The effects of micronutrient deficiencies on bacterial species from the human gut microbiota

Abstract

Vitamin and mineral (micronutrient) deficiencies afflict 2 billion people. Although the impact of these imbalances on host biology has been studied extensively, much less is known about their effects on the gut microbiota of developing or adult humans. Therefore, we established a community of cultured, sequenced human gut–derived bacterial species in gnotobiotic mice and fed the animals a defined micronutrient-sufficient diet, followed by a derivative diet devoid of vitamin A, folate, iron, or zinc, followed by return to the sufficient diet. Acute vitamin A deficiency had the largest effect on bacterial community structure and metatranscriptome, with Bacteroides vulgatus, a prominent responder, increasing its abundance in the absence of vitamin A. Applying retinol selection to a library of 30,300 B. vulgatus transposon mutants revealed that disruption of acrR abrogated retinol sensitivity. Genetic complementation studies, microbial RNA sequencing, and transcription factor–binding assays disclosed that AcrR is a repressor of an adjacent AcrAB-TolC efflux system. Retinol efflux measurements in wild-type and acrR-mutant strains plus treatment with a pharmacologic inhibitor of the efflux system revealed that AcrAB-TolC is a determinant of retinol and bile acid sensitivity in B. vulgatus. Acute vitamin A deficiency was associated with altered bile acid metabolism in vivo, raising the possibility that retinol, bile acid metabolites, and AcrAB-TolC interact to influence the fitness of B. vulgatus and perhaps other microbiota members. This type of preclinical model can help to develop mechanistic insights about the effects of, and more effective treatment strategies for micronutrient deficiencies.

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GUT MICROBIOTA 2017 © The Authors,
some rights reserved;
exclusive licensee
American Association
for the Advancement
of Science.
The effects of micronutrient deficiencies on bacterial
species from the human gut microbiota
Matthew C. Hibberd,
1,2
Meng Wu,
1
Dmitry A. Rodionov,
3,4
Xiaoqing Li,
4
Jiye Cheng,
1,2
Nicholas W. Griffin,
1,2
Michael J. Barratt,
1,2
Richard J. Giannone,
5
Robert L. Hettich,
5
Andrei L. Osterman,
4
Jeffrey I. Gordon
1,2
*
Vitamin and mineral (micronutrient) deficiencies afflict 2 billion people. Although the impact of these imbalances
on host biology has been studied extensively, much less is known about their effects on the gut microbiota of
developing or adult humans. Therefore, we established a community of cultured, sequenced human gut–derived
bacterial species in gnotobiotic mice and fed the animals a defined micronutrient-sufficient diet, followed by a
derivative diet devoid of vitamin A, folate, iron, or zinc, followed by return to the sufficient diet. Acute vitamin A
deficiency had the largest effect on bacterial community structure and metatranscriptome, with Bacteroides vulgatus, a
prominent responder, increasing its abundance in the absence of vitamin A. Applying retinol selection to a library of
30,300 B. vulgatus transposon mutants revealed that disruption of acrR abrogated retinol sensitivity. Genetic
complementation studies, microbial RNA sequencing, and transcription factor–binding assays disclosed that AcrR is
a repressor of an adjacent AcrAB-TolC efflux system. Retinol efflux measurements in wild-type and acrR-mutant strains
plus treatment with a pharmacologic inhibitor of the efflux system revealed that AcrAB-TolC is a determinant of retinol
and bile acid sensitivity in B. vulgatus. Acute vitamin A deficiency was associated with altered bile acid metabolism
in vivo, raising the possibility that retinol, bile acid metabolites, and AcrAB-TolC interact to influence the fitness of
B. vulgatus and perhaps other microbiota members. This type of preclinical model can help to develop mechanistic
insights about the effects of, and more effective treatment strategies for micronutrient deficiencies.
INTRODUCTION
Dietary micronutrients (vitamins and minerals) are cofactors for myriad
enzymes whose functions are essential for health. The “hidden hunger”
of micronutrient deficiencies represents a global health challenge,
affecting 2 billion individuals, with deficiencies in iron, zinc, folate,
and vitamin A representing major contributors to this problem (1,2).
Risk is compounded in low- and middle-income countries, where dietary
insufficiency and lack of dietary diversity are common (3).
Vitamin A plays important roles in vision, growth, and immune
function (4). In settings where its deficiency is a public health problem,
the World Health Organization recommends high-dose vitamin A
supplementation for infants and children 6 to 59 months of age (5).
Vitamin A is typically given in the form of retinyl esters, which are hy-
drolyzed in the gut before uptake of retinol by enterocytes (6). The
effectiveness of vitamin A supplementation has been confirmed in a
meta-analysis of 43 studies showing a reduction in mortality in children
under 5 (7). However, knowledge of the short- and long-term effects of
high-dose supplementation of infants and children is incomplete. Given
the large body of knowledge that has accumulated regarding the effects
of retinoids on eukaryotic cellular biology, vitamin A imbalances are
typically viewed from the perspective of their effects on the host, rather
than on the microbiota.
Deficiencies of other micronutrients are also associated with mor-
bidity in children under 5 (1). Given the prevalence of combined defi-
ciencies in individuals living in low- and middle-income countries,
many studies have been conducted to examine the benefits of multiple
micronutrient powders. A meta-analysis of 16 controlled studies of
supplementation with these powders in 6-month-old to 11-year-old
children revealed a reduction in anemia and improved serum hemoglobin
levels but no impact on growth (8). There was also evidence of increased
diarrhea (9). Studies of weaning Kenyan infants revealed evidence of
increased intestinal inflammation, increased enteropathogen burden,
and decreases in the representation of bifidobacteria associated with
administration of micronutrient powders containing iron (10). More-
over, iron supplementation may potentiate the risk for certain systemic
infections (for example, malaria) (11–14). Together, these findings raise
questions about whether current protocols for dosing and duration of
treatment of micronutrient deficiencies are optimal, and to what extent
unintended deleterious effects accompany such interventions.
Recent studies have revealed a program of gut microbial community
development, defined by changing patterns of abundances of a group of
age-discriminatory bacterial strains, that is executed during the first 2 to
3 years of postnatal life (15–17). This developmental program is shared
among healthy, biologically unrelated infants and children living in
culturally and geographically distinct low-income countries, and it is
disrupted in infants and children with undernutrition, resulting in
bacterial community configurations that appear younger (more immature)
compared to those encountered in chronologically age-matched in-
dividuals with healthy growth phenotypes (14,16,18). Preclinical
evidence indicates that this immaturity is not simply an effect of under-
nutrition but rather is a contributing cause. Recently weaned gnoto-
biotic mice colonized with immature gut microbiota samples from
undernourished Malawian children exhibited impaired growth com-
pared to recipients of microbiota from chronologically age-matched
healthy donors, although animals in all treatment groups consumed
the same amounts of a macronutrient- and micronutrient-deficient
diet designed to resemble the diets of the microbiota donor population.
Analysis of the gut microbial communities of recipient mice identified
1
Center for Genome Sciences and Systems Biology, Washington University School
of Medicine, St. Louis, MO 63110, USA.
2
Center for Gut Microbiome and Nutrition
Research, Washington University School of Medicine, St. Louis, MO 63110, USA.
3
A. A. Kharkevich Institute for Information Transmission Problems, Russian Acad-
emy of Sciences, Moscow 127994, Russia.
4
Sanford Burnham Prebys Medical Dis-
covery Institute, La Jolla, CA 92037, USA.
5
Chemical Sciences Division, Oak Ridge
National Laboratory, Oak Ridge, TN 37830, USA.
*Corresponding author. Email: jgordon@wustl.edu
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bacterial strains that are growth-discriminatory: They include a subset
of the age-discriminatory strains (16). These observations suggest a
testable hypothesis, namely, that various types of micronutrient im-
balances may disrupt various features of a developing microbiota,
including the representation and expressed functions of age- and
growth-discriminatory taxa as well as pathobionts and enteropathogens.
Moreover, such disruptions, occurring during a critical period of com-
munity assembly, may persist, resulting in deleterious effects on host
biology. A corollary is that the microbiota may be a useful marker of
micronutrient intake (19) and a means to assess the efficacy and safety
of current dosing regimens for the treatment of deficient states.
Earlier studies comparing germ-free animals and their convention-
ally raised counterparts provided evidence that the gut microbiota can
have beneficial effects in the face of micronutrient deficiencies [for
example, enhanced iron uptake and storage in rats and rabbit models
(20,21)] or detrimental effects [increased mortality on vitamin A–
deficient diets (22,23) and increased dietary zinc requirements (21,24)
inrats].Thespecificmicrobesandunderlying mechanisms responsible
for these observed effects were not defined in these reports.
There is little information about the effects of specific micronutrient
imbalances on the human gut microbiota (10,19,25–28). A limited
number of published reports have been descriptive, focused on com-
munity structure (generally at low levels of taxonomic resolution),
and are confounded by (i) the challenges of conducting randomized
controlled trials in this area, (ii) the fact that various combinations of
micronutrient deficiencies can occur within at-risk populations, and
(iii) the challenge of distinguishing between primary effects of dietary
micronutrient content, versus host effects, on the microbiota. Here,
we examine the effects of acute dietary micronutrient deficiencies on
members of the microbiota using gnotobiotic mice colonized with a
large and phylogenetically diverse consortium of cultured and se-
quenced human gut bacterial strains, including strains representing
species that are age- and/or growth-discriminatory in models of micro-
biota development (14,16,17). Mice were subjected to a diet oscillation
that began with a defined micronutrient-sufficient diet followed by a
derived diet with one of four types of single micronutrient deficiency,
or a diet representing combined deficiencies, followed by return to
the original micronutrient-sufficient diet. This model of acute defi-
ciency allowed us to focus on effects on the gut microbiota, rather
than having to disentangle potentially cofounding effects of combined
community and host deficiency states. Bacterial community DNA
and mRNA analyses, combined with in vitro genetic, biochemical,
and pharmacologic studies, allowed us to characterize the mechanisms
that underlie the pronounced effects of vitamin A, specifically retinol,
deficiency on the fitness of Bacteroides vulgatus, a growth-discriminatory
species identified in the gnotobiotic mouse model of human gut micro-
biota development described above. Our findings provide a rationale
and a preclinical method for examining the impact of current vitamin
A dosing regimens, and by extension other critical micronutrients, on
members of our microbial communities, including the developing gut
microbiota of children with undernutrition.
RESULTS
A compositionally well-defined, micronutrient-sufficient diet and a set
of highly similar derivative diets devoid of one or more micronutrients
were designed (table S1). Protein was represented in all diets only by
amino acids; this feature allowed us to avoid the potentially confounding
problem of having to vary protein type due to differences in their con-
tent of bound minerals. Adult (8- to 9-week-old) germ-free C57BL/6J
mice that had been weaned onto a standard mouse chow, rich in plant
polysaccharides and low in fat, were placed on a micronutrient-sufficient
diet for 4 days before colonization. Animals then received a single oral
gavage of a consortium of 92 sequenced human gut–derived bacterial
type strains, containing 348,834 known or predicted protein-coding
genes and encompassing the major phyla present in the human micro-
biota. Sixteen of these strains represented species corresponding to
strains that had been identified as age- and/or growth-discriminatory
in random forests–derived models of normal gut microbiota develop-
ment (table S2). Mice in each experimental group were maintained
on the micronutrient-sufficient diet for 14 days, followed by a 21-day
period of acute micronutrient deficiency (one of five diets: vitamin A–,
folate-, iron-, zinc-, and multiple micronutrient–deficient), followed by
a 14-day period of reexposure to the micronutrient-sufficient diet. A
control group was maintained on the micronutrient-sufficient diet
throughout the course of the experiment (Fig. 1A). All diets were pro-
vided ad libitum.
Short-read shotgun sequencing [community profiling by sequenc-
ing (COPRO-Seq)] of DNA prepared from fecal samples collected
over time was used to define the efficiency and reproducibility of col-
onization within and across treatment groups and the relative abun-
dance of each community member as a function of diet. A group of
44 strains comprised a core group of organisms that was represented in
the fecal microbiota of all mice after the initial micronutrient-sufficient
diet phase; the number of additional strains found in each treatment
group was small (range, 0 to 3) (table S3, A and B). This similarity in
community membership across treatment groups at the end of this
stage of the experiment was reflected in principal coordinates analysis
of pairwise comparisons using the Bray-Curtis dissimilarity metric
(Fig. 1B and fig. S1). We cannot rule out the possibility that other mem-
bers of the consortium of 92 organisms established themselves in dif-
ferent regions of the gut, although they were not detectable in feces.
Investigating this possibility would require sacrifice of multiple animals
at multiple time points in each of the multiple treatment groups to
perform a detailed analysis of the biogeographical features of their mi-
crobial communities.
