Flavonoid-Modifying Capabilities of the Human Gut Microbiome—An In Silico Study

Flavonoids are a major group of dietary plant polyphenols and have a positive health impact, but their modification and degradation in the human gut is still widely unknown. Due to the rise of metagenome data of the human gut microbiome and the assembly of hundreds of thousands of bacterial metagenome-assembled genomes (MAGs), large-scale screening for potential flavonoid-modifying enzymes of human gut bacteria is now feasible. With sequences of characterized flavonoid-transforming enzymes as queries, the Unified Human Gastrointestinal Protein catalog was analyzed and genes encoding putative flavonoid-modifying enzymes were quantified. The results revealed that flavonoid-modifying enzymes are often encoded in gut bacteria hitherto not considered to modify flavonoids. The enzymes for the physiologically important daidzein-to-equol conversion, well studied in Slackiaisoflavoniconvertens, were encoded only to a minor extent in Slackia MAGs, but were more abundant in Adlercreutzia equolifaciens and an uncharacterized Eggerthellaceae species. In addition, enzymes with a sequence identity of about 35% were encoded in highly abundant MAGs of uncultivated Collinsella species, which suggests a hitherto uncharacterized daidzein-to-equol potential in these bacteria. Of all potential flavonoid modification steps, O-deglycosylation (including derhamnosylation) was by far the most abundant in this analysis. In contrast, enzymes putatively involved in C-deglycosylation were detected less often in human gut bacteria and mainly found in Agathobacter faecis (formerly Roseburia faecis). Homologs to phloretin hydrolase, flavanonol/flavanone-cleaving reductase and flavone reductase were of intermediate abundance (several hundred MAGs) and mainly prevalent in Flavonifractor plautii. This first comprehensive insight into the black box of flavonoid modification in the human gut highlights many hitherto overlooked and uncultured bacterial genera and species as potential key organisms in flavonoid modification. This could lead to a significant contribution to future biochemical-microbiological investigations on gut bacterial flavonoid transformation. In addition, our results are important for individual nutritional recommendations and for biotechnological applications that rely on novel enzymes catalyzing potentially useful flavonoid modification reactions.

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Flavonoid-modifying capabilities of the gut microbiome – an in silico study

10.21203/rs.3.rs-588616/v1 ◽

2021 ◽

Author(s):

Tobias Goris ◽

Rafael Cuadrat ◽

Annett Braune

Keyword(s):

Large Scale ◽

Health Impact ◽

Gut Bacteria ◽

Human Gut ◽

Positive Health ◽

Nutritional Recommendations ◽

In Silico Study ◽

Gut Metagenome ◽

Insight Into ◽

Large Scale Screening

Abstract Flavonoids are a major group of dietary plant polyphenols and have a positive health impact, but their modification and degradation in the human gut is still widely unknown. Due to the rise of human gut metagenome data and the assembly of hundreds of thousands of bacterial metagenome-assembled genomes (MAGs), large-scale screening for potential flavonoid-modifying enzymes is now feasible. With sequences from characterized flavonoid-transforming enzymes as queries, the Unified Human Gastrointestinal Protein catalog was analyzed and quantification of putative flavonoid-modifying enzymes was carried out. The results revealed that flavonoid-modifying enzymes are often highly abundant in bacteria hitherto not considered as flavonoid-modifying gut bacteria. The enzymes for the physiologically important daidzein to equol conversion, well studied in Slackia isoflavoniconvertens, were encoded only to a low extent in Slackia MAGs, but more abundant in Adlercreutzia equolifaciens and an uncharacterizedEggerthellaceae species. In addition, a high abundance of genes with a similarity of only about 35% in uncultivated Collinsella species suggest a hitherto uncharacterized Daidzein-to-equol potential in these bacteria. Of all potential flavonoid modification steps, O-deglycosylation (including derhamnosylation) was by far the most abundant in this analysis. In contrast, enzymes putatively involved in C-deglycosylation were detected less often in human gut bacteria and mainly found in Agathobacter faecis (formerly Roseburia faecis). Phloretin hydrolase, flavanonol/flavanone-cleaving reductase and flavone reductase (all three most abundant in Flavonifractor plautii) and O-demethylase (Intestinibacter bartlettii) homologs were of intermediate prevalence (several hundreds of MAGs). This first comprehensive insight into the black box of flavonoid modification in the human gut highlights many hitherto overlooked and uncultured bacterial genera and species as key organisms in flavonoid modification by the human gut microbiota. This could lead to a significant contribution to future biochemical-microbiological investigations on gut bacterial flavonoid transformation. In addition, our results are important for individual nutritional recommendations and for biotechnological applications which rely on novel enzymes catalyzing potentially useful flavonoid modification reactions.

