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The American Journal of Gastroenterology Dec 2022Irritable bowel syndrome (IBS) includes diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes. We combined breath testing and stool microbiome...
INTRODUCTION
Irritable bowel syndrome (IBS) includes diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes. We combined breath testing and stool microbiome sequencing to identify potential microbial drivers of IBS subtypes.
METHODS
IBS-C and IBS-D subjects from 2 randomized controlled trials (NCT03763175 and NCT04557215) were included. Baseline breath carbon dioxide, hydrogen (H 2 ), methane (CH 4 ), and hydrogen sulfide (H 2 S) levels were measured by gas chromatography, and baseline stool microbiome composition was analyzed by 16S rRNA sequencing. Microbial metabolic pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes collection databases.
RESULTS
IBS-C subjects had higher breath CH 4 that correlated with higher gut microbial diversity and higher relative abundance (RA) of stool methanogens, predominantly Methanobrevibacter , as well as higher absolute abundance of Methanobrevibacter smithii in stool. IBS-D subjects had higher breath H 2 that correlated with lower microbial diversity and higher breath H 2 S that correlated with higher RA of H 2 S-producing bacteria, including Fusobacterium and Desulfovibrio spp. The predominant H 2 producers were different in these distinct microtypes, with higher RA of Ruminococcaceae and Christensenellaceae in IBS-C/CH 4 + (which correlated with Methanobacteriaceae RA) and higher Enterobacteriaceae RA in IBS-D. Finally, microbial metabolic pathway analysis revealed enrichment of Kyoto Encyclopedia of Genes and Genomes modules associated with methanogenesis and biosynthesis of methanogenesis cofactor F420 in IBS-C/CH 4 + subjects, whereas modules associated with H 2 S production, including sulfate reduction pathways, were enriched in IBS-D.
DISCUSSION
Our findings identify distinct gut microtypes linked to breath gas patterns in IBS-C and IBS-D subjects, driven by methanogens such as M. smithii and H 2 S producers such as Fusobacterium and Desulfovibrio spp, respectively.
Topics: Humans; Irritable Bowel Syndrome; Hydrogen Sulfide; Gastrointestinal Microbiome; RNA, Ribosomal, 16S; Bacteria
PubMed: 36114762
DOI: 10.14309/ajg.0000000000001997 -
Osteoarthritis and Cartilage Dec 2021There is considerable evidence for relationship between gut microbiota and osteoarthritis (OA), but no studies have investigated their causal relationship.
OBJECTIVE
There is considerable evidence for relationship between gut microbiota and osteoarthritis (OA), but no studies have investigated their causal relationship.
METHOD
This study utilized large-scale genome-wide association studies (GWAS) summary statistics to evaluate the causal association between gut microbiota and OA risk. Specifically, two-sample Mendelian randomization (MR) approach was used to identify the causal microbial taxa for OA. Comprehensively sensitive analyses were performed to validate the robustness of results and novel multivariable MR analyses were further conducted to ensure the independence of causal association. Reverse-direction MR analyses were performed to rule out the possibility of reverse associations. Finally, enrichment analyses were used to investigate the biofunction.
RESULTS
After correction, three microbial taxa were identified to be causally associated with diverse joint OA (P < 0.100), namely Methanobacteriaceae family for knee OA (P = 0.043) and any OA (P = 0.028), Desulfovibrionales order for knee OA (P = 0.045) and Ruminiclostridium5 genus for knee OA (P = 0.063). In addition, we also identified five suggestive microbial taxa that were significant with three different methods under the nominal significance (P < 0.05). Sensitive analysis excluded the influence of heterogeneity and horizontal pleiotropy and multivariable MR analysis ruled out the possibility of horizontal pleiotropy of BMI. GO enrichment analysis illustrates the protective mechanism of the identified taxa against OA.
CONCLUSIONS
This study found that several microbial taxa were causally associated with diverse joint OA. The results enhanced our understanding of gut microbiota in the pathology of OA.
Topics: Causality; Gastrointestinal Microbiome; Genome-Wide Association Study; Humans; Mendelian Randomization Analysis; Osteoarthritis
PubMed: 34425228
DOI: 10.1016/j.joca.2021.08.003 -
Nature Jun 2021Loss of gut microbial diversity in industrial populations is associated with chronic diseases, underscoring the importance of studying our ancestral gut microbiome....
