European Journal of Nutrition
https://doi.org/10.1007/s00394-019-02088-0

ORIGINAL CONTRIBUTION

Metabolically and immunologically beneficial impact of extra virgin
olive and flaxseed oils on composition of gut microbiota in mice
Jasmine Millman1 · Shiki Okamoto1 · Aoki Kimura2 · Tsugumi Uema1 · Moeko Higa1 · Masato Yonamine1 ·Toyotaka Namba1 · Emi Ogata3 · Satoru Yamazaki3 · Michio Shimabukuro3 · Masato Tsutsui4 · Masayuki Matsushita5 ·Shinya Ikematsu2 · Hiroaki Masuzaki1
Received: 2 April 2019 / Accepted: 30 August 2019
© The Author(s) 2019

Abstract
Purpose Extra virgin olive oil (EVOO) and flaxseed oil (FO) contain a variety of constituents beneficial for chronic inflammation and cardio-metabolic derangement. However, little is known about the impact of EVOO and FO on dysbiosis of gut
microbiota, intestinal immunity, and barrier. We, therefore, aimed to assess the impact of EVOO and FO on gut microbiota,
mucosal immunity, barrier integrity, and metabolic health in mice.
Methods C57BL/6 J mice were exposed to a low-fat (LF), lard (HF), high fat-extra virgin olive oil (HF-EVOO), or highfat-flaxseed oil (HF-FO) diet for 10 weeks. Gut microbiota assessment was undertaken using 16S rRNA sequencing. Levels
of mRNA for genes involved in intestinal inflammation and barrier maintenance in the intestine and bacterial infiltration in
the liver were measured by qPCR.
Results HF-EVOO or HF-FO mice showed greater diversity in gut microbiota as well as a lower abundance of the Firmicutes
phylum in comparison with HF mice (P < 0.05). The qPCR analyses revealed that mRNA level of FoxP3, a transcription
factor, and IL-10, an inducer of regulatory T cells, was significantly elevated in the intestines of mice-fed HF-EVOO incomparison with mice-fed HF (P < 0.05). The mRNA level of the antimicrobial peptide, RegӀӀӀγ, was markedly elevated in
the intestines of HF-EVOO and HF-FO compared with HF group (P < 0.05).
Conclusions Our data suggest that the consumption of EVOO or FO can beneficially impact gut microbiota, enhance gut
immunity, and assist in the preservation of metabolic health in mice.
Keywords Gut microbiota · Flaxseed oil · Extra virgin olive oil · Antimicrobial peptide · Regulatory T cells

Introduction

Electronic supplementary material The online version of thisarticle (https​://doi.org/10.1007/s0039​4-019-02088​-0) contains
supplementary material, which is available to authorized users.
* Shiki Okamoto
shiki@med.u‑ryukyu.ac.jp* Hiroaki Masuzaki
hiroaki@med.u‑ryukyu.ac.jp
1

Division of Endocrinology, Diabetes and Metabolism,
Hematology, Rheumatology, (Second Department of Internal
Medicine), Graduate School of Medicine, Universityof the Ryukyus, Okinawa, Japan2

Department of Bioresources Engineering, National Instituteof Technology, Okinawa College, Okinawa, Japan

Metabolic health can be described as the body’s ability tosuccessfully balance fuel oxidation and fuel availability inan endocrine-influenced environment [1]. In today’s current obesogenic climate, however, attaining this metabolic
3

Department of Diabetes, Endocrinology and Metabolism,
School of Medicine, Fukushima Medical University,
Fukushima, Japan

4

Department of Pharmacology, Graduate School of Medicine,
University of the Ryukyus, Okinawa, Japan

5

Department of Molecular and Cellular Physiology, Graduate
School of Medicine, University of the Ryukyus, Okinawa,
Japan

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balance is becoming ever more difficult to achieve, and as aconsequence, the burden of lifestyle-related metabolic diseases is rapidly increasing worldwide. Low-grade, sustained
systemic inflammation, considered to lie at the heart of metabolic dysfunction, is strongly linked to the consumptionof obesogenic diets high in pro-inflammatory-saturated fats
[2, 3]. Chronic intake of diets high in saturated fats not only
leads to inevitable weight gain, but can also compromise
intestinal barrier function, causing metabolic endotoxemia,
inducing systemic inflammation partly through toll-likereceptor (TLR) signaling [4].
A number of mechanisms employed by the mucosal
immune system aim to assist in the protection of gut barrier and preservation of host defense. Antimicrobial peptides (AMPs) are one such example. Primarily produced by
Paneth cells, however, also by enterocytes in the intestine,
they create a physical barrier, separating gut microbiota fromintestinal cells, employing a variety of bacteriostatic andbactericidal mechanisms [5]. For example, RegӀӀӀγ, a representative AMP of the Lectin family, uses non-enzymatic
attack to destroy pathogenic bacteria through the TLR–myeloid differentiation primary response 88 (MyD88)-signaling
pathway via recognition of microbiota-associated molecularpatterns (MAMPs), eventually promoting its transcription
and subsequent production [5]. The adaptive immune system also plays a fundamental role in modulating the local
gut environment and composition of microbiota, with key
players such as intestinal regulatory T (Treg) cells, aiding
mucosal tolerance, and enforcing commensalism [6, 7].
It has been expeditiously recognized that the gut microbiota is crucial in both mediating host inflammation andinfluencing host metabolic health, with diet as a critical factor in determining the diversity and function of the microbialcommunity [8]. A diet high in saturated fatty acids (SFAs)
and low in dietary fibers is considered to be one of the main
factors driving dysbiosis of gut microbiota, decreasing
microbial diversity and beneficial species of bacteria as wellas increasing the abundance of certain pathogenic speciesof bacteria, overall negatively impacting metabolic health inboth humans and rodents [9].
To date, however, little is known about the potential
impact of specific plant-derived dietary fats and oils on
composition of gut microbiota and host metabolic health.
Extra Virgin Olive oil (EVOO) and Flaxseed oil (FO), twomajor oils marketed as ‘functional foods,’ contain a wide
variety of compounds purported to have anti-inflammatory
properties and beneficial effects on markers of metabolic
health including blood glucose and lipids, body weight, andinflammation [10, 11]. EVOO, a staple in the mediterraneandiet, contains appreciable amounts of monounsaturated fatty
acids (MUFAs) and also a variety of phenolic compounds
which possess antioxidant, anti-inflammatory, and anti-bacterial activity that are transformed into bioactive metabolites

13

by resident gut microbiota [12, 13]. In comparison, FO is anabundant source of α-linolenic acid (ALA), an essential ω3polyunsaturated fatty acid (PUFA), but contains only a small
quantity of phenolic compounds [11, 12].
In this context, the present study aims to characterize thepotential impact of EVOO and FO on mouse gut microbiota, with a particular focus on gut barrier integrity, mucosal
immunity, and metabolic health—specifically in relation tofuel homeostasis, inflammation, and gut permeability. Our
hypothesis is that the ingestion of either EVOO or FO will
exert superior effects on mouse gut microbiota compositionand diversity, intestinal immune function, and metabolic
health in comparison with mice-fed HF or even LF diets.

