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The Journal of Nutrition
Genomics, Proteomics, and Metabolomics
Early-Life Sugar Consumption Affects the Rat
Microbiome Independently of Obesity
1–3
Emily E Noble,
4
Ted M Hsu,
4,5
Roshonda B Jones,
7
Anthony A Fodor,
7
Michael I Goran,
6,8
and Scott E Kanoski
4,5,8
*
4
Human and Evolutionary Biology Section, Department of Biological Sciences,
5
Neuroscience Program, and
6
Department of Preventative
Medicine, University of Southern California, Los Angeles, CA; and
7
Department of Bioinformatics and Genetics, University of North
Carolina at Charlotte, Charlotte, NC
Abstract
Background: The gut microbiome has been implicated in various metabolic and neurocognitive disorders and is heavily
influenced by dietary factors, but there is a paucity of research on the effects of added sugars on the gut microbiome.
Objective: With the use of a rodent model, our goal was to determine how added-sugar consumption during the juvenile
and adolescent phase of development affects the gut microbiome.
Methods: Forty-two juvenile male Sprague-Dawley rats [postnatal day (PND) 26; 50–70 g] were given access to 1 of 3
different 11%-carbohydrate solutions designed to model a range of monosaccharide ratios commonly consumed in sugar-
sweetened beverages: 1) 35% fructose:65% glucose, 2) 50% fructose:50% glucose, 3) 65% fructose:35% glucose, and 4)
control (no sugar). After ad libitum access to the respective solutions for the juvenile and adolescent period (PND 26–80), fecal
samples were collected for next-generation 16S ribosomal RNA sequencing and multivariate microbial composition analyses.
Energy intake, weight change, and adiposity index were analyzed in relation to sugar consumption and the microbiota.
Results: Body weight, adiposity index, and total caloric intake did not differ as a result of sugar consumption. However,
sugar consumption altered the gut microbiome independently of anthropometric measures and caloric intake. At the
genus level, Prevotella [linear discriminant analysis (LDA) score = 24.62; P< 0.001] and Lachnospiraceae incertae sedis
(LDA score = 23.01; P= 0.03) were reduced, whereas Bacteroides (LDA score = 4.19; P< 0.001), Alistipes (LDA score =
3.88; P< 0.001), Lactobacillus (LDA score = 3.78; P< 0.001), Clostridium sensu stricto (LDA score = 3.77; P< 0.001),
Bifidobacteriaceae (LDA score = 3.59; P= 0.001), and Parasutterella (LDA score = 3.79; P= 0.004) were elevated by sugar
consumption. No overall pattern could be attributable to monosaccharide ratio.
Conclusions: Early-life sugar consumption affects the gut microbiome in rats independently of caloric intake, body weight, or
adiposity index; these effects are robust across a range of fructose-to-glucose ratios. JNutrdoi: 10.3945/jn.116.238816.
Keywords: glucose, fructose, gut microbiota, juvenile, adolescence
Introduction
Colonization of the gut microbiome, which consists of an
estimated 100 trillion microorganisms (1), begins during
birth and continues into early childhood (2). Early-life gut
microbial populations play a critical role in the development
of the nervous system and the immune response and have been
shown to affect behaviors such as anxiety and motor control
into adulthood (3–6). Current findings also identified a role for the
gut microbiome in the development of gastrointestinal diseases,
such as ulcerative colitis and irritable bowel syndrome (7, 8), in
metabolic pathologies such as insulin resistance (9) and obesity
(10, 11) and in neurological disorders such as autism (12),
Parkinson disease (13), and Alzheimer disease (14). Due to
evidence linking the gut microbiome with human health and
disease, it has been suggested that nurturing the development of a
healthy patient/microbial ‘‘superorganism’’ is a cornerstone in the
future of medicine (15, 16). Thus, an understanding of how
modifiable environmental factors affect the gut microbiome,
particularly during early-life developmental periods in which there
is rapid microorganism colonization of the gut, is of critical
importance for human health and disease prevention.
Recent experimental rodent studies revealed that dietary
factors robustly affect the gut microbiome (17). In particular, an
1
Supported by the Universityof Southern CaliforniaDiabetes and Obesity Research
Institute (SEK) and The Robert and C Veronica Atkins Foundation (MIG).
2
Author disclosures: EE Noble, TM Hsu, RB Jones, AA Fodor, MI Goran, and SE
Kanoski, no conflicts of interest.
