scholarly journals Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors

mBio ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Marc A. Sze ◽  
Patrick D. Schloss
Keyword(s):  
Colorectal Cancer ◽  
Random Forest ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Meta Analysis ◽  
Tumor Diagnosis ◽  
Rrna Gene ◽  
Modeling Framework ◽  
Data Set ◽  
Colonic Microbiota

ABSTRACTAn increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal to or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the area under the curve. The ability to correctly classify individuals with adenomas was poor, and the ability to classify individuals with carcinomas was considerably better using sequences from feces or tissue.IMPORTANCEColorectal cancer is a significant and growing health problem in which animal models and epidemiological data suggest that the colonic microbiota have a role in tumorigenesis. These observations indicate that the colonic microbiota is a reservoir of biomarkers that may improve our ability to detect colonic tumors using noninvasive approaches. This meta-analysis identifies and validates a set of 8 bacterial taxa that can be used within a Random Forest modeling framework to differentiate individuals as having normal colons or carcinomas. When models trained using one data set were tested on other data sets, the models performed well. These results lend support to the use of fecal biomarkers for the detection of tumors. Furthermore, these biomarkers are plausible candidates for further mechanistic studies into the role of the gut microbiota in tumorigenesis.

scholarly journals Leveraging Existing 16S rRNA Gene Surveys to Identify Reproducible Biomarkers in Individuals with Colorectal Tumors

2018 ◽  
Author(s):  
Marc A Sze ◽  
Patrick D Schloss
Keyword(s):  
Colorectal Cancer ◽  
Random Forest ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Meta Analysis ◽  
Tumor Diagnosis ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Modeling Framework ◽  
Colonic Microbiota

AbstractAn increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the Area Under the Curve. The ability to correctly classify individuals with adenomas was poor and the ability to classify individuals with carcinomas was considerably better using sequences from fecal or tissue.ImportanceColorectal cancer is a significant and growing health problem in which animal models and epidemiological data suggest that the colonic microbiota have a role in tumorigenesis. These observations indicate that the colonic microbiota is a reservoir of biomarkers that may improve our ability to detect colonic tumors using non-invasive approaches. This meta-analysis identifies and validates a set of 8 bacterial taxa that can be used within a Random Forest modeling framework to differentiate individuals as having normal colons or carcinomas. When models trained using one dataset were tested on other datasets, the models performed well. These results lend support to the use of fecal biomarkers for the detection of tumors. Furthermore, these biomarkers are plausible candidates for further mechanistic studies into the role of the gut microbiota in tumorigenesis.

scholarly journals High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Julia L. Drewes ◽  
James R. White ◽  
Christine M. Dejea ◽  
Payam Fathi ◽  
Thevambiga Iyadorai ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Resolution ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Meta Analysis ◽  
Rrna Gene ◽  
Gene Profile ◽  
Bacterial 16S Rrna Gene

scholarly journals The Termite Group I Phylum Is Highly Diverse and Widespread in the Environment

2007 ◽  
Vol 73 (20) ◽  
pp. 6682-6685 ◽  
Author(s):  
Daniel P. R. Herlemann ◽  
Oliver Geissinger ◽  
Andreas Brune
Keyword(s):  
Phylogenetic Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Rrna Gene ◽  
Gene Sequences ◽  
16S Rrna Gene Sequences ◽  
Specific Primers ◽  
Environmental Distribution ◽  
Data Set ◽  
Group I

ABSTRACT The bacterial candidate phylum Termite Group I (TG-1) presently consists mostly of “Endomicrobia,” which are endosymbionts of flagellate protists occurring exclusively in the hindguts of termites and wood-feeding cockroaches. Here, we show that public databases contain many, mostly undocumented 16S rRNA gene sequences from other habitats that are affiliated with the TG-1 phylum but are only distantly related to “Endomicrobia.” Phylogenetic analysis of the expanded data set revealed several diverse and deeply branching lineages comprising clones from many different habitats. In addition, we designed specific primers to explore the diversity and environmental distribution of bacteria in the TG-1 phylum.

