Gut Microbiota Composition and Structure of the Ob/Ob and Db/Db Mice

Yang, Mingsheng; Liu, Yixin; Xie, Hengchang; Wen, Zhengzheng; Zhang, Yunxia; Wu, Changjing; Huang, Li; Wu, Jie; Xie, Chensheng; Wang, Tao; Peng, Weifeng; Liu, Shangqi; Chen, Long; Liu, Xiaomeng

doi:https://doi.org/10.1155/2019/1394097

International Journal of Endocrinology

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Abstract Introduction Materials and Methods Results Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2019 | Article ID 1394097 | https://doi.org/10.1155/2019/1394097

Gut Microbiota Composition and Structure of the Ob/Ob and Db/Db Mice

Mingsheng Yang,¹Yixin Liu,²Hengchang Xie,¹Zhengzheng Wen,¹Yunxia Zhang,¹Changjing Wu,¹Li Huang,¹Jie Wu,¹Chensheng Xie,¹Tao Wang,¹Weifeng Peng,¹and Shangqi Liu¹ et al.

Academic Editor: Dario Pitocco

Received12 Sept 2018

Revised28 Dec 2018

Accepted14 Jan 2019

Published11 Mar 2019

Abstract

Introduction. Gut microbiota is involved in the progression of metabolic diseases such as obesity and type 2 diabetes. The ob/ob and db/db mice are extensively used as models in studies on the pathogenesis of these diseases. The goal of this study is to characterize the composition and structure of gut microbiota in these model mice at different ages. Materials and methods. High-throughput sequencing was used to obtain the sequences of the highly variable 16S rRNA V3-V4 region from fecal samples. The taxa with high abundance in both model mice were identified by bioinformatics analysis. Moreover, the taxa with divergent abundance in one model mice at different ages or in both model mice at the same age were also recognized. Discussion and conclusion. The high abundance of Bacteroidetes and Firmicutes in microbiota composition and their imbalanced ratio in both model mice reflect the state of metabolic disorders of these mice. Differences in microbiota composition between the two model mice of the same age or in one model mice with different ages were assumed to be closely linked to the fluctuation of their blood glucose levels with age. The data on gut microbiota in ob/ob and db/db mice investigated herein has broad implications for the pathogenesis study and drug discovery on obesity and related complications.

1. Introduction

Gut microbiota represents a microbial community resident in the human intestine that exerts a crucial impact on human health. The prevalence of obesity and type 2 diabetes (T2D) worldwide is increasing, which has brought huge medical costs to society in past decades [1–3]. Thus, studies on the link between gut microbiota and obesity and T2D have been intensely conducted in recent years. Epidemiological studies demonstrated that the microbial composition of individuals with T2D is quite different from their normal controls, and it is characterized by a lower abundance of universal butyrate-producing bacteria and an increase in opportunistic pathogens [4, 5]. In model animals, Backhed et al. [6] reported that germ-free mice which received microbiota from normal mice produced a 60% increase in body fat content 14 days later, even though they consumed less food than their germ-free controls. In diet-induced obesity, low-grade inflammation was regarded as a factor contributing to obesity-related metabolic disorders such as T2D [7]. Further studies clarified that a high-fat diet can change microbiota composition and increase intestinal permeability. Consequently, more lipopolysaccharides (LPS) are released from the gut, which initiates toll-like receptor-4 (TLR4) signaling and inflammatory response, contributing to the development of obesity and T2D [8–10]. Collectively, the aforementioned evidence indicates that gut microbiota is closely associated with the progression of obesity and T2D.

Leptin is mainly produced by adipocytes, and it plays a crucial role in the regulation of energy homeostasis, skeletal growth, and the immune responses [11–13]. Impaired leptin signaling is closely associated with metabolic diseases, such as obesity and type 2 diabetes. Ob/ob mice are genetically obese animals because their leptin-encoding gene has a systemic nonsense and is biologically inactive [14]. In contrast, in db/db mice, the receptor-encoding sequence of leptin is mutated, rendering these mice with high circulatory levels of leptin but lacking intracellular leptin action [15]. Hence, db/db mice are characterized as genetically diabetic. Interestingly, previous studies have recognized that the blood glucose levels of ob/ob and db/db mice fluctuate with age. In ob/ob mice, hyperglycemia can be observed from 4 weeks of age, but this state starts to decline after 12 weeks and blood glucose levels are comparable to control mice until 28 weeks [16, 17]. In contrast, db/db mice develop hyperglycemia at 8 weeks of age, and this state is not transient later [17, 18].

