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 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 Diet induces reproducible alterations in the mouse and human gut microbiome

2019 ◽  
Author(s):  
Jordan E. Bisanz ◽  
Vaibhav Upadhyay ◽  
Jessie A. Turnbaugh ◽  
Kimberly Ly ◽  
Peter J. Turnbaugh
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Meta Analysis ◽  
Alpha Diversity ◽  
Operational Taxonomic Unit ◽  
Molecular Testing ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Dietary Interventions

SummaryThe degree to which diet reproducibly alters the human and mouse gut microbiota remains unclear. Here, we focus on the consumption of a high-fat diet (HFD), one of the most frequently studied dietary interventions in mice. We employed a subject-level meta-analysis framework for unbiased collection and analysis of publicly available 16S rRNA gene and metagenomic sequencing data from studies examining HFD in rodent models. In total, we re-analyzed 27 studies, 1101 samples, and 106 million reads mapping to 16S rRNA gene sequences. We report reproducible changes in gut microbial community structure both within and between studies, including a significant increase in the Firmicutes phylum and decrease in the Bacteroidetes phylum; however, reduced alpha diversity is not a consistent feature of HFD. Finer taxonomic analysis revealed that the strongest signal of HFD on microbiota species composition is Lactococcus spp., which we demonstrate is a common dietary contaminant through the molecular testing of dietary ingredients, culturing, microscopy, and germ-free mouse experiments. After in silico removal of Lactococcus spp., we employed machine learning to define a unique operational taxonomic unit (OTU)-based signature capable of predicting the dietary intake of mice and demonstrate that phylogenetic and gene-family transformations of this model are capable of accurately predicting human samples in controlled feeding settings (area under the receiver operator curve = 0.75 and 0.88 respectively). Together, these results demonstrate the utility of microbiome meta-analyses in identifying robust bacterial signals for mechanistic studies and creates a framework for the routine meta-analysis of microbiome studies in preclinical models.

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 Dynamics of Soil Microbial Communities During Diazepam and Oxazepam Biodegradation in Soil Flooded by Water From a WWTP

2021 ◽  
Vol 12 ◽  
Author(s):  
Marc Crampon ◽  
Coralie Soulier ◽  
Pauline Sidoli ◽  
Jennifer Hellal ◽  
Catherine Joulian ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Treated Wastewater ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Rrna Gene Sequencing

The demand for energy and chemicals is constantly growing, leading to an increase of the amounts of contaminants discharged to the environment. Among these, pharmaceutical molecules are frequently found in treated wastewater that is discharged into superficial waters. Indeed, wastewater treatment plants (WWTPs) are designed to remove organic pollution from urban effluents but are not specific, especially toward contaminants of emerging concern (CECs), which finally reach the natural environment. In this context, it is important to study the fate of micropollutants, especially in a soil aquifer treatment (SAT) context for water from WWTPs, and for the most persistent molecules such as benzodiazepines. In the present study, soils sampled in a reed bed frequently flooded by water from a WWTP were spiked with diazepam and oxazepam in microcosms, and their concentrations were monitored for 97 days. It appeared that the two molecules were completely degraded after 15 days of incubation. Samples were collected during the experiment in order to follow the dynamics of the microbial communities, based on 16S rRNA gene sequencing for Archaea and Bacteria, and ITS2 gene for Fungi. The evolution of diversity and of specific operating taxonomic units (OTUs) highlighted an impact of the addition of benzodiazepines, a rapid resilience of the fungal community and an evolution of the bacterial community. It appeared that OTUs from the Brevibacillus genus were more abundant at the beginning of the biodegradation process, for diazepam and oxazepam conditions. Additionally, Tax4Fun tool was applied to 16S rRNA gene sequencing data to infer on the evolution of specific metabolic functions during biodegradation. It finally appeared that the microbial community in soils frequently exposed to water from WWTP, potentially containing CECs such as diazepam and oxazepam, may be adapted to the degradation of persistent contaminants.

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 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 The archives are half-empty: an assessment of the availability of microbial community sequencing data

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Stephanie D. Jurburg ◽  
Maximilian Konzack ◽  
Nico Eisenhauer ◽  
Anna Heintz-Buschart
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
16S Rrna Gene Sequencing ◽  
Data Reuse ◽  
Data Availability ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Data Archiving ◽  
Sequencing Studies ◽  
Rrna Gene Sequencing

AbstractAs DNA sequencing has become more popular, the public genetic repositories where sequences are archived have experienced explosive growth. These repositories now hold invaluable collections of sequences, e.g., for microbial ecology, but whether these data are reusable has not been evaluated. We assessed the availability and state of 16S rRNA gene amplicon sequences archived in public genetic repositories (SRA, EBI, and DDJ). We screened 26,927 publications in 17 microbiology journals, identifying 2015 16S rRNA gene sequencing studies. Of these, 7.2% had not made their data public at the time of analysis. Among a subset of 635 studies sequencing the same gene region, 40.3% contained data which was not available or not reusable, and an additional 25.5% contained faults in data formatting or data labeling, creating obstacles for data reuse. Our study reveals gaps in data availability, identifies major contributors to data loss, and offers suggestions for improving data archiving practices.

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