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Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome

The ISME Journal ◽

10.1038/ismej.2017.139 ◽

2017 ◽

Vol 11 (12) ◽

pp. 2848-2863 ◽

Cited By ~ 41

Author(s):

Francesco Beghini ◽

Edoardo Pasolli ◽

Tin Duy Truong ◽

Lorenza Putignani ◽

Simone M Cacciò ◽

...

Keyword(s):

Gut Microbiome ◽

Large Scale ◽

Human Gut ◽

Human Gut Microbiome ◽

Comparative Metagenomics

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Alterations in human gut microbiome composition and metabolism after exposure to glyphosate and Roundup and/or a spore-based formulation using the SHIME® technology

10.1101/2021.12.16.472928 ◽

2021 ◽

Author(s):

Robin Mesnage ◽

Marta Calatayud ◽

Cindy Duysburgh ◽

Massimo Marzorati ◽

Michael Antoniou

Keyword(s):

Gut Microbiota ◽

Gut Microbiome ◽

Large Scale ◽

Epidemiological Studies ◽

Human Gut ◽

Microbiome Composition ◽

Human Gut Microbiome ◽

Ammonium Production ◽

Profiling Techniques ◽

Microbial Environment

Despite extensive research into the toxicology of the herbicide glyphosate, there are still major unknowns regarding its effects on the human gut microbiome. As a step in addressing this knowledge gap, we describe for the first time the effects of glyphosate and a Roundup glyphosate-based herbicide on infant gut microbiota using SHIME technology, which mimics the entire gastrointestinal tract. SHIME microbiota culture was undertaken in the presence of a concentration of 100 mg/L (corresponding to a dose of 1.6 mg/kg/day) glyphosate and the same glyphosate equivalent concentration of Roundup, which is in the range of the US chronic reference dose, and subjected to molecular profiling techniques to assess outcomes. Roundup and to a lesser extent glyphosate caused an increase in fermentation activity, resulting in acidification of the microbial environment. This was also reflected by an increase in lactate and acetate production concomitant to a decrease in the levels of propionate, valerate, caproate and butyrate. Ammonium production reflecting proteolytic activities was increased by Roundup exposure. Global metabolomics revealed large scale disturbances in metabolite profiles, including an increased abundance of long chain polyunsaturated fatty acids (n3 and n6). Although changes in bacterial composition measured by qPCR and 16S rRNA sequencing were less clear, our results suggested that lactobacilli had their growth stimulated as a result of microenvironment acidification. Co-treatment with the spore-based probiotic formulation MegaSporeBiotic reverted some of the changes in short-chain fatty acid levels. Altogether, our results suggest that glyphosate can exert effects on human gut microbiota at permitted regulatory levels of exposure, highlighting the need for epidemiological studies aimed at evaluating the effects of glyphosate herbicides on human gut microbiome function.

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Large-scale association analyses identify host factors influencing human gut microbiome composition

Nature Genetics ◽

10.1038/s41588-020-00763-1 ◽

2021 ◽

Vol 53 (2) ◽

pp. 156-165

Author(s):

Alexander Kurilshikov ◽

Carolina Medina-Gomez ◽

Rodrigo Bacigalupe ◽

Djawad Radjabzadeh ◽

Jun Wang ◽

...

Keyword(s):

Gut Microbiome ◽

Large Scale ◽

Host Factors ◽

Human Gut ◽

Association Analyses ◽

Microbiome Composition ◽

Human Gut Microbiome ◽

Factors Influencing

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GutCyc: a Multi-Study Collection of Human Gut Microbiome Metabolic Models

10.1101/055574 ◽

2016 ◽

Cited By ~ 1

Author(s):

Aria S. Hahn ◽

Tomer Altman ◽

Kishori M. Konwar ◽

Niels W. Hanson ◽

Dongjae Kim ◽

...

Keyword(s):

Gut Microbiome ◽

Large Scale ◽

High Throughput Sequencing ◽

Human Microbiome ◽

Therapeutic Interventions ◽

Human Gut ◽

Human Gut Microbiome ◽

Disease States ◽

Health And Disease ◽

Inflammatory Bowel

AbstractAdvances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GUTCYC, a compendium of environmental pathway genome databases constructed from 418 assembled human microbiome datasets using METAPATHWAYS, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the PATHWAY TOOLS software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GUTCYC provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GUTCYC data products are searchable online, or may be downloaded and explored locally using METAPATHWAYS and PATHWAY TOOLS.

