Replication of Obesity and Associated Signaling Pathways Through

1624 Diabetes Volume 63, May 2014

Frank A. Duca,1,2,3 Yassine Sakar,1,2 Patricia Lepage,1,2 Fabienne Devime,1,2 Bénédicte Langelier,1,2 Joël Doré,1,2 and Mihai Covasa1,2,4,5

Replication of Obesity and Associated Signaling Pathways Through Transfer of Microbiota From Obese-Prone Rats AS Aberrations in gut microbiota are associated with animals harbored specifi c speciesH from Oscillibacter metabolic disorders, including obesity. However, and Clostridium clustersE XIVa and IV that were whether shifts in the microbiota profile during obesity completelyL absent from OR animals. In conclusion, are a characteristic of the phenotype or susceptibilityC to obesity is characterized by an D a consequence of obesogenic feeding remains Iunfavorable microbiome predisposing the host to fl E elusive. Therefore, we aimed to determine differencesT peripheral and central in ammationT and promoting in the gut microbiota of obese-prone (OP)R and obese- weight gain and adiposity during obesogenic resistant (OR) rats and examinedA the contribution of feeding. C Diabetes 2014;63:1624–1636 | DOI: 10.2337/db13-1526 OBESITY STUDIES this microbiota to the behavioral and metabolic A characteristicsI duringS obesity. We found that OP rats R displayH a gut microbiota distinct from OR rats fed the T Tsame high-fat diet, with a higher Firmicutes-to- EThe overabundance of energy-dense foods in developed Bacteroidetes ratio and significant genera R westernized societies has transformed obesity from an differences. Transfer of OP but not OR microbiota to American burden to a worldwide epidemic, with grave germ-free (GF) mice replicatedN the characteristics of health and socioeconomic consequences. Recent the OP phenotype, includingE reduced intestinal and advancements in DNA sequencing techniques and meta- hypothalamic satiationE signaling, hyperphagia, genomic profiling have established a link between the increased B weight gain and adiposity, and enhanced trillions of microbial inhabitants of the gut (i.e., gut lipogenesis and adipogenesis. Furthermore, microbiota) and the development of obesity-related increased gut permeability through metabolic dysregulations (1). Gut microbiota are signifi- conventionalization resulted in inflammation by cantly altered in humans and animal models of obesity, proinflammatory nuclear factor (NF)-kB/inhibitor of with reduction in bacterial diversity (2) as well as overall NF-kB kinase subunit signaling in adipose tissue, compositional shifts, such as a reduced abundance of liver, and hypothalamus. OP donor and GF recipient Bacteroidetes and a proportional increase in Firmicutes

1UMR1913-Microbiologie de l’Alimentation au Service de la Santé, l’Institut Corresponding author: Mihai Covasa, [email protected]. National de la Recherche Agronomique, Jouy-en-Josas, France Received 3 October 2013 and accepted 5 January 2014. 2UMR1913-Microbiologie de l’Alimentation au Service de la Santé, This article contains Supplementary Data online at http://diabetes AgroParisTech, Jouy-en-Josas, France .diabetesjournals.org/lookup/suppl/doi:10.2337/db13-1526/-/DC1. 3Doctoral School of Physiology and Pathophysiology, University Pierre and Marie Currie, Paris, France F.A.D. and Y.S. contributed equally to this study. 4Department of Basic Medical Sciences, College of Osteopathic Medicine, © 2014 by the American Diabetes Association. See http://creativecommons Western University of Health Sciences, Pomona, CA .org/licenses/by-nc-nd/3.0/ for details. 5Department of Human Health and Development, University of Suceava, Suceava, Romania diabetes.diabetesjournals.org Duca and Associates 1625

phylum (3–6). Gut microbiota regulate several host a HF diet (4.2 kcal/g; D12334B; Research Diets, New metabolic functions, and microbial dysbiosis is associated Brunswick, NJ) for 12 weeks, until sacrifice (11). Addi- with altered energy homeostasis (1). Obesity is charac- tional OP (n = 5) and OR (n = 4) rats, kept in the same terized by an enrichment in genes encoding enzymes housing conditions, were maintained on chow through- responsible for extracting calories from otherwise in- out and used for microbiota analysis. digestible polysaccharides (2,4). In addition to increased Mice energy harvest, the microbial metabolic byproducts, such as short-chain fatty acids (FAs), modulate secre- Male C57BL/6J GF mice (n = 20) from our GF colonies tion and gene expression of gut peptides controlling (Animalerie Axénique de Micalis, Jouy-en-Josas, France) satiety, such as glucagon like peptide-1 (GLP-1) and were used for inoculation studies. Two groups (n =10 peptideYY(PYY),byactingonG-coupledprotein each) were housed separately in two Trexler-type iso- receptors (GPRs) in intestinal enteroendocrine cells, lators (Igenia, France), with animals housed individually suggesting a role for gut microbiota in modulating sa- in polycarbonate cages with cedar bedding. tiation (7). Obesity and high-fat (HF) feeding are also Conventionalization associated with intestinal, systemic, and adipose tissue inflammation (1). Intestinal inflammation is an early GF mice (n = 10 for each phenotype) were con- consequence of HF feeding, present before the onset of ventionalized (CV) with fecal microbiota from one OP obesity and insulin resistance (8), supporting a direct, and OR donor rat, both maintained on the HF diet. Feces causal role for HF-induced microbiota changes in the from each donor were freshly collected, quickly diluted, development of obesity. and homogenized in liquid casein yeast mediumS (1:100 Conventionalization studies have demonstrated the w/v) in anaerobic conditions. GF mice (12 weeks old) were inoculated immediately thereafterA by oral gavage contribution of the gut microbiota to the development of metabolic disease because the metabolic phenotype is (250 mL) and maintained onH standard chow (2.83 kcal/g; transmissible by gut microbiota transplantation (3,4,9). R03, Safe Diets)E for 4 weeks in separate gnotobiotic These early studies investigated the effect of gut micro- isolators.L Afterward, half of each group was switched to biota using genetic, transgenic, or HF-fed models of Ca HF diet (4.73 kcal/g; D12451, Research Diets) for D obesity, none of which are an accurate reflection of hu-I 8 weeks, while the remaining half was fed chow. All animals were killed and tissue collected forE analyses. man obesity that encompasses the interaction betweenT T genes and the environment (3,4,9,10).R Therefore, in the Quantitative RT-PCR and WesternC Blotting current study, we examined theA role of the gut microbiota in obese-prone (OP) and obese-resistant (OR) rats, For all experiments,A protein and RNA extraction, and S subsequent Western blotting and quantitative (q)PCR a model thatI closely mimics the characteristics of the R humanH obese phenotype, including a polygenic mode of wereT performed as previously described (11). Briefly, Tinheritance, whereby some, but not all, individuals areERNA was extracted by using TRiZol, quantified, 10 mg susceptible to weight gain when exposed toR an obeso- RNA was reverse transcribed, and cDNA was diluted fivefold for qRT-PCR using TaqMan gene expression genic environment (10). First, by using both chow- and HF-fed OP and OR rats, we determinedN whether micro- assays (Applied Biosystems). Data are expressed as the biota shifts solely resultE from consumption of an obe- relative mRNA normalized to b-actin and analyzed –DDCT sogenic diet orE are a manifestation of the obese according to the 2 method. phenotype. B Second, we transplanted microbiota from OP For Western blotting, tissues were thawed on ice and and OR rats fed a HF diet into germ-free (GF) mice to suspended in 1 mL radioimmunoprecipitation assay examine the capacity of microbiota to influence pheno- buffer containing protease inhibitors (Sigma-Aldrich, type and associated metabolic and molecular changes, Lyon, France). Cells were lysed, homogenized, and including gut–brain satiation signaling, lipid storage, and centrifuged for 20 min at 14,000g at 4°C. After quanti- systemic and hypothalamic inflammation. fying, soluble protein (25–100 mg) was run on SDS-PAGE gels containing 8–12% acrylamide, transferred to nitro- RESEARCH DESIGN AND METHODS cellulose membranes, and probed with antibodies (Santa Animals Cruz Biotechnology and Abcam). Immune complexes were detected by chemiluminescence and quantified by All experiments were done in accordance with the European scanning densitometry using ImageJ software against Guidelines for the Care and Use of Laboratory Animals. b-actin (cytosolic proteins) or Ras-related nuclear protein Rats (nucleus proteins) as internal controls. OP-CD (n = 7) and OR-CD (n = 4) rats from Charles River Laboratories (Wilimington, MA) were used. Ani- Plasma Analysis mals were housed individually in a temperature- Plasma was analyzed for glucose, triglycerides, total controlled vivarium with 12:12-h light/dark cycle (lights cholesterol, and total HDL by an AU 400 automated on at 0700). Starting at 8 weeks of age, rats were fed biochemical analyzer (Olympus). Gut hormones and 1626 Microbiota Transfer and Obesity Diabetes Volume 63, May 2014

