Page 1 of 46 Diabetes
Replication of obesity and associated signaling pathways through transfer of microbiota from obese prone rat
Frank A Duca1,2,3,*, Yassine Sakar1,2,*, Patricia Lepage1,2, Fabienne Devime1,2, Bénédicte Langelier1,2 Joël Doré1,2, Mihai Covasa1,2,4,5
1 UMR1913 MICALIS, INRA, Jouy en Josas, 78352, France 2 UMR1913 MICALIS, AgroParisTech, Jouy en Josas, 78352, France 3Doctoral School of Physiology and Pathophysiology, University Pierre and Marie Currie, Paris, 75005, France 4Department of Basic Medical Sciences, College of Osteopathic Medicine, Western University of Health Sciences, Pomona, CA, 91766, USA 5Department of Human Health and Development, University of Suceava, Suceava, 720229, Romania *FAD and YS contributed equally to the manuscript
Corresponding Author: Mihai Covasa INRA MICALIS Domaine de Vilvert 78350 Jouy en Josas, France [email protected] Tel: +33 (0)1 34 65 27 78 Fax: +33 (0)1 34 65 24 92
Short Title: microbiota transfer and obesity Word Count:3997 Number of Tables and Figures: 7
1
Diabetes Publish Ahead of Print, published online January 15, 2014 Diabetes Page 2 of 46
Abstract
Aberrations in gut microbiota are associated with metabolic disorders, including obesity.
However, whether shifts in microbiota profile during obesity is a characteristic of the phenotype or a consequence of obesogenic feeding remains elusive. Therefore, we aimed to determine differences in the gut microbiota of obese prone (OP) and obese resistant (OR) rats and examined the contribution of this microbiota to the behavioral and metabolic characteristics during obesity. We found that OP rats display a distinct gut microbiota from obese resistant OR rats fed the same high fat diet, with a higher Firmicutes/Bacteroidetes ratio and significant genera differences. Transfer of OP but not OR microbiota to GF mice replicated the characteristics of the OP phenotype: reduced intestinal and hypothalamic satiation signaling, hyperphagia, increased weight gain and adiposity, and enhanced lipogenesis and adipogenesis. Furthermore, increased gut permeability through conventionalization resulted in inflammation via proinflammatory NF κB/IKKβ signaling in adipose tissue, liver, and hypothalamus. OP donor and GF recipient animals harbored specific species from Oscillibacter and Clostridium Clusters XIVa and IV, that were completely absent from OR animals. In conclusion, susceptibility to obesity is characterized by an unfavorable microbiome predisposing the host to peripheral and central inflammation, promoting weight gain and adiposity during obesogenic feeding.
2
Page 3 of 46 Diabetes
The overabundance of energy dense foods in developed westernized societies has
transformed obesity from an American burden, to a worldwide epidemic, with grave health
and socio economic consequences. Recent advancements in DNA sequencing techniques and
metagenomic profiling have established a link between the trillions of microbial inhabitants
of the gut (i.e. gut microbiota) and the development of obesity related metabolic
dysregulations(1). Gut microbiota is significantly altered in humans and animal models of
obesity, with reduction in bacterial diversity(2) as well as overall compositional shifts, such
as reduced abundance of Bacteroidetes and a proportional increase in Firmicutes phylum(3
6). Gut microbiota regulates several host metabolic functions and microbial dysbiosis is
associated with altered energy homeostasis(1). Obesity is characterized by an enrichment in
genes encoding enzymes responsible for extracting calories from otherwise indigestible
polysaccharides(2,4). In addition to increased energy harvest, the microbial metabolic
byproducts, such as short chain fatty acids (SCFAs), modulate secretion and gene expression
of gut peptides controlling satiety, such as glucagon like peptide 1 (GLP 1) and peptide YY
(PYY), by acting on G coupled protein receptors (GPRs) in intestinal enteroendocrine cells,
suggesting a role for gut microbiota in modulating satiation(7). Obesity and high fat (HF)
feeding are also associated with intestinal, systemic, and adipose tissue inflammation(1).
Intestinal inflammation is an early consequence of HF feeding, present before the onset of
obesity and insulin resistance(8), supporting a direct, causal role for HF induced microbiota
changes in the development of obesity.
Conventionalization studies have demonstrated the contribution of the gut microbiota
to the development of metabolic disease, as the metabolic phenotype is transmissible via gut
microbiota transplantation(3,4,9). These early studies investigated the impact of gut
microbiota using either genetic, transgenic, or HF fed models of obesity, neither of which are
an accurate reflection of human obesity that encompasses the interaction between genes and 3
Diabetes Page 4 of 46
the environment(3,4,9,10). Therefore, in the current study, we examined the role of the gut microbiota in obese prone (OP) and obese resistant (OR) rats, a model that closely mimics the characteristics of human obese phenotype including a polygenic mode of inheritance, whereby some, but not all, individuals are susceptible to weight gain when exposed to an obesogenic environment(10). First, by utilizing both chow and HF fed OP and OR rats, we determined whether microbiota shifts are solely resulting from consumption of an obesogenic diet or a manifestation of the obese phenotype. Secondly, we transplanted microbiota from
OP and OR rats fed a HF diet into germ free (GF) mice to examine the capacity of microbiota to influence phenotype and associated metabolic and molecular changes including gut brain satiation signaling, lipid storage, and systemic and hypothalamic inflammation.
Experimental Procedures
Animals
All experiments were carried out in accordance with the European Guidelines for the Care and Use of Laboratory Animals.
Rats: OP CD (Obese Prone; n=7) and OR CD (Obese Resistant; n=4) from Charles River
Laboratories (Wilimington, MA) were used. Animals were housed individually in temperature controlled vivarium with 12:12 h light/dark cycle (lights on at 0700). Starting at
8wks of age, rats were fed a HF diet (Research Diets, NJ, D12334B, 4.2 kcal/g) for 12wks, until sacrifice(11). Additional OP (n=5) and OR (n=4) rats, kept in the same housing conditions, were maintained on chow throughout, and were used for microbiota analysis.
Mice: Male C57BL/6J GF mice (n=20), from our germ free colonies (ANAXEM, Jouy en
Josas, France) were used for inoculation studies. Two groups (n=10/group) were housed separately in two Trexler type isolators (Igenia, France), with animals housed individually in polycarbonate cages with cedar bedding. 4
Page 5 of 46 Diabetes
Conventionalization
GF mice (n=10 for each phenotype) were conventionalized with fecal microbiota from one
OP and OR donor rat, both maintained on HF diet. Feces from each donor was freshly
collected, quickly diluted and homogenized in liquid casein yeast medium (1:100 w/vol) in
anaerobic conditions. Twelve week GF mice were inoculated immediately thereafter, via oral
gavage (250 l), and were maintained on standard chow (Safe Diets, R03, 2.83 kcal/g) for 4
wks in separate gnotobiotic isolators. Afterwards, half of each group was switched to a HF
diet (Research Diets, D12451, 4.73 kcal/g) for 8wks, while the remaining half was kept on
chow. All animals were sacrificed and tissue collected for analyses.
qRT-PCR and Western Blotting
For all experiments, protein and RNA extraction, and subsequent western blotting and qPCR
were performed as previously described(11). Briefly, RNA was extracted via TRiZol,
quantified, and 10 g of RNA was reverse transcribed and cDNA was diluted 5 fold for qRT
PCR using TaqMan gene expression assays (Applied Biosystems). Data is expressed as the
relative mRNA normalized to β actin and analyzed according to the 2 CT method. For
western blotting, tissues were thawed on ice and suspended in 1 ml RIPA buffer containing
protease inhibitors (Sigma, France). Cells were lysed, homogenized, and centrifuged for 20
min at 14,000×g at 4°C. After quantifying, soluble protein (25 100 g) was run on SDS
PAGE gels containing 8–12% acrylamide, transferred to nitrocellulose membranes, and
probed with antibodies (Santa Cruz Biotechnology and Abcam). Immune complexes were
detected by chemiluminescence and quantified by scanning densitometry using ImageJ.
against β actin (cytosolic proteins) or Ras related nuclear protein (Ran; nucleus proteins) as
internal controls.
5
Diabetes Page 6 of 46
Plasma Analysis
Plasma was analyzed for glucose, triglycerides, total cholesterol and total HDL by AU 400 automated biochemical analyzer (Olympus). Additionally, gut hormones and cytokines were determined in duplicate using a mouse gut hormone panel or mouse cytokine magnetic bead panel (Millipore), measured with Luminex technology (St. Antoine, Paris) following manufacturer’s instructions.
Adipose Tissue Immunodetection
Adipose tissue was fixed with 4% formaldehyde overnight, stored in 75% ethanol, embedded in paraffin, and 4 m thick microtome cut sections were processed using standard procedures. For macrophage infiltration, sections were incubated overnight at 4°C with primary anti F4/80 antibody (sc:71088; Santa Cruz, 1:100) followed by 1 h incubation with secondary antibody (1:200, goat anti rat IgG: sc 2041), then developed with DAB (Dako Kit
K3465) about 5 min. The number of F4/80 positive cells per microscope field was counted and divided by the total number of adipocytes in the field (% macrophages/adipocyte), with a total of 5 to 8 fields counted per animal sample (n=4 animals per group). For immunofluorescence of tumor necrosis factor α (TNF α), sections were incubated overnight at 4°C with primary, anti TNF α antibody (D2D4 #11948; Cell Signaling Technology, 1:100) followed by 1 h incubation at room temperature in the dark with secondary antibody (Anti rabbit IgG (Alexa Fluor® 488 Conjugate #4412, 1:200; Cell Signaling). Images were acquired with an inverted confocal microscope LSM510 with 40x oil immersed objective using AxioVision SE64 Rel.4.8 software. The corrected total cell fluorescence (CTCF) was computed via ImageJ, using uniform sized adipocyte images and handled based on the mean fluorescence detected in the internal controls sections. CTCF = Integrated Density (Area of selected cell X Mean fluorescence of background readings). Background readings were run to control for false negatives or positives of the first and secondary antibody. 6
Page 7 of 46 Diabetes
Immunofluorescence for GLP-2 enteroendocrine cells and intestinal macrophage
infiltration
For GLP 2, sections were incubated overnight at 4°C with primary anti GLP 2 (C 20) (sc
7781; Santa Cruz; 1:100) followed by 1 h incubation at room temperature in the dark with
secondary antibody (anti goat IgG CFL 488: sc 362255; Santa Cruz, 1:200). GLP 2
containing EEC were quantified by counting both total villi and GLP 2 positive stained cells
throughout the entire length of the section (over 20, non overlapping microscopic areas) for
each animal (n=4 per group). For macrophage infiltration, sections were incubated with
mouse anti F4/80+ antibody and revealed with immunoflorescence secondary antibody
coupled with fluochrome alexa 647 (1:100, goat anti mouse IgG H&L Alexa Fluor® 647).
Data was expressed as the number of F4/80+ macrophage per high power field (HPF: visible
area of a slide under magnification). In total between 3 to 6 fields were counted per sample (8
samples) for number of F4/80+ macrophage per HPF All images were acquired with an
inverted confocal microscope LSM510 with 40x oil immersed objective, and processed by
AxioVision SE64 Rel.4.8 software. Controls were run for false negatives or positives of the
first and secondary antibody.
Microbiota analysis
Total DNA was extracted from 200 mg of caecal content from 20 rats (chow: OP=5, OR=4,
HF: OP=7, OR=4) and 16 mice (CVOP=9, CVOR=7)(12). Microbiota composition was
assessed by 454 pyrosequencing (GS FLX TI technology, Genoscreen, Lille, France)
targeting the V3 V4 region of the bacterial 16S rRNA gene (V3fwd:
5’TACGGRAGGCAGCAG3’, V4rev: 5’GGACTACCAGGGTATCTAAT3’). Sequences
were trimmed for barcodes, PCR primers, and binned for a minimal sequence length of
300pb, a minimal base quality threshold of 27, a maximum homopolymers length of 6. 7
Diabetes Page 8 of 46
Resulting sequences were assigned to the different taxonomic levels, from phylum to genus using the Ribosomal Database Project (release 10, update 26 and release 11, update 1)(13).
Sequences were further clustered into operational taxonomic units (OTUs) or phylotypes at
97% of identity using Quantitative Insights Into Microbial Ecology (QIIME) pipeline(14) and cdhit(15). OTUs were assigned to closest taxonomic neighbors and relative bacterial species using Seqmatch and Blastall.
Statistical Analyses
Data are expressed as mean ± SEM. Unless otherwise noted, statistics were performed by either GraphPad Prism (v. 5) or R software (packages ade4, randomForest and lattice). For statistical analysis between groups during HF feeding, t test with Welch’s corrections was used, and ANOVA with Bonferroni post hoc test was used for analyses between groups and diet conditions. Principal component analysis (PCA) was computed based on bacterial genus composition and statistically assessed by a Monte Carlo rank test, and Wilcoxon test was applied for differences in bacterial composition. Significance was accepted at p < 0.05. For heatmaps representation, log10 transformation was applied on the bacterial relative abundance data matrix, which allowed visualizing similarities or differences between samples that affect members of the community that may make up less than 1% of the relative abundance in a sample.. Machine learning techniques employing the Random Forests classifier was applied to relative abundance data of distributed bacterial genera(16).
Results
OP and OR rats have different gut microbiota profiles only during HF-feeding
After 12wks of HF feeding, OP rats had increased 24h food consumption, weighed significantly more, and had increased adiposity relative to OR rats (Figure S1). Among the
8
Page 9 of 46 Diabetes
198,611 obtained 16S rRNA gene sequences, 121,887 passed quality filters and were further
assigned to taxonomic levels, from phylum to bacterial species and OTUs (Operational
Taxonomic Units).. As highlighted by the PCA, during chow feeding, OP and OR microbiota
did not differ. However, 12wks of HF feeding resulted in a differing microbiota of the HF
fed rats compared to their chow counterparts, and, more interestingly, a divergence between
the OP and OR phenotypes during the HF feeding period (Figure 1A). There were no
differences at the phyla level during chow, but HF feeding resulted in significantly increased
Bacteroidetes and Proteobacteria percentages and decreased Firmicutes in both groups.
