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Anti-aging food that improves markers of health in senior dogs by modulating gut microbiota and metabolite profiles

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Abstract

Dysbiosis is one of the major changes in aging that leads to an accumulation of toxic microbial metabolites. The aim of this study was to evaluate the effect of a test food containing components of citrus, carrot, spinach and tomato on gut microbiota and age-related metabolites in senior dogs. The study was conducted on 36 dogs between 8 and 13 years of age. All dogs were maintained on a control food (control 1), which used corn as major source of fiber. After 30 days, the dogs were divided into two groups of 18 dogs. One of the groups received the test food for 30 days while the other group received the control 2 food, containing multiple whole grains as the test food but without the above added sources of fiber present in the test food. After a washout period on the control 1 food for 30 days, a crossover was performed so that the test or the control 2 food was fed for 30 days to those dogs which had not yet been fed that food. Samples from feces and blood were collected after each 30 days period to analyze changes in gut microbial composition and metabolites. The consumption of the test food led to increased proportions of Adlercreutzia, Oscillospira, Phascolarcobacteria, Faecalibacterium and Ruminococcus, Christensenellaceae, Ruminococcaceae, Cyanobacteria and Acidobacteria and decreased proportions of Megamonas, Salmonella, Enterobacteriaceae and Fusobacterium. Pets had higher levels of glycerol and fatty acids and lower levels of pyrraline and mucin amino acids in feces. The test food also reduced circulating levels of pyrraline, symmetric dimethylarginine and phenolic uremic toxins, including the microbial brain toxin, 4-ethylphenyl sulfate. Christensenellaceae abundance was strongly associated with the observed health benefits. Fermentable fibers from fruits and vegetables enhance health in senior dogs by modulating the gut bacteria and metabolites involved in aging, kidney, brain and gut health.
1
1Anti-aging food that improves markers of health in senior dogs by modulating gut microbiota
2and metabolite profiles
3Eden Ephraim Gebreselassie1*, Matthew I. Jackson1, Maha Yerramilli2 and Dennis E. Jewell1
41Hill’s Pet Nutrition, Topeka, Kansas, United States of America
52IDEXX Laboratories Inc., Westbrook, Maine, United States of America
6
7
8
9
10
11 *Corresponding Author
12 Email: eden_ephraim_gebreselassie@hillspet.com
13
14
15
16
17
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2
18 Abstract
19 Dysbiosis is one of the major changes in aging that leads to an accumulation of toxic microbial
20 metabolites. The aim of this study was to evaluate the effect of a test food containing components of
21 citrus, carrot, spinach and tomato on gut microbiota and age-related metabolites in senior dogs. The
22 study was conducted on 36 dogs between 8 and 13 years of age. All dogs were maintained on a control
23 food (control 1), which used corn as major source of fiber. After 30 days, the dogs were divided into
24 two groups of 18 dogs. One of the groups received the test food for 30 days while the other group
25 received the control 2 food, containing multiple whole grains as the test food but without the above
26 added sources of fiber present in the test food. After a washout period on the control 1 food for 30
27 days, a crossover was performed so that the test or the control 2 food was fed for 30 days to those dogs
28 which had not yet been fed that food. Samples from feces and blood were collected after each 30 days
29 period to analyze changes in gut microbial composition and metabolites. The consumption of the test
30 food led to increased proportions of Adlercreutzia, Oscillospira, Phascolarcobacteria,
31 Faecalibacterium and Ruminococcus, Christensenellaceae, Ruminococcaceae, Cyanobacteria and
32 Acidobacteria and decreased proportions of Megamonas, Salmonella, Enterobacteriaceae and
33 Fusobacterium. Pets had higher levels of glycerol and fatty acids and lower levels of pyrraline and
34 mucin amino acids in feces. The test food also reduced circulating levels of pyrraline, symmetric
35 dimethylarginine and phenolic uremic toxins, including the microbial brain toxin, 4-ethylphenyl
36 sulfate. Christensenellaceae abundance was strongly associated with the observed health benefits.
37 Fermentable fibers from fruits and vegetables enhance health in senior dogs by modulating the gut
38 bacteria and metabolites involved in aging, kidney, brain and gut health.
39
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3
40 Introduction
41 Aging is associated with shifts in the composition of gut microbiota. An example of this is the
42 increase in the number of facultative anaerobes and a decline in the proportion of beneficial bacteria
43 associated with aging (1, 2). This shift in the microbial composition leads to the accumulation of toxic
44 microbial metabolites in the body causing inflammation, oxidative stress and contributing to various
45 diseases prominent in the aging condition (3). The reduction in the proportion of beneficial bacteria
46 may lead to constipation, mal-absorption and longer colonic transit time. Decreased absorption of
47 dietary protein in the upper intestine and longer colonic transit times encourage increased abundance
48 of proteolytic bacteria, whose fermentation products deteriorate intestinal barrier integrity (4).
49
50 Foods containing fermentable fibers are known to benefit dogs by increasing nutrient
51 absorption and reducing enteric infection (5). In an in vitro study, Swanson et al. (6) confirmed the
52 fermentability of fruits and vegetables by canine fecal microflora with the resulting production of short
53 chain fatty acids. This study evaluates the effect of a test food containing components of citrus, carrot,
54 spinach and tomato on the microbial composition as well as metabolites associated with aging, kidney,
55 brain and gut health in senior dogs. A recent study by Hall et al (7) showed that the consumption of a
56 food with similar composition as the test food employed in the current study led to improvement of
57 markers of kidney health in geriatric dogs with early stage kidney disease. This study was designed to
58 evaluate changes in fecal microbial composition and age-related markers of health attributed to the
59 consumption of the test food by healthy senior dogs.