3
Supplemental Figures 1–5 and Supplemental Table 1 are available from the
‘‘Online Supporting Material’’ link in the online posting of the article and from the
same link in the online table of contents at http://jn.nutrition.org.
8
These authors contributed equally to this work.
*To whom correspondence should be addressed. E-mail: kanoski@usc.edu.
ã2016 American Society for Nutrition.
Manuscript received July 21, 2016. Initial review completed September 10, 2016. Revision accepted October 31, 2016. 1of9
doi: 10.3945/jn.116.238816.
The Journal of Nutrition. First published ahead of print November 30, 2016 as doi: 10.3945/jn.116.238816.
Copyright (C) 2016 by the American Society for Nutrition
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obesogenic high-fat diet (HFD)
9
, typically composed of 45–60% fat
with sucrose as the primary carbohydrate source, has been studied
in rodents with regard to changes in bacterial populations. For
example, an HFD reduces populations in the phylum Bacteroidetes
and increases both Firmicutes and Proteobacteria relative to control
diets that are 10–15% kilocalories from fat with complex carbo-
hydrates (starch) as the primary carbohydrate source (18, 19).
Bacteroidetes aid in promoting T cell–mediated immune responses
in the host and prevent the overgrowth of more harmful pathogens
(20–22), whereas Proteobacteria and Firmicutes are generally
associated with gut dysbiosis (23) and obesity (24), respectively.
However, given that rodent HFD models produce obesity and
metabolic syndrome, it is unclear whether HFD-mediated gut
microbiome alterations are based directly on dietary factors or,
rather, are secondary to increased adiposity and associated
metabolic derangements.
Although many experimental rodent models showed that a diet
that is high in both FAs and simple sugars (e.g., sucrose) affects the
gut microbiome, there is a paucity of research on the contribution
of added sugars independent of elevated dietary fat. In humans,
over the past half-century, a large increase in the consumption of
added sugars has occurred, particularly from sugar-sweetened
beverages (SSBs) (25). For example, in children [the highest sugar
consumers of any age group (26, 27)], 40% of added sugars come
from SSBs (26), which is associated with increased risk of
cardiovascular and metabolic disease (27–29) and weight gain
(30). Moreover, changes in the food industry in the past decades
have created a shift in the biochemical form in which sugars are
frequently consumed (25, 31). For example, in the United States,
the natural disaccharide sucrose (chemically bound fructose and
glucose molecules, 50:50 ratio) has been largely replaced with
sweeteners containing unbound fructose and glucose monosaccha-
ride molecules, typically comprising an overall elevation in the
fructose-to-glucose ratio compared with sucrose (e.g., high-fructose
corn syrup) (32). A current study that used HPLC revealed that the
percentage of fructose in high-fructose corn syrupinpopular,
commercially available SSBs ranged from 47% to 65%, with a mean
fructose content of 59% (33). Thus, due to the excessive use of
added sugars in the modern food environment, particularly in the
form of SSBs, and the industrial development of sweeteners with
monosaccharide ratios that diverge from those present in foods in
their native form (typically with a higher fructose content than
sugar), the current study elucidates the impact of SSBs that vary in
the glucose-to-fructose ratio on gut microbial populations.
Evidence from human studies suggests that the gut microbiota
that is present during early postnatal and adolescent periods likely
plays a major role in subsequent health and disease (34, 35).
Rodent studies in which the gut microbiota was manipulated
during this critical period confirmed that developmental abnor-
malities present in germ-free mice are reversible when these mice
were colonized with intestinal bacteria during early life but not
during adulthood (36, 37). Because added sugars make up an
increasing proportion of the diet during early-life periods of
development in humans, and the gut microbiota during early life
may have profound implications for health during the life span,
our goal was to determine (by using a rodent model) how the
consumption of added sugars (with varying fructose-to-glucose
ratios, and in the form of SSBs) during the juvenile and adolescent
period of development affects the gut microbiome, and whether
thesesugar-inducedmicrobiomealterationsarerelatedtocaloric
intake and body weight gain.