Given the prevalence of obesity and T2D worldwide, great efforts are being made in clarifying its pathogenesis and in screening the drugs of these diseases. In these efforts, ob/ob and db/db mice are routinely used as model organisms (e.g., Wang et al. [19], Liu et al. [20], and Ahmad et al. [21]). The close link between gut microbiota and these metabolic diseases indicates that understanding the gut microbiota composition and structure of ob/ob and db/db mice is very indispensable. Geurts et al. [22] reported the difference of the gut microbiota between six-week-old db/db and lean mice, emphasizing the specific relationships between the gut microbiota and the regulation of the apelinergic system. Most other studies on this issue only sequenced and analyzed the gut microbiota of the model mice at a specific age [23–25]. However, the dynamic changes of gut microbiota with age of model mice have seldom been investigated. In this study, we set out to investigate the composition and structure of gut microbiota in ob/ob and db/db mice as well as their dynamic change with age using a high-throughput sequencing method. Our results would provide broad implications for further investigations on the pathophysiology and pharmacology of obesity and T2D.

2. Materials and Methods

2.1. Animals

Six-week-old male ob/ob () and db/db () mice in a C57BL/6J background were purchased from The Jackson Laboratory (Bar Harbor, ME). Animals were maintained in an atmosphere controlled at and relative humidity with a 12 h light and 12 h dark cycle. Animals were housed under barrier-maintained conditions with free access to water and a normal chow diet. Body weight was measured biweekly. All animal experiments were approved by the Animal Ethics Committee (approval ID: ZKNU2017023) of Zhoukou Normal University.

2.2. Fecal Collection and DNA Extraction

Total metagenomic DNA was extracted from fecal samples using the QIAmp Fast DNA Stool Mini Kit according to the manufacturer’s protocol (cat. no. 51604; Qiagen, Hilden, Germany). Fecal samples were collected from 3 mice per group, and each group included the ob/ob or db/db mice at 8, 12, or 18 weeks of age. DNA quantification was performed on a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific Inc.).

2.3. High-Throughput Sequencing of 16S rRNA Genes and Analysis

High-throughput sequencing was performed with the Illumina HiSeq PE250 platform at the Shanghai Ruiyi Biotechnology Co. Ltd. (Shanghai, China). Amplification of the highly variable V3-V4 region of the 16S rRNA gene was conducted using universal primers 341F (5-CCTACGGGRSGCAGCAG-3) and 806R (5-GGACTACVSGGGTATCTAAT-3). Raw paired-end reads were assembled by PANDAseq, and the following conditions were used to define the quality filter: reads with an average quality score of more than 20, reads with less than three ambiguous N bases, and reads with a long range from 220 to 500 nucleotides [26]. After the singletons were discarded, the sequences were clustered into operational taxonomic units (OTUs) using the software USEARCH at ≥97% similarity [27]. Linear discriminant analysis (LDA) effect size (LEfSe) was conducted to assess the effect size of each differentially abundant OUT [28]. Subsequent bioinformatics steps were analyzed using a QIIME software package [29].

2.4. Statistical Analysis

Statistical analysis on the body weight of animals was performed using GraphPad Prism version 6 (San Diego, CA). All data are expressed as . In LEfSe analysis, the parameters of wilcox.test and kruskal.test were used to define the differences in the relative abundance of bacteria between groups.

3. Results

3.1. Body Weight of Ob/Ob and Db/Db Mice

To examine the phenotype change with age of the ob/ob and db/db mice, we measured the body weight of the model mice aged from 6 to 18 weeks. The average body weight of the 4 ob/ob mice measured consistently increased with age except for that of mice at 16 weeks of age (Figure 1(a)). There was almost an 8 g difference in body weight between the 6-week and 18-week age groups, with the former weighing and the latter . A tendency for the increased average body weight of the 4 db/db mice can be observed during the treated time, although there is a slight decrease at 14 and 16 weeks relative to the mice aged 12 weeks (Figure 1(b)). At the end of the experiment, the average body weight was , an increase of about 8 g relative to the initial .

