DOI: 10.5061/dryad.dz08kps0v
title: Yogurt supplementation attenuates insulin resistance in obese mice by reducing metabolic endotoxemia and inflammation

description: Background Inflammation is an underlying mechanism for development of
 obesity-related health complications. Yogurt consumption inhibits
 obesity-associated inflammation, but the tissue-specific mechanisms have
 not been adequately described. Objectives We aimed to determine the
 tissue-specific responses by which yogurt supplementation inhibits
 inflammation. Methods C57BL/6 male mice (5 weeks old) were fed a Teklad
 Global 14% Protein Rodent Maintenance diet as a control or a high-fat diet
 (60% calories from fat) to induce obesity for 11 weeks, followed by
 feeding a Western diet (WD; 43% carbohydrate & 42% fat) or WD
 supplemented with 5.6 % lyophilized yogurt powder for 3 weeks to test for
 the impact of yogurt supplementation. Markers of metabolic endotoxemia and
 inflammation were assessed in plasma and tissues. Cecal and fecal
 microbiota were profiled by 16S rRNA sequencing. Results In obese mice,
 relative to the WD control group, yogurt supplementation attenuated
 HOMA-IR by 57%) (p=0.020), plasma TNF-a by 31% (p&lt;0.05) and colonic
 IFN-g by 46% (p=0.0034), which were accompanied by a 40% reduction in
 plasma LBP (p=0.0019) and 45% less colonic Lbp expression (p=0.037), as
 well as alteration in the beta diversity of cecal microbiota (p=0.0090)
 and relative abundance of certain cecal microbes (e.g., Lachnospiraceae
 Dorea longicatena with p=0.049). There were no differences in the LBP,
 Lbp, and Cd14 levels in the liver and small intestine between obese mice
 with and without yogurt supplementation (p&gt;0.05). Conclusions
 Yogurt consumption inhibited obesity-induced inflammation in mice by
 modulating colonic endotoxin detoxification, changing the gut microbiota,
 and improving glucose metabolism. This work helps to establish the
 underlying mechanisms by which yogurt consumption affects markers of
 metabolic and immune health.
description: <strong>Animals and experimental
 design</strong> C57BL/6 male mice were obtained
 from Jackson Laboratory (Bar Harbor, ME) at three weeks old and acclimated
 for 2 weeks. Mice were housed together with 2 to 5 per cage and maintained
 by the University of Wisconsin-Madison Research Animal Resource Center and
 reared under 12 h light-dark cycle. Mice were received in small batches
 from the breeding center and assigned to the same group and co-housed to
 avoid fighting. All experiments were approved by the Institutional Animal
 Care and Use Committee of the University of Wisconsin-Madison
 (A005914). Western diets (WDs) induce metabolic stress
 through the TLR4 pathway. The objective of the feeding protocol was to
 test the impact of yogurt consumption in obese and non-obese mice in the
 context of a WD. WDs were selected to more closely mimic dietary patterns
 in which yogurt reduced inflammation in human participants. Therefore, in
 this study, a standard high-fat diet (HFD) was used to induce obesity in
 mice, then obese and non-obese groups consumed Western diets with or
 without yogurt supplementation. For 11 weeks, mice were
 randomized by cage and fed a Teklad Global 14% Protein Rodent Maintenance
 Diet (2014C: 13, 20, 67 % calories were from fat, protein, carbohydrate;
 ENVIGO, IN) as a control group (C group: n=33) or a HFD (TD.06414: 60, 18,
 21 % calories were from fat, protein, carbohydrate; ENVIGO) to
 sufficiently induce obesity (O group: n=37). From Week 11, approximately
 half of both the C group and O group were randomized by cages and fed
 either a WD (TD.07201: 42, 15, 43 % calories were from fat, protein,
 carbohydrate; ENVIGO) (LC group: n=18; OC group: n=18) or a WD
 supplemented with 5.6% lyophilized yogurt in place of corn starch in
 TD.07201 (TD.190642: 44, 16, 40% calories were from fat, protein,
 carbohydrate; ENVIGO) (LY group: n =15 OY group: n=19) for another 3
 weeks. The yogurt powder was produced as follows: 2 % reduced-fat milk
 (Kemps, MN) was pasteurized at 80-85 °C for 30 min and fermented by Yo-Mix
 495 LYO 750 DCU (<em>Streptococcus thermophilus</em> and
 <em>Lactobacillus delbrueckii </em>subsp.