Methods
Experimental design
Effect of different monosaccharide ratios of sugar on the fecal
microbiome. Forty-two juvenile male Sprague-Dawley rats [Envigo;
postnatal day (PND) 26; 50–70 g] were housed individually in standard
conditions with a 12:12 light-dark cycle and were classified into 4 groups on
the basis of solution feeding of the following: 1) 35% fructose and 65%
glucose (n= 11), 2) 65% fructose and 35% glucose (n= 11), 3) 50% fructose
and 50% glucose (n= 10), and 4) control (no sugar; n= 10). For each of the
sugar groups, the concentration of total sugar in solution was 11% wt:vol
(comparable to SSBs typically consumed by humans) in reverse osmosis–
filtered water. In addition to sugar solutions (or an extra water bottle for the
control group), rats were given access to Lab Diet 5001 (29.8% kilocalories
from protein, 13.4% kilocalories from fat, 56.7% kilocalories from carbo-
hydrate; PMI Nutrition International) and water ad libitum. Food intake,
solution intake, and body weights were monitored 3 times/wk from PNDs
26 to 61, with additional recordings taken at PND 80 (fecal collection) and a
terminal recording at PND 92. The percentage of kilocalories consumed from
each macronutrient was estimated by first multiplying the grams of feed pellets
consumed by the gram percentage of each macronutrient in the feed pellets.
The gram contribution of each macronutrient was converted to kilocalories by
using the 4-, 4-, and 9-kcal/g conversion factors for carbohydrate, protein, and
fat, respectively. This number was then divided by the total number of
kilocalories consumed for a percentage contribution of each macronutrient.
Feces were collected from the rats according to the following methods: each rat
was placed in a sterile cage and gently restrained while lifting its tail until
defecation occurred. Feces were immediately placed into dry ice and stored at
280°C until time of processing for RNA sequencing. All of the
experiments were performed in accordance with the approval of the Animal
Care and Use Committee at the University of Southern California.
A separate group of male Sprague-Dawley rats (n= 42; PND 26; 50–
70 g) were housed individually in standard conditions with a 12:12 light-
dark cycle and were classified into 4 groups in an identical design to
cohort 1. After 6 wk in the same respective conditions, body weights and
intakes of feed, sugar, and total kilocalories were similar to those in
cohort 1 (Supplemental Figure 1). Body composition was assessed by
using a Bruker NMR Minispec LF 90II (Bruker Daltonics, Inc.). The
adiposity index was calculated as [fat mass (g)/lean mass (g)] 3100.
Taxonomic classification of 16S ribosomal RNA gene sequences.
Fecal microbiome populations were identified by using next-generation
high-throughput sequencing of the V3–V4 variable region of 16S
ribosomal RNA (rRNA; Vaiomer SAS). Genomic DNA was isolated
and collected from fecal samples, and DNA concentrations were
determined by using UV spectroscopy (Nanodrop 2000; ThermoScien-
tific). PCR amplification was performed by using 16S universal primers
targeting the V3–V4 region of the bacterial 16S ribosomal gene (Vaiomer
universal 16S primers), with a joint pair length encompassing 476-bp
amplicons (MiSeq Reagent Kit V3, Illumina Inc.). The detection of
sequencing fragments was performed by using MiSeq Illumina technol-
ogy (Illumina Inc.). The 16S targeted sequences were then clustered into
operational taxonomic units (OTUs) before taxonomic assignment and
analyzed by using the bioinformatics pipeline as described previously
(38, 39). The OTUs and taxonomy classifications were tabulated, and to
account for differences in raw counts across the samples the tables were
log normalized by using Equation 1:
Log10 Raw Count
# of sequences in samplex
3ðAverage # of sequences per sampleÞþ1ð1Þ
Multidimensional scaling was performed on the tables by using the
‘‘capscale’’ function of the R statistical software package ‘‘vegan’’ (40)
with Bray-Curtis dissimilarity.
9
Abbreviations used: FDR, false discovery rate; HFD, high-fat diet; LDA, linear
discriminant analysis; OTU, operational taxonomic unit; PND, postnatal day;
rRNA, ribosomal RNA; SSB, sugar-sweetened beverage.
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Statistical analysis
Two-factor ANOVA (time 3group) with Holm-Sidak post hoc analyses
were used to determine whether there were group differences in body
weight and food, sugar, and total intakes. Data for fat mass, lean mass,
and adiposity index were each statistically compared by using 1-factor
ANOVA with an alevel of 0.05 (GraphPad Prism, version 6.0).
