insulin resistance, microbiota and fat distribution...

1

Insulin resistance, microbiota and fat distribution changes by a new model of vertical sleeve

gastrectomy in obese rats

Running title: glandular gastrectomy in obese rats

Nicola Basso1, Emanuele Soricelli

1, Lidia Castagneto-Gissey

1, Giovanni Casella

1, Davide

Albanese3, Francesca Fava

2, Claudio Donati

3, Kieran Tuohy

2, Giulia Angelini

4, Federica La

Neve4, Anna Severino

4, Virginia Kamvissi-Lorenz

5,6, Andrea L. Birkenfeld

5,6, Stefan Bornstein

5,6,

Melania Manco7, Geltrude Mingrone

6, 8

1Surgical-Medical Department for Digestive Diseases, Policlinico Umberto I, University of Rome Sapienza, Rome,

Italy

2 Department of Food Quality and Nutrition, and 3 Computational Biology, Research and Innovation Centre-Fondazione

Edmund Mach, San Michele all’Adige, Trento, Italy.

4Institute of Cardiology, Catholic University, Rome, Italy

5Department of Medicine III, Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden,

Dresden, Germany.

6Diabetes and Nutritional Sciences, Hodgkin Building, Guy's Campus, King's College London, London, UK.

7Research Unit for Multifactorial Diseases, Scientific Directorate, Bambino Gesù Children Hospital, Rome, Italy.

8 Department of Internal Medicine, Catholic University, Rome, Italy

Text: 4369 words, 2 tables and 6 figures

Corresponding author

Geltrude Mingrone, MD, PhD

Department of Internal Medicine

Catholic University

Largo A. Gemelli 8 – 00168 Rome, Italy

Email: [email protected]; [email protected]

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Diabetes Publish Ahead of Print, published online July 18, 2016

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ABSTRACT

Metabolic-surgery improves insulin-resistance and type-2 diabetes, possibly related to weight-loss.

We have performed a novel sleeve-gastrectomy in rats that resects ≈80% of the glandular portion

leaving the forestomach almost intact (Glandular Gastrectomy,GG) and compared subsequent

metabolic re-modeling with a sham-operation (SO).

GG did not affect body weight, at least after 10 weeks, improved hepatic and peripheral insulin

sensitivity likely through the increased Akt, GSK-3 and AMPK phosphorylation, and reduced

ectopic fat deposition and hepatic glycogen over-accumulation. The body adipose tissue was

redistributed with reduction of intra-abdominal fat. We found reduction of circulating ghrelin levels,

increased GLP1 plasma concentration and remodeling of the gut microbiome diversity,

characterized by lower relative abundance of Ruminococcus and by higher relative abundance of

Lactobacillus and Collinsella. These data suggest that, at least in rat, the glandular stomach plays a

central role in the improvement of insulin resistance even if obesity persists. GG provides a new

model of the metabolically healthy obese phenotype.

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INTRODUCTION

While lifestyle changes should be the first choice for treating the global pandemic of obesity and

diabetes, long-term compliance is poor (1). Metabolic surgery (MS) is, at present, the most effective

therapy for morbid obesity. In the last few years, randomized controlled trials have shown that MS

is effective in determining diabetes remission also in the long term (2-5). Insulin resistance is the

primary defect of type-2 diabetes (T2D) and plays a central role in its pathogenesis, while β-cell

failure develops only afterwards (6). Interestingly, some types of MS, including Bilio-Pancreatic

Diversion (BPD), Sleeve Gastrectomy (SG) and Roux-en-Y Gastric Bypass (RYGB), determine a

net amelioration of insulin resistance with subsequent reduction of insulin secretion even if the

patients remain obese (7,8). Based on clinical observations in SG patients, we formulated a “gastric

hypothesis” for diabetes resolution with early involvement of ghrelin and GLP-1 independent of

weight change or modified food intake (9).

To understand the resolution of diabetes upon MS a relevant problem arises from the difficulty in

separating the effect of weight loss per se, from that of gastrointestinal manipulations and

subsequent metabolic recovery. The rat stomach possesses two distinct portions, the proximal

(forestomach), non-glandular part, lined by keratinized squamous epithelium which functions as a

reservoir for food, and the distal (corpus), glandular part, which is lined by simple columnar

epithelium responsible for exocrine and endocrine secretions (10). The modified sleeve gastrectomy

or glandular gastrectomy (GG), devised by us, resects approximately 80% of the glandular portion

in order to induce the hormonal effects but leave the non-glandular portion almost intact, thereby

limiting loss of gastric volume and its sequel on body weight. In high-fat diet (HFD) - obese rats

the effect of GG on body fat distribution, insulin sensitivity, insulin signaling and gut microbiota

was compared with that of a gastric sham operation (SO).

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MATERIALS AND METHODS

Animals

Sixty male Wistar rats aged 10 weeks were housed individually in hanging wire cages in a

controlled room at 22°C with a 12-h day/night cycle (lights on from 07:00 to 19:00).

The animals received a purified tripalmitin-based HFD ad libitum (Rieper AG SpA, Bolzano, Italy)

with an energy density of 21.10 kJ/g that supplied 59% of energy as fat and 20% as carbohydrate

comprised of cornstarch and sucrose (2:1 wt/wt) and containing corn oil (1.9/100g diet) to prevent

essential fatty acid deficiency. The HFD was continued for 10 weeks before and 10 weeks after the

operation.

The animals randomly underwent GG or SO. Survival rates were 90% after sham operations and

75% after GG. All experimental procedures were approved by the Catholic University of Rome

Institutional Animal Care Committee.

Interventions

The rats were anesthetized using ketamine (75 mg/kg intramuscularly) and xylazine (10 mg/kg

intramuscularly). Ten milliliters of sterile 0.9% NaCl were administered s.c. prior to surgery.

Access to the peritoneal cavity was obtained by 3 cm laparotomy. About 80% of the glandular

stomach was removed by resecting the organ along a line as in Figure S1, and the non-glandular

forestomach was left almost intact. The gastric remnant was closed by single-layer continuous

suture using 6–0 Prolene thread (Ethicon Inc. USA). The sham-operated rats had the same

anesthesia as described above. A midline laparotomy was performed, and the stomach was exposed

and gently manipulated. The abdominal cavity was kept open for the same amount of time required

to perform GG. A 1 cm gastrotomy was performed and then closed as in the previous group.

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Post-operative care

At the end of the surgical procedures, all rats received sterile 0.9% NaCl (10 ml i.p. and 10 ml s.c.)

to maintain hydration during healing. The animals received ketoprophen (5 mg/kg) as analgesic.