Prominent effects of dietary vitamin A deficiency on
community structure and metatranscriptome
We applied mixed-effects linear models to log-transformed, rarefied
COPRO-Seq data to identify organisms with significant interactions
between treatment group and diet stage. We also performed least-
squares means comparisons between mice in the control group monot-
onously fed the micronutrient-sufficient diet and those assigned to the
experimental treatments (table S4, A and B). Communities sampled at
the end of the initial micronutrient-sufficient diet phase (experimental
day 14), 14 days after switch to the deficient diet (day 28), and 14 days
after return to the sufficient diet (day 49) were included in the analyses.
The results revealed that among the five acute dietary deficiency states
tested, vitamin A had the greatest effect, significantly affecting the abun-
dances of the largest number of organisms (tables S3 and S5). Compar-
isons of Bray-Curtis dissimilarities showed that the community
structure of mice in the vitamin A treatment group differed significantly
from that of the control group during the deficient diet stage [experi-
mental days 28 and 35; P< 0.01 for each experimental day based on
randomization tests and false discovery rate (FDR) correction, but
was no longer significantly different after returning to the sufficient diet
(experimental day 49)] (fig. S1). The number of species representing
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age- and growth-discriminatory strains that were affected by acute mi-
cronutrient deficiency was small (range, 0 to 3 per deficiency type; see
table S4A for details and table S4B for a summary).
We subsequently used microbial RNA sequencing (RNA-seq) to
characterize transcriptional responses to the various diets. Data gen-
erated from fecal samples collected from each mouse in each treatment
group at the end of the micronutrient sufficiency phase (day 14), at
the end of the micronutrient deficiency phase (day 35), and 14 days
after return to the sufficient diet (day 49) were compared. Statistically
significant differences in gene expression as a function of time/diet
were identified (see Materials and Methods). Responsive genes were
binned into Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthol-
ogy (KO) groups. Analogous to the COPRO-Seq analysis, animals con-
suming the micronutrient-sufficient diet monotonously served as a
reference control for temporal effects independent of diet transitions.
Table S6 describes the results of this community-wide (top-down)
RNA-seq analysis for all treatment groups. Vitamin A deficiency elicited
alargernumberofsignificantalterationsinthemetatranscriptomethan
any of the other acute deficiency states (summarized in table S6A, with
KEGG summaries of differentially expressed genes and associated
Pvalues provided in table S6B). The two top-ranked KO groups that
incorporated transcripts whose expression changed significantly as
a function of the presence and absence of vitamin A were K02014
(TonB-dependent receptors) and K00936 (phosphotransferases with
a nitrogenous group as acceptor) (table S6B).
B. vulgatus, a species positively correlatedwithhostgrowthinpre-
clinical models of gut microbiota development (16), was one of the few
age- and/or growth-discriminatory taxa that exhibited significant
changes in abundance as a function ofanyoftheacutedietarymicro-
nutrient deficiencies applied; its proportional representation in the
community increased significantly during the vitamin A deficiency
phase and decreased significantly when mice were transitioned back
to the sufficient diet (P< 0.001, one-way ANOVA, Tukey’s HSD test,
FDR correction; Fig. 1C and fig. S2A). Mixed-effects linear modeling
revealed that no other single micronutrient deficiency produced a
statistically significant increase in the representation of B. vulgatus
(table S4). The only other single micronutrient deficiency state that
affected its representation was iron deficiency, but the change occurred
in a direction opposite to that observed with vitamin A deficiency
(Fig. 1C, fig. S2A, and table S4). The direction and specificity of the re-
sponse to vitamin A deficiency were notable, because none of the other
Bacteroides in the community exhibited this pattern (table S5).
The microbial RNA-seq data set revealed the specificity and dis-
tinctive breadth of responses of B. vulgatus to vitamin A availability
(see table S7 for a KO group-level summary of significant changes in
its in vivo gene expression profile in response to diet oscillations in-
volving vitamin A, iron, zinc, or folate, and combined deficiency, as well
as in the control group monotonously fed the micronutrient-sufficient
diet; see table S8 for a KO group-level summary of transcriptional
responses for each member of the gut microbial community defined
as responsive to dietary vitamin A manipulation based on changes in
their relative abundance). None of the micronutrient-deficient diets
× 5
C57BL/6J
C57BL/6J
× 5
Gavage: 92 strains
Micronutrient
–
sufficient diet
Micronutrient
–
sufficient diet (monotonous)
Fecal sample (COPRO-Seq)
Fecal sample (microbial RNA-seq)
Micronutrient
–
deficient diet
Micronutrient
–
sufficient diet
Experimental
day
–
40 7 14 21 28 35 42 49
A
BMicronutrient
–
sufficient diet
(experimental day 14)
Micronutrient
–
deficient diet
(experimental day 28)
Micronutrient
–
deficient diet
(experimental day 35)
Micronutrient
–
sufficient diet
(experimental day 49)
Vitamin A–
sufficient
Day 14
Control
Day 14
Control
Day 35
Control
Day 49
Control
Day 28
Vitamin A–
deficient
Day 28 Vitamin A–
deficient
Day 35
Vitamin A–
sufficient
Day 49
–
0.2
–
0.1 0 0.1
–
0.15
–
0.10
–
0.05
0
0.05
0.10
0.15
PCo 1 (16.7%)
PCo 2 (5.5%)
Micronutrient sufficient Multiple
–
deficient
Iron
–
deficient
Zinc
–
deficient
Vitamin A–deficient Folate
–
deficient
Sufficient (day 14) versus
Deficient (day 35)
Deficient (day 35) versus
Sufficient (day 49)
C
–
4
–
2
2
0
0
4
6
8
10
12
14
–
7
–
5
–
3
–
1
1
3
5
7
Change in % relative
abundance of B. vulgatus
Change in % relative
abundance of B. dorei
***
** ***
**
***
**
***
Folate
–
deficient
Iron
–
deficient
Micronutrient
–
sufficient (monotonous diet control)
Multiple micronutrient
–
deficient
Vitamin A–deficient
Zinc
–
deficient
Fig. 1. The effect of dietary micronutrient deficiency on the configuration of a
defined human gut microbiota established in gnotobiotic mice. (A) Experimental
design. (B) Principal coordinates analysis of pairwise comparisons of fecal microbiota
using Bray-Curtis dissimilarities of Wisconsin square root–transformed abundance data
obtained from COPRO-Seq analysis. Fecal samples were obtained from mice in the
indicated treatment groups at the indicated time points. Gray shaded ellipses and
spokes indicate the SEM of sample group centroids from the vitamin A–deficient and
the micronutrient-sufficient (monotonous diet control) groups in each experimen-
tal phase. PCo 1, principal coordinate 1. (C) COPRO-Seq analysis of the effects of the
micronutrient-deficient diets versus micronutrient-sufficient diets on the abun-
dance of B. vulgatus and Bacteroides dorei in the fecal microbiota of gnotobiotic
mice. Means ± SEM. **P< 0.01, ***P< 0.001 [one-way analysis of variance (ANOVA),
Tukey’s honest significant difference (HSD), FDR correction; n= 5 mice per treat-
ment group].
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resulted in a significant change in body
weight in these adult mice (P>0.05,two-
way and repeated-measures ANOVA,
Dunnett’s test; table S9).
Identification of a member of the
TetR family of transcriptional
repressors that mediates the
sensitivity of B. vulgatus to retinol
To examine the mechanisms underlying the
in vivo response of B. vulgatus to dietary vi-
tamin A availability, we cultured the strain
in vitro, under anaerobic conditions, in a
defined medium, with or without addition
of a range of concentrations of retinol, ret-
inal, retinyl palmitate, all-trans retinoic
acid, or b-carotene; the diterpene alcohol
geranylgeraniol was used as a control. Ret-
inoid sensitivity was defined as the ratio
between the time required to reach an
OD
600
(optical density at 600 nm) thresh-
old of 0.3 for treated cultures versus control
cultures containing vehicle alone [0.02%
dimethyl sulfoxide (DMSO)]. The con-
centration range (0.1 to 10 mM) of retinol
and other retinoids used was based on pre-
vious reports of their concentrations in
mouse small intestinal contents and feces,
and human feces (29,30). Treatment with
10 mM retinol completely inhibited the
growth of B. vulgatus [P< 0.0001, com-
pared to control incubations containing
vehicle (0.02% DMSO) alone]; retinal (P<
0.0001) and retinyl palmitate (P= 0.0002)
also produced significant, though much
weaker, growth inhibition compared to
retinol at the same concentration. In con-
trast, geranylgeraniol had no signifi-
cant effect (P> 0.99, one-way ANOVA,
Bonferroni multiple comparisons test;
Fig. 2, A and B, andtable S10A). A primary
isolate of B. vulgatus recovered from a
Malawian child, characterized as a growth-
discriminatory strain in previous gnotobi-
otic mouse experiments (16), also exhibited
marked, dose-dependent growth suppres-
sion in the presence of retinol (Fig. 2C and
table S10B).
The COPRO-Seq data set revealed that
B. dorei strain DSM 17855 exhibited a response to vitamin A deficiency
that was the opposite of that manifested by B. vulgatus,namely,it
increased rather than decreased its relative abundance in these presence
of vitamin A (Fig. 1C, fig. S2B, and table S5). Consistent with these re-
sponses documented in vivo, retinol produced significantly less growth
inhibition of B. dorei in vitro compared to B. vulgatus (P< 0.0001 for
both 5 and 10 mM retinol compared to vehicle alone control incuba-
tions, ttest, Bonferroni correction; Fig. 2B and table S10A).
Whole-genome transposon (Tn) mutagenesis [insertion sequencing
(INSeq) (31,32)] was subsequently used to identify the genetic deter-
minants of the response ofB. vulgatus ATCC 8482 to retinol. A library
of 30,300 isogenic Tn mutants was generated (see Materials and
Methods). Seventy-one percent (2894) of the strain’spredictedgenes
contained Tn mutants positioned within the first 80% of each open
reading frame (ORF) (average of 10.5 Tn mutants per ORF repre-
sented in the library; 1 Tn insertion per mutant bacterial strain) (fig.
S3A). Simulating the number of unique mutants required to cover all
nonessential ORFs (defined as genes that are not required for survival in
the medium used to generate the library) (32) revealed that the INSeq
library approached saturation (fig. S3B).
A
0.25
0.50
0.75
1.00
1.25
061218243036
0
Growth (OD600)
Time (hour)
Retinol
Retinyl palmitate Retinal
B
β-Carotene
DMSO (0.02%)
Geranylgeraniol
Retinoic acid
Retinol
Retinal
Retinyl palmitate
0
2
4
6
8
10
12
14
16
18
20
22
24
Retinoid sensitivity (treated/control)
510µM 1 510 1 510 1 510 1 510 1 510 1
B. vulgatus ATCC 8482
B. vulgatus ATCC 8482
B. dorei DSM 17855
**
***
***
*** C
510μM
B. vulgatus
1510 1
0
2
4
6
8
10
12
14
16
18
20
22
24
Retinol sensitivity (treated/control)
ATCC 8482 257B_H4
Fig. 2. The distinct retinol sensitivity phenotypes of B. vulgatus strains and B. dorei in vitro. (A)Growthcurvesof
B. vulgatus in defined medium with and without various retinoids. Thehorizontaldashedlineindicatesthethreshold
used for calculating time–to–log phase measurements. (Band C) Bar plots indicating mean (±SEM) retinoid sensitivity,
calculated as time–to–log phase for treated cultures versus time–to–log phase for vehicle alone (DMSO) control cultures
for B. vulgatus ATCC 8482 versus B. dorei DSM 17855 (B) or B. vulgatus strain ATCC 8482 versus B. vulgatus strain 257_H4
(C) isolated from a healthy Malawian infant (16). For example, the sensitivity value of 20.8 ± 2.1 for wild-type B. vulgatus
incubated in medium containing 10 mM retinol in (B) was calculated by dividing the total incubation period (in this case,
95 hours) by the time required for vehicle alone–treated control cultures of the same strain to cross the OD
600
threshold
of 0.3. Means ± SEM are shown except under those conditions in (C), where the concentration of retinol tested com-
pletely inhibited growth. n= 2 independent experiments, each in triplicate for (B), and 1 experiment performed in
triplicate for (C). **P< 0.01, ***P< 0.001 (one-way ANOVA, Bonferroni multiple comparisons test).