 <em>bulgaricus</em>) and HOWARU® Dophilus LYO 100 DCU-S
 (<em>Lactobacillus acidophilus</em> NCFM) supplied by
 Danisco-Dupont at 40.6 °C for 6 h, followed by lyophilization to obtain
 the dried yogurt powder. No animals died throughout the study.
 Body weight and food intake were recorded weekly. At Week 14,
 after 4 h of fasting, mice were euthanized by isoflurane and the following
 samples were collected and frozen with liquid nitrogen to be stored at -80
 °C: plasma, gonadal, retroperitoneal, and omental adipose tissues, liver,
 small intestine, colon, and feces and cecal samples. Upon collection, the
 tissue samples were weighed. <strong>Plasma
 glucose and insulin assessment</strong> Plasma
 glucose level was assessed using Glucose Colorimetric Assay Kit (Cayman
 Chemical, MI). Plasma insulin level was assessed by Mouse Insulin ELISA
 kit (Mercodia, NC). Homeostatic model assessment for insulin resistance
 (HOMA-IR) was calculated as follows: Glucose (mg/dL) * Insulin (mU/L) /
 405. <strong>Cytokine
 assessment</strong> Interleukin (IL)-1b, IL-6,
 IL-10, tumor necrosis factor (TNF)-a, and leptin in the plasma, as well as
 keratinocytes-derived chemokine (KC), TNF-a, IL-1b, IL-4, IL-6, IL-10,
 IL-17A, and interferon (IFN)-g levels in the omental adipose tissue and
 distal colon samples measured by a customized multiplex cytokine panel
 using QuickPlex SQ 120 imager (Meso Scale Discovery, Rockville, MD). Some
 samples were removed from statistical analysis because the concentrations
 were below detection limit, suspected of a technical error, or of
 insufficient sample volume to complete the analysis.
 <strong>LBP and (s)CD14 assessment</strong>
 Plasma and liver LBP were measured by Mouse LBP ELISA kit (Abcam,
 Cambridge, UK) and plasma sCD14 by Mouse CD14 Quantikine ELISA kit
 (R&D Systems, Inc., MN). Total protein was extracted from liver
 samples as follows: 30 mg tissue was mixed with 1.5 mL of ice-cold lysis
 buffer (25 mM Tris-HCl at pH 7.4, 150 mM NaCl, 1 mM EDTA, 1 % NP-40, and 5
 % glycerol), homogenized by bead beating for 45 sec at 5 m/s, incubated
 for 2 h with constant agitation at 4 °C, centrifuged for 20 min at 14,000
 ×g at 4 °C, and the supernatant was collected for the ELISA assay.
 <em>Lbp</em> and <em>Cd14</em> expression levels
 in the liver, small intestine, and colon samples were assessed by RT-qPCR
 as described by others. Briefly, RNA was extracted using TRIzol
 (Invitrogen, Waltham, MA) and cleaned up using RNeasy Mini Kit and DNase
 set (Qiagen, Valencia, CA). cDNA was synthesized by
 iScript<sup>TM</sup> cDNA Synthesis Kit (Bio-Rad, Hercules,
 CA) and qPCR was run with Fast SYBR<sup>TM</sup> Green Master
 Mix (Thermo Fisher) using Bio-Rad CFX96 system (Bio-Rad). The mRNA
 expressions were normalized using that of ribosomal protein large P0 since
 its gene expression level showed stable expression in the tissues of
 interest in HFD-fed C57BL/6J male mice.