They were placed on a heated mat until they recovered and then returned to their home cages. The

rats were allowed to drink purified water for 12 hours after surgery and then a liquid diet containing

5% glucose and 0.2% KCl was provided for the next 48 hours. Successively they received the HFD

until 10 weeks after surgery.

Oral glucose tolerance test (OGTT)

The OGTT was performed at sacrifice. Animals were fasted overnight and then received a 50% D-

glucose solution (1g/kgbw) by oral gavage. Blood was collected from the tail for measurement of

glucose and insulin concentrations at 0, 15, 30, 60, 90, and 120 min at the end of the study. At the

end of the OGTT blood was obtained by cardiac puncture and placed in tubes containing EDTA,

aprotinin and a dipeptidyl peptidase-IV inhibitor, and analysed for GLP1 and ghrelin.

After centrifugation, plasma was divided into appropriate subsamples and stored at −20°C until

analyses.

Histology

SO and GG rats were euthanized, fresh portions of liver, muscle and adipose tissue from each rat

were cut, fixed in neutral buffered formalin (10%), then dehydrated using grades of ethanol (70%,

80%, 90%, 95%, and 100%). Dehydration was followed by clearing the samples in two changes of

xylene. The samples were then embedded in paraffin and cut with a microtome (3-4 µm).

Hematoxylin-eosin staining was used. Periodic-acid-Schiff (PAS) staining has been employed to

evaluate glycogen storage. Photographs of stained sections from sham and distal gastrectomy rats

were taken using an optical microscope (Microscope ZEISS Primo Star HAL).

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Western blot analysis

Muscle biopsies were rinsed in phosphate buffer saline (PBS) and homogenized in RIPA buffer

containing a cocktail of protease inhibitors. Homogenates were cleared by centrifugation (13.000

rpm; 30 min, 4°C). Protein content was determined using Bradford Protein Assay (Biorad).

Protein lysates (30 µg) were separated on 8% SDS-PAGE and transferred on PVDF membrane.

Membrane were probed overnight with the following antibodies pAktSer473 (D9E), pAMPKα and

GSK3α-β Ser21/9 (9331). Membrane were stripped for 30 min at 56°C and re-probed overnight

respectively with Aktpan (C67E7), AMPKα (D5A2) and GSK3α-β (D75D3). All the antibodies are

from Cell Signaling. Detection and analysis were performed respectively with Chemidoc XRS

Image system and Image Lab 5.0 software (Biorad). All the results are expressed as

phosphoprotein/total protein ratio.

16S rRNA Gene Sequencing by Roche GS FLX+ Platform

Bacterial genomic DNA was extracted from rat fecal samples using the FastDNA Spin Kit for Feces

(MP Biomedicals LLC). The V3–V5 16S rRNA gene variable regions were amplified by PCR using

454 adapter-linked and bar-coded primers 338F (5’-TCCTACGGGAGGCAGCAG-3’) and 934R

(5’-TGTGCGGGCCCCCGTCAATT-3’). Each PCR reaction was carried out in triplicate with 0,2

µM dNTPs, 0,4 µM primers, 0.05U Taq Polymerase (FastStart™ High Fidelity PCR System,

Roche), and 40ng template DNA. The following thermal cycles were employed: 95°C for 5

minutes; 30 cycles at 95°C for 30 seconds, 58°C for 30 seconds and 72°C for 1 minute; a final

elongation step at 72°C for 8 minutes. PCR products were analysed by Lab-on-Chip electrophoresis

(2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA, USA) and cleaned using the Agencourt

AMPure XP system (Beckman Coulter, Brea, CA, USA) following the manufacturer’s instructions.

Purified products were quantitated by qPCR (LightCycler 480, Roche) using the KAPA Library

quantification kit (KAPA Biosystems, Boston, MA, USA) and pooled in an equimolar way in a

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final amplicon library. The pyrosequencing was carried out on the GS FLX+ system using XL+

chemistry (454 Life Sciences, Roche, Basel, Switzerland) following the manufacturer’s

recommendations.

16S rDNA pyrosequencing resulted in a total of 1,088,841 16S rDNA reads with a mean of 35,123

sequences per sample. Average sequence lengths were 671 nt with an average phred quality score of

31. Raw 454 files were demultiplexed using the Roche's sff file software, and available at the

European Nucleotide Archive (www.ebi.ac.uk) under the accession study PRJEB11901. Sample

accessions and metadata are available in Supplementary Table S1. Reads were preprocessed using

the MICCA pipeline (version 0.1, http://micca.org ). Forward and reverse

primer trimming and quality filtering were performed using micca-preproc (parameters

-fTCCTACGGGAGGCAGCAG -rTGTGCGGGCCCCCGTCAATT-O 16 -q 20 -l 300) truncating

reads shorter than 300 nt. De-novo sequence clustering, chimera filtering and taxonomy assignment

were performed by micca-otu-denovo (parameters -s 0.97 -d -l 200 -c): operational taxonomic units

(OTUs) were assigned by clustering the sequences with a threshold of 97% pair-wise identity, and

their representative sequences were classified using the RDP software version 2.8. Template-guided

multiple sequence alignment (MSA) was performed using PyNAST (version 0.1) against the

multiple alignment of the Greengenes database (release 13_05) filtered at 97% similarity. Finally, a

phylogenetic tree was inferred using FastTree and micca-phylogeny (parameters: -a template-

template-min-perc 75). Sampling heterogeneity was reduced by rarefaction (12,562 sequences per

sample). Alpha (within sample richness) and beta-diversity (between-sample dissimilarity)

estimates were computed using the phyloseq R package. Permutational ANOVA (PERMANOVA)

tests were performed using the adonis function of the vegan R package with 999 permutations.

The Random Forest model was computed using the supervised_learning.py script of QIIME

(doi:10.1038/nmeth.f.303) version 1.8.0, with 1000 trees and 10-fold cross validation.

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Analytical methods

Plasma glucose levels were analyzed by the glucose-oxidase method (Glucose Analyzer II;

Beckman, Fullerton, CA, USA). The plasma concentrations of cholesterol and triacylglycerol

(TAG) were measured using spectrophotometric methods. Serum insulin was measured by a rat

insulin ultrasensitive ELISA (Biovendor GmbH, Kassel, Germany), with a sensitivity of 0.025

ng/ml and an intra- and inter-assay precision of 10%. Plasma GLP17-36 and active ghrelin were

measured by rodent/rat-specific ELISAs (Millipore, St Charles, MO, USA). Sensitivity was

5.2 pg/ml, intra-assay <11%, inter-assay <19%, accuracy 83% for GLP17–36. The intra-assay was 1–

6%, the inter-assay was 1-5% and the accuracy 94-105% for ghrelin. Total plasma bile acids were

determined with a bile acid assay kit (Trinity Biotech, Jamestown, NY, USA). Plasma

lipopolysaccharide (LPS) levels were determined using a Limulus amebocyte lysate kit (QCL-1000,

Lonza, Walkersville, MD).

