scholarly journals Mouse Gut Microbiome-Encoded β-Glucuronidases Identified Using Metagenome Analysis Guided by Protein Structure

mSystems ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Benjamin C. Creekmore ◽  
Josh H. Gray ◽  
William G. Walton ◽  
Kristen A. Biernat ◽  
Michael S. Little ◽  
...  
Keyword(s):  
Protein Structure ◽  
Active Site ◽  
Human Microbiome ◽  
Drug Efficacy ◽  
Human Microbiome Project ◽  
Structural Features ◽  
Model Organisms ◽  
Mouse Strains ◽  
Sequencing Data ◽  
Metagenome Analysis

ABSTRACT Gut microbial β-glucuronidase (GUS) enzymes play important roles in drug efficacy and toxicity, intestinal carcinogenesis, and mammalian-microbial symbiosis. Recently, the first catalog of human gut GUS proteins was provided for the Human Microbiome Project stool sample database and revealed 279 unique GUS enzymes organized into six categories based on active-site structural features. Because mice represent a model biomedical research organism, here we provide an analogous catalog of mouse intestinal microbial GUS proteins—a mouse gut GUSome. Using metagenome analysis guided by protein structure, we examined 2.5 million unique proteins from a comprehensive mouse gut metagenome created from several mouse strains, providers, housing conditions, and diets. We identified 444 unique GUS proteins and organized them into six categories based on active-site features, similarly to the human GUSome analysis. GUS enzymes were encoded by the major gut microbial phyla, including Firmicutes (60%) and Bacteroidetes (21%), and there were nearly 20% for which taxonomy could not be assigned. No differences in gut microbial gus gene composition were observed for mice based on sex. However, mice exhibited gus differences based on active-site features associated with provider, location, strain, and diet. Furthermore, diet yielded the largest differences in gus composition. Biochemical analysis of two low-fat-associated GUS enzymes revealed that they are variable with respect to their efficacy of processing both sulfated and nonsulfated heparan nonasaccharides containing terminal glucuronides. IMPORTANCE Mice are commonly employed as model organisms of mammalian disease; as such, our understanding of the compositions of their gut microbiomes is critical to appreciating how the mouse and human gastrointestinal tracts mirror one another. GUS enzymes, with importance in normal physiology and disease, are an attractive set of proteins to use for such analyses. Here we show that while the specific GUS enzymes differ at the sequence level, a core GUSome functionality appears conserved between mouse and human gastrointestinal bacteria. Mouse strain, provider, housing location, and diet exhibit distinct GUSomes and gus gene compositions, but sex seems not to affect the GUSome. These data provide a basis for understanding the gut microbial GUS enzymes present in commonly used laboratory mice. Further, they demonstrate the utility of metagenome analysis guided by protein structure to provide specific sets of functionally related proteins from whole-genome metagenome sequencing data.

scholarly journals phylogenize: correcting for phylogeny reveals genes associated with microbial distributions

2019 ◽  
Vol 36 (4) ◽  
pp. 1289-1290
Author(s):  
Patrick H Bradley ◽  
Katherine S Pollard
Keyword(s):  
Community Composition ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Shotgun Sequencing ◽  
Supplementary Information ◽  
Phylogenetic Comparative Methods ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Phylogenetic Regression ◽  
Project Data

Abstract Summary Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. Availability and implementation phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Active-site differences between substrate-free and ritonavir-bound cytochrome P450 (CYP) 3A5 reveal plasticity differences between CYP3A5 and CYP3A4

2019 ◽  
Vol 294 (20) ◽  
pp. 8015-8022 ◽  
Author(s):  
Mei-Hui Hsu ◽  
Eric F. Johnson
Keyword(s):  
Cytochrome P450 ◽  
Drug Metabolism ◽  
Active Site ◽  
Drug Efficacy ◽  
Structural Features ◽  
Crystallographic Structure ◽  
Access Channel ◽  
Active Site Cavity ◽  
Human Adults ◽  
Related Enzyme