Materials and methods
Animals and diets
6-week-old male C57BL/6 J mice were obtained fromCharles River Laboratories Japan, Inc. (Kanagawa,Japan) and were housed (5 mice per cage) at 24 °C undera 12 h/12 h light/dark cycle. All animal care and experimental procedures were approved by the Animal Experiment Ethics Committee of the University of the Ryukyus(No. 5352, 5718 and 5943). Mice were allowed to acclimatize for 2 weeks prior to the start of the dietary intervention and were fed ad libitum with water and a low-fat
purified diet. After 2 weeks of acclimatization, mice (n = 5
per group) were randomly allocated to receive one of four
experimental diets (Research Diets, New Brunswick, NJ,
USA). Experimental diets were as follows; low-fat, purified(LF) (10% energy from fat), saturated fat (HF) (45% energy
from fat, 35% from lard), extra virgin olive oil (HF-EVOO)(45% energy from fat, 35% from EVOO), and flaxseed oil(HF-FO) (45% energy from fat, 35% from FO). A low-fat,purified diet was used in replace of a grain-based standard
chow to control the effect of dietary fiber on gut microbiota
with an increased proportion of corn starch and maltodextrin
used as opposed to an increased proportion fat used in otherdiets. In all diets, sucrose and protein were matched. Macronutrient distribution of the experimental diets is shown in
Table 1. Composition regarding major fatty acids composition in the experimental diets is shown in Table 2.
Mice had free access to each of the diets for a period of
10 weeks. Body weight and food intake were measured andrecorded weekly. To assess diet-related changes in metabolic markers, on the morning following last day of the
dietary treatment phase, mice were sacrificed by decapitation. Liver, intestinal tissue samples, and caecum contents
were collected immediately and flash frozen in liquid nitrogen. Whole blood was collected into heparinized tubes andplasma was obtained after centrifugation at 4 °C, at 880×g

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Table 1  Macronutrient distribution in the experimental diets
LF

HF

HF-EVOO

HF-FO

%

G

Kcal

g

kcal

g

kcal

g

kcal

Protein
Carbohydrate
Fat
Total

19
67
4

20
70
10
100

24
41
24

20
35
45
100

24
41
24

20
35
45
100

24
41
24

20
35
45
100

LF

HF

HF-EVOO

HF-FO

Ingredient

g

kcal

g

kcal

g

kcal

g

kcal

Corn starch
Maltodextrin
SucroseCellulose
Soybean oil
Flaxseed oil
Extra virgin
olive oil
Lard
Total

452.2
75
172.8
50
25
0
0

1809
300
691
0
225
0
0

72.8
100
172.8
50
25
0
0

291
400
691
0
225
0
0

72.8
100
172.8
50
24.8
0
157.8

291
400
691
0
223.4
0
1420

72.8
100
172.8
50
24.8
157.8
0

291
400
691
0
223.4
1420
0

20.33
1055.4

183
4060

177.6
858.2

1598
4057

20.3
858.5

183
4061

20.3
858.5

183
4061

LF diet contained 3.8 kcal/g of energy and HF, HF-EVOO and HF-FO diets contained 4.7 kcal/g of energy, respectively
Table 2  Major fatty acid composition of Lard, EVOO, and FO usedin the experimental diets
Major fatty acid

LF

HF

HF-EVOO HF-FO

16:0 Palmitic acid
18:0 Stearic acid18:1 Oleic acid (MUFA)
18:3 Linoleic
18:3 α-Linolenic acid (n-3 PUFA)
SFA
MUFA
PUFA
Trans-fatty acid

14.4
6.9
27.4
39.5
4.7
23.7
30.0
46.3
0

18.2
9.8
31.7
27.8
2.1
31.7
35.6
32.7
0

12.1
4.0
63.0
15.8
1.6
17.3
64.5
18.0
0.2

7.2
4.2
19.1
21.1
46.1
12.3
19.9
67.8
0.1

% of total fat

for 15 min. All samples were stored at − 80 °C until further
processing.

DNA extraction and 16S rRNA sequencing
Microbial DNA was isolated from caecum contents using
QIAamp Fast DNA Stool Mini Kit (QIAGEN, Tokyo, Japan)
as per manufacturer’s instructions. PCR amplification was
performed using 16S rRNA universal primers targeting theV3–V4 region; 341F (TCG​TCG​GCA​GCG​TCA​GAT​GTG​
GTA​TAA​GAG​ACA​GCC​TAC​GGG​NGGC​WGC​AG) and
806R (GTC​TCG​TGG​GCT​CGG​AGA​TGT​GTA​TAA​GAG​
ACA​ G GG​ ACTACHVGGG​ T WT ​ C TAAT). Sequencing

fragments were detected and analyzed using MiSeq Illumina
platform (Illumina, San Diego, CA). Raw sequences were
filtered using the QIIME software package (Version 1.9.1).
Amplicon reads were clustered into operational taxonomic
units (OTUs) at 97% identity threshold and aligned against
Greengenes reference database (Version 13.5). A total of
193 representative sequences (most abundant OTUs) werealigned using Greengenes reference database and taxonomically assigned the Ribosomal Database project Classifier.
Microbial alpha diversity was measured by phylogenetic
diversity (PD) whole tree, Chao1, observed OTUs and Shannon indices and rarefaction plots generated using QIIME.
Microbial beta diversity was displayed as Principal Coordinates Analysis (PCoA) plots based on unweighted UniFrac
distances using Emperor, [14] displaying the relative relatedness of species among groups and the percentage varianceamong groups explained by the principal coordinate axes.
Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predict
differences in metagenome functions using OTUs generated
from 16S rRNA-sequencing data [15]. Normalized OTUs
for Kyoto Encyclopedia of Genes and Genomes (KEGG)
orthologs were then used to predict functions based on genecounts for each sample.