 <strong>16S rRNA sequencing
 analysis</strong> DNA was extracted from the
 cecal and fecal samples using DNeasy PowerSoil Kit according to the
 manufacturer’s protocol. 16S Metagenomic Sequencing Library Preparation
 Protocol (Part # 15044223 Rev. B, Illumina Inc., San Diego, CA) was used
 with the following modifications. The 16S rRNA gene V3/V4 variable region
 was amplified with fusion primers and cleaned with a 0.7x volume of
 AxyPrep Mag PCR clean-up beads (Axygen Biosciences, Union City, CA). Then,
 Illumina dual indexes and sequencing adapters were added using the primers
 shown in Supplementary Table 5, which was then cleaned using the same kit
 written above. Quality and quantity of the constructed libraries were
 assessed by an Agilent 4200 TapeStation DNA 1000 kit (Agilent
 Technologies, Santa Clara, CA) and Qubit dsDNA HS Assay Kit (Thermo Fisher
 Scientific), respectively. Libraries were pooled in an equimolar fashion
 and appropriately diluted prior to sequencing. Paired end 300 bp
 sequencing was performed using the Illumina MiSeq Sequencer and a MiSeq
 600 bp (v3) sequencing cartridge in the University of Wisconsin-Madison
 Biotechnology Center. Data was analyzed using the standard Illumina DATA2
 Pipeline, version 1.8.2 to generate amplicon sequence variant table for
 further data analyses. <strong>Statistical
 analyses</strong> The sample sizes among groups
 varied by assays and tissue availability. Prior to applying the following
 statistics, the Shapiro test was used to assess data distribution, and
 Levene’s test was used to test for data homogeneity. Datasets that had
 non-normal distribution (datasets except for weight, diet intake, and
 individual bacterial abundance datasets), log10 transformation was applied
 to establish normal distribution. For measurements
 obtained over multiple time points, a repeated-measure three-way analysis
 of variance (ANOVA) with a mixed-effects model was used due to the
 unbalanced sample size among groups. The statistics were applied for
 “Obese” (body condition at Week 11; L or O groups), “Yogurt” (with or
 without yogurt supplementation in WD; C or Y groups), “Time,” and the
 interaction terms, where the resulting p-values are summarized in Tables
 1-2 or directly in Figures (e.g., p<sub>obese</sub>,
 p<sub>yogurt</sub>). As post-hoc test, two-stage linear
 step-up procedure of Benjamini, Krieger, and Yekutieli was used, and
 p-values are summarized in figures. A two-way ANOVA was applied for the
 rest of the measurements collected at single time point, except for the
 microbiota dataset, for the effects of Obese, Yogurt, and the interaction
 terms. Sidak’s multiple comparison test was used for post-hoc tests.
 Pearson correlation test was used to assess the correlation between plasma
 LBP, sCD14, cytokines, or leptin, and HOMA-IR. The interpretation of
 effect size (correlation coefficient, R) is as follows: small when R is
 less than 0.30, moderate when R is between 0.3–0.5, high when R is above
 0.5. Statistical results with p&lt;0.05 were defined to be significant
 and false discovery rate corrected p-values were used for multiple
 comparison analyses. Bacterial abundance with
 significant group difference was identified by likelihood-ratio test in
 DESeq2 (version 1.32.0). P-values from the statistics were adjusted by the
 Benjamini-Hochberg method, and features with adjusted p-values less than
 0.05 were considered to have significant group difference. Those with
 identified genera were reported in the manuscript. Rarefied amplicon
 sequence variant counts were used to assess the alpha and beta diversity.
 Alpha diversity was measured using Shannon index. For beta diversity, the
 data dispersion was assessed by betadisper (vegan package, version 2.5.7),
 which showed that the homogeneity of data dispersion between groups was
 significantly different in fecal samples. Therefore, the group difference
 in the centroids was assessed by analysis of similarities (ANOSIM). Beta
 diversity was visualized by non-metric multi-dimensional scaling plots
 based on Bray Curtis distance.