60
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61 Materials and methods
62 Dogs
63 All study protocols were reviewed and approved by the Institutional Animal Care and Use
64 Committee, Hill’s Pet Nutrition, Inc., Topeka, KS, USA. Criteria for inclusion were healthy dogs
65 above the age of 7 years. Dogs having chronic disease conditions such as inflammatory bowel disease,
66 dermatitis, food allergy, cancer, tumor, kidney disease, liver disease and chronic urinary tract
67 infections were excluded from the study. A total of 36 dogs between the ages of 8 and 13 years were
68 grouped into a two groups of 18 each. Each group contained equal number of female and male dogs.
69 All dogs were spayed or neutered. A summary of the description of the dogs included in this study is
70 shown in Table 1.
71 Table 1: Description of dogs used in the study
Species
Dogs
Age
Group 1: 10.6 ± 1.3; Group 2: 10.2 ± 1.1
Sex
Control: 9M, 9F, Test: 9M, 9F
Breed
Beagles
Initial body weight
Control: 11.2 ± 2.1 Kg, Test: 11.5 ± 1.8 Kg
Reproductive status
All dogs were spayed or neutered
Health status
Healthy
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72 Foods
73 The study used a test food and two control foods; all in dry form. All foods were produced by
74 Hill’s Pet Nutrition, Inc. Topeka, KS and were essentially isocaloric with respect to metabolizable
75 energy (control 1 = 3674 kcal/kg; control 2 = 3666 kcal/kg; test = 3684 kcal/kg). The foods were
76 formulated to meet similar nutrient profiles (Table 2) and contained grain sources such as rice, millet,
77 oat groats, corn, wheat and/or barley. The test food contained added fiber sources from citrus, carrot,
78 tomato and spinach in addition to the multiple grains. Unlike the test food, the first control (control 1)
79 and the second control (control 2) foods did not have the unique fiber sources from fruit and
80 vegetables. The first control food (control 1) used corn as major source of grain fiber and did not have
81 multiple grain sources as the test or the control 2 food. The composition of the foods expressed as
82 percentage of food as fed is shown in Table 2. Food analytical measurements were determined by
83 Eurofins Scientific Inc. (Des Moines, IA) using Association of Analytical Communities (AOAC)
84 methods.
85
86
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87 Table 2. Comparison of the three different foods as fed (g/100g)
Nutrient
Control 1
Control 2
Test Food
Moisture
7.6
8.91
9
Ash
4.52
4.8
4.41
Crude Fiber
1.2
2
2.5
Crude Protein
19.6
17.77
19.89
Carbohydrates*
54.11
53.64
48.92
Soluble Fiber
0.5
1.8
2.7
Insoluble Fiber
6.6
7.2
5.8
Crude Fat
12.67
12.98
15.18
C18:2 Omega 6 (Linoleic)
3.43
3.29
3.32
C18:3 omega 3 (alpha-Linolenic)
0.36
0.41
0.75
C20:4 Omega 6
0.05
0.05
0.09
C20:5 EPA Omega 3
0.01
0.01
0.08
C22:6 DHA Omega 3
0.01
0.01
0.06
Omega 3 Sum
0.4
0.43
0.92
Omega 6 Sum
3.54
3.41
3.49
C16:1 Palmitoleic
0.28
0.26
0.26
C18:0 Stearic
0.93
0.91
0.91
Lysine
0.92
0.84
1.27
Threonine
0.7
0.62
0.76
Tryptophan
0.24
0.19
0.29
88 *Carbohydrate (Nitrogen-free extract) =100% - (%Protein + %Fat + %Fiber + %Ash + %Moisture)
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89 Study design and sample collections
90 All dogs were maintained on control 1 food for 30 days and were divided into two groups. At
91 the beginning of the test food feeding period, one of the groups received the test food while the other
92 group received control 2 food for 30 days. Both groups were then fed the control 1 food for the next
93 30 days after which a cross-over was performed so that the test or the control 2 food were fed for 30
94 days to dogs which did not eat them during the first assignment to test foods. Water was available ad
95 libitum. All dogs were meal fed from electronic feeders, where fresh food was offered daily with
96 amounts calculated to maintain body weight. Exposure to food was allowed for up to 30 minutes to
97 complete diet consumption. Daily food intake (g/d) was recorded for each dog. Body weights were
98 measured weekly. Blood and fecal samples were collected at the end of each 30 days period to
99 compare the effect of food on the abundance of various bacterial genera and various metabolites
100 (Table 3).
101 Table 3. Sample analyses and measurement
Sample/measurement
Analysis
Phase
Days
Pre-feed
25
Blood
Blood chemistry,
SDMA,
inflammatory
cytokines,
metabolomics
Treatment
25, 55
Pre-feed
23, 24
Feces
Microbiome
sequencing,
metabolomics
Treatment
23, 24, 53, 54
102
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103 Sequencing of the 16S rRNA gene
104 Fecal samples were collected within 30 minutes of defecation and stored at -80oC until
105 processed. Approximately 25mg of frozen stool homogenate was used for DNA isolation with MoBio
106 PowerFecal® Kit (MoBio, Carlsbad, CA). Instructions provided by the manufacturer were followed
107 except that a sonication step was added before vortexing the bead tubes with feces samples for 15
108 minutes. The DNA extracts were stored at -20oC until further processed. One microliter of each DNA
109 sample was used to amplify the V3V4 region of the 16S rRNA gene using primers 347F and 803R
110 containing Illumina adapters (8). Amplification was performed on BioRad C1000 Touch Thermal
111 Cycler under the following conditions: 25 cycles of denaturation at 95°C for 30seconds, annealing
112 at 55°C for 30seconds extension at 72°C for 45seconds, and a final elongation step at 72°C for
113 5minutes. An internal normalized mock community DNA and PCR-grade water were used as positive
114 and negative controls, respectively. The mock community was formed by mixing genomic DNA of 28
115 bacterial species representing 25 genera obtained from the American Type Culture Collection (ATCC,
116 Rockville, MD). The mock community represented equal copy numbers of the 16S rRNA gene of each
117 species as described by Diaz et al. (9).
