Title |
Differences Between the Intestinal Lumen Microbiota of Aberrant Crypt Foci (ACF)-Bearing and Non-bearing Rats
|
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Published in |
Digestive Diseases and Sciences, July 2018
|
DOI | 10.1007/s10620-018-5180-7 |
Pubmed ID | |
Authors |
Xiuli Xiao, Wenbo Long, Tingyu Huang, Tian Xia, Rupei Ye, Yong Liu, Hanan Long |
Abstract |
Multiple factors including host-microbiota interaction could contribute to the conversion of healthy mucosa to sporadic precancerous lesions. An imbalance of the gut microbiota may be a cause or consequence of this process. The goal was to investigate and analyze the composition of gut microbiota during the genesis of precancerous lesions of colorectal cancer. To analyze the composition of gut microbiota in the genesis of precancerous lesions, a rat model of 1, 2-dimethylhydrazine (DMH)-induced aberrant crypt foci (ACF) was established. The feces of these rats and healthy rats were collected for 16S rRNA sequencing. The diversity and density of the rat intestinal microbiota were significantly different between ACF-bearing and non-bearing group. ACF were induced in rats treated with DMH and showed increased expression of the inflammatory cytokines IL-6, IL-8, and TNF-α. Firmicutes was the most predominant phylum in both ACF-bearing and non-bearing group, followed by Bacteroidetes. Interestingly, although the density of Bacteroidetes decreased from the fifth week to the 17th week in both groups, it was significantly reduced in ACF-bearing group at the 13th week (P < 0.01). At the genus level, no significant difference was observed in the most predominant genus, Lactobacillus. Instead, Bacteroides and Prevotella were significantly less abundant (P < 0.01), while Akkermansia was significantly more abundant (P < 0.05) in ACF-bearing group at the 13th week. Imbalance of the intestinal microbiota existed between ACF-bearing and non-bearing rats, which could be used as biomarker to predict the genesis of precancerous lesions in the gut. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 20 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 3 | 15% |
Student > Bachelor | 3 | 15% |
Other | 2 | 10% |
Student > Master | 2 | 10% |
Other | 1 | 5% |
Unknown | 6 | 30% |
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Engineering | 3 | 15% |
Immunology and Microbiology | 2 | 10% |
Agricultural and Biological Sciences | 1 | 5% |
Neuroscience | 1 | 5% |
Other | 1 | 5% |
Unknown | 7 | 35% |