The following analyses were performed by using the statistical
software R (41). Bacterial taxa that were differentially abundant in
pairwise analysis of dietary groups were identified by using the Kruskal-
Wallis nonparametric test, followed by the Benjamini-Hochberg post
hoc test with a false discovery rate of P< 0.10. The identified features
were then subjected to the linear discriminant analysis (LDA) model
with a threshold logarithmic LDA score set at 3.0 and ranked (42).
Respective cladograms were generated with genus at the lowest level.
Differences in the abundances of bacteria classified at a given
taxonomic level relative to the type of sugar consumed were determined
by using the following model:
Abundance of bacteriai¼Fructose fraction þeð2Þ
where ‘‘Abundance’’ represents log-normalized counts and the fructose
fraction was a quantitative variable ranging from 35 to 65. Statistical
models were only built for ‘‘nonrare taxa,’’ which were present in $25%
of all samples. To determine whether the gut microbiota could expl ain
differences in body weight (grams) or energy intake (kilocalories), a
series of linear models were built, which we named intake variables, as
follows:
Abundance of bacteriai¼Sugar vs:control þIntake variable
þIntake variable 3Sugar vs:control þeð3Þ
One model was built for each combination of the abundance of bacteria
(the log-normalized counts at a phylogenetic level) and the intake
variables [body weight (g), energy intake (kcal)]. Differences needed to
survive the Benjamini-Hochberg post hoc test with a false discovery rate
(FDR) of P< 0.10 were deemed to be significant.
Results
Effects of early-life sugar consumption on anthropometric
measures. Consistent with our previous report (43), there was
no effect of sugar or monosaccharide ratio on body weight gain
when sugar was made available ad libitum throughout the entire
juvenile and adolescent period (Figure 1A), and there were no
differences in overall energy intake between the 4 groups (Figure 1B).
Sugar consumption did not differ between groups fed the solutions
with different monosaccharide ratios (Figure 1C). The lack of
elevated weight gain in the sugar-fed groups is based, at least in part,
by compensatory reductions in food intake in the sugar consumers
(Figure 1D). When comparing 24-h food intake (Figure 1D), 2-factor
ANOVA revealed a significant interaction (time 3sugar group;
P< 0.0001) with main effects of time (P< 0.0001) and sugar
group (P< 0.0001). Post hoc analyses revealed that the sugar
groups consumed significantly less food than did controls at each
time point (P< 0.0001). Thus, the lack of elevated body weight gain
in the sugar-fed groups is based, at least in part, by compensatory
reductions in food intake in the sugar consumers. All 3 sugar groups
consumed a significantly lower percentage of energy from fat
(P<0.0001) and protein (P<0.0001), likely due to compensatory
reductions in food intake as a result of consuming sugar. There were
no differences in body fat (Figure 2A), lean mass (Figure 2B), or
overall adiposity index (Figure 2C) between the 4 groups.
Taxonomic classification of 16S rRNA sequence reads.
The Ribosomal Database Project classifier was used to assign
taxonomy to the 16S rRNA sequence reads and QIIME was used
to cluster the sequence reads into OTUs (Table 1).
FIGURE 1 Effects of consumption of rats fed 11% sugar solutions
containing varying fructose-to-glucose ratios on body weight (A) and
total (B), sugar solution (C), and food energy intakes (D) in male rats
from PNDs 26 to 61. Values are means 6SEMs; n= 10 or 11.
*Different from 35F:65G, P,0.05; #different from 50F:50G, P,0.05;
$different from 65F:35G, P,0.05. BW, body weight; PND, postnatal
day; 35F:65G, 35% fructose:65% glucose; 50F:50G, 50% fructose:50%
glucose; 65F:35G, 65% fructose:35% glucose.
Dietary sugar and the gut microbiome 3 of 9
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Sugar solution consumption resulted in microbial separation.
Results from our multidimensional scaling analysis on the
taxonomic classification tables at all phylogenetic levels
represent a summary of gut microbial composition (Figure 3).
Rats fed the sugar solution (black symbols) compared with
water (gray symbols) had distinct clustering patterns (Figure
3A–F). There was no clear separation based on the monosac-
charide ratio of the sugar solutions administered (Figure 3G–L).