(a)

(b)

3.2. Fecal Microbiota Composition

Based on the Illumina MiSeq sequencing platform, the highly variable V3-V4 regions of the 16S rRNA gene from 18 samples were sequenced. A clustering analysis based on a 97% similarity identified 14,292 operational taxonomic units (OTUs). As shown in Figure 2 and Tables 1 and 2, of the 11 microbial phyla defined by these OTUs, Bacteroidetes and Firmicutes dominate the microbial composition in ob/ob mice, and show 39.28% and 52.86% relative abundance on average, respectively. In db/db mice, they also exhibit relatively high abundance, with an average of 46.06% and 46.58%, respectively. This was followed by Proteobacteria, Tenericutes, Actinobacteria, and Cyanobacteria, with a relative abundance of 3.08%, 2.9%, 0.3%, and 0.38% in ob/ob mice, respectively, and 2.96%, 1.75%, 0.57%, and 0.44% in db/db mice, respectively. The other five phyla, including Acidobacteria, Deferribacteres, Gemmatimonadetes, Lentisphaerae, and Verrucomicrobia, showed extremely low abundance in both ob/ob and db/db mice. Overall, no significant difference can be recognized in the α-diversity of fecal microbiota from one model animal with different ages or the two model animals of the same age (Figure 3).

(a)

(b)

(c)

(d)

3.3. The Change of Microbial Compositions in Ob/Ob and Db/Db Mice with Age

Gut microbial compositions in both ob/ob and db/db mice changed with increasing age. With the LDA as the threshold for significance, LefSe analysis revealed that the Bacillales of fecal microbiota from 12-week-old ob/ob mice was significantly high relative to that of another age group. However, the abundance of Moraxellaceae in mice aged 18 weeks was significantly higher than that of younger ob/ob mice investigated in this study (Figure 4). In db/db mice, the Coriobacteriaceae, Ochrobactrum, and Bradyrhizobiaceae of fecal microbiota from 8 weeks of age were significantly high relative to that of other two age groups. In 12-week-old db/db mice, a significant enrichment to the taxa was Desulfovibrio. However, the abundance of Brachybacterium, Pseudonocardia, and Bacteroidales in mice aged 18 weeks was significantly higher than that of younger db/db mice investigated in this study (Figure 5).

3.4. Comparison of Microbial Compositions between the Ob/Ob and Db/Db Mice of the Same Age

With the LDA as the threshold for significance, LefSe analysis revealed that there were significant differences in microbial composition between ob/ob and db/db mice at the same age. At 8 weeks of age, a significant enrichment to the taxa in ob/ob mice were Bacteroidaceae, Desulfovibrionaceae, Pasteurellaceae, and Anaeroplasmataceae at the family level and Bacteroides, Flavisolibacter, Anaerotruncus, Desulfovibrio, Aggregatibacter, and Anaeroplasma at the genus level. In db/db mice with the same age, a significant enrichment to the taxa were Propionibacteriaceae, Prevotellaceae, Dehalobacteriaceae, Ruminococcaceae, Sphingomonadaceae, Eterobacteriaceae, and Verrucomicrobiaceae at the family level and Propionibacterium, Parabacteroides, Prevotella, Dehalobacterium, Oscillospira, Kalstobacter, Citrobacter, and Akkermansia at the genus level (Figure 6). As shown in Figure 7, when the mice were 18 weeks of age, larger proportions of Brevundimonas, Devosia, Rubellimicrobium, and Kaistobacter at the genus level were found in ob/ob mice, whereas Bacteroides, Mucispirillum, and Erwinia at the genus level were the significant enrichment to the taxa in db/db mice.

4. Discussion

The ob/ob and db/db mice are extensively employed as animal models in studies on the pharmacology and pathogenesis of metabolic diseases such as obesity and type 2 diabetes [17]. Given the involvement of gut microbiota in the pathogenesis of these diseases, we provide the structure and composition of gut microbiota in ob/ob and db/db mice at different ages in the present study. To our knowledge, this is the first study focusing on the gut microbiota of the ob/ob and db/db mice at different ages, and these data would provide broad implications for the management of obesity and related complications.

