthy individuals. Here we assessed the biological processes undertaken in-vivo by microbes and the host in the intestinal tract by conducting a metaproteome analysis from a total of 48 faecal samples of 16 healthy adults participating in a placebo-controlled probiotic intervention trial. Half of the subjects received placebo and the other half consumed Lactobacillus rhamnosus GG for three weeks (1010 cfu per day). Faecal samples were collected just before and at the end of the consumption phase as well as after a three-week follow-up period, and were processed for microbial composition and metaproteome analysis. A common core of shared microbial protein functions could be identified in all subjects. Furthermore, we observed marked differences in expressed proteins between subjects that resulted in the definition of a stable and personalized microbiome both at the mass-spectrometry-based proteome level and the functional level based on the KEGG pathway analysis. No significant changes in the metaproteome were attributable to the probiotic intervention. A detailed taxonomic assignment of peptides and comparison to phylogenetic microarray data made it possible to evaluate the activity of the main phyla as well as key species, including Faecalibacterium prausnitzii. Several correlations were identified between human and bacterial proteins. Proteins of the human host accounted for approximately 14% of the identified metaproteome and displayed variations both between and within individuals. The individually different human intestinal proteomes point to personalized host-microbiota interactions. Our findings indicate that analysis of the intestinal metaproteome can complement gene-based analysis and contributes to a thorough understanding of the activities of the microbiome and the relevant pathways in health and disease. [ABSTRACT FROM AUTHOR]

Objective: The assessment of potentially confounding factors affecting colon microbiota composition is essential to the identification of robust microbiome based disease markers. Here, we investigate the link between gut microbiota variation and stool consistency using Bristol Stool Scale classification, which reflects faecal water content and activity, and is considered a proxy for intestinal colon transit time. Design: Through 16S rDNA Illumina profiling of faecal samples of 53 healthy women, we evaluated associations between microbiome richness, Bacteroidetes:Firmicutes ratio, enterotypes, and genus abundance with self-reported, Bristol Stool Scale-based stool consistency. Each sample's microbiota growth potential was calculated to test whether transit time acts as a selective force on gut bacterial growth rates. Results: Stool consistency strongly correlates with all known major microbiome markers. It is negatively correlated with species richness, positively associated to the Bacteroidetes:Firmicutes ratio, and linked to Akkermansia and Methanobrevibacter abundance. Enterotypes are distinctly distributed over the BSS-scores. Based on the correlations between microbiota growth potential and stool consistency scores within both enterotypes, we hypothesise that accelerated transit contributes to colon ecosystem differentiation. While shorter transit times can be linked to increased abundance of fast growing species in Ruminococcaceae- Bacteroides samples, hinting to a washout avoidance strategy of faster replication, this trend is absent in Prevotella-enterotyped individuals. Within this enterotype adherence to host tissue therefore appears to be a more likely bacterial strategy to cope with washout. Conclusions: The strength of the associations between stool consistency and species richness, enterotypes and community composition emphasises the crucial importance of stool consistency assessment in gut metagenome-wide association studies. [ABSTRACT FROM AUTHOR]

During the past decade, meta-omics approaches have revolutionized microbiology, allowing for a cultivation-free assessment of the composition and functional properties of entire microbial ecosystems. On the one hand, a phylogenetic and functional interpretation of such data relies on accumulated genetic, biochemical, metabolic, and phenotypic characterization of microbial variation. On the other hand, the increasing availability of extensive microbiome data sets and corresponding metadata provides a vast, underused resource for the microbiology field as a whole. To demonstrate the potential for integrating big data into a functional microbiology workflow, we review literature on trimethylamine (TMA), a microbiota-generated metabolite linked to atherosclerosis development. Translating recently elucidated microbial pathways resulting in TMA production into genomic orthologs, we demonstrate how to mine for their presence in public (meta-) genomic databases and link findings to associated metadata. Reviewing pathway abundance in public data sets shows that TMA production potential is associated with symptomatic atherosclerosis and allows identification of currently uncharacterized TMA-producing bacteria. [ABSTRACT FROM AUTHOR]