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The library of Tn mutants was subjected to in vitro selection in the
presence of 10 mM retinol. Aliquots were withdrawn from primary
culturesduringthelonglagphase(indicatingthatgrowthofmostmem-
bers of the library was severely inhibited by retinol) and during sta-
tionary phase. An aliquot from the stationary phase culture was then
reinoculated into fresh medium containing 10 mMretinolforasecond
round of selection; log and stationary phase samples were withdrawn
from these secondary cultures (n= 3 replicate primary and secondary
cultures;Fig.3A).TheselectedB. vulgatus libraries contained only four
mutants (Fig. 3B). These mutants map to two adjacent genes in the
B. vulgatus ATCC 8482 genome: BVU0240, which encodes a homolog
of Escherichia coli AcrR (17% identity and 22% similarity) (a member of
the TetR family of transcription factors), and BVU0241, which encodes
a homolog of E. coli LpxA (33% identity and 35% similarity). LpxA is
a UDP-N-acetylglucosamine O-acyltransferase that functions as the
first enzyme in the biosynthetic pathway for the lipid A moiety of
TolC
B
Percent abundance of mutant in input library
0
20
40
60
80
100
Percent abundance in selected library
0
20
40
60
80
100
0
20
40
60
80
100
AD
OD600
Subculture
Lag Stationary
Log
phase Stationary
Input
INSeq library
Output
selected
library
Replicate
1
Replicate
2
Replicate
3
C
BVU0245
BVU0244 BVU0241
BVU0240
BVU0239 BVU0238
BVU0236 BVU0234 BVU0232
BVU0243
AcrAB-TolC 1
BVU0242 BVU0237
BVU0235 BVU0233
lpxA_362422::IN
lpxA_
362422::IN
0
0.01
0.02
0.03
0.04
0.05
Input
acrR_361472::IN
acrR_
361472::IN
acrR_361646::IN
acrR_
361646::IN
lpxA_361958::IN
lpxA_
361958::IN
LpxA AcrR TolC2 AcrA2 AcrB2
Hypothetical
protein
Hypothetical
protein
Hypothetical
protein
Alginate O-
acetylation protein
AcrA AcrB
Retinol (10 uM)
AcrAB-TolC 2
E
0
2
4
6
8
10
12
14
16
18
Retinol sensitivity
(treated/control)
WT B. dorei
WT B. vulgatus
B. vulgatus acrR_361646::IN
B. vulgatus lpxA_361958::IN
B. vulgatus acrR_361472::IN
B. vulgatus lpxA_362422::IN
Retinol (µM) 10 1510151015101510151015
*** *** *** **
*** *** *** ***
***
***
COMPOUND
F
0
2
4
6
8
10
12
14
Strain WT acrR_
361472::IN
acrR_
361472::IN
+ pNBU2
alone
acrR_
361472::IN
+ pNBU2_
acrR
Retinol sensitivity (treated/control)
**
***
**
Comp. ––
Outer
membrane
Inner
membrane
TolC
AcrA AcrA
Periplasm
Cytoplasm
Compound
H+
AcrB
Fig. 3. Selection of retinol-resistant B. vulgatus Tn
mutants. (A) Experimental design. The mutant library
was inoculated into defined medium containing 10 mM
retinol or 0.02% (v/v) DMSO (three cultures per treat-
ment). In the first round of selection, mutant libraries
were allowed to grow to stationary phase and were
then passaged to fresh medium and subjected to a
second round of selection. Aliquots were withdrawn
in lag and stationary phases from the primary cultures
and in log and stationary phases of the secondary
cultures. The site of insertion of the Tn was defined
in the retinol-resistant mutants using INSeq. (B)Per-
cent abundance of Tn mutants in retinol-selected B. vulgatus
libraries. The left portion of the panel indicates the abun-
dance of each selected mutant in the input library. Each
set of four bars shown in the right portion of the panel
indicates the abundance of the Tn mutants at the indi-
cated growth phases from both primary and passaged
cultures. (C)SchematicoftheB. vulgatus locus containing
the retinol-resistant mutants identified from screening
the Tn library. Annotation is based on the National Center
for Biotechnology Information reference assembly
NC_009614.1. The genomic location of each selected Tn
mutant is indicated by a downward pointing arrow anno-
tated with the corresponding color from (B) and the
corresponding genome coordinate for the site of Tn
insertion. (D) Schematic of components comprising the
E. coli AcrAB-TolC efflux system (adapted from http://
2013.igem.org/Team:Ciencias-UNAM/Project). (E)Retinol
sensitivity of B. vulgatus wild-type (WT) and Tn mutants
grown in monoculture in defined medium treated with
1, 5, and 10 mM retinol versus 0.02% (v/v) DMSO as a
reference vehicle control. Means ± SEM of the ratio be-
tween treated and control cultures for each strain are
shown. The sensitive B. vulgatus WT strain and resistant
B. dorei WT strain are shown as positive and negative
controls, respectively. (F) Retinol (10 mM) sensitivity of
the WT, acrR::IN (genome location 361472) mutant, and
complemented B. vulgatus acrR::IN +pNBU2_acrR mutant
strains (abbreviated Comp), plus a control B. vulgatus acrR::IN
strain containing the empty vector. The results shown in
(E) are from two independent experiments, each per-
formed in triplicate, whereas those in (F) are from three
independent experiments, each performed in triplicate
or quadruplicate. *P< 0.05, **P< 0.01, ***P< 0.001
(one-way ANOVA, Bonferroni multiple comparisons test).
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lipopolysaccharide [transfers (R)-2-hydroxymyristate from its acyl
carrier protein thioester to the 3′-OH of UDP-N-acetylglucosamine
(33)]. Control DMSO-treated cultures displayed no selection for these
mutantsineithertheprimaryorsecondarycultures.
AsshowninFig.3C,theB. vulgatus acrR ortholog is positioned in
the middle of a locus containing 12 similarly oriented ORFs. Three
ORFs located upstream of acrR and three ORFs located immediately
downstream each encode orthologs of components of the E. coli AcrAB-
TolC complex. AcrAB-TolC is the prototypic example of broad-specificity
multidrug efflux systems belonging to the resistance–nodulation–
cell division superfamily (34,35). The AcrB component of this complex
is a homotrimeric integral inner membrane transporter powered by
a proton gradient. TolC is also a homotrimer and resides in the outer
bacterial membrane. AcrA, a homohexamer, is a periplasmic adapter
protein that bridges AcrB and TolC. Substrates are captured in the lower
cleft region of AcrB (through a process determined in part by an asso-
ciated small accessory protein, AcrZ), transported through the binding
pocket, through the gate, and finally to the AcrA funnel that connects
AcrB to TolC (Fig. 3D) (36,37). Although the transcriptional repressor
AcrR is an important modulator of expression of the AcrAB-TolC
system, this efflux pump is also subject to additional regulation by other
transcription factors, including those involved in mediating responses
to cellular stress signals (38,39).
Monocultures of each mutant strain exhibited a marked decrease
in retinol sensitivity compared to the wild-type strain (P< 0.0001 for
10 mM retinol, one-way ANOVA, Bonferroni multiple comparisons
test; Fig. 3E). To confirm that the B. vulgatus AcrR ortholog was a key
regulator of the retinol sensitivity phenotype, we used the integrating
expression vector pNBU2_tetQ (40) to complement the mutant contain-
ing the Tn insertion at genomic location 361472 [138 nucleotides (nt)
downstream of the start of the AcrR ORF]. To do so, we linked the
BVU0240 (AcrR) ORF and the 21-nt intergenic region between BVU0240
and BVU0241 to the promoter region of rpoD (BVU2738), assembled them
into pNBU2_acrR, and conjugated them into the B. vulgatus acrR_361472::
IN strain (abbreviated acrR::IN) (see Materials and Methods and table
S11). Insertion occurred at the attBV site positioned at the 3′end of
BVU2094 [one of two serine transfer RNAs (tRNAs) in the B. vulgatus
genome]. Complementation of acrR::IN with the vector carrying
PrpoD_acrR (pNBU2_acrR) restored retinol sensitivity to that of the
wild-type strain, whereas complementation of acrR::IN with the empty
pNBU2_tetQ vector had no effect on retinol sensitivity (P> 0.05 and
P= 0.01, respectively, one-way ANOVA, Bonferroni multiple compar-
isons test; Fig. 3F and table S10, C and D).
Characterizing the regulon controlled by AcrR
Having established that acrR (BVU0240) is a key genetic determinant
of retinol sensitivity in vitro, we characterized its regulon. Triplicate
cultures of the wild-type, acrR::IN,andlpxA_362422::IN (abbreviated
lpxA::IN)strainsweregrowntomid-logphase in the absence of retinol.
Microbial RNA-seq revealed that expression of the lpxA ortholog
(BVU0241) was completely ablated by the Tn insertion in its ORF
(lpxA::IN mutant). Low expression of acrR was detectable in the
acrR::IN mutant, but all reads mapped to the area encompassed by
the 5′138 nt of the gene, indicating that only truncated transcripts
derivedfromtheregionofBVU0240 upstream of the Tn insertion site
at genome coordinate 361472 were produced. Transcription of the
12-gene locus containing acrR and lpxA was affected in similar ways
by Tn mutagenesis of either acrR (BVU0240)orlpxA (BVU0241), that
is, expression of upstream genes was significantly increased (and expres-
sion of downstream genes significantly decreased, as would be antici-
pated by polar effects of the upstream Tn insertion) (Fig. 4 and table
S12B). Together, these results support a conclusion that the AcrR
homolog encoded by BVU0240 acts as a transcriptional repressor.
A total of 220 genes with statistically significant differences in their
expression were identified when comparing the isogenic wild-type and
acrR::IN strains, and 165 genes when comparing wild-type and lpxA::IN
strains, with 92 genes common to both data sets. The fold differences in
expression of these 92 genes between the wild-type versus acrR::IN
mutants and the wild-type versus lpxA::IN mutants were highly cor-
related (Pearson’sr=0.92,P< 0.0001); these differentially expressed
genes are functionally annotated in table S12 (A and B), which also
describes the fold differences in their expression in the comparisons
of wild-type and acrR::IN strains and wild-type and lpxA::IN strains.
Table S12A highlights genes with shared transcriptional responses to
insertional mutagenesis of acrR or lpxA in vitro: A number of these genes
are present in clusters (putative operons) distributed throughout the
B. vulgatus genome, including polysaccharide utilization loci, capsular
polysaccharide biosynthesis loci, and loci involved in carbohydrate,
amino acid, and DNA metabolism, as well as drug resistance.
Inspection of the RNA-seq data sets generated from the fecal micro-
biota of mice subjected to dietary vitamin A deficiency disclosed that
only 18 genes in the putative regulon identified from the in vitro analysis
satisfied our statistical threshold for significant differential regulation in
vivo. The log-normalized fold changes for these genes ranged from −4.9
to 4.6; however, only nine of these genes displayed a transcriptional
response in the same direction as that expected based on the in vitro
data (table S12B). Notably, genes in the AcrAB-TolC locus did not reach
the level of statistical significance we required for designation as dif-
ferentially regulated in vivo.
Identification of a candidate AcrR-binding motif
Comparison of the genomes of other Bacteroides strains represented
in the defined community, other human gut Bacteroides, and other
members of the family Bacteroidaceae demonstrated that acrR ortho-
logs are positioned in loci containing genes encoding components of
multidrug efflux systems belonging to the resistance–nodulation–
cell division superfamily (Fig. 5A and table S13). Figure S4 presents
a phylogenetic tree of AcrR orthologs identified in these organisms
(note that B. dorei has the highest degree of similarity to B. vulgatus
AcrR encoded by BVU0240; table S13B).
We used comparative genomics of B. vulgatus ATCC 8482 and
related organisms to identify a conserved 30–base pair (bp) palindrome
as a candidate AcrR-binding motif (Fig. 5, B and C) (this motif is located
just upstream of BVU0244, the first gene in the locus encoding AcrAB-
TolC1 and AcrAB-TolC2 shown in Fig. 3C). Genome scans with this
motif allowed reconstruction of predicted AcrR regulons, including
orthologs of BVU0244-BVU0233 (table S13A). As noted above, in
B. vulgatus ATCC 8482, this gene cluster encodes two homologous
AcrAB-TolC efflux pumps; however, their respective components only
share20to27%aminoacididentity.InBacteroides thetaiotaomicron
and several other Bacteroides genomes, the orthologous AcrR-associated
gene cluster is broken into two separate loci, each encoding one paralog
of the AcrAB-TolC efflux system and preceded by a candidate AcrR-
binding site (Fig. 5A and table S13A), suggesting coregulation by AcrR
orthologs in these genomes.
Genomic searches yielded one additional locus in B. vulgatus ATCC
8482 (BVU0421-BVU0415) that is preceded by a high-scoring candidate
AcrR-binding site (Fig. 5B). This additional candidate AcrR target
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operon contains genes encoding an uncharacterized outer membrane
protein (oma87), sodium/sulfate symporter (slt), 3′-phosphoadenosine
5′-phosphosulfate (cysQ), adenylylsulfate kinase (cysC), and sulfate
adenylyltransferase (cysDN). The latter enzymes are involved in the
sulfate assimilation pathway. These B. vulgatus genes exhibited increases
in their in vitro expression in acrR::IN and lpxA::IN strains compared
to wild-type strains (that is, they are part of the regulon controlled by
AcrR) but did not manifest differences in vivo as a function of dietary
vitamin A content (see table S12B, which includes Pvalues).
