scholarly journals Effect of Disordered Gut Microbiota on Serum Metabolome Alterations in Colorectal Tumor-Bearing Mice Under The Intervention of Fufangchangtai

2021 ◽  
Author(s):  
Mengmeng Cai ◽  
Zhibing Lin ◽  
Ya Xiao ◽  
Jinmiao Lu ◽  
Xiaoyu Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Gut Microbiota ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Colorectal Tumor ◽  
Rrna Gene ◽  
Tumor Bearing ◽  
Rrna Gene Sequencing

Abstract Background: The occurrence and development of colorectal cancer is related to the compositional and functional variation of gut microbiota. Compared with healthy people, gut microbiota of patients with colorectal cancer is in disorder. Most traditional Chinese Medicine is effective by oral administration and has both anti-tumor effect and enteric microecological regulative effect. However, whether the dysbiosis of gut microbiota under tumor burden affects the serum metabolome of human body that related to traditional Chinese medicine is unclear. In this study, Fufangchangtai (FFCT) was chosen to be the model prescription to explore the correlation between gut microbiota and the serum metabolism related to FFCT in anti colorectal cancer treatment. Results: The gut microbiota between colorectal tumor-bearing mice and healthy mice were determined by 16S rRNA gene sequencing, showing quite differences between the two groups and suggesting that Firmicutes , Deferribacteres , Bacteroidetes and Proteobacteria were marked differential intestinal bacteria. The alternations in serum metabolome in the FFCT-treating tumor-bearing mice and simple FFCT-treating mice were detected using Liquid chromatogragh-mass spectrometer (LC/MS), showing significant differences between the two groups as well. Metabolites of FFCT like Citric acid, （±）12-HEPE, Cycloartanyl ferulate were much more in simple FFCT-treating mice, indicating that the present of tumor could affect the absorption and metabolism of FFCT. Additionally, these differential metabolites of FFCT involved in multiple pathways including the Alanine, aspartate and glutamate metabolism, Central carbon metabolism in cancer, Biosynthesis of amino acids. Different doses of FFCT were given to the tumor-bearing mice through oral administration, and the results of gut microbiota 16S rRNA gene sequencing showing that FFCT-treating groups has higher abundance of Firmicutes , Turicibacter and Roseburia than tumor-bearing group, moreover, the abundance of these bacteria was positively correlated to the drug concentration. Firmicutes and Bacteroidetes in FFCT-treating groups showed a similar trend with Healthy group, indicating the modulation of FFCT on gut microbiota of colorectal tumor-bearing mice.Conclusions: Collectively, we concluded that the dysbiosis of gut microbiota in tumor-bearing mice could affect the serum metabolome of human body that related to FFCT, and FFCT could correct the gut microbiota of colorectal tumor-bearing mice. It was pointed out that the GM should be concerned during the therapy of FFCT. The more healthier intestinal microenvironment was conductive to the better clinical curative effect.

scholarly journals Gut microbiota accelerates cisplatin-induced acute liver injury associated with robust inflammation and oxidative stress in mice

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Shenhai Gong ◽  
Yinglin Feng ◽  
Yunong Zeng ◽  
Huanrui Zhang ◽  
Meiping Pan ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
16S Rrna ◽  
Gut Microbiota ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Metabolomic Analysis ◽  
Rrna Gene Sequencing ◽  
And Oxidative Stress

Abstract Background Gut microbiota has been reported to be disrupted by cisplatin, as well as to modulate chemotherapy toxicity. However, the precise role of intestinal microbiota in the pathogenesis of cisplatin hepatotoxicity remains unknown. Methods We compared the composition and function of gut microbiota between mice treated with and without cisplatin using 16S rRNA gene sequencing and via metabolomic analysis. For understanding the causative relationship between gut dysbiosis and cisplatin hepatotoxicity, antibiotics were administered to deplete gut microbiota and faecal microbiota transplantation (FMT) was performed before cisplatin treatment. Results 16S rRNA gene sequencing and metabolomic analysis showed that cisplatin administration caused gut microbiota dysbiosis in mice. Gut microbiota ablation by antibiotic exposure protected against the hepatotoxicity induced by cisplatin. Interestingly, mice treated with antibiotics dampened the mitogen-activated protein kinase pathway activation and promoted nuclear factor erythroid 2-related factor 2 nuclear translocation, resulting in decreased levels of both inflammation and oxidative stress in the liver. FMT also confirmed the role of microbiota in individual susceptibility to cisplatin-induced hepatotoxicity. Conclusions This study elucidated the mechanism by which gut microbiota mediates cisplatin hepatotoxicity through enhanced inflammatory response and oxidative stress. This knowledge may help develop novel therapeutic approaches that involve targeting the composition and metabolites of microbiota.