Loss of gut microbial diversity in industrial populations is associated with chronic diseases, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000-2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.
Topics: Anti-Bacterial Agents; Bacteria; Biodiversity; Biological Evolution; Chronic Disease; Developed Countries; Developing Countries; Diet, Western; Feces; Gastrointestinal Microbiome; Genome, Bacterial; History, Ancient; Host Microbial Interactions; Humans; Industrial Development; Methanobrevibacter; Mexico; Sedentary Behavior; Southwestern United States; Species Specificity; Symbiosis
PubMed: 33981035
DOI: 10.1038/s41586-021-03532-0 -
PLoS Computational Biology Dec 2022Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure...
Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa may be associated. We developed endoR, a method to interpret tree ensemble models. First, endoR simplifies the fitted model into a decision ensemble. Then, it extracts information on the importance of individual features and their pairwise interactions, displaying them as an interpretable network. Both the endoR network and importance scores provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed endoR on both simulated and real metagenomic data. We found endoR to have comparable accuracy to other common approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to explore associations between human gut methanogens and microbiome components. Indeed, these hydrogen consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems.
Topics: Humans; Bacteria; Gastrointestinal Microbiome; Machine Learning; Metagenome; Microbiota
PubMed: 36516158
DOI: 10.1371/journal.pcbi.1010714 -
International Journal of Molecular... May 2020The microbial cell membrane is affected by physicochemical parameters, such as temperature and pH, but also by the specific growth rate of the host organism.... (Review)
Review
The microbial cell membrane is affected by physicochemical parameters, such as temperature and pH, but also by the specific growth rate of the host organism. Homeoviscous adaption describes the process of maintaining membrane fluidity and permeability throughout these environmental changes. Archaea, and thereby, spp. exhibit a unique lipid composition of ether lipids, which are altered in regard to the ratio of diether to tetraether lipids, number of cyclopentane rings and type of head groups, as a coping mechanism against environmental changes. The main biotechnological application of the membrane lipids of spp. are so called archaeosomes. Archaeosomes are liposomes which are fully or partly generated from archaeal lipids and harbor the potential to be used as drug delivery systems for vaccines, proteins, peptides and nucleic acids. This review summarizes the influence of environmental parameters on the cell membrane of spp. and the biotechnological applications of their membrane lipids.
Topics: Biotechnology; Cell Membrane; Cyclopentanes; Drug Delivery Systems; Hydrogen-Ion Concentration; Liposomes; Membrane Lipids; Membranes, Artificial; Methanobacterium; Natronococcus; Peptides; Sulfolobus; Temperature; Viscosity
PubMed: 32486295
DOI: 10.3390/ijms21113935 -
Clinical and Experimental Rheumatology Jan 2024To assess whether there is a bidirectional causal relationship between the composition of gut microbiota and rheumatoid arthritis (RA), and to identify specific...
OBJECTIVES
To assess whether there is a bidirectional causal relationship between the composition of gut microbiota and rheumatoid arthritis (RA), and to identify specific pathogenic bacterial taxa via the Mendelian randomisation (MR) analysis.
METHODS
We acquired single nucleotide polymorphisms (SNPs) associated with the composition of gut microbiota (n=18,340) and with RA (n=331,313) from publicly available genome-wide association studies (GWAS). The genome-wide threshold was 1 × 10-5 in the forward MR analysis and was 5 × 10-8 in the reverse MR analysis. Inverse variance weighted (IVW) was the main method to analyse causality, and MR results were verified by several sensitivity analyses including weighted median, MR Egger, and MR Pleiotropy Residual Sum and Outlier (PRESSO).
