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      Modulation of gut microbiota by berberine and metformin during the treatment of high-fat diet-induced obesity in rats

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          Abstract

          Accumulating evidence suggests that the gut microbiota is an important factor in mediating the development of obesity-related metabolic disorders, including type 2 diabetes. Metformin and berberine, two clinically effective drugs for treating diabetes, have recently been shown to exert their actions through modulating the gut microbiota. In this study, we demonstrated that metformin and berberine similarly shifted the overall structure of the gut microbiota in rats. Both drugs showed reverting effects on the high-fat diet-induced structural changes of gut microbiota. The diversity of gut microbiota was significantly reduced by both berberine- and metformin-treatments. Nearest shrunken centroids analysis identified 134 operational taxonomic units (OTUs) responding to the treatments, which showed close associations with the changes of obese phenotypes. Sixty out of the 134 OTUs were decreased by both drugs, while those belonging to putative short-chain fatty acids (SCFA)-producing bacteria, including Allobaculum, Bacteriodes, Blautia, Butyricoccus, and Phascolarctobacterium, were markedly increased by both berberine and, to a lesser extent, metformin. Taken together, our findings suggest that berberine and metformin showed similarity in modulating the gut microbiota, including the enrichment of SCFA-producing bacteria and reduction of microbial diversity, which may contribute to their beneficial effects to the host.

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          Most cited references16

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          Through Ageing, and Beyond: Gut Microbiota and Inflammatory Status in Seniors and Centenarians

          Background Age-related physiological changes in the gastrointestinal tract, as well as modifications in lifestyle, nutritional behaviour, and functionality of the host immune system, inevitably affect the gut microbiota, resulting in a greater susceptibility to infections. Methodology/Principal Findings By using the Human Intestinal Tract Chip (HITChip) and quantitative PCR of 16S rRNA genes of Bacteria and Archaea, we explored the age-related differences in the gut microbiota composition among young adults, elderly, and centenarians, i.e subjects who reached the extreme limits of the human lifespan, living for over 100 years. We observed that the microbial composition and diversity of the gut ecosystem of young adults and seventy-years old people is highly similar but differs significantly from that of the centenarians. After 100 years of symbiotic association with the human host, the microbiota is characterized by a rearrangement in the Firmicutes population and an enrichment in facultative anaerobes, notably pathobionts. The presence of such a compromised microbiota in the centenarians is associated with an increased inflammatory status, also known as inflammageing, as determined by a range of peripheral blood inflammatory markers. This may be explained by a remodelling of the centenarians' microbiota, with a marked decrease in Faecalibacterium prauznitzii and relatives, symbiotic species with reported anti-inflammatory properties. As signature bacteria of the long life we identified specifically Eubacterium limosum and relatives that were more than ten-fold increased in the centenarians. Conclusions/Significance We provide evidence for the fact that the ageing process deeply affects the structure of the human gut microbiota, as well as its homeostasis with the host's immune system. Because of its crucial role in the host physiology and health status, age-related differences in the gut microbiota composition may be related to the progression of diseases and frailty in the elderly population.
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            NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes

            Microbiologists conducting surveys of bacterial and archaeal diversity often require comparative alignments of thousands of 16S rRNA genes collected from a sample. The computational resources and bioinformatics expertise required to construct such an alignment has inhibited high-throughput analysis. It was hypothesized that an online tool could be developed to efficiently align thousands of 16S rRNA genes via the NAST (Nearest Alignment Space Termination) algorithm for creating multiple sequence alignments (MSA). The tool was implemented with a web-interface at . Each user-submitted sequence is compared with Greengenes' ‘Core Set’, comprising ∼10 000 aligned non-chimeric sequences representative of the currently recognized diversity among bacteria and archaea. User sequences are oriented and paired with their closest match in the Core Set to serve as a template for inserting gap characters. Non-16S data (sequence from vector or surrounding genomic regions) are conveniently removed in the returned alignment. From the resulting MSA, distance matrices can be calculated for diversity estimates and organisms can be classified by taxonomy. The ability to align and categorize large sequence sets using a simple interface has enabled researchers with various experience levels to obtain bacterial and archaeal community profiles.
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              Rapid denoising of pyrosequencing amplicon data: exploiting the rank-abundance distribution

              We developed a fast method for denoising pyrosequencing for community 16S rRNA analysis. We observe a 2–4 fold reduction in the number of observed OTUs (operational taxonomic units) comparing denoised with non-denoised data. ~50,000 sequences can be denoised on a laptop within an hour, two orders of magnitude faster than published techniques. We demonstrate the effects of denoising on alpha and beta diversity of large 16S rRNA datasets.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                23 September 2015
                2015
                : 5
                : 14405
                Affiliations
                [1 ]State Key Laboratory of Microbial Metabolism, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai, 200240, China
                [2 ]Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University , Shanghai, 200240, China
                Author notes
                Article
                srep14405
                10.1038/srep14405
                4585776
                26396057
                91a9b665-6a5f-4dff-8b50-0f3aa41b2d54
                Copyright © 2015, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 17 February 2015
                : 07 August 2015
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