Statistical analysis

Values are expressed as mean±SEM. The homeostatic model assessment of insulin resistance

(HOMA-IR) index(11) was calculated as HOMA-IR = (FBG x FPI)/405, where FBG denotes

fasting blood glucose (mg/dl) and FPI is fasting plasma insulin (µU/ml). The factor 405 accounts

for measurement units.

The incremental AUCs were computed using the trapezoidal rule. Insulin sensitivity after the OGTT

was measured as the glucose/insulin AUCs ratio. Glucose and insulin AUC data intra-group

variations were assessed by Wilcoxon while inter-group differences by Mann Whitney U test due to

lack of normal distribution by Shapiro-Wilk test.

Differences were considered significant at P < 0.05. Spearman’s correlation analysis was performed

to detect the correlations among variables.

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RESULTS

All animals lost weight in the week following surgery with about 10% weight loss in the SO and

15% in the GG group, respectively. Rats in the GG group recovered more slowly but from the 3rd

week after surgery, their weight was not statistically different from that of SO animals (Figure

S2A). Food intake also did not differ between the groups from week 3 post-surgery (Figure S2B).

At 10 weeks after the operation, mesenteric and retroperitoneal fat content was higher in the SO

than in the GG group (9.67±1.26 vs. 8.11±1.22 g, P<0.01 and 8.75±1.16 vs. 7.30±0.95 g, P<0.01

respectively). In contrast, subcutaneous fat was more represented in GG than in SO rats (2.64±0.65

vs. 4.10±0.78 g, P<0.0001).

Insulin sensitivity

The data in Table 1 and in the Figures refer to the end of the study. Insulin sensitivity was

significantly improved in GG compared to SO rats as demonstrated both by HOMA-IR values,

which were reduced by about 50%, and by the ratio AUCglucose/AUCinsulin which was about 55%

lower in GG than in SO rats (Table 1). The time courses of blood glucose and plasma insulin

concentrations after the OGTT are reported in Figure 1A and 1B, respectively. In spite the glycemic

curve was significantly lower after GG than after SO, blood glucose levels did not return to the

baseline. Both Cholesterol and TAG levels dropped in GG rats (Table1).

Active plasma ghrelin levels (Table1) in GG rats (2.24±4.04 vs.48.32±5.38 fmol/ml), were

significantly reduced (P<0.0001) with respect to the SO group.

Plasma levels of GLP1 were significantly higher (P<0.0001) in GG rats (5.15±1.52 pmol/ml)

compared to SO ones (3.01±0.84 pmol/ml)(Table1). We cannot exclude a more rapid gastric

emptying after GG which could have stimulated GLP1 secretion.

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LPS levels were almost halved while BAs were nearly doubled in GG as compared with SO animals

(Table 1).

Table 2 shows the coefficients of Spearman’s correlation between relevant metabolic and microbial

measures. In a multiple regression analysis (R2=0.84, P<0.0001) with HOMA-IR as dependent

variable and ghrelin, GLP1, BAs, TAG and mesenteric and retroperitoneal fat as independent

variables, the significant predictors were ghrelin levels (β=0.26,P=0.034), GLP1 (β=-0.32,P=0.019),

BAs (β=−0.26,P=0.040) and TAG (β=0.49,P=0.002).

Ectopic fat deposition and hepatic glycogen

Since fat deposition in muscle and liver is associated with insulin resistance, the histological effects

of both surgical procedures in these sites were evaluated.

Skeletal muscle fat infiltration was present in SO but not in GG rats (Figure 2, panels 2A and 2B,

respectively).

All SO animals showed moderate-to-marked hepatic steatosis and some ballooning degeneration

(Figure 2, panel 2C). Rats in the GG group showed normal liver architecture (Figure 2, panel 2D).

Furthermore, SO rats exhibited higher glycogen liver storage than the GG group (Figure 2 panels

2E and 2F, respectively). Finally, an increase in both interstitial cellular infiltration and fibrosis was

observed in the visceral adipose tissue of SO rats as compared with GG rats (Figure 2, panels 2G

and 2H respectively).

Western blots

When compared to SO rats, GG rats exhibited increased phosphorylation of Akt on Ser473 in

muscle (Figure3, panel A, left side) (21.5 ±8.2 vs. 74.03 ±11.8%; P=0.006) and in liver (Figure3,

panel B, left side) (8.7±0.8 vs. 13.9±1.7%; P=0.03). No significant difference of Akt

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phosphorylation in the subcutaneous adipose tissue (Figure3 panel C, left side) (16.1±3.4 vs.

9.4±3.4%) and of phosphorylation of GSK3β in both muscle (7.4%±0.5 vs. 9.4±0.2%, not reported

in the figure) and adipose tissue (Figure3, panel C, right side) (38.7±11.2 vs. 12.8±3.3%) between

SO and GG rats was observed. The liver (Figure3, panel B, right side) showed an increased

phosphorylation of GSK3β in GG when compared to SO rats (4.6±1.3 vs. 10.5±2.0%; P=0.049).

GSK3α isoform phosphorylation (Ser21) in the muscle showed no significant differences between

SO and GG rats (5.3±1.0 vs. 6.5±2.2%) while it was not even detectable in the liver and adipose

tissue.

The muscle phosphorylated AMPKα (Tyr172) form was 53% higher in GG than in SO rats (Figure

3, panel A, right side) (1.9% ±0.18 vs. 2.9% ±0.2; P=0.007).

Gut microbiota

GG wrought significant changes in microbiome structure, with GG animals showing significantly

higher taxa richness (α-diversity) calculated using the Shannon index (P=0.0064, Wilcoxon rank-

sum test, FDR corrected) (Figure 4). GG and SO animals were significantly different in terms of β-

diversity using unweighted UniFrac (Figure 4b), weighted UniFrac and Bray-Curtis between-

samples dissimilarity measures (P=0.002, 0.034 and 0.002 respectively, PERMANOVA test).