RESULTS
The IVW method suggested that eight taxa were positively correlated with RA, including: MollicutesRF9 (pIVW <0.01), Alphaproteobacteria (pIVW <0.01), Betaproteobacteria (p IVW =0.04), Bacteroidaceae (pIVW <0.01), Adlercreutzia (pIVW <0.01), Bacteroides (pIVW <0.01), Butyricimonas (p IVW =0.03) and Holdemanella (pIVW =0.03). Six bacterial taxa were negatively correlated with RA, including Desulfovibrionales (pIVW = 0.01), Methanobacteriales (pIVW <0.01), Methanobacteria (PIVW <0.01), Desulfovibrionaceae (pIVW <0.01), Methanobacteriaceae (pIVW <0.01) and Butyrivibrio (pIVW =0.02). Heterogeneity (p>0.05) and pleiotropy (p>0.05) analysis confirmed the robustness of the MR results.
CONCLUSIONS
We identified some specific bacterial taxa that were causally associated with the risk of RA, providing new insights into prevention and diagnosis of RA.
Topics: Humans; Gastrointestinal Microbiome; Genome-Wide Association Study; Arthritis, Rheumatoid; Polymorphism, Single Nucleotide
PubMed: 37812479
DOI: 10.55563/clinexprheumatol/p9ig7c -
Animal : An International Journal of... Jan 2021The greenhouse gases (GHGs) derived from agriculture include carbon dioxide, nitrous oxide, and methane (CH). Of these GHGs, CH, in particular, constitutes a major... (Review)
Review
The greenhouse gases (GHGs) derived from agriculture include carbon dioxide, nitrous oxide, and methane (CH). Of these GHGs, CH, in particular, constitutes a major component of the GHG emitted by the agricultural sector. Along with environmental concerns, CH emission also leads to losses in gross energy intake with economic implications. While ruminants are considered the main source of CH from agriculture, nonruminant animals also contribute substantially, and the CH emission intensity of nonruminants remains comparable to that of ruminants. Means of mitigating CH emissions from enteric fermentation have therefore been sought. Methane is produced by methanogens-archaeal microorganisms that inhabit the digestive tracts of animals and participate in fermentation processes. As the diversity of methanogen communities is thought to be responsible for the differences in CH production among nonruminant animals, it is necessary to investigate the archaeal composition of specific animal species. Methanogens play an important role in energy metabolism and adipose tissue deposition in animals. Higher abundances of methanogens, along with their higher diversity, have been reported to contribute to lean phenotype in pigs. In particular, a greater abundance of Methanosphaera spp. and early dominance of Methanobrevibacter smithii have been reported to correlate with lower body fat formation in pigs. Besides the contribution of methanogens to the metabolic phenotype of their hosts, CH release reduces the productivity that could be achieved through other hydrogen (H) disposal pathways. Enhanced participation of acetogenesis in H disposal, leading to acetate formation, could be a more favorable direction for animal production and the environment. Better knowledge and understanding of the archaeal communities of the gastrointestinal tract (GIT), including their metabolism and interactions with other microorganisms, would thus allow the development of new strategies for inhibiting methanogens and shifting toward acetogenesis. There are a variety of approaches to inhibiting methanogens and mitigating methanogenesis in ruminants, which can find an application for nonruminants, such as nutritional changes through supplementation with biologically active compounds and management changes. We summarize the available reports and provide a comprehensive review of methanogens living in the GIT of various nonruminants, such as swine, horses, donkeys, rabbits, and poultry. This review will help in a better understanding of the populations and diversity of methanogens and the implications of their presence in nonruminant animals.
Topics: Animals; Animals, Domestic; Horses; Methane; Methanobrevibacter; Rabbits; Rumen; Ruminants; Swine
PubMed: 33516013
DOI: 10.1016/j.animal.2020.100060 -
NPJ Biofilms and Microbiomes Sep 2022A meta-analysis approach was used, to study the microbiomes of biofilms and planktonic communities underpinning microbial electrosynthesis (MES) cells. High-throughput... (Meta-Analysis)
Meta-Analysis
A meta-analysis approach was used, to study the microbiomes of biofilms and planktonic communities underpinning microbial electrosynthesis (MES) cells. High-throughput DNA sequencing of 16S rRNA gene amplicons has been increasingly applied to understand MES systems. In this meta-analysis of 22 studies, we find that acetogenic and methanogenic MES cells share 80% of a cathodic core microbiome, and that different inoculum pre-treatments strongly affect community composition. Oxygen scavengers were more abundant in planktonic communities, and several key organisms were associated with operating parameters and good cell performance. We suggest Desulfovibrio sp. play a role in initiating early biofilm development and shaping microbial communities by catalysing H production, to sustain either Acetobacterium sp. or Methanobacterium sp. Microbial community assembly became more stochastic over time, causing diversification of the biofilm (cathodic) community in acetogenic cells and leading to re-establishment of methanogens, despite inoculum pre-treatments. This suggests that repeated interventions may be required to suppress methanogenesis.
