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 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 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 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.

Human Microbiome Project, Data Analysis, and Coordination Center

2015 ◽  
pp. 229-230
Author(s):  
Olukemi O. Abolude ◽  
Heather H. Creasy ◽  
Anup A. Mahurkar ◽  
Owen White ◽  
Michelle G. Giglio
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Coordination Center ◽  
Project Data

scholarly journals Phylogenetic Comparative Methods on Phylogenetic Networks with Reticulations

2018 ◽  
Vol 67 (5) ◽  
pp. 800-820 ◽  
Author(s):  
Paul Bastide ◽  
Claudia Solís-Lemus ◽  
Ricardo Kriebel ◽  
K William Sparks ◽  
Cécile Ané
Keyword(s):  
Phylogenetic Signal ◽  
Statistical Tests ◽  
Recursive Algorithm ◽  
Species Traits ◽  
Comparative Methods ◽  
Hybrid Vigor ◽  
Phylogenetic Networks ◽  
Phylogenetic Comparative Methods ◽  
Data Set ◽  
Phylogenetic Regression

Abstract The goal of phylogenetic comparative methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species’ traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel’s $\lambda$ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a data set of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

scholarly journals Interactive exploratory data analysis of Integrative Human Microbiome Project data using Metaviz

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 601
Author(s):  
Justin Wagner ◽  
Jayaram Kancherla ◽  
Domenick Braccia ◽  
James Matsumara ◽  
Victor Felix ◽  
...  
Keyword(s):  
Data Analysis ◽  
Human Microbiome ◽  
Disease Status ◽  
Human Microbiome Project ◽  
Second Phase ◽  
Bioconductor Package ◽  
The Rich ◽  
Rich Data ◽  
Project Data ◽  
Microbiome Data

The rich data produced by the second phase of the Human Microbiome Project (iHMP) offers a unique opportunity to test hypotheses that interactions between microbial communities and a human host might impact an individual’s health or disease status. In this work we describe infrastructure that integrates Metaviz, an interactive microbiome data analysis and visualization tool, with the iHMP Data Coordination Center web portal and the HMP2Data R/Bioconductor package. We describe integrative statistical and visual analyses of two datasets from iHMP using Metaviz along with the metagenomeSeq R/Bioconductor package for statistical analysis of differential abundance analysis. These use cases demonstrate the utility of a combined approach to access and analyze data from this resource.

scholarly journals Evaluation of Microbiome Alterations Following Consumption of BIOHM, a Novel Probiotic

2021 ◽  
Vol 43 (3) ◽  
pp. 2135-2146
Author(s):  
Mahmoud A. Ghannoum ◽  
Thomas S. McCormick ◽  
Mauricio Retuerto ◽  
Gurkan Bebek ◽  
Susan Cousineau ◽  
...  
Keyword(s):  
Human Subjects ◽  
Statistical Significance ◽  
Gastrointestinal Symptoms ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Saccharomyces Boulardii ◽  
P Value ◽  
Significant Difference ◽  
Bowel Diseases ◽  
Project Data

Gastrointestinal microbiome dysbiosis may result in harmful effects on the host, including those caused by inflammatory bowel diseases (IBD). The novel probiotic BIOHM, consisting of Bifidobacterium breve, Saccharomyces boulardii, Lactobacillus acidophilus, L. rhamnosus, and amylase, was developed to rebalance the bacterial–fungal gut microbiome, with the goal of reducing inflammation and maintaining a healthy gut population. To test the effect of BIOHM on human subjects, we enrolled a cohort of 49 volunteers in collaboration with the Fermentation Festival group (Santa Barbara, CA, USA). The profiles of gut bacterial and fungal communities were assessed via stool samples collected at baseline and following 4 weeks of once-a-day BIOHM consumption. Mycobiome analysis following probiotic consumption revealed an increase in Ascomycota levels in enrolled individuals and a reduction in Zygomycota levels (p value < 0.01). No statistically significant difference in Basidiomycota was detected between pre- and post-BIOHM samples and control abundance profiles (p > 0.05). BIOHM consumption led to a significant reduction in the abundance of Candida genus in tested subjects (p value < 0.013), while the abundance of C. albicans also trended lower than before BIOHM use, albeit not reaching statistical significance. A reduction in the abundance of Firmicutes at the phylum level was observed following BIOHM use, which approached levels reported for control individuals reported in the Human Microbiome Project data. The preliminary results from this clinical study suggest that BIOHM is capable of significantly rebalancing the bacteriome and mycobiome in the gut of healthy individuals, suggesting that further trials examining the utility of the BIOHM probiotic in individuals with gastrointestinal symptoms, where dysbiosis is considered a source driving pathogenesis, are warranted.

scholarly journals Identification of Complex Multidimensional Patterns in Microbial Communities

2019 ◽  
Author(s):  
Golovko George ◽  
Khanipov Kamil ◽  
Albayrak Levent ◽  
Fofanov Yuriy
Keyword(s):  
Microbial Communities ◽  
Statistical Significance ◽  
Source Code ◽  
Three Dimensional ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Site Specific ◽  
Information Coefficient ◽  
Complex Relationships ◽  
Project Data

AbstractMotivationIdentification of complex relationships within members of microbial communities is key to understand and guide microbial transplantation and provide personalized anti-microbial and probiotic treatments. Since members of a given microbial community can be simultaneously involved in multiple relations that altogether will determine their abundance, not all significant relations between organisms are expected to be manifested as visually uninterrupted patterns and be detected using traditional correlation nor mutual information coefficient based approaches.ResultsThis manuscript proposes a pattern specific way to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relations patterns between abundance profiles of two organisms which can be extended to three or more dimensional patterns. Presented approach can also be extended by including a variety of physical (pH, temperature, oxygen concentration) and biochemical (antimicrobial susceptibility, nutrient and metabolite concentration) variables into the search for multidimensional patterns. The presented approach has been tested using 2,380 microbiome samples from the Human Microbiome Project resulting in body-site specific networks of statistically significant 2D patterns. We also were able to demonstrate the presence of several 3D patterns in the Human Microbiome Project data.AvailabilityC++ source code for two and three-dimensional patterns, as well as executable files for the Pickle pipeline, are in the attached supplementary [email protected]

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