Cytochrome P450 (CYP) 3A4 is a major contributor to hepatic drug and xenobiotic metabolism in human adults. The related enzyme CYP3A5 is also expressed in adult liver and has broader age and tissue distributions. However, CYP3A5 expression is low in most Caucasians because of the prevalence of an allele that leads to an incorrectly spliced mRNA and premature termination of translation. When expressed, CYP3A5 expands metabolic capabilities and can augment CYP3A4-mediated drug metabolism, thereby reducing drug efficacy and potentially requiring dose adjustments. The extensive role of CYP3A4 in drug metabolism reflects in part the plasticity of the substrate-free enzyme to enlarge its active site and accommodate very large substrates. We have previously shown that the structure of the CYP3A5–ritonavir complex differs substantially from that of the CYP3A4–ritonavir complex. To better understand whether these differences are conserved in other CYP3A5 structures and how they relate to differential plasticity, we determined the X-ray crystallographic structure of the CYP3A5 substrate-free complex to 2.20 Å resolution. We observed that this structure exhibits a much larger active site than substrate-free CYP3A4 and displays an open substrate access channel. This reflected in part a lower trajectory of the helix F–F′ connector in CYP3A4 and more extensive π–CH interactions between phenylalanine residues forming the roof of the active-site cavity than in CYP3A5. Comparison with the CYP3A5–ritonavir complex confirmed conserved CYP3A5 structural features and indicated differences in plasticity between CYP3A4 and CYP3A5 that favor alternative ritonavir conformations.

scholarly journals A resistome roadmap: from the human body to pristine environments

2021 ◽  
Author(s):  
Lucia Maestre-Carballa ◽  
Manuel Martínez-García ◽  
Vicente Navarro-López
Keyword(s):  
Oral Cavity ◽  
Resistance Genes ◽  
Human Body ◽  
Human Microbiome ◽  
Antibiotic Resistance Genes ◽  
Human Microbiome Project ◽  
Individual Variability ◽  
Body Parts ◽  
Sequencing Data ◽  
The One

A comprehensive characterization of the human body resistome (sets of antibiotic resistance genes (ARGs)) is yet to be done and paramount for addressing the antibiotic microbial resistance threat. Here, we study the resistome of 771 samples from five major body parts (skin, nares, vagina, gut and oral cavity) of healthy subjects from the Human Microbiome Project and addressed the potential dispersion of ARGs in pristine environments. A total of 28,731 ARGs belonging to 344 different ARG types were found in the HMP proteome dataset (n=9.1x107 proteins analyzed). Our study reveals a distinct resistome profile (ARG type and abundance) between body sites and high inter-individual variability. Nares had the highest ARG load (≈5.4 genes/genome) followed by the oral cavity, while the gut showed one of the highest ARG richness (shared with nares) but the lowest abundance (≈1.3 genes/genome). Fluroquinolone resistance genes were the most abundant in the human body, followed by macrolide-lincosamide-streptogramin (MLS) or tetracycline. Most of the ARGs belonged to common bacterial commensals and multidrug resistance trait was predominant in the nares and vagina. Our data also provide hope, since the spread of common ARG from the human body to pristine environments (n=271 samples; 77 Gb of sequencing data and 2.1x108 proteins analyzed) thus far remains very unlikely (only one case found in an autochthonous bacterium from a pristine environment). These findings broaden our understanding of ARG in the context of the human microbiome and the One-Health Initiative of WHO uniting human host-microbes and environments as a whole.

scholarly journals Implications of error-prone long-read whole-genome shotgun sequencing on characterizing reference microbiomes

2020 ◽  
Author(s):  
Yu Hu ◽  
Li Fang ◽  
Christopher Nicholson ◽  
Kai Wang
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Error Rates ◽  
Tool Development ◽  
Sequencing Data ◽  
Bacterial Strains ◽  
Microbial Genomes ◽  
Oxford Nanopore ◽  
Long Read ◽  
Adequate Coverage

SummaryLong-read sequencing techniques, such as the Oxford Nanopore Technology, can generate reads that are tens of kilobases in length, and are therefore particularly relevant for microbiome studies. However, due to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored. Here we deeply sequenced two human microbiota mock community samples (HM-276D and HM-277D) from the Human Microbiome Project. We showed that assembly programs consistently achieved high accuracy (~99%) and completeness (~99%) for bacterial strains with adequate coverage. We also found that long-read sequencing provides accurate estimates of species-level abundance (R=0.94 for 20 bacteria with abundance ranging from 0.005% to 64%). Our results demonstrate the feasibility to characterize complete microbial genomes and populations from error-prone Nanopore sequencing data, but also highlight necessary bioinformatics improvements for future metagenomics tool development.

scholarly journals HLA-A alleles including HLA-A29 affect the composition of the gut microbiome: a potential clue to the pathogenesis of birdshot retinochoroidopathy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter R. Sternes ◽  
Tammy M. Martin ◽  
Michael Paley ◽  
Sarah Diamond ◽  
Mark J. Asquith ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Bacterial Species ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Eye Disease ◽  
Intestinal Biopsy ◽  
Healthy Individuals ◽  
Sequencing Data ◽  
Immune Mediated ◽  
Bacterial Profiling