We subsequently used in vitro DNA binding assays to test the pre-
dicted AcrR-binding site upstream of BVU0244 and determined whether
retinol affects binding of AcrR from B. vulgatus (AcrR
BV
)andB. dorei
(AcrR
BD
,encodedbyBACDOR00223). The predicted 30-nt binding site
upstream of the orthologous AcrAB-TolC operon in B. dorei has a single-
nucleotide variation located inside the nonconserved center of the AcrR-
binding motif (see underlined nucleotide in Fig. 5B). AcrR
BV
and AcrR
BD
differ by three amino acid substitutions. Two substitutions are located in
the N-terminal DNA binding domain, whereas a single substitution is
positioned inside the effector-binding domain. We also noticed that the
annotated AcrR
BV
ORF in B. vulgatus is 24 nt shorter than its ortholog
in B. dorei, which encodes an additional eight–amino acid segment at its
N terminus. This N-terminal sequence is conserved in AcrR orthologs
across all analyzed Bacteroides spp., suggesting its functional relevance
and that the site of initiation of translation of the transcript arising from
acrR in B. vulgatus may have been previously misannotated. Therefore,
we expressed the full-length (AcrR
BV
) and truncated (AcrR*
BV
) ver-
sions of AcrR, as well as its B. dorei DSM 17855 ortholog (AcrR
BD
),
each fused to a Smt3-His6 tag, in E. coli. The recombinant proteins
were purified, their tag was removed, and their ability to bind the pre-
dicted DNA operator upstream of BVU0244 (acrA)wastestedby
electrophoretic mobility shift assay and by fluorescence polarization
assay (Fig. 5D and fig. S5, A to C). Fluorescence polarization assays
yielded K
d
(dissociation constant) values of 18.5 ± 6 nM and 25.5 ±
11 nM for AcrR
BV
and AcrR
BD
, respectively (fig. S5B). In contrast, the
truncated AcrR*
BV
protein did not interact with the same target DNA
fragment. Retinol (up to 125 mM) did not have notable effects on the
binding of either purified AcrR ortholog to the target DNA in either assay
(fig. S5, A and B). These findings are consistent with our analysis of the in
vivo transcriptional responses of B. vulgatus; that is, components of its
AcrAB-TolC efflux pump, as well as the great majority of other genes
in its predicted AcrR regulon, did not show significant differences in
their expression as a function of the presence or absence of vitamin A
***
Strain
WT
acrR_361472::IN
lpxA_362422::IN
Normalized transcript count
***
***
***
***
***
**
***
*
*** ***
***
***
BVU0244 BVU0241 BVU0240 BVU0239 BVU0238 BVU0236 BVU0234 BVU0233
BVU0243 BVU0242 BVU0237 BVU0235
TolC
BVU0245
BVU0244
BVU0241
BVU0240
BVU0239 BVU0238
BVU0236
BVU0234
BVU023
2
BVU0243 BVU0242 BVU0237 BVU0235 BVU0233
LpxA
AcrR
TolC2
AcrA2
AcrB2
Hypothetical
protein
Hypothetical
protein
Hypothetical
protein
Alginate O-
acetylation protein
AcrA
AcrB
0
500
1000
1500
2000
2500
3000
Fig. 4. Transcriptional response of the B. vulgatus locus containing the AcrAB-TolC efflux pump to insertional mutagenesis of acrR or lpxA.Microbial RNA-seq
analysis of gene expression in mid-log phase B. vulgatus WT, acrR::IN, and lpxA::IN (genome location 362422) strains cultured in the absence of retinol. Transcript counts,
normalized by DESeq2, for each gene in the putative BVU0244-BVU0233 operon are shown. Bars indicate means ± SEM for n= 3 independent cultures of each strain.
Significant differences in gene expression were defined by DESeq2 analysis. *P< 0.05; **P< 0.01; ***P< 0.001.
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in the diet. This conclusion is also supported by the results of a high-
resolution quantitative mass spectrometry (MS) analysis of the wild-
type B. vulgatus proteome expressed after growth in medium
containing a subinhibitory concentration of retinol (1 mM) versus
vehicle alone (0.02% DMSO; table S14). Finally, these binding assays
also suggest that the differential sensitivity of the two Bacteroides
species cannot be ascribed to differences in the interactions of AcrR
with either retinol or the identified target DNA binding site.
Further evidence that the bacterial AcrAB-TolC efflux system
affects retinol sensitivity
We hypothesized that the AcrAB-TolC efflux system, whose expression
was up-regulated when expression of the acrR repressor was abrogated,
operated to reduce local toxic concentrations of retinol in either the
periplasm or cytoplasm. To test this hypothesis more directly, we
measured the efflux of retinol from cultures of wild-type B. vulgatus
and the isogenic acrR::IN and PrpoD_acrR complemented acrR::IN
mutants. Equal numbers of stationary phase cells were pelleted and
resuspended in phosphate-buffered saline (PBS) containing cysteine
and 10 mM retinol. Ultra-performance liquid chromatography–MS
(UPLC-MS) was used to quantitate retinol in cell-free supernatants
harvested at various time points during a 2-hour incubation. We found
a statistically significant increase in extracellular retinol in the acrR::IN
mutant, where the efflux machinery was transcriptionally up-regulated,
but not in the wild-type or complemented strains (P= 0.01, acrR::IN
versus wild-type and acrR::IN versus complemented strains, two-way
repeated-measures ANOVA, Tukey’sHSDtest;Fig.6A).
Phenylalanine-arginine b-naphthylamide (PAbN) is a known inhib-
itor of multidrug efflux systems, including AcrAB-TolC (41). Wild-
type, acrR::IN, and complemented strains of B. vulgatus were treated
with PAbN in the presence or absence of retinol; wild-type B. dorei
was used as a reference control (unlike wild-type B. vulgatus,itis
resistant to retinol-mediated growth inhibition; Fig. 2B). Incubation
of the B. vulgatus acrR::IN strain (which exhibits increased expression of
its efflux system components compared to wild-type) or wild-type
B. dorei with PAbN(25mg/ml) in the absence of retinol did not result in
a statistically significant effect on growth compared to untreated control
cultures (P> 0.99, B. vulgatus acrR::IN;P> 0.99, wild-type B. dorei,
one-way ANOVA, Bonferroni multiple comparisons test; table S15A).
However, addition of the efflux pump inhibitor produced a significant
increase in retinol sensitivity in the B. vulgatus acrR::IN mutant (P<
0.0001) and in wild-type B. dorei (P< 0.0001) compared to cultures
with retinol alone (one-way ANOVA, Bonferroni multiple comparisons
test;Fig.6BandtableS15A).Whereasthewild-typeandcomplemented
mutant strains of B. vulgatus displayed a modest but significant in-
hibition of growth in the presence of PAbN (25 mg/ml) (P< 0.0001,
both strains), their growth was completely inhibited by 10 mM retinol in
the presence and absence of the efflux pump inhibitor (one-way ANOVA,
Bonferroni multiple comparisons test; Fig. 6B and table S15A).
Together, these results support the notion that the AcrAB-TolC efflux
pump mediates resistance to the growth inhibitory effects of retinol. The
precise mechanism by which retinol suppresses growth of B. vulgatus
remains to be defined.
The effects of bile acids on growth of B. vulgatus
UPLC-MS disclosed differences in the proportional representation of
several bile acid species in the fecal microbiota of gnotobiotic animals fed
the vitamin A–deficient and multiple micronutrient–deficient diets com-
pared to the control group monotonously fed the micronutrient-sufficient
AcrRBV
tolC
tolC2
Bacteroides vulgatus
C
BVU0244
Bacteroides dorei
Bacteroides thetaiotaomicron
OMA87 slt
cysC
cysD BVU0415
cysN
cysQ
OMA87 slt
cysC
cysD BVU0415
BVU0417cysNcysQ
acrA
acrA
acrB
acrB
tolC lpxA
acrR
BVU0236
acrB2
BVU0235
BVU0234
BVU0233
acrA2
tolC2
BVU0242
BVU0243
BVU0240
BVU0241
BVU0239
BVU0238
BVU0237
acrB2
BVU0421
BVU0420
BVU0419
BVU0417
BVU0418
BVU0416
B
A
BVU0244
-
79 6.57 TAAAAACTAATTGGGTTTAAAAAGTTTTCT
BVU0236
-
14 5.52 TAAAAAATGGAATAATGAAATCAGTTTTCA
BVU0421
-
65 6.04 TGAAAACTCATTTGCCGTAGAAAGTTTTTT
BACDOR00219
-
79 6.49 TAAAAACTAATTGGGTTCAAAAAGTTTTCT
BACDOR00022
-
116 6.04 TGAAAACTCATTTGCCGTAGAAAGTTTTTT
BT3339
-
115 6.72 CGGAAACTTATTTCATGGAATAAGTTTCCG
BT2689
-
109 7.14 AGGAAACTTGATTTGTTAATTGAGTTTTCT
BT0411
-
59 6.04 GGGAAACTTTTTTCGTAAATAAAGTTTTTA
BT1162
-
81 6.37 TGAAAACTAATCGAAATAAAATAGTTTCCT
Position Score SequenceLocus tag
D
2
1
Bits
01
55 1015202530
3
BVU0235
BVU0234
BVU0233
OMA87 slt
cysC
cysD BVU0415
cysNcysQ
tolC
lpxA
acrR acrA2
acrB2 BVU0236
BVU0235
BVU0234
BVU0233
BVU0236
acrR acrA2
tolC2
acrA
acrB lpxA
AcrRBD AcrRBV
00.2 0.5 1 0.50 1 0.5 12
Target DNA
Position
Control DNA
AcrRBD
Protein
µM
0 0.2 0.5 1 2
Fig. 5. Interactions between the AcrR transcription factor and its target DNA
binding site. (A) Predicted AcrR-regulated operons in the genomes of human gut
Bacteroides species. Boxes indicate clusters of coregulated genes. Filled black
circles indicate predicted AcrR-binding sites. Orthologous gene symbols are indicated
for each species; unnamed genes are indicated using the orthologous B. vulgatus
locus designation. (B) Sequences of predicted AcrR-binding sites. BVU, B. vulgatus;
BACDOR, B. dorei;BT,B. thetaiotaomicron.(C) Consensus binding site motif. (D)Elec-
trophoretic mobility shift assay of the interactions between AcrR
BV
and AcrR
BD
and
their predicted target DNA sequences.
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diet. For example, vitamin A deficiency was associated with a statisti-
cally significant increase in tauro-b-muricholic acid sulfate at the end
of the micronutrient deficiency phase at experimental day 35 (P=0.02
for both the vitamin A–deficient and multiple micronutrient–deficient
diet groups compared to micronutrient-sufficient controls, two-way
ANOVA, Bonferroni multiple comparisons test; table S16). Tauro-
b-muricholic acid is a potent antagonist of the farnesoid X receptor
(42). Concentrations of this bile acid, and its sulfated form, correlate
with slower intestinal transit time (43).
Examination of the community metatranscriptome revealed that
vitamin A deficiency was associated with differential expression of
several genes involved in bile acid metabolism, including bile salt hydro-
lases (EC3.5.1.24) from B. vulgatus (BVU2699 and BVU3993), B. dorei
(BACDOR00823), and B. thetaiotaomicron (Bthe7330970). Of the two
B. vulgatus bile salt hydrolase transcripts, BVU2699 was identified as a
member of the acrR regulon (table S12B).
AcrAB-TolC systems have been reported to contribute to bile acid
resistance in bacteria (35,44). Therefore, we tested the in vitro sensitiv-
ities of the wild-type and acrR::IN strains of B. vulgatus to six bile acid
species (cholic, deoxycholic, glycocholic, taurocholic, b-muricholic, and
tauro-b-muricholic acids; concentration range, 25 to 1000 mM) in the
presence or absence of the pharmacologic inhibitor of the efflux pump.
Wild-type B. vulgatus displayed the greatest sensitivity to deoxycholic
acid (table S15B). Moreover, the sensitivity of wild-type B. vulgatus to
150 mM deoxycholic acid was significantly increased in the presence
of the efflux pump inhibitor (25 mg/ml) (P< 0.0001, one-way ANOVA,
Bonferroni multiple comparisons test; Fig. 6C). In contrast, sensitivity
to deoxycholic acid was significantly reduced in the presence of the
same concentration of the inhibitor when B. vulgatus acrR was mutated
(and expression of the efflux pump was increased) (P< 0.0001, one-way
ANOVA, Bonferroni multiple comparisons test; Fig. 6C). The effect
of the efflux pump was also seen with 250 mM cholic acid, glycocholic
acid, and taurocholic acid, where sensitivity was significantly reduced
in the mutant compared to wild-type strains in the presence of the in-
hibitor (P= 0.02, P=0.002, and P= 0.006, respectively, ttest, Bonferroni
correction; table S15B). Complementation of the B. vulgatus acrR::IN
mutant partially restored sensitivity to deoxycholic acid (Fig. 6C and
table S15B).
Compared to wild-type B. vulgatus, wild-type B. dorei was signif-
icantly less sensitive to deoxycholic acid in the presence of PAbN(P<
0.0001, one-way ANOVA, Bonferroni multiple comparisons test;
Fig. 6C), just as it was significantly less sensitive to retinol in the pres-
ence of the inhibitor (Fig. 6B). This reduced sensitivity to both compounds
was measured in two ways: by noting the fold difference in sensitivity of
each organism to retinol or deoxycholic acid as a function of treatment
with the inhibitor and by noting the absolute difference in sensitivity
values between the organisms in the presence of deoxycholic acid plus
the inhibitor or retinol plus the inhibitor. These measures provide
one operational definition of the increased efflux capacity of B. dorei
for these two compounds.