Differences in the relative abundance of bacteria within the gut microbiota of GG and SO animals

are shown in Figure 5 at different taxonomic levels (phylum to genus). No statistically significant

differences were observed between the groups at phylum level. At the Class level, γ-Proteobacteria

(P=0.020), Actinobacteria (P=0.034), Bacilli (P=8.0x10-5) and Erysipelotrichia (P=0.016) were all

significantly enriched in the GG group compared to the SO rats, while the Clostridia were enriched

in the SO group (P=0.031). At the order level, Aeromonadales (P=0.04), Actinomycetales (P=0.03),

Coriobacteriales (P=0.04), Lactobacilli (P<0.0001), Clostridia (P=0.03) and Erysipelotrichales

(P=0.02) differed between groups. According to the Wilcoxon ranked-sum test significant

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differences occurred between GG and SO animals at genus level, with GG animals harbouring

higher relative abundance of Lactobacillus (P=0.0003) and Collinsella (P=0.0095), while SO

animals had higher Ruminococcus (P=0.0095).

To define bacteria characteristic of GG or SO groups at order and genus taxonomic levels, we

performed a Random Forests model in a 10-fold cross-validation (CV) scheme obtaining an

estimated test error of 13.3±18.5% (order level abundances) and 12.5±22.65% (genus level

abundances). The top ranked orders and genera (mean decrease in classification accuracy>0.1%)

identified by Random Forests are shown in Figure 6 in decreasing order of their discriminatory

importance. Lactobacilli were the main drivers of difference between groups at both taxonomic

levels.

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DISCUSSION

In this study we describe the results of a novel experimental model of metabolic surgery, the

glandular gastrectomy, which is associated with

1) Reduction of ectopic fat deposition and redistribution of body fat;

2) Improvement of insulin resistance;

3) Changes in the gut microbiota profile with increase primarily of lactobacilli but also

increase of microbial diversity and alteration of BA pool size;

4) Decrease of the low-grade inflammation state, as shown by the reduction of the

concentrations of circulating LPS and by the reduction of cellular infiltrates and fibrosis in

the adipose tissue.

As compared with SO, GG did not induce any significant body weight loss or food intake

reduction after the first 3 weeks following the operations. However, we did not measure the impact

of GG on intestinal transit or whether it altered meal frequency or size.

The weight loss observed in GG and SO rats during the first 2 weeks after surgery is in

agreement with that reported in the literature for rodents under a high fat diet after conventional

sleeve gastrectomy or sham operation (12,13), with about 18% weight reduction from baseline.

However, while the weight gain curve post conventional sleeve gastrectomy remains significantly

lower than SO rats (12,13), in our series there was no difference in weight gain between GG and SO

animals over time. This suggests that the rapid fall of circulating ghrelin levels following GG

acutely reduced appetite, but since the aglandular stomach remained intact (the gastric section

responsible for food storage and digestion in the rat), this may have permitted a normal food intake,

comparable with that of SO rats. Our model was not designed to measure the early dynamic effects

of glandular gastrectomy, but rather to shed some light on the steady effects of this operation. In

fact, the GG rats ate about 50% less calories for a full 2 weeks following the operation and this

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might be resulted in loss of fat and lean mass and improvements in glycemic control. We do not

know if this weight loss period might have influenced the findings observed at the end of the study,

although 8 further weeks during which GO animals rapidly gained weight should be a period of

time long enough to minimize the effect of these 2 weeks of hypo-caloric intake. Given that days in

rats translates into months for humans, we cannot extrapolate our results to the human situation,

where some types of bariatric/metabolic operations improve insulin action just few days after

surgery, independently of weight loss.

Further monitoring of insulin sensitivity during the weight loss period as well as in the

regaining phase would be useful and would provide additional mechanistic evidence.

The HFD determined insulin resistance and fat deposition in both liver and skeletal muscle

tissue in SO animals, while GG prevented these features and determined redistribution of the

adiposity pattern characterized by decreased visceral fat and by proportionally increased

subcutaneous fat. Fat redistribution may have played an important role in modulating insulin

resistance. Visceral fat weight was directly associated with HOMA-IR. It is worth noting that partial

surgical removal of intra-abdominal fat prevents the onset of age-dependent insulin resistance and

delays significantly the onset of glucose intolerance and diabetes in rodent models of obesity and

diabetes (14). GG used in our series provides a model of metabolically healthy obesity. In humans,

metabolically healthy but obese individuals (MHO) have significantly lower levels of visceral fat

and are resistant to the metabolic and cardiovascular risks deriving from obesity (15). Furthermore,

MHO appear to have higher microbial diversity, both in species and gene richness compared to

other obese subjects (16).

It has been shown that BAs (ursodeoxycholic acid) ingestion decreases age-related adiposity

and inflammation in mice (17). BAs circulating levels and composition are regulated by gut

microbiota (18, 19) and, indeed, we found that the plasma levels of total BAs were significantly and

inversely correlated with HOMA-IR in a multiple regression analysis.

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In addition, accumulation of glycogen was observed only in the liver of SO rats. A net liver

glycogen accumulation is described in about 80% of diabetic patients (20)

as well as in obese

patients (21) and has been suggested to be linked to an enhanced gluconeogenesis.

Therefore, GG modifies adipose tissue distribution preventing ectopic fat deposition,

reduces liver glycogen accumulation possibly as a consequence of the evidenced drastic reduction

of ghrelin secretion and of the less pronounced GLP-1 increased levels, which have been linked to

changes in the gut microbiome and circulating BAs (22-24).

Furthermore, GG improved insulin signaling. We found, in fact, a significantly higher

phosphorylation of Akt in both liver and skeletal muscle of GG as compared with SO rats. Once

phosphorylated on both Thr308 and Ser473 Akt is fully activated and phosphorylates its

downstream effector, GSk3β (25). This latter was in fact significantly more phosphorylated in GG

than in SO rats. GSk3β phosphorylation on Ser9 or Ser21, depending on tissue specific isoform

expression, decreases its active site availability. Since GSK-3 is a protein kinase that

phosphorylates and inactivates glycogen synthesis, the rate-limiting enzyme in glycogen formation

(26), its inactivation stimulates glycogen production. Therefore, although we did not measure it

directly, the accumulation of glycogen in SO rat tissues is a likely consequence of impaired

glycogen breakdown. Cellular glycogen, in fact, results from the balance between synthesis and

glycogenolysis. Indeed, both hyperglycemia and hyperinsulinemia are potent inhibitors of

glycogenolysis (27,28) and thus HFD by inducing insulin resistance may reduce glycogenolysis

with subsequent glycogen accumulation. In addition, in hyperglycemic conditions glucose passively

enters the hepatocytes by insulin-independent GLUT2, and is rapidly phosphorylated, with

inhibition of its release from hepatocytes (29).