We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities. [ABSTRACT FROM AUTHOR]

Kinetic analyses of bacterial growth, carbohydrate consumption, and metabolite production of five butyrate- producing clostridial cluster XIVa colon bacteria grown on acetate plus fructose, oligofructose, inulin, or lactate were performed. A gas chromatography method was set up to assess H2 and CO2 production online and to ensure complete coverage of all metabolites produced. Method accuracy was confirmed through the calculation of electron and carbon recoveries. Fermentations with Anaerostipes caccae DSM 14662T, Roseburia faecis DSM 16840T, Roseburia hominis DSM 16839T, and Roseburia intestinalis DSM 14610T revealed similar patterns of metabolite production with butyrate, CO2, and H2 as the main metabolites. R. faecis DSM 16840T and R. intestinalis DSM 14610T were able to degrade oligofructose, displaying a nonpreferential breakdown mechanism. Lactate consumption was only observed with A. caccae DSM 14662T. Roseburia inulinivorans DSM 16841T was the only strain included in the present study that was able to grow on fructose, oligofructose, and inulin. The metabolites produced were lactate, butyrate, and CO2, without H2 production, indicating an energy metabolism distinct from that of other Roseburia species. Oligofructose degradation was nonpreferential. In a coculture of R. inulinivorans DSM 16841T with the highly competitive strain Bifidobacterium longum subsp. longum LMG 11047 on inulin, hardly any production of butyrate and CO2 was detected, indicating a lack of competitiveness of the butyrate producer. Complete recovery of metabolites during fermentations of clostridial cluster XIVa butyrate-producing colon bacteria allowed stoichiometric balancing of the metabolic pathway for butyrate production, including H2 formation. [ABSTRACT FROM AUTHOR]

Deep sequencing of the gut microbiomes of 1135 participants from a Dutch population-based cohort shows relations between the microbiome and 126 exogenous and intrinsic host factors, including 31 intrinsic factors, 12 diseases, 19 drug groups, 4 smoking categories, and 60 dietary factors. These factors collectively explain 18.7% of the variation seen in the interindividual distance of microbial composition. We could associate 110 factors to 125 species and observed that fecal chromogranin A (CgA), a protein secreted by enteroendocrine cells, was exclusively associated with 61 microbial species whose abundance collectively accounted for 53% of microbial composition. Low CgA concentrations were seen in individuals with a more diverse microbiome. These results are an important step toward a better understanding of environment-diet-microbe-host interactions. [ABSTRACT FROM AUTHOR]

FLEX i GUT is the first large-scale exposomics study focused on chronic low-grade inflammation. It aims to characterize human life course environmental exposure to assess and validate its impact on gut inflammation and related biological processes and diseases. The cumulative influences of environmental and food contaminants throughout the lifespan on certain biological responses related to chronic gut inflammation will be investigated in two Flemish prospective cohorts, namely the "ENVIRONAGE birth cohort", which provides follow-up from gestation to early childhood, and the "Flemish Gut Flora Project longitudinal cohort", a cohort of adults. The exposome will be characterised through biomonitoring of legacy and emerging contaminants, mycotoxins and markers of air pollution, by analysing the available metadata on nutrition, location and activity, and by applying state-of-the-art -omics techniques, including metagenomics, metabolomics and DNA adductomics, as well as the assessment of telomere length and measurement of inflammatory markers, to encompass both exposure and effect. Associations between exposures and health outcomes will be uncovered using an integrated -omics data analysis framework comprising data exploration, pre-processing, dimensionality reduction and data mining, combined with machine learning-based pathway analysis approaches. This is expected to lead to a more profound insight in mechanisms underlying disease progression (e.g. metabolic disorders, food allergies, gastrointestinal cancers) and/or accelerated biological ageing. [ABSTRACT FROM AUTHOR]