The distribution of Pvalues derived from ttests performed
separately for each family showed that approximately one-
quarter of nonrare bacteria at the family level were significantly
different between samples from rats fed a sugar solution and
control samples at a 10% FDR (Table 2). These findings are
also represented in the distribution of Pvalues derived
from statistical tests at the OTU level, which, unlike the case for
sugar compared with no sugar (Figure 4A), are approximately
uniform (Figure 4B) when evaluating the linear models in which
the fructose-to-glucose ratio is compared with the relative
abundance of each OTU. Likewise, none of the family-level
bacteria had a difference in abundance with respect to sugar
group at a FDR threshold of 10% (Supplemental Table 1).
Relation of fecal microbiota to body weight and energy
intake. To determine how members of the microbial community
are associated with body weight and calorie intake, we
executed a series of linear regression models comparing these
intake variables with log-normalized adjusted counts. All of
these models included a categorical variable for sugar compared
with nonsugar. By using Equation 3as described in Methods at
a 10% FDR threshold, there were no significant associations
with body weight or food intake to any member of the microbial
community and there was no association with any of the
interaction terms at any phylogenetic level (Supplemental
Figures 2–5). Likewise, the distribution of Pvalues for body
weight or calorie intake generated by Equation 3produced near-
uniform Pvalues, suggesting little association (Figure 4C, D).
Effects of added sugar on abundance of fecal microbiota
at different taxonomic levels. To further explore differences in
the microbial community associated with sugar group, pairwise
comparisons were made comparing sugar with the control at each
phylogenetic level (from phylum to genus; Figure 5). At the
phylum level, Proteobacteria (t-statistic =3.44, P=0.005)and
Actinobacteria (t-statistic =4.70, P=0.002)wereelevatedinall
sugar groups compared with controls. At the class level, Actino-
bacteria (t-statistic =4.71, P= 0.002) and Bacilli (t-statistic =4.48,
P< 0.001) (of the phyla Actinobacteria and Firmicutes, respec-
tively) were significantly elevated by sugar, as were Alpha-
(t-statistic =2.01, P=0.04),Beta- (t-statistic =2.61, P=0.02),
and Gamma- (t-statistic =8.17, P< 0.001) Proteobacteria (of the
phylum Proteobacteria). Bacteria of the orders Lactobacillales
(t-statistic =4.45, P< 0.001), Actinobacteridae (t-statistic =3.45,
P< 0.001), Burkholderiales (t-statistic =2.59, P= 0.02), and
Enterobacteriales (t-statistic =7.07, P< 0.001) were significantly
elevated by added sugar. According to our LDA effective size
analysis,many taxa weresignificantly different between sugar and
control at the family level: for example, Clostridiaceae 1 (LDA
score = 3.97, P< 0.001), Lactobacillaceae (LDA score = 3.78,
P< 0.001), Rikenellaceae (LDA score = 3.93, P< 0.001),
Porphyromonadaceae (LDA score = 3.36, P= 0.03), Bacteroida-
ceae (LDA score = 4.19, P< 0.001), Bifidobacteriales (LDA score =
3.59, P= 0.001), Sutterellaceae (LDA score = 3.73, P= 0.02),
and Enterobacteriaceae (LDA score = 3.09, P< 0.001) were
elevated by sugar, whereas Prevotellaceae (LDA score = 24.61,
P= 0.002), Ruminococcaceae (LDA score = 24.24, P=0.02),
and Lachnospiraceae (LDA score = 24.37, P= 0.04) were
reduced due to sugar consumption. At the genus level, Prevotella
(LDA score = 24.62, P< 0.001) and Lachnospiracea incertae
sedis (LDA score = 23.01, P= 0.03) were reduced by sugar
consumption, whereas Bacteroides (LDA score = 4.19,
P<0.001),Alistipes (LDA score = 3.88, P<0.001),Lactobacillus
(LDA score = 3.78, P< 0.001), Clostridium sensu stricto (LDA
score = 3.77, P< 0.001), Bifidobacteriaceae (LDA score = 3.59,
P= 0.001), and Parasutterella (LDA score = 3.79, P= 0.004) were
all significantly elevated by sugar consumption.
Discussion
Herein we report, with the use of a rat model, that the gut
microbiome is affected by added-sugar consumption during the
FIGURE 2 Effect of consumption of rats fed 11% sugar solutions
containing varying fructose-to-glucose ratios on changes in body fat
(A), lean mass (B), and adiposity index (C) in male rats from PNDs 26
to 61. Values are means 6SEMs; n= 10 or 11. PND, postnatal day;
35F:65G, 35% fructose:65% glucose; 50F:50G, 50% fructose:50%
glucose; 65F:35G, 65% fructose:35% glucose.