Fully phosphorylated Akt, as observed in GG rats, suppresses hepatic glucose production by

decreasing the expression of gluconeogenic enzymes by phosphorylation and nuclear exclusion of

the forkhead box protein, FOXO1, and its pro-gluconeogenic targets (30). These functional cellular

findings may explain why HOMA-IR, an index of hepatic insulin resistance, and the AUCs glucose-

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to-insulin ratio, indicative of peripheral insulin resistance, were markedly reduced only in GG rats.

In fact, HFD impairs insulin signaling determining hepatic and peripheral insulin resistance by

reducing Akt and GSK phosphorylation and function. We note, however, that glycemia did not

return to baseline values in both GG and SO rats after the OGTT (see Figure 1) suggesting that the

glucose clearance from the circulation to the metabolic compartment is not fully normalized by

glandular gastrectomy.

Looking for the mechanism of action of GG on the normalization of insulin resistance, three

possible factors, namely the abolition of ghrelin secretion, the increased circulating GLP1 and the

elevation of BAs, might have played a significant role.

In accordance to our findings, i.e. that our GG rats did not appreciably lose weight, ghrelin -/- mice

do not show appetite reduction or weight loss under a HFD (31). Ghrelin−/−

mice show improved

glucose tolerance and increased insulin sensitivity (32). Interestingly, also in ghrelin receptor

knockout mice, i.e. in mice producing ghrelin which however cannot bind to its receptor, peripheral

insulin sensitivity is greatly increased while hepatic glucose production is reduced (33).

Thus, it appears that the most important role of ghrelin is that of regulating insulin sensitivity and

glucose homeostasis.

We cannot exclude, however, that the marked improvement of insulin sensitivity observed in our

series can be partially explained by the increase of GLP1 secretion or by changes in gut microbiota

profile and resultant changes in BA pool size. It has, in fact, been demonstrated that the lack of

dipeptidyl peptidase IV (DPPIV), the enzyme that degrades GLP1, improves both HOMA-IR and

the AUC in the OGTT in genetically DPPIV deficient rats (F344/DuCrj), under a HFD compared

with F344/Crj control rats (34). Interestingly, microbially produced short-chain fatty-acids (SCFA),

BAs and certain probiotic strains, e.g. Lactobacillus reuteri SD5865, stimulate glucose-induced

GLP1 release (35-37).

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The literature data on the gut microbiota profiles in obesity and after metabolic surgery are

consistent in showing dramatic effects on microbial make-up but less so in terms of which

organisms or microbial related metabolic activities are affected.

The first studies in ob/ob mice demonstrated that the Firmicutes were more represented whereas

the Bacteroidetes were correspondingly reduced (38), and this was considered as an imprinting of

obesity. However, successively using an ob/ob mouse model fed either low-fat or high-fat diets and

compared with wild-type mice, Murphy et al. (39) demonstrated that this increased Firmicutes to

Bacteroidetes ratio was the resultant of the high-fat diet rather than genetic obesity. Accordingly,

Turnbaugh et al. (40) found that in the diet-induced obese mice, the difference in

Firmicutes/Bacteroidetes ratio was mainly related to the increase in a single class of bacteria, the

Mollicutes belonging to the Firmicutes phylum.

After the gastric bypass in a rat model, Li et al. (41) showed a simultaneous reduction of both

Firmicutes (4.5-fold) and Bacteroidetes (2-fold) in comparison to sham-operated rats, but a

significant increase of Proteobacteria (52-fold), which are a phylum of gram-negative bacteria.

In our study, we found no significant changes in the Firmicutes/Bacteroidetes ratio between GG and

SO rats under a HFD but rather dramatic shifts in relative abundance within the Firmicutes, with

increased relative abundance of Lactobacillus and reduced abundance of Ruminococcus

characterizing improved metabolic health in the GG animals. Indeed, our Random Forest analysis

showed lactobacilli to be the main bacteria taxa distinguishing GG from SO animals.

GG was associated with a reduced endotoxemia as shown by the lower circulating levels of LPS in

comparison with SO rats. Luche et al. (42) demonstrated that metabolic endotoxemia increases the

macrophage number in the mice adipose tissue in a CD14 mediated manner. Accordingly, the

visceral adipose tissue of SO rats showed an increased cellular infiltration and fibrosis, not observed

after GG.

We acknowledge some limitations of our study. Our model is markedly different from the

human model, where improvements in glycemic control are generally observed during the period of

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weight loss. However, we note that after bilio-pancreatic diversion the patients become extremely

insulin sensitive even though they have lost negligible amounts of weight (8) or they pass from

super obesity to morbid obesity (43). In addition, the gastric anatomy of the rats is completely

different from that of humans, where the glands containing endocrine cells are distributed

throughout the entire gastric surface, thus preventing replication of this operation in patients.

Finally, we did not test the mechanistical aspects of the observed metabolic changes.

In conclusion, the GG devised by us shows a role for the stomach in glycemia even though the rats

remained essentially obese. It lowered insulin resistance – through the improvement of both insulin-

mediated hepatic and peripheral insulin sensitivity, with the increase of Akt and GSK-3

phosphorylation, and insulin-independent glucose disposal, as demonstrated by the increased

muscular phosphorylation of AMPK – and reduced ectopic fat deposition and hepatic glycogen

over-accumulation. These findings are associated with body adipose tissue redistribution, with net

reduction of intra-abdominal fat, dramatic reduction of circulating ghrelin levels, increased GLP1

plasma concentration and with remodeling of gut microbiome with increased taxonomic richness,

increased relative abundance of lactobacilli and increased circulating BA pool. Collectively, these

data suggest that, in the rat, the secreting portion of the stomach plays a central role in the

improvement of the metabolic syndrome.

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Acknowledgments

Professor Geltrude Mingrone is the guarantor of this study.

NB, GM and SF designed the study. NB, ES, LCG and GC researched data and operated the

animals. NB, GM, VK, FF and KT wrote the manuscript. DA, FF, CD and KT performed the

experiments on gut microbiota and made statistical analysis. GA, FLN and AS performed the

western blots. AB, MM, LCG and GC contributed discussion.

We would like to thank Mrs. Anna Caprodossi for her excellent technical support.

This study was partially supported by the grant 70201398 - LINEA D.1 2015 from the Catholic

University.