118 PCR amplicons (25µl) were purified by using Agencourt AmPure XP beads (Beckman
119 Coulter) and concentrations were measured by using Qubit fluorometer 3.0 (Life Technologies). The
120 quality of the amplicon was assessed by using Agilent 2100 Bioanalyzer. Index PCR, library
121 quantification, normalization and pooling were performed following the Illumina’s 16S metagenomic
122 sequencing library preparation protocol (Part # 15044223 Rev. A, Illumina, CA). Libraries were
123 mixed with Illumina generated PhiX control library and denatured using fresh NaOH. Final
124 sequencing libraries were then loaded onto the Illumina Miseq v3 reagent cartridge and 251-base
125 paired-end reads were generated using Miseq Control Software (MCS) 2.4., RTA 1.18.54 and Miseq
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126 Reporter 2.4. For every Miseq run, a mock community sample and water were run as a positive and a
127 negative control, respectively.
128 The reads were de-multiplexed using Miseq built-in workflow to obtain FASTQ files
129 processed using Mothur, version 1.32 (10). Sequences were retained based on criteria such as having
130 reads between 431 and 458 base pairs, maximum ambiguous bases of 0 and maximum homopolymer
131 length of 6. The remaining sequences were chimera detected using the UCHIME algorithm
132 implemented in MOTHUR and excluded from further processing (11). All retained sequences were
133 aligned to the GreenGenes 16S rRNA gene reference database of (gg.13.5.99). The database was used
134 for taxonomical assignment of operational taxonomic units (OTUs) at an 80% confidence threshold by
135 using the naïve Baysian algorithm (12) implemented in MOTHUR.
136
137 Blood and fecal metabolites
138 Metabolomic profiles of blood and fecal samples were determined by Metabolon (Durham,
139 NC). The methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and
140 a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated
141 electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass
142 resolution. Different aliquots of sample extracts were analyzed under different chromatographic
143 conditions optimized for hydrophilic or hydrophobic compounds (13). Standards present in each
144 aliquot were used to ensure injection and chromatography consistency. Peaks were identified and
145 processed using proprietary hardware and software. The relative quantification of the metabolites was
146 performed by using area-under-the curve.
147
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148 Symmetric dimethylarginine (SDMA) concentrations in blood samples were determined using
149 liquid chromatography-mass spectroscopy (LC-MS) as described by Hall et al. (14).
150
151 Statistical analysis
152 Matched-pair analyses were performed with JMP version 12 (SAS Institute, Carry, NC) to
153 compare differences between means of the microbial abundances and relative levels of metabolites on
154 samples collected from the same dog after the consumption of the test or the control 2 food. P values
155 were calculated for differences between means and false discovery rate (FDR) corrections were made
156 on each group of markers. FDR-P values less than 0.05 were considered significant. A bivariate
157 regression analysis was performed to evaluate correlations between the changes in the microbial
158 abundance and fecal metabolites.
159
160 Results
161 Food intake and body weight
162 All dogs completed the study successfully and there was no adverse health report. There was a
163 trend (P=0.06) inthe body weights of the dogs consuming the test food (10.96 Kg) to increase when
164 compared to the control 2 food (10.74 Kg). There was very little difference in intakes of the test
165 (113.41 Kcal/Body weight^0.75, SE=3.89) or the control 2 food (113.9 Kcal/Body weight^0.75,
166 SE=4.15).
167
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168 Changes in the gut microbial composition
169 The test food led to significant changes in the proportions of bacteria at various taxa levels. Fig
170 1 summarizes the log (base 2) fold changes of the taxa after the consumption of the test food compared
171 to the control 2 food. At the phylum level, Acidobacteria and Cyanobacteria increased by 0.91 and
172 0.72 log fold changes, respectively. This was accompanied by a -0.65 log fold reduction in the phylum
173 Fusobacteria. This was equivalent to 87% and 65% increase in Acidobacteria and Cyanobacteria,
174 respectively, and a 66% reduction in Fusobacteria. At the family level, Christensenellaceae and
175 Ruminococcaceae increased by 1.76 and 0.68 log fold changes, respectively. These were equivalent to
176 a 138% and 60% increase in the proportions of Christensenellaceae and Ruminococcaceae,
177 respectively, compared to their levels on the Control 2 food. On the contrary, the test food led to 1.65
178 log fold reduction (68%) in Enterobacteriaceae. At the genus level, Adlercreutzia and
179 Phascolarctobacterium increased by 77% and 68%, respectively.
180
181 Fig 1. Log fold-changes (base 2) of the different taxa after the consumption of the test food. The
182 test food led to a significant reduction in the proportion of bacteria belonging to the genus
183 Megamonas, an unclassified genus in family Enterobacteriacea, Salmonella and Peptostreptococcus.
184 The consumption of the test food significantly increased the proportions of the genera Adlercreutzia,
185 Oscillospira, Phascolarcobacterium, Faecalibacterium and Ruminococcus. At the family level, the
186 test food led to a significant increase in Christensenellaceae and Ruminococcaceae and a significant
187 reduction in Enterobacteriaceae. At the phylum level, the test food increased the phyla Acidobacteria
188 and Cyanobacteria and led to a significant reduction in Fusobacteria.