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A decrease in iron availability to human gut microbiome reduces the growth of potentially pathogenic gut bacteria; an in vitro colonic fermentation study

The Journal of Nutritional Biochemistry ◽

10.1016/j.jnutbio.2019.01.010 ◽

2019 ◽

Vol 67 ◽

pp. 20-27 ◽

Cited By ~ 19

Author(s):

Bhavika A Parmanand ◽

Lee Kellingray ◽

Gwenaelle Le Gall ◽

Abdul W Basit ◽

Susan Fairweather-Tait ◽

...

Keyword(s):

Gut Microbiome ◽

Gut Bacteria ◽

Iron Availability ◽

Human Gut ◽

Colonic Fermentation ◽

Human Gut Microbiome

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Revealing microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

10.1101/664219 ◽

2019 ◽

Cited By ~ 1

Author(s):

Shion Hosoda ◽

Suguru Nishijima ◽

Tsukasa Fukunaga ◽

Masahira Hattori ◽

Michiaki Hamada

Keyword(s):

Data Mining ◽

Cluster Analysis ◽

Gut Microbiome ◽

Large Scale ◽

Latent Dirichlet Allocation ◽

Human Gut ◽

Human Gut Microbiome ◽

Mining Methods ◽

Gut Metagenome ◽

Dirichlet Allocation

AbstractRecent research has revealed that there are various microbial species in the human gut microbiome. To clarify the structure of the human gut microbiome, many data mining methods have been applied to microbial composition data. Cluster analysis, one of the key data mining methods that have been used in human gut microbiome research, can classify the human gut microbiome into three clusters, called enterotypes. The human gut microbiome has been suggested to be composed of the microbial assemblages or groups of co-occurring microbes, and one human gut microbiome can contain several microbial assemblages. However, cluster analysis can cluster samples into groups without capturing minor assemblages. In addition, a reliable method of assemblage detection has not been established, and little is known about the distributions of microbial assemblages at a population-level scale. Accordingly, the purpose of this study was to clarify the microbial assemblages in the human gut microbiome. In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method, which was first proposed for the classification of documents in natural language processing. We applied LDA to a large-scale human gut metagenome dataset and found that a four-assemblage LDA model can represent relationships between enterotypes and assemblages with high interpretability. This model indicates that each individual tends to have several assemblages, and each of three assemblages corresponded to each enterotype. However, the C-assemblage can exist in all enterotypes. Interestingly, the dominant genera of the C-assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Finally, we revealed that genera mainly appearing in the same assemblage were correlated to each other. We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA, a powerful method for detection of microbial assemblages. This approach has the potential to reveal the structure of the human gut microbiome.

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Evidence for a multi-level trophic organization of the human gut microbiome

10.1101/603365 ◽

2019 ◽

Cited By ~ 1

Author(s):

Tong Wang ◽

Akshit Goyal ◽

Veronika Dubinkina ◽

Sergei Maslov

Keyword(s):

Gut Microbiome ◽

Large Scale ◽

Real Data ◽

Trophic Levels ◽

Human Gut ◽

Human Gut Microbiome ◽

Complex Ecosystem ◽

Metabolic Interactions ◽

Multi Level ◽

High Level

AbstractThe human gut microbiome is a complex ecosystem, in which hundreds of microbial species and metabolites coexist, in part due to an extensive network of cross-feeding interactions. However, both the large-scale trophic organization of this ecosystem, and its effects on the underlying metabolic flow, remain unexplored. Here, using a simplified model, we provide quantitative support for a multi-level trophic organization of the human gut microbiome, where microbes consume and secrete metabolites in multiple iterative steps. Using a manually-curated set of metabolic interactions between microbes, our model suggests about four trophic levels, each characterized by a high level-to-level metabolic transfer of byproducts. It also quantitatively predicts the typical metabolic environment of the gut (fecal metabolome) in approximate agreement with the real data. To understand the consequences of this trophic organization, we quantify the metabolic flow and biomass distribution, and explore patterns of microbial and metabolic diversity in different levels. The hierarchical trophic organization suggested by our model can help mechanistically establish causal links between the abundances of microbes and metabolites in the human gut.

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