Analyses of quantitative RT‑PCR
Total RNA was extracted from liver, ileum, and proximal colon samples using Trizol reagent (Thermo Fisher,13

European Journal of Nutrition

Analyses of plasma short‑chain fatty acids (SCFAs)
SCFAs (acetate, propionate, butyrate, and valerate) inplasma were measured using LC–MS/MS. Standard SCFA
solutions (Merck, MO, USA) were used for generating calibration curves. Acetic acid-d4 (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) was used as an internal

Fig. 1  Selected metabolic parameters in mice-fed LF, HF, HF-EVOO,
and HF-FO diets. a Body weight gain (*Indicates difference between
HF and LF; #Indicates difference between HF and HF-EVOO
groups), b average energy intake and c blood glucose levels when

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Fig. 2  Plasma SCFA profile of mice-fed LF, HF, HF-EVOO, and
HF-FO diets. a Total concentration of plasma SCFAs, b–e individual plasma SCFA concentrations (acetate, propionate, butyrate, andvalerate, respectively) and f plasma ratio of acetate to butyrate plus
propionate. Data indicate mean ± SEM. All data were analyzed using
one-way ANOVA followed by post hoc Tukey’s multiple comparison test. n = 5 mice per group. *P < 0.05; **P < 0.01, ***P < 0.001;****P < 0.0001

standard. Samples were first subject to deproteinizationand SCFAs derived using 2-nitrophenylhydrazine. SCFA
derivatives were then extracted by methyl tert-butyl ether
and injected into the LC–MS/MS system using ACQUITYUPLC system (Waters, Milford, MA, USA) equipped withan analytical column (AQUITY HSS T3 2.1 × 150 mm,
1.8 μm, Waters). Electrospray ionization was carried out
with the API4000 (AB Sciex, Foster City, CA, USA) operating in negative ionization and multiple reaction-monitoring
mode. The multiple reaction-monitoring transitions labeled
for acetic acid, propionic acid, butyric acid, valeric acid, andacetic acid-d4 were m/z 194-164, m/z 208-178, m/z 222-192,
m/z 236-206, and m/z 197-93, respectively.

fed ad libitum (taken from tail vein). All data were analyzed using
one-way ANOVA followed by post hoc Tukey’s multiple comparison test. Data indicate mean ± SEM. n = 5 mice per group. *P < 0.05;**P < 0.01◂

Carlsbad, CA) and quantified using nanodrop (Thermo
Fisher, Scientific). The cDNAs were synthesized using
an iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA)according to the manufacturers’ instructions. Quantitative
real-time PCR was performed using Applied Biosystems
StepOneplus Real-Time PCR systems (Applied Biosystems)
and SYBR Green (Takara Bio, Shiga, Japan), following the
manufacturers’ instructions. All samples were analyzed
in duplicate and mRNA levels normalized to those of 18S
rRNA. The following quantitative PCR conditions were as
follows: 95 °C for 10 min, followed by 40 cycles of 95 °C for
15 s and 60 °C for 60 s. The primer sets used for quantitative real-time PCR analysis are shown in the Supplementary
Materials section (Supplementary Table S1).

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European Journal of Nutrition◂ Fig. 3  Diversity and composition of gut microbiota in mice-fed LF,

HF, HF-EVOO, and HF-FO diets. a Rarefaction curve for PD, b box
plot showing alpha diversity measured by PD, c principal coordinate
analysis of gut microbiota composition based on unweighted UniFrac,d correlation of observed OTUs with blood glucose levels, e relative abundance of major phyla composition, f–h relative abundanceof selected phyla highlighting significant differences among groups,
i relative abundance of major genera and j–r relative abundance of
selected genera highlighting significant differences among groups, mcorrelation of Bacteroides genera with plasma TG concentration. Alldata were analyzed using non-parametric, Kruskal–Wallis test. Data
indicate mean ± SEM. n = 5 mice per group *P < 0.05; **P < 0.01,***P < 0.001

Measurements of metabolic parameters
At the end of the 10-week dietary intervention, blood glucose levels, using blood taken from tail vein, were determined using One Touch glucose analyzer (Lifescan, Japan).
Both plasma triglyceride (TG) and total cholesterol (TC)levels, with samples obtained by decapitation, were measured using the Cholesterol E kit and Triglyceride E kits
(Wako, Japan), respectively, according to manufacturer’s
instructions.

Statistical analyses
Data are shown as mean ± SEM. One-way analysis of variance (ANOVA) followed by multiple comparison tests (Post
Hoc, Tukey’s) were used to analyze differences between
groups. Non-parametric Kruskal–Wallis tests were used
to compare differences in microbiota compositional data
among groups. Associations between plasma metabolic
markers and microbiota data were examined using linear
regression statistics. Levels of statistical significance wereset at *: P < 0.05, **: P < 0.01, ***: P < 0.001, and ****:
P < 0.0001. Statistical analyses were performed in Graph
Pad Prism, version 8.0 (Graph Pad Software, San Diego,
CA, USA).

Results
EVOO and FO prevent obesity and metabolic
dysfunction
We tested whether consumption of EVOO or FO would
beneficially impact weight gain, energy intake, and fuelhomeostasis during and at the completion of the 10-weekdietary intervention. From 4 weeks, a significant increasein body weight was observed in HF group compared to LF
group which continued throughout the 10-week intervention (P < 0.05) (Fig. 1a). HF-EVOO- and HF-FO-fed micealso exhibited an increase in body weight in comparison
with mice-fed LF throughout the 10-week period. However,there were no significant differences between two groups.
A significant increase in energy intake was seen only in HF
compared to the LF-fed mice throughout the 10-week intervention (P = 0.0081) (Fig. 1b). At the end of the 10-weekintervention, a significant increase in blood glucose level
was observed in HF group compared to LF (P = 0.0214),
HF-EVOO (P = 0.0054), and HF-FO groups (P = 0.0085)(Fig. 1c). In addition, mice-fed HF-EVOO and HF-FO
showed a trend to decrease plasma TG and TC in comparison with mice-fed HF. However, differences were not statistically significant (Fig. S2c, d).

EVOO and FO influence productionof microbial‑derived metabolites
Total concentration of SCFAs in plasma was significantly
increased in HF (P = 0.0082), HF-EVOO (P = 0.0022), and
HF-FO (P = 0.0128)-fed mice in comparison with LF-fed
mice (Fig. 2a). Higher levels of acetate, the most abundant
SCFA, were observed in mice-fed HF (P = 0.0064), HFEVOO (P = 0.0009) and HF-FO (P = 0.0049) vs. mice-fed
LF (Fig. 2b). Propionate concentration in plasma was significantly decreased in HF-EVOO (P = 0.0220, P < 0.0001)
and HF-FO (P = 0.0144, P < 0.0001) mice in comparison
with mice-fed HF and LF, respectively (Fig. 2c). Plasma
levels of butyrate were significantly elevated in HF micevs. HF-FO (P = 0.0168) mice; however, levels of this SCFA
were scarce (Fig. 2d). Plasma concentration of the minor
SCFA, valerate, was elevated in mice-fed HF as well as
mice-fed HF-EVOO, with a very small amount detected inmice-fed LF and no detectable levels observed in mice-fed
HF-FO diets (Fig. 2e).
Ratio of acetate to butyrate plus propionate, a proposed
marker of gut microbiota balance contributing to anti-obesity effects [16, 17], was significantly elevated in HF-FO(P = 0.0002, P = 0.0039) mice as compared with LF and HF
mice, respectively (Fig. 2f). This ratio was also significantly
elevated in HF-EVOO (P = 0.0022) mice compared to LF
mice.