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189 Although they did not meet the statistical significance, Oscillospira and Ruminococcus
190 increased by 73% and 39%, respectively, after the consumption of the test food. This was
191 accompanied by reductions in the proportions of the genera Salmonella, Megamonas,
192 Peptostreptococcus and an unknown genus (OTU_10001 in the family Enterobacteriaceae) by 58%,
193 81%, 32%, 80%, respectively. S1 Table provides the means and standard errors of the proportions of
194 the above taxa on the test and the control 2 foods.
195
196 Levels of fatty acids and glycerol
197 The levels of fecal and circulating unsaturated fatty acids increased after the consumption of
198 the test food (Table 4). In plasma, levels of docosahexaenoate (DHA; 24:6n3), docosapentaenoate
199 (DPA; 22:5n6), eicosapentaenoate (EPA; 22:5n3), linolenate (18:3n3) and stearidonate (18:4n3) were
200 increased. In feces, in addition to these fatty acids, docosapentaenoate (DPA; 22:5n3) and palmitoleate
201 (16:1n7) were increased on the test food. Despite the similar levels of circulating glycerol on both
202 foods, fecal levels of glycerol increased by 24% when the pets consumed the test food compared to
203 the control 2 food (P=0.001) (Table 4).
204
205
206
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207 Table 4. Change in levels of fatty acids and glycerol in feces and plasma of the senior dogs
208 during the consumption of the Test versus the Control 2 food.
Feces
Plasma
Metabolite in
feces
% change
on Test
P Value
(FDR)
Mean
difference
(test-
Control 2)
SE
% change
on Test
P Value
(FDR)
Mean
diference
(test-
Control 2)
SE
arachidonate (20:4n6)
-16.24
1.49
-0.2
0.12
10.51
2.46
0.11
.08
docosahexaenoate
(DHA; 22:6n3)
256.47
3.52E-11
2.17
0.21
177.33
1.02E-10
1.44
0.14
docosapentaenoate
(DPA; 22:5n3)
43.85
0.02
0.45
0.13
35.53
0.11
0.36
0.12
docosapentaenoate (n6
DPA; 22:5n6)
120.98
1.84E-07
1.1
0.15
57.13
0.0003
0.57
0.11
eicosapentaenoate
(EPA; 20:5n3)
434.21
1.22E-13
3.97
0.3
191.50
4.83E-08
1.61
0.21
glycerol
31.70
0.01
0.36
0.1
3.33
1.0*
0.04
0.09
laurate (12:0)
-4.52
1.0*
-0.05
0.06
2.45
1.0*
0.03
0.08
linoleate (18:2n6)
-4.84
1.0*
-0.05
0.07
7.86
1.0*
.081
0.08
linolenate (18:3n3 or
3n6)
45.47
0.004
0.44
0.1
40.36
0.02
0.42
0.12
myristate (14:0)
10.73
1.0*
0.11
0.07
5.10
1.0*
0.06
0.1
oleate/vaccenate
(18:1)
8.80
1.0*
0.1
0.08
9.49
1.0*
.096
0.07
palmitate (16:0)
1.01
1.0*
0.01
0.09
7.64
1.0*
0.08
0.06
palmitoleate (16:1n7)
121.28
2.01E-09
1.23
0.1
26.21
0.51
0.28
0.13
stearate (18:0)
-22.59
0.08
-0.26
0.09
3.03
1.0*
0.03
0.06
stearidonate (18:4n3)
776.56
1.33E-14
8.1
0.6
132.47
7.68E-06
1.29
0.21
209 Changes that were statistically significant (P<0.05) are marked grey.
210 1.0*: FDR P values greater than 1 are referred as 1.
211
212 Fecal levels of mucin amino acids
213 The relative fecal levels of amino acids that make up the mucin layer, such as aspartate,
214 proline, serine and threonine were significantly affected by the type of food consumed by the senior
215 dogs. Compared to the control 2 food, the consumption of the test food led to a 28 – 61% reduction in
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216 levels of these amino acids in the feces (Table 5).
217 Table 5. Relative levels of mucin amino acids in feces
218
219
220
221
222 Advanced glycation
223 end products (AGE)
224 High levels of circulating advanced glycation end products (AGE) are associated with aging
225 and various age-related diseases. The test food led to about 70% reductions in both circulating
226 (P=1.15E-07 and fecal (P=5.29E-13) levels of one of the AGE, pyrraline. The circulating level of
227 another AGE, N6-carboxymethyllysine (CML), was not affected by the different diets; but the fecal
228 levels were higher on the test food. The third AGE, N6-carboxyethyllysine (CEL), was detected only
229 in feces and did not change during the consumption of the different diets (Table 6).
230
231
Amino acid in feces
% change on Test
P Value (FDR)
Asparagine
-61.03
0.007
Proline
-28.5
0.007
Serine
-36.2
0.004
Threonine
-35.6
0.001
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232 Table 6. Changes in levels of advanced glycation end products (AGE)
Matrix
AGE
% change
on Test
P Value
(FDR)
Mean difference
(Test-Control 2)
SE
Feces
Pyrraline
-69.65
5.29E-13
-1.74
0.15
Plasma
Pyrraline
-68.82
1.15E-07
-1.56
.23
Feces
N6-carboxymethyllysine
48.86
0.0006
0.44
0.11
Plasma
N6-carboxymethyllysine
4.25
0.89
0.05
.07
Feces
N6-carboxyethyllysine
9.15
1.0
0.08
.09
233 Changes that were statistically significant are marked grey.
234 Changes in circulating uremic toxins
235 Uremic toxins are among the major toxic metabolites that lead to renal and associated diseases
236 in aging. Some uremic toxins originate from protein fermentation in the colon by proteolytic bacteria.