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juvenile and adolescent stage of development and that these
differences are independent of obesity status and caloric intake.
Moreover, the monosaccharide ratio of fructose to glucose did
not significantly contribute to the overall effects of sugar
consumption on microbial populations. Given that we used
added sugars designed to model those commonly consumed in
TABLE 1 Summary characteristics of 16S rRNA sequence reads from fecal samples of rats fed 11%
sugar solutions containing varying fructose-to-glucose ratios
1
OTUs, nSequence reads, nReads, n/sample
Minimum reads,
n/sample
Maximum reads,
n/sample
16S reads generated 1,237,456 29,463.24 6411.07 21,939 34,034
RDP classified
Phylum 11 1,208,210 28,766.90 6394.82 21,508 33,392
Class 21 1,191,926 28,379.19 6393.58 21,191 32,837
Order 35 1,187,343 28,270.07 6392.22 21,127 32,733
Family 84 1,086,649 25,872.60 6383.17 19,116 30,283
Genus 211 738,112 17,574.10 6327.48 12,728 21,924
QIIME OTUs
2
4703 918,964 21,880.10 6394.77 15,833 28,531
1
Values are means 6SEMs unless otherwise indicated; n= 42 samples total. The treatment groups refer to the 3 groups of rats fed sugar solutions
(35F:65G, n= 11; 50F:50G, n= 10; 65F:35G, n= 11) and the control group had no access to sugar but rece ived a second water bottle instead. OT U,
operational taxonomic unit; RDP, Ribosomal Database Project; rRNA, ribosomal RNA; 35F:65G, 35% fructose:65% glucose; 50F:50G, 50%
fructose:50% glucose; 65F:35G, 65% fructose:35% glucose.
2
More than 25% of samples.
FIGURE 3 Summary of clustering patterns of fecal microbiota from rats fed 11% sugar solutions containing varying fructose-to-glucose ratios or
controls by using multidimensional scaling. A distinct clustering pattern of fecal microbiota was observed between rats fed a sugar solution (n= 32) and
the control group that received water instead of sugar (n= 10) (A–F). No distinct clustering pattern of fecal microbiota was observed at any phylogenetic
level as a result of fructose-to-glucose ratio (n= 10 or 11) (G–L; based on a significance level of P,0.05). MDS, multidimensional scaling; OTU,
operational taxonomic unit; 35F:65G, 35% fructose:65% glucose; 50F:50G, 50% fructose:50% glucose; 65F:35G, 65% fructose:35% glucose.
Dietary sugar and the gut microbiome 5 of 9
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beverages in human populations, both in terms of caloric
content and monosaccharide ratio, the present findings may
have implications with regard to the relation between added
sugars and the gut microbiome in humans, although the
translational potential of the present results requires further
epidemiologic and experimental studies in humans.
To our knowledge, this is the first investigation of how added
sugars affect the gut microbiome during the juvenile and adolescent
period of development, during which the brain is particularly
vulnerable to the effects of sugar and other dietary factors (44).
However, the effects of added dietary sugars on gut microbial
populations have been investigated in adult rodents. For
example, high-sucrose diets (containing 70% of kcal from
carbohydrate, mainly in the form of sucrose) have previously
been shown to elevate Clostridiales (a class of Firmicutes) and
reduce Bacteroidales (an order of the phylum Bacteroidetes) in
adult rodents (45). In the present study we did not observe changes
in either one of these populations due to sugar consumption. The
age of the rats (adult compared with juvenile and adolescent), the
percentage of total kilocalories from sugar (70% compared with
;40%), and the chemical composition of the sugar (disaccharide
sucrose compared with free monosaccharides) may contribute to
these differences. Another study found that a diet enriched in the
monosaccharide fructose increases the population of the genus
Coprococcus and Ruminococcus (both in the phylum Firmicutes)
in adult rodents, and that either antibiotic treatment or a fecal
microbiome transfer from rodents fed a healthy control diet
reduces both the populations of these species as well the fructose-
induced metabolic disease (46). We did not observe differences in
Coprococcus or Ruminococcus in rats fed the highest dose of
fructose; however, our study differed in that fructose was
consumed by free choice in liquid form compared with 20.4%
fructose in the feed pellets in this previous study. We did not see
an effect of added sugars during the juvenile and adolescent
period on body weight gain in our rodent model, nor did we
observe an association between fecal microbiota and bodyweight.