scholarly journals Fecal Short-Chain Fatty Acids Are Not Predictive of Colonic Tumor Status and Cannot Be Predicted Based on Bacterial Community Structure

mBio ◽  
2019 ◽  
Vol 10 (4) ◽  
Author(s):  
Marc A. Sze ◽  
Begüm D. Topçuoğlu ◽  
Nicholas A. Lesniak ◽  
Mack T. Ruffin ◽  
Patrick D. Schloss
Keyword(s):  
Colorectal Cancer ◽  
Fatty Acids ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Sequence Data ◽  
Short Chain Fatty Acids ◽  
Tumor Burden ◽  
Short Chain ◽  
Rrna Gene ◽  
Chain Fatty Acids

ABSTRACT Colonic bacterial populations are thought to have a role in the development of colorectal cancer with some protecting against inflammation and others exacerbating inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory properties and are produced in large quantities by colonic bacteria that produce SCFAs by fermenting fiber. We assessed whether there was an association between fecal SCFA concentrations and the presence of colonic adenomas or carcinomas in a cohort of individuals using 16S rRNA gene and metagenomic shotgun sequence data. We measured the fecal concentrations of acetate, propionate, and butyrate within the cohort and found that there were no significant associations between SCFA concentration and tumor status. When we incorporated these concentrations into random forest classification models trained to differentiate between people with healthy colons and those with adenomas or carcinomas, we found that they did not significantly improve the ability of 16S rRNA gene or metagenomic gene sequence-based models to classify individuals. Finally, we generated random forest regression models trained to predict the concentration of each SCFA based on 16S rRNA gene or metagenomic gene sequence data from the same samples. These models performed poorly and were able to explain at most 14% of the observed variation in the SCFA concentrations. These results support the broader epidemiological data that questions the value of fiber consumption for reducing the risks of colorectal cancer. Although other bacterial metabolites may serve as biomarkers to detect adenomas or carcinomas, fecal SCFA concentrations have limited predictive power. IMPORTANCE Considering that colorectal cancer is the third leading cancer-related cause of death within the United States, it is important to detect colorectal tumors early and to prevent the formation of tumors. Short-chain fatty acids (SCFAs) are often used as a surrogate for measuring gut health and for being anticarcinogenic because of their anti-inflammatory properties. We evaluated the fecal SCFA concentrations of a cohort of individuals with different colonic tumor burdens who were previously analyzed to identify microbiome-based biomarkers of tumors. We were unable to find an association between SCFA concentration and tumor burden or use SCFAs to improve our microbiome-based models of classifying people based on their tumor status. Furthermore, we were unable to find an association between the fecal community structure and SCFA concentrations. Our results indicate that the association between fecal SCFAs, the gut microbiome, and tumor burden is weak.

scholarly journals 16S rRNA Gene Amplicon Profiling of Baby and Adult Captive Elephants in Thailand

2020 ◽  
Vol 9 (24) ◽  
Author(s):  
Sangam Kandel ◽  
Supaphen Sripiboon ◽  
Piroon Jenjaroenpun ◽  
David W. Ussery ◽  
Intawat Nookaew ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Bacterial Diversity ◽  
Sequence Data ◽  
Rrna Gene ◽  
Data Set ◽  
Geographical Locations

ABSTRACT Here, we present a 16S rRNA gene amplicon sequence data set and profiles demonstrating the bacterial diversity of baby and adult elephants from four different geographical locations in Thailand. The dominant phyla among baby and adult elephants were Bacteroidetes, Firmicutes, Proteobacteria, Kiritimatiellaeota, Euryarchaeota, and Tenericutes.

scholarly journals PCR/DGGE and 16S rRNA gene library analysis of the colonic microbiota of HLA-B27/beta2-microglobulin transgenic rats