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 Inbreeding Alters the Gut Microbiota of the Banna Minipig

Animals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 2125
Author(s):  
Limin Wei ◽  
Bo Zeng ◽  
Siyuan Zhang ◽  
Feng Li ◽  
Fanli Kong ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Gene Sequencing ◽  
Alpha Diversity ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rrna Gene Sequencing ◽  
Isoleucine Metabolism ◽  
Dominant Bacteria ◽  
The Impact ◽  
Predicted Function

The gut microbiota coevolve with the host and can be stably transmitted to the offspring. Host genetics plays a crucial role in the composition and abundance of gut microbiota. Inbreeding can cause a decrease of the host’s genetic diversity and the heterozygosity. In this study, we used 16S rRNA gene sequencing to compare the differences of gut microbiota between the Diannan small-ear pig and Banna minipig inbred, aiming to understand the impact of inbreeding on the gut microbiota. Three dominant bacteria (Stenotrophlomonas, Streptococcus, and Lactobacillus) were steadily enriched in both the Diannan small-ear pig and Banna minipig inbred. After inbreeding, the gut microbiota alpha diversity and some potential probiotics (Bifidobacterium, Tricibacter, Ruminocaccae, Christensenellaceae, etc.) were significantly decreased, while the pathogenic Klebsiella bacteria was significantly increased. In addition, the predicted metagenomic analysis (PICRUSt2) indicated that several amino acid metabolisms (‘‘Valine, leucine, and isoleucine metabolism’’, ‘‘Phenylalanine, tyrosine, and tryptophan biosynthesis’’, ‘‘Histidine metabolism’’) were also markedly decreased after the inbreeding. Altogether our data reveal that host inbreeding altered the composition and the predicted function of the gut microbiome, which provides some data for the gut microbiota during inbreeding.

scholarly journals Potential for Monitoring Gut Microbiota for Diagnosing Infections and Graft-versus-Host Disease in Cancer and Stem Cell Transplant Patients

2017 ◽  
Vol 63 (11) ◽  
pp. 1685-1694 ◽  
Author(s):  
Andrew Y Koh
Keyword(s):  
Stem Cell ◽  
Next Generation Sequencing ◽  
Gut Microbiota ◽  
Stem Cell Transplant ◽  
Gene Sequencing ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Cell Transplant ◽  
Rrna Gene Sequencing ◽  
Generation Sequencing

Abstract BACKGROUND Gut microbiota, the collective community of microorganisms inhabiting the intestine, have been shown to provide many beneficial functions for the host. Recent advances in next-generation sequencing and advanced molecular biology approaches have allowed researchers to identify gut microbiota signatures associated with disease processes and, in some cases, establish causality and elucidate underlying mechanisms. CONTENT This report reviews 3 commonly used methods for studying the gut microbiota and microbiome (the collective genomes of the gut microorganisms): 16S rRNA gene sequencing, bacterial group or species-specific quantitative polymerase chain reaction (qPCR), and metagenomic shotgun sequencing (MSS). The technical approaches and resources needed for each approach are outlined, and advantages and disadvantages for each approach are summarized. The findings regarding the role of the gut microbiota in the health of patients with cancer and stem cell transplant (SCT) patients (specifically in modulating the development of gut-derived bacterial infections and a posttransplant immune-mediated complication known as graft-vs-host-disease) are reviewed. Finally, there is discussion of the potential viability of these approaches in the actual clinical treatment of cancer and SCT patients. SUMMARY Advances in next-generation sequencing have revolutionized our understanding of the importance of the gut microbiome to human health. Both 16S rRNA gene sequencing and MSS are currently too labor-intensive or computationally burdensome to incorporate into real-time clinical monitoring of gut microbiomes. Yet, the lessons learned from these technologies could be adapted to currently used methods (e.g., qPCR) that could then be rigorously tested in the clinical care of these patients.

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.

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