TABLE 2 Comparison of fecal bacteria at the family level between rats fed sugar solution and control rats (no sugar)
1
Bacteria family
Mean log-normalized sequence
count in sugar samples 6SE
Mean log-normalized sequence
count in control samples 6SE t-Statistic
2
P
Sugar vs. control BH-corrected
Enterobacteriaceae 1.524 60.017 0.477 60.035 7.082 ,0.001 ,0.001
Carnobacteriaceae 0.900 60.011 0.184 60.025 7.248 ,0.001 ,0.001
Corynebacteriaceae 0.265 60.010 0.000 60.000 4.790 ,0.001 ,0.001
Bifidobacteriaceae 1.749 60.030 0.609 60.059 4.519 ,0.001 0.002
Rikenellaceae 2.970 60.006 2.425 60.029 5.511 ,0.001 0.002
Clostridiaceae.1 2.611 60.019 1.580 60.057 4.918 ,0.001 0.002
Cryomorphaceae 0.318 60.009 0.057 60.012 4.151 ,0.001 0.002
Lactobacillaceae 2.684 60.009 2.266 60.025 4.467 ,0.001 0.003
Moraxellaceae 0.421 60.014 0.086 60.014 3.801 ,0.001 0.004
Micrococcaceae 0.499 60.013 0.099 60.023 3.866 0.001 0.004
Bacteroidaceae 3.371 60.005 3.151 60.014 4.102 0.001 0.005
Prevotellaceae 3.583 60.007 3.804 60.016 23.521 0.002 0.012
Coriobacteriaceae 1.842 60.008 1.636 60.016 3.075 0.005 0.029
Pseudomonadaceae 0.428 60.013 0.191 60.017 2.629 0.012 0.07
1
Values are presented from bacteria classified at the family level whose abundances are significantly different with a BH-corrected P,0.10. The sugar group refers to the 3
groups of rats fed sugar solutions combined (35F:65G, n= 11; 50F:50G, n= 10; 65F:35G, n= 11; n= 32 total) and the control group had no access to sugar but received a second
water bottle instead (n= 10). BH, Benjamini-Hochberg; 35F:65G, 35% fructose:65% glucose; 50F:50G, 50% fructose:50% glucose; 65F:35G, 65% fructose:35% glucose.
2
The t-statistic is positive when the mean abundance of the bacteria is higher in sugar samples and negative if higher in control samples.
FIGURE 4 Differences in bacterial abundance between rats fed 11% sugar solutions and control rats (no sugar). The Pdistribution was obtained from
ttests with the null hypothesis that there is no difference in OTU-level bacterial abundance in sugar (n= 32) and control (n= 10) samples (A) and a
regression model with the null hypothesis that there is no significant difference in bacterial abundance between rats fed 1 of 3 sugar solutions differing in
fructose-to-glucose ratio (B). Also presented is the Pdistribution obtained from dependent variables of a simple linear regression with OTU-level bacteria
abundance as an independent variable and either body weight (C) or energy intake (D) as the dependent variable. OTU, operational taxonomic unit.
6 of 9 Noble et al.
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Thus, we were able to investigate the impact of added sugars on
the gut microbiome during early life independently of obesity.
This is an important benefit to our design, because obesity is
associated with an altered composition of the gut microbiome
(10, 47–49). Added sugars have been shown to contribute to
obesity and metabolic disease (27–30); therefore, an understand-
ing of how sugars affect the gut microbiome during early-life
periods and independent of obesity may help identify putative
causal factors for the obesogenic effects of added sugar. Related to
this, we observed that early-life sugar consumption significantly
elevated Proteobacteria, and more specifically within this phylum,
microbes from Enterobacteriaceae were increased by added-sugar
consumption. Enterobacteriaceae is an abundant family of gram-
negative bacteria that are also elevated in type 2 diabetes (50) and
has recently been linked with artificial sweetener consumption (9).
SSB consumption is associated with type 2 diabetes (51), and thus
further investigations on the impact of Enterobacteriaceae on host
glucose metabolism may be a promising avenue for future research.
At the genus level, several species were elevated in all 3 sugar
groups, some of which were previously associated with various
health and disease processes. Parabacteroides, a genus in the
phylum Bacteroidetes, were significantly elevated by sugar.
