scholarly journals Meta-analysis To Define a Core Microbiota in the Swine Gut

mSystems ◽  
2017 ◽  
Vol 2 (3) ◽  
Author(s):  
Devin B. Holman ◽  
Brian W. Brunelle ◽  
Julian Trachsel ◽  
Heather K. Allen
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Meta Analysis ◽  
Country Of Origin ◽  
Rrna Gene ◽  
Gi Tract ◽  
Potential Health ◽  
Core Microbiota ◽  
Composition And Structure

ABSTRACT The results of this meta-analysis demonstrate that “study” and GI sample location are the most significant factors in shaping the swine gut microbiota. However, in comparisons of results from different studies, some biological factors may be obscured by technical variation among studies. Nonetheless, there are some bacterial taxa that appear to form a core microbiota within the swine GI tract regardless of country of origin, diet, age, or breed. Thus, these results provide the framework for future studies to manipulate the swine gut microbiota for potential health benefits. The swine gut microbiota encompasses a large and diverse population of bacteria that play a significant role in pig health. As such, a number of recent studies have utilized high-throughput sequencing of the 16S rRNA gene to characterize the composition and structure of the swine gut microbiota, often in response to dietary feed additives. It is important to determine which factors shape the composition of the gut microbiota among multiple studies and if certain bacteria are always present in the gut microbiota of swine, independently of study variables such as country of origin and experimental design. Therefore, we performed a meta-analysis using 20 publically available data sets from high-throughput 16S rRNA gene sequence studies of the swine gut microbiota. Next to the “study” itself, the gastrointestinal (GI) tract section that was sampled had the greatest effect on the composition and structure of the swine gut microbiota (P = 0.0001). Technical variation among studies, particularly the 16S rRNA gene hypervariable region sequenced, also significantly affected the composition of the swine gut microbiota (P = 0.0001). Despite this, numerous commonalities were discovered. Among fecal samples, the genera Prevotella, Clostridium, Alloprevotella, and Ruminococcus and the RC9 gut group were found in 99% of all fecal samples. Additionally, Clostridium, Blautia, Lactobacillus, Prevotella, Ruminococcus, Roseburia, the RC9 gut group, and Subdoligranulum were shared by >90% of all GI samples, suggesting a so-called “core” microbiota for commercial swine worldwide. IMPORTANCE The results of this meta-analysis demonstrate that “study” and GI sample location are the most significant factors in shaping the swine gut microbiota. However, in comparisons of results from different studies, some biological factors may be obscured by technical variation among studies. Nonetheless, there are some bacterial taxa that appear to form a core microbiota within the swine GI tract regardless of country of origin, diet, age, or breed. Thus, these results provide the framework for future studies to manipulate the swine gut microbiota for potential health benefits.

scholarly journals Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francesco Durazzi ◽  
Claudia Sala ◽  
Gastone Castellani ◽  
Gerardo Manfreda ◽  
Daniel Remondini ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Shotgun Sequencing ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Experimental Conditions ◽  
Rrna Gene Sequencing

AbstractIn this paper we compared taxonomic results obtained by metataxonomics (16S rRNA gene sequencing) and metagenomics (whole shotgun metagenomic sequencing) to investigate their reliability for bacteria profiling, studying the chicken gut as a model system. The experimental conditions included two compartments of gastrointestinal tracts and two sampling times. We compared the relative abundance distributions obtained with the two sequencing strategies and then tested their capability to distinguish the experimental conditions. The results showed that 16S rRNA gene sequencing detects only part of the gut microbiota community revealed by shotgun sequencing. Specifically, when a sufficient number of reads is available, Shotgun sequencing has more power to identify less abundant taxa than 16S sequencing. Finally, we showed that the less abundant genera detected only by shotgun sequencing are biologically meaningful, being able to discriminate between the experimental conditions as much as the more abundant genera detected by both sequencing strategies.