EVOO and FO impact the composition of gut
microbiota and microbial diversity
At the end of the 10-week period, microbiota analysis
using caecum contents revealed a number of distinct
changes in microbial community structure in mice-fed
HF-EVOO and HF-FO diets in comparison with micefed HF or LF. Alpha diversity (diversity within samples)
was markedly affected in mice-fed HF-EVOO and HF-FO,
showing a significant increase in PD in comparison withmice-fed HF diet (Fig. 3a, b). Specifically, the rarefaction
curve in Fig. 3a showing PD (units) (y-axis) increasing(particularly in HF-EVOO and HF-FO groups) as sampling13

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Fig. 3  (continued)

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Fig. 3  (continued)

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Fig. 4  Relative mRNA level inproximal colon of mice-fed LF,
HF, HF-EVOO, and HF-FO
diets for a FOXP3 and b IL-10.
All data were analyzed using
one-way ANOVA followedby post hoc Tukey’s multiple
comparison test. Data indicate
mean ± SEM. n = 5 mice pergroup. *P < 0.05

depth increases (x-axis), with the curve plateauing whenmaximum sampling depth has been reached. The box plot
in Fig. 3b showed a significant increase in PD (units)(y-axis) between HF-EVOO and HF (P = 0.0327), and
HF-FO and HF (P = 0.0031)-fed mice. Such increments inalpha diversity observed in HF-EVOO and HF-FO groups
also showed similar trends when employing other diversity metrics: Chao1, observed OTUs, and Shannon indexes(Figs. S4, S5). In addition, principal coordinates analysis (PCoA) using unweighted UniFrac distances, a distance measure for comparison of microbial communities,
with the percentage of variation explained by PC1, PC2,and PC3, revealed distinct clustering of samples basedon respective diets. Specifically, HF-EVOO and HF-FO
showed closer similarities compared to LF and HF diets,
highlighting that changes in microbial communities amonggroups were dependent largely on the quantity of different
oil/fat consumed (Fig. 3c). Interestingly, richness in microbiota (observed OTUs) showed an inverse correlation withblood glucose levels (Fig. 3d).
Taxonomy-based analysis of relative abundance of gut
microbiota (Fig. 3e), with each column representing an
individual sample from one of the dietary groups, revealed
significantly higher relative abundance of Firmicutes(depicted in purple) in HF vs. LF (P = 0.0125), HF-EVOO(P = 0.0148), and HF-FO groups (P = 0.0184) (Fig. 3f).
Higher relative abundance of other phyla (depicted in red)and Defferibacteres (depicted in green) was also observedin mice-fed EVOO in comparison with HF and LF groups,
respectively. Specifically, a higher abundance of the otherphylum, comprising a number of rarer, less abundant yet
important bacterial species, was observed in mice-fed
HF-EVOO in comparison with mice-fed HF (P = 0.0166)(Fig. 3g). In addition, a significant elevation in the average relative abundance of Defferibacteres was observed inmice-fed HF-EVOO compared to mice-fed LF (P = 0.0197)(Fig. 3h). Although the abundance of the phylum Verrucomicrobia was relatively high among groups (Fig. 3e,

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depicted in pink), significant differences were observed
between HF and LF groups only (P = 0.0245).
A number of significant differences were also observedin mice-fed HF-EVOO and HF-FO in comparison withmice-fed LF or HF diets at the genus level (Fig. 3i, Supplementary Table S2). A select number of bacterial genera
were significantly elevated in mice-fed HF-EVOO in comparison with mice-fed LF (Figs. 3j–l). Specifically, significantly higher abundance of bacteria from the Mucispirillum
genera was observed in mice-fed HF-EVOO vs. micefed LF (P = 0.0197) (Fig. 3j). This significant increase in
Mucispirillum was endorsed by findings at the phylum
level showing the increased abundance in Defferibacteres
phylum observed in mice-fed HF-EVOO vs. mice-fed LF
was driven entirely by Mucispirillum at the genus level. A
higher abundance of the bacteria from the genera Lachnospiraceae (P = 0.0080) and Bacteroides (P = 0.0037) was
also observed in HF-EVOO-fed mice in comparison with
LF-fed mice (Fig. 3k, l). The increased abundance of Bacteroides was associated with lower levels of plasma TG(R2 = 0.39, P = 0.003) (Fig. 3m).
An increased abundance of bacteria from the Allobaculum
and Coriobacteriaceae genera was also observed in mice-fed
HF-EVOO (P = 0.0234, P = 0.0107) and HF-FO (P = 0.0139,
P = 0.0018), respectively, in comparison with mice-fed HF(Fig. 3n, o). Moreover, a significantly lower abundancein bacteria from carbohydrate-degrading genera, S24-7
spp., (Bacteroidetes phylum), was observed in HF-EVOO(P = 0.0097) and HF (P = 0.0139) groups compared to LF
group (Fig. 3p). Lower levels in the abundance of Clostridiales spp. (Firmicutes phylum), associated with obesity andincreased in mice-fed HF lard diet [18], was observed in FOfed mice in contrast to mice-fed HF (P = 0.0005) (Fig. 3q).
In comparison, bacteria from the Unclassified Clostridiales
genera (Firmicutes phylum) dominated in mice-fed HF-FO
compared to mice-fed LF (P = 0.0001) (Fig. 3r). A significant difference in this genus was also observed in mice-fed
HF-EVOO (P = 0.0004) and HF (P = 0.0028) vs. HF-FO.

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Fig. 5  Relative mRNA level of a RegӀӀӀγ in ileum, b RegӀӀӀγ inproximal colon and c lipopolysaccharide-binding protein (LBP) in
liver of mice-fed LF, HF, HF-EVOO, or HF-FO diets. All data wereanalyzed using one-way ANOVA followed by post hoc Tukey’s multiple comparison test. Data indicate mean ± SEM. n = 5 mice per group.
*P < 0.05; **P < 0.01; ***P < 0.001

EVOO increases the expression of genes involvedin the accumulation of intestinal Treg

as well as bacterial infiltration in the liver. The mRNA
expression of RegӀӀӀγ in the ileum was significantly elevated in mice-fed HF-EVOO in comparison with mice-fed
HF (P = 0.0295) (Fig. 5a). Similarly, the level in the proximal colon was also increased HF-FO mice vs. mice-fed HF(P = 0.0475) (Fig. 5b). Hepatic mRNA expression of lipopolysaccharide-binding protein (LBP) was markedly increasedin the HF group in comparison with LF (P = 0.0005) and
HF-EVOO (P = 0.0041) groups (Fig. 5c).