Morever, during HF feeding, OP rats had significantly less bacteria from Proteobacteria
phyla (Fig. 1B) and a trend towards more bacteria belonging to the Firmicutes (p= 0.06667)
with an average ratio Firmicutes/Bacteroidetes higher in OP rats compared to OR (1.67 vs
1.28). During chow feeding, very few differences were observed at deeper taxonomic levels,
except for bacteria belonging to the genus Clostridium cluster XIVb and Flavonifractor. HF
feeding resulted in many significant genus differences between chow and HF fed animals
(Table S1). More interesting is that specifically during HF feeding, within the Firmicutes
phylum, OP rats exhibited significantly higher proportions of Ruminococcus, Oscillibacter,
and Alistipes genera (Fig. 1C) compared to HF fed OR rats. On the other hand, Bacteroidetes,
Beta proteobacteria (Parasutterella) and Gamma proteobacteria (Escherichia/Shigella) were
significantly more represented in OR rats. This is intriguing given that high levels of E.coli
were also noticed in humans undergoing weight loss following gastric bypass(5). Supervised
classification with random forest classifier analysis demonstrated the variable importance of
genera predicted to belong to OP and OR phenotype under HF diet with an estimate error rate
of 20% (17% for OP and 25% for OR). The main predictive genera for phenotype
discrimination were unclassified Ruminococcaceae, unclassified Porphyromonadaceae,
Clostridium Cluster XIVb, Escherichia, Parasutterella, and Alistipes (Fig. S1). On the 9
Diabetes Page 10 of 46
contrary, for animals on chow diet, the estimate error rate of supervised classification was higer (56%, with 75% error rate for OP classification and 40% for OR animals classification).
Most important genera in data classficiation were related to Flavonifractor, Clostridium cluster XIVb, Johnsonella, Unclassified clostridiales and Lachnospiraceae incertae sedis, although bacterial genera discriminatory power was weak in chow fed rats. Interestingly, some specific bacterial species were differentially represented between OP and OR rats (Fig.
1D; see Table S1 for all species differences), and in particular, Bacteroides vulgatus is reduced in OP rats, similar to obese children(17).
Gut microbiota transfer replicates signatures of OP phenotype
To determine the overall strength of the OP and OR microbiota profiles in the promotion and generation of their metabolic phenotypes, we inoculated GF C57BL/6J mice
(n=10/phenotype) with microbiota from an OP or OR donor, termed CVOP and CVOR respectively, that were placed on either chow or HF diet. Strikingly, phenotype and behavioral differences between OP and OR rats were reliably transferred to CVOP and
CVOR animals maintained on HF diet. After 2wks, HF CVOP mice gained significantly more weight than their chow counterparts, and after 7wks, they were heavier than HF CVOR mice (Fig. 2A). More importantly, mice inoculated with OP microbiota had significantly greater adiposity index (~30% increase) compared to CVOR during HF , but not chow feeding. Similar to OP rats, 24 hour food intake of CVOP mice was increased only during
HF feeding (Fig. 2B). Additionally, homeostatic model assessment of insulin resistance
(HOMA IR; Fig. 2C) and circulating leptin and insulin levels were significantly increased in
HF CVOP animals (Fig. 2D), as were triglyceride and glycemia levels (Fig. S2), features all associated with metabolic syndrome.
10
Page 11 of 46 Diabetes
Similar to OP donors, we found that hyperphagia in HF CVOP mice was associated
with reduced plasma GLP 1 and PYY (Fig. 2E), as well as decreased intestinal PYY and
GLP 1 protein expression (Fig. S2). Furthermore, gene and protein expression of intestinal
GPRs, which mediate nutrient induced satiety peptide secretion(7,18,19), was increased in
OP compared to OR rats, an effect transferred to GF recipients, irrespective of their
maintenance diet (Fig. 2F).
OP microbiota enhances adipogenesis and lipogenesis
Gut microbiota alters expression of host genes regulating adipogenesis and
lipogenesis (20). In adipose tissue, HF CVOP mice exhibited increased fatty acid synthetase
(FAS), and reduced phosphorylated acetyl CoA carboxylase (ACC)/total ACC ratio,
indicating increased capacity for de novo FA synthesis. Furthermore, sterol regulatory
element binding protein 1c, a key transcription factor of glucose induced hepatocyte
lipogenesis and activator of ACC and FAS, was significantly upregulated in CVOP mice
(Fig. 3A and 3B). Additionally, angiopoietin like 4 (Angptl4), an inhibitor of lipoprotein
lipase, was reduced in HF OP donors and their GF recipients (Fig. 3A and S4). This is of
significance as others have shown that intestinal Angptl4 is directly regulated by gut
microbiota, consistent with our observations of reduced intestinal Angptl4 in CVOP animals
(data not shown) (20). As expected, all of these changes in lipogenic markers and enzymes
resulted in increased adipocyte hyperplasia in CVOP mice (Fig. 3E). Furthermore, we found
identical trends in the liver of HF CVOP mice, where FAS and SREPB 1c were increased,
while p ACC/ACC and Angptl4 were both reduced (Fig. 3A and 3B). Additionally,
peroxisome proliferator activated receptor γ (PPARγ), a transcription factor of adipogenesis,
was increased in CVOP mice (Fig. 3C). In the liver, PPARα protein levels were significantly
decreased, contributing to reduced hepatic FA oxidation and increased circulating 11
Diabetes Page 12 of 46
triglycerides. Finally, protein expression of adipose and hepatic FA transporters, cluster of differentiation 36 (CD36) and fatty acid binding protein (FABP), were increased in OP and
CVOP compared with OR rats and CVOR mice, during HF but not chow feeding (Fig. S4 and 3D), supporting its role in increased intracellular shuttling of FAs and lipid accumulation in adipose and hepatic tissue.
“Obese” microbiota alters tight junction proteins and increases inflammation
During HF but not chow feeding, both OP and CVOP animals exhibited altered tight junction protein levels. Specifically, zonula occludens protein 1 (ZO 1) and occludin levels were decreased in distal intestinal epithelial cells, while phosphorylation of myosin light chain (MLC), a mechanism that results in cytoskeleton contraction and disruption of tight junction integrity(21), was increased in HF fed OP and CVOP (Fig. 4A and S5).
Furthermore, jejunal and colonic TNF α, a biomarker of intestinal inflammation, was increased in HF CVOP animals (Fig. 4C), likely due to enhanced intestinal macrophage infiltration (Fig. 4E F). Impairment in markers of tight junction disruption of HF CVOP mice via gut microbiota was further associated with significant decreases in L cells immunostained with GLP 2, a trophic gut hormone shown to regulate gut permeability (22), in the ileum and colon, which contain the largest amounts of L cells (Fig. 4D). Additionally,
HF fed OP rats had higher circulating inflammatory markers: TNF α, monocyte chemotactic protein 1 (MCP 1), macrophage inflammatory protein 1 α (MIP 1α), interleukin (IL) 6, and
IL 1α (Fig. S5). Conventionalization with OP gut microbiota replicated increases in plasma
TNF α, MIP 1α, IL 6, and IL 1a in OP compared to OR recipients on HF diet (Fig. 4B).
12
Page 13 of 46 Diabetes
“Obese” microbiota increases adipose tissue and liver inflammation.
We observed increased macrophage infiltration in CVOP compared to CVOR mice on
HF diet (37.1% vs. 27.4%; p<0.05; Fig. 5A), as well as increased total F4/80 and CD3 (Fig.
S6), markers of macrophage and T lymphocytes infiltration, respectively. Activation of
TLR4 regulates inflammatory process and, as such, TLR4 mRNA levels were increased in
adipose tissue of HF fed OP donors and their CVOP recipients (Fig. 5B and S6), as were the
expression of inflammatory chemokines TNF α, plasminogen activator inhibitor 1 (PAI 1),
and IL 6 (Fig. 5C and 5F and S6). Similarly, HF feeding resulted in upregulation of
inflammatory gene expression TNF α, and IL 6 in the liver of OP donor rats and their GF
mice recipients (Fig. 5D and S6).
Obese animals exhibited an increase in the inflammatory nuclear factor kappa light
chain enhancer of activated β cells/inhibitor of kappa β kinase (NF kB/IKKβ) pathway.
Specifically, phosphorylation/activation of the p65 subunit of NF kB at its ser536 residue and
of IKKβ at residues ser177 and ser181 was significantly increased in adipose and hepatic
tissue of OP and CVOP animals during HF but not chow feeding (Fig. 5E and S6).
Activation of IKK allows NF κB to dissociate and enter the nucleus and induce gene
expression of inflammatory factors. IKKβ activation is accomplished via TLR4, TNF
receptor, and IL 1 receptor activation(23). Here we demonstrate that TLR4 and its TNF α
ligand is increased in adipocytes.
“Obese” microbiota increases hypothalamic inflammation and satiety neuropeptide
expression
Conventionalization of GF mice with OP microbiota significantly increased
expression of hypothalamic IL 6, TNF α, and TLR4 genes compared with CVOR during HF
feeding (Fig. 6A). When examining the effect of conventionalization on hypothalamic 13
Diabetes Page 14 of 46
energy regulating peptides, we found that, on HF diet, CVOP mice had reduced proopiomelanocortin (POMC), and increased Agouti related peptide (AgRP) and neuropeptides Y (NPY) (Fig. 6B). These changes were reliably transferred from OP donors characterized by decreased anorexigenic, and increased orexigenic peptides(24,25), and are the first demonstration of the ability of the gut microbiota to alter CNS energy homeostatic signaling proteins.
Microbiota-related phenotype is preserved and transferred to GF mice
Although specific microbial phyla present in the OP and OR donors were also detected in recolonized germ free mice, phyla differences were not replicated, as the average
Firmicutes/Bacteroidetes ratio was not different in CVOP (1.22) versus CVOR (1.45) mice.
However, bacterial genera distribution differed between HF fed CVOP and CVOR mice (Fig.
7A) and several genera differences between OP and OR donors were replicated in GF recipients (Fig. 7B; see Fig S1 for supervised classification analysis). Clostridium cluster IV and Oscillibacter from the Clostridiaceae family and some unclassified Ruminococcaceae were significantly more represented in OP and CVOP compared to OR animals. Furthermore, we observed 25 bacterial molecular species (or OTUs) that were specific to both OP and
CVOP and were totally absent from OR and CVOR animals (Fig. 7C and Table S7). As such, the 25 OTUs all clustered within the Firmicutes phylum and were related to 10 bacterial isolates, mostly from Clostridium Cluster XIVa, Clostridium Cluster IV, and Oscillibacter.
Clostridium cluster XIVa, part of the E.rectale-C.coccoides group contain the currently recognized butyrate producing bacteria in the gut(26), resulting in more efficient energy extraction from the diet. Furthermore, Clostridia strains from both Clostridium Cluster XIVa and Clostridium Cluster IV have been shown to evoke a proinflammatory cytokine response in vitro(27) while Oscillibacter has been positively correlated with gut permeability(28). 14
Page 15 of 46 Diabetes
Therefore, the potential ability of these specific bacterial strains to increase energy harvest
and promote intestinal inflammation could explain the increases in adiposity and gut
permeability and subsequent systemic inflammation in OP donors and their GF recipients fed
a HF diet.
Discussion
Our studies provide new evidence demonstrating that i) obese prone and obese
resistant phenotypes are associated with distinct and differing gut microbial communities
during HF feeding that are not present during chow feeding, and that ii) transfer of OP
microbiota replicates the obese phenotype of the donor, as well as associated differences in
the chemosensory, metabolic, and neural dysregulations. While some studies suggest the
presence of a specialized “obese” microbiota capable of increased energy storage, most are
confounded by the fact that the observed obesity is often resultant from an obesogenic,
western diet, known to rapidly alter the gut microbiota(29); thus, making it difficult to
ascertain the influence of the host metabolic phenotype vs. the diet on microbial composition
during the obese state. Indeed, HF feeding of humanized gnotobiotic mice results in a rapid
shift in microbiome and its metabolic pathways preceding increased adiposity(29). Similarly,
genetically obese resistant mice exhibit decreased Bacteroidetes, and increased Firmicutes
during HF feeding, emphasizing that diet and not host phenotype may be the main
determinant of microbiota shifts(30). However, our results clearly demonstrate that the
“obese” gut microbiota profile is not a mere result of HF feeding, but instead is unique and
conserved to the obese state, as chow fed OP and OR rats exhibited similar microbiota
profiles that diverged during HF feeding. Therefore, this distinct gut microbiota, with phyla,
genera, and species specific differences is a signature of the obese host phenotype, not only
of HF feeding. This finding is consistent with results from a recent study demonstrating that 15
Diabetes Page 16 of 46
transplantation of gut microbiota from twins discordant for obesity into GF mice resulted in increased body mass and adiposity of mice receiving the obese co twins compared to the lean co twin, whether the groups were maintained on a low or high saturated fat diet(31).
We observed a high Firmicutes to Bacteroidetes ratio in OP rats, however, not all studies replicated the low Bacteroidetes levels in obesity (32), suggesting that differences at the genus and species levels play a greater role. Indeed, we found high levels of bacteria from the Ruminococcus genus in OP rats, similar to that observed in obese humans and HF fed mice(33,34). Ruminococcus is phylogenetically heterogenous, and most species fall under several Clostridium clusters, including Clostridium cluster IV and Clostridium cluster XIVa.
