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

2022 ◽  
Vol 2 (1) ◽  
Author(s):  
Lalhaba Oinam ◽  
Fumi Minoshima ◽  
Hiroaki Tateno
Keyword(s):  
Flow Cytometry ◽  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Deinococcus Radiodurans ◽  
16S Rrna Gene Sequencing ◽  
Appropriate Technology ◽  
Rrna Gene ◽  
Adult Mice ◽  
Glycan Profiling

AbstractBacterial 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. In this study, we applied Glycan-seq technology for glycan profiling of the intact gut microbiota of mice. 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 glycan profiling. We thus applied Glycan-seq for comparative glycan profiling of pups and adult mice gut microbiotas. The glycans of the pups and adult microbiotas had significantly distinct glycan profiles, which reflect the different bacterial compositions of pups and adult gut microbiotas based on 16S rRNA gene sequencing.α2-6Sia-binders bound specifically to the pups microbiota. Lectin pull-down followed by 16S rRNA gene sequencing of the pups microbiota identified Lactobacillaceae as the most abundant bacterial family with glycans reacting with α2-6Sia-binders. The Glycan-seq system can reveal the glycan profile of the intact bacterial gut microbiota.

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 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.

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 Monosodium Glutamate Induces Changes in Hepatic and Renal Metabolic Profiles and Gut Microbiome of Wistar Rats

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1865
Author(s):  
Kanokwan Nahok ◽  
Jutarop Phetcharaburanin ◽  
Jia V. Li ◽  
Atit Silsirivanit ◽  
Raynoo Thanan ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Wistar Rats ◽  
Monosodium Glutamate ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Male Wistar Rats ◽  
The Impact

The short- and long-term consumption of monosodium glutamate (MSG) increases urinary pH but the effects on the metabolic pathways in the liver, kidney and the gut microbiota remain unknown. To address this issue, we investigated adult male Wistar rats allocated to receive drinking water with or without 1 g% MSG for 2 weeks (n = 10, each). We performed a Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolomic study of the jejunum, liver, and kidneys, while faecal samples were collected for bacterial DNA extraction to investigate the gut microbiota using 16S rRNA gene sequencing. We observed significant changes in the liver of MSG-treated rats compared to controls in the levels of glucose, pyridoxine, leucine, isoleucine, valine, alanine, kynurenate, and nicotinamide. Among kidney metabolites, the level of trimethylamine (TMA) was increased, and pyridoxine was decreased after MSG-treatment. Sequencing of the 16S rRNA gene revealed that MSG-treated rats had increased Firmicutes, the gut bacteria associated with TMA metabolism, along with decreased Bifidobacterium species. Our data support the impact of MSG consumption on liver and kidney metabolism. Based on the gut microbiome changes, we speculate that TMA and its metabolites such as trimethylamine-N-oxide (TMAO) may be mediators of the effects of MSG on the kidney health.

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