cytokines were determined in duplicate using a mouse acquired with an inverted confocal microscope LSM510 gut hormone panel or mouse cytokine magnetic bead with 340 oil-immersed objective and processed by panel (Millipore), measured with Luminex technology AxioVision SE64 Rel.4.8 software. Controls were run for (St. Antoine, Paris, France), following the manufacturers’ false negatives or positives of the ﬁrst and secondary instructions. antibody.

Adipose Tissue Immunodetection Microbiota Analysis Adipose tissue was fixed with 4% formaldehyde over- Total DNA was extracted from 200 mg caecal content night, stored in 75% ethanol, embedded in paraffin, and from 20 rats (chow: OP = 5, OR = 4; HF: OP = 7, OR = 4) 4-mm-thick microtome cut sections were processed using and 16 mice (CVOP = 9, CVOR = 7) (12). Microbiota standard procedures. For macrophage infiltration, sec- composition was assessed by 454 pyrosequencing (GS tions were incubated overnight at 4°C with primary anti- FLX TI technology; Genoscreen, Lille, France) targeting F4/80 antibody (sc:71088, 1:100; Santa Cruz) followed the V3-V4 region of the bacterial 16S rRNA gene (V3 by 1-h incubation with secondary antibody (goat antirat forward: 59TACGGRAGGCAGCAG39; V4 reverse: IgG, sc-2041, 1:200), then developed with DAB (Dako 59GGACTACCAGGGTATCTAAT39). Sequences were Kit-K3465) for ;5 min. The number of F4/80+ cells trimmed for barcodes, PCR primers, and binned for per microscope field was counted and divided by the a minimal sequence length of 300 pb, a minimal base total number of adipocytes in the field (percentage quality threshold of 27, and a maximum homopolymers macrophages-to-adipocyte), with five to eight fields length of 6. Resulting sequences were assigned to the counted per animal sample (n = 4 animals per group). For different taxonomic levels, from phylum to genusS using immunofluorescence of tumor necrosis factor-a (TNF-a), the Ribosomal Database Project (release 10, update 26; release 11, update 1) (13). Sequences wereA further clus- sections were incubated overnight at 4°C with primary, – tered into operational taxonomicH units (OTUs) or phy- anti TNF-a antibody (D2D4 #11948, 1:100; Cell Signal- ing Technology), followed by 1-h incubation at room lotypes at 97% ofE identity using Quantitative Insights temperature in the dark with secondary anti-rabbit IgG Into MicrobialL Ecology (QIIME) pipeline (14) and cdhit antibody (Alexa Fluor 488 Conjugate #4412, 1:200; Cell (15).C OTUs were assigned to the closest taxonomic D Signaling). Images were acquired with an inverted Ineighbors and relative bacterial species using SeqmatchE confocal microscope LSM510 with 340 oil-immersedT and Blastall. T objective using AxioVision SE64 Rel.4.8R software. The fl Statistical Analyses C corrected total cell uorescenceA (CTCF) was computed by Data are expressed as mean 6 SEM. Unless otherwise ImageJ software using uniformly sized adipocyte images A S fl noted, statistics were performed by GraphPad Prism 5 and handled basedI on the mean uorescence detected R in theH internal controls sections. CTCF = integrated softwareT or R software (packages ade4, randomForest, density – (area of selected cell 3 mean fluorescence ofEand lattice). For statistical analysis among groups during T ’ background readings). Background readings wereR run to HF feeding, a t test with Welch s corrections was used, fi and ANOVA with Bonferroni post hoc test was used for control for false negatives or positives of the rst and N analyses between groups and diet conditions. Principal secondary antibody. E component analysis was computed based on bacterial ImmunofluorescenceE for GLP-2 Enteroendocrine Cells genus composition and statistically assessed by a Monte and IntestinalB Macrophage Infiltration Carlo rank test. The Wilcoxon test was applied for differences in bacterial composition. Significance was For GLP-2, sections were incubated overnight at 4°C with , primary anti–GLP-2 (C-20) (sc-7781, 1:100; Santa Cruz), accepted at P 0.05. For heat maps representation, followed by 1-h incubation at room temperature in the log10-transformation was applied on the bacterial relative dark with secondary antibody (anti-goat IgG-CFL 488: sc- abundance data matrix, which allowed visualizing simi- 362255, 1:200; Santa Cruz). GLP-2 containing EEC was larities or differences between samples that affect quantified by counting total villi and GLP-2+–stained members of the community that may make up less than cells throughout the entire length of the section (.20 1% of the relative abundance in a sample. Machine fi nonoverlapping microscopic areas) for each animal (n = learning techniques, using the random forest classi er, four per group). For macrophage infiltration, sections were applied to relative abundance data of distributed were incubated with mouse anti-F4/80+ antibody and bacterial genera (16). revealed with immunofluorescence secondary antibody coupled with goat anti-mouse IgG (H&L)–Alexa Fluor RESULTS 647 (1:100). Data were expressed as the number of F4/ OP and OR Rats Have Different Gut Microbiota Profiles 80+ macrophages per high-power field (HPF: visible area Only During HF Feeding of a slide under magnification). Between three and six After 12 weeks of HF feeding, OP rats had increased 24-h fields were counted per sample (eight samples) for the food consumption, weighed significantly more, and had number of F4/80+ macrophages per HPF. All images were increased adiposity relative to OR rats (Supplementary diabetes.diabetesjournals.org Duca and Associates 1627