As such, Clostridium leptum (cluster IV) has been associated with both obesity and weight loss(35,36), and was increased in OP and CVOP animals. Further, OTUs only present in OP and their GF recipients were assigned to Clostridium cluster XIVa, which is directly correlated with fat pad mass and BMI(35,37,38), and contains bacterial species known to break down polysaccharides, promoting monosaccharide absorption, enhanced lipogenesis and lipid storage(4,20). In agreement with this, OP microbiota conventionalization resulted in increase in adipogenic and lipogenic enzyme expression in both the liver and adipose tissue, as well as increased FA transporters providing a more efficient transfer and storage of fermented byproducts. Furthermore, we demonstrate, for the first time, the ability of cross species transfer (rat to mouse) of the obese phenotype. Interestingly, OP and OR bacterial phyla level differences were not found in CV animals, indicating that while overall phyla shifts may be indicative and representative of the obese state, they are not the main determinants in shaping the metabolic profile of the host recipient, emphasizing the role of more specific and detailed taxonomic differences in the development of obesity.
Obesity is characterized by increase in gut paracellular permeability likely promoting metabolic endotoxemia(22,39). In this regard, we found that OP donors and GF recipients 16
Page 17 of 46 Diabetes
exhibited disruptions in tight junction protein levels, indicative of increased gut
permeability(39). This was associated with systemic inflammation, likely through LPS or
saturated FAs, as indicated by increased adipose macrophage recruitment and hepatic NF
κB/IKKβ inflammatory signalling pathways(40,41).
Our currents results provide new evidence showing that aberrations in microbiota
during obesity results in alterations in peripheral and central satiety signaling promoting
hyperphagia and weight gain. Gut microbiota modulates expression of intestinal nutrient
receptors(7,11), and here we demonstrate that obesity associated differences in GPRs were
replicated in GF recipient mice, affecting the ability of EECs to sense and respond to
intestinal nutrients. In line with this, both intestinal and circulating satiety peptide levels were
reduced in OP and CVOP animals, in addition to reduced L cell number. Changes in gut
peptide signaling and inflammation may also be secondary to increased adiposity and/or
increased fat consumption, possibly a result of increased energy harvest from OP microbiota,
although the relationship between diet, obesity, and energy extraction is much more complex
than previously thought(42). Interestingly, HF feeding causes rapid microbiota shifts
occurring before changes in adiposity(29), suggesting that direct microbe host crosstalk
influences intestinal signaling mechanism, which precede and result in hyperphagia and
promote weight gain. Indeed, certain bacterial species or their metabolic byproducts have
been shown to influence gut peptide signaling either by directly altering expression of
signaling proteins of enteroendocrine cells or by activating enteroendocrine cells through
GPR signaling,, respectively(7,43 45). Nevertheless, future studies using pair fed animals to
control for dietary fat intake, or time course experiments assessing the development of
obesity with changes in microbiota and intestinal satiation signaling would aid in delineating
their effects,
17
Diabetes Page 18 of 46
There is a growing appreciation of the bidirectional interaction between gut microbiota and the brain with microbiota modulating CNS activity through endocrine and neural pathways(46). Impaired hypothalamic activity, as well as diet induced hypothalamic inflammation, has been causally linked with the development of obesity(47,48). Interestingly, hypothalamic inflammatory signaling occurs rapidly within a few days of HF feeding(49), that corresponds with changes in diet induced microbiota shifts(29). Therefore, it is plausible that CNS inflammation is a direct result of aberrations in gut microbiota. Here we show, for the first time, the ability of the gut microbiota to alter hypothalamic anorexigenic and orexigenic peptides. Although this may be due to increased hypothalamic inflammation, which directly alters leptin and insulin resistance in these neurons, causing a shift in neuropeptides, it is possible that direct signaling pathways between the microbes and the brain may also impact central functions. For example, peripheral endotoxins and cytokines evoke neural activation through vagal afferents(50,51), while the stress reducing effects of prebiotics require intact vagal signaling(52). Furthermore, several bacteria have the capacity to produce neurometabolites, such as GABA, serotonin, and dopamine(46). Nevertheless, the current study clearly shows that “obese” microbiota is associated with hypothalamic energy regulating peptides that promote energy intake and adiposity.
In conclusion, this study demonstrates the broad and extensive contribution of the
“obese” gut microbiota to the modulation of complex molecular signaling machinery responsible for host metabolism, energy storage, intestinal nutrient sensing, and inflammatory pathways, Taken together, it demonstrates that humans susceptible to obesity may harbor a disadvantageous gut microbiome that exacerbates adiposity during HF feeding, that could be used as a potential marker for susceptibility to obesity in humans. Further, by identifying and characterizing specific bacterial groups or species that play a major role in the promotion and
18
Page 19 of 46 Diabetes
perpetuation of obesity, these findings open the potential for future therapeutic treatments
that target these organisms and alleviate the metabolic dysregulation of the host.
19
Diabetes Page 20 of 46
Acknowledgements
We wish to thank Gary Schwartz from Albert Einstein College of Medicine and Jeffrey
Felton from Western University of Health Sciences for their editorial input. We also thank the ANAXEM team of INRA Jouy en Josas, and Timothy Swartz from Institut Cochin
(INSERM) for assisting with the behavioral experiments.
F.A.D and M.C. designed the study, researched data, and wrote the manuscript. Y.S. researched data and reviewed the article. P.L. analyzed metagenomic data and reviewed the article. F.D and B.L. researched data. J.D. reviewed the article.
The study was supported by INRA through a scientific package awarded to M. C. and by the
Romanian National Program PN II ID PCE 2012 4 0608 no. 48/02.09.2013, “Analysis of novel risk factors influencing control of food intake and regulation of body weight”.
No potential conflicts of interest relevant to this article were reported.
M.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
20
Page 21 of 46 Diabetes
References
1. Tremaroli V, Backhed F: Functional interactions between the gut microbiota and host metabolism. Nature 2012;489:242 249 2. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI: A core gut microbiome in obese and lean twins. Nature 2009;457:480 484 3. Turnbaugh PJ, Backhed F, Fulton L, Gordon JI: Diet induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell host & microbe 2008;3:213 223 4. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI: An obesity associated gut microbiome with increased capacity for energy harvest. Nature 2006;444:1027 1031 5. Furet JP, Kong LC, Tap J, Poitou C, Basdevant A, Bouillot JL, Mariat D, Corthier G, Dore J, Henegar C, Rizkalla S, Clement K: Differential adaptation of human gut microbiota to bariatric surgery induced weight loss: links with metabolic and low grade inflammation markers. Diabetes 2010;59:3049 3057 6. Ley RE, Turnbaugh PJ, Klein S, Gordon JI: Microbial ecology: human gut microbes associated with obesity. Nature 2006;444:1022 1023 7. Samuel BS, Shaito A, Motoike T, Rey FE, Backhed F, Manchester JK, Hammer RE, Williams SC, Crowley J, Yanagisawa M, Gordon JI: Effects of the gut microbiota on host adiposity are modulated by the short chain fatty acid binding G protein coupled receptor, Gpr41. Proceedings of the National Academy of Sciences 2008;105:16767 16772 8. Ding S, Chi MM, Scull BP, Rigby R, Schwerbrock NM, Magness S, Jobin C, Lund PK: High fat diet: bacteria interactions promote intestinal inflammation which precedes and correlates with obesity and insulin resistance in mouse. PLoS One 2010;5:e12191 9. Vijay Kumar M, Aitken JD, Carvalho FA, Cullender TC, Mwangi S, Srinivasan S, Sitaraman SV, Knight R, Ley RE, Gewirtz AT: Metabolic syndrome and altered gut microbiota in mice lacking Toll like receptor 5. Science 2010;328:228 231 10. Levin BE: Developmental gene x environment interactions affecting systems regulating energy homeostasis and obesity. Front Neuroendocrinol 2010;31:270 283 11. Duca FA, Swartz TD, Sakar Y, Covasa M: Increased oral detection, but decreased intestinal signaling for fats in mice lacking gut microbiota. PLoS One 2012;7:e39748 12. Lepage P, Seksik P, Sutren M, de la Cochetiere MF, Jian R, Marteau P, Dore J: Biodiversity of the mucosa associated 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, Fish J, Chai B, Farris RJ, Kulam Syed Mohideen AS, McGarrell DM, Marsh T, Garrity GM, Tiedje JM: The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 2009;37:D141 145 14. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R: QIIME allows analysis of high throughput community sequencing data. Nat Methods 2010;7:335 336 15. Li W, Godzik A: Cd hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006;22:1658 1659 21
Diabetes Page 22 of 46
