ExHiBITT shows that microbiome from colon biopsies, caecal fluid from colonoscopies and faecal samples shape different microbiome-host interactions
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PROVIDER: EGAS00001007313 | EGA |
REPOSITORIES: EGA
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Project description:The objective of the colonoscopy study is to carry out the 16s sequencing of colon biopsies and faecal samples provided in ExHiBITT study to compare potential fluctuations in the microbiota of different sites.
| EGAD00001010936 | EGA
Project description:In this randomised placebo-controlled trial, irritable bowel syndrome (IBS) patients were treated with faecal material from a healthy donor (n=8, allogenic FMT) or with their own faecal microbiota (n=8, autologous FMT). The faecal transplant was administered by whole colonoscopy into the caecum (30 g of stool in 150 ml sterile saline). Two weeks before the FMT (baseline) as well as two and eight weeks after the FMT, the participants underwent a sigmoidoscopy, and biopsies were collected at a standardised location (20-25 cm from the anal verge at the crossing with the arteria iliaca communis) from an uncleansed sigmoid. In patients treated with allogenic FMT, predominantly immune response-related genes sets were induced, with the strongest response two weeks after FMT. In patients treated with autologous FMT, predominantly metabolism-related gene sets were affected.
2019-10-14 | GSE138297 | GEO
Project description:The incidence rate of colorectal cancer in younger adults has been rising in developed countries. This trend may be attributed to environmental exposures as a result of lifestyle changes. Many of the lifestyle factors that promote colorectal cancer can also affect the gut microbiome, which may be associated with colorectal cancer risks. The role of the microbiome in the ongoing rise of early-onset colorectal cancer is unknown. Here, we aimed to investigate age-related differences in the gut microbiome of patients with colorectal cancer and healthy individuals by examining both the fecal and tumor microbiomes. We utilized the publicly accessible data on fecal shotgun metagenomics from CuratedMetagenomeData and TCGA via the GDC Data Portal. Comparison of 701 colorectal cancer and 693 controls revealed that microbial features were age dependent, with a significant difference in species enrichment between early-onset (<50 years) and late-onset (>65 years) patients with colorectal cancer. Analysis of the tumor-associated microbiome in a separate dataset of 85 patients with colorectal cancer verified age-specific differences in taxon abundance between early- and late-onset patients with colorectal cancer. Finally, using host gene expression data, we found a stronger microbe-host interaction in early- vs. late-onset colorectal cancers. Altogether, these findings indicate that microbial features were age-dependent with stronger microbial-host interactions at the tumor site in early-onset colorectal cancers, suggesting a direct role of microbes in tumorigenesis via interaction with cancer-related pathways in this age group.Prevention relevanceEarly-onset colorectal cancer is on the rise, presumably because of changes in environmental exposures. Lifestyle changes may contribute to colorectal cancer via alterations in gut microbes. Here, we show that microbial association with colorectal cancer is age-dependent, and microbe interactions with tumor pathways are stronger in young versus older colorectal cancers.
| S-EPMC10842926 | biostudies-literature
Project description:BackgroundCalves undergo nutritional, metabolic, and behavioural changes from birth to the entire weaning period. An appropriate selection of weaning age is essential to reduce the negative effects caused by weaning-related dietary transitions. This study monitored the faecal microbiome and plasma metabolome of 59 female Holstein calves during different developmental stages and weaning times (early vs. late) and identified the potential associations of the measured parameters over an experimental period of 140 days.ResultsA progressive development of the microbiome and metabolome was observed with significant differences according to the weaning groups (weaned at 7 or 17 weeks of age). Faecal samples of young calves were dominated by bifidobacterial and lactobacilli species, while their respective plasma samples showed high concentrations of amino acids (AAs) and biogenic amines (BAs). However, as the calves matured, the abundances of potential fiber-degrading bacteria and the plasma concentrations of sphingomyelins (SMs), few BAs and acylcarnitines (ACs) were increased. Early-weaning at 7 weeks significantly restructured the microbiome towards potential fiber-degrading bacteria and decreased plasma concentrations of most of the AAs and SMs, few BAs and ACs compared to the late-weaning event. Strong associations between faecal microbes, plasma metabolites and calf growth parameters were observed during days 42-98, where the abundances of Bacteroides, Parabacteroides, and Blautia were positively correlated with the plasma concentrations of AAs, BAs and SMs as well as the live weight gain or average daily gain in calves.ConclusionThe present study reported that weaning at 17 weeks of age was beneficial due to higher growth rate of late-weaned calves during days 42-98 and a quick adaptability of microbiota to weaning-related dietary changes during day 112, suggesting an age-dependent maturation of the gastrointestinal tract. However, the respective plasma samples of late-weaned calves contained several metabolites with differential concentrations to the early-weaned group, suggesting a less abrupt but more-persistent effect of dietary changes on host metabolome compared to the microbiome.