The protective effects of yellow tea extract against loperamide-induced constipation in mice

2021 ◽  
Author(s):  
Pei-Qin Cao ◽  
Xiu-Ping Li ◽  
Jian Ou-Yang ◽  
Rong-Gang Jiang ◽  
Fang-Fang Huang ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Protective Effects ◽  
Rrna Gene Sequencing ◽  
Tea Extract

We evaluated the effects of yellow tea extract on relieving constipation induced by loperamide and evaluated the changes of gut microbiota based on 16S rRNA gene sequencing.

scholarly journals Diversity and Structure of the Endophytic Bacterial Communities Associated With Three Terrestrial Orchid Species as Revealed by 16S rRNA Gene Metabarcoding

2020 ◽  
Vol 11 ◽  
Author(s):  
Pasquale Alibrandi ◽  
Sylvia Schnell ◽  
Silvia Perotto ◽  
Massimiliano Cardinale
Keyword(s):  
Bacterial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Host Plant ◽  
Bacterial Communities ◽  
Reproductive Organs ◽  
Distribution Model ◽  
Rrna Gene ◽  
Orchid Species ◽  
Core Microbiota

The endophytic microbiota can establish mutualistic or commensalistic interactions within the host plant tissues. We investigated the bacterial endophytic microbiota in three species of Mediterranean orchids (Neottia ovata, Serapias vomeracea, and Spiranthes spiralis) by metabarcoding of the 16S rRNA gene. We examined whether the different orchid species and organs, both underground and aboveground, influenced the endophytic bacterial communities. A total of 1,930 operational taxonomic units (OTUs) were obtained, mainly Proteobacteria and Actinobacteria, whose distribution model indicated that the plant organ was the main determinant of the bacterial community structure. The co-occurrence network was not modular, suggesting a relative homogeneity of the microbiota between both plant species and organs. Moreover, the decrease in species richness and diversity in the aerial vegetative organs may indicate a filtering effect by the host plant. We identified four hub OTUs, three of them already reported as plant-associated taxa (Pseudoxanthomonas, Rhizobium, and Mitsuaria), whereas Thermus was an unusual member of the plant microbiota. Core microbiota analysis revealed a selective and systemic ascent of bacterial communities from the vegetative to the reproductive organs. The core microbiota was also maintained in the S. spiralis seeds, suggesting a potential vertical transfer of the microbiota. Surprisingly, some S. spiralis seed samples displayed a very rich endophytic microbiota, with a large number of OTUs shared with the roots, a situation that may lead to a putative restoring process of the root-associated microbiota in the progeny. Our results indicate that the bacterial community has adapted to colonize the orchid organs selectively and systemically, suggesting an active involvement in the orchid holobiont.

16S rRNA gene sequencing reveals altered composition of gut microbiota in individuals with kidney stones

Urolithiasis ◽  
2018 ◽  
Vol 46 (6) ◽  
pp. 503-514 ◽  
Author(s):  
Ruiqiang Tang ◽  
Yonghua Jiang ◽  
Aihua Tan ◽  
Juan Ye ◽  
Xiaoying Xian ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Kidney Stones ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rrna Gene Sequencing

scholarly journals Gut Microbiota Analysis Results Are Highly Dependent on the 16S rRNA Gene Target Region, Whereas the Impact of DNA Extraction Is Minor

2017 ◽  
Vol 28 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Anniina Rintala ◽  
Sami Pietilä ◽  
Eveliina Munukka ◽  
Erkki Eerola ◽  
Juha-Pekka Pursiheimo ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Dna Extraction ◽  
Rrna Gene ◽  
Gene Target ◽  
Target Region ◽  
Microbiota Analysis ◽  
The Impact ◽  
The 16S Rrna Gene

Impact of a probiotic, inulin, or their combination on the piglets’ microbiota at different intestinal locations

2015 ◽  
Vol 6 (4) ◽  
pp. 473-483 ◽  
Author(s):  
V.A. Sattler ◽  
K. Bayer ◽  
G. Schatzmayr ◽  
A.G. Haslberger ◽  
V. Klose
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Real Time ◽  
Relative Abundance ◽  
Real Time Pcr ◽  
Denaturing Gradient Gel Electrophoresis ◽  
Feed Additives ◽  
Rrna Gene ◽  
16S Rrna Gene Pyrosequencing