16. Cutler DR, Edwards TC, Jr., Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ: Random forests for classification in ecology. Ecology 2007;88:2783 2792 17. Bervoets L, Van Hoorenbeeck K, Kortleven I, Van Noten C, Hens N, Vael C, Goossens H, Desager KN, Vankerckhoven V: Differences in gut microbiota composition between obese and lean children: a cross sectional study. Gut pathogens 2013;5:10 18. Hirasawa A, Tsumaya K, Awaji T, Katsuma S, Adachi T, Yamada M, Sugimoto Y, Miyazaki S, Tsujimoto G: Free fatty acids regulate gut incretin glucagon like peptide 1 secretion through GPR120. Nat Med 2005;11:90 94 19. Li X, Staszewski L, Xu H, Durick K, Zoller M, Adler E: Human receptors for sweet and umami taste. Proc Natl Acad Sci U S A 2002;99:4692 4696 20. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI: The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 2004;101:15718 15723 21. Turner JR: Molecular basis of epithelial barrier regulation: from basic mechanisms to clinical application. Am J Pathol 2006;169:1901 1909 22. Cani PD, Possemiers S, Van de Wiele T, Guiot Y, Everard A, Rottier O, Geurts L, Naslain D, Neyrinck A, Lambert DM, Muccioli GG, Delzenne NM: Changes in gut microbiota control inflammation in obese mice through a mechanism involving GLP 2 driven improvement of gut permeability. Gut 2009;58:1091 1103 23. Olefsky JM, Glass CK: Macrophages, inflammation, and insulin resistance. Annu Rev Physiol 2010;72:219 246 24. Gao J, Ghibaudi L, van Heek M, Hwa JJ: Characterization of diet induced obese rats that develop persistent obesity after 6 months of high fat followed by 1 month of low fat diet. Brain Res 2002;936:87 90 25. Tian DR, Li XD, Shi YS, Wan Y, Wang XM, Chang JK, Yang J, Han JS: Changes of hypothalamic alpha MSH and CART peptide expression in diet induced obese rats. Peptides 2004;25:2147 2153 26. Louis P, Scott KP, Duncan SH, Flint HJ: Understanding the effects of diet on bacterial metabolism in the large intestine. J Appl Microbiol 2007;102:1197 1208 27. Tuovinen E, Keto J, Nikkila J, Matto J, Lahteenmaki K: Cytokine response of human mononuclear cells induced by intestinal Clostridium species. Anaerobe 2012; 28. Lam YY, Mitchell AJ, Holmes AJ, Denyer GS, Gummesson A, Caterson ID, Hunt NH, Storlien LH: Role of the gut in visceral fat inflammation and metabolic disorders. Obesity (Silver Spring) 2011;19:2113 2120 29. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI: The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 2009;1:6ra14 30. Hildebrandt MA, Hoffmann C, Sherrill Mix SA, Keilbaugh SA, Hamady M, Chen YY, Knight R, Ahima RS, Bushman F, Wu GD: High fat diet determines the composition of the murine gut microbiome independently of obesity. Gastroenterology 2009;137:1716 1724 e1711 1712 31. Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, Griffin NW, Lombard V, Henrissat B, Bain JR, Muehlbauer MJ, Ilkayeva O, Semenkovich CF, Funai K, Hayashi DK, Lyle BJ, Martini MC, Ursell LK, Clemente JC, Van Treuren W, Walters WA, Knight R, Newgard CB, Heath AC, Gordon JI: Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 2013;341:1241214 32. Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P, Flint HJ: Human colonic microbiota associated with diet, obesity and weight loss. Int J Obes (Lond) 2008;32:1720 1724 22
Page 23 of 46 Diabetes
33. Ferrer M, Ruiz A, Lanza F, Haange SB, Oberbach A, Till H, Bargiela R, Campoy C, Segura MT, Richter M, von Bergen M, Seifert J, Suarez A: Microbiota from the distal guts of lean and obese adolescents exhibit partial functional redundancy besides clear differences in community structure. Environ Microbiol 2012; 34. Kim KA, Gu W, Lee IA, Joh EH, Kim DH: High Fat Diet Induced Gut Microbiota Exacerbates Inflammation and Obesity in Mice via the TLR4 Signaling Pathway. PLoS One 2012;7:e47713 35. Santacruz A, Marcos A, Warnberg J, Marti A, Martin Matillas M, Campoy C, Moreno LA, Veiga O, Redondo Figuero C, Garagorri JM, Azcona C, Delgado M, Garcia Fuentes M, Collado MC, Sanz Y: Interplay between weight loss and gut microbiota composition in overweight adolescents. Obesity (Silver Spring) 2009;17:1906 1915 36. Geurts L, Lazarevic V, Derrien M, Everard A, Van Roye M, Knauf C, Valet P, Girard M, Muccioli GG, Francois P, de Vos WM, Schrenzel J, Delzenne NM, Cani PD: Altered gut microbiota and endocannabinoid system tone in obese and diabetic leptin resistant mice: impact on apelin regulation in adipose tissue. Front Microbiol 2011;2:149 37. Munukka E, Wiklund P, Pekkala S, Volgyi E, Xu L, Cheng S, Lyytikainen A, Marjomaki V, Alen M, Vaahtovuo J, Keinanen Kiukaanniemi S: Women with and without metabolic disorder differ in their gut microbiota composition. Obesity (Silver Spring) 2012;20:1082 1087 38. Nadal I, Santacruz A, Marcos A, Warnberg J, Garagorri JM, Moreno LA, Martin Matillas M, Campoy C, Marti A, Moleres A, Delgado M, Veiga OL, Garcia Fuentes M, Redondo CG, Sanz Y: Shifts in clostridia, bacteroides and immunoglobulin coating fecal bacteria associated with weight loss in obese adolescents. Int J Obes (Lond) 2009;33:758 767 39. Cani PD, Bibiloni R, Knauf C, Waget A, Neyrinck AM, Delzenne NM, Burcelin R: Changes in gut microbiota control metabolic endotoxemia induced inflammation in high fat diet induced obesity and diabetes in mice. Diabetes 2008;57:1470 1481 40. Takeda K, Kaisho T, Akira S: Toll like receptors. Annu Rev Immunol 2003;21:335 376 41. Kleinridders A, Schenten D, Konner AC, Belgardt BF, Mauer J, Okamura T, Wunderlich FT, Medzhitov R, Bruning JC: MyD88 signaling in the CNS is required for development of fatty acid induced leptin resistance and diet induced obesity. Cell Metab 2009;10:249 259 42. Murphy EF, Cotter PD, Healy S, Marques TM, O'Sullivan O, Fouhy F, Clarke SF, O'Toole PW, Quigley EM, Stanton C, Ross PR, O'Doherty RM, Shanahan F: Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut 2010;59:1635 1642 43. Fredborg M, Theil PK, Jensen BB, Purup S: G protein coupled receptor120 (GPR120) transcription in intestinal epithelial cells is significantly affected by bacteria belonging to the Bacteroides, Proteobacteria, and Firmicutes phyla. J Anim Sci 2012;90 Suppl 4:10 12 44. Everard A, Lazarevic V, Derrien M, Girard M, Muccioli GG, Neyrinck AM, Possemiers S, Van Holle A, Francois P, de Vos WM, Delzenne NM, Schrenzel J, Cani PD: Responses of gut microbiota and glucose and lipid metabolism to prebiotics in genetic obese and diet induced leptin resistant mice. Diabetes 2011;60:2775 2786 45. Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB, Guiot Y, Derrien M, Muccioli GG, Delzenne NM, de Vos WM, Cani PD: Cross talk between Akkermansia muciniphila and intestinal epithelium controls diet induced obesity. Proc Natl Acad Sci U S A 2013;110:9066 9071 46. Cryan JF, Dinan TG: Mind altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci 2012;13:701 712
23
Diabetes Page 24 of 46
47. Milanski M, Degasperi G, Coope A, Morari J, Denis R, Cintra DE, Tsukumo DM, Anhe G, Amaral ME, Takahashi HK, Curi R, Oliveira HC, Carvalheira JB, Bordin S, Saad MJ, Velloso LA: Saturated fatty acids produce an inflammatory response predominantly through the activation of TLR4 signaling in hypothalamus: implications for the pathogenesis of obesity. J Neurosci 2009;29:359 370 48. Zhang X, Zhang G, Zhang H, Karin M, Bai H, Cai D: Hypothalamic IKKbeta/NF kappaB and ER stress link overnutrition to energy imbalance and obesity. Cell 2008;135:61 73 49. Thaler JP, Yi CX, Schur EA, Guyenet SJ, Hwang BH, Dietrich MO, Zhao X, Sarruf DA, Izgur V, Maravilla KR, Nguyen HT, Fischer JD, Matsen ME, Wisse BE, Morton GJ, Horvath TL, Baskin DG, Tschop MH, Schwartz MW: Obesity is associated with hypothalamic injury in rodents and humans. J Clin Invest 2012;122:153 162 50. Gakis G, Mueller MH, Hahn J, Glatzle J, Grundy D, Kreis ME: Neuronal activation in the nucleus of the solitary tract following jejunal lipopolysaccharide in the rat. Auton Neurosci 2009;148:63 68 51. Holzer P, Danzer M, Schicho R, Samberger C, Painsipp E, Lippe IT: Vagal afferent input from the acid challenged rat stomach to the brainstem: enhancement by interleukin 1beta. Neuroscience 2004;129:439 445 52. Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG, Bienenstock J, Cryan JF: Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad Sci U S A 2011;108:16050 16055
24
Page 25 of 46 Diabetes
Figure Legends
Fig 1. 16S rRNA gene analysis reveal phyla, genus, and species level differences in
microbiota of HF fed OP and OR rats. (A) PCA clustering of gut microbial communities
from individual OP and OR rats fed chow or HF diet for 12wks, clustered according to
principal coordinates analysis. (B) Relative abundance of bacterial phyla present in OP and
OR rats. (C) Heatmap 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 ± SEM. *P<0.05, **P<0.01 denotes difference
between phenotype within diet.
Fig 2. Conventionalization 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)
Percent body weight gain of mice inoculated with either OP or OR microbiota and fed chow
or HF diet. (B) Adiposity index (top), daily calorie intake (bottom) and (C) HOMA IR. (D)
Plasma Leptin and insulin and (E) Gastrointestinal peptides levels following 5hr fast. (F)
Relative mRNA expression of GPRs in intestinal epithelial cells of CVOP and CVOR mice.
Data are expressed as mean ± SEM. *P<0.05, **P<0.01, ***P<0.0001. †denotes significant
difference from chow fed diet condition within phenotype, †P<0.05, ††P<0.01, †††P<0.0001.
Fig 3. Increased lipogenesis and adipogenesis of OP rats were transferred to GF recipients via
microbiota. (A) Protein expression of lipogenic and adipogenic enzymes in liver and adipose
tissue of CVOP and CVOR. (B) Phosphorylation of ACC. (C) PPARγ protein expression in
adipose tissue (top) and PPARα expression in liver (bottom). (D)Western blot images of
fatty acid transporters, in adipose and hepatic tissue. (E) Adipocyte surface area (see Fig.5F
for representative image). Data are expressed as mean ± SEM. *P<0.05, **P<0.01.
25
Diabetes Page 26 of 46
Fig 4. Conventionalization with OP microbiota altered marekers 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 5hr fast. (C) TNF α gene expression levels in the jejunum and colon. (D) Immunofluorescent (IF) image (40x) of GLP 2 stained
L cells in the ileum and L cell counts; arrow indicates a fluorescent L cell. Insert represents digital zoom of identified fluorescent L cell. (E) Immunofluorescent 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 ± SEM. *P<0.05, **P<0.01. †denotes significant difference from chow fed diet condition within phenotype, †P<0.05, ††P<0.01.
Fig 5. HF fed CVOP mice exhibit increased adipose and hepatic inflammation via enhanced macrophage infiltration and NF κB/IKKβ signaling. (A) Immunohistochemistry images and quantitative analysis of macrophage infiltration (indicated by arrows) in adipose tissue of
CVOR (left) and CVOP (middle) mice. Right panel: CVOP section without secondary antibody. (B) TLR4 mRNA expression in liver and adipose tissue of CVOP and CVOR mice.
(C,D) mRNA transcript levels of inflammatory cytokines in adipose and hepatic tissue, respectively. (E) Western blot images of NF κB and IKKβ proteins. (F) Immunofluorescence
