Abstract Obesity and type 2 diabetes are characterized by altered gut microbiota, inflammation, and gut barrier disruption. Microbial composition and the mechanisms of interaction with the host that affect gut barrier function during obesity and type 2 diabetes have not been elucidated. We recently isolated Akkermansia muciniphila, which is a mucin-degrading bacterium that resides in the mucus layer. The presence of this bacterium inversely correlates with body weight in rodents and humans. However, the precise physiological roles played by this bacterium during obesity and metabolic disorders are unknown. This study demonstrated that the abundance of A. muciniphila decreased in obese and type 2 diabetic mice. We also observed that prebiotic feeding normalized A. muciniphila abundance, which correlated with an improved metabolic profile. In addition, we demonstrated that A. muciniphila treatment reversed high-fat diet-induced metabolic disorders, including fat-mass gain, metabolic endotoxemia, adipose tissue inflammation, and insulin resistance. A. muciniphila administration increased the intestinal levels of endocannabinoids that control inflammation, the gut barrier, and gut peptide secretion. Finally, we demonstrated that all these effects required viable A. muciniphila because treatment with heat-killed cells did not improve the metabolic profile or the mucus layer thickness. In summary, this study provides substantial insight into the intricate mechanisms of bacterial (i.e., A. muciniphila) regulation of the cross-talk between the host and gut microbiota. These results also provide a rationale for the development of a treatment that uses this human mucus colonizer for the prevention or treatment of obesity and its associated metabolic disorders.

Gut microbiota were once characterized as bystanders in the intestinal tract, but their active role in intestinal physiology is now widely investigated. In particular, the mutualism that exists between gut microbiota and the host has received much attention. Obesity and type 2 diabetes are characterized by altered gut microbiota (1), inflammation (2), and gut barrier disruption (3⇓–5). We recently demonstrated an association of obesity and type 2 diabetes with increased gut permeability, which induced metabolic endotoxemia and metabolic inflammation (3⇓–5). Unequivocal evidence demonstrates that gut microbiota influence whole-body metabolism (1, 6) by affecting the energy balance (6), gut permeability (4, 5), serum lipopolysaccharides [i.e., metabolic endotoxemia (7)], and metabolic inflammation (3⇓–5, 7) that are associated with obesity and associated disorders. However, the microbial composition and the exact mechanisms of interaction between these two partners that affect host–gut barrier function and metabolism remain unclear.

The intestinal epithelium is the interface for the interaction between gut microbiota and host tissues (8). This barrier is enhanced by the presence of a mucus layer and immune factors that are produced by the host (9). Antimicrobial peptides for innate immunity are produced by Paneth cells (e.g., α-defensins, lysozyme C, phospholipases, and C-type lectin, primarily regenerating islet-derived 3-gamma, RegIIIγ) or enterocytes (RegIIIγ) (10⇓–12). Adaptive immune system effectors that are secreted into the intestinal lumen, such as IgA, may also restrict bacterial penetration into the host mucus and mucosal tissue (13). These immune factors allow the host to control its interactions with gut microbiota and shape its microbial communities (11).

The endocannabinoid system has also been implicated in the control of the gut barrier and inflammation (5, 14). One lipid in this system, 2-arachidonoylglycerol (2-AG), reduces metabolic endotoxemia and systemic inflammation (15). Another acylglycerol, 2-palmitoylglycerol (2-PG), potentiates the antiinflammatory effects of 2-AG (16). Importantly, 2-oleoylglycerol (2-OG) stimulates the release of gut peptides, such as glucagon-like peptide-1 (GLP-1) and glucagon-like peptide-2 (GLP-2), from intestinal L-cells (17). These peptides are implicated in the control of glucose homeostasis and gut barrier function, respectively (4).