Abstract Birdshot retinochoroidopathy occurs exclusively in individuals who are HLA-A29 positive. The mechanism to account for this association is unknown. The gut microbiome has been causally implicated in many immune-mediated diseases. We hypothesized that HLA-A29 would affect the composition of the gut microbiome, leading to a dysbiosis and immune-mediated eye disease. Fecal and intestinal biopsy samples were obtained from 107 healthy individuals from Portland, Oregon environs, 10 of whom were HLA-A29 positive, undergoing routine colonoscopy. Bacterial profiling was achieved via 16S rRNA metabarcoding. Publicly available whole meta-genome sequencing data from the Human Microbiome Project (HMP), consisting of 298 healthy controls mostly of US origin, were also interrogated. PERMANOVA and sparse partial least squares discriminant analysis (sPLSDA) demonstrated that subjects who were HLA-A29 positive differed in bacterial species composition (beta diversity) compared to HLA-A29 negative subjects in both the Portland (p = 0.019) and HMP cohorts (p = 0.0002). The Portland and HMP cohorts evidenced different subsets of bacterial species associated with HLA-A29 status, likely due to differences in the metagenomic techniques employed. The functional composition of the HMP cohort did not differ overall (p = 0.14) between HLA-A29 positive and negative subjects, although some distinct pathways such as heparan sulfate biosynthesis showed differences. As we and others have shown for various HLA alleles, the HLA allotype impacts the composition of the microbiome. We hypothesize that HLA-A29 may predispose chorioretinitis via an altered gut microbiome.

scholarly journals phylogenize: correcting for phylogeny reveals genes associated with microbial distributions

2018 ◽  
Author(s):  
Patrick H. Bradley ◽  
Katherine S. Pollard
Keyword(s):  
Community Composition ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Comparative Methods ◽  
Shotgun Sequencing ◽  
Phylogenetic Comparative Methods ◽  
Sequencing Data ◽  
Link Type ◽  
Phylogenetic Regression ◽  
Project Data

AbstractSummaryPhylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME2, and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.Availabilityphylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/[email protected]

scholarly journals Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

2021 ◽  
Author(s):  
Camilo Valdes ◽  
Vitalii Stebliankin ◽  
Daniel Ruiz-Perez ◽  
Ji In Park ◽  
Hajeong Lee ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Supplementary Information ◽  
Metagenomic Sequencing ◽  
Hilbert Curve ◽  
Sequencing Data ◽  
Space Filling ◽  
Space Filling Curve ◽  
Spatio Temporal ◽  
Disease Study

AbstractMotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent is complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number in the thousands. We present a technique for succinct visualization of abundance profiles using a space-filling curve that transforms a profile into an interpretable 2D image.ResultsJasper is a tool for visualizing profiles from metagenomic whole-genome sequencing and 16S, and orders taxa along a space-filling Hilbert curve. The result is a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order the taxa in one of two ways, and depending on the ordering, the microbiome maps can highlight “hot spots” of microbes that are either dominant in taxonomic clades or to the biological conditions under study.We use Jasper to visualize samples from the Human Microbiome Project and from a Chronic Kidney Disease study, and discuss a variety of ways in which the microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can be converted into animated movies that bring to life the dynamicity of microbiomes.AvailabilityJasper will be available as free software from the Mac App Store and biorg.cs.fiu.edu/jasperSupplementary informationSupplementary materials are available at biorg.cs.fiu.edu/jasper

scholarly journals Accessible, curated metagenomic data through ExperimentHub

2017 ◽  
Author(s):  
Edoardo Pasolli ◽  
Lucas Schiffer ◽  
Paolo Manghi ◽  
Audrey Renson ◽  
Valerie Obenchain ◽  
...  
Keyword(s):  
Open Source Software ◽  
Large Scale ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Disease Classification ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Data Types ◽  
Command Line Interface ◽  
Functional Profiles

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut “enterotypes”, and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

scholarly journals OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

2021 ◽  
Author(s):  
Qiyun Zhu ◽  
Shi Huang ◽  
Antonio Gonzalez ◽  
Imran McGrath ◽  
Daniel McDonald ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Shotgun Metagenomics ◽  
Biologically Relevant ◽  
Metagenome Analysis ◽  
Host Sex

We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies.

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