(IF) images of TNF α in adipose tissue and quantitative measurement (integrated density) of
IF. Top panels: representative image of stained adipocytes with TNF α immunofluorescence ; lower panels: TNF α immunofluorescence only. (H) Protein expression of F4/80 and CD3 in adipose tissue. Data are expressed as mean ± SEM. *P<0.05, **P<0.01, ***P<0.0001. † denotes significant difference from chow fed diet condition within phenotype, †P<0.05,
††P<0.01, †††P<0.0001.
26
Page 27 of 46 Diabetes
Fig 6. Conventionalization of GF mice promotes hypothalamic inflammation and alter 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. Data are expressed as mean ± SEM. *P<0.05, **P<0.01,
***P<0.0001. †denotes significant difference from chow fed diet condition within phenotype,
†P<0.05, ††P<0.01, †††P<0.0001.
Fig 7. Distinct genera and species level differences were transferable from obese donors to
conventionalized GF mice. (A) PCA clusters of gut microbial communities from individual
HF fed CVOP and CVOR mice, clustered according to principal coordinates analysis. The
two first components explain 44.61% of the variability (Component 1 = 25.16%; Component
2 = 19.45%). (B) Comparisons of significant genera differences between phenotypes (OP/OR
or CVOP/CVOR). (C) 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 grey, respectively. Data are expressed as mean ± SEM.
*P<0.05
27
A CDiabetes * Page 28 of 46 * * ** * **
Comp1=23.6% Comp2=9.0% D Simulated p-value: 1e-06 8 Clostridiales OP Chow 6 OR Chow 100% Other OP HF B OR HF 4 Tenericutes ** * 80% Deferribacteres 2 ** Verrucomicrobia 0 Actinobacteria 60% Rel. of Ruminococcus Rel. of bacterium Clostridium Ruminococcus sp. Proteobacteria Lachnospiraceae flavefaciens ASF500 symbiosum M-1 bacterium 19gly4 LRC017 REM001 Bacteroidetes 20 40% Firmicutes Bacteroidales 16 **
12 20% 8 *
Sequences of Total Percentage 4 0% * 0 OR OP OR OP Bacteroides vulgatus Bacteroides vulgatus Bacteroides caccae Rel. of Gram-negative Chow High-Fat BCRC12903 ATCC8482 JCM9498 bacterium cTPY13 A B 15 C 4 Page60 29 of 46 CVOR CHOW Diabetes CVOR † † † CVOR † † † † † † † † † CVOP CVOP *** CVOP CHOW *** 3 † † * * 10 CVOR HF † † 2 40 CVOP HF † †
5 HOMA-IR † 1 † Index Adiposity 0 0 20 CHOW HF CHOW HF 15 † † % Weight Body Gain * 10 0 0 1 2 3 4 5 6 7 8 Weeks on Diet 5
Kilocalories (per day)(per Kilocalories 0 CHOW HF
CHOW D E CHOW F 4 40000 200 CVOR CVOR CVOR † † † CVOP ** CVOP CVOP 3 30000 ** 150
2 * 20000 100 ** 1 Leptin (pg/mL) Leptin 10000
50 mRNA Expression mRNA
0 0 CHOW HF 0 GPR40 GPR41 GPR120
Plasma Concentration (pg/mL) Concentration Plasma GLP-1 PYY 4000 † † † CVOR ** HF-Diet HF-Diet CVOP 200 5 † 3000 CVOR ** CVOP CVOP 4 CVOP 2000 150 † † † 3 † ** 100 * †
Insulin (pg/mL) Insulin 1000 2 † † ** † † 0 50 1 CHOW HF * Expression mRNA
0 0
Plasma Concentration (pg/mL) Concentration Plasma GLP-1 PYY GPR40 GPR41 GPR120 Diabetes Page 30 of 46 A CHOW B C CHOW 2.0 2.5 * CVOR 4 CVOR CVOR 1.5 * CVOP CVOP 2.0 CVOP
-Actin 3
**
1.0 1.5 / RAN
2 1.0
0.5
PPAR Relative to Relative pACC/T-ACC 1 0.5 0.0 FAS SREPB-1 Angptl4 FAS SREPB-1 Angptl4 0 0.0 ADIPOSE LIVER Adipose Liver CHOW HF
HF-Diet HF-Diet 4 2.5 4 CVOR CVOR CVOR CVOP 2.0 CVOP 3 3 * CVOP
-Actin * 1.5 / RAN *
** 2 2
* ** 1.0 ** pACC/T-ACC ** 1 PPAR
1 * 0.5 Relative to Relative
0 0 0.0 FAS SREPB-1 Angptl4 FAS SREPB-1 Angptl4 Adipose Liver CHOW HF ADIPOSE LIVER
D )
E 2 ADIPOSE CHOW HF-Diet 2000 ** CVOR CVOR CVOP CVOR CVOP CVOP CD36 1500 FABP Actin 1000
LIVER CHOW HF-Diet 500 CVOR CVOP CVOR CVOP 0
CD36 area (µm surface Adipoctye FABP Actin CHOW HF-Diet APage 31 of 46 Diabetes C CHOW 2.0 2.0 CVOR CVOR 5 CVOP CVOP CVOR
1.5 1.5 4 CVOP
-Actin
-Actin 3 1.0 1.0 * 2
0.5 0.5 Expression mRNA
* 1 Relative to Relative Relative to Relative **
0.0 0.0 TNF- 0 ZO1 Occludin p-MLC ZO1 Occludin p-MLC Jejunum Colon B CHOW HF-Diet HF-Diet 5 150 200 †† CVOR CVOR CVOR ** * 4 CVOP CVOP 150 CVOP 100 3 100 ** † **
50 50 2 mRNA Expression mRNA
1 10 10 * **
5 5 TNF- 0 Concentration (pg/mL) Concentration Concentration (pg/mL) Concentration Jejunum Colon 0 0 TNF- IL-6 MCP-1 MIP-1a IL-1a TNF- IL-6 MCP-1 MIP-1a IL-1a
100 E + 80
60 D HF-Diet 40 2.5 CVOR
Number of F4/80 of Number 20
2.0 CVOP per HPF macrophage CVOR 50 µm 0 1.5 CVOR CVOP 1.0 F 0.5 HF-Diet GLP-2 cells per villi GLP-2 cells CVOR CVOP 0.0 Illeum Colon F40/80 CD3 Actin
CVOP 50 µm Diabetes Page 32 of 46 HF-Diet 50 E CHOW HF-Diet A CVOR CVOP CONTROL CVOR CVOP CVOR CVOP 40 * pSer536-NF-κB p65 30 NF-κB 20 pSer177/181-IKKβ
10 IKKβ 50 µm
% Macrophage/Adipocyte 0 CVOR CVOP F CVOR CVOP B CHOW HF-Diet 5 5 † CVOR CVOR *** 4 CVOP 4 CVOP
3 3
2 2 50 µm
1 1
mRNA expression mRNA mRNA expression mRNA
0 0 Liver Adipose Liver Adipose ADIPOSE C CHOW HF-Diet 15 15 CVOR CVOR ††† CVOP CVOP *** 10 10 8.010 6 † CVOR CVOP ** ** 6.010 6 5 5 †
6 mRNA expression mRNA mRNA expression mRNA 4.010
0 0 6
TNF- PAI-1 IL-6 TNF- PAI-1 IL-6 2.010 Intergrated Density Intergrated LIVER 0 D CHOW HF-Diet CVOR 10 10 CVOR †† CVOP 8 CVOP 8 ** †† ** 6 6
4 4
2 2
mRNA expression mRNA mRNA expression mRNA
0 0 TNF- PAI-1 IL-6 TNF- PAI-1 IL-6 Page 33 of 46 Diabetes
A CHOW HF-Diet
2.0 CVOR 2.0 CVOR CVOP CVOP † ** † 1.5 1.5 ** **
1.0 1.0
0.5 0.5
mRNA expression mRNA expression mRNA
0.0 0.0 TNF- IL-6 TLR-4 TNF-a IL-6 TLR-4
CHOW HF-Diet B 2.0 CVOR 2.0 CVOR CVOP CVOP ** 1.5 1.5 * 1.0 1.0 † * † †
0.5 0.5
mRNA expression mRNA mRNA expression mRNA
0.0 0.0 POMC CART AgRP NPY POMC CART AgRP NPY A d = 2 C Diabetes Page 34 of 46 CVOP
CVOR
CVOR
B 15 * OR OP CVOR 10 * CVOP
5 * Percentage of Total Bacteria Total of Percentage * * * * * 0 *
Blautia Clostridium IV Oscillibacter Parasutterella Clostridium XlVb
unclassified Ruminococcaceae Lachnospiracea incertae sedis Page 35 of 46 Diabetes
Supplemental Table 1- Bacterial genus differences between chow and HF-feeding
High-Fat Diet Chow Diet P-values High-Fat Diet Chow Diet P-values Phylum Genus Mean SD FQ (%) Mean SD FQ (%) HF vs Chow Phylum Genus Mean SD FQ (%) Mean SD FQ (%) HF vs Chow Actinobacteria Bifidobacterium 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Marvinbryantia 0.00 0.01 10.00 0.06 0.05 77.78 0.0031 Actinobacteria Adlercreutzia 0.04 0.04 50.00 0.01 0.02 11.11 0.0671 Firmicutes Moryella 0.00 0.00 0.00 0.10 0.08 88.89 0.0003 Actinobacteria Asaccharobacter 0.01 0.01 20.00 0.00 0.00 0.00 0.1930 Firmicutes Oribacterium 0.00 0.01 10.00 0.02 0.01 66.67 0.0083 Actinobacteria Coriobacterium 0.00 0.00 0.00 0.00 0.00 0.00 NA Firmicutes Parasporobacterium 0.00 0.00 0.00 0.05 0.06 55.56 0.0099 Actinobacteria Cryptobacterium 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Pseudobutyrivibrio 0.00 0.00 0.00 0.01 0.01 22.22 0.1457 Actinobacteria Enterorhabdus 0.01 0.02 20.00 0.00 0.00 0.00 0.1930 Firmicutes Robinsoniella 0.02 0.05 10.00 0.24 0.15 88.89 0.0008 Actinobacteria Olsenella 0.00 0.00 0.00 0.01 0.01 22.22 0.1457 Firmicutes Roseburia 0.77 0.53 100.00 0.64 0.57 100.00 0.4470 Actinobacteria Paraeggerthella 0.00 0.00 0.00 0.01 0.01 22.22 0.1457 Firmicutes Syntrophococcus 0.00 0.00 0.00 0.01 0.03 33.33 0.0624 Actinobacteria Corynebacterium 0.01 0.03 20.00 0.00 0.00 0.00 0.1930 Firmicutes unclassified_Lachnospiraceae 9.81 4.46 100.00 39.77 4.00 100.00 0.0000 Actinobacteria Rothia 0.03 0.07 30.00 0.00 0.01 11.11 0.3306 Firmicutes Desulfonispora 0.03 0.02 70.00 0.01 0.02 22.22 0.0774 Ascomycota Saccharomyces 0.00 0.00 0.00 0.01 0.01 22.22 0.1457 Firmicutes Peptococcus 2.84 0.79 100.00 0.17 0.12 88.89 0.0000 Bacteroidetes Bacteroides 24.74 10.69 100.00 1.25 0.48 100.00 0.0000 Firmicutes Clostridium_XI 1.33 0.39 100.00 0.14 0.14 77.78 0.0003 Bacteroidetes Barnesiella 2.21 1.70 100.00 6.23 3.31 100.00 0.0021 Firmicutes Acetanaerobacterium 0.00 0.00 0.00 0.06 0.07 66.67 0.0035 Bacteroidetes Dysgonomonas 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Acetivibrio 0.39 0.49 80.00 0.21 0.17 100.00 1.0000 Bacteroidetes Parabacteroides 0.82 0.26 100.00 0.10 0.07 100.00 0.0000 Firmicutes Anaerotruncus 0.08 0.05 90.00 0.67 0.64 100.00 0.0000 Bacteroidetes Porphyromonas 0.04 0.07 40.00 0.11 0.07 100.00 0.0181 Firmicutes Butyricicoccus 0.13 0.14 90.00 0.44 0.26 100.00 0.0030 Bacteroidetes Tannerella 0.00 0.00 0.00 0.01 0.02 33.33 0.0624 Firmicutes Clostridium_III 0.08 0.09 60.00 0.09 0.04 100.00 0.6519 Bacteroidetes unclassified_Porphyromonadaceae 0.98 0.92 90.00 5.95 2.11 100.00 0.0000 Firmicutes Clostridium_IV 3.59 1.99 100.00 4.27 1.42 100.00 0.6038 Bacteroidetes Hallella 0.00 0.00 0.00 0.01 0.03 22.22 0.1457 Firmicutes Ethanoligenens 0.03 0.10 20.00 0.04 0.06 55.56 0.2028 Bacteroidetes Prevotella 0.04 0.08 40.00 1.06 0.39 100.00 0.0002 Firmicutes Faecalibacterium 0.02 0.04 20.00 0.00 0.00 0.00 0.1930 Bacteroidetes unclassified_Prevotellaceae 0.00 0.00 0.00 0.04 0.05 55.56 0.0099 Firmicutes Flavonifractor 1.42 0.92 100.00 0.16 0.09 100.00 0.0021 Bacteroidetes Alistipes 9.47 5.04 100.00 0.98 0.65 100.00 0.0030 Firmicutes Hydrogenoanaerobacterium 0.07 0.08 70.00 0.14 0.12 100.00 0.0546 Bacteroidetes Sphingobacterium 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Oscillibacter 1.38 0.91 90.00 6.80 2.23 100.00 0.0000 Deferribacteres Mucispirillum 0.11 0.32 20.00 0.07 0.05 77.78 0.0470 Firmicutes Pseudoflavonifractor 0.37 0.26 90.00 2.05 1.08 100.00 0.0000 Euryarchaeota Pyrococcus 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Ruminococcus 5.68 4.35 80.00 1.44 0.93 100.00 0.0940 Firmicutes Globicatella 0.00 0.01 10.00 0.00 0.00 0.00 0.3991 Firmicutes Sporobacter 0.02 0.04 20.00 0.05 0.05 77.78 0.0703 Firmicutes Sediminibacillus 0.00 0.00 0.00 0.01 0.02 11.11 0.3428 Firmicutes unclassified_Ruminococcaceae 3.32 1.90 100.00 7.25 2.58 100.00 0.0003 Firmicutes Enterococcus 0.01 0.02 30.00 0.00 0.00 0.00 0.0947 Firmicutes unclassified_Clostridiales 1.76 1.37 100.00 2.43 0.95 100.00 0.4470 Firmicutes Lactobacillus 0.25 0.25 90.00 0.30 0.14 100.00 0.2110 Firmicutes Clostridium_XVIII 0.01 0.02 30.00 0.00 0.00 0.00 0.0947 Firmicutes Staphylococcus 0.01 0.02 10.00 0.01 0.02 11.11 0.9390 Firmicutes Coprobacillus 0.02 0.03 40.00 0.00 0.00 0.00 0.0452 Firmicutes Lactococcus 0.06 0.06 60.00 0.00 0.00 0.00 0.0087 Firmicutes Erysipelotrichaceae_incertae_sedis 0.07 0.08 60.00 0.00 0.00 0.00 0.0087 Firmicutes Streptococcus 0.03 0.04 50.00 0.00 0.00 0.00 0.0205 Firmicutes Holdemania 0.01 0.02 20.00 0.00 0.00 0.00 0.1930 Firmicutes Anaerosporobacter 0.02 0.03 30.00 0.17 0.14 88.89 0.0051 