Recently, Akkermansia muciniphila has been identified as a mucin-degrading bacteria that resides in the mucus layer (18), and it is the dominant human bacterium that abundantly colonizes this nutrient-rich environment (18). A. muciniphila may represent 3–5% of the microbial community (18, 19) in healthy subjects, and its abundance inversely correlates with body weight (20⇓⇓–23) and type 1 diabetes (24) in mice and humans, although a recent metagenomic study found that some of the genes belonging to A. muciniphila were enriched in type 2 diabetic subjects (25).

We recently discovered that the administration of prebiotics (oligofructose) to genetically obese mice increased the abundance of A. muciniphila by ∼100-fold (23). However, the direct implications of A. muciniphila for obesity and type 2 diabetes have not been determined, and the precise physiological roles it plays during these processes are not known.

Our previous results and the close proximity of this bacterium to the human intestinal epithelium support the hypothesis that A. muciniphila plays a crucial role in the mutualism between the gut microbiota and host that controls gut barrier function and other physiological and homeostatic functions during obesity and type 2 diabetes. We administered alive or heat-killed A. muciniphila to mice that were fed a high-fat diet and investigated the gut barrier, glucose homeostasis, and adipose tissue metabolism to test this hypothesis.

Discussion This study demonstrated a dramatic decrease in A. muciniphila in genetically and diet-induced obese mice. We demonstrated that prebiotic (oligofructose) treatment restored A. muciniphila abundance and improved gut barrier and metabolic parameters. However, the mechanisms that were responsible for the bloom in A. muciniphila caused by prebiotic administration are not clear. A. muciniphila does not grow on oligofructose-enriched media (in vitro), which suggests that complex cross-feeding interactions contributed to this effect. However, it has been previously shown in rats that oligofructose feeding increases the number of goblet cells and mucus layer thickness (29). Thus, whether oligofructose feeding increases A. muciniphila by providing the main source of energy for this bacterium and thereby favoring its growth or whether the increase of A. muciniphila increases mucus production and degradation (i.e., turnover) remain to be demonstrated. Oligofructose changes more than 100 different taxa in mice (23). Therefore, we cannot exclude that oligofructose induces specific changes in the gut bacteria and cross-feeding promoting the growth of A. muciniphila. In the present study, we investigated the direct impact of A. muciniphila. We reversed the pathological phenotype by restoring the physiological abundance of this strain in obese and diabetic mice. These results demonstrated the key role of A. muciniphila in the physiopathology of obesity, type 2 diabetes, and metabolic inflammation. These experiments clearly demonstrate that viable A. muciniphila controls gut barrier function, fat mass storage, and glucose homeostasis in obese and type 2 diabetic mice via several mechanisms. These results provide proof of this concept in this context. The major weaknesses in investigations of the role of gut microbiota in the etiology of obesity and type 2 diabetes is the reliance on conclusions that are based on correlative data between bacteria (or one genus) and physiological parameters, because most of the gut bacteria have been identified at the phylogenetic level (i.e., through metagenomic approaches) but have never been cultured. Several reports have demonstrated the importance of selected bacteria [i.e., Lactobacillus spp (30, 31), Bifidobacterium spp (32, 33), and Bacteroides uniformis CECTT 7771 (34)] on fat mass development during diet-induced obesity, but the aims of these studies were different from that of the present study. These studies investigated the impact of supplementation with one specific probiotic strain or strains that were isolated from healthy infants on physiological parameters. Here we investigated the strain that is affected during obesity and type 2 diabetes in humans and rodents (18, 23). Probiotics have far fewer opportunities for direct contact with the mucosa, but A. muciniphila may induces differential host responses because of more intensive contact with the host mucosa (26). To further confirm this hypothesis, we have treated HF-fed mice with a probiotic (i.e., Lactobacillus plantarum WCFS1). We found that L. plantarum administration did not change fat mass development, adipose tissue metabolism, mucus layer thickness, colon Reg3g mRNA, and metabolic endotoxemia (Fig. S6 A–E). Therefore, these data suggest that A. muciniphila induces specific host responses compared with other putative beneficial microbes. A. muciniphila is a Gram-negative bacteria (i.e., it contains LPS) that constitutes 3–5% of the gut microbial community. However, our study clearly demonstrated the lack of a direct relationship between the abundance of Gram-negative bacteria within the gut and metabolic endotoxemia (i.e., that is caused by serum LPS) because gut colonization by A. muciniphila decreased metabolic endotoxemia arising on an HF diet. One explanation for this counterintuitive result may be that A. muciniphila regulates gut barrier function at different levels. Previous data suggest that gut microbiota contribute to gut barrier alterations during obesity and metabolic endotoxemia (4). However, the different mechanisms of interaction between bacteria and the host that affect gut barrier function during obesity and type 2 diabetes have not been elucidated. This study identified an association of obesity with a decrease in mucus thickness, which supports an additional mechanism of increased gut permeability (i.e., metabolic endotoxemia) that is characteristic of obesity and associated disorders. Furthermore, we demonstrated that A. muciniphila restored this mucus layer, which suggests that this mechanism contributes to the reduction in metabolic endotoxemia that was observed during A. muciniphila treatment. Moreover, we found that viable A. muciniphila induces these effects, whereas heat-killed A. muciniphila did not protect the mice from diet-induced obesity and associated disorders. These results suggest that the presence of viable A. muciniphila within the mucus layer is a crucial mechanism in the control of host mucus turnover (19), which improves gut barrier function. However, we cannot exclude additional mechanisms that have been implicated in the regulation