Fig. 1). Among the 198,611 obtained 16S rRNA gene OP rats than in OR rats (1.67 vs. 1.28). During chow sequences, 121,887 passed quality filters and were fur- feeding, very few differences were observed at deeper ther assigned to taxonomic levels, from phylum to bac- taxonomic levels, except for bacteria belonging to the terial species and OTUs. As highlighted by the principal genus Clostridium cluster XIVb and Flavonifractor.HF component analysis, OP and OR microbiota did not differ feeding resulted in many significant genus differences during chow feeding. However, 12 weeks of HF feeding between chow- and HF-fed animals (Supplementary Ta- resulted in a differing microbiota of the HF-fed rats ble 1). More interesting is that specifically during HF compared with their chow-fed counterparts, and more feeding, within the Firmicutes phylum, OP rats exhibited interestingly, a divergence between the OP and OR significantly higher proportions of Ruminococcus, Oscil- phenotypes during the HF-feeding period (Fig. 1A). There libacter, and Alistipes genera (Fig. 1C) compared with were no differences at the phyla level during chow HF-fed OR rats. On the other hand, Bacteroidetes, feeding, but HF feeding resulted in significantly increased b-Proteobacteria (Parasutterella)andg-Proteobacteria Bacteroidetes and Proteobacteria percentages and de- (Escherichia/Shigella) were significantly more represented creased Firmicutes in both groups. Moreover, during HF in OR rats. This is intriguing, given that high levels of feeding, OP rats had significantly less bacteria from Escherichia coli were also noticed in humans undergoing Proteobacteria phyla (Fig. 1B) and a trend toward more weight loss after gastric bypass (5). Supervised classifi- bacteria belonging to the Firmicutes (P = 0.06667), with cation with random forest classifier analysis demon- an average Firmicutes-to-Bacteroidetes ratio higher in strated the variable importance of genera predictedS to HA LE TIC TED AR AC HIS TR T RE EN BE

Figure 1—16S rRNA gene analysis reveals phyla-, genus-, and species-level differences in microbiota of HF-fed OP and OR rats. A: Gut microbial communities from individual OP and OR rats fed chow or the HF diet for 12 weeks clustered according to principal coordinates analysis. B: Relative abundance of bacterial phyla present in OP and OR rats. C: Heat map of differentially expressed bacterial genera for individual OP and OR rats. Rats with the highest and lowest bacterial levels are red and white, respectively. D: Differences in speciﬁc species belonging to Clostridiales and Bacteroidales groups between OP and OR rats. Data are expressed as mean 6 SEM. *P < 0.05, **P < 0.01 denotes difference between phenotype within diet. 1628 Microbiota Transfer and Obesity Diabetes Volume 63, May 2014

belong to OP and OR phenotypes fed the HF diet, with Bacteroides vulgatus was reduced in OP rats, similar to an estimate error rate of 20% (17% for OP and 25% for obese children (17). OR). The main predictive genera for phenotype dis- crimination were unclassified Ruminococcaceae, un- Gut Microbiota Transfer Replicates Signatures of OP classified Porphyromonadaceae, Clostridium cluster XIVb, Phenotype Escherichia, Parasutterella, and Alistipes (Supplementary To determine the overall strength of the OP and OR Fig.1).Onthecontrary,foranimalsfedthechowdiet, microbiota profiles in the promotion and generation of the estimate error rate of supervised classification was their metabolic phenotypes, we inoculated GF-C57BL/6J higher at 56%, with a 75% error rate for OP classifi- mice (n = 10/phenotype) with microbiota from an OP or cation and 40% for OR classification. Most important, OR donor, termed CVOP and CVOR, respectively, that genera in data classification were related to Flavoni- were fed chow or the HF diet. Strikingly, phenotype and fractor, Clostridium cluster XIVb, Johnsonella,un- behavioral differences between OP and OR rats were classified Clostridiales, and Lachnospiraceae incertae reliably transferred to CVOP and CVOR animals main- sedis, although bacterial genera discriminatory power tained on the HF diet. After 2 weeks, HF-CVOP mice was weak in chow-fed rats. Interestingly, some specific gained significantly more weight than their chow coun- bacterial species were differentially represented be- terparts, and after 7 weeks, were heavier than the HF- tween OP and OR rats (Fig. 1D; see Supplementary CVOR mice (Fig. 2A). More importantly, mice inoculated Table 1 for all species differences), and in particular, with OP microbiota had a significantly greater adiposityS HA LE TIC TED AR AC HIS TR T RE EN BE