Firmicutes Turicibacter 0.43 0.35 80.00 0.04 0.07 55.56 0.0181 Firmicutes Caloramator 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Dialister 0.01 0.04 10.00 0.00 0.00 0.00 0.3991 Firmicutes Clostridium_sensu_stricto 5.30 2.45 100.00 0.12 0.16 55.56 0.0003 Firmicutes Mitsuokella 0.01 0.05 10.00 0.00 0.00 0.00 0.3991 Firmicutes Fervidicella 0.00 0.00 0.00 0.01 0.02 11.11 0.3428 Firmicutes Selenomonas 0.00 0.00 0.00 0.05 0.02 100.00 0.0001 Firmicutes Oxobacter 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes unclassified_Firmicutes 0.00 0.01 10.00 0.04 0.05 55.56 0.0334 Firmicutes unclassified_Clostridiaceae_1 0.04 0.14 10.00 0.00 0.00 0.00 0.3991 Proteobacteria Gemmiger 0.01 0.04 10.00 0.00 0.00 0.00 0.3991 Firmicutes Alkaliphilus 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Proteobacteria Kiloniella 0.18 0.37 40.00 0.07 0.09 55.56 0.7573 Firmicutes Tindallia 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Proteobacteria Fodinicurvata 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Sporosalibacterium 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Proteobacteria Insolitispirillum 0.17 0.26 50.00 0.02 0.02 55.56 0.4122 Firmicutes Sporanaerobacter 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Proteobacteria Rhodospirillum 0.00 0.01 10.00 0.01 0.02 11.11 0.9390 Firmicutes Anaerovorax 0.01 0.03 10.00 0.10 0.06 100.00 0.0007 Proteobacteria Janthinobacterium 0.01 0.02 10.00 0.00 0.00 0.00 0.3991 Firmicutes Eubacterium 0.20 0.28 50.00 0.10 0.07 88.89 1.0000 Proteobacteria Parasutterella 2.22 1.20 100.00 0.33 0.27 100.00 0.0003 Firmicutes Gracilibacter 0.00 0.00 0.00 0.01 0.03 22.22 0.1457 Proteobacteria Vampirovibrio 0.00 0.00 0.00 0.01 0.02 11.11 0.3428 Firmicutes Clostridium_XII 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Proteobacteria Helicobacter 0.01 0.02 10.00 0.00 0.00 0.00 0.3991 Firmicutes Anaerostipes 0.18 0.30 30.00 0.17 0.08 100.00 0.1428 Proteobacteria Aeromonas 0.00 0.00 0.00 0.01 0.01 22.22 0.1457 Firmicutes Blautia 7.95 3.81 100.00 0.23 0.18 100.00 0.0000 Proteobacteria Enterobacter 0.01 0.01 20.00 0.00 0.00 0.00 0.1930 Firmicutes Butyrivibrio 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Proteobacteria Escherichia/Shigella 1.00 1.39 80.00 0.18 0.16 88.89 0.3066 Firmicutes Clostridium_XlVa 3.73 1.76 100.00 13.14 5.00 100.00 0.0001 Proteobacteria Proteus 0.05 0.06 60.00 0.00 0.01 11.11 0.0208 Firmicutes Clostridium_XlVb 3.39 1.05 100.00 0.10 0.10 77.78 0.0003 Proteobacteria unclassified_Enterobacteriaceae 0.00 0.01 10.00 0.00 0.01 11.11 0.9390 Firmicutes Coprococcus 0.70 0.83 90.00 0.04 0.03 55.56 0.0074 Proteobacteria Acinetobacter 0.03 0.09 10.00 0.00 0.00 0.00 0.3991 Firmicutes Dorea 0.01 0.02 10.00 0.04 0.07 33.33 0.2294 Proteobacteria Pasteurella 0.06 0.12 40.00 0.02 0.03 44.44 0.8203 Firmicutes Hespellia 0.00 0.00 0.00 0.01 0.02 11.11 0.3428 Proteobacteria unclassified_Pasteurellaceae 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Firmicutes Johnsonella 0.00 0.00 0.00 0.04 0.08 44.44 0.0256 Tenericutes Anaeroplasma 0.05 0.10 20.00 0.01 0.03 11.11 0.5630 Firmicutes Lachnospiracea_incertae_sedis 1.96 1.41 100.00 0.97 0.51 100.00 0.0535 unclassified_Bacteria unclassified_Bacteria 0.11 0.15 70.00 0.33 0.37 100.00 0.0787 Firmicutes Lactonifactor 0.00 0.00 0.00 0.00 0.01 11.11 0.3428 Verrucomicrobia Akkermansia 0.03 0.05 40.00 0.00 0.00 0.00 0.0452 1
Diabetes Page 36 of 46
Supplemental Table 2 - Bacterial species differences between OP and OR and CVOP and CVOR animals during high-fat feeding
OP OR CVOP CVOR p-value 1st HIT AN E- S_ab FQ FQ FQ FQ p-value OP CVOP vs Phylum Genus GenBank score Seqmatch 1st isolate score Mean SD (%) Mean SD (%) Mean SD (%) Mean SD (%) vs OR CVOR Adlercreutzia equolifaciens; FJC-M48; Actinobacteria Uncl. Coriobacterineae gb|GU241872.1| 0.0 AB434709 0.980 0.03 0.05 29 0.03 0.05 50 0.06 0.08 60 0.01 0.02 25 0.743 0.227 Actinobacteria Uncl. Coriobacterineae gb|GQ300123.1| 0.0 Enterorhabdus caecimuris (T); B7; DQ789120 0.885 0.00 0.01 14 0.02 0.03 25 0.03 0.02 80 0.06 0.12 25 0.674 0.443 Enterorhabdus mucosicola (T); type strain: Actinobacteria Uncl. Coriobacterineae gb|JF254730.1| 0.0 Mt1B8; AM747811 1.000 0.00 0.00 0 0.00 0.00 0 0.01 0.02 40 0.04 0.03 75 NA 0.201 Bacteroidetes Alistipes gb|JF246324.1| 0.0 Alistipes finegoldii; ANH 2437; AJ518874 0.802 3.87 0.54 100 2.26 2.09 100 4.19 2.46 100 6.37 2.60 100 0.230 0.191 Bacteroidetes Alistipes gb|HQ789275.1| 0.0 Alistipes finegoldii; CIP 107999; AY643084 0.993 0.93 0.56 100 0.08 0.05 100 1.69 1.16 100 0.56 0.18 100 0.006 0.063 Bacteroidetes Alistipes gb|FJ838147.1| 0.0 Bacteroides sp. Smarlab 3302398; AY538696 0.849 0.02 0.06 14 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.571 NA Bacteroidetes Bacteroides gb|JQ894003.1| 0.0 Bacteroides acidifaciens; A29; AB021160 0.976 2.03 1.40 100 5.42 3.88 100 2.80 1.52 100 3.54 1.23 100 0.042 0.556 Bacteroidetes Bacteroides gb|JQ084931.1| 0.0 Bacteroides acidifaciens; A43; AB021165 0.993 0.12 0.10 100 0.44 0.43 100 0.28 0.17 100 0.35 0.26 100 0.164 1.000 Bacteroidetes Bacteroides gb|JQ084931.1| 0.0 Bacteroides acidifaciens; A43; AB021165 1.000 0.04 0.03 86 0.19 0.16 75 0.06 0.07 80 0.07 0.08 100 0.185 0.730 Bacteroidetes Bacteroides gb|JQ891250.1| 0.0 Bacteroides acidifaciens; SLC1-3; AB599942 0.992 4.19 1.95 100 2.21 0.60 100 4.11 3.67 100 1.83 0.89 100 0.006 0.413 Bacteroidetes Bacteroides gb|HQ789470.1| 0.0 Bacteroides caccae; JCM9498; EU136686 0.970 0.36 0.75 57 3.98 2.44 100 0.97 1.27 80 0.71 0.50 100 0.010 0.905 Bacteroidetes Bacteroides gb|JQ317269.1| 3E-149 Bacteroides vulgatus ATCC 8482; CP000139 1.000 2.90 1.01 100 5.04 0.70 100 1.90 0.61 100 1.01 0.51 100 0.012 0.111 Bacteroidetes Bacteroides gb|JQ607721.1| 0.0 Bacteroides vulgatus; BCRC12903; EU136687 0.995 10.59 4.26 100 16.95 1.96 100 6.28 1.33 100 3.54 1.67 100 0.042 0.032 Bacteroidetes Bacteroides gb|HQ792859.1| 0.0 Bacteroides vulgatus; DJF_B081; EU728705 0.984 0.10 0.07 86 0.08 0.05 100 0.07 0.05 100 0.00 0.00 0 0.927 0.015 Barnesiella intestinihominis (T); YIT 11860; Bacteroidetes Barnesiella gb|FJ835261.1| 0.0 AB370251 0.667 0.72 0.35 100 0.21 0.28 75 3.09 2.12 100 3.98 1.96 100 0.073 0.730
Bacteroidetes Barnesiella gb|JQ893854.1| 0.0 Gram-negative bacterium cL10-2b-4; AY239469 0.745 1.30 0.42 100 0.52 0.53 100 2.51 1.25 100 1.68 0.53 100 0.073 0.286 Bacteroidetes Parabacteroides dbj|AB702762.1| 0.0 Bacteroides sp. ASF519; ASF 519; AF157056 1.000 0.02 0.04 29 0.04 0.06 50 0.58 0.98 100 1.83 0.51 100 0.584 0.111 Parabacteroides distasonis; JCM5825; Bacteroidetes Parabacteroides gb|EU457296.1| 3E-171 EU136681 0.637 0.49 0.47 86 0.03 0.04 50 0.00 0.00 0 0.00 0.00 0 0.070 NA Parabacteroides goldsteinii; JCM13446; 14; Bacteroidetes Parabacteroides dbj|AB702762.1| 0.0 EU136697 0.995 0.72 0.27 100 0.76 0.24 100 10.73 3.40 100 8.80 2.39 100 0.927 0.556 Bacteroidetes Porphyromonas gb|JF259494.1| 0.0 Porphyromonas sp. MI10-1288x; HM583587 0.671 0.02 0.03 29 0.00 0.00 0 0.17 0.10 100 0.22 0.06 100 0.327 0.413 Bacteroidetes Prevotella gb|JQ892741.1| 0.0 Prevotella sp. DJF_B116; EU728713 0.708 0.01 0.03 29 0.00 0.00 0 0.00 0.00 0 0.02 0.02 50 0.327 0.131 Bacteroidetes Uncl. "Porphyromonadaceae" dbj|AB702775.1| 0.0 Gram-negative bacterium cTPY-13; AY239461 0.606 1.14 0.61 100 0.16 0.23 50 1.52 0.65 100 1.57 1.16 75 0.011 0.905 Porphyromonadaceae bacterium C941; Bacteroidetes Uncl. "Porphyromonadaceae" dbj|AB702775.1| 0.0 JF803519 0.596 0.04 0.05 57 0.02 0.04 25 0.11 0.05 100 0.07 0.05 75 0.410 0.413
Deferribacteres Mucispirillum gb|JQ891435.1| 0.0 Flexistipes group bacterium HRI1cae; AF059187 0.972 0.17 0.37 43 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.186 NA Clostridiales bacterium oral taxon F32; VO026; Firmicutes Acetivibrio gb|GQ448052.1| 0.0 HM099644 0.870 0.50 0.37 100 0.01 0.01 25 0.01 0.02 40 0.02 0.05 25 0.010 1.000 Firmicutes Allobaculum gb|JQ893241.1| 0.0 Clostridium sp. ID4; AY960571 0.985 0.00 0.00 0 0.00 0.00 0 0.07 0.10 60 0.11 0.09 75 NA 0.533 Firmicutes Anaerosporobacter gb|FJ880932.1| 0.0 bacterium NLAE-zl-C556; JQ608256 0.903 0.20 0.26 71 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.050 NA Eubacterium hadrum; type strain: DSM 3319; Firmicutes Anaerostipes gb|FJ881307.1| 0.0 FR749934 0.832 0.23 0.25 57 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.101 NA Anaerotruncus colihominis (T); 14565; Firmicutes Anaerotruncus gb|JQ891773.1| 1E-178 AJ315980 0.985 0.01 0.02 43 0.00 0.00 0 0.07 0.10 80 0.09 0.11 50 0.186 0.901
Firmicutes Anaerotruncus gb|JQ891988.1| 0.0 Anaerotruncus colihominis; HKU19; DQ002932 0.863 0.03 0.03 57 0.01 0.03 25 0.11 0.16 60 0.41 0.51 100 0.410 0.268 Firmicutes Blautia gb|JF258992.1| 0.0 bacterium NLAE-zl-C551; JQ608251 0.952 0.31 0.25 86 0.79 0.25 100 0.00 0.00 0 0.01 0.01 25 0.042 0.371 Firmicutes Blautia gb|GQ493551.1| 0.0 Blautia glucerasea (T); HFTH-1; AB439724 0.987 0.06 0.07 86 0.07 0.07 75 0.00 0.00 0 0.00 0.00 0 0.850 NA Firmicutes Blautia gb|EU772879.1| 0.0 Blautia sp. M25; HM626178 0.930 3.35 1.99 100 5.06 1.61 100 0.01 0.02 40 0.02 0.03 50 0.164 0.687 Firmicutes Blautia gb|HM124291.1| 0.0 butyrate-producing bacterium SR1/1; AY305321 0.874 0.41 0.19 100 0.55 0.20 100 0.04 0.05 60 0.23 0.12 100 0.230 0.019
2 Page 37 of 46 Diabetes
Lachnospiraceae bacterium DJF_RP14; Firmicutes Blautia gb|EU772879.1| 0.0 EU728751 0.936 0.53 0.59 86 0.81 0.33 100 0.00 0.00 0 0.00 0.00 0 0.230 NA Firmicutes Blautia gb|HQ804988.1| 0.0 Ruminococcus schinkii (T); Bie 41; X94964 0.898 0.24 0.28 86 0.22 0.21 75 0.00 0.00 0 0.01 0.02 25 0.850 0.371 Firmicutes Blautia gb|FJ881025.1| 0.0 Ruminococcus sp. DJF_VR67; EU728791 0.891 0.53 0.60 100 0.14 0.13 100 0.39 0.25 80 1.17 0.55 100 0.073 0.111 Firmicutes Blautia gb|JF260206.1| 0.0 Ruminococcus sp. M-1; AB125231 1.000 0.47 0.32 100 1.28 0.48 100 0.04 0.04 60 0.04 0.04 75 0.024 0.901 Firmicutes Clostridium IV gb|FJ880059.1| 0.0 bacterium NLAE-zl-C231; JQ608310 0.743 0.02 0.02 71 0.00 0.00 0 0.15 0.21 60 0.15 0.17 75 0.050 0.901 Firmicutes Clostridium IV gb|HM848627.1| 0.0 Clostridium leptum (T); DSM 753T; AJ305238 0.841 0.20 0.10 100 0.16 0.17 75 0.09 0.08 80 0.13 0.12 100 0.527 0.905 Firmicutes Clostridium IV dbj|AB506382.1| 0.0 Clostridium sp. BS-1; FJ805840 0.781 0.07 0.06 86 0.11 0.08 75 0.04 0.05 60 0.06 0.07 50 0.344 0.701 Firmicutes Clostridium IV gb|HQ805671.1| 0.0 Clostridium sp. Culture Jar-13; AB622826 0.847 0.02 0.03 29 0.00 0.00 0 0.12 0.10 100 0.06 0.03 100 0.327 0.556 Firmicutes Clostridium IV gb|GU959097.1| 0.0 Clostridium sp. YIT 12069; AB491207 0.676 0.15 0.16 57 0.02 0.04 25 0.98 0.79 100 0.26 0.33 50 0.216 0.110 Firmicutes Clostridium IV gb|GQ492087.1| 0.0 Clostridium sp. YIT 12070; AB491208 1.000 0.14 0.07 100 0.06 0.02 100 0.08 0.10 60 0.10 0.08 100 0.073 0.389 Clostridium viride (T); T2-7 (DSM 6836); Firmicutes Clostridium IV gb|JQ084713.1| 0.0 X81125 0.843 0.25 0.09 100 0.11 0.10 100 1.49 0.26 100 0.33 0.19 100 0.073 0.016 Firmicutes Clostridium IV gb|HM124074.1| 0.0 Eubacterium siraeum; 70/3; EU266550 0.825 0.12 0.25 43 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.186 NA Firmicutes Clostridium IV gb|GU959458.1| 0.0 Lachnospiraceae bacterium 19gly4; AF550610 0.722 8.66 2.58 100 3.27 4.22 75 0.00 0.00 0 0.90 1.76 75 0.073 0.042 Firmicutes Clostridium sensu stricto gb|HQ782503.1| 0.0 Clostridiaceae bacterium FN062; AB298770 1.000 0.01 0.03 14 0.20 0.26 50 0.00 0.00 0 0.00 0.00 0 0.186 NA