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Project description:Gut bacteria can affect key aspects of host fitness, such as development, fecundity, and lifespan, while the host, in turn, shapes the gut microbiome. However, it is unclear to what extent individual species versus community interactions within the microbiome are linked to host fitness. Here, we combinatorially dissect the natural microbiome of Drosophila melanogaster and reveal that interactions between bacteria shape host fitness through life history tradeoffs. Empirically, we made germ-free flies colonized with each possible combination of the five core species of fly gut bacteria. We measured the resulting bacterial community abundances and fly fitness traits, including development, reproduction, and lifespan. The fly gut promoted bacterial diversity, which, in turn, accelerated development, reproduction, and aging: Flies that reproduced more died sooner. From these measurements, we calculated the impact of bacterial interactions on fly fitness by adapting the mathematics of genetic epistasis to the microbiome. Development and fecundity converged with higher diversity, suggesting minimal dependence on interactions. However, host lifespan and microbiome abundances were highly dependent on interactions between bacterial species. Higher-order interactions (involving three, four, and five species) occurred in 13-44% of possible cases depending on the trait, with the same interactions affecting multiple traits, a reflection of the life history tradeoff. Overall, we found these interactions were frequently context-dependent and often had the same magnitude as individual species themselves, indicating that the interactions can be as important as the individual species in gut microbiomes.
| S-EPMC6304949 | biostudies-literature
Project description:Host metabolome and faecal microbiome shows potential interactions impacted by age and weaning times in calves
| PRJEB48866 | ENA
Project description:The gut microbiome plays a critical role in regulating host immunity and can no longer be regarded as a bystander in human health and disease. In recent years, circadian (24 h) oscillations have been identified in the composition of the microbiota, its biophysical localization within the intestinal tract and its metabolic outputs. The gut microbiome and its key metabolic outputs, such as short chain fatty acids and tryptophan metabolites contribute to maintenance of intestinal immunity by promoting barrier function, regulating the host mucosal immune system and maintaining the function of gut-associated immune cell populations. Loss of rhythmic host-microbiome interactions disrupts host immunity and increases risk of inflammation and metabolic complications. Here we review factors that drive circadian variation in the microbiome, including meal timing, dietary composition and host circadian clocks. We also consider how host-microbiome interactions impact the core molecular clock and its rhythmic outputs in addition to the potential impact of this relationship on circadian control of immunity.
| S-EPMC7456996 | biostudies-literature
Project description:Circadian regulation of the host by the microbiome
| PRJEB16726 | ENA
Project description:BackgroundHost-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that the logical representation of the knowledge derived from these data and the standardized representation of experimental variables and processes can foster integration of data and reproducibility of experiments and thereby further HMI knowledge discovery.MethodsThrough a multi-institutional collaboration, a community-based Ontology of Host-Microbiome Interactions (OHMI) was developed following the Open Biological/Biomedical Ontologies (OBO) Foundry principles. As an OBO library ontology, OHMI leverages established ontologies to create logically structured representations of (1) microbiomes, microbial taxonomy, host species, host anatomical entities, and HMIs under different conditions and (2) associated study protocols and types of data analysis and experimental results.ResultsAligned with the Basic Formal Ontology, OHMI comprises over 1000 terms, including terms imported from more than 10 existing ontologies together with some 500 OHMI-specific terms. A specific OHMI design pattern was generated to represent typical host-microbiome interaction studies. As one major OHMI use case, drawing on data from over 50 peer-reviewed publications, we identified over 100 bacteria and fungi from the gut, oral cavity, skin, and airway that are associated with six rheumatic diseases including rheumatoid arthritis. Our ontological study identified new high-level microbiota taxonomical structures. Two microbiome-related competency questions were also designed and addressed. We were also able to use OHMI to represent statistically significant results identified from a large existing microbiome database data analysis.ConclusionOHMI represents entities and relations in the domain of HMIs. It supports shared knowledge representation, data and metadata standardization and integration, and can be used in formulation of advanced queries for purposes of data analysis.
| S-EPMC6937947 | biostudies-literature
Project description:BackgroundThe effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.ResultsWe propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).ConclusionThese results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.
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