Figure 2—CV of GF mice with OP and OR microbiota results in replication of donors’ phenotype and altered intestinal nutrient and satiety signaling during HF feeding. A: Percentage of body weight gain of mice inoculated with OP or OR microbiota and fed chow or the HF diet. B: Adiposity index (top) and daily calorie intake (bottom). C: Homeostasis model assessment of insulin resistance (HOMA-IR). Plasma leptin and insulin (D) and gastrointestinal peptides levels (E) are shown after a 5-h fast. F: Relative mRNA expression of GPRs in intestinal epithelial cells of CVOP and CVOR mice. Data are expressed as mean 6 SEM. *P < 0.05, **P < 0.01, ***P < 0.0001. †P < 0.05, ††P < 0.01, †††P < 0.0001 indicating signiﬁcant difference from chow-fed diet condition within phenotype. diabetes.diabetesjournals.org Duca and Associates 1629

index (;30% increase) than CVOR mice during HF HF-CVOP mice exhibited increased FA synthetase (FAS) feeding but not chow feeding. Similar to OP rats, 24-h and a reduced phosphorylated-acetyl-CoA carboxylase food intake of CVOP mice was increased only during HF (ACC)–to–total ACC ratio, indicating increased capacity feeding (Fig. 2B). In addition, homeostasis model as- for de novo FA synthesis. Furthermore, sterol regulatory sessment of insulin resistance (Fig. 2C) and circulating element-binding protein-1c (SREPB-1c), a key transcrip- leptin and insulin levels were significantly increased in tion factor of glucose-induced hepatocyte lipogenesis and HF-CVOP animals (Fig. 2D), as were triglyceride and activator of ACC and FAS, was significantly upregulated glycemia levels (Supplementary Fig. 2), features all as- in CVOP mice (Fig. 3A and B). In addition, angiopoietin- sociated with metabolic syndrome. like 4 (Angptl4), an inhibitor of lipoprotein lipase, was Similar to OP donors, we found that hyperphagia in reduced in HF-OP donors and their GF recipients (Fig. 3A HF-CVOP mice was associated with reduced plasma and Supplementary Fig. 4). This is significant, because GLP-1 and PYY (Fig. 2E) as well as decreased intestinal others have shown that intestinal Angptl4 is directly PYY and GLP-1 protein expression (Supplementary regulated by gut microbiota, consistent with our obser- Fig. 2). Furthermore, gene and protein expression of vations of reduced intestinal Angptl4 in CVOP animals intestinal GPRs, which mediate nutrient-induced satiety (data not shown) (20). As expected, all of these changes peptide secretion (7,18,19), was increased in OP com- in lipogenic markers and enzymes resulted in increased pared with OR rats, an effect transferred to GF recipi- adipocyte hyperplasia in CVOP mice (Fig. 3E). Further- ents, irrespective of their maintenance diet (Fig. 2F). more, we found identical trends in the livers of HF-CVOP OP Microbiota Enhances Adipogenesis and Lipogenesis mice, where FAS and SREPB-1c were increased, but Gut microbiota alter expression of host genes regulating phosphorylated (p)-ACC/ACC and Angptl4 wereS both adipogenesis and lipogenesis (20). In adipose tissue, reduced (Fig. 3A and B). InH addition,A peroxisome LE TIC TED AR AC HIS TR T RE EN BE

Figure 3—Increased lipogenesis and adipogenesis of OP rats were transferred to GF recipients by microbiota. A: Protein expression of lipogenic and adipogenic enzymes in liver and adipose tissue of CVOP and CVOR. B: Phosphorylation of ACC. C: PPARg protein expression in adipose tissue (top) and PPARa expression in liver (bottom). D: Western blot images of FA transporters in adipose and hepatic tissue. E: Adipocyte surface area (see Fig. 5F for representative image). Data are expressed as mean 6 SEM. *P < 0.05, **P < 0.01. 1630 Microbiota Transfer and Obesity Diabetes Volume 63, May 2014

proliferator–activated receptor g (PPARg), a transcription levels. Specifically, zonula occludens protein-1 (ZO-1) and factor of adipogenesis, was increased in CVOP mice occludens levels were decreased in distal intestinal epi- (Fig. 3C). In the liver, PPARa protein levels were signifi- thelial cells, whereas phosphorylation of myosin light cantly decreased, contributing to reduced hepatic FA oxi- chain, a mechanism that results in cytoskeleton con- dation and increased circulating triglycerides. Finally, traction and disruption of tight junction integrity (21), protein expression of adipose and hepatic FA transporters, was increased in HF-OP and -CVOP animals (Fig. 4A and cluster of differentiation 36 (CD36), and FA binding Supplementary Fig. 5). Furthermore, jejunal and colonic protein (FABP) were increased in OP and CVOP compared TNF-a, a biomarker of intestinal inflammation, was in- with OR rats and CVOR mice during HF feeding but not creased in HF-CVOP animals (Fig. 4C), likely due to en- chow feeding (Fig. 3D and Supplementary Fig. 4), sup- hanced intestinal macrophage infiltration (Fig. 4E and F). porting its role in increased intracellular shuttling of FAs Impairment in markers of tight junction disruption of and lipid accumulation in adipose and hepatic tissue. HF-CVOP mice by gut microbiota was further associated with significant decreases in L cells immunostained with “Obese” Microbiota Alters Tight Junction Proteins and GLP-2, a trophic gut hormone shown to regulate gut Increase Inflammation permeability (22), in the ileum and colon, which contain During HF feeding but not chow feeding, OP and CVOP the largest amounts of L cells (Fig. 4D). HF-fed OP rats animals both exhibited altered tight junction protein also had higher circulating levels of the inflammatoryS HA LE TIC TED AR AC HIS TR T RE EN BE

Figure 4—CV with OP microbiota altered markers of gut permeability and inflammation along with associated increases in circulating inflammatory cytokines. A: Protein expression of tight junction proteins in the distal intestine of CVOP and CVOR mice. B: Circulating inflammatory markers in mice after a 5-h fast. C: TNF-a gene expression levels in the jejunum and colon. D: Immunofluorescent (IF) image (original magnification 340) of GLP-2–stained L cells in the ileum and L-cell counts; arrow indicates a fluorescent L cell. The insert represents a digital zoom of identified fluorescent L cell. E: IF images and quantitative analysis of macrophage infiltration of distal intestine of CVOR (top) and CVOP (bottom) mice during HF feeding. F: Protein expression of F4/80 and CD3 in distal intestinal epithelial cells. Data are expressed as mean 6 SEM. *P < 0.05, **P < 0.01. †P < 0.05, ††P < 0.01 denoting significant difference from chow-fed diet condition within phenotype. diabetes.diabetesjournals.org Duca and Associates 1631