Our study shows that the abundance of Bacteroidetes and Firmicutes is the highest in microbiota composition from both ob/ob and db/db mice, reaching 42.67% and 49.72% on average, respectively. These values are comparable to that of either the ob/ob and db/db mice or the normal C57Bl/6J mice provided in other studies [30, 31]. Moreover, a series of studies have observed that the ratio of Firmicutes/Bacteroidetes is positively correlated with obesity and glucose tolerance severity [30, 32, 33]. In this study, the ratios of Bacteroidetes/Firmicutes are 1.35 in ob/ob mice and 1.01 in db/db mice, which are evidently higher than that of normal C57Bl/6J mice investigated in other studies (i.e., Roopchand et al., [34]), indicating that the model mice we used are indeed under the state of metabolic disorders. As expected, the microbial compositions between the ob/ob and db/db mice with the same age are quite different, which may be associated with their different genetic background. Indeed, ob/ob and db/db mice are also used as different animal models in studies (e.g., Park et al. [35] and Jia et al. [36]).

Gut microbiota play a crucial role in glucose homeostasis and some beneficial bacteria have been identified such as the genera Akkermansia [34, 37–39] and Oscillospira [38]. These recognized taxa have promoted the development of gut microbiota-based agents against insulin resistance and other metabolic disorders. In the present study, the fluctuation of the blood glucose levels of ob/ob and db/db mice with age reported before prompted us to suppose that the gut microbiota composition in them may be altered with age as well. Interestingly, our study revealed that microbial composition showed a similar fluctuation pattern with that of the blood glucose values in ob/ob and db/db mice with age. This reflects that the gut microbiota, especially the taxa with a relatively higher abundance at a specific age, may be involved in glucose homeostasis. However, additional work is needed to establish whether and how these taxa functionally modulate the glucose homeostasis, providing microbiota recourse for developing novel therapies against obesity and associated metabolic disorders.

5. Conclusions

The ob/ob and db/db mice are extensively employed as models in studies on the pathogenesis and drug discovery of metabolic diseases such as obesity and type 2 diabetes. In the present study, we sequenced and analyzed the gut microbiota of the model mice across different ages. Our results showed that the microbial composition exhibits a similar fluctuation pattern with that of the blood glucose values in ob/ob and db/db mice with age. The microbial taxa with a relatively higher abundance at the specific age identified herein would provide microbiota recourse for the development of novel therapies against obesity and associated metabolic disorders.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflict of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 81500659), the Scientific and Technological Innovation Talent Project of Henan Province (19HASTIT015), the International Science and Technology Cooperation Project of Henan Province (No. 172102410020), and the Science and Technology Program of Henan Province (No. 172102310450).