To investigate whether Treg expansion may occur in theproximal colon of mice fed either HF-EVOO or HF-FO, we
measured Treg-related gene expressions such as Forkhead
box P3 (FoxP3) and Interleukin10 (IL-10). The mRNA level
for FoxP3, a transcription factor critical for the development of Treg, was significantly elevated in the proximal
colon of mice-fed HF-EVOO compared with mice-fed HF(P = 0.0125) (Fig. 4a). The level for IL-10, both producedby as well as an inducer of Treg, was also significantly
increased in mice-fed HF-EVOO as compared with micefed HF (P = 0.0136) (Fig. 4b).

EVOO and FO influence gut barrier and potentiallyupregulate intestinal antimicrobial defense
mechanisms
RT-PCR was performed to determine mRNA levels of genes
involved in AMP production in the ileum and proximal colon

Discussion
The present study clearly demonstrates that mice-fed HFEVOO and HF-FO exhibited a variety of beneficial effects
on markers of health. Specifically, HF-EVOO- and HFFO-fed mice exemplified a significantly positive impact onblood glucose compared to HF-fed mice. Noticeably, thesebeneficial effects on glucose homeostasis were observed
despite no significant differences in body weight gain
or energy intake among groups. In light of this finding,13

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specific components within EVOO and FO, including theω9 and ω3 fatty acids from EVOO and FO, respectively,
may be partially accountable for such a favorable impact.
In accordance with our results, Oliveira et al. recently
reported that the substitution of diets with FO and olive
oil (OO) for obese mice on a high fat diet resulted in beneficial effects on glucose homeostasis, and found that thiseffect was partially mediated via ω9 and ω3 fatty acids
from these oils [12]. Briefly, ω9 and ω3 fatty acids wereshown to increase glucagon-like peptide-1 (GLP-1) in theintestine, reverse insulin resistance as well as exert antiinflammatory activities through G-protein-coupled receptor (GPR) 120 and GPR 40 pathways in liver, skeletal muscle, and adipose tissue [12]. In agreement with this notion,
mice-fed HF-EVOO and HF-FO showed a trend to lower
levels of plasma TG and TC compared to mice-fed HF.
The present study demonstrates that plasma concentrations of SCFAs differed significantly among groups. Total
SCFA concentration as well as the major SCFA, acetate,
was significantly lower in mice-fed LF in comparison withmice-fed HF-EVOO, HF-FO, and HF diets. Although thefiber content in all experimental diets was equal, the present study used an LF purified diet as opposed to a grainbased standard chow containing significantly more fiber, inturn leading to the increased production of SCFA, which iscommonly observed in animals on the standard chow diets[19]. On the other hand, propionate concentration in plasma
was much lower in both HF-EVOO- and HF-FO-fed micein comparison with LF and HF mice. Considering a recent
study demonstrating that higher circulating level of propionate is implicated in exaggerated gluconeogenesis and insulin
resistance in both mice and humans [20], our data support
the notion that the SCFA profile of mice-fed HF-EVOO
and HF-FO is metabolically beneficial. Concentrations ofbutyrate were significantly lower in HF-FO mice comparedto HF mice, although amounts of this SCFA were scarce.
The ratio of acetate to butyrate plus propionate in plasma
typically increased in lean subjects [16, 17], was significantly lower in mice fed both HF and LF diets in comparison with mice-fed HF-EVOO and HF-FO. These data arepartly consistent with a previous report of Nishitsuji et al.
demonstrating that not only a trend to increase in the ratio ofFirmicutes to Bacteroidetes (Fig. S6), but also the ratio wassignificantly lower in mice that develop obesity–diabetes
syndrome [16]. Despite LF mice exhibiting less body weightgain than HF mice, the lower ratio observed in the LF groupis likely the consequence of a low-fat purified diet being
used, contributing to lower levels of acetate in particular.
A substantial impact on the composition of gut microbiota was observed in mice-fed EVOO and FO. HF-EVOO
and HF-FO-fed mice displayed significantly higher diversity in gut microbiota compared to HF-fed mice as wellas β-diversity plots revealing clear differences between

13

HF-EVOO and HF-FO groups vs. LF and HF groups. Thesefindings are in line with the previous reports that a diet highin saturated fats and low in dietary fibers, as consumed by
HF-fed mice in the present study, lowers total gut microbial
gene count and adversely affects microbial diversity [21, 22].
The present study demonstrates that microbial richness also
showed an inverse correlation with blood glucose levels. In
mice-fed HF-EVOO and HF-FO, higher levels of microbial
diversity and lower blood glucose levels were observed compared to HF mice. In line with this finding, a recent cohort
study reported that individuals with low bacterial richness
show more insulin resistance and other metabolic abnormalities in comparison with individuals with high bacterial
richness [23].
The differing constituents within EVOO and FO may
be responsible for their positive effects on gut microbiota
diversity, yet likely operate under different mechanisms. For
example, the higher diversity in gut microbiota observed in
HF-EVOO mice compared to HF mice may also be partially
due to the presence of certain phenolic compounds within
EVOO (Supplementary Table S3), and their ability to act
as prebiotics, stimulating the growth of beneficial bacterial
species and impacting microbial diversity [24]. In contrast,
the presence of the ω3 fatty acids, α-linolenic acid (ALA)in FO, and its ability to be converted into the more potent
ω3 fatty acid, docosahexaenoic acid (DHA), may play a rolein increasing microbiota diversity observed in HF-FO micevs. HF mice [25]. Although it is well known that majorityof dietary fats are indeed digested and absorbed in the small
intestine, there is evidence that dietary fats, in particular,
MUFAs and PUFAs, can indeed enter the large intestine,
potentially exerting effects on gut tissue as well as interacting with resident microbiota [26]. Our data support that
this notion in that lipids were extractable from caecum contents of mice and subsequent measurements of TG and TC
in stool revealed that diets richer in fats (HF, HF, EVOO,
and HF-FO) showed higher levels of these lipids in comparison with mice-fed LF (Fig. S2a, b). Interestingly, a significant elevation in TC in caecum contents was observedin mice-fed HF-EVOO vs. LF. This difference may be partially explained by the presence of polyphenols in EVOO
that have been shown to impair cholesterol absorption in theintestine by reducing micellar solubility [27]. In addition,
KEGG-functional analyses showed that a number of pathways involved in lipid metabolism that were significantly
enriched in HF-EVOO- and HF-FO-fed mice compared toeither HF or LF groups, providing further evidence of thepresence of fats in the caecum of these mice, and their potential to interact with microbiota (Fig. S3).
In the present study, at the phylum level, a significantly
lower relative abundance of the Firmicutes phylum was seenin LF, HF-EVOO, and HF-FO groups in comparison with
HF group. An increased abundance of Firmicutes, and lower