Firmicutes Clostridium sensu stricto gb|JN885835.1| 0.0 Clostridium disporicum (T); DSM 5521; Y18176 1.000 3.95 2.04 100 5.48 2.47 100 0.00 0.00 0 0.00 0.00 0 0.315 NA Clostridium sp. enrichment culture clone Y234; Firmicutes Clostridium sensu stricto gb|JN792362.1| 0.0 JF345341 0.868 0.02 0.03 43 0.18 0.10 100 0.00 0.00 0 0.00 0.00 0 0.016 NA Firmicutes Clostridium XI gb|JQ892143.1| 0.0 Clostridium ruminantium; LA1; EU089964 0.953 1.06 0.36 100 1.36 0.27 100 0.00 0.00 0 0.00 0.00 0 0.315 NA Firmicutes Clostridium XlVa gb|GU959587.1| 0.0 bacterium NLAE-zl-P613; JQ607197 0.799 0.26 0.25 71 0.00 0.00 0 0.83 0.43 100 0.00 0.00 0 0.050 0.015
Firmicutes Clostridium XlVa gb|JQ891394.1| 0.0 butyrate-producing bacterium M62/1; AY305309 0.914 2.55 1.03 100 3.44 1.52 100 4.14 1.59 100 5.38 2.58 100 0.315 0.730 Firmicutes Clostridium XlVa gb|GU959587.1| 0.0 Clostridiaceae bacterium SK082; AB298754 0.829 1.15 1.12 71 0.00 0.00 0 4.06 2.17 100 0.00 0.00 0 0.050 0.015 Firmicutes Clostridium XlVa gb|FJ881247.1| 0.0 Clostridium asparagiforme (T); N6; AJ582080 0.859 0.76 0.68 71 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.050 NA Clostridium glycyrrhizinilyticum (T); ZM35; Firmicutes Clostridium XlVa gb|JF243922.1| 0.0 AB233029 0.662 0.01 0.03 29 0.00 0.00 0 0.13 0.11 100 0.00 0.00 0 0.327 0.015 Firmicutes Clostridium XlVa gb|JQ608200.1| 0.0 Clostridium hathewayi; AY552788 0.968 0.02 0.05 14 0.03 0.04 50 0.05 0.08 40 0.13 0.13 75 0.400 0.443 Clostridium lavalense (T); CCRI-9842; Firmicutes Clostridium XlVa gb|JQ820141.1| 0.0 EF564277 1.000 0.18 0.22 86 0.20 0.25 75 0.16 0.08 100 0.14 0.12 75 0.570 0.905 Firmicutes Clostridium XlVa gb|HQ792005.1| 0.0 Clostridium leptum; 10900; AF262239 0.909 0.20 0.25 86 0.38 0.30 100 0.13 0.18 60 0.64 0.63 100 0.230 0.065 Firmicutes Clostridium XlVa gb|HQ791726.1| 0.0 Clostridium nexile (T); DSM 1787; X73443 0.959 0.66 0.54 86 2.03 0.70 100 0.00 0.00 0 0.00 0.00 0 0.024 NA Clostridium saccharolyticum (T); DSM 2544; Firmicutes Clostridium XlVa gb|GU959587.1| 1E-179 Y18185 0.818 0.25 0.47 57 0.00 0.00 0 1.28 0.82 100 0.00 0.00 0 0.101 0.015 Clostridium scindens; JCM 10420 M-18; Firmicutes Clostridium XlVa gb|EU511662.1| 0.0 AB020729 0.881 0.01 0.03 29 0.00 0.00 0 0.47 0.41 80 0.00 0.00 0 0.327 0.044 Firmicutes Clostridium XlVa gb|JQ892151.1| 0.0 Clostridium sp. Culture-41; AB622820 0.848 0.48 0.16 100 1.06 0.66 100 2.53 0.80 100 2.94 0.58 100 0.164 0.413 Firmicutes Clostridium XlVa emb|X73449.1| 1E-174 Clostridium sp. Kas203-2; AB114230 0.797 0.14 0.17 71 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.050 NA Firmicutes Clostridium XlVa gb|EU509924.1| 0.0 Clostridium sp.; LIP5; Y12289 0.893 0.14 0.35 29 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.327 NA Firmicutes Clostridium XlVa gb|FJ880297.1| 0.0 Clostridium symbiosum; 69; EF025909 0.849 0.00 0.00 0 0.00 0.00 0 0.03 0.07 20 0.11 0.13 75 NA 0.227 Firmicutes Clostridium XlVa gb|JQ607604.1| 0.0 Clostridium symbiosum; REM001; EF442669 1.000 1.12 0.64 100 2.62 1.16 100 1.31 0.56 100 0.99 0.50 100 0.073 0.286 Firmicutes Clostridium XlVa gb|JF794951.1| 0.0 rumen bacterium NK4A66; GU124467 0.920 0.29 0.18 86 0.24 0.32 50 0.00 0.00 0 2.65 5.30 25 0.634 0.371 Firmicutes Clostridium XlVb gb|FJ880656.1| 2E-167 Clostridia bacterium S130(2)-2; GU136556 0.727 0.01 0.02 29 0.00 0.00 0 0.00 0.00 0 0.21 0.09 100 0.327 0.011 Firmicutes Clostridium XlVb gb|FJ880656.1| 0.0 Clostridiales bacterium 21-4c; HQ452858 0.817 2.50 0.39 100 4.13 0.53 100 0.90 0.83 60 1.91 0.52 100 0.006 0.110 Clostridium lactatifermentans (T); G17; Firmicutes Clostridium XlVb gb|FJ880656.1| 1E-168 AY033434 0.778 0.06 0.04 71 0.14 0.04 100 0.03 0.03 60 0.09 0.04 100 0.018 0.065 Firmicutes Coprococcus gb|DQ777926.1| 0.0 butyrate-producing bacterium SL7/1; AY305312 0.874 0.02 0.03 43 0.06 0.07 75 0.06 0.04 80 0.06 0.11 50 0.276 0.533 Firmicutes Coprococcus gb|EU508827.1| 0.0 Clostridium sp. ID6; AY960572 0.887 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.01 0.03 25 NA 0.371 Firmicutes Coprococcus gb|JQ084127.1| 0.0 Coprococcus catus; GD/7; EU266552 0.820 0.00 0.00 0 0.00 0.00 0 0.17 0.07 100 0.05 0.09 50 NA 0.110 Firmicutes Coprococcus gb|JF255576.1| 0.0 Coprococcus catus; L8; AB361624 0.830 0.10 0.24 29 0.00 0.00 0 0.66 0.37 100 4.42 4.56 100 0.327 0.191 Coprococcus eutactus (T); ATCC 27759; Firmicutes Coprococcus gb|FJ880289.1| 0.0 EF031543 0.878 0.90 0.83 86 0.10 0.11 50 0.00 0.00 0 0.00 0.00 0 0.070 NA
3 Diabetes Page 38 of 46
Firmicutes Desulfonispora gb|GU959689.1| 0.0 Desulfonosporus sp. AAN04; AB436739 0.648 0.55 0.18 100 0.88 0.31 100 0.78 0.25 100 0.74 0.31 100 0.042 0.905
Firmicutes Dorea gb|FJ879722.1| 0.0 Clostridiaceae bacterium DJF_B063; EU728703 0.923 0.00 0.01 14 0.00 0.00 0 0.27 0.36 60 0.00 0.00 0 0.571 0.109 Eubacterium coprostanoligenes (T); HL; Firmicutes Eubacterium gb|GU959638.1| 0.0 HM037995 0.787 0.21 0.24 71 0.05 0.10 25 0.00 0.00 0 0.21 0.22 75 0.276 0.042 Clostridium orbiscindens (T); DSM 6740; Firmicutes Flavonifractor gb|JF258942.1| 0.0 Y18187 1.000 0.11 0.17 71 0.27 0.06 100 0.34 0.61 100 0.49 0.29 100 0.218 0.191 Firmicutes Flavonifractor gb|JF258942.1| 0.0 Flavonifractor plautii; AIP029.07; EU541439 0.983 0.55 0.69 86 1.09 0.29 100 0.35 0.33 100 0.53 0.23 100 0.073 0.191 Hydrogenoanaerobacterium saccharovorans Firmicutes Hydrogenoanaerobacterium dbj|AB702849.1| 0.0 (T); SW512; EU158190 0.751 0.04 0.06 43 0.05 0.05 75 1.05 1.24 100 0.05 0.05 75 0.766 0.191 Firmicutes Lachnospiracea_incertae_sedis gb|HQ770584.1| 0.0 Clostridium sp. cTPY-17; AY239462 0.956 0.11 0.08 86 0.26 0.08 100 0.04 0.04 60 0.25 0.16 100 0.012 0.019 Firmicutes Lachnospiracea_incertae_sedis gb|HQ785850.1| 0.0 Clostridium sp. ID5; AY960574 0.955 0.23 0.20 86 0.24 0.20 100 0.03 0.04 40 0.10 0.17 50 0.788 0.893 Ruminococcus gnavus (T); ATCC 29149; Firmicutes Lachnospiracea_incertae_sedis gb|HQ814441.1| 0.0 X94967 0.939 0.14 0.14 86 0.97 0.77 100 0.00 0.00 0 0.00 0.00 0 0.024 NA Ruminococcus torques (T); ATCC 27756; Firmicutes Lachnospiracea_incertae_sedis gb|HQ792300.1| 0.0 L76604 0.925 0.65 0.48 100 0.99 0.74 100 0.00 0.00 0 0.00 0.00 0 0.412 NA Firmicutes Lactobacillus gb|JQ894318.1| 0.0 Lactobacillus murinus; ONS2; AY324630 1.000 0.09 0.10 71 0.24 0.24 75 0.04 0.06 40 0.03 0.02 75 0.391 1.000 Firmicutes Lactobacillus gb|JQ892923.1| 0.0 Lactobacillus sp. L-YJ; AY157573 1.000 0.04 0.04 57 0.05 0.07 50 0.00 0.00 0 0.08 0.12 50 1.000 0.131 Lactococcus lactis subsp. lactis; ZG7-10; Firmicutes Lactococcus gb|JQ754446.1| 0.0 HE646430 0.993 0.04 0.06 57 0.07 0.06 75 0.06 0.07 80 0.03 0.03 50 0.439 0.709 Firmicutes Oribacterium dbj|AB702893.1| 1E-118 Oribacterium sp. OBRC12; HQ616355 0.782 0.00 0.00 0 0.00 0.00 0 0.06 0.14 20 0.00 0.00 0 NA 0.502 Firmicutes Oscillibacter gb|JQ892723.1| 0.0 Oscillibacter sp. G2; HM626173 0.850 0.35 0.45 71 0.00 0.00 0 1.57 0.39 100 0.00 0.00 0 0.050 0.015
Firmicutes Oscillibacter gb|EF436308.2| 0.0 Oscillibacter valericigenes Sjm18-20; AP012044 0.832 0.08 0.11 43 0.05 0.07 50 0.00 0.00 0 0.25 0.14 100 0.918 0.011 Firmicutes Oscillibacter gb|HM124080.1| 0.0 Oscillospira guilliermondii; OSC4; AB040498 0.838 0.15 0.20 86 0.00 0.00 0 1.08 1.03 80 0.23 0.26 100 0.023 0.413 Oscillospiraceae bacterium AIP 1035.11; Firmicutes Oscillibacter emb|FP083032.1| 0.0 JQ246091 0.873 0.01 0.01 29 0.01 0.01 25 0.00 0.00 0 0.09 0.05 100 1.000 0.011 Oscillospiraceae bacterium NML 061048; Firmicutes Oscillibacter gb|HQ790721.1| 0.0 EU149939 0.813 1.12 0.71 86 0.52 0.45 75 0.16 0.09 100 0.60 0.62 100 0.130 0.413 Peptococcus sp. canine oral taxon 344; 1O039; Firmicutes Peptococcus gb|HQ789613.1| 0.0 JN713513 0.673 1.67 0.23 100 2.11 0.72 100 2.43 0.52 100 1.88 0.48 100 0.230 0.111 Firmicutes Pseudoflavonifractor gb|JQ892170.1| 0.0 bacterium ASF500; ASF 500; AF157051 0.951 2.66 1.29 100 0.75 0.49 100 4.89 1.26 100 9.72 4.82 100 0.012 0.016 Firmicutes Pseudoflavonifractor gb|JQ084092.1| 0.0 Clostridiales bacterium 53-4c; HQ452851 0.889 0.17 0.19 71 0.16 0.05 100 0.21 0.11 100 0.32 0.16 100 0.925 0.730 Firmicutes Pseudoflavonifractor gb|FJ880774.1| 0.0 Clostridium sp. NML 04A032; EU815224 0.745 0.00 0.01 14 0.02 0.04 25 0.00 0.00 0 0.13 0.15 75 0.674 0.042 Firmicutes Robinsoniella gb|GU959587.1| 7E-141 Clostridium sp. Clone-16; AB622836 0.718 0.01 0.01 29 0.00 0.00 0 0.16 0.13 80 0.00 0.00 0 0.327 0.044 Firmicutes Roseburia gb|DQ456475.1| 0.0 Roseburia faecis (T); M72/1; AY305310 0.982 0.61 0.26 100 0.70 0.64 100 1.27 0.29 100 0.31 0.42 75 0.927 0.032 Firmicutes Roseburia gb|JQ307268.1| 0.0 Roseburia intestinalis (T); L1-82; AJ312385 0.989 0.03 0.04 43 0.05 0.06 50 0.02 0.03 40 0.00 0.00 0 0.471 0.240 Firmicutes Roseburia gb|JQ891177.1| 0.0 Roseburia intestinalis; XB6B4; AM055815 0.817 0.00 0.00 0 0.00 0.00 0 0.16 0.12 80 0.64 0.60 100 NA 0.191 Firmicutes Ruminococcus gb|HQ785669.1| 0.0 Clostridiales bacterium 80/3; EU266551 0.880 1.04 0.65 86 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.023 NA Firmicutes Ruminococcus dbj|AB606289.1| 0.0 Ruminococcus bromii; X85099 0.917 0.28 0.26 86 0.07 0.08 50 0.36 0.27 100 0.62 0.59 100 0.105 0.730 Ruminococcus flavefaciens; 007; LRC017; Firmicutes Ruminococcus gb|GQ369777.1| 0.0 AF030447 0.966 5.96 2.71 100 1.20 2.00 50 0.00 0.00 0 1.15 2.31 25 0.029 0.371 Firmicutes Turicibacter gb|JQ893864.1| 0.0 Turicibacter sanguinis (T); AF349724 0.840 0.39 0.31 86 0.19 0.27 50 0.00 0.00 0 0.00 0.00 0 0.294 NA Uncl. Clostridiales_Incertae Sedis Peptostreptococcaceae bacterium canine oral Firmicutes XII gb|GU105102.1| 0.0 taxon 019; OB001; JN713180 0.501 0.00 0.00 0 0.00 0.00 0 0.04 0.07 40 0.02 0.03 25 NA 0.771 Firmicutes Uncl. Lachnospiraceae gb|JF794962.1| 0.0 bacterium CT-m2; HM989805 0.822 0.05 0.07 57 0.00 0.00 0 1.10 1.13 100 0.84 1.45 50 0.101 0.539 bacterium enrichment culture clone ecb12; Firmicutes Uncl. Lachnospiraceae gb|HQ786419.1| 3E-144 HQ395206 0.655 0.00 0.01 14 0.00 0.00 0 0.02 0.03 60 0.04 0.04 75 0.571 1.000 Firmicutes Uncl. Lachnospiraceae gb|HQ789605.1| 0.0 bacterium NLAE-zl-C545; JQ608245 0.976 0.05 0.09 29 0.01 0.01 25 0.01 0.03 20 0.12 0.10 75 0.810 0.081 Firmicutes Uncl. Lachnospiraceae gb|JF794908.1| 0.0 bacterium YE257; FJ966227 0.709 0.03 0.05 43 0.00 0.00 0 0.14 0.10 80 0.02 0.03 50 0.186 0.105 Blautia sp. canine oral taxon 143; PP006; Firmicutes Uncl. Lachnospiraceae gb|JN713310.1| 0.0 JN713310 0.959 0.13 0.15 71 0.37 0.28 75 0.00 0.00 0 0.00 0.00 0 0.215 NA