Parabacteroides were previously shown to be elevated due to the
consumption of soluble corn fiber, and Parabacteroides counts
were negatively correlated with calcium absorption in adoles-
cents (52). Clostridium sensu stricto was also elevated by sugar
and has been correlated with the development of food allergy or
food sensitization during early life (53, 54) and atopic dermatitis
during childhood (55). Lactobacillus, a strain of bacteria
associated with promoting regulatory T helper cells (56) and
preserving tight junctions in the epithelial cells of the intestinal
tract (57), was increased by sugar consumption, whereas
previous studies showed that this strain is reduced in mice fed an
HFD (58–61). Alistipes (genus in the family Rikenellaceae)
and Bacteroides (genus in the family Bacteroidaceae) were
also significantly elevated in all sugar groups compared with
controls. A recent study identified both Alistipes and Bacteroides
as being rapidly elevated in people who consumed an animal-
based diet (consisting of meats, eggs, and cheese) compared with a
plant-based diet (containing grains, legumes, fruit, and vege-
tables) (17). Bacteroides are highly equipped to utilize poly-
saccharides and contain many enzymes for hydrolyzing glycans,
suggesting that they might thrive on a more polysaccharide-rich
diet (62, 63). However, in our current study, rats that were fed
sugar solutions consumed a greater proportion of calories from
monosaccharides and a reduced contribution of calories from
the polysaccharide-rich feed pellets (Lab Diet 5001). Thus, the
increased amount of Bacteroides observed in the sugar groups
is unexpected. Koropatkin et al. (63) postulated that elevated
proportions of bacteria with glycolytic activity (e.g., Bacteroides)
observed after the consumption of a high-fat and low-fiber
diet could be consequent to the capacity for Bacteroides to
metabolize host mucosal glycans. Thus, it is possible that the
elevated consumption of simple sugars, which can be readily
absorbed from the proximal intestine, promotes a competitive
advantage for microbes in the distal gastrointestinal tract that
are capable of finding an alternative food source (e.g., the host
mucosal glycans).
Sugar consumption reduced counts of Prevotellaceae,a
member of the Bacteroidetes class. This effect was primarily
driven by reductions in the genus Prevotella, a gram-negative
bacterium that aids in the breakdown of protein and carbohy-
drates (64). In humans, Prevotella amounts also correlate with
regular fiber intake and are reduced with a high-protein diet
FIGURE 5 Cladogram (A) and LDA scores (B) indicating statistical differences between microbial populations in rats fed 11% sugar solution
compared with control (no sugar) groups. Data are presented from the phylum to the genus level with the higher order at the outermost level (i.e.,
phylum, class, order, family, genus). Colors indicate the group with the highest mean of differential features in which significant differences were
found. Values are means 6SEMs; n= 10 or 11. LDA, linear discriminant analysis.
Dietary sugar and the gut microbiome 7 of 9
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(17). Thus, it is possible that the reduced Prevotella we observed
after SSB consumption was due to lower intakes of complex
carbohydrate and/or fiber relative to controls. Oscillibacter and
Lachnospiracea incertae sedis, which are both genera of the
Firmicutes phylum and the Clostridia class, were also reduced by
sugar. Oscillibacter is negatively correlated with Crohn disease
(65, 66) and is implicated as a potential treatment for ulcerative
colitis after fecal transfer (67). Lachnospiracea incertae sedis are
involved in the fermentation of starches to produce SCFAs (68).
Further research is needed to determine the implications of the
reduction in these intestinal health–promoting bacteria due to
added sugars on colonic health and disease.
In summary, early-life sugar consumption significantly alters
the gut microbiome independently of obesity and total caloric
intake in a rodent model. Sugar promoted multiple differences in
the microbiota at all taxonomic levels; however, there was no
apparent effect of glucose-to-fructose ratio between the groups.
These seemingly novel findings lay the groundwork for future
studies to focus on the functional implications of these sugar-
induced alternations in the gut microbiota on metabolic and
cognitive disorders associated with elevated sugar consumption
during the juvenile and adolescent period of development.
Acknowledgments
EEN wrote the manuscript and contributed to the research project
design; TMH conducted the experiments and analyzed the data;
RBJ analyzed the data and contributed to writing the manuscript;
AAF analyzed the data and contributed to the project design; and
MIG and SEK edited the manuscript, designed the research project,
and had primary responsibility for the research content. All authors
read and approved the final manuscript.
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