80
90
100
110
120
130
140
150
160
170
Retinol in cell-free
supernatant (% of initial)
B. vulgatus strain WT acrR_361472::IN acrR_361472::IN
Time (hour) 0 0.5 2.0 0 0.5 2.0 0 0.5 2.0
**
A
+ pNBU2_acrR
DCA (μM)
PAβN (μg/ml) 0 25 0 25 0 25 0 25
Deoxycholic acid sensitivity
(treated/control)
0
0.5
1.0
1.5
2.5
2.0
0
2
4
6
8
10
12
> 14
Retinol (μM) 10 10 10 10 10 10 10 10
PAβN (μg/ml) 0 25 0 25 0 25 0 25
B. vulgatus
acrR::IN
+ pNBU2_
acrR
B. vulgatus
WT
B. vulgatus
acrR::IN
B. dorei
WT
B. vulgatus
acrR::IN
+ pNBU2_
acrR
B. vulgatus
WT
B. vulgatus
acrR::IN
B. dorei
WT
Retinol sensitivity
(treated/control)
***
***
***
***
***
***
C
B
***
***
150 150 150 150 150 150 150 150
Fig. 6. Role of theAcrAB-TolCeffluxpumpin regulatingthe sensitivityof B. vulgatus
to retinol and deoxycholic acid. (A) Retinol efflux assay. Stationary phase cultures of
the WT, acrR::IN, and complemented acrR::IN + pNBU2_acrR strains were resus-
pended in PBS containing cysteine and 10 mM retinol. Samples were collected over
a 2-hour time period, and retinol concentrations in cell-free supernatants were
quantified by UPLC-MS. Four independent experiments were performed; n=1to
3 replicates per experiment (two-way, repeated-measures ANOVA, Tukey’s HSD
test). (B) Sensitivity of the WT, acrR::IN,andacrR-complemented strains of B. vulgatus
and WT B. dorei to 10 mM retinol in the presence and absence of PAbN, a chemical
inhibitor of multidrug efflux systems (see table S15A for further details). Data repre-
sent one experiment, performed in triplicate (one-way ANOVA, Bonferroni multiple
comparisons test). (C) Sensitivity of B. vulgatus and B. dorei strains to deoxycholic acid
(DCA) in the presence and absence of PAbN. Data represent one experiment, per-
formed in triplicate (one-way ANOVA, Bonferroni multiple comparisons test). Means ±
SEM. *P< 0.05, **P< 0.01, ***P< 0.001.
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Follow-up electrophoretic mobility shift assays revealed that none
of these bile acid species affected the binding of AcrR to its target DNA
sequence (fig. S5A), a finding independently confirmed by fluorescence
polarization assays, which showed minimal effects on K
d
(fig. S5C).
Considered together, our findings indicate that these bile acids are not
direct regulators of AcrR activity. However, we cannot rule out the pos-
sibility that bile acids (or retinol) are metabolized to derivatives that
operate as AcrR effectors or on some other pathway or additional
regulator that affects AcrR. Our observations also support the notion
that in vivo dietary retinol availability and bile acid metabolites gen-
erated through biotransformation by members of the gut microbiota
may interact to influence the fitness of B. vulgatus via its AcrAB-TolC
efflux system. Moreover, the fact that the B. vulgatus AcrAB-TolC1/2
locus did not significantly change its expression in vivo as a function
of the vitamin A manipulations applied to our gnotobiotic mouse model
of the human gut microbiota suggests that the constitutive level of
activity of the efflux pump is a key determinant of the sensitivity of
B. vulgatus compared to other bacterial members of the community
(for example, B. dorei) to changes in retinoid availability. However, ad-
ditional studies are needed before any conclusions can be made about
whether the AcrR-mediated resistance to retinol inhibition observed in
vitro is a critical determinant of the broader reconfiguration of the
defined bacterial community that occurs in vivo.
DISCUSSION
Deliberate manipulation of dietary iron, folate, zinc, or vitamin A
produced the unanticipated finding that vitamin A had the greatest
effect on the structure and metatranscriptome of a phylogenetically
diverse, defined human gut bacterial community in gnotobiotic mice.
Much information has accumulated about the effects of retinoids on
host rather than microbial cell biology, including their pivotal role in
signal transduction. The current study provides preclinical evidence
supporting the concept that the presence and subsequent treatment
of micronutrient imbalances need to be considered from the perspective
of not only the human host but also the host’sgutmicrobiota.
As noted above, B. vulgatus is one of a number of growth-
discriminatory strains identified in a preclinical gnotobiotic mouse
model of human gut microbiota development (16). The underrepre-
sentation of some of these growth-discriminatory strains in the gut
microbial communities of undernourished children provides a rationale
for developing nutritional interventions designed to increase their
abundance and expressed beneficial functions (18). Although our
results establish that vitamin A deficiency increases, and repletion
decreases, the representation of B. vulgatus in our model community,
we lack preclinical or clinical evidence of its contribution to host growth
relative to other growth-discriminatory strains in the developing micro-
biota. Hence, we cannot conclude at this time that vitamin A imbalances
influence host growth through their effects on B. vulgatus (or that an
increase in the abundance of this putative growth-promoting taxon
in a vitamin A–deficient state would represent an adaptive response).
However, our results do suggest that the impact of vitamin A dose
and pharmacokinetics on this and other growth-discriminatory or-
ganisms present in the developing microbiota of children at risk for
or with already manifest undernutrition should be evaluated. Studies
conducted in gnotobiotic animal models where diet and microbiota
composition can be precisely controlled and manipulated provide a
useful starting point for addressing this challenging problem. There
are formidable problems with directly proceeding to human studies
without previous knowledge gleaned from preclinical animal models.
These problems include distinguishing primary effects of dietary mi-
cronutrient content on the microbiota from host effects, as well as the
challenge of stratifying a target human population composed of indi-
viduals of varying ages, with varying combinations of micronutrient
deficiencies and microbiota compositions, and with varied histories of
exposure to supplements containing various combinations and doses
of micronutrients.
The current study extends previous work performed with defined
human gut communities composed of reference “domesticated”bac-
terial strains derived from diverse human donors to characterize the
interactions of specified deliberate dietary nutrient manipulations on
the structural and functional features of these artificial microbiota.
The diversity of the community described in the present report and
the reliability of its installation in gnotobiotic mice represent an advance
over our previous gnotobiotic models (32,45–48). This capacity to
reliably assemble a more complex defined model human gut micro-
biota, where all component organisms and their gene content are
known, expands our capacity to (i) capture the structural and func-
tional responses of a broader range of community members to a variety
of dietary manipulations (for example, by COPRO-Seq, microbial
RNA-seq, and MS-based proteomic or metabolomics studies) and (ii)
subsequently prioritize further analyses based on the observed effect
size and nature of the responses. These studies also illustrate how once
such prioritization is made, an informative next step is to construct
INSeq libraries from the most responsive taxa and perform in vitro
screens with identified bioactive dietary compounds to decipher the
molecular underpinnings of the observed in vivo response. Nonetheless,
our experiments illustrate one challenge in characterizing the effects
of deliberate perturbations of defined artificial human gut commu-
nities with increasing degrees of complexity: Whereas consistent
community membership was achieved across cohoused animals within
and across treatment groups based on the criterion of “presence/
absence”and Bray-Curtis dissimilarity measurements, we observed
changes in relative abundance over time even in the untreated control
group. We accommodated this apparent stochasticity by performing
several types of comparisons: (i) a “within-treatment group”com-
parison where each mouse served as its own control, documenting com-
munity structure and metatranscriptome before, during, and after a
single type of acute micronutrient deficiency is applied, and where mice
were also compared to one another; (ii) a between-group compari-
son where the responses of a given treatment group were referenced
to members of the control group that monotonously consumed a
micronutrient-sufficient diet; and (iii) in the case of vitamin A, a
between-group comparison where the responses to this single dietary
micronutrient deficiency were compared to the responses to the mul-
tiply deficient diet. The robustness of responses to dietary or other
types of manipulations can be examined further in communities with
the degree of complexity described in this report, or even greater
degrees of complexity, by performing multiple independent
experiments with cohoused coprophagic animals.
These approaches set the stage for future studies that apply speci-
fied dietary micronutrient deficiencies to gnotobiotic mice of varying
ages harboring (i) defined collections of cultured age- and growth-
discriminatory human gut bacterial strains representing the different
stages of assembly of the human gut microbiota or (ii) intact, uncultured,
normally developing microbiota from children with healthy growth
phenotypes, or immature microbiota from those with undernutrition.
The results should not only allow further dissection of the mechanisms
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by which micronutrients interact with community members to shape
microbiota and host development but also may inform new approaches
for more effectively treating (and ultimately preventing) the short- and
longer-term sequelae of deficiency states. These animal models can also
support tests of whether current dosing protocols for micronutrient
repletion may have unintended and deleterious effects on the develop-
ing microbiota of the very children whose healthy growth such treat-
ments are intended to promote.
MATERIALS AND METHODS
Study design
Each experimental group consisted of five mice housed in a single cage,
with all cages housed in a single gnotobiotic isolator. Groups of mice
were age- and weight-matched before colonization (see below), and diet
treatments were randomly assigned to each group. Experimental diets
were custom-designed and manufactured by Harlan Teklad/Envigo. Six
diets were produced: four were devoid of one micronutrient (vitamin A,
iron, zinc, or folic acid), all four micronutrients were absent in another,
and one contained sufficient amounts of all of these micronutrients. To
design a consistent, defined base diet for all experimental diets, a nutri-
tionally replete mixture of individual amino acids was used in place of
complete protein. Custom vitamin and mineral mixes containing only
the micronutrients appropriate to each diet were then added to the base
diet to generate each experimental diet. Diets were measured into ~500-g
portions, placed into 3-mm-thick vacuum-sealed bags (Uline Inc.), and
then put into a second bag that was also vacuum-sealed. Diets were
shipped overnight on ice for sterilization by g-radiation (20 to 50 kGy;
STERIS Corp.). The nutritional characteristics of each irradiated diet were
calculated on the basis of diet formulation and are reported in table S1.
Investigators were not blinded to diet treatments. All animals studied
were included in subsequent analyses.
Bacterial strains and culture conditions
Reference type strains used in this study are listed in table S2. Strains
were grown in gut microbiota medium (GMM) (49) or on brain-heart
infusion agar (BHI; Becton-Dickinson) supplemented with 10% horse
blood, under anaerobic conditions (atmosphere: 5% H
2
, 20% CO
2
,and
75% N
2
) in a soft-sided plastic anaerobic chamber (Coy Laboratory
Products). The identity of each strain was confirmed by sequencing
full-length 16Sribosomal RNA (rRNA) gene amplicons generated using
the universal primers 8F and 1391R. Strains were arrayed into a 96-well
format and preserved at −80°C in GMM containing 15% glycerol.
Additional manipulations of the arrayed collection were performed
inside the anaerobic chamber using a Precision XS liquid-handling robot
(BioTek Instruments Inc.).
A primary human isolate of B. vulgatus (16) was cultured anaerobically
in BHI broth supplemented with L-cysteine (0.5 g/liter), L-histidine
(0.2 mM), hematin (1.9 mM), and vitamin K
3
(1 mg/liter) (referred to as
BHI+ broth) or on BHI-blood plates. E. coli S17 lpir was used for routine
cloning and as a conjugation donor for genetic experiments involving
B. vulgatus;itwasgrowninLBMillerbrothoronLBplates(BDDifco).Anti-
biotics were added to media as appropriate: ampicillin (100 mg/ml), eryth-
romycin (25 mg/ml), tetracycline (2 mg/ml), and gentamicin (200 mg/ml).
Gnotobiotic mouse experiments
All experiments involving mice were performed using protocols ap-
proved by the Animal Studies Committee of the Washington University
School of Medicine.
Colonization of germ-free mice
A−80°C stock plate of the clonally arrayed culture collection was
thawed in the anaerobic chamber. A 96-well, deep-well plate (Thermo
Scientific Nunc) was filled robotically with 960 ml of GMM broth. A
40-ml aliquot was withdrawn from each well of the culture collection
and inoculated into the recipient plate, which was then covered with an
atmosphere-permeable seal (VWR). The inoculated plate was incubated
under anaerobic conditions for 48 hours at 37°C, after which time an
aliquot of each well was assayed for growth by measuring OD
600
.
Equal volumes of each well culture were pooled, mixed, transferred
to 1.8-ml crimp seal glass vials (Wheaton), and sealed for transport to
the gnotobiotic mouse facility. Vials were immediately fogged into gno-
tobiotic isolators, and 500 ml of the pooled culture was introduced into
each recipient germ-free mouse by a single oral gavage.
Adult (8- to 9-week-old) male CF57BL/6J mice were maintained
in a flexible plastic film gnotobiotic isolator and fed a nutritionally
sufficient standard diet (B&K autoclavable chow #7378000, Zeigler Bros
Inc.) ad libitum. Four days before gavage of the defined 92-strain
culture collection, all mice were transitioned to the nutritionally suffi-
cient experimental diet. On experimental day 0, mice received 500 mlof
the strain mixture.