markers TNF-a, monocyte chemotactic protein-1 (MCP- adipose tissue of HF-fed OP donors and their CVOP 1), macrophage inflammatory protein-1 a (MIP-1a), in- recipients (Fig. 5B and Supplementary Fig. 6), as were the terleukin (IL)-6, and IL-1a (Supplementary Fig. 5). CV expression of inflammatory chemokines TNF-a, plas- with OP gut microbiota replicated increases in plasma minogen activator inhibitor-1 (PAI-1), and IL-6 (Fig. 5C TNF-a, MIP-1a, IL-6, and IL-1a in OP compared with OR and F and Supplementary Fig. 6). Similarly, HF feeding recipients fed the HF diet (Fig. 4B). resulted in upregulation of inflammatory gene expression TNF-a and IL-6 in the liver of OP donor rats and their “Obese” Microbiota Increases Adipose Tissue and GF mice recipients (Fig. 5D and Supplementary Fig. 6). Liver Inflammation Obese animals exhibited an increase in the in- We observed increased macrophage infiltration in CVOP flammatory nuclear factor (NF)-kB/inhibitor of kb compared with CVOR mice fed the HF diet (37.1% vs. kinase (IKKb) pathway. Specifically, phosphorylation/ 27.4%, P , 0.05; Fig. 5A), as well as increased total F4/ activation of the p65 subunit of NF-kB at its ser536 80 and CD3 (Supplementary Fig. 6), markers of macro- residue and of IKKb at residues ser177 and ser181 was phage and T-cell infiltration, respectively. Activation of significantly increased in adipose and hepatic tissue of Toll-like receptor 4 (TLR4) regulates inflammatory pro- OP and CVOP animals during HF feeding but not chow cess and, as such, TLR4 mRNA levels were increased in feeding (Fig. 5E and Supplementary Fig. 6). Activation of S HA LE TIC TED AR AC HIS TR T RE EN BE

Figure 5—HF-fed CVOP mice exhibit increased adipose and hepatic inflammation by enhanced macrophage infiltration and NF-kB/IKKb signaling. A: Immunohistochemistry images and quantitative analysis is shown for macrophage infiltration (indicated by arrows) in adipose tissue of CVOR (left) and CVOP (middle) mice; CVOP section without secondary antibody (right). B: TLR4 mRNA expression in liver and adipose tissue of CVOP and CVOR mice. mRNA transcript levels of inflammatory cytokines in adipose (C) and hepatic tissue (D). E: Western blot images of NF-kB and IKKb proteins. F: Immunofluorescence (IF) images of TNF-a in adipose tissue and quantitative measurement (integrated density) of IF; representative image of stained adipocytes with TNF-a IF (top panels); TNF-a IF only (lower panels). Data are expressed as mean 6 SEM. *P < 0.05, **P < 0.01, ***P < 0.0001. †P < 0.05, ††P < 0.01, †††P < 0.0001 denote a significant difference from chow-fed diet condition within phenotype. 1632 Microbiota Transfer and Obesity Diabetes Volume 63, May 2014

IKKb allows NF-kB to dissociate and enter the nucleus between OP and OR donors were replicated in GF and induce gene expression of inflammatory factors. recipients (Fig. 7B; see Supplementary Fig. 1 for super- IKKb activation is accomplished by activation of TLR4, vised classification analysis). Clostridium cluster IV and TNF receptor, and IL-1 receptor (23). Here we demon- Oscillibacter from the Clostridiaceae family and some strate that TLR4 and its TNF-a ligand are increased in unclassified Ruminococcaceae were significantly more adipocytes. represented in OP and CVOP compared with OR animals. “ ” Furthermore, we observed 25 bacterial molecular species Obese Microbiota Increases Hypothalamic fi Inflammation and Satiety Neuropeptide Expression (or OTUs) that were speci c to OP and CVOP but were totally absent from OR and CVOR animals (Fig. 7C and CV of GF mice with OP microbiota significantly increased Supplementary Table 7). As such, the 25 OTUs all clus- expression of hypothalamic IL-6, TNF-a, and TLR4 genes tered within the Firmicutes phylum and were related to compared with CVOR during HF feeding (Fig. 6A). When 10 bacterial isolates, mostly from Clostridium cluster examining the effect of CV on hypothalamic energy- XIVa, Clostridium cluster IV, and Oscillibacter. Clostridium regulating peptides, we found that CVOP mice fed the HF cluster XIVa, part of the Eubacterium rectale-C. coccoides diet had reduced proopiomelanocortin and increased group contain the currently recognized butyrate- Agouti-related peptide and neuropeptides Y (Fig. 6B). producing bacteria in the gut (26), resulting in more ef- These changes were reliably transferred from OP donors ficient energy extraction from the diet. Furthermore, characterized by decreased anorexigenic and increased Clostridia strains from Clostridium cluster XIVa and orexigenic peptides (24,25), and are the first demon- Clostridium cluster IV have been shown to evoke stration of the ability of the gut microbiota to alter a proinflammatory cytokine response in vitro (27), central nervous system (CNS) energy homeostatic- S whereas Oscillibacter has been positively correlated with signaling proteins. A gut permeability (28). Therefore, the potential ability of Microbiota-Related Phenotype Is Preserved and these specific bacterial strains H to increase energy harvest Transferred to GF Mice and promote intestinalE inflammation could explain the Although specific microbial phyla present in the OP and increases inL adiposity and gut permeability and the OR donors were also detected in recolonized GF mice, subsequentC systemic inflammation in OP donors and D phyla differences were not replicated, because the aver- Itheir GF recipients fed a HF diet. E age Firmicutes-to-Bacteroidetes ratio was not differentT in DISCUSSION T CVOP (1.22) versus CVOR (1.45) mice. However,R bacte- C rial genera distribution differed A between HF-fed CVOP Our studies provide new evidence demonstrating that 1) and CVOR mice (Fig. 7A), and several genera differences OP and OR phenotypesA are associated with distinct and HIS TR T RE EN BE

Figure 6—CV of GF mice promotes hypothalamic inﬂammation and alters overall central energy homeostasis signaling during HF feeding. A: mRNA expression of hypothalamic cytokines in CVOP and CVOR mice. B: Gene expression of anorexigenic and orexigenic peptides in mice. AgRP, Agouti-related peptide; CART, cocaine- and amphetamine-regulated transcript; NPY, neuropeptides; POMC, proopiomelanocortin. Data are expressed as mean 6 SEM. *P < 0.05, **P < 0.01. †P < 0.05 denotes a signiﬁcant difference from chow-fed diet condition within phenotype. diabetes.diabetesjournals.org Duca and Associates 1633