237 Products of the putrefaction process are absorbed and converted to toxic derivatives causing an
238 increased burden on kidney function. We detected a total of 14 phenolic and indolic uremic toxins in
239 plasma (Fig 2). The phenolic uremic toxins, 3-methyl catechol sulfate (P=0.0015, SE=0.19), 4-
240 ethylphenyl sulfate (P=2.38E-09, SE=0.05), 3-methoxycatechol sulfate (P=0.02, SE=0.11) and 4-
241 vinylphenol sulfate (P=0.05, SE=0.05) declined by 175%, 73%, 67% and 23%, respectively after the
242 consumption of the test food. On the contrary, the indolic uremic toxins 5-hydroxyindole sulfate
243 (P=2.23E-06, SE=0.05) and 7-hydroxyindole sulfate (P=2.97E-10, SE=0.06) increased by 29% and
244 43%, respectively, after the consumption of the test food. None of the other uremic toxins were
245 significantly influenced by the different foods.
246
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247 Fig 2. Changes in circulating levels of 9 phenolic (black bars) and 5 indolic (grey bars) uremic
248 toxins after the consumption of the test food. The test food led to significant (*: False discovery rate
249 corrected (FDR) P-value <0.05) reductions in levels of phenolic uremic toxins such as 3-methyl
250 catechol sulfate, 4-ethylphenyl sulfate, 3-methoxycatechol sulfate and 4-vinylphenol sulfate. Two of
251 the indolic uremic toxins, namely 5-hydroxyindole sulfate and 7-hydroxyindole sulfate, increased after
252 the consumption of the test food. There were no significant changes in the typical uremic toxins such
253 as 3-indoxyl sulfate or P-cresol sulfate.
254 Symmetric dimethylarginine (SDMA) is a uremic toxin originating from the host metabolism
255 and methylation of arginine (15, 16). The test food resulted in a significant reduction in blood
256 concentrations of SDMA (P=0.035, SE=0.2) in the senior dogs compared to the control 2 food (Fig 3).
257
258 Fig 3. Changes in circulating levels of the renal health marker symmetric dimethylarginine
259 (SDMA). Matched pair analyses of each dog on the test food versus the control 2 food showed
260 significant reduction in plasma concentrations of symmetric dimethylarginine (SDMA) (P=0.035,
261 SE=0.2) after the consumption of the test food.
262
263 Correlations of microbial taxa with changes in metabolites
264 The genus Adlercreutzi and the family Christensenellaceae were strong positive predictors of
265 glycerol levels in feces (Table 7). Faecalibacterium prausnitzii, family Ruminococcaceae, genus
266 Phascolarctobacterium and phylum Actinobacteria also correlated positively with fecal levels of
267 glycerol. On the contrary, phylum Fusobacterium and genus Salmonella negatively correlated with
268 glycerol levels in feces (Table 7). The genera Oscillospira and Adlercreutzia also had a negative
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17
269 correlation with levels of pyrraline in feces. The phylum Fusobacterium correlated positively with
270 fecal levels of pyrraline and threonine. Salmonella also had a positive correlation with pyrraline levels
271 in feces (Table 7).
272
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18
273 Table 7. Correlations of different taxa with fecal levels of glycerol, pyrraline and mucin amino
274 acids
Correlations with glycerol in feces
FDR P-value
R
Genus Adlercreutzi
3.95E-09
0.51
Family Christensenellaceae
0.0001
0.42
Faecalibacterium prausnitzii
0.02
0.39
Family Ruminococcaceae
0.02
0.39
Genus Phascolarctobacterium
0.003
0.33
Phylum Actinobacteria
0.0005
0.32
Phylum Fusobacterium
0.002
-0.4
Genus Salmonella
0.005
-0.25
Correlations with pyrraline in feces
Family Christensenellaceae
1.05E-12
-0.52
Genus Oscillospira
0.002
-0.48
Genus Adlercreutzia
1.52E-14
-0.44
Genus Salmonella
0.026
0.48
Phylum Fusobacterium
0.017
0.41
Correlations with mucin amino acids in feces
Family Christensenellaceae and threonine
0.0004
-0.32
Family Christensenellaceae and serine
0.0007
-0.27
Phylum Fusobacterium and threonine
0.00015
0.33
Phylum Fusobacterium and serine
0.02
0.25
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19
275 Discussion
276 The test food increased the relative abundance of health-promoting bacteria belonging to the
277 genera Adlercreutzia and Phascolarctobacterium. The abundance of the short-chain fatty acids
278 producers Phascolarctobacterium was reported to have a positive association with positive mood and
279 their number declines in elderly humans (17). Both Adlercreutzia and Phascolarctobacterium have the
280 capacity to metabolize isoflavones to equol, which has been implicated to have antioxidative
281 properties and prevent various age-related diseases including diabetes and obesity (18-20). Equol is
282 also associated with a decreased risk of certain types of cancer; therefore increasing the abundance of
283 equol-producing gut microbiota has been recommended to reduce this risk (21). In this study, although
284 it did not reach statistical significance (P=0.17), the fecal level of equol increased by 59.6% after the
285 consumption of the test food. Polyphenols bound to fruits and vegetables present in the test diet may
286 have led to the increased abundance of these bacteria in the senior dogs.