All mice were maintained under a strict light cycle (lights on at 0600
and lights off at 1800). All diets were provided ad libitum. All animals in
all treatment groups were observed on a daily basis and weighed weekly.
Autoclaved bedding (Aspen wood chips, Northeastern Products) was
changed weekly and at the beginning of each diet oscillation.
The timing of fecal sampling for COPRO-Seq and microbial RNA-
seq analyses is described in Fig. 1A. All fecal sampleswere collected from
individual mice into 2-ml screw cap tubes (Axygen). Once sampling
of the animals in an isolator had been completed, tubes were removed
promptly, snap-frozen in liquid N
2
, and transferred to a −80°C freezer.
Community profiling by sequencing
The microbial community structure in each fecal sample was analyzed
by COPRO-Seq (47). Briefly, DNA was isolated by subjecting each fecal
pellet (n= 5 samples per experimental group per time point) to bead
beading in a mixture containing 500 ml of buffer A (200 mM NaCl,
200 mM tris, 20 mM EDTA), 210 ml of 20% SDS, 500 ml of phenol/
chloroform/isoamyl alcohol (pH 7.9) (25:24:1; Ambion), and 250 ml
of 0.1-mm zirconium beads (BioSpec Products) for 3 min (Mini-
BeadBeater-8, BioSpec). The aqueous phase was collected after centri-
fugation at 4°C for 3 min at 8000g, and nucleic acids were purified
(QIAquickcolumns,Qiagen)andelutedinto10mMtris.
COPRO-Seq libraries were prepared by first sonicating 100 mlofa
solution of DNA (5 ng/ml) from each fecal sample (Bioruptor Pico,
Diagenode) (10 cycles of 30-s on/30-s off at 4°C). Fragmented DNA
was cleaned up in MinElute 96 UF PCR Purification plates (Qiagen).
The fragments were blunt-ended, an A-tail was added, and the reaction
products were ligated to Illumina paired-end sequencing adapters
containing sample-specific 8-bp barcodes. Size selection was per-
formed (1% agarose gels); 250- to 350-bp fragments were excised,
and the DNA was purified (MinElute Gel Extraction, Qiagen). Adapter-
linked fragments were enriched by a 20-cycle polymerase chain re-
action (PCR) using Illumina PCR Primers PE 1.0 and 2.0, followed
by MinElute PCR Purification (Qiagen); if agarose gels indicated
adapter dimers, an additional size selection was performed (AMPure
XP SPRI bead cleanup, Beckman Coulter). Libraries were pooled and
sequenced using Illumina MiSeq or HiSeq instruments (unidirectional
50-nt reads).
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Sequence data were demultiplexed and mapped to the reference
genomes of community members plus three “distractor”genomes
(Bacteroides fragilis NCTC 9343, Clostridium perfringens ATCC
13124, and Shigella sp. D9). The proportion of reads mapping to
the distractor genomes was used to set a minimum count threshold
cutoff indicating the presence/absence of an organism in the commu-
nity on a per-sample basis. Normalized counts for each bacterial strain
in each sample were used to produce a relative abundance table (sum-
marized in table S3). Before statistical analyses, the table was further
filtered to exclude organisms not present at ≥0.1% relative abundance
in >25% of samples collected.
To identify bacterial taxa whose relative abundances were influenced
by the micronutrient-deficient diet treatments, we rarefied the abundance
table to 7000 reads per sample and used linear mixed-effects models of
log-transformed abundances (plus one pseudocount). For each taxon,
models were generated for each of the five dietary micronutrient defi-
ciencies, using the micronutrient-sufficient group as a control in each
model. Each model included (i) experimental stage (end of first suffi-
cient diet phase at experimental day 14; 14 days after initiation of the
micronutrient deficient diets; 14 days after return to the sufficient
diet), (ii) treatment group (deficiency versus control), and (iii) their
interaction as fixed effects, with individual mice treated as a random
effect. A significant interaction term was considered evidence of a po-
tentially interesting influence of the micronutrient deficiency, and tests
of differences of least-squares means between the control and deficiency
groups in each experimental stage, followed by Pvalue adjustments
using Holm’s method, were used to further explore the effects of the
experimental treatments.
In addition, relative abundances before and after diet switch were
compared using the group_significance.py script in QIIME version
1.9.0 (50). A follow-up analysis was performed in R (version 3.2.3)
(51). Relative abundance data and associated metadata files were read
into R, and the change in relative abundance for each organism within
an individual mouse between two diet phases was calculated. These
values were compared across experimental groups toidentify changes
in relative abundance that were significantly different from relative
abundance responses in other experimental groups. For all univariate
analyses, both nonparametric and parametric statistical tests were per-
formed, and the results were compared.
Microbial RNA-seq
Triplicate cultures of wild-type B. vulgatus and isogenic mutants were
diluted 1:100 from overnight cultures into 5 ml of fresh Bacteroides
defined medium (BDM) [prepared by mixing equal volumes of a 2×
concentrate of the carbohydrate-free medium stock with a 2× concen-
trated carbon source solution, as described previously (48)] and grown
to mid-log phase (OD
600
, 0.4 to 0.6) under anaerobic conditions in
sealed Balch tubes. Once cultures reached mid-log phase, they were
treated with RNAprotect Bacteria Reagent (Qiagen), vortexed, and
incubated at room temperature for 5 min. Cultures were then transferred
to clean 15-ml tubes and centrifuged for 10 min at 3023gsupernatants
were decanted, and the pellets were stored at −80°C. Both cryopreserved
in vitro cultures and fecal pellets (n= 3 samples per experimental group
pertimepoint)werethawedandresuspendedin500mlofbufferAim-
mediately before extraction of total RNA.
Microbial RNA-seq was performed as previously described (45,47,48).
After acid phenol extraction, precipitation with isopropanol, and two
rounds of deoxyribonuclease treatment [each followed by cleanup using
a MEGAclear column (Ambion)], RNA integrity was confirmed by gel
electrophoresis, and PCR-based checks for genomic DNA contami-
nation were performed. 23SrRNA, 16SrRNA, and 5SrRNA were
removed (Ribo-Zero Kit, Illumina), and purified bacterial mRNA was
precipitated with ethanol in the presence of GlycoBlue (Ambion; used
to ensure subsequent complete resuspension in nuclease-free H
2
O).
Double-stranded complementary DNA was synthesized using ran-
dom hexamers and SuperScript II (Invitrogen). Illumina library
preparation was performed as described above for COPRO-Seq;
however, size selection was performed in the 200- to 300-bp range.
Libraries were subjected to sequencing first on the Illumina MiSeq
platform for quality control purposes, after which library balance adjust-
ments were made where necessary and final sequencing at greater depth
was performed using the Illumina HiSeq platform (50-nt unidirectional
reads).
Data analysis
The pipeline we used for processing short-read metatranscriptomic data
is described in a previous publication (45). Briefly, sequence data were
demultiplexed, and Bowtie version 1.1.0 (52)wasusedtomapreadsto
the genomes of community members. Raw counts were subsetted, then
normalized, and analyzed using DESeq2 (53) in R 3.2.3 using two
complementary strategies (“top-down”and “bottom-up”). To analyze
data at the community level (top-down view of the metatranscriptome),
raw count data for each comparison were filtered at a low abundance
threshold of ≥3 raw reads and for consistent representation in biological
replicates (present in ≥2 samples in both micronutrient-sufficient and
micronutrient-deficient diet groups being compared, or present in all
samples in one group and in none of the other) and then imported into
R. Size factors and dispersions were estimated in DESeq2. Significant
differential expression was defined using the Wald test based on nega-
tive binomial model fitting. To obtain a strain-level view of transcrip-
tional responses (bottom-up analysis), RNA-seq data were subsetted
by strain, filtering for low abundance and sample representation as
above, and the resulting data set was analyzed in R using DESeq2.
Rarefaction was used to determine the fraction of expressed protein-
coding sequences in each organism that was detected across all RNA-
seq samples. Examination of the saturation characteristics of per-strain
rarefaction curves across all samples allowed us to stratify organisms by
predicted transcriptome saturation. Strains that colonized gnotobi-
otic mice (by COPRO-Seq analysis) but for which saturation was low
(Bacteroides finegoldii DSM 17565, Bacteroides ovatus ATCC 8483,
Bifidobacterium adolescentis L2-32, Enterobacter cancerogenus ATCC
35316, Megamonas funiformis DSM 19343, Parabacteroides distasonis
ATCC 8503, and Proteus penneri ATCC 35198) were excluded from
the bottom-up analysis (all transcriptomic data were included in the
top-down community-level analysis).
Functional annotation of differentially expressed genes
The general strategy and bioinformatic tools used for functional analysis
of microbial RNA-seq data are described in an earlier publication (47).
Predicted protein-coding genes in the genomes of community members
were annotated by BLASTP query (e-value threshold of 1 × 10
−5
)
against the KEGG database (released 4 January 2016). Annotation
results were used to match coding sequence locus tags to KO identifiers.
KO lookup tables for each genome were used to annotate transcriptional
data, which were then used to determine the representation of various
levels of the KEGG hierarchy, including “General,”“Categories,”
“Pathways,”“Functions,”and “Enzyme Commission numbers”in each
RNA-seq data set. A more detailed (manual) curation of functional
assignments for genes implicated in the predicted AcrR regulon was
performed using comparative genomics tools in the SEED database
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andRAST(RapidAnnotationofmicrobial genomes using Subsystems
Technology) (54).
Pathway enrichment analyses
Hypergeometric enrichment and gene set enrichment analysis (GSEA)
were performed using the R packages clusterProfiler (55) and GAGE
(56), respectively. Gene set information for both top-down (community-
wide) and bottom-up (strain-level) analyses were derived from the
KEGG-based functional annotations described above. For hyper-
geometric enrichment tests, the set of differentially expressed genes
(as defined by DESeq2) was supplied to the generalized “enricher”tool
in clusterProfiler, along with the corresponding gene set information
(that is, their corresponding KEGG-level functional group). For GSEA,
DESeq2-normalized counts were supplied to GAGE along with the
corresponding gene set information and with options “same.dir = F (genes
allowed to change expression in different directions),”“saaTest = gs.
KSTest (use non-parametric Kolmogorov-Smirnov test to order genes),”
and “rank.test = F (required for gs.KSTest).”For both enrichment
analyses, Pvalues were adjusted to control FDR by the Benjamini-
Hochberg method.
Phenotypic screen for the effects of various retinoids on the
growth of Bacteroides spp.
Stocks (50 mM) of retinol, retinal, retinoic acid, retinyl palmitate,
b-carotene, and geranylgeraniol (Sigma-Aldrich) were prepared in
DMSO under low-light conditions and stored in N
2
-purged amber vials
at −80°C. Bacterial strains were struck from −80°C glycerol stocks
onto BHI-blood plates containing antibiotics where appropriate and
grown for 48 hours at 37°C under anaerobic conditions. Single colonies
were picked into BHI+ broth and grown overnight under anaerobic
conditions at 37°C. Cultures were diluted to an OD
600
of 0.05 in fresh
BHI+ broth and grown to mid-log phase. Either retinoids or DMSO
(vehicle control) was added to BDM at specified concentrations, and
150 ml of the media was aliquoted into wells of a 96-well plate (Techno
Plastic Products AG) using a liquid-handling robot housed in the
Coy chamber. Mid-log test cultures were subsequently diluted 1:25
into 1 ml of BDM/0.02% (v/v) DMSO in deep-well plates; 50 ml of the
diluted cultures was transferred to recipient wells in the plate by ro-
bot, yielding 1× treatment and 1:100 final dilutions of the bacterial
strains (n= 3 to 4 cultures per treatment per experiment; experiments
were performed in duplicate unless indicated). Plates were sealed with
an optically clear film (Axygen UC500) and transferred to a plate
stacker-reader system housed in the anaerobic chamber (BioTek
Eon and BioStack 4). For data collection, each plate was placed in the
Eon plate reader and incubated at 37°C, with OD
600
values determined at
15-min intervals. For multiplate data collection, the anaerobic chamber
was heated to 37°C, and plates were placed in the plate-handling ro-
bot and draped with laboratory diapers to achieve low-light conditions;
OD
600
measurements were performed for each plate at 15-min intervals.
For multidrug efflux inhibitor studies, cultures were prepared as
described above for retinoid sensitivity testing and were treated with
PAbN(0or25mg/ml) (Sigma-Aldrich), with or without 10 mMretinol
in 200 mlofBDM.Platesweresealed,andOD
600
was monitored.
At the conclusion of each experiment, data were exported to text file,
and in-house perl scripts were used to plot growth curves and calculate
growthrate,thetimeatwhicheachgrowthcurvecrossedauser-defined
OD
600
threshold and the maximum OD
600
that was achieved. Curve
parameters were normalized to corresponding values from control
cultures of the same strain in 0.02% DMSO before performing compar-
isons between strains (Prism 6.0, GraphPad Software).