of gut barrier. For example, we previously demonstrated that gut microbiota control gut peptides (e.g., GLP-2) that regulate epithelial cell proliferation and gut barrier function (4). Prebiotics stimulate GLP-1 and GLP-2 secretion by acting on the enteroendocrine L-cells that are primarily in the ileum and colon (6). The abundance of A. muciniphila is associated with higher L-cell activity (i.e., GLP-1 and GLP-2 secretion) (4, 23), but the mechanisms underlying this relationship are not known. Here, we demonstrated that A. muciniphila administration significantly increased intestinal levels of 2-OG, which stimulates glucagon-like peptide secretion from intestinal L-cells (17). Altogether our data suggest that this could be a key mechanism by which A. muciniphila controls gut barrier function, metabolic endotoxemia, and metabolism. We also demonstrated that A. muciniphila administration increased 2-AG intestinal levels. We recently demonstrated that an increase in 2-AG endogenous levels induced by selective monoacylglycerol lipase inhibitor protects against trinitrobenzene sulfonic acid-induced colitis in mice (15) and reduces metabolic endotoxemia as well as the level of circulating inflammatory cytokines and peripheral and brain inflammation. Therefore, the increased 2-AG levels that were observed after A. muciniphila treatment may have also contributed to the reduced inflammation. However, whether the induction of these endocannabinoids after A. muciniphila treatment constitutes the molecular event that links these metabolic features warrants further investigation. Specifically, we demonstrated that the restoration of the physiological abundance of A. muciniphila reduced diet-induced body weight gain, fat mass development, and fasting hyperglycemia without affecting food intake. This variation in energy storage is explained by the normalization of adipose tissue adipogenesis (i.e., differentiation and lipogenesis) and fatty acid oxidation. We have previously demonstrated that higher circulating LPS levels inhibit adipose tissue differentiation and lipogenesis, thereby contributing to altered adipose tissue metabolism characterizing obesity (5). Thus, we postulate that A. muciniphila restores gut barrier function and thereby contributes to normalize metabolic endotoxemia and adipose tissue metabolism. We found that A. muciniphila improved glucose tolerance and decreased endogenous hepatic glucose production. These findings are not in agreement with the apparent but low association of A.muciniphila genes with type 2 diabetes-associated metagenome-wide associated studies (25). Nevertheless, the data by Qin et al. remain to be confirmed because this related to only 337 of the 2,176 A.muciniphila genes (35) and may be confounded by dietary or pharmaceutical treatments specifically favoring its growth in the human intestine. Dynamic insulin resistance assessments and the present results suggest improved insulin sensitivity. However, we cannot exclude the possibility that the improvements in glucose and lipid metabolism occurred via an LPS-dependent mechanism, as demonstrated previously (5, 7). We confirmed (7, 36) that an HF diet profoundly affected the gut microbiota composition, whereas A. muciniphila administration did not significantly affect this profile. Therefore, it is tempting to extrapolate our findings as a single-species-dependent modulation of the gut microbiota. Moreover, because heat-killing of A. muciniphila completely abolished the metabolic effects it is unlikely that specific A. muciniphila-derived cell-envelope components may directly contribute to the phenotype observed with viable A. muciniphila. It is worth noting that this observation also minimizes the possibility that the host response was caused by a substance in the culture media. However, although not directly fitting with the aim of the present study, follow-up studies of the gut microbiome after viable A. muciniphila administration may identify the components that contribute to disease or the host physiological response (37). Finally, we demonstrated that A. muciniphila regulates intestinal antimicrobial peptides in the colon (e.g., RegIIIγ). A. muciniphila exerted minor effects on antimicrobial peptide production in the ileum. RegIIIγ exerts direct bactericidal activity against Gram-positive bacteria in the intestine. Therefore, A. muciniphila may manipulate host immunity to favor its own survival through an increase in RegIIIγ expression, which reduces the competition for resources and induces long-term tolerance for the development in the mucus layer. Here, we clearly found that viable A. muciniphila significantly increased RegIIIγ, whereas heat-killed A. muciniphila did not affect this parameter. Whether the effect on RegIIIγ should be considered as beneficial or harmful for the host remain to be determined. These results link the colonization of the colon, but not the ileum, by A. muciniphila with the fundamental immune mechanisms through which RegIIIγ promotes host–bacterial mutualism and regulates the spatial relationships between the microbiota and host (38). Finally, A. muciniphila is known to degrade human mucus (18). However, whether the beneficial effects observed here may be extended to other pathological situations in which the mucus layer is altered (e.g., intestinal inflammatory diseases) (39) remain to be elucidated. We recently demonstrated that germ-free mice that were monoassociated with A. muciniphila exhibit important modulations of gene expression; the most marked changes were observed in the colon (442 genes), followed by the ileum (253 genes) and the cecum (211 genes) (26). In the colon, 60 genes, including 16 genes encoding CD antigen markers and 10 genes encoding immune cell membrane receptors, were up-regulated after A. muciniphila colonization (26). Several pathways that regulate lipid metabolism, cell signaling, and molecular transport are mostly affected in the ileum (26). These data have uncovered mechanisms of bacterial interaction with the host to control gut permeability and metabolism. Further studies should explore the cellular processes and identify the bacterial products that regulate the host cell responses and metabolic effects of A. muciniphila. In summary, this study provided unique and substantial insights into the intricate regulation of the cross-talk between the host and A. muciniphila bacteria. These results provide a rationale for the development of a treatment that uses this human mucus colonizer for the prevention or treatment of obesity and its associated metabolic disorders.