ALB was supported by a grant from the German Diabetes Association (DDG)

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Glandular gastrectomy Sham operation P value

Weight (g) 299.83±47.21 296.15±55.56 0.953

Fasting plasma glucose

(mg/dl)

138.76±21.39 166.47±28.18 0.001

Fasting plasma insulin

(ng/ml)

1.27±0.25 2.42±0.67 <0.0001

HOMA-IR 14.93±3.20 23.72±7.10 <0.0001

Glucose AUC 11753.57±8024.38 17210.16±5554.65 0.029

Insulin AUC 138.24±55.66 254.53±114.01 0.001

AUCglucose/AUCinsulin 64.53±62.90 143.32±70.70 0.001

Cholesterol (mg/dl) 122.33±9.55 167.81±25.21 <0.0001

Triacylglycerol (mg/dl) 133.28±10.23 189.85±21.40 <0.0001

Ghrelin (fmol/ml) 4.20±11.75 48.69±5.79 <0.0001

GLP1 (pmol/ml) 5.15±1.57 2.93±0.93 <0.0001

LPS (EU/ml) 4.65±0.87 7.84±0.98 <0.0001

BAs (µmol/l) 22.96±3.79 14.17±2.76 <0.0001

Table 1: weight and metabolic parameters in SO rats and in rats after GG. The blood

samples were taken at sacrifice. Data are mean±SD. Significance (P) was computed by

Wilcoxon signed-rank test.

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Variables

Spearman’s coefficients of correlation

GRL GLP1 Cholesterol TAG HOMA-

IR Weight

Mesenteric

adipose tissue

Retroperitoneal

adipose tissue

Subcutaneous

adipose tissue LPS BA

Lacto

bacillus

Collin

sella

Ruminoc

occus

GRL 1.00 -0.52* 0.01 0.32 0.70* 0.09 0.42 0.31 -0.02 0.18 -0.39 0.22 -0.49* 0.20

GLP1 -0.52* 1.00 -0.07 -0.36 -0.64* -0.31 -0.36 -0.33 -0.10 -0.08 0.21 -0.14 0.09 0.08

Cholesterol 0.01 -0.07 1.00 0.53* 0.45 0.61* 0.65* 0.69* 0.79* 0.64* -0.55* 0.20 0.02 -0.55*

TAG 0.32 -0.36 0.53* 1.00 0.64* 0.80* 0.52* 0.54* 0.56* 0.50* -0.25 0.29 -0.16 -0.33

HOMA-IR 0.70* -0.64* 0.45 0.64* 1.00 0.45 0.57* 0.50* 0.40 0.42 -0.62* 0.17 -0.30 -0.19

Weight 0.09 -0.31 0.61* 0.80* 0.45 1.00 0.55* 0.59* 0.53* 0.46 -0.08 0.14 0.05 -0.45

Mesenteric

adipose tissue 0.42 -0.36 0.65* 0.52* 0.57* 0.55* 1.00 0.97* 0.61* 0.47* -0.54* 0.36 0.05 -0.52*

Retroperitoneal

adipose tissue 0.31 -0.33 0.69* 0.54* 0.50* 0.59* 0.97* 1.00 0.67* 0.52* -0.54* 0.42 0.08 -0.57*

Subcutaneous

adipose tissue -0.02 -0.10 0.79* 0.56* 0.40 0.53* 0.61* 0.67* 1.00 0.43 -0.38 0.26 0.19 -0.69*

LPS 0.18 -0.08 0.64* 0.50* 0.42 0.46 0.47* 0.52* 0.43 1.00 -0.65* 0.16 -0.34 -0.08

BAs -0.39 0.21 -0.55* -0.25 -0.62* -0.08 -0.54* -0.54* -0.38 -0.65* 1.00 -0.17 0.20 0.13

Lactobacillus 0.22 -0.14 0.20 0.29 0.17 0.14 0.36 0.42 0.26 0.16 -0.17 1.00 0.03 -0.21

Collinsella -0.49* 0.09 0.02 -0.16 -0.30 0.05 0.05 0.08 0.19 -0.34 0.20 0.03 1.00 -0.69*

Ruminococcus 0.20 0.08 -0.55* -0.33 -0.19 -0.45 -0.52* -0.57* -0.69* -0.08 0.13 -0.21 -0.69* 1.00

Table 2. Coefficients of Spearman’s correlation between relevant metabolic and microbial measures within the GG animals (n=18). * indicates

statistically significant correlation (P<0,05).

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FIGURE LEGENDS

Figure 1

Time courses of plasma glucose (Figure 1, panel A) and of plasma insulin (Figure 1, panel B) in

SO, continuous lines, and GG, dotted lines. Both plasma glucose and insulin concentrations were

reduced in GG as compared with SO rats.

The OGTT was performed the day of animal sacrifice.

Figure 2

Representative hematoxylin and eosin stained muscle sections from SO and GG rats fed with HFD.

Sham rats exhibited predominantly steatosis in muscle (A) and liver (C) sections. After glandular

gastrectomy rats showed no steatosis in muscle (B) and liver (D) sections. SO rats (G) show

abundant interstitial cellular infiltration (possibly macrophages) of the retroperitoneal adipose tissue

with pronounced fibrosis; these features are absent in GG rats (H).

Representative PAS stained sections of liver from SO and GG rats fed with HFD. Sham rats

exhibited a higher glycogen storage in liver (E) sections. After GG, rats showed a decrease in

glycogen deposition in liver (F) sections.

Magnification x20.

Figure 3

The graphs depict densitometric analyses of phosphorylated form/total protein in 20 muscle samples

of SO and GG rats obtained at sacrifice. Results are expressed as the mean±SEM. Representative

blot of a single rat (Sham and GG) is shown.

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(A, left side) Level of Akt phosphorylation (Ser473) in muscle biopsies of SO and GG rats.

There is a low level of phosphorylation of Ser473 in sham operated rats and a significant increase of

phosphorylation after GG (21.5% ±8.2 vs. 74.03% ±11.8; P=0.006).

(A, right side) Rats undergoing glandular gastrectomy exhibit increased phosphorylation of

AMPKα (Tyr172) than sham operated rats (1.9% ±0.18 vs. 2.9% ±0.2; P=0.007).

(B, left side) Level of Akt phosphorylation (Ser473) in liver biopsies of SO and GG rats.

There is a low level of phosphorylation of Ser473 in sham operated rats and a significant increase of

phosphorylation after GG (8.7% ±0.8 vs. 13.9% ±1.7; P=0.03).

(B, right side) Level of GSK3β phosphorylation (Ser9) in liver biopsies of SO and GG rats. There is

a low level of phosphorylation of Ser9 in sham operated rats and a significant increase of

phosphorylation after GG (4.6% ±1.3 vs. 10.5% ±2; P=0.05). GSK3α was not detected.