High-resolution quantitative MS-based proteomics
Wild-type B. vulgatus ATCC 8482 was cultured in BDM in the presence
of 1 mM retinol or vehicle alone (0.02% DMSO) (n= 2 monocultures per
treatment), pelleted, and frozen for storage at −80°C. Proteins were
extracted and digested with trypsin, and 25 mg of peptides was measured
across 11 salt cuts of a 24-hour MudPIT LC-MS/MS analysis using an
Orbitrap Elite MS (48). Resulting MS/MS spectra were searched against
the B. vulgatus proteome database (concatenated with common con-
taminants and decoy sequences to assess FDRs) using the MyriMatch
v. 2.1 search algorithm (57). Peptide spectrum matches were scored and
filtered (peptide-level FDR < 1%) via IDPicker v. 3.0 (58), assigned
matched ion intensities, and peptide abundance distributions were
normalized and assembled to proteins using InfernoRDN, as described
previously (59,60).
INSeq-based identification of B. vulgatus mutants that affect
retinol sensitivity
B. vulgatus ATCC 8482 taxon-specific barcodes were introduced into
the INSeq mutagenesis vector pSAM_Bt (31) by PCR amplification,
using the primer pairs described in table S11. Amplification conditions
were as follows: initial denaturation at 94°C for 2 min, followed by
25 cycles of denaturation (94°C for 15 s), annealing (58°C for 30 s),
and amplification (58°C for 90 s). Vector DNA was digested with Kpn I
and Bam HI, and the linear product was ligated to the PCR amplicon,
yielding the barcoded Tn mutagenesis vector. Whole-genome Tn
mutagenesis of B. vulgatus ATCC 8482 was performed using a pub-
lished protocol (32).
Aliquots of the mutant library were inoculated into BDM containing
retinol (10 mM) or 0.02% DMSO. Triplicate, large-volume cultures
(250 ml each; starting OD
600
of 0.05) were incubated anaerobically at
37°C. Aliquots were removed during lag phase and after retinol-selected
cultures reached stationary phase. Additionally, a stationary phase
aliquot from the primary culture was used to inoculate 250 ml of fresh
selection medium. The resulting secondary cultures were sampled in
mid-log phase (OD
600
, 0.4 to 0.6) and at stationary phase.
DNA was isolated from all cultures/time points, and the abundance
and genome location of mutants in input, control, and selected samples
were determined by INSeq as follows. The mariner Tn contains engi-
neered Mme I sites at both of its ends; thus, DNA was digested with
Mme I (which cuts 20 bp distal to its recognition site), yielding products
with flanking genomic sequence tags at both ends. AMPure XP bead-
based and gel-based size selection was used to isolate and purify the
products. Custom, indexed Illumina adapters were ligated to these
fragments, which were then sequenced (Illumina HiSeq platform; 50-nt
reads). INSeq reads were mapped to the B. vulgatus genome and ana-
lyzed (32) to obtain the identity and abundance of each Tn mutant
present in the input library and the selected or control libraries.
Retinol-resistant Tn mutants were isolated by plating dilutions of
cryopreserved, stationary phase, selected cultures on BHI-blood
containing erythromycin (25 mg/ml). Colony PCR using primer pairs
that spanned inserted Tn borders was used to confirm the identities
of mutants. Confirmed colonies were grown overnight in BHI+ and
archived as 15% glycerol stocks at −80°C. Retinoid sensitivity experiments
were performed as described above using monocultures of isolated
Tn mutant strains.
Complementation of B. vulgatus Tn mutants
Tn mutants of B. vulgatus were complemented using the genomic
insertion vector pNBU2_tetQ (40). The coding sequence of acrR
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(BVU0240) was amplified by PCR with Phusion High-Fidelity Master
Mix (New England Biolabs) using purified genomic DNA from wild-type
B. vulgatus as the template. To drive constitutive expression of comple-
mented genes, a 300-bp region upstream of the B. vulgatus rpoD gene
(PrpoD) was also amplified by PCR. pNBU2_tetQ was digested with
Xba I and Pst I; Gibson Assembly (New England Biolabs) was used to as-
semble PrpoD and the B. vulgatus acrR coding sequence into the digested
vector, yielding pNBU2_tetQ_PrpoD_acrR (abbreviated pNBU2_acrR).
Assembled recombinant and control vectors were transformed into
E. coli S17 lpir, and the configuration of the constructs were confirmed
by junction-spanning PCR. Vectors were mobilized from the E. coli do-
nor to the B. vulgatus recipient strains by conjugation. Briefly, overnight
cultures of both donor and recipient were inoculated into corresponding
rich media with antibiotics where appropriate and grown for 16 to 20 hours
at 37°C (E. coli, aerobically with shaking at 225 rpm; B. vulgatus,
anaerobically without shaking). Stationary phase cultures were then se-
rially diluted into fresh medium and incubated for 4 hours at 37°C.
Cultures of donor and recipient cells with equivalent optical densities
were pelleted by centrifugation, resuspended as a mixture in 1 ml of
fresh medium without antibiotics, and plated on BHI-blood agar. Af-
ter a 24-hour incubation under aerobic conditions, the surface of each
plate was scraped, resuspended in 5 ml of BHI+ liquid, and plated on
BHI+ agar with tetracycline (2 mg/ml). After a 48-hour incubation under
anaerobic conditions, colonies were picked and restruck on BHI+ agar
with tetracycline. The site of insertion and orientation of introduced genes
in selected colonies were verified by PCR and sequencing. Confirmed
complemented Tn mutants carrying either the empty pNBU2_tetQ
vector or pNBU2_acrR were subjectedtoretinoidsensitivityassaysas
described above.
Transcription factor–binding site analyses
Analysis of Bacteroides AcrR regulons
We applied an integrative comparative genomics approach to recon-
struct the AcrR regulon in Bacteroides species [as implemented in the
RegPredict Web server (http://regpredict.lbl.gov)] (61). This approach
combines identification of candidate regulator-binding sites with
cross-genomic comparison of regulons and functional context analysis
of candidate target genes (62). The upstream regions of BVU0240 and
its orthologs in 11 Bacteroides genomes (representing a nonredun-
dant set of species excluding closely related strains) were analyzed
using a DNA motif recognition program (the “Discover Profile”
procedure implemented in RegPredict) to identify a conserved palin-
dromic DNA motif. After construction of a position-weight matrix
for the candidate AcrR-binding motif, we searched for additional AcrR-
binding sites in the analyzed Bacteroides genomes. Finally, we per-
formed a consistency check or cross-species comparison of the predicted
AcrR regulons. Scores of candidate binding sites were calculated as
the sum of positional nucleotide weights. The score threshold was
defined as the lowest score observed in the training set. The sequence
logo for the derived DNA binding motif was drawn using the WebLogo
package (63).
Cloning, expression, and protein purification of B. vulgatus
and B. dorei AcrR-like regulators
Genes encoding orthologous AcrR-like regulators from B. vulgatus
(BVU0240, AcrR
BV
) and B. dorei (BACDOR_00223, AcrR
BD
) were
amplified by PCR from genomic DNA using two sets of specific primers
containing Bam HI and Hind III restriction sites (table S11). A truncated
variant of the AcrR
BV
protein (AcrR*
BV
)thatlackseightN-terminalamino
acids was generated using an alternative forward primer (table S11).
This “truncated variant”corresponds to an alternative translational start
of this protein, as currently reflected in GenBank (WP_008782083.1).
Amplicons specifying the full-length AcrR orthologs from B. vulgatus
and B. dorei and the truncated AcrR*
BV
variant were cloned into the
pSMT3 expression vector. The recombinant proteins were expressed,
under control of the T7 promoter, with an N-terminal His6-Smt3-tag
in E. coli BL21/DE3 (64). Cells were grown in LB medium (50 ml) to
an OD
600
of ~1.0, and protein expression was induced with 0.2 mM
isopropyl-b-D-thiogalactopyranoside. Cells were harvested after 18 hours
of additional shaking at 20°C.
To purify the recombinant AcrR
BV
,AcrR*
BV
,andAcrR
BD
proteins,
harvested cells were first resuspended in 20 mM Hepes buffer (pH 7)
containing 100 mM NaCl, 0.03% Brij-35, 2 mM b-mercaptoethanol,
and 2 mM phenylmethylsulfonyl fluoride (Sigma-Aldrich). Cells were
then lysed by incubation with lysozyme (1 mg/ml) for 30 min at 4°C,
followed by a freeze-thaw cycle, sonication, and centrifugation. Tris-
HCl buffer (pH 8) was added to the resulting supernatant (final concen-
tration, 50 mM), which was subsequently loaded onto a Ni–nitrilotriacetic
acid agarose minicolumn (0.3 ml; Qiagen). After washing with the
starting buffer containing 1 M NaCl and 0.3% Brij-35, bound proteins
were eluted with 0.3 ml of the starting buffer containing 300 mM im-
idazole. Protein size and purity were verified by SDS–polyacrylamide
gel electrophoresis. Protein concentration was determined by using
the Quick Start Bradford Protein AssayKit (Bio-Rad). The N-terminal
His6-Smt3-tag was cleaved off the purified proteins by digestion with
Ulp1 protease [overnight incubation at 4°C in a reaction mixture
containing the protease (0.07 mg/ml)].
DNA binding assays
Interactions between the purified recombinant transcription factors and
their predicted DNA binding sites were assayed using two techniques:
electrophoretic mobility shift assay and fluorescence polarization assay.
DNA oligonucleotide targets were synthesized (table S11). 5′-biotin–
labeled DNA fragments were used for electrophoretic mobility shift
assays, whereas fluorescence polarization assays used DNA frag-
ments 3′-labeled with 6-carboxyfluorescein. As a negative control, we
used a 41-bp DNA fragment from the BT0356 gene of B. thetaiotaomicron
VPI-5482; this fragment contains the verified binding site of an un-
related transcriptional regulator, AraR (65).
For electrophoretic mobility shift assays, the target DNA fragment
(0.25 nM) was mixed with increasing concentrations of the purified tag-
free AcrR
BV
and AcrR
BD
proteins in a total reaction volume of 20 ml.
The binding buffer contained 20 mM tris-HCl (pH 8.0), 150 mM KCl,
5mMMgCl
2
, 1 mM dithiothreitol, 0.05% NP-40, and 2.5% glycerol.
After a 25-min incubationat 37°C, the reaction mixturewas subjected
to electrophoresis (conditions: native 5% polyacrylamide gels in 45 mM
tris, 45 mM borate buffer (0.5× TB), 100-min run time at 90 V, room tem-
perature). DNA was electrophoretically transferred onto a Hybond-N
+
membrane (Pierce) and fixed by ultraviolet cross-linking. Biotin-labeled
DNA was detected with the LightShift chemiluminescent electrophoretic
mobility shift assay kit (Pierce).
For fluorescence polarization assays, fluorescently labeled double-
stranded DNA fragments (3 nM) were incubated with increasing con-
centrations of the purified tag-free AcrR
BV
and AcrR
BD
proteins in
triplicate 100-ml reaction mixtures conducted in 96-well black plates
(VWR). The binding buffer contained 20 mM tris-HCl (pH 7.5) and
100 mM NaCl, and the incubation was conducted for 20 min at 24°C.
Herring sperm DNA (1 mg) was added to the reaction mixture to
suppress nonspecific binding. Fluorescence polarization measurements
were made using a Beckman multimode plate reader (DTX 880) with
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excitation and emission filters set at 495 and 520 nm. The fluorescence
polarization assay values were determined as described previously (66).
Bile sensitivity assays
The bile acid sensitivities of isogenic wild-type, acrR::IN,andpNBU2_acrR-
complemented strains of B. vulgatus, plus wild-type B. dorei, were
assayed using methods analogous to those used for defining their
sensitivity to different retinoids. Cultures were grown to stationary phase
and then diluted to OD
600
of ~0.01 in BDM containing 0, 250 mM, or
1 mM of glycocholic acid, taurocholic acid, cholic acid, deoxycholic
acid, b-muricholic acid, or tauro-b-muricholic acid, with or without
PAbN (25 mg/ml) (n= 3 cultures per treatment). An additional ex-
periment was performed with 150 mM deoxycholic acid in the presence
or absence of the efflux pump inhibitor. Bile species were obtained from
Sigma-Aldrich except for b-muricholic acid (Steraloids) and tauro-
b-muricholicacid(SantaCruzBiotechnology). Assays were performed
in a volume of 200 ml in triplicate in 96-well plates.