European Journal of Nutritionlevels of Bacteroidetes, have been observed in both miceand human subjects with obesity [28]. The other phylumof bacteria as well as Deferribacteres was observed in HFEVOO-fed mice vs. LF-fed mice. Other phylum of bacteria
contains a numerous number of unclassifiable rarer speciesof bacteria, contributing to gut microbiota diversity. The
Deferribacteres phylum contains the genus Mucispirillum, which was significantly higher in HF-EVOO mice ascompared with LF mice. This bacterium, a core member ofthe mouse gut microbiome, associates intimately with themucosal tissue, influencing gene expression and playing a
key role in induction of Treg [29, 30]. A recent study byCampbell et al. demonstrated that when the border-dwelling bacteria Mucispirillum was depleted in Treg-deficient
mice, a subsequent increase in type 2 responses occurred,
negatively altering epithelial homeostasis [30, 31]. They also
noted that the growth and expansion of Mucispirillum werein turn enhanced by Treg by promoting immune tolerance tothis bacterium, subsequently shaping the microbiota [30]. In
accordance with these previous reports, we also observed asubstantial elevation in mRNA level of FoxP3, a transcription factor produced by Treg, in the proximal colon of micefed HF-EVOO in comparison with HF-fed mice [32, 33]. In
addition, a higher abundance of bacteria from the Lachnospiraceae and Bacteroides genera, also capable of increasing Treg [7], was observed in mice-fed HF-EVOO in comparison with mice-fed LF. Furthermore, bacteria from the
Bacteroides genera also showed an inverse association withplasma TG levels, highlighting the possibility that an elevation of this genera observed in HF-EVOO mice may have led
to the lower levels of plasma TG observed in this group. This
genus also was found to negatively correlate with plasma
TG levels in a large, recent cohort study [34]. Moreover, inboth HF-EVOO and HF-FO mice, a significant elevation ofbacteria from Allobaculum and Coriobacteriaceae, which
are associated with lean phenotype [16], was observed incomparison with HF mice. It should be noted that mice-fed
HF-FO showed a significantly higher abundance of bacteria
from the Unclassified clostridiales genera in comparison
with LF, HF, and HF-EVOO. Bacteria belonging to thisgenus express bile acid-inducible (bai) genes and generate secondary bile acids which exert wide spread effects onhost health including antimicrobial effects [35]. Importantly,
our KEGG data demonstrated that expression level of bai
genes is apparently increased in the microbiota of mice-fed
HF-FO as reflected in the augmentation of pathways for bothprimary and secondary bile acid synthesis, particularly incomparison with HF mice (Fig. S3). Although mechanisms
remain to be elucidated, an increase in this genera of bacteria
and subsequent enrichment in genes involved in pathways ofbile acid production would be a key impact factor of HF-FO
consumption on gut microbiota in comparison with otherdietary groups.

In the ileum, mice-fed HF-EVOO exhibited a significantly
higher level of RegӀӀӀ-γ mRNA in comparison with HF-fed
mice, while mice-fed HF-FO showed significantly higher
levels of RegӀӀӀ-γ mRNA in the proximal colon compared tomice-fed HF. The RegӀӀӀ-γ protein produced in response to
specific Gram-positive, pathogen-causing bacteria, rapidly
kills or inactivates bacteria, shaping the bacterial community [5, 36]. Previous studies reported a significant reduction
in RegӀӀӀγ expression in both the small intestine and colon
under HF and obese conditions [36]. Bacterial–epithelialinteractions initiated by lipopolysaccharide and nucleotidebinding oligomerization domain containing molecules(NODs) stimulate MyD88-TLR signaling, thereby activating
innate lymphoid cells (ILCs) and Th17 cells and regulatingRegӀӀӀγ expression [37–39]. Unequal distribution of Paneth
cells throughout the murine intestinal tract as well as distinct
defense mechanisms operating in the small vs. large intestine
are possible factors contributing to the difference observedRegӀӀӀγ mRNA expression in the ileum of HF-EVOO-fed
mice vs. the proximal colon of mice-fed HF-FO [37, 38].
Such an apparent difference between HF-EVOO- and HFFO-fed mice may be attributable to the distinct constituents
within the oil-targeting specific regions of the intestine,
modulating not only gut microbiota, but also influencing
specific intestinal cell types such as Paneth vs. epithelial
cells. Along with RegӀӀӀγ, mRNA expression of MyD88 andIL-22 also showed a trend to increase in mice-fed HF-EVOO
and HF-FO (Fig. S8). Notably, IL-22, an interleukin produced by innate ILCs and Th17 cells, modulates epithelial
cell function and plays a critical role in licensing epithelial
cells for RegӀӀӀγ production [5]. Recently, Fatkhulina et al.
uncovered an atheroprotective role of IL-22 via its ability toregulate production of AMPs such as RegӀӀӀγ and preventthe expansion of species of bacteria with pro-atherogenic
properties [40]. Specifically, these species of bacteria deliver
pro-atherogenic metabolites such as LPS due to a defective
intestinal barrier, causing chronic low-grade inflammation
and atherosclerosis [40]. In accordance with this notion,
we also observed significantly lower levels of LBP mRNAin the liver of mice-fed EVOO or FO in comparison withmice-fed HF diet and a trend for plasma LBP to show higher
levels in HF mice, indicating pathophysiologic aggravationof intestinal permeability and subsequent bacterial infiltration in mice-fed HF (Fig. S7). Moreover, an increasing trend
in mRNA level of intestinal barrier markers, in particular,
ZO-1 and Occludin, was also observed in mice-fed EVOO
or FO (Fig S9).
Collectively, our data provide novel evidence that the consumption of EVOO and FO can beneficially impact the composition of gut microbiota, increase microbial diversity as
well as influence the production of microbial-derived metabolites contributing to the activation of mucosal immune system, enhancement of markers of gut barrier integrity, and13

European Journal of Nutrition

ultimately support metabolic health in mice. Further studies
are warranted to test this hypothesis in human clinic-based
dietary interventions.
Acknowledgements The authors would like to thank Y Murayama, C
Horiguchi, I Nomura, and T Ikematsu for technical assistance (University of the Ryukyus, Japan). We would also like to thank M Hirata, I
Asato, H Kaneshiro, and C Noguchi (University of the Ryukyus, Japan)
for secretarial assistance. This work was supported in part by CrossMinisterial Strategic Innovation Promotion Program (SIP, Japanese
Government), ‘Technologies for Creating Next-Generation Agriculture, Forestry and Fisheries,’ New Energy and Industrial Technology
Development Organization (NEDO, Japanese Government), Uehara
Memorial Foundation, Japan Agency for Medical Research and Development (AMED, Japanese government) and Rotary Yoneyama Memorial Foundation Fellowships.

