scholarly journals Gut Microbiota Interplay With COVID-19 Reveals Links to Host Lipid Metabolism Among Middle Eastern Populations

2021 ◽  
Vol 12 ◽  
Author(s):  
Mohammad Tahseen Al Bataineh ◽  
Andreas Henschel ◽  
Mira Mousa ◽  
Marianne Daou ◽  
Fathimathuz Waasia ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
16S Rrna Gene ◽  
Middle Eastern ◽  
Ether Lipid ◽  
Infected Group ◽  
Host Cells ◽  
Control Group ◽  
Rrna Gene ◽  
Microbial Profiling

The interplay between the compositional changes in the gastrointestinal microbiome, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) susceptibility and severity, and host functions is complex and yet to be fully understood. This study performed 16S rRNA gene-based microbial profiling of 143 subjects. We observed structural and compositional alterations in the gut microbiota of the SARS-CoV-2-infected group in comparison to non-infected controls. The gut microbiota composition of the SARS-CoV-2-infected individuals showed an increase in anti-inflammatory bacteria such as Faecalibacterium (p-value = 1.72 × 10–6) and Bacteroides (p-value = 5.67 × 10–8). We also revealed a higher relative abundance of the highly beneficial butyrate producers such as Anaerostipes (p-value = 1.75 × 10–230), Lachnospiraceae (p-value = 7.14 × 10–65), and Blautia (p-value = 9.22 × 10–18) in the SARS-CoV-2-infected group in comparison to the control group. Moreover, phylogenetic investigation of communities by reconstructing unobserved state (PICRUSt) functional prediction analysis of the 16S rRNA gene abundance data showed substantial differences in the enrichment of metabolic pathways such as lipid, amino acid, carbohydrate, and xenobiotic metabolism, in comparison between both groups. We discovered an enrichment of linoleic acid, ether lipid, glycerolipid, and glycerophospholipid metabolism in the SARS-CoV-2-infected group, suggesting a link to SARS-CoV-2 entry and replication in host cells. We estimate the major contributing genera to the four pathways to be Parabacteroides, Streptococcus, Dorea, and Blautia, respectively. The identified differences provide a new insight to enrich our understanding of SARS-CoV-2-related changes in gut microbiota, their metabolic capabilities, and potential screening biomarkers linked to COVID-19 disease severity.

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 Effects of Antibiotics on the Bacterial Community, Metabolic Functions and Antibiotic Resistance Genes in Mariculture Sediments during Enrichment Culturing

2020 ◽  
Vol 8 (8) ◽  
pp. 604
Author(s):  
Meng-Qi Ye ◽  
Guan-Jun Chen ◽  
Zong-Jun Du
Keyword(s):  
Antibiotic Resistance ◽  
Bacterial Community ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
High Throughput ◽  
Antibiotic Resistance Genes ◽  
Control Group ◽  
Rrna Gene ◽  
Metabolic Functions ◽  
Culture Dependent

The effect of antibiotics on the diversity and functioning of indigenous microorganisms in the environment has attracted much attention. In this study, effects of exposure to six different antibiotics on the bacterial community, metabolic functions and antibiotic resistance genes (ARGs) in marine sediments during enrichment culturing were investigated. Classical culture-dependent method and high-throughput 16S rRNA gene sequencing method were both applied. In the culture-dependent analysis, the obtained 1549 isolates belonged to four phyla (Proteobacteria, Firmicutes, Bacteroidetes and Actinobacteria) and 155 genera. Proteobacteria and Firmicutes were the dominant phyla. The diversity and abundance of obtained bacteria after antibiotic processing exhibited different degrees of decrease. Enrichment culturing for different time could also affect the bacterial community composition. Some genera of bacteria were not isolated in the control group, but they could be isolated in the antibiotic-treated groups. In high-throughput 16S rRNA gene amplicon sequencing analyses, all the effective reads were clustered into 2822 OTUs at 97% similarity cutoff; they were annotated to 49 phyla, 103 class, 220 orders, 347 families, 624 genera and 1122 species. An alpha diversity analysis indicated that the community diversity and richness decreased under antibiotic exposure. The changes at the genus level were much more obvious. Only 48 genera of 129 genera were shared by all the samples. A total of 29 genera which were not detected in the initial control sample could be detected in at least one antibiotic-treated group. SIMPER analysis showed that OTU2543 and OTU1450 were the most common taxa to the dissimilarity of bacterial community between antibiotic-treated groups and the control group. OTU2034 and OUT2543 were the most contributive taxa to dissimilarity of groups incubating for different time. Metabolism was the predominant bacterial function. A total of 30 ARGs were detected in the samples. This study mainly focused on the changes of microbiota under the selective pressure of antibiotics for different time and the results demonstrated that the antibiotic could affect the bacterial diversity and richness in the marine ecosystem.

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 The Effect of Xuebijing on the Gut Microbiota and Metabolic of Heat 1 Stroke Rat 2

2021 ◽  
Author(s):  
Qiang wen ◽  
Xuan He ◽  
Yu Shao ◽  
Lun Peng ◽  
Li Zhao ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Heat Stroke ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Control Group ◽  
Rrna Gene ◽  
Metabolism Pathway ◽  
Rrna Gene Sequencing

Abstract The goal of the present study was to evaluate the fecal microbiome and serum metabolites in 16 Xuebijing (XBJ)-injected rats after heat stroke using 16S rRNA gene sequencing and gas chromatography-mass spectrometry (GC-MS) metabolomics. Eighteen rats were divided into the control group (CON), heat stroke group (HS), and XBJ group. The 16S rRNA gene sequencing results revealed that the abundance of Bacteroidetes was overrepresented in the XBJ group compared to the HS group, while Actinobacteria was underrepresented. Metabolomic profiling showed that the pyrimidine metabolism pathway, pentose phosphate pathway, and glycerophospholipid metabolism pathway were upregulated in the XBJ group compared to the HS group. Taken together, these results demonstrated that heat stroke not only altered the gut microbiome community structure of rats but also greatly affected metabolic functions, leading to gut microbiome toxicity.

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