2006 ◽  
Vol 42 (2) ◽  
pp. 165-171 ◽  
Author(s):  
W. McBurney ◽  
M. Mangold ◽  
K. Munro ◽  
M. Schultz ◽  
H.C. Rath ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Rrna Gene ◽  
Gene Library ◽  
Hla B27 ◽  
Transgenic Rats ◽  
Colonic Microbiota ◽  
16S Rrna Gene Library

scholarly journals Allometry and Ecology of the Bilaterian Gut Microbiome

mBio ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. e00319-18 ◽  
Author(s):  
Scott Sherrill-Mix ◽  
Kevin McCormick ◽  
Abigail Lauder ◽  
Aubrey Bailey ◽  
Laurie Zimmerman ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Island Biogeography ◽  
Rrna Gene ◽  
Microbial Composition ◽  
Data Set ◽  
Community Construction ◽  
Wide Range ◽  
Niche Diversification ◽  
Bacterial Lineages

ABSTRACT Classical ecology provides principles for construction and function of biological communities, but to what extent these apply to the animal-associated microbiota is just beginning to be assessed. Here, we investigated the influence of several well-known ecological principles on animal-associated microbiota by characterizing gut microbial specimens from bilaterally symmetrical animals (Bilateria) ranging from flies to whales. A rigorously vetted sample set containing 265 specimens from 64 species was assembled. Bacterial lineages were characterized by 16S rRNA gene sequencing. Previously published samples were also compared, allowing analysis of over 1,098 samples in total. A restricted number of bacterial phyla was found to account for the great majority of gut colonists. Gut microbial composition was associated with host phylogeny and diet. We identified numerous gut bacterial 16S rRNA gene sequences that diverged deeply from previously studied taxa, identifying opportunities to discover new bacterial types. The number of bacterial lineages per gut sample was positively associated with animal mass, paralleling known species-area relationships from island biogeography and implicating body size as a determinant of community stability and niche complexity. Samples from larger animals harbored greater numbers of anaerobic communities, specifying a mechanism for generating more-complex microbial environments. Predictions for species/abundance relationships from models of neutral colonization did not match the data set, pointing to alternative mechanisms such as selection of specific colonists by environmental niche. Taken together, the data suggest that niche complexity increases with gut size and that niche selection forces dominate gut community construction. IMPORTANCE The intestinal microbiome of animals is essential for health, contributing to digestion of foods, proper immune development, inhibition of pathogen colonization, and catabolism of xenobiotic compounds. How these communities assemble and persist is just beginning to be investigated. Here we interrogated a set of gut samples from a wide range of animals to investigate the roles of selection and random processes in microbial community construction. We show that the numbers of bacterial species increased with the weight of host organisms, paralleling findings from studies of island biogeography. Communities in larger organisms tended to be more anaerobic, suggesting one mechanism for niche diversification. Nonselective processes enable specific predictions for community structure, but our samples did not match the predictions of the neutral model. Thus, these findings highlight the importance of niche selection in community construction and suggest mechanisms of niche diversification.

scholarly journals Uncovering a hidden diversity: optimized protocols for the extraction of bacteriophages from soil

2019 ◽  
Author(s):  
Pauline C. Göller ◽  
Jose M. Haro-Moreno ◽  
Francisco Rodriguez-Valera ◽  
Martin J. Loessner ◽  
Elena Gómez-Sanz
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Ecological Model ◽  
Extraction Process ◽  
Extraction Methods ◽  
Soil Samples ◽  
Rrna Gene ◽  
Optimization Strategy ◽  
Data Set ◽  
Soil Matrix