4 Page 39 of 46 Diabetes
Butyrivibrio crossotus; type strain: DSM2876; Firmicutes Uncl. Lachnospiraceae gb|JQ084267.1| 0.0 FR733670 0.883 0.92 1.16 57 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.101 NA Firmicutes Uncl. Lachnospiraceae gb|HQ765927.1| 0.0 Clostridiaceae bacterium FH052; AB298768 0.784 0.02 0.06 14 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.571 NA Firmicutes Uncl. Lachnospiraceae gb|HQ786419.1| 0.0 Clostridiales bacterium APP2; GQ863487 0.654 0.06 0.08 71 0.01 0.03 25 0.22 0.09 100 0.13 0.06 100 0.276 0.191 Firmicutes Uncl. Lachnospiraceae gb|AY990989.1| 4E-179 Clostridium sp. ASF502; ASF 502; AF157053 0.959 0.00 0.00 0 0.00 0.00 0 0.04 0.03 80 0.01 0.03 25 NA 0.201 Firmicutes Uncl. Lachnospiraceae gb|EF097418.1| 0.0 Clostridium sp. Clone-17; AB622837 0.917 0.00 0.00 0 0.00 0.00 0 0.67 0.27 100 0.43 0.23 100 NA 0.413 Firmicutes Uncl. Lachnospiraceae gb|FJ683052.1| 0.0 Clostridium sp. Clone-7; AB622834 0.928 0.07 0.07 86 0.08 0.06 100 0.23 0.12 100 1.53 1.58 100 0.649 0.016 Firmicutes Uncl. Lachnospiraceae gb|FJ880972.1| 0.0 Clostridium sp. Culture-54; AB622823 0.932 0.01 0.02 14 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.571 NA Firmicutes Uncl. Lachnospiraceae gb|FJ880915.1| 0.0 intestinal bacterium CG19-1; FJ711049 0.842 0.01 0.03 29 0.00 0.00 0 1.16 0.60 100 0.13 0.09 100 0.327 0.016 Firmicutes Uncl. Lachnospiraceae gb|GQ298173.1| 0.0 Lachnospiraceae bacterium 14-2; DQ789124 0.934 0.04 0.07 43 0.00 0.00 0 0.06 0.04 100 0.00 0.00 0 0.186 0.015 Firmicutes Uncl. Lachnospiraceae gb|JQ891326.1| 0.0 Lachnospiraceae bacterium 607; AB700365 0.855 0.23 0.58 29 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.327 NA Firmicutes Uncl. Lachnospiraceae dbj|AB700360.1| 0.0 Lachnospiraceae bacterium A2; DQ789117 1.000 0.00 0.00 0 0.01 0.01 25 0.00 0.00 0 0.62 0.31 100 0.257 0.011 Firmicutes Uncl. Lachnospiraceae gb|EU622771.1| 0.0 Lachnospiraceae bacterium A4; DQ789118 0.828 0.11 0.11 71 0.00 0.00 0 0.00 0.00 0 0.01 0.03 25 0.050 0.371 Firmicutes Uncl. Lachnospiraceae gb|GQ493579.1| 0.0 Ruminococcus sp. YE58; AY442822 0.909 0.28 0.16 100 0.46 0.15 100 0.01 0.03 20 0.07 0.07 75 0.164 0.140 Clostridiaceae bacterium NML 060002; Firmicutes Uncl. Ruminococcaceae gb|FJ878981.1| 0.0 EU815225 0.730 0.06 0.04 86 0.09 0.10 50 0.15 0.09 100 0.15 0.11 75 0.924 0.730 Clostridiaceae bacterium NML 061030; Firmicutes Uncl. Ruminococcaceae gb|JN713248.1| 0.0 EU183300 0.997 0.61 0.68 86 0.30 0.10 100 0.64 0.29 100 1.23 1.56 100 0.927 0.905 Firmicutes Uncl. Ruminococcaceae gb|JQ084116.1| 0.0 Clostridiales bacterium 30-4c; HQ452853 0.880 0.49 0.44 86 0.36 0.37 75 0.03 0.07 20 0.83 0.06 100 0.449 0.015 Clostridiales bacterium canine oral taxon 061; Firmicutes Uncl. Ruminococcaceae gb|FJ880909.1| 0.0 OF002; JN713225 0.830 0.00 0.01 14 0.02 0.04 25 0.00 0.00 0 0.20 0.10 100 0.674 0.011 Firmicutes Uncl. Ruminococcaceae gb|GU958847.1| 0.0 Clostridium sp. Culture-1; AB622814 0.960 0.46 0.82 71 0.00 0.00 0 7.11 1.64 100 0.01 0.01 50 0.050 0.019 Firmicutes Uncl. Ruminococcaceae gb|FJ880287.1| 0.0 rumen bacterium NK4A214; GU324404 0.906 0.76 0.21 100 0.14 0.20 75 0.00 0.00 0 0.69 0.23 100 0.006 0.011 Firmicutes Uncl. Firmicutes dbj|AB606234.1| 0.0 bacterium NLAE-zl-C589; JQ608288 0.808 0.01 0.03 14 0.00 0.00 0 0.00 0.00 0 0.86 1.35 100 0.571 0.011 Firmicutes Uncl. Firmicutes gb|JF172118.1| 0.0 Christensenella minuta; YIT 12065; AB490809 0.652 0.04 0.05 57 0.00 0.00 0 0.01 0.02 40 0.02 0.02 50 0.101 0.893 Firmicutes Uncl. Firmicutes gb|HQ814888.1| 0.0 Eubacterium desmolans (T); L34618 0.855 0.10 0.11 57 0.08 0.09 100 0.00 0.00 0 0.03 0.06 25 0.924 0.371 Peptostreptococcaceae bacterium canine oral Firmicutes Uncl. Firmicutes dbj|AB626919.1| 0.0 taxon 333; 1G023; JN713503 0.848 0.16 0.15 86 0.01 0.01 25 0.05 0.10 40 0.27 0.24 100 0.053 0.062 Proteobacteria Escherichia/Shigella gb|HQ771207.1| 0.0 Escherichia coli; 228; EF560774 0.997 0.04 0.04 57 0.50 0.32 100 0.00 0.00 0 0.00 0.00 0 0.010 NA Proteobacteria Escherichia/Shigella gb|HQ759058.1| 0.0 Escherichia coli; 4090; FJ463818 0.992 0.03 0.04 43 0.28 0.15 100 0.00 0.00 0 0.02 0.05 25 0.016 0.371 Proteobacteria Escherichia/Shigella gb|JX040337.1| 0.0 Escherichia coli; PK3; X80731 1.000 0.08 0.08 71 1.05 0.81 100 0.00 0.00 0 0.00 0.00 0 0.018 NA Insolitispirillum peregrinum subsp. integrum; Proteobacteria Insolitispirillum gb|JF260247.1| 0.0 IAM 14946; AB074521 0.596 0.23 0.28 71 0.04 0.07 25 0.89 0.34 100 0.38 0.20 100 0.198 0.063 Proteobacteria Kiloniella gb|JF260740.1| 0.0 Thalassospira sp. 2ta1; FJ952805 0.526 0.51 0.70 71 0.00 0.00 0 0.00 0.00 0 0.00 0.00 0 0.050 NA Parasutterella excrementihominis (T); YIT Proteobacteria Parasutterella gb|HQ774176.1| 0.0 11859; AB370250 1.000 1.26 0.49 100 3.17 0.77 100 1.86 0.80 100 2.11 0.29 100 0.006 0.905 Verrucomicrobia Akkermansia gb|HQ793323.1| 0.0 Akkermansia muciniphila (T); Muc; AY271254 0.968 0.01 0.01 29 0.02 0.03 25 0.06 0.06 80 0.02 0.03 50 0.105 0.730 unclassified_Bact eria Uncl. Bacteria gb|GU959660.1| 0.0 bacterium YE57; AY442821 0.848 0.11 0.13 100 0.03 0.05 25 0.25 0.14 100 0.33 0.27 100 0.391 0.371 unclassified_Bact eria Uncl. Bacteria gb|GU958610.1| 0.0 rumen bacterium R-7; AB239481 0.794 1.58 0.86 86 0.79 1.56 50 0.00 0.00 0 0.63 1.27 25 1.000 0.383
5 Diabetes Page 40 of 46
Supplemental Figures
S1. Variable importance for OP versus OR on either a chow or HF-diet by supervised classification with random forest classifier analysis. Main predictive genera for each group of animals are observed on the top of the chart, with accuracy of predication decreasing down the genera column.
Variable importance for OP vs. OR Chow HF-diet
6
Page 41 of 46 Diabetes
S2. OP rats exhibit increased weight gain, adiposity and adiposity hormones during HF- feeding (data related to Fig. 1 and Fig. 2, main text). (A) Body weight of OP and OR rats during 12wks of HF-feeding. (B) Adiposity index, (C) daily food intake, and D) circulating plasma levels of leptin and insulin after brief 5hr fast in OP and OR rats. Data are expressed as mean ± SEM. **P < 0.01, ***P < 0.0001.
7 Diabetes Page 42 of 46
S3. During HF-feeding, OP rats and GF recipients have altered lipid profile, and reductions in proteins involved in intestinal satiety signaling (Supplementary data related to Fig. 2, main text). (A) Lipid profile of donor OP and OR rats. (B) Lipid profile of CVOP and CVOR animals after brief fast. (C) Plasma GLP-1 and PYY. (D) Intestinal protein expression of gut peptides in donors and recipient mice. (E-F) mRNA transcript of OP and OR rats, and protein expression of GPRs in intestinal epithelial cells of donor rats and recipient mice. Data are expressed as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.0001. † denotes significant difference from chow-fed diet condition within phenotype, †P < 0.05, ††P < 0.01, †††P < 0.0001.
8 Page 43 of 46 Diabetes
S4. OP rats exhibit increases in lipid storage via enhanced lipogenesis and fatty-acid
transporters in both liver and adipose tissue compared to OR rats during HF-feeding (data
related to Fig. 3, main text). (A) Liver and adipose tissue protein expression of lipogenic and
adipogenic enzymes in OP and OR rats during HF-feeding (B) Ratio of phosphorylated to total
ACC in liver and adipose. (C) Western blots of fatty-acid transporters, CD36 and FABP. Data
are expressed as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.0001.
9 Diabetes Page 44 of 46
S5. HF-feeding results in increased gut permeability and systemic inflammation in OP rats
(data related to Fig. 4, main text). A) Protein expression of tight junction proteins, ZO-1, occluding, and phosphyorylated-MLC, in the distal intestine of donor rats on HF-diet. (B)
Circulating inflammatory markers in OP and OR rats after 5hr fast. Data are expressed as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.0001.
10 Page 45 of 46 Diabetes
S6. HF-fed OP rats have increased inflammatory signaling in liver and adipose tissue (data
related to Fig. 5, main text). (A-B) Gene expression of inflammatory cytokines in liver and
adipose tissue. (C) TLR4 gene expression in liver and adipose tissue. (D) NF-κB/IKKβ
inflammatory signaling pathway of OP and OR rats depicted by western blots. Data are
expressed as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.0001.
11 Diabetes Page 46 of 46
S7. Variable importance for CVOP versus CVOR maintained on HF-diet by supervised classification with random forest classifier analysis. Main predictive genera for each group of animals are observed on the top of the chart, with accuracy of predication decreasing down the genera column.
Variable importance for CVOP vs CVOR
Pseudoflavonifractor unclassified_Ruminococcaceae Robinsoniella Lachnospiracea_incertae_sedis Clostridium_IV Clostridium_XlVb Oscillibacter Blautia Eubacterium Insolitispirillum Coprococcus Flavonifractor Bacteroides Barnesiella Prevotella unclassified_Porphyromonadaceae Dorea Lactobacillus Parasutterella Alistipes Hydrogenoanaerobacterium Parabacteroides Porphyromonas Mucispirillum Allobaculum Anaerosporobacter Anaerostipes Clostridium_sensu_stricto Clostridium_XI Clostridium_XlVa Oribacterium Turicibacter Escherichia_Shigella Kiloniella Roseburia Akkermansia Anaerotruncus Coriobacterineae Ruminococcus unclassified_Clostridiales_Incertae_Sedis_XII unclassified_Lachnospiraceae Acetivibrio Peptococcus Desulfonispora Lactococcus
-1 0 1 2 3 4 MeanDecreaseAccuracy
12