Materials and Methods Mice. Male C57BL/6 mice were used in the four series of experiments. Cecal contents from genetic (ob/ob) and HF-fed obese and type 2 diabetic mice treated or not with prebiotics (oligofructose, 0.3 g per mouse per day) were harvested, immersed in liquid nitrogen, and stored at −80 °C for further A. muciniphila analysis. A subset of 10-wk-old C57BL/6J was fed a control diet (CT) or an HF diet (60% fat). The mice were treated with A. muciniphila by oral gavage at a dose 2.108 cfu/0.2 mL suspended in sterile anaerobic PBS (CT-Akk and HF-Akk), or heat-killed A. muciniphila (HF-K-Akk). Control groups were orally administered an equivalent volume of sterile anaerobic PBS containing a similar end concentration of glycerol (2.5% vol/vol) (CT and HF). Treatments were continued for 4 wk. A. muciniphila MucT (ATTC BAA-835) was grown anaerobically in a mucin-based basal medium as described previously (18). The cultures were washed and concentrated in anaerobic PBS that included 25% (vol/vol) glycerol to an end concentration of 1.1010 cfu/mL under strict anaerobic conditions. Body composition was assessed using a 7.5-MHz time-domain NMR. Blood, adipose depots, liver, cecal content, and intestinal segments (ileum, cecum, and colon) were collected at death and analyzed. A complete description of the mouse experiments and bacteria preparation is provided in SI Material and Methods. Gut Microbiota Analysis. Gut microbiota analyses were performed using real-time quantitative PCR (qPCR) analysis and the MITChip, which is a phylogenetic microarray consisting of 3,580 different oligonucleotide probes that target two hypervariable regions of the 16S rRNA gene (the V1 and V6 regions). Analyses of the MITChip were performed as described previously (23, 40) and in SI Material and Methods. Gene Expression Analysis. The expression of metabolic genes of interest and RNA expression profiles were analyzed using real-time qPCR analysis as described in SI Material and Methods. Measurement of Endocannabinoid Intestinal Levels. Intestinal endocannabinoids were measured using an LTQ Orbitrap mass spectrometer as described in SI Material and Methods. Biochemical Analysis. Plasma insulin and fecal IgA were analyzed using ELISA as described in SI Material and Methods. The thickness of the mucus layer was measured in proximal colon segments that were fixed in Carnoy’s solution and in 5-µm paraffin sections stained with alcian blue as described in SI Material and Methods. LPS concentrations in portal vein blood were measured using Endosafe-Multi-Cartridge System based on the limulus amebocyte lysate kinetic chromogenic methodology as described the in SI Material and Methods. Statistical Analysis. Data are expressed as means ± SEM. Differences between two groups were assessed using the unpaired two-tailed Student t test. Data sets that involved more than two groups were assessed using ANOVA followed by Newman-Keuls post hoc tests. Correlations were analyzed using Pearson’s correlation. In the figures, data with different superscript letters are significantly different at P < 0.05, according to post hoc ANOVA statistical analyses. Data were analyzed using GraphPad Prism version 5.00 for Windows (GraphPad Software). The results were considered statistically significant when P < 0.05.