S HA LE TIC TED AR AC Figure 7—DistinctS genera- and species-level differences were transferable from obese donors to CV GF mice. A:Gutmicrobial communities fromI individual HF-fed CVOP and CVOR mice clustered accordingR to principal coordinates analysis. The two first componentsH explain 44.61% of the variability (component 1, 25.16%;T component 2,19.45%). B: Comparisons of significant genera Tdifferences between phenotypes (OP/OR or CVOP/CVOR).EC: Heat map displays the 25 OTUs represented in individual OP and CVOP animals and completely absent from OR and CVOR animals. Animals with the highest and lowest OTUs are red and gray, respectively. Data are expressed as mean 6 SEM.R *P < 0.05. EEN differing B gut microbial communities during HF feeding phenotype, may be the main determinant of microbiota that are not present during chow feeding and that 2) shifts (30). However, our results clearly demonstrate that transfer of OP microbiota replicates the obese phenotype the “obese” gut microbiota profile is not a mere result of of the donor as well as associated differences in the HF feeding but instead is unique and conserved to the chemosensory, metabolic, and neural dysregulations. Al- obese state, because chow-fed OP and OR rats exhibited though some studies suggest the presence of a specialized similar microbiota profiles that diverged during HF “obese” microbiota capable of increased energy storage, feeding. Therefore, this distinct gut microbiota, with most are confounded by the observation that obesity phyla-, genera-, and species-specific differences, is a sig- often results from an obesogenic, western diet known to nature of the obese host phenotype not only of HF rapidly alter the gut microbiota (29), thus making it feeding. This finding is consistent with results from difficult to ascertain the influence of the host metabolic a recent study demonstrating that transplantation of gut phenotype versus the diet on microbial composition microbiota from twins discordant for obesity into GF during the obese state. Indeed, HF feeding of humanized mice resulted in increased body mass and adiposity of gnotobiotic mice results in a rapid shift in the micro- mice receiving the obese cotwins compared with the lean biome and its metabolic pathways preceding increased cotwin, whether the groups were maintained on a diet adiposity (29). Similarly, genetically OR mice exhibit low or high in saturated fat (31). decreased Bacteroidetes and increased Firmicutes during We observed a high Firmicutes-to-Bacteroidetes ratio HF feeding, emphasizing that diet, and not host in OP rats; however, not all studies replicated the low 1634 Microbiota Transfer and Obesity Diabetes Volume 63, May 2014

Bacteroidetes levels in obesity (32), suggesting that dif- obesity, and energy extraction is much more complex ferences at the genus and species levels play a greater than previously thought (42). Interestingly, HF feeding role. Indeed, we found high levels of bacteria from the causes rapid microbiota shifts occurring before changes Ruminococcus genus in OP rats, similar to that observed in adiposity (29), suggesting that direct microbe host in obese humans and HF-fed mice (33,34). Ruminococcus cross talk influences intestinal-signaling mechanisms is phylogenetically heterogenous, and most species fall that precede and result in hyperphagia and promote under several Clostridium clusters, including Clostridium weight gain. Indeed, certain bacterial species or their clusters IV and XIVa. As such, C leptum (cluster IV) has metabolic byproducts have been shown to influence gut been associated with both obesity and weight loss (35,36) peptide signaling by directly altering expression of sig- and was increased in OP and CVOP animals. Further, naling proteins of enteroendocrine cells or by activating OTUs only present in OP and their GF recipients were enteroendocrine cells through GPR signaling, respectively assigned to Clostridium cluster XIVa, which is directly (7,43–45). Nevertheless, future studies using pair-fed correlated with fat pad mass and BMI (35,37,38) and animals to control for dietary fat intake, or time course contains bacterial species known to break down poly- experiments assessing the development of obesity with saccharides, promoting monosaccharide absorption, en- changes in microbiota and intestinal satiation signaling, hanced lipogenesis, and lipid storage (4,20). In would aid in delineating their effects. agreement with this, OP microbiota CV resulted in in- There is a growing appreciation of the bidirectional crease in adipogenic and lipogenic enzyme expression in interaction between gut microbiota and the brain, with the liver and adipose tissue as well as increased FA microbiota modulating CNS activity through endocrine transporters, providing a more efficient transfer and and neural pathways (46). Impaired hypothalamicS activ- storage of fermented byproducts. Furthermore, we ity, as well as diet-induced hypothalamic inflammation, fi A demonstrate, for the rst time, the ability of cross- has been causally linked with the development of obesity species transfer (rat to mouse) of the obese phenotype. (47,48). Interestingly, hypothalamic H inflammatory sig- Interestingly, OP and OR bacterial phyla-level differ- naling occurs rapidlyE within a few days of HF -feeding ences were not found in CV animals, indicating that (49) that correspondsL with changes in diet-induced although overall phyla shifts may be indicative and microbiotaC shifts (29). Therefore, it is plausible that CNSD representative of the obese state, they are not the main Iinflammation is a direct result of aberrations inE gut determinants in shaping the metabolic profile ofT the microbiota. Here we show, for the firstT time, the ability host recipient, emphasizing the role ofR more specific of the gut microbiota to alterC hypothalamic anorexigenic and detailed taxonomic differencesA in the development and orexigenic peptides. Although this may be due to fl of obesity. S increased hypothalamicA in ammation, which directly Obesity is characterizedI by increase in gut paracellular alters leptinR and insulin resistance in these neurons and permeabilityH likely promoting metabolic endotoxemia causesT a shift in neuropeptides, it is possible that direct T(22,39). In this regard, we found that OP donors and GFEsignaling pathways between the microbes and the brain recipients exhibited disruptions in tight junction R protein may also affect central functions. For example, peripheral levels, indicative of increased gut permeability (39). This endotoxins and cytokines evoke neural activation was associated with systemic inflNammation, likely through vagal afferents (50,51), whereas the stress- through lipopolysaccharideE or saturated FAs, as indicated reducing effects of prebiotics require intact vagal signaling by increased adiposeE macrophage recruitment and he- (52). Furthermore, several bacteria have the capacity to patic NF-BkB/IKKb inflammatory-signaling pathways produce neurometabolites, such as g-aminobutyric acid, (40,41). serotonin, and dopamine (46). Nevertheless, the current Our currents results provide new evidence showing study clearly shows that “obese” microbiota are associ- that aberrations in microbiota during obesity result ated with hypothalamic energy-regulating peptides that in alterations in peripheral and central satiety signaling promote energy intake and adiposity. that promote hyperphagia and weight gain. Gut In conclusion, this study demonstrates the broad and microbiota modulate expression of intestinal nutrient extensive contribution of the “obese” gut microbiota to receptors (7,11), and here we demonstrate that obesity- the modulation of complex molecular-signaling machin- associated differences in GPRs were replicated in GF ery responsible for host metabolism, energy storage, in- recipient mice, affecting the ability of enteroendocrine testinal nutrient sensing, and inflammatory pathways, cells to sense and respond to intestinal nutrients. In line Taken together, it demonstrates that humans susceptible with this, intestinal and circulating satiety peptide levels to obesity may harbor a disadvantageous gut microbiome were both reduced in OP and CVOP animals in addition that exacerbates adiposity during HF feeding and could to reduced L-cell number. Changes in gut peptide sig- be used as a potential marker for susceptibility to obesity naling and inflammation may also be secondary to in- in humans. Further, by identifying and characterizing creased adiposity and/or increased fat consumption, specific bacterial groups or species that play a major role possibly a result of increased energy harvest from OP in the promotion and perpetuation of obesity, these microbiota, although the relationship between diet, findings open the potential for future therapeutic diabetes.diabetesjournals.org Duca and Associates 1635