Topics: Methane; Methanobacterium; Microbiota; Oxygen; RNA, Ribosomal, 16S
PubMed: 36138044
DOI: 10.1038/s41522-022-00337-5 -
Anaerobe Oct 2022Archaea comprise a unique domain of organisms with distinct biochemical and genetic differences from bacteria. Methane-forming archaea, methanogens, constitute the... (Review)
Review
Archaea comprise a unique domain of organisms with distinct biochemical and genetic differences from bacteria. Methane-forming archaea, methanogens, constitute the predominant group of archaea in the human gut microbiota, with Methanobrevibacter smithii being the most prevalent. However, the effect of methanogenic archaea and their methane production on chronic disease remains controversial. As perturbation of the microbiota is a feature of chronic conditions, such as cardiovascular disease, neurodegenerative diseases and chronic kidney disease, assessing the influence of archaea could provide a new clue to mitigating adverse effects associated with dysbiosis. In this review, we will discuss the putative role of archaea in the gut microbiota in humans and the possible link to chronic diseases.
Topics: Humans; Archaea; Gastrointestinal Microbiome; Methanobrevibacter; Euryarchaeota; Methane; Chronic Disease
PubMed: 35985606
DOI: 10.1016/j.anaerobe.2022.102629 -
Microbiology Spectrum Jun 2022Trophic interactions between microbes are postulated to determine whether a host microbiome is healthy or causes predisposition to disease. Two abundant taxa, the...
Trophic interactions between microbes are postulated to determine whether a host microbiome is healthy or causes predisposition to disease. Two abundant taxa, the Gram-negative heterotrophic bacterium Bacteroides thetaiotaomicron and the methanogenic archaeon Methanobrevibacter smithii, are proposed to have a synergistic metabolic relationship. Both organisms play vital roles in human gut health; B. thetaiotaomicron assists the host by fermenting dietary polysaccharides, whereas M. smithii consumes end-stage fermentation products and is hypothesized to relieve feedback inhibition of upstream microbes such as B. thetaiotaomicron. To study their metabolic interactions, we defined and optimized a coculture system and used software testing techniques to analyze growth under a range of conditions representing the nutrient environment of the host. We verify that B. thetaiotaomicron fermentation products are sufficient for M. smithii growth and that accumulation of fermentation products alters secretion of metabolites by B. thetaiotaomicron to benefit M. smithii. Studies suggest that B. thetaiotaomicron metabolic efficiency is greater in the absence of fermentation products or in the presence of M. smithii. Under certain conditions, B. thetaiotaomicron and M. smithii form interspecies granules consistent with behavior observed for syntrophic partnerships between microbes in soil or sediment enrichments and anaerobic digesters. Furthermore, when vitamin B, hematin, and hydrogen gas are abundant, coculture growth is greater than the sum of growth observed for monocultures, suggesting that both organisms benefit from a synergistic mutual metabolic relationship. The human gut functions through a complex system of interactions between the host human tissue and the microbes which inhabit it. These diverse interactions are difficult to model or examine under controlled laboratory conditions. We studied the interactions between two dominant human gut microbes, B. thetaiotaomicron and M. smithii, using a seven-component culturing approach that allows the systematic examination of the metabolic complexity of this binary microbial system. By combining high-throughput methods with machine learning techniques, we were able to investigate the interactions between two dominant genera of the gut microbiome in a wide variety of environmental conditions. Our approach can be broadly applied to studying microbial interactions and may be extended to evaluate and curate computational metabolic models. The software tools developed for this study are available as user-friendly tutorials in the Department of Energy KBase.
Topics: Bacteroides; Fermentation; Gastrointestinal Microbiome; Humans; Methanobrevibacter; Microbial Interactions
PubMed: 35536023
DOI: 10.1128/spectrum.01067-22