Compliance with ethical standards
Conflict of interest The authors have no conflict of interest.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat​iveco​
mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made.

References
1. Galgani J, Ravussin E (2008) Energy metabolism, fuel selection
and body weight regulation. Int J Obes (Lond) 32(Suppl 7):S109–
S119. https​://doi.org/10.1038/ijo.2008.246
2. Gregor MF, Hotamisligil GS (2011) Inflammatory mechanisms in
obesity. Annu Rev Immunol 29:415–445. https:​ //doi.org/10.1146/
annur​ev-immun​ol-03121​0-10132​2
3. Vannice G, Rasmussen H (2014) Position of the academy ofnutrition and dietetics: dietary fatty acids for healthy adults. J
Acad Nutr Diet 114(1):136–153. https ​ : //doi.org/10.1016/j.
jand.2013.11.001
4. Yiu JH, Dorweiler B, Woo CW (2017) Interaction between gut
microbiota and toll-like receptor: from immunity to metabolism.
J Mol Med (Berl) 95(1):13–20. https​://doi.org/10.1007/s0010​
9-016-1474-4
5. Mukherjee S, Hooper LV (2015) Antimicrobial defense of theintestine. Immunity 42(1):28–39. https:​ //doi.org/10.1016/j.immun​
i.2014.12.028
6. Honda K, Littman DR (2016) The microbiota in adaptive immune
homeostasis and disease. Nature 535(7610):75–84. https​://doi.
org/10.1038/natur​e1884​8
7. Tanoue T, Atarashi K, Honda K (2016) Development andmaintenance of intestinal regulatory T cells. Nat Rev Immunol
16(5):295–309. https​://doi.org/10.1038/nri.2016.36
8. Graf D, Di Cagno R, Fak F, Flint HJ, Nyman M, Saarela M, Watzl
B (2015) Contribution of diet to the composition of the humangut microbiota. Microb Ecol Health Dis 26:26164. https​://doi.
org/10.3402/mehd.v26.26164​
9. Benítez-Páez A, Gómez Del Pulgar EM, Kjølbæk L, Brahe
LK, Astrup A, Larsen L, Sanz Y (2016) Impact of dietary fiberand fat on gut microbiota re-modeling and metabolic health.
Trends Food Sci Technol 57:201–212. https​://doi.org/10.1016/j.
tifs.2016.11.001

13

10. Aparicio-Soto M, Sanchez-Hidalgo M, Rosillo MA, Castejon ML,
Alarcon-de-la-Lastra C (2016) Extra virgin olive oil: a key functional food for prevention of immune-inflammatory diseases. Food
Funct 7(11):4492–4505. https​://doi.org/10.1039/c6fo0​1094f​
11. Kajla P, Sharma A, Sood DR (2015) Flaxseed-a potential functional food source. J Food Sci Technol 52(4):1857–1871. https​://doi.org/10.1007/s1319​7-014-1293-y
12. Oliveira V, Marinho R, Vitorino D, Santos GA, Moraes JC,
Dragano N, Sartori-Cintra A, Pereira L, Catharino RR, da Silva
AS, Ropelle ER, Pauli JR, De Souza CT, Velloso LA, Cintra
DE (2015) Diets containing alpha-linolenic (omega3) or Oleic(omega9) fatty acids rescues obese mice from insulin resistance.
Endocrinology 156(11):4033–4046. https​://doi.org/10.1210/
en.2014-1880
13. Cicerale S, Lucas LJ, Keast RS (2012) Antimicrobial, antioxidant
and anti-inflammatory phenolic activities in extra virgin olive oil.
Curr Opin Biotechnol 23(2):129–135. https​://doi.org/10.1016/j.
copbi​o.2011.09.006
14. Vazquez-Baeza Y, Pirrung M, Gonzalez A, Knight R (2013)
EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2(1):16. https ​ : //doi.
org/10.1186/2047-217x-2-16
15. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights
D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL,
Knight R, Beiko RG, Huttenhower C (2013) Predictive functional profiling of microbial communities using 16S rRNA
marker gene sequences. Nat Biotechnol 31(9):814–821. https​://doi.org/10.1038/nbt.2676
16. Nishitsuji K, Xiao J, Nagatomo R, Umemoto H, Morimoto Y,
Akatsu H, Inoue K, Tsuneyama K (2017) Analysis of the gut
microbiome and plasma short-chain fatty acid profiles in a spontaneous mouse model of metabolic syndrome. Sci Rep 7(1):15876.
https​://doi.org/10.1038/s4159​8-017-16189​-5
17. Duncan SH, Belenguer A, Holtrop G, Johnstone AM, Flint HJ,
Lobley GE (2007) Reduced dietary intake of carbohydrates byobese subjects results in decreased concentrations of butyrate
and butyrate-producing bacteria in feces. Appl Environ Microbiol
73(4):1073–1078. https​://doi.org/10.1128/aem.02340​-06
18. Kubeck R, Bonet-Ripoll C, Hoffmann C, Walker A, Muller VM,
Schuppel VL, Lagkouvardos I, Scholz B, Engel KH, Daniel H,
Schmitt-Kopplin P, Haller D, Clavel T, Klingenspor M (2016)
Dietary fat and gut microbiota interactions determine dietinduced obesity in mice. Mol Metab 5(12):1162–1174. https​://doi.org/10.1016/j.molme​t.2016.10.001
19. Pellizzon MA, Ricci MR (2018) The common use of improper
control diets in diet-induced metabolic disease research confounds
data interpretation: the fiber factor. Nutr Metab (Lond) 15:3. https​://doi.org/10.1186/s1298​6-018-0243-5
20. Tirosh A, Calay ES, Tuncman G, Claiborn KC, Inouye KE, Eguchi
K, Alcala M, Rathaus M, Hollander KS, Ron I, Livne R, Heianza
Y, Qi L, Shai I, Garg R, Hotamisligil GS (2019) The short-chain
fatty acid propionate increases glucagon and FABP4 production,
impairing insulin action in mice and humans. Sci Transl Med.
https​://doi.org/10.1126/scitr​anslm​ed.aav01​20
21. Sonnenburg JL, Backhed F (2016) Diet-microbiota interactions as
moderators of human metabolism. Nature 535(7610):56–64. https​://doi.org/10.1038/natur​e1884​6
22. Menni C, Jackson MA, Pallister T, Steves CJ, Spector TD, Valdes
AM (2017) Gut microbiome diversity and high-fibre intake
are related to lower long-term weight gain. Int J Obes (Lond)41(7):1099–1105. https​://doi.org/10.1038/ijo.2017.66
23. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, FalonyG, Almeida M, Arumugam M, Batto JM, Kennedy S, Leonard P,
Li J, Burgdorf K, Grarup N, Jorgensen T, Brandslund I, Nielsen
HB, Juncker AS, Bertalan M, Levenez F, Pons N, Rasmussen S,
Sunagawa S, Tap J, Tims S, Zoetendal EG, Brunak S, Clement

European Journal of Nutrition

24.
25.