287 The consumption of the test food led to an increase in the proportion of the butyrate producer
288 Faecalibacterium prausnitizii. F. prausnitzii has been reported to have anti-inflammatory effects (22)
289 and the abundance of Faecalibacterium species declines during active inflammatory bowel disease
290 (23). The family Christensenellacea also increased after the consumption of the test food. Our
291 correlation analysis showed Christensenellacea abundance was a strong positive predictor of fecal
292 levels of glycerol. In a study that compared the microbiome of 416 twin-pairs, Christensenellacea
293 were in a greater abundance in lean individuals compared to obese (24). Christensenellacea have also
294 been reported to have the capability to produce short-chain fatty acids (SCF) (25). SCF are known to
295 improve the intestinal barrier integrity, which is in line with the result of our correlation analysis
296 showing a negative association between fecal levels of mucin amino acids and the proportion of
297 Christensenellacea. Their increased abundance may have reduced the degradation of the mucin layer,
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20
298 which in turn would decrease inflammation attributed to the translocation of bacteria and their
299 secretions through the gut barrier.
300 In a study that compared the microbial composition of Japanese people ranging from infants to
301 the elderly (26), the relative proportions of bacteria in the genera Fusobacterium and Megamonas
302 increased with age. The positive correlation of Fusobacterium with fecal levels of mucin amino acids
303 supports a previous report that showed the capacity of Fusobacterium species to degrade mucin (27).
304 Odamaki et al. (26) showed a negative correlation between Enterobacteriaceae and a cluster of
305 butyrate producing bacteria including Faecalibacterium. The decline in the proportions of
306 Enterobacteriaceae in senior dogs after the consumption of the test food may have benefited the
307 senior dogs as some of these bacteria are endotoxin producers, which compromise intestinal barrier
308 integrity leading to inflammation. Salmonella belonging to Enterobacteriaceae also declined due to
309 the test food consumption. Some species of Salmonella are major public health concerns causing
310 salmonellosis. Although dogs are subclinical carriers of Salmonella, the intimate relationship between
311 dogs and humans may lead to the risk of human exposure to Salmonella. The test food led to a
312 significant relative reduction in Salmonella shedding by increasing the proportion of other bacteria
313 that may have an anti-pathogenic effect.
314 Although not statistically significant, the relative proportions of the genera Ruminococcus and
315 Oscillospira increased by 43% and 73%, respectively, after the consumption of the test food. Both
316 genera belong to the family Ruminococcaceae, which increased significantly after the consumption of
317 the test food. These bacteria are known to produce short-chain fatty acids (SCF) that are beneficial to
318 the host mainly due to their anti-inflammatory effects (28, 29). They also serve as an energy source for
319 enterocytes, regulate intestinal motility and ameliorating leaky gut syndrome (30). Bacteria in the
320 genus Ruminococcus are fiber degraders and major producers of butyrate, which serves as an energy
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321 source for intestinal epithelial cells and has anti-inflammatory effects (31). Members of the genus also
322 produce bacteriocins, which have anti-microbial effects against a wide variety of pathogenic bacteria
323 (32).
324 Oscillospira are known to produce butyrate by relying on fermentation products secreted by
325 other bacterial species (33). In humans, Oscillospira have been associated with leanness or lower
326 body mass index in both infants and adults (34, 35). A meta-analysis by Kaakoush et al. (36), showed
327 a negative association of the abundance of Oscillospira with pediatric inflammatory bowel disease. In
328 ruminants, the abundance of Oscillospira is increased during the consumption of fresh green leaves
329 and decreases upon consumption of grain containing diets (37). The presence of increased fruit and
330 vegetable fiber in the test food may have encouraged the increase in the abundance of Oscillospira in
331 the senior dogs. Conley et al. (2) showed the genus that declines the most in aged mice compared to
332 young is Oscillospira. The decline in Oscillospira was accompanied by an increase in the marker of
333 inflammation, monocyte chemoattractant protein-1 (MCP-1). A similar reduction in the abundance of
334 Oscillospira was also associated with paracellular permeability and a decline in the anti-inflammatory
335 cytokine, IL-10 as reported by Hamilton et al. (38).
336 Despite dietary levels of threonine being higher in the test food, fecal threonine declined when
337 the dogs consumed the test food. This suggests that the increased fecal excretion of threonine is
338 associated with the degradation of the mucin layer as reported by Weir et al, (39). The composition of
339 the gut microbiota is a key factor in maintaining intestinal barrier integrity. The reduction in the
340 proportion of beneficial bacteria may lead to constipation, mal-absorption and longer colonic transit
341 time. This encourages increased presence of proteolytic bacteria, whose products of fermentation
342 deteriorate the intestinal barrier.
343 The consumption of the test food reduced levels of the advanced glycation end product,
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22
344 pyrraline. AGE are a complex group of compounds derived from the non-enzymatic glycation of
345 proteins, lipids, and nucleic acids. They can also be acquired from food; thus restriction of foods with
346 high levels of AGE has been recommended to decrease circulating AGE in the body (40). AGE are
347 known to accelerate the process of aging and they are linked to a number of age-related diseases such
348 as diabetes, vascular and renal diseases mainly by inducing inflammation and oxidative stress (41, 42).
349 Fecal microorganisms have been shown to be capable of degrading various AGE including pyrraline
350 (43). The negative correlation of pyrraline with Oscillospira, Christensenellaceae and Adlercreutzia
351 suggests the capability of these bacteria to degrade pyrraline or prevent its formation. The
352 consumption of diets rich in AGE has been shown to shift the microbiota towards a more detrimental
353 composition (44). This is in line with our correlation analyses that showed a positive association of
354 pyrraline with Salmonella and Fusobacterium. After the consumption of the test food, the level of
355 pyrraline in blood declined by almost the same amount (70%) as in feces. This implies induced
356 microbial degradation of pyrraline or its precursors by the above microbes as a more likely mechanism
357 than the test food influencing absorption of pyrraline from the GI tract. Interestingly, one of the AGE,
358 N6-carboxymethyllysin (CML), increased in feces after the consumption of the test food. However,
359 the level of CML in the blood did not change. This may suggest that fecal levels of CML may not be
360 biologically significant.