treatments that target these organisms and alleviate the 12. Lepage P, Seksik P, Sutren M, et al. Biodiversity of the mucosa-associated metabolic dysregulation of the host. microbiota is stable along the distal digestive tract in healthy individuals and patients with IBD. Inflamm Bowel Dis 2005;11:473–480 13. Cole JR, Wang Q, Cardenas E, et al. The Ribosomal Database Project: Acknowledgments. The authors thank Gary Schwartz, from Albert improved alignments and new tools for rRNA analysis. Nucleic Acids Res Einstein College of Medicine, and Jeffrey Felton, from Western University of 2009;37(Database issue):D141–D145 Health Sciences, for their editorial input, and the Animalerie Axénique de 14. Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of Micalis team of l’Institut National de la Recherche Agronomique–Jouy-en- high-throughput community sequencing data. Nat Methods 2010;7:335– Josas, and Timothy Swartz, from Institut Cochin (INSERM), for assisting with 336 the behavioral experiments. 15. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large Funding. The study was supported by INRA through a scientific package sets of protein or nucleotide sequences. Bioinformatics 2006;22:1658– awarded to M.C. and by the Romanian National Program PN-II-ID-PCE-2012-4- 1659 0608 No. 48/02.09.2013, “Analysis of novel risk factors influencing control of fi food intake and regulation of body weight.” 16. Cutler DR, Edwards TC Jr, Beard KH, et al. Random forests for classi - cation in ecology. Ecology 2007;88:2783–2792 Duality of Interest. No potential conflicts of interest relevant to this article were reported. 17. Bervoets L, Van Hoorenbeeck K, Kortleven I, et al. Differences in gut microbiota composition between obese and lean children: a cross- Author Contributions. F.A.D. and M.C. designed the study, sectional study. Gut Pathog 2013;5:10 researched data, and wrote the manuscript. Y.S. researched data and reviewed the manuscript. P.L. analyzed metagenomic data and reviewed the manuscript. 18. Hirasawa A, Tsumaya K, Awaji T, et al. Free fatty acids regulate gut incretin – F.D. and B.L. researched data. J.D. reviewed the manuscript. M.C. is the glucagon-like peptide-1 secretion through GPR120. Nat Med 2005;11:90 guarantor of this work and, as such, had full access to all the data in the study 94 S and takes responsibility for the integrity of the data and the accuracy of the 19. Li X, Staszewski L, Xu H, Durick K, Zoller M, Adler E. Human receptors data analysis. for sweet and umami taste. Proc Natl AcadA Sci U S A 2002;99:4692– Prior Presentation. These data were partly presented in abstract form 4696 H at the 2012 Experimental Biology Conference, San Diego, CA, 23 April 2012. 20. Bäckhed F, DingE H, Wang T, et al. The gut microbiota as an environmental factorL that regulates fat storage. Proc Natl Acad Sci U S A 2004;101: References C15718–15723 D 1. Tremaroli V, Bäckhed F. Functional interactions between the gut microbiotaI 21. Turner JR. Molecular basis of epithelial barrier regulation: from basic – E– and host metabolism. Nature 2012;489:242 249 T mechanisms to clinical application. Am J PatholT 2006;169:1901 1909 2. Turnbaugh PJ, Hamady M, Yatsunenko T, et al.R A core gut microbiome in 22. Cani PD, Possemiers S, Van de Wiele T, et al. Changes in gut microbiota – fl C obese and lean twins. Nature 2009;457:480A484 control in ammation in obese mice through a mechanism involving GLP-2- driven improvementA of gut permeability. Gut 2009;58:1091–1103 3. Turnbaugh PJ, Bäckhed F, Fulton L, Gordon JI. Diet-induced obesity is S fl linked to markedI but reversible alterations in the mouse distal gut mi- 23. OlefskyR JM, Glass CK. Macrophages, in ammation, and insulin resistance. crobiome.H Cell Host Microbe 2008;3:213–223 TAnnu Rev Physiol 2010;72:219–246 T4. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, GordonE JI. An 24. Gao J, Ghibaudi L, van Heek M, Hwa JJ. Characterization of diet-induced obesity-associated gut microbiome with increased capacityR for energy obese rats that develop persistent obesity after 6 months of high-fat – – harvest. Nature 2006;444:1027 1031N followed by 1 month of low-fat diet. Brain Res 2002;936:87 90 5. Furet JP, Kong LC, Tap J, et al. Differential adaptation of human gut 25. Tian DR, Li XD, Shi YS, et al. Changes of hypothalamic alpha-MSH and microbiota to bariatric surgery-inducedE weight loss: links with metabolic CART peptide expression in diet-induced obese rats. Peptides 2004;25: and low-gradeE inflammation markers. Diabetes 2010;59:3049–3057 2147–2153 6. LeyB RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut 26. Louis P, Scott KP, Duncan SH, Flint HJ. Understanding the effects of diet on microbes associated with obesity. Nature 2006;444:1022–1023 bacterial metabolism in the large intestine. J Appl Microbiol 2007;102: – 7. Samuel BS, Shaito A, Motoike T, et al. Effects of the gut microbiota on host 1197 1208 adiposity are modulated by the short-chain fatty-acid binding G protein- 27. Tuovinen E, Keto J, Nikkila J, Matto J, Lahteenmaki K. Cytokine response coupled receptor, Gpr41. Proc Natl Acad Sci U S A 2008;105:16767– of human mononuclear cells induced by intestinal Clostridium species. 16772 Anaerobe 2013;19:70–76 8. Ding S, Chi MM, Scull BP, et al. High-fat diet: bacteria interactions promote 28. Lam YY, Mitchell AJ, Holmes AJ, et al. Role of the gut in visceral fat intestinal inflammation which precedes and correlates with obesity and inflammation and metabolic disorders. Obesity (Silver Spring) 2011;19: insulin resistance in mouse. PLoS ONE 2010;5:e12191 2113–2120 9. Vijay-Kumar M, Aitken JD, Carvalho FA, et al. Metabolic syndrome and 29. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect altered gut microbiota in mice lacking Toll-like receptor 5. Science 2010; of diet on the human gut microbiome: a metagenomic analysis in hu- 328:228–231 manized gnotobiotic mice. Sci Transl Med 2009;1:6ra14 10. Levin BE. Developmental gene x environment interactions affecting sys- 30. Hildebrandt MA, Hoffmann C, Sherrill-Mix SA, et al. High-fat diet deter- tems regulating energy homeostasis and obesity. Front Neuroendocrinol mines the composition of the murine gut microbiome independently of 2010;31:270–283 obesity. Gastroenterology 2009;137:1716–1724.e2 11. Duca FA, Swartz TD, Sakar Y, Covasa M. Increased oral detection, but 31. Ridaura VK, Faith JJ, Rey FE, et al. Gut microbiota from twins discor- decreased intestinal signaling for fats in mice lacking gut microbiota. PLoS dant for obesity modulate metabolism in mice. Science 2013;341: ONE 2012;7:e39748 1241214 1636 Microbiota Transfer and Obesity Diabetes Volume 63, May 2014