References

E. A. Finkelstein, J. G. Trogdon, J. W. Cohen, and W. Dietz, “Annual medical spending attributable to obesity: payer- and service-specific estimates,” Health Affairs, vol. 28, no. 5, pp. w822–w831, 2009.
View at: Publisher Site | Google Scholar
Y. C. Wang, K. McPherson, T. Marsh, S. L. Gortmaker, and M. Brown, “Health and economic burden of the projected obesity trends in the USA and the UK,” The Lancet, vol. 378, no. 9793, pp. 815–825, 2011.
View at: Publisher Site | Google Scholar
M. Ng, T. Fleming, M. Robinson et al., “Global, regional, and national prevalence of overweigh and obesity on children and adults during 1920–2013: a systematic analysis for the global burden of disease study,” The Lancet, vol. 384, pp. 766–781, 2013.
View at: Google Scholar
N. Larsen, F. K. Vogensen, F. W. J. van den Berg et al., “Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults,” PLoS One, vol. 5, no. 2, article e9085, 2010.
View at: Publisher Site | Google Scholar
J. Qin, Y. Li, Z. Cai et al., “A metagenome-wide association study of gut microbiota in type 2 diabetes,” Nature, vol. 490, no. 7418, pp. 55–60, 2012.
View at: Publisher Site | Google Scholar
F. Backhed, H. Ding, T. Wang et al., “The gut microbiota as an environmental factor that regulates fat storage,” Proceedings of the National Academy of Sciences, vol. 101, no. 44, pp. 15718–15723, 2004.
View at: Publisher Site | Google Scholar
L. K. Heilbronn and L. V. Campbell, “Adipose tissue macrophages, low grade inflammation and insulin resistance in human obesity,” Current Pharmaceutical Design, vol. 14, no. 12, pp. 1225–1230, 2008.
View at: Publisher Site | Google Scholar
P. D. Cani, R. Bibiloni, C. Knauf et al., “Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice,” Diabetes, vol. 57, no. 6, pp. 1470–1481, 2008.
View at: Publisher Site | Google Scholar
P. D. Cani, J. Amar, M. A. Iglesias et al., “Metabolic endotoxemia initiates obesity and insulin resistance,” Diabetes, vol. 56, no. 7, pp. 1761–1772, 2007.
View at: Publisher Site | Google Scholar
M. F. Gregor and G. S. Hotamisligil, “Inflammatory mechanisms in obesity,” Annual Review of Immunology, vol. 29, no. 1, pp. 415–445, 2011.
View at: Publisher Site | Google Scholar
J. M. Friedman and J. L. Halaas, “Leptin and the regulation of body weight in mammals,” Nature, vol. 395, no. 6704, pp. 763–770, 1998.
View at: Publisher Site | Google Scholar
D. M. Muoio and G. Lynis Dohm, “Peripheral metabolic actions of leptin,” Best Practice & Research Clinical Endocrinology & Metabolism, vol. 16, no. 4, pp. 653–666, 2002.
View at: Publisher Site | Google Scholar
Q. L. Lam and L. Lu, “Role of leptin in immunity,” Cellular & Molecular Immunology, vol. 4, no. 1, pp. 1–13, 2007.
View at: Google Scholar
V. R. Drel, N. Mashtalir, O. Ilnytska et al., “The leptin-deficient (ob/ob) mouse: a new animal model of peripheral neuropathy of type 2 diabetes and obesity,” Diabetes, vol. 55, no. 12, pp. 3335–3343, 2006.
View at: Publisher Site | Google Scholar
K. Kobayashi, T. M. Forte, S. Taniguchi, B. Y. Ishida, K. Oka, and L. Chan, “The db/db mouse, a model for diabetic dyslipidemia: molecular characterization and effects of Western diet feeding,” Metabolism, vol. 49, no. 1, pp. 22–31, 2000.
View at: Publisher Site | Google Scholar
K. Srinivasan and P. Ramarao, “Animal models in type 2 diabetes research: an overview,” The Indian Journal of Medical Research, vol. 125, no. 3, pp. 451–472, 2007.
View at: Google Scholar
B. Wang, P. C. Chandrasekera, and J. J. Pippin, “Leptin- and leptin receptor-deficient rodent models: relevance for human type 2 diabetes,” Current Diabetes Reviews, vol. 10, no. 2, pp. 131–145, 2014.
View at: Publisher Site | Google Scholar
T. J. Allen, M. E. Cooper, and H. Y. Lan, “Use of genetic mouse models in the study of diabetic nephropathy,” Current Atherosclerosis Reports, vol. 6, no. 3, pp. 197–202, 2004.
View at: Publisher Site | Google Scholar
G. Wang, X. Xu, X. Yao et al., “Latanoprost effectively ameliorates glucose and lipid disorders in db/db and ob/ob mice,” Diabetologia, vol. 56, no. 12, pp. 2702–2712, 2013.
View at: Publisher Site | Google Scholar
X. Liu, S. Wang, Y. You et al., “Brown adipose tissue transplantation reverses obesity in ob/ob mice,” Endocrinology, vol. 156, no. 7, pp. 2461–2469, 2015.
View at: Publisher Site | Google Scholar
A. Ahmad, T. Ali, M. W. Kim et al., “Adiponectin homolog novel osmotin protects obesity/diabetes-induced NAFLD by upregulating AdipoRs/PPARα signaling in ob/ob and db/db transgenic mouse models,” Metabolism, vol. 90, pp. 31–43, 2019.