361 The level of glycerol in the feces of the senior dogs was higher when they were fed the test
362 food. Glycerol is known to increase water retention in the colon and thus it is used to treat constipation
363 (45). Prolonged transit times are risks to develop various diseases due to the exposure to toxic
364 products accumulating due to putrefaction (46). A shorter transit time leads to a limited accumulation
365 of such products that may cause various diseases (46). People with functional constipation have been
366 shown to contain bacteria with more abundant genes to degrade glycerol (47). The strong negative
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367 correlation between fecal levels of glycerol versus Fusobacteria and Salmonella may suggest the
368 capacity of these bacteria to degrade glycerol. The reduction in the proportions of these bacteria after
369 the consumption of the test food may have led to the increased levels of glycerol in feces. On the other
370 hand, other taxa such as Adlercreutzi, Christensenellaceae, Faecalibacterium prausnitzii,
371 Ruminococcaceae, Phascolarctobacterium and Actinobacteria correlated positively with glycerol.
372 Weir et al. (39) found a positive association of a Rumonococcus species with fecal levels of glycerol
373 and free fatty acids. To our knowledge, this study is the first to show the associations of the other taxa
374 with fecal glycerol concentration. This may suggest the test food altered the microbial composition
375 towards a population with higher lipase activity. Along with the increased level of glycerol in feces,
376 the level of both fecal and plasma levels of omega fatty acids, DHA, EPA, DPA also increased in
377 feces after the consumption of the test food due to added fish oil. Omega-3 fatty acids have health
378 benefits throughout life by improving cardiovascular, immune, cognitive and other functions (48).
379 Similarly, the increased level of linoleate while consuming the test food is due to the increased dietary
380 concentration. Despite the similar levels of C:16:1 and C:18:0 fatty acids measured in the foods (Table
381 2), the levels of these fatty acids in feces increased after the consumption of the test food. This
382 supports the possible increased microbial lipase activity attributed to the change in the microbial
383 composition.
384 Uremic toxins are among the major metabolites that cause age-related complications. There
385 was a marked improvement in markers of kidney health after the consumption of the test food.
386 Circulating symmetric dimethylarginine (SDMA) has been shown to be a good biomarker of kidney
387 function in dogs as it detects reduction in glomerular filtration rate (GFR) much earlier than serum
388 creatinine (16). The reduction in circulating concentration of SDMA in the senior dogs after the
389 consumption of the test food may thus indicate an increased GFR and improved kidney function.
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390 Furthermore, the test food reduced several phenolic uremic toxins originating from microbial
391 fermentation of protein (49). One of these metabolites was 4-ethylphenyl sulfate (4-EPS), which is
392 also known to have a negative impact on brain health by causing anxiety-like symptoms (50, 51). The
393 role of foods containing fruits and vegetables in improving kidney health has been reported (7, 52).
394 The significant reduction in such metabolites in the senior dogs after the consumption of the test food
395 may be due to the changes in the microbial composition. The two indolic uremic toxins, 5-
396 hydroxyindole sulfate and 7-hydroxyindole sulfate, increased after the consumption of the test food.
397 Indolic uremic toxins originate from colonic fermentation of the amino acid tryptophan (53). The test
398 food was formulated to contain 53% higher tryptophan compared to the control 2 food. The presence
399 of more substrate may have led to increased levels of the two indolic metabolites. However, the typical
400 indolic uremic toxin, 3-indoxyl sulfate, was not affected by the consumption of the different diets.
401 In conclusion, old dogs fed fiber sources from vegetables and fruits containing high soluble
402 fiber benefit by having a gut microbial composition promoting healthier metabolic profiles.
403
404
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405 Acknowledgments
406 E. E. G. and D. E. J designed and conducted research; M. I. J. and E.E.G. analyzed data; E.E.G. wrote
407 the paper and had primary responsibility for final content. All authors read and approved the final
408 manuscript. The work was funded by and performed at the Pet Nutrition Center, Hill’s Pet Nutrition,
409 Topeka, Kansas.
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26
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... In correlation with our results, in a human study, psyllium supplementation increased the fecal acetate concentration along with the amount of both succinate-specific utilizer Phascolarctobacterium and acetate metabolizing Faecalibacterium species [15]. Furthermore, according to other studies, increased fiber content of feed resulted in intensely elevated acetogenic bacterial content such as Ruminococcus, Phascolarctobacteria, Christensenellaceae and Ruminococcacea species in dogs [47]. ...
... According to our results, it can be also concluded that only psyllium was able to increase the concentration of n-butyrate, which can be in correlation with the shift in the canine intestinal microbiota towards Faecalibacterium, Christensenellaceae, Oscillospira and Ruminococcus spp. as the effect of fiber-rich diet, described in previous studies, since these bacteria are able to effectively convert sugars, acetate and other substrates into nbutyrate [15,47]. On the other hand, according to recent studies, the composition of the intestinal microbiota of healthy humans was less altered by psyllium administration than that of constipated people [15,65]. ...
