Human Papillomavirus Community in Healthy Persons, Defined by Metagenomics Analysis of Human Microbiome Project Shotgun Sequencing Data Sets

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.

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phylogenize: correcting for phylogeny reveals genes associated with microbial distributions

10.1101/425231 ◽

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]

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

mSystems ◽

10.1128/msystems.00452-19 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 5

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.

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A resistome roadmap: from the human body to pristine environments

10.1101/2021.10.08.463752 ◽

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.

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

10.1101/2020.03.05.978866 ◽

2020 ◽

Cited By ~ 1

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.

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HMP16SData: Efficient Access to the Human Microbiome Project through Bioconductor

10.1101/299115 ◽

2018 ◽

Cited By ~ 1

Author(s):

Lucas Schiffer ◽

Rimsha Azhar ◽

Lori Shepherd ◽

Marcel Ramos ◽

Ludwig Geistlinger ◽

...

Keyword(s):

Phase 1 ◽

Human Microbiome ◽

Data Access ◽

Human Microbiome Project ◽

Amplicon Sequencing ◽

Data Sets ◽

Healthy Human ◽

Variable Regions ◽

Efficient Access ◽

Data Objects

AbstractPhase 1 of the NIH Human Microbiome Project (HMP) investigated 18 body subsites of 239 healthy American adults, to produce the first comprehensive reference for the composition and variation of the “healthy” human microbiome. Publicly-available data sets from amplicon sequencing of two 16S rRNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

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MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data

Microorganisms ◽

10.3390/microorganisms8050684 ◽

2020 ◽

Vol 8 (5) ◽

pp. 684

Author(s):

Nathanael J. Bangayan ◽

Baochen Shi ◽

Jerry Trinh ◽

Emma Barnard ◽

Gabriela Kasimatis ◽

...

Keyword(s):

High Throughput Sequencing ◽

Human Microbiome ◽

Shotgun Sequencing ◽

Strain Level ◽

Multi Locus Sequence Typing ◽

Metagenomic Data ◽

Sequencing Analysis ◽

Metagenomic Sequencing ◽

Healthy Skin ◽

Sequencing Data

The microbiome plays an important role in human physiology. The composition of the human microbiome has been described at the phylum, class, genus, and species levels, however, it is largely unknown at the strain level. The importance of strain-level differences in microbial communities has been increasingly recognized in understanding disease associations. Current methods for identifying strain populations often require deep metagenomic sequencing and a comprehensive set of reference genomes. In this study, we developed a method, metagenomic multi-locus sequence typing (MG-MLST), to determine strain-level composition in a microbial community by combining high-throughput sequencing with multi-locus sequence typing (MLST). We used a commensal bacterium, Propionibacterium acnes, as an example to test the ability of MG-MLST in identifying the strain composition. Using simulated communities, MG-MLST accurately predicted the strain populations in all samples. We further validated the method using MLST gene amplicon libraries and metagenomic shotgun sequencing data of clinical skin samples. MG-MLST yielded consistent results of the strain composition to those obtained from nearly full-length 16S rRNA clone libraries and metagenomic shotgun sequencing analysis. When comparing strain-level differences between acne and healthy skin microbiomes, we demonstrated that strains of RT2/6 were highly associated with healthy skin, consistent with previous findings. In summary, MG-MLST provides a quantitative analysis of the strain populations in the microbiome with diversity and richness. It can be applied to microbiome studies to reveal strain-level differences between groups, which are critical in many microorganism-related diseases.

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

Scientific Reports ◽

10.1038/s41598-020-74751-0 ◽

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.

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Comparison of Three Commercial Tools for Metagenomic Shotgun Sequencing Analysis

Journal of Clinical Microbiology ◽

10.1128/jcm.00981-19 ◽

2019 ◽

Vol 58 (3) ◽

Cited By ~ 1

Author(s):

Matthew Thoendel ◽

Patricio Jeraldo ◽

Kerryl E. Greenwood-Quaintance ◽

Janet Yao ◽

Nicholas Chia ◽

...

Keyword(s):

Diagnostic Method ◽

Shotgun Sequencing ◽

Metagenomic Analysis ◽

Data Sets ◽

Sequencing Analysis ◽

Sequencing Data ◽

Data Set ◽

Significant Challenge ◽

Number Of Species

ABSTRACT Metagenomic shotgun sequencing for the identification of pathogens is being increasingly utilized as a diagnostic method. Interpretation of large and complicated data sets is a significant challenge, for which multiple commercial tools have been developed. Three commercial metagenomic shotgun sequencing tools, CosmosID, One Codex, and IDbyDNA, were compared to determine whether they result in similar interpretations of the same sequencing data. We selected 24 diverse samples from a previously characterized data set derived from DNA extracted from biofilms dislodged from the surfaces of resected arthroplasties (sonicate fluid). Sequencing data sets were analyzed using the three commercial tools and compared to culture results and prior metagenomic analysis interpretation. Identical interpretations from all three tools occurred for 6 samples. The total number of species identified included 28 by CosmosID, 59 by One Codex, and 41 by IDbyDNA. All of the tools performed similarly in detecting those microorganisms identified by culture, including polymicrobial mixes. These data show that while all of the tools performed well overall, there were some differences, particularly in their predilection for identifying low-abundance or contaminant organisms as present.

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Complete Genome Assemblies for Three Variants of the Wolbachia Endosymbiont of Drosophila melanogaster

Microbiology Resource Announcements ◽

10.1128/mra.00956-19 ◽

2019 ◽

Vol 8 (45) ◽

Cited By ~ 1

Author(s):

Preston J. Basting ◽

Casey M. Bergman

Keyword(s):

Drosophila Melanogaster ◽

Complete Genome ◽

Shotgun Sequencing ◽

Data Sets ◽

Sequencing Data ◽

Content Type ◽

Wolbachia Pipientis ◽

Long Read ◽

Wolbachia Endosymbiont ◽

Genome Assemblies

Here, we report genome assemblies for three strains of Wolbachia pipientis, assembled from unenriched, unfiltered long-read shotgun sequencing data of geographically distinct strains of Drosophila melanogaster. Our simple methodology can be applied to long-read data sets of other Wolbachia-infected species with limited Wolbachia-host lateral gene transfers to produce complete assemblies for this important model symbiont.

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