View at: Publisher Site | Google Scholar
L. Geurts, V. Lazarevic, M. Derrien et al., “Altered gut microbiota and endocannabinoid system tone in obese and diabetic leptin-resistant mice: impact on apelin regulation in adipose tissue,” Frontiers in Microbiology, vol. 2, 2011.
View at: Publisher Site | Google Scholar
J. X. Song, H. Ren, Y. F. Gao et al., “Dietary capsaicin improves glucose homeostasis and alters the gut microbiota in obese diabetic ob/ob mice,” Frontiers in Physiology, vol. 8, p. 602, 2017.
View at: Publisher Site | Google Scholar
R. E. Ley, F. Backhed, P. Turnbaugh, C. A. Lozupone, R. D. Knight, and J. I. Gordon, “Obesity alters gut microbial ecology,” Proceedings of the National Academy of Sciences, vol. 102, no. 31, pp. 11070–11075, 2005.
View at: Publisher Site | Google Scholar
X. Mei, X. Zhang, Z. Wang et al., “Insulin sensitivity-enhancing activity of phlorizin is associated with lipopolysaccharide decrease and gut microbiota changes in obese and type 2 diabetes (db/db) mice,” Journal of Agricultural and Food Chemistry, vol. 64, no. 40, pp. 7502–7511, 2016.
View at: Publisher Site | Google Scholar
A. P. Masella, A. K. Bartram, J. M. Truszkowski, D. G. Brown, and J. D. Neufeld, “PANDAseq: paired-end assembler for illumina sequences,” BMC Bioinformatics, vol. 13, no. 1, p. 31, 2012.
View at: Publisher Site | Google Scholar
R. C. Edgar, “UPARSE: highly accurate OTU sequences from microbial amplicon reads,” Nature Methods, vol. 10, no. 10, pp. 996–998, 2013.
View at: Publisher Site | Google Scholar
N. Segata, J. Izard, L. Waldron et al., “Metagenomic biomarker discovery and explanation,” Genome Biology, vol. 12, no. 6, p. R60, 2011.
View at: Publisher Site | Google Scholar
J. G. Caporaso, J. Kuczynski, J. Stombaugh et al., “QIIME allows analysis of high-throughput community sequencing data,” Nature Methods, vol. 7, no. 5, pp. 335-336, 2010.
View at: Publisher Site | Google Scholar
A. Everard, V. Lazarevic, M. Derrien et al., “Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet-induced leptin-resistant mice,” Diabetes, vol. 60, no. 11, pp. 2775–2786, 2011.
View at: Publisher Site | Google Scholar
J. F. Garcia-Mazcorro, I. Ivanov, D. A. Mills, and G. Noratto, “Influence of whole-wheat consumption on fecal microbial community structure of obese diabetic mice,” PeerJ, vol. 4, article e1702, 2016.
View at: Publisher Site | Google Scholar
R. E. Ley, P. J. Turnbaugh, S. Klein, and J. I. Gordon, “Microbial ecology: human gut microbes associated with obesity,” Nature, vol. 444, no. 7122, pp. 1022-1023, 2006.
View at: Publisher Site | Google Scholar
W. Liu, S. Zhao, J. Wang et al., “Grape seed proanthocyanidin extract ameliorates inflammation and adiposity by modulating gut microbiota in high-fat diet mice,” Molecular Nutrition & Food Research, vol. 61, no. 9, 2017.
View at: Publisher Site | Google Scholar
D. E. Roopchand, R. N. Carmody, P. Kuhn et al., “Dietary polyphenols promote growth of the gut bacterium Akkermansia muciniphila and attenuate high-fat diet-induced metabolic syndrome,” Diabetes, vol. 64, no. 8, pp. 2847–2858, 2015.
View at: Publisher Site | Google Scholar
M.-Y. Park, S. J. Kim, E. K. Ko, S.-H. Ahn, H. Seo, and M. K. Sung, “Gut microbiota-associated bile acid deconjugation accelerates hepatic steatosis in ob/ob mice,” Journal of Applied Microbiology, vol. 121, no. 3, pp. 800–810, 2016.
View at: Publisher Site | Google Scholar
L. Jia, D. Li, N. Feng et al., “Anti-diabetic effects of Clostridium butyricum CGMCC0313.1 through promoting the growth of gut butyrate-producing bacteria in type 2 diabetic mice,” Scientific Reports, vol. 7, no. 1, 2017.
View at: Publisher Site | Google Scholar
C. H. F. Hansen, L. Krych, D. S. Nielsen et al., “Early life treatment with vancomycin propagates Akkermansia muciniphila and reduces diabetes incidence in the NOD mouse,” Diabetologia, vol. 55, no. 8, pp. 2285–2294, 2012.
View at: Publisher Site | Google Scholar
N.-R. Shin, J.-C. Lee, H.-Y. Lee et al., “An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice,” Gut, vol. 63, no. 5, pp. 727–735, 2014.
View at: Publisher Site | Google Scholar
F. F. Duan, J. H. Liu, and J. C. March, “Engineered commensal bacteria reprogram intestinal cells into glucose-responsive insulin secreting cells for the treatment of diabetes,” Diabetes, vol. 64, no. 5, pp. 1794–1803, 2015.
View at: Publisher Site | Google Scholar

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Copyright © 2019 Mingsheng Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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