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The intestinal microbiome of dogs can be influenced by a number of factors such as non-starch polysaccharides as well as some non-digestible oligo- and disaccharides. These molecules are only decomposed by intestinal anaerobic microbial fermentation, resulting in the formation of volatile fatty acids (VFAs), which play a central role in maintaining the balance of the intestinal flora and affecting the health status of the host organism. In the present study, the effects of lactulose and psyllium husk (Plantago ovata) were investigated regarding their influence on concentrations of various VFAs produced by the canine intestinal microbiome. Thirty dogs were kept on a standard diet for 15 days, during which time half of the animals received oral lactulose once a day, while the other group was given a psyllium-supplemented diet (in 0.67 and in 0.2 g/kg body weight concentrations, respectively). On days 0, 5, 10 and 15 of the experiment, feces were sampled from the rectum, and the concentration of each VFA was determined by GC-MS (gas chromatography–mass spectrometry). Lactulose administration caused a significant increase in the total VFA concentration of the feces on days 10 and 15 of the experiment (p = 0.035 and p < 0.001, respectively); however, in the case of psyllium supplementation, the concentration of VFAs showed a significant elevation only on day 15 (p = 0.003). Concentrations of acetate and propionate increased significantly on days 5, 10 and 15 after lactulose treatment (p = 0.044, p = 0.048 and p < 0.001, respectively). Following psyllium administration, intestinal acetate, propionate and n-butyrate production were stimulated on day 15, as indicated by the fecal VFA levels (p = 0.002, p = 0.035 and p = 0.02, respectively). It can be concluded that both lactulose and psyllium are suitable for enhancing the synthesis of VFAs in the intestines of dogs. Increased acetate and propionate concentrations were observed following the administration of both supplements; however, elevated n-butyrate production was found only after psyllium treatment, suggesting that the applied prebiotics may exert slightly different effects in the hindgut of dogs. These findings can be also of great importance regarding the treatment and management of patients suffering from intestinal disorders as well as hepatic encephalopathy due to portosystemic shunt.
... The peas in the extruded diet might explain the higher prevalence of Adlercreutzia equolifaciens because peas are a legume and usually have moderate to high isoflavone concentrations, depending on the processing and portions of the peas used (Bhagwat et al., 2008). Additionally, in a dog study, the food containing fiber sources such as citrus, carrot, tomato, and spinach, had a numerical but not significant increase in fecal equol concentration and had a significantly increased relative abundance of Adlercreutzia (Gebreselassie et al., 2018). Similarly, Asaccharobacter celatus, closely related to Adlercreutzia equolifaciens, can also convert the isoflavone, daidzein, to equol (Clavel et al., 2014). ...
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... Selon le type de fibre utilisé la population bactérienne favorisée ne sera pas la même, ni les produits de son métabolisme 89,90 . Ainsi, un apport équilibré et diversifié en fibre permet de maintenir une population microbienne équilibrée et diversifiée 91 . Parmi les produits du métabolisme bactérien, on retrouve notamment des acides gras volatils. ...
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Introduction. Age is the primary risk factor for major human chronic diseases, including cardiovascular disorders, cancer, type 2 diabetes, and neurodegenerative diseases. Chronic, low-grade, systemic inflammation is associated with aging and the progression of immunosenescence. Immunosenescence may play an important role in the development of age-related chronic disease and the widely observed phenomenon of increased production of inflammatory mediators that accompany this process, referred to as “inflammaging.” While it has been demonstrated that the gut microbiome and immune system interact, the relationship between the gut microbiome and age remains to be clearly defined, particularly in the context of inflammation. The aim of our study was to clarify the associations between age, the gut microbiome, and pro-inflammatory marker serum MCP-1 in a C57BL/6 murine model. Results. We used 16S rRNA gene sequencing to profile the composition of fecal microbiota associated with young and aged mice. Our analysis identified an association between microbiome structure and mouse age and revealed specific groups of taxa whose abundances stratify young and aged mice. This includes the Ruminococcaceae, Clostridiaceae, and Enterobacteriaceae. We also profiled pro-inflammatory serum MCP-1 levels of each mouse and found that aged mice exhibited elevated serum MCP-1, a phenotype consistent with inflammaging. Robust correlation tests identified several taxa whose abundance in the microbiome associates with serum MCP-1 status, indicating that they may interact with the mouse immune system. We find that taxonomically similar organisms can exhibit differing, even opposite, patterns of association with the host immune system. We also find that many of the OTUs that associate with serum MCP-1 stratify individuals by age. Discussion. Our results demonstrate that gut microbiome composition is associated with age and the pro-inflammatory marker, serum MCP-1. The correlation between age, relative abundance of specific taxa in the gut microbiome, and serum MCP-1 status in mice indicates that the gut microbiome may play a modulating role in age-related inflammatory processes. These findings warrant further investigation of taxa associated with the inflammaging phenotype and the role of gut microbiome in the health status and immune function of aged individuals.
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Background: Gut microbial community, which may influence our mood, can be shaped by modulating the gut ecosystem through dietary strategies. Understanding the gut-brain correlationship in healthy people is important for maintenance of mental health and prevention of mental illnesses. Methods: A case study on the correlation between gut microbial alternation and mood swing of healthy adults was conducted in a closed human life support system during a 105-day experiment. Gut microbial community structures were analyzed using high-throughput sequencing every 2 weeks. A profile of mood states questionnaire was used to record the mood swings. Correlation between gut microbes and mood were identified with partial least squares discrimination analysis. Key results: Microbial community structures in the three healthy adults were strongly correlated with mood states. Bacterial genera Roseburia, Phascolarctobacterium, Lachnospira, and Prevotella had potential positive correlation with positive mood, while genera Faecalibacterium, Bifidobacterium, Bacteroides, Parabacteroides, and Anaerostipes were correlated with negative mood. Among which, Faecalibacterium spp. had the highest abundance, and showed a significant negative correlation with mood. Conclusions & inferences: Our results indicated that the composition of microbial community could play a role in emotional change in mentally physically healthy adults.