(C, left side) Level of Akt phosphorylation (Ser473) in adipose tissue biopsies of SO and GG rats.

There is no significant difference of phosphorylation after GG (16.1% ±3.4 vs. 9.4% ±3.4).

(C, right side) Level of GSK3β phosphorylation (Ser9) in adipose tissue biopsies of SO and GG

rats. There is no significant difference of phosphorylation after GG (38.7% ±11.2 vs. 12.8% ±3.3).

GSK3α was not detected.

Figure 4

4a: Alpha diversity from Genus to Phylum level for GG (A) and sham operated rats (B) measured

using the number of observed taxa, the Chao1 index and the Shannon entropy. The central line in

the box indicates the median, and the box extends to the 25th and 75th percentiles.

4b: PCoA of the between samples distances (beta diversity) computed using the Unweighted

UniFrac distance. Red dots represent the GG rats (A) and green dots the sham operated rats (B).

The right panel shows the same PCoA plot with the ten most abundant Genera (showing the highest

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average relative abundance across all the samples) overlaid as colored squares with a size

proportional to their mean relative abundance. Genus coordinates were calculated as the weighted

average across sample coordinates.

Figure 5

Differences in relative abundance of taxa from phylum to genus level for GG (A) and sham-

operated (B) animals.

Taxa with relative abundance <1% are merged into the other label.

Figure 6

Top ranked orders (A) and genera (B) identified by Random Forests according to their ability to

discriminate the microbiota of GG and sham operated rats in decreasing order of their

discriminatory importance (mean decrease in classification accuracy >0.1%). Importance was

determined based on the expected mean decrease in classification accuracy when the feature was

ignored. Error bars indicate the standard deviations.

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50

100

150

200

250

300

350

400

450

0 20 40 60 80 100 120

Pla

sma

glu

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(m

g/m

l)

Time (minutes)

0

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2

3

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0 20 40 60 80 100 120

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lin (

ng/

ml)

Time (minutes)

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Sample accession Secondary accession Sample unique name Study accession Experiment accession Run accession ID Name BarcodeSequence

LinkerPrimerSequence ReversePrimer Rat Cage Status GRL..fmol.ml. GLP1..pmol.ml. Chol..mg.dl. TAG..mg.dl. HOMA.IR AUC.glu

AUC.ins AUGglu.AUCins WEIGHT LPS..EU.ml. BA..micromol.l. MESENTERIC RETROPERITONEAL subcutaneous

ERS986129 SAMEA3678980 rat_gastrectomy1 PRJEB11901 ERX1225758 ERR1146956 454Reads.10265 10-265 CTCTACGCTC

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 30 10 B 45 2.5 158.2 205 34.9 14205 273.9 51.9

350 9.03 14.3 9.69 9.55 3.16

ERS986130 SAMEA3678981 rat_gastrectomy2 PRJEB11901 ERX1225759 ERR1146957 454Reads.10320 10-320 TGTACTACTC

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 20 10 B 49 2.1 152 181 21 17355 59.7 290.9

320 7.77 16.2 10.6 9.29 1.65

ERS986131 SAMEA3678982 rat_gastrectomy3 PRJEB11901 ERX1225760 ERR1146958 454Reads.11290 11-290 TGTGAGTAGT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 17 11 A 1.2 4.4 111 130 10.2 17010 30.6 555.9

298 4.14 24.3 6.9 6.54 3.15

ERS986132 SAMEA3678983 rat_gastrectomy4 PRJEB11901 ERX1225761 ERR1146959 454Reads.11310 11-310 TCTAGCGACT


195 3.56 26.2 6.64 5.91 2.75

ERS986133 SAMEA3678984 rat_gastrectomy5 PRJEB11901 ERX1225762 ERR1146960 454Reads.12275 12-275 AGTACGCTAT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 11 12 A 0.6 4.2 136 136 11.9 10830 43 252

335 5.55 23.5 8.51 7.22 4.11

ERS986134 SAMEA3678985 rat_gastrectomy6 PRJEB11901 ERX1225763 ERR1146961 454Reads.12295 12-295 TGACGTATGT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 25 13 B 39 4.6 139 169 10.1 15135 55.7 272

335 6.49 12.7 9.86 9.54 2.26

ERS986135 SAMEA3678986 rat_gastrectomy7 PRJEB11901 ERX1225764 ERR1146962 454Reads.13330 13-330 ACGACTACAG

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 21 13 B 56 3.7 136 178 19.4 6795 133.9 50.8

189 7.53 15.2 8.75 8.91 2.55

ERS986136 SAMEA3678987 rat_gastrectomy8 PRJEB11901 ERX1225765 ERR1146963 454Reads.14310 14-310 CGTACTCAGA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 29 14 B 57 2 190.6 215 41.4 23407.5 306.3

76.4 322 9.15 13.6 9.64 9.15 4.02

ERS986137 SAMEA3678988 rat_gastrectomy9 PRJEB11901 ERX1225766 ERR1146964 454Reads.14320 14-320 TACGCTGTCT


355 9.26 9.2 10.95 8.81 2.11

ERS986138 SAMEA3678989 rat_gastrectomy10 PRJEB11901 ERX1225767 ERR1146965 454Reads.15320 15-320 TCGCACTAGT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 18 15 A 1.3 4 119 141 12.3 1777.5 102.1 17.4

of 44 Diabetes

311 5.15 19.4 8.77 7.41 4.16

ERS986139 SAMEA3678990 rat_gastrectomy11 PRJEB11901 ERX1225768 ERR1146966 454Reads.15330 15-330 CTACGCTCTA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 12 15 A 0.6 5.4 119 141 9.6 6930 18 385

329 3.77 28 7.44 7.09 3.77

ERS986140 SAMEA3678991 rat_gastrectomy12 PRJEB11901 ERX1225769 ERR1146967 454Reads.16305 16-305 ATAGAGTACT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 22 16 B 44 4.2 174 170 25.4 13740 548.9 25

270 8.36 12.3 8.64 8.11 2.78

ERS986141 SAMEA3678992 rat_gastrectomy13 PRJEB11901 ERX1225770 ERR1146968 454Reads.16405 16-405 ACTGTACAGT


191 7.21 16.5 8.16 6.96 3.29

ERS986142 SAMEA3678993 rat_gastrectomy14 PRJEB11901 ERX1225771 ERR1146969 454Reads.17255 17-255 CTATAGCGTA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 13 17 A 1.2 3.9 120 132 10.8 11557.5 95.7

