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.

scholarly journals Correction: 16S rRNA gene sequencing and healthy reference ranges for 28 clinically relevant microbial taxa from the human gut microbiome

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0212474 ◽  
Author(s):  
Daniel E. Almonacid ◽  
Laurens Kraal ◽  
Francisco J. Ossandon ◽  
Yelena V. Budovskaya ◽  
Juan Pablo Cardenas ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Reference Ranges ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Rrna Gene Sequencing

scholarly journals 16S rRNA gene sequencing and healthy reference ranges for 28 clinically relevant microbial taxa from the human gut microbiome

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0176555 ◽  
Author(s):  
Daniel E. Almonacid ◽  
Laurens Kraal ◽  
Francisco J. Ossandon ◽  
Yelena V. Budovskaya ◽  
Juan Pablo Cardenas ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Reference Ranges ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Rrna Gene Sequencing

scholarly journals Influence of fecal collection conditions and 16S rRNA gene sequencing protocols at two centers on human gut microbiota analysis

2017 ◽  
Author(s):  
Jocelyn Sietsma Penington ◽  
Megan A S Penno ◽  
Katrina M Ngui ◽  
Nadim J Ajami ◽  
Alexandra J Roth-Schulze ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Multicenter Studies ◽  
16S Rrna Analysis ◽  
The Usa ◽  
Rrna Gene Sequencing

AbstractBackgroundTo optimise fecal sampling and analysis yielding reproducible microbiome data, and gain further insight into sources of its variation, we compared different collection conditions and 16S rRNA gene sequencing protocols in two centers. Fecal samples were collected on three sequential days from six healthy adults and placed in commercial collection tubes (OMNIgeneGut OMR-200) at room temperature or in sterile 5 ml screw-top tubes in a home fridge or home freezer for 6-24 h, before transfer at 4°C to the laboratory and storage at - 80°C within 24 hours. Replicate samples were shipped on dry ice to centers in Australia and the USA for DNA extraction and sequencing of the V4 region of the 16S rRNA gene, using different PCR protocols. Sequences were analysed with the QIIME pipeline and Greengenes database at the Australian center and with an in-house pipeline and SILVA database at the USA center.ResultsVariation in gut microbiome composition and diversity was dominated by differences between individuals. Minor differences in the abundance of taxa were found between collection-processing methods and day of collection. Larger differences were evident between the two centers, including in the relative abundances of genus Akkermansia, in phylum Verrucomicrobiales, and Bifidobacteria in Actinobacteria.ConclusionsCollection with storage and transport at 4°C within 24 h is adequate for 16S rRNA analysis of the gut microbiome. However, variation between sequencing centers suggests that cohort samples should be sequenced by the same method in one center. Differences in handling, shipping and methods of PCR gene amplification and sequence analysis in different centers introduce variation in ways that are not fully understood. These findings are particularly relevant as microbiome studies shift towards larger population-based and multicenter studies.

scholarly journals 16S rRNA Gene Sequencing for Deciphering the Colorectal Cancer Gut Microbiome: Current Protocols and Workflows

2018 ◽  
Vol 9 ◽  
Author(s):  
Muhammad-Afiq Osman ◽  
Hui-min Neoh ◽  
Nurul-Syakima Ab Mutalib ◽  
Siok-Fong Chin ◽  
Rahman Jamal
Keyword(s):  
Colorectal Cancer ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rrna Gene Sequencing

scholarly journals Dysfunctional Gut Microbiome Networks in Childhood IgE-Mediated Food Allergy

2021 ◽  
Vol 22 (4) ◽  
pp. 2079 ◽  
Author(s):  
Khui Hung Lee ◽  
Jing Guo ◽  
Yong Song ◽  
Amir Ariff ◽  
Michael O’Sullivan ◽  
...  
Keyword(s):  
Food Allergy ◽  
Network Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Gut Microbiome ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Ige Mediated ◽  
Rrna Gene Sequencing

The development of food allergy has been reported to be related with the changes in the gut microbiome, however the specific microbe associated with the pathogenesis of food allergy remains elusive. This study aimed to comprehensively characterize the gut microbiome and identify individual or group gut microbes relating to food-allergy using 16S rRNA gene sequencing with network analysis. Faecal samples were collected from children with IgE-mediated food allergies (n = 33) and without food allergy (n = 27). Gut microbiome was profiled by 16S rRNA gene sequencing. OTUs obtained from 16S rRNA gene sequencing were then used to construct a co-abundance network using Weighted Gene Co-expression Network Analysis (WGCNA) and mapped onto Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We identified a co-abundance network module to be positively correlated with IgE-mediated food allergy and this module was characterized by a hub taxon, namely Ruminococcaceae UCG-002 (phylum Firmicutes). Functional pathway analysis of all the gut microbiome showed enrichment of methane metabolism and glycerolipid metabolism in the gut microbiome of food-allergic children and enrichment of ubiquinone and other terpenoid-quinone biosynthesis in the gut microbiome of non-food allergic children. We concluded that Ruminococcaceae UCG-002 may play determinant roles in gut microbial community structure and function leading to the development of IgE-mediated food allergy.

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