Acknowledgments We thank R. M. Goebbels for histological assistance, and B. Es Saadi and R. Selleslagh for technical assistance. P.D.C. is a research associate from the Fonds de la Recherche Scientifique (FRS-FNRS, Belgium) and is the recipient of FSR and FRSM (Fonds Spéciaux de Recherches, Université catholique de Louvain, Belgium; Fonds de la Recherche Scientifique Médicale, Belgium) and Société Francophone du Diabète (France) subsidies. A.E. is a doctoral fellow from the FRS-FNRS. G.G.M. is the recipient of subsidies from the FSR and FRSM and from FRS-FNRS Grant FRFC 2.4555.08. J.P.O. and C.B. were funded by European Research Council Advance Grant 250172-MicrobesInside, awarded to W.M.d.V., whose work was further supported by an unrestricted Spinoza Award of the Netherlands Organization for Scientific Research.

Footnotes Author contributions: P.D.C. designed research; A.E., C.B., L.G., J.P.O., C.D., L.B.B., M.D., G.G.M., W.M.d.V., and P.D.C. performed research; C.B., J.P.O., Y.G., M.D., G.G.M., N.M.D., W.M.d.V., and P.D.C. contributed new reagents/analytic tools; A.E., C.B., L.G., J.P.O., Y.G., G.G.M., W.M.d.V., and P.D.C. analyzed data; and A.E., C.B., W.M.d.V., and P.D.C. wrote the paper.

The authors declare no conflict of interest.

↵*This Direct Submission article had a prearranged editor.

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