120.8 355 4.33 25.4 8.12 7.19 4.09

ERS986143 SAMEA3678994 rat_gastrectomy15 PRJEB11901 ERX1225772 ERR1146970 454Reads.17325 17-325 CACGCTACGT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 4 17 A 0 6.6 128 134 9.8 14872.5 9

1647.6 300 4.29 25.2 6.5 5.8 5.56

ERS986144 SAMEA3678995 rat_gastrectomy16 PRJEB11901 ERX1225773 ERR1146971 454Reads.18340 18-340 CGTACAGTCA


215 4.51 19.6 9.31 8 4.36

ERS986145 SAMEA3678996 rat_gastrectomy17 PRJEB11901 ERX1225774 ERR1146972 454Reads.2290 2-290 CGTCTAGTAC

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 19 8 B 53 2 185 209 33.8 16485 162 101.7

265 6.58 18 11.78 10.06 3.11

ERS986146 SAMEA3678997 rat_gastrectomy18 PRJEB11901 ERX1225775 ERR1146973 454Reads.3A315 3A-315 TCTATACTAT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 9 3A A 0 7.1 127 118 7.3 13680 64.1 213.6

285 5.25 18.2 7.25 7 3.49

ERS986147 SAMEA3678998 rat_gastrectomy19 PRJEB11901 ERX1225776 ERR1146974 454Reads.3A335 3A-335 CGACGTGACT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 5 3A A 2.1 4.6 129 139 13.1 2872.5 11.9 240.8

320 6.1 16.9 8.64 7.36 4.12

ERS986148 SAMEA3678999 rat_gastrectomy20 PRJEB11901 ERX1225777 ERR1146975 454Reads.4A310 4A-310 CGTGTCTCTA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 2 4A A 5.2 6.1 135 155 18.5 6795 67 101.4

320 6.27 16.5 9.56 8.02 5

ERS986149 SAMEA3679000 rat_gastrectomy21 PRJEB11901 ERX1225778 ERR1146976 454Reads.4A340 4A-340 TACGTCATCA

of 44Diabetes

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 14 4A A 2.1 3.1 126 143 14.7 3007.5 135.6 22.2

315 4.51 20.1 7.55 7.14 4.01

ERS986150 SAMEA3679001 rat_gastrectomy22 PRJEB11901 ERX1225779 ERR1146977 454Reads.535512 5-355 1/2

AGTGCTACGA TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 23 5 B 47 2.6 188.5 202 25.1 6727.5

1.6 4219 355 7.55 15.4 11.9 11.06 2.19

ERS986151 SAMEA3679002 rat_gastrectomy23 PRJEB11901 ERX1225780 ERR1146978 454Reads.535522 5-355 2/2

ACTACTATGT TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 27 5 B 45 3.1 163.4 177 23.1 11850

68.4 173.2 310 6.52 18.3 8.51 8.36 1.96

ERS986152 SAMEA3679003 rat_gastrectomy24 PRJEB11901 ERX1225781 ERR1146979 454Reads.5A320 5A-320 ACTAGCAGTA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 15 5A A 1 4.6 128 144 8.8 4027.5 110.6 36.4

356 5.26 25.5 9.5 8.01 4.48

ERS986153 SAMEA3679004 rat_gastrectomy25 PRJEB11901 ERX1225782 ERR1146980 454Reads.5A360 5A-360 CATAGTAGTG

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 3 5A A 1.2 8.2 115 121 6.3 19095 89.4 213.6

251 5.12 25 6.91 6.91 3.36

ERS986154 SAMEA3679005 rat_gastrectomy26 PRJEB11901 ERX1225783 ERR1146981 454Reads.7250 7-250 CGCGTATACA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 28 7 B 47 4.1 168 196 28.2 12592.5 145.4

86.6 288 8.29 11.5 8.74 7.55 2.04

ERS986155 SAMEA3679006 rat_gastrectomy27 PRJEB11901 ERX1225784 ERR1146982 454Reads.7A320 7A-320 CGTCGATCTC

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 16 7A A 0 8.6 103 116 5.2 7350 45.5 161.6

250 3.19 30 6.69 6.49 3.12

ERS986156 SAMEA3679007 rat_gastrectomy28 PRJEB11901 ERX1225785 ERR1146983 454Reads.8355 8-355 CGATCGTATA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 24 8 B 54 2.2 175.8 172 28.6 12420 52.5 236.3

300 8.19 11 10.71 8.14 3.11

ERS986157 SAMEA3679008 rat_gastrectomy29 PRJEB11901 ERX1225786 ERR1146984 454Reads.9330 9-330 TAGTGTAGAT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 6 9 A 3.2 3.9 131 141 16.9 11557.5 43.3

267.2 371 5.02 23.1 10.11 9.89 5.12

ERS986158 SAMEA3679009 rat_gastrectomy30 PRJEB11901 ERX1225787 ERR1146985 454Reads.9335 9-335 ATACGACGTA

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 10 9 A 1 5.6 133 129 9.4 15870 15.8 1002.8

301 3.59 22.3 9.64 8.05 5

ERS986159 SAMEA3679010 rat_gastrectomy31 PRJEB11901 ERX1225788 ERR1146986 454Reads.A260 A-260 ACGAGTGCGT

TCCTACGGGAGGCAGCAG TGTGCGGGCCCCCGTCAATT 1 A A 51 4.2 112 129 12.9 8295 49.4 168

290 4.11 24 8.5 6.89 2.68

of 44 Diabetes

X

of 44Diabetes

120

160

200

240

280

320

360

400

0 2 4 6 8 10

Bo

dy

wei

ght

(g)

Weeks

* *

of 44 Diabetes

10

15

20

25

30

35

0 2 4 6 8 10

Foo

d in

take

(g/

day

)

weeks

* *

of 44Diabetes

Observed Chao1 Shannon

6

8

10

12

5.0

7.5

10.0

12.5

15.0

0.4

0.6

0.8

1.0

20

30

40

20

30

40

50

0.50

0.75

1.00

1.25

1.50

1.75

Alp

ha D

ivers

ity M

easu

re

16

20

24

15

20

25

30

0.50

0.75

1.00

1.25

1.50

1.75

30

40

50

60

70

30

40

50

60

70

80

1.50

1.75

2.00

2.25

2.50

50

60

70

80

90

100

60

70

80

90

100

110

2.0

2.4

2.8

3.2

A B A B A B

Status

Phylum

Class

Order

Family

Genus

of 44 Diabetes

insulin resistance, microbiota and fat distribution...

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