AbstractBackgroundBacteriophages are the most numerous biological entities on earth and play a crucial role in shaping microbial communities. Investigating the bacteriophage community from soil samples will shed light not only on the yet largely unknown phage diversity, but also may result in novel insights into phage biology and functioning. Unfortunately, the study of soil viromes lags far behind any other ecological model system, due to the heterogeneous soil matrix that rises major technical difficulties in the extraction process. Resolving these technical challenges and establishing a standardized extraction protocol is therefore a fundamental prerequisite for replicable results and comparative virome studies.ResultsWe here report the optimization of protocols for extraction of bacteriophage DNA from soil preceding metagenomic analysis such that the protocol can equally be harnessed for phage isolation. As an optimization strategy, soil samples were spiked with a viral community consisting of phages from different families (106 PFU/g soil): Listeria phage ΦA511 (Myovirus), Staphylococcus phage Φ2638AΔLCR (Siphovirus), and Escherichia phage ΦT7 (Podovirus). The efficacy of bacteriophage (i) elution, (ii) filtration, (iii) concentration, and (iv) DNA extraction methods was tested. Successful extraction routes were selected based on spiked phage recovery and low bacterial 16S rRNA gene contaminants. Natural agricultural soil viromes were then extracted with the optimized methods and shotgun sequenced. Our approach yielded sufficient amounts of inhibitor-free viral DNA for non-amplification dependent sequencing and low 16S rRNA gene contamination levels (≤ 0.2 ‰). Compared to previously published protocols, the number of bacterial read contamination was decreased by 65 %. In addition, 468 novel circularized soil phage genomes in size up to 235 kb were obtained from over 29,000 manually identified viral contigs, promising the discovery of a large, previously inaccessible viral diversity.ConclusionWe have shown a dramatically enhanced extraction of the soil phage community by protocol optimization that has proven robustness in both a culture-depended as well as through metaviromic analysis. Our huge data set of manually curated soil viral contigs roughly doubles the amount of currently available soil virome data, and provide insights into the yet largely undescribed soil viral sequence space.

scholarly journals Substrate-Specific Clades of Active Marine Methylotrophs Associated with a Phytoplankton Bloom in a Temperate Coastal Environment

2008 ◽  
Vol 74 (23) ◽  
pp. 7321-7328 ◽  
Author(s):  
Josh D. Neufeld ◽  
Rich Boden ◽  
Hélène Moussard ◽  
Hendrik Schäfer ◽  
J. Colin Murrell
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Atmospheric Chemistry ◽  
Methyl Bromide ◽  
Dimethyl Sulfide ◽  
Phytoplankton Bloom ◽  
Stable Isotope Probing ◽  
Rrna Gene ◽  
Surface Seawater ◽  
Data Set

ABSTRACT Marine microorganisms that consume one-carbon (C1) compounds are poorly described, despite their impact on global climate via an influence on aquatic and atmospheric chemistry. This study investigated marine bacterial communities involved in the metabolism of C1 compounds. These communities were of relevance to surface seawater and atmospheric chemistry in the context of a bloom that was dominated by phytoplankton known to produce dimethylsulfoniopropionate. In addition to using 16S rRNA gene fingerprinting and clone libraries to characterize samples taken from a bloom transect in July 2006, seawater samples from the phytoplankton bloom were incubated with 13C-labeled methanol, monomethylamine, dimethylamine, methyl bromide, and dimethyl sulfide to identify microbial populations involved in the turnover of C1 compounds, using DNA stable isotope probing. The [13C]DNA samples from a single time point were characterized and compared using denaturing gradient gel electrophoresis (DGGE), fingerprint cluster analysis, and 16S rRNA gene clone library analysis. Bacterial community DGGE fingerprints from 13C-labeled DNA were distinct from those obtained with the DNA of the nonlabeled community DNA and suggested some overlap in substrate utilization between active methylotroph populations growing on different C1 substrates. Active methylotrophs were affiliated with Methylophaga spp. and several clades of undescribed Gammaproteobacteria that utilized methanol, methylamines (both monomethylamine and dimethylamine), and dimethyl sulfide. rRNA gene sequences corresponding to populations assimilating 13C-labeled methyl bromide and other substrates were associated with members of the Alphaproteobacteria (e.g., the family Rhodobacteraceae), the Cytophaga-Flexibacter-Bacteroides group, and unknown taxa. This study expands the known diversity of marine methylotrophs in surface seawater and provides a comprehensive data set for focused cultivation and metagenomic analyses in the future.

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