32. Duncan SH, Lobley GE, Holtrop G, et al. Human colonic microbiota associated 43. Fredborg M, Theil PK, Jensen BB, Purup S. G protein-coupled receptor120 with diet, obesity and weight loss. Int J Obes (Lond) 2008;32:1720–1724 (GPR120) transcription in intestinal epithelial cells is significantly affected 33. Ferrer M, Ruiz A, Lanza F, et al. Microbiota from the distal guts of lean and by bacteria belonging to the Bacteroides, Proteobacteria, and Firmicutes – obese adolescents exhibit partial functional redundancy besides clear phyla. J Anim Sci 2012;90(Suppl. 4):10 12 differences in community structure. Environ Microbiol 2013;15:211–226 44. Everard A, Lazarevic V, Derrien M, et al. Responses of gut microbiota and 34. Kim KA, Gu W, Lee IA, Joh EH, Kim DH. High fat diet-induced gut micro- glucose and lipid metabolism to prebiotics in genetic obese and diet- biota exacerbates inflammation and obesity in mice via the TLR4 signaling induced leptin-resistant mice. Diabetes 2011;60:2775–2786 pathway. PLoS ONE 2012;7:e47713 45. Everard A, Belzer C, Geurts L, et al. Cross-talk between Akkermansia 35. Santacruz A, Marcos A, Wärnberg J, et al.; EVASYON Study Group. In- muciniphila and intestinal epithelium controls diet-induced obesity. Proc terplay between weight loss and gut microbiota composition in overweight Natl Acad Sci U S A 2013;110:9066–9071 adolescents. Obesity (Silver Spring) 2009;17:1906–1915 46. Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut 36. Geurts L, Lazarevic V, Derrien M, et al. Altered gut microbiota and endo- microbiota on brain and behaviour. Nat Rev Neurosci 2012;13:701–712 cannabinoid system tone in obese and diabetic leptin-resistant mice: im- 47. Milanski M, Degasperi G, Coope A, et al. Saturated fatty acids produce an pact on apelin regulation in adipose tissue. Front Microbiol 2011;2:149 inflammatory response predominantly through the activation of TLR4 sig- 37. Munukka E, Wiklund P, Pekkala S, et al. Women with and without met- naling in hypothalamus: implications for the pathogenesis of obesity. J abolic disorder differ in their gut microbiota composition. Obesity (Silver Neurosci 2009;29:359–370 Spring) 2012;20:1082–1087 48. Zhang X, Zhang G, Zhang H, Karin M, Bai H, Cai D. Hypothalamic IKKbeta/ 38. Nadal I, Santacruz A, Marcos A, et al. Shifts in clostridia, bacteroides and NF-kappaB and ER stress link overnutrition to energy imbalance and immunoglobulin-coating fecal bacteria associated with weight loss in obesity. Cell 2008;135:61–73 obese adolescents. Int J Obes (Lond) 2009;33:758–767 49. Thaler JP, Yi CX, Schur EA, et al. Obesity is associated with hypothalamic – S 39. Cani PD, Bibiloni R, Knauf C, et al. Changes in gut microbiota control injury in rodents and humans. J Clin Invest 2012;122:153 162 metabolic endotoxemia-induced inflammation in high-fat diet-induced A 50. Gakis G, Mueller MH, Hahn J, Glatzle J, Grundy D, Kreis ME. Neuronal obesity and diabetes in mice. Diabetes 2008;57:1470–1481 activation in the nucleus of the solitaryH tract following jejunal lipopoly- 40. Takeda K, Kaisho T, Akira S. Toll-like receptors. Annu Rev Immunol 2003; saccharide in the rat.E Auton Neurosci 2009;148:63–68 21:335–376 51. Holzer P,L Danzer M, Schicho R, Samberger C, Painsipp E, Lippe IT. Vagal 41. Kleinridders A, Schenten D, Könner AC, et al. MyD88 signaling in the CNS Cafferent input from the acid-challenged rat stomach to the brainstem: D is required for development of fatty acid-induced leptin resistance and I enhancement by interleukin-1beta. Neuroscience 2004;129:439–445 – E diet-induced obesity. Cell Metab 2009;10:249 259 T 52. Bravo JA, Forsythe P, Chew MV, et al. IngestionT of Lactobacillus strain 42. Murphy EF, Cotter PD, Healy S, et al. CompositionR and energy harvesting regulates emotional behavior and central GABA receptor expression in capacity of the gut microbiota: relationshipA to diet, obesity and time in a mouse via the vagus nerve. Proc NatlC Acad Sci U S A 2011;108:16050– mouse models. Gut 2010;59:1635 –1642 16055 A HIS TR T RE EN BE