Natural feed additives are used to maintain health and to promote performance of pigs without antibiotics. Effects of a probiotic, inulin, and their combination (synbiotic), on the microbial diversity and composition at different intestinal locations were analysed using denaturing gradient gel electrophoresis (DGGE), real-time PCR, and 16S rRNA gene pyrosequencing. Bacterial diversity assessed by DGGE and/or pyrosequencing was increased by inulin in all three gut locations and by the synbiotic in the caecum and colon. In contrast, the probiotic did only affect the microbiota diversity in the ileum. Shifts in the DGGE microbiota profiles of the caecum and colon were detected for the pro- and synbiotic fed animals, whereas inulin profiles were more similar to the ones of the control. 16S rRNA gene pyrosequencing revealed that all three additives could reduce Escherichia species in each gut location, indicating a potential beneficial effect on the gut microbiota. An increase of relative abundance of Clostridiaceae in the large intestine was found in the inulin group and of Enterococcaceae in the ileum of probiotic fed pigs. Furthermore, real-time PCR results showed that the probiotic and synbiotic increased bifidobacterial numbers in the ileum, which was supported by sequencing results. The probiotic and inulin, to different extents, changed the diversity, relative abundance of phylotypes, and community profiles of the porcine microbiota. However, alterations of the bacterial community were not uniformly between gut locations, demonstrating that functionality of feed additives is site specific. Therefore, gut sampling from various locations is crucial when investigations aim to identify the composition of a healthy gut microbiota after its manipulation through feed additives.

scholarly journals Glycomic profiling of the gut microbiota by Glycan-seq

2021 ◽  
Author(s):  
Lalhaba Oinam ◽  
Fumi Minoshima ◽  
Hiroaki Tateno
Keyword(s):  
Flow Cytometry ◽  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Appropriate Technology ◽  
Fluorescence Labeling ◽  
Rrna Gene ◽  
Rrna Gene Sequencing

Background: There has been immense interest in studying the relationship between the gut microbiota and human health. Bacterial glycans modulate the cross talk between the gut microbiota and its host. However, little is known about these glycans because of the lack of appropriate technology to study them. Methods: We previously developed a sequencing-based glycan profiling method called Glycan-seq, which is based on the use of 39 DNA-barcoded lectins. In this study, we applied this technology to analyze the glycome of the intact gut microbiota of mice. Fecal microbiota was incubated with 39 DNA-barcoded lectins exposed to UV, and the number of released DNA barcodes were counted by next-generation sequencing to obtain a signal for each lectin bound to the microbiota. In parallel, the bacterial composition of the gut microbiota was analyzed by 16S rRNA gene sequencing. Finally, we performed a lectin pull-down experiment followed by 16S rRNA gene sequencing to identify lectin-reactive bacteria. Results: The evaluation of cultured gram-positive (Deinococcus radiodurans) and gram-negative (Escherichia coli) bacteria showed significantly distinct glycan profiles between these bacteria, which were selected and further analyzed by flow cytometry. The results of flow cytometry agreed well with those obtained by Glycan-seq, indicating that Glycan-seq can be used for bacterial glycomic analysis. We thus applied Glycan-seq to comparatively analyze the glycomes of young and old mice gut microbiotas. The glycomes of the young and old microbiotas had significantly distinct glycan profiles, which reflect the different bacterial compositions of young and old gut microbiotas based on 16S rRNA gene sequencing. Therefore, the difference in the glycomic profiles between young and old microbiotas may be due to their differing bacterial compositions. α2-6Sia-binders bound specifically to the young microbiota. Lectin pull-down followed by 16S rRNA gene sequencing of the young microbiota identified Lactobacillaceae as the most abundant bacterial family with glycans reacting with α2-6Sia-binders. Conclusion: The Glycan-seq system can, without any prior culturing and fluorescence labeling, reveal the glycomic profile of the intact bacterial gut microbiota. A combination of lectin pull-down and 16S rRNA gene sequencing can identify lectin-reactive bacteria.

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.

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