scholarly journals Variations in the Post-weaning Human Gut Metagenome Profile As Result of Bifidobacterium Acquisition in the Western Microbiome

2016 ◽  
Vol 07 ◽  
Author(s):  
Matteo Soverini ◽  
Simone Rampelli ◽  
Silvia Turroni ◽  
Stephanie L. Schnorr ◽  
Sara Quercia ◽  
...  
Keyword(s):  
Human Gut ◽  
Gut Metagenome

scholarly journals An assessment of Oxford Nanopore sequencing for human gut metagenome profiling: A pilot study of head and neck cancer patients

2019 ◽  
Vol 166 ◽  
pp. 105739 ◽  
Author(s):  
Thidathip Wongsurawat ◽  
Mayumi Nakagawa ◽  
Omar Atiq ◽  
Hannah N. Coleman ◽  
Piroon Jenjaroenpun ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Pilot Study ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Nanopore Sequencing ◽  
Human Gut ◽  
Oxford Nanopore ◽  
Gut Metagenome

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

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.

scholarly journals Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244876
Author(s):  
Moamen M. Elmassry ◽  
Sunghwan Kim ◽  
Ben Busby
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Metagenomic Data ◽  
Efficacy And Safety ◽  
Human Gut ◽  
Microbial Enzymes ◽  
Sequence Read Archive ◽  
Structural Analogues ◽  
Gut Metagenome

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.

scholarly journals Comprehensive mouse gut metagenome catalog reveals major difference to the human counterpart

2021 ◽  
Author(s):  
Silas Kieser ◽  
Evgeny M. Zdobnov ◽  
Mirko Trajkovski
Keyword(s):  
Gut Microbiota ◽  
Gut Microbiome ◽  
New Order ◽  
Human Gut ◽  
Rarefaction Analysis ◽  
Human Counterpart ◽  
The Impact ◽  
Human And Mouse ◽  
Gut Metagenome ◽  
Mouse Gut Microbiota

AbstractMouse is the most used model for studying the impact of microbiota on its host, but the repertoire of species from the mouse gut microbiome remains largely unknown. Here, we construct a Comprehensive Mouse Gut Metagenome (CMGM) catalog by assembling all currently available mouse gut metagenomes and combining them with published reference and metagenome-assembled genomes. The 50’011 genomes cluster into 1’699 species, of which 78.1% are uncultured, and we discovered 226 new genera, 7 new families, and 1 new order. Rarefaction analysis indicates comprehensive sampling of the species from the mouse gut. CMGM enables an unprecedented coverage of the mouse gut microbiome exceeding 90%. Comparing CMGM to the human gut microbiota shows an overlap 64% at the genus, but only 16% at the species level, demonstrating that human and mouse gut microbiota are largely distinct.

scholarly journals Identifying Core Operons in Metagenomic Data

2019 ◽  
Author(s):  
Xiao Hu ◽  
Iddo Friedberg
Keyword(s):  
Functional Unit ◽  
High Accuracy ◽  
Metagenomic Data ◽  
Human Gut ◽  
Microbial Genetics ◽  
Polycistronic Mrna ◽  
Functional Complexity ◽  
Powerful Mechanism ◽  
Gut Metagenome ◽  
Metagenome Sample

AbstractAn operon is a functional unit of DNA whose genes are co-transcribed on polycistronic mRNA, in a co-regulated fashion. Operons are a powerful mechanism of introducing functional complexity in bacteria, and are therefore of interest in microbial genetics, physiology, biochemistry, and evolution. Here we present a Pipeline for Operon Exploration in Metagenomes or POEM. At the heart of POEM lies the concept of a core operon, a functional unit enabled by a predicted operon in a metagenome. Using a series of benchmarks, we show the high accuracy of POEM, and demonstrate its use on a human gut metagenome sample. We conclude that POEM is a useful tool for analyzing metagenomes beyond the genomic level, and for identifying multi-gene functionalities and possible neofunctionalization in metagenomes. Availability: https://github.com/Rinoahu/POEM_py3k

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

2019 ◽  
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.

scholarly journals Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Syed Shujaat Ali Zaidi ◽  
Masood Ur Rehman Kayani ◽  
Xuegong Zhang ◽  
Younan Ouyang ◽  
Imran Haider Shamsi
Keyword(s):  
Type 2 Diabetes ◽  
Secondary Metabolites ◽  
Metagenomic Data ◽  
Whole Genome ◽  
Functional Information ◽  
Human Gut ◽  
E Coli ◽  
Operon Prediction ◽  
Gut Metagenome

Abstract Background Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions. Results In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101). Conclusion With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

scholarly journals HumGut: a comprehensive human gut prokaryotic genomes collection filtered by metagenome data

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Pranvera Hiseni ◽  
Knut Rudi ◽  
Robert C. Wilson ◽  
Finn Terje Hegge ◽  
Lars Snipen
Keyword(s):  
Superior Performance ◽  
Reference Database ◽  
Human Gut ◽  
Healthy Human ◽  
Identity Resolution ◽  
Sequence Identity ◽  
Prokaryotic Genomes ◽  
Major Bottleneck ◽  
Metagenome Sequencing ◽  
Gut Metagenome

Abstract Background A major bottleneck in the use of metagenome sequencing for human gut microbiome studies has been the lack of a comprehensive genome collection to be used as a reference database. Several recent efforts have been made to re-construct genomes from human gut metagenome data, resulting in a huge increase in the number of relevant genomes. In this work, we aimed to create a collection of the most prevalent healthy human gut prokaryotic genomes, to be used as a reference database, including both MAGs from the human gut and ordinary RefSeq genomes. Results We screened > 5,700 healthy human gut metagenomes for the containment of > 490,000 publicly available prokaryotic genomes sourced from RefSeq and the recently announced UHGG collection. This resulted in a pool of > 381,000 genomes that were subsequently scored and ranked based on their prevalence in the healthy human metagenomes. The genomes were then clustered at a 97.5% sequence identity resolution, and cluster representatives (30,691 in total) were retained to comprise the HumGut collection. Using the Kraken2 software for classification, we find superior performance in the assignment of metagenomic reads, classifying on average 94.5% of the reads in a metagenome, as opposed to 86% with UHGG and 44% when using standard Kraken2 database. A coarser HumGut collection, consisting of genomes dereplicated at 95% sequence identity—similar to UHGG, classified 88.25% of the reads. HumGut, half the size of standard Kraken2 database and directly comparable to the UHGG size, outperforms them both. Conclusions The HumGut collection contains > 30,000 genomes clustered at a 97.5% sequence identity resolution and ranked by human gut prevalence. We demonstrate how metagenomes from IBD-patients map equally well to this collection, indicating this reference is relevant also for studies well outside the metagenome reference set used to obtain HumGut. All data and metadata, as well as helpful code, are available at http://arken.nmbu.no/~larssn/humgut/.

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