26.

27.
28.
29.

30.

31.
32.

K, Dore J, Kleerebezem M, Kristiansen K, Renault P, SicheritzPonten T, de Vos WM, Zucker JD, Raes J, Hansen T, Meta HITc,
Bork P, Wang J, Ehrlich SD, Pedersen O (2013) Richness ofhuman gut microbiome correlates with metabolic markers. Nature
500(7464):541–546. https​://doi.org/10.1038/natur​e1250​6Castro-Barquero S, Lamuela-Raventos RM, Domenech M, Estruch
R (2018) Relationship between mediterranean dietary polyphenol
intake and obesity. Nutrients. https:​ //doi.org/10.3390/nu1010​ 1523​
Menni C, Zierer J, Pallister T, Jackson MA, Long T, Mohney
RP, Steves CJ, Spector TD, Valdes AM (2017) Omega-3 fatty
acids correlate with gut microbiome diversity and production of
N-carbamylglutamate in middle aged and elderly women. Sci Rep7(1):11079. https​://doi.org/10.1038/s4159​8-017-10382​-2
Hoyles L, John Wallace R (2019) Gastrointestinal tract: intestinal
fatty acid metabolism and implications for health. In: Goldfine H(ed) Health consequences of microbial interactions with hydrocarbons, oils, and lipids. Springer, Cham, pp 1–19. https​://doi.
org/10.1007/978-3-319-72473​-7_31-1
Amiot MJ, Riva C, Vinet A (2016) Effects of dietary polyphenols
on metabolic syndrome features in humans: a systematic review.
Obes Rev 17(7):573–586. https​://doi.org/10.1111/obr.12409​
Al-Assal K, Martinez AC, Torrinhas RS, Cardinelli C, Waitzberg
D (2018) Gut microbiota and obesity. Clinical Nutrition Experimental 20:60–64. https​://doi.org/10.1016/j.yclne​x.2018.03.001
Loy A, Pfann C, Steinberger M, Hanson B, Herp S, Brugiroux S,
Gomes Neto JC, Boekschoten MV, Schwab C, Urich T, RamerTait AE, Rattei T, Stecher B, Berry D (2017) Lifestyle and horizontal gene transfer-mediated evolution of mucispirillum schaedleri, a core member of the murine gut microbiota. mSystems.
https​://doi.org/10.1128/msyst​ems.00171​-16
Campbell C, Dikiy S, Bhattarai SK, Chinen T, Matheis F, Calafiore M, Hoyos B, Hanash A, Mucida D, Bucci V, Rudensky AY(2018) Extrathymically generated regulatory T cells establish
a niche for intestinal border-dwelling bacteria and affect physiologic metabolite balance. Immunity 48(6):1245–1257. https​://doi.org/10.1016/j.immun​i.2018.04.013
Gefen T, Geva-Zatorsky N (2018) What Came First: the Microbiota or the Tr(egg) Cells? Immunity 48(6):1072–1074. https​://doi.org/10.1016/j.immun​i.2018.06.005
Murai M, Turovskaya O, Kim G, Madan R, Karp CL, Cheroutre
H, Kronenberg M (2009) Interleukin 10 acts on regulatory T cells

33.

34.

35.

36.

37.
38.

39.
40.

to maintain expression of the transcription factor Foxp3 and suppressive function in mice with colitis. Nat Immunol 10(11):1178–
1184. https​://doi.org/10.1038/ni.1791
Round JL, Lee SM, Li J, Tran G, Jabri B, Chatila TA, Mazmanian SK (2011) The Toll-like receptor 2 pathway establishescolonization by a commensal of the human microbiota. Science332(6032):974–977. https​://doi.org/10.1126/scien​ce.12060​95
Fu J, Bonder MJ, Cenit MC, Tigchelaar EF, Maatman A, Dekens JA, Brandsma E, Marczynska J, Imhann F, Weersma RK,
Franke L, Poon TW, Xavier RJ, Gevers D, Hofker MH, Wijmenga C, Zhernakova A (2015) The gut microbiome contributes
to a substantial proportion of the variation in blood lipids. Circ
Res 117(9):817–824. https​://doi.org/10.1161/CIRCR​ESAHA​.115.30680​7
Vital M, Rud T, Rath S, Pieper DH, Schlüter D (2019) Diversity ofbacteria exhibiting bile acid-inducible 7α-dehydroxylation genes
in the human gut. Comput Struct Biotechnol J 17:1016–1019.
https​://doi.org/10.1016/j.csbj.2019.07.012
Everard A, Lazarevic V, Gaia N, Johansson M, Stahlman M,
Backhed F, Delzenne NM, Schrenzel J, Francois P, Cani PD(2014) Microbiome of prebiotic-treated mice reveals novel targets
involved in host response during obesity. ISME J 8(10):2116–
2130. https​://doi.org/10.1038/ismej​.2014.45
Okumura R, Takeda K (2017) Roles of intestinal epithelial cells
in the maintenance of gut homeostasis. Exp Mol Med 49(5):e338.
https​://doi.org/10.1038/emm.2017.20
Bowcutt R, Forman R, Glymenaki M, Carding SR, Else KJ,
Cruickshank SM (2014) Heterogeneity across the murine small
and large intestine. World J Gastroenterol 20(41):15216–15232.
https​://doi.org/10.3748/wjg.v20.i41.15216​
Gassler N (2017) Paneth cells in intestinal physiology and pathophysiology. World J Gastrointest Pathophysiol 8(4):150–160. https​://doi.org/10.4291/wjgp.v8.i4.150
Fatkhullina AR, Peshkova IO, Dzutsev A, Aghayev T, McCulloch
JA, Thovarai V, Badger JH, Vats R, Sundd P, Tang HY, Kossenkov AV, Hazen SL, Trinchieri G, Grivennikov SI, Koltsova EK(2018) An Interleukin-23-Interleukin-22 axis regulates intestinal
microbial homeostasis to protect from diet-induced atherosclerosis. Immunity 49(5):943–957.e949. https​://doi.org/10.1016/j.
immun​i.2018.09.011

13