Targeted UPLC-MS
Retinoids
Aliquots of cultures of the wild-type, acrR::IN,andacrR-complemented
acrR::IN strains of B. vulgatus were diluted into supplemented BHI
medium and grown to stationary phase at 37°C under anaerobic con-
ditions. Cultures were then diluted to an OD
600
of 0.5 in 10 ml of PBS/
0.2% (w/v) cysteine, and retinol was added to a final concentration of
10 mM(n= 1 to 3 cultures per treatment, 3 experiments). At indicated
time points, a 500-ml aliquot of each culture was placed into a 1.5-ml
tube, and cells were pelleted by centrifugation for 3 min. The super-
natant was withdrawn and transferred to a 1-ml sample vial (Waters),
anddeuterated(D5)retinol(Toronto Research Chemicals) was added
to a final concentration of 10 mM as a spike-in control. MS analyses were
performed using Acquity UPLC I-Class System (Waters) coupled to
LTQ Orbitrap Discovery (Thermo Scientific). Mobile phases for posi-
tive ionization were (i) 0.1% formic acid in water and (ii) 0.1% formic
acid in acetonitrile. Retinol quantification was achieved by compar-
ing measured values to standard curves generated from stocks of ret-
inol and D5-retinol.
Bile acids
UPLC-MS analysis of fecal bile acids was performed using protocols
described in an earlier report (43).
Statistical analyses
Routine statistical analyses were performed in R (version 3.2.3) or in
Prism 6.0 as indicated. P< 0.05 was considered statistically significant
after appropriate correction for multiple hypothesis testing. Specific
statistical tests and Pvalue corrections are noted throughout the text,
in figure legends, and in the Supplementary Materials.
SUPPLEMENTARY MATERIALS
www.sciencetranslationalmedicine.org/cgi/content/full/9/390/eaal4069/DC1
Fig. S1. Comparisons of community structure across experimental stages.
Fig. S2. Relative abundances of B. vulgatus ATCC 8482 and B. dorei DSM 17855 in gnotobiotic
mice across experimental stages and treatment groups.
Fig. S3. Characterization of the B. vulgatus ATCC 8482 INSeq library.
Fig. S4. Maximum likelihood phylogenetic tree of BVU0240/AcrR orthologs identified in human
gut–associated Bacteroides and other members of the family Bacteroidaceae.
Fig. S5. DNA binding characteristics of AcrR
BV
and AcrR
BD
in the presence and absence of
possible effectors.
Table S1. Nutritional characteristics of experimental micronutrient-deficient and micronutrient-
sufficient diets.
Table S2. Ninety-two sequenced, human gut–derived bacterial strains.
Table S3. COPRO-Seq analysis of community composition in fecal samples.
Table S4. Influence of micronutrient deficiencies on the relative abundances of specific taxa.
Table S5. Identification of community members that exhibit significant changes in their
abundance as a function of diet treatment and/or time.
Table S6. Microbial RNA-seq analysis of changes in community metatranscriptome as a
function of diet treatment with grouping of transcripts into KO groups.
Table S7. Microbial RNA-seq analysis of changes in B. vulgatus gene expression as a function of
diet treatment with grouping of transcripts into KO groups.
Table S8. Strain-level microbial RNA-seq analysis of the effects of vitamin A on gene expression
(summarized at the level of KO groups).
Table S9. Mouse weights as a function of diet treatment and time.
Table S10. In vitro retinoid sensitivities of Bacteroides strains.
Table S11. Strains, primers, and plasmids used in this study.
Table S12. Microbial RNA-seq analysis of differential gene expression between wild-type
B. vulgatus and Tn mutants (DESeq2).
Table S13. Bioinformatic characterization of AcrR regulons in human gut Bacteroides strains.
Table S14. High-resolution quantitative MS-based proteomic analysis of wild-type B. vulgatus
cultured in the presence of 1 mM retinol versus vehicle alone (0.02% DMSO).
Table S15. Effects of retinol, bile acids, and PAbN on growth of wild-type and mutant strains of
B. vulgatus and wild-type B. dorei.
Table S16. UPLC-MS analysis of the effects of dietary micronutrient deficiency on fecal bile acid
metabolites.
REFERENCES AND NOTES
1. R. L. Bailey, K. P. West Jr., R. E. Black, The epidemiology of global micronutrient
deficiencies. Ann. Nutr. Metab. 66,22–33 (2015).
2. Institute of Medicine Committee on Micronutrient Deficiencies; C. P. Howson,
E. T. Kennedy, A. Horwitz, Eds., Prevention of Micronutrient Deficiencies: Tools for
Policymakers and Public Health Workers (National Academy Press, 1998).
3. R. E. Black, L. H. Allen, Z. A. Bhutta, L. E. Caulfield, M. de Onis, M. Ezzati, C. Mathers,
J. Rivera; Maternal and Child Undernutrition Study Group, Maternal and child
undernutrition: Global and regional exposures and health consequences. Lancet 371,
243–260 (2008).
4. B. A. Underwood, Hypovitaminosis A: International programmatic issues. J. Nutr. 124,
1467S–1472S (1994).
5. World Health Organization, Guideline: Vitamin A Supplementation in Infants and Children
6–59 Months of Age (World Health Organization, 2011).
6. E. H. Harrison, Enzymes catalysing the hydrolysis of retinyl esters. Biochim. Biophys. Acta
1170,99–108 (1993).
7. E. Mayo-Wilson, A. Imdad, K. Herzer, M. Y. Yakoob, Z. A. Bhutta, Vitamin A supplements for
preventing mortality, illness, and blindness in children aged under 5: Systematic review
and meta-analysis. BMJ 343, d5094 (2011).
8. R. A. Salam, C. MacPhail, J. K. Das, Z. A. Bhutta, Effectiveness of micronutrient powders
(MNP) in women and children. BMC Public Health 3(suppl. 13), S22 (2013).
9. T. Gera and H. P. S. Sachdev, Effect of iron supplementation on incidence of infectious
illness in children: Systematic review. BMJ 325, 1142 (2002).
10. T. Jaeggi, G. A. M. Kortman, D. Moretti, C. Chassard, P. Holding, A. Dostal, J. Boekhorst,
H. M. Timmerman, D. W. Swinkels, H. Tjalsma, J. Njenga, A. Mwangi, J. Kvalsvig, C. Lacroix,
M. B. Zimmermann, Iron fortification adversely affects the gut microbiome, increases
pathogen abundance and induces intestinal inflammation in Kenyan infants. Gut 64,
731–742 (2014).
11. S. Sazawal, R. E. Black, M. Ramsan, H. M. Chwaya, R. J. Stoltzfus, A. Dutta, U. Dhingra,
I. Kabole, S. Deb, M. K. Othman, F. M. Kabole, Effects of routine prophylactic
supplementation with iron and folic acid on admission to hospital and mortality in
preschool children in a high malaria transmission setting: Community-based,
randomised, placebo-controlled trial. Lancet 367, 133–143 (2006).
12. J. Veenemans, P. Milligan, A. M. Prentice, L. R. A. Schouten, N. Inja, A. C. van der Heijden,
L. C. C. de Boer, E. J. S. Jansen, A. E. Koopmans, W. T. M. Enthoven, R. J. Kraaijenhagen,
A. Y. Demir, D. R. A. Uges, E. V. Mbugi, H. F. J. Savelkoul, H. Verhouf, Effect of
supplementation with zinc and other micronutrients on malaria in Tanzanian children: A
randomised trial. PLOS Med. 8, e1001125 (2011).
13. H. Drakesmith,A. M. Prentice,Hepcidinand the iron-infection axis.Science338,768–772 (2012).
14. S. Soofi, S. Cousens, S. P. Iqbal, T. Akhund, J. Khan, I. Ahmed, A. K. M. Zaidi, Z. A. Bhutta,
Effect of provision of daily zinc and iron with several micronutrients on growth and
morbidity among young children in Pakistan: A cluster-randomised trial. Lancet 382,
29–40 (2013).
15. S. Subramanian, S. Huq, T. Yatsunenko, R. Haque, M. Mahfuz, M. A. Alam, A. Benezra,
J. DeStefano, M. F. Meier, B. D. Muegge, M. J. Barratt, Q. Zhang, M. A. Province,
W. A. Petri Jr., T. Ahmed, J. I. Gordon, Persistent gut microbiota immaturity in
malnourished Bangladeshi children. Nature 509, 417–421 (2014).
SCIENCE TRANSLATIONAL MEDICINE |RESEARCH ARTICLE
Hibberd et al., Sci. Transl. Med. 9, eaal4069 (2017) 17 May 2017 15 of 17
by guest on November 28, 2017http://stm.sciencemag.org/Downloaded from

16. L. V. Blanton, M. R. Charbonneau, T. Salih, M. J. Barratt, S. Venkatesh, O. Ilkaveya,
S. Subramanian, M. J. Manary, I. Trehan, J. M. Jorgensen, Y.-M. Fan, B. Henrissat, S. A. Leyn,
D. A. Rodionov, A. L. Osterman, K. M. Maleta, C. B. Newgard, P. Ashorn, K. G. Dewey,
J. I. Gordon, Gut bacteria that prevent growth impairments transmitted by microbiota
from malnourished children. Science 351, aad3311 (2016).
17. J. D. Planer, Y. Peng, A. L. Kau, L. V. Blanton, I. M. Ndao, P. I. Tarr, B. B. Warner, J. I. Gordon,
Development of the gut microbiota and mucosal IgA responses in twins and gnotobiotic
mice. Nature 534, 263–266 (2016).
18. L. V. Blanton, M. J. Barratt, M. R. Charbonneau, T. Ahmed, J. I. Gordon, Childhood
undernutrition, the gut microbiota, and microbiota-directed therapeutics. Science 352,
1533 (2016).
19. S.Mandal,K.M.Godfrey,D.McDonald,W.V.Treuren,J.V.Bjørnholt,T.Midtvedt,
B.Moen,K.Rudi,R.Knight,A.L.Brantsæter,S.D.Peddada,M.Eggesbø,Fatand
vitamin intakes during pregnancy have stronger relations with a pro-inflammatory
maternal microbiota than does carbohydrate intake. Microbiome 4, 55 (2016).
20. B. S. Reddy, J. R. Pleasants, D. R. Zimmerman, B. S. Wostmann, Iron and copper utilization
in rabbits as affected by diet and germfree status. J. Nutr. 87, 189–196 (1965).
21. B. S. Reddy, J. R. Pleasants, B. S. Wostmann, Effect of intestinal microflora on iron and
zinc metabolism, and on activities of metalloenzymes in rats. J. Nutr. 102, 101–107
(1972).
22. J. G. Bieri, E. G. McDaniel, W. E. Rogers Jr., Survival of germfree rats without vitamin A.
Science 163, 574–575 (1969).
23. W. E. Rogers Jr., J. G. Bieri, E. G. McDaniel, Vitamin A deficiency in the germfree state.
Fed. Proc. 30, 1773–1778 (1971).
24. J. C. Smith, E. G. McDaniel, L. D. McBean, F. S. Doft, J. A. Halsted, Effect of microorganisms
upon zinc metabolism using germfree and conventional rats. J. Nutr. 102, 711–719
(1972).
25. H. K. Biesalski, Nutrition meets the microbiome: Micronutrients and the microbiota.
Ann. N Y. Acad. Sci. 1372,53–64 (2016).
26. J.M.Carrothers,M.A.York,S.L.Brooker,K.A.Lackey,J.E.Williams,B.Shafii,
W. J. Price, M. L. Settles, M. A. McGuire, M. K. McGuire, Fecal microbial community
structure is stable over time and related to variation in macronutrient and
micronutrient intakes in lactating women. J. Nutr. 14 5, 2379–2388 (2015).
27. T. Lee, T. Clavel, K. Smirnov, A. Schmidt, I. Lagkouvardos, A. Walker, M. Lucio, B. Michalke,
P. Schmitt-Kopplin, R. Fedorak, D. Haller, Oral versus intravenous iron replacement
therapy distinctly alters the gut microbiota and metabolome in patients with IBD. Gut 66,
863–871 (2017).
28. T. Werner, S. J. Wagner, I. Martínez, J. Walter, J.-S. Chang, T. Clavel, S. Kisling,
K. Schuemann, D. Haller, Depletion of luminal iron alters the gut microbiota and prevents
Crohn’s disease-like ileitis. Gut 60, 325–333 (2011).
29. L. Liu, X.-H. Tang, L. J. Gudas, Homeostasis of retinol in lecithin: Retinol acyltransferase
gene knockout mice fed a high retinol diet. Biochem. Pharmacol. 75, 2316–2324
(2008).
30. E. Hernandez-Alvarez, B. I. Pérez-Sacristán, I. Blanco-Navarro, E. Donoso-Navarro,
R. A. Silvestre-Mardomingo, F. Granado-Lorencio, Analysis of microsamples of human
faeces: A non-invasive approach to study the bioavailability of fat-soluble bioactive
compounds. Eur. J. Nutr. 54, 1371–1378 (2015).
31. A. L. Goodman, N. P. McNulty, Y. Zhao, D. Leip, R. D. Mitra, C. A. Lozupone, R. Knight,
J. I. Gordon, Identifying genetic determinants needed to establish a human gut symbiont
in its habitat. Cell Host Microbe 6, 279–289 (2009).
32. M. Wu, N. P. McNulty, D. A. Rodionov, M. S. Khoroshkin, N. W. Griffin, J. Cheng, P. Latreille,
R. A. Kerstetter, N. Terrapon, B. Henrissat, A. L. Osterman, J. I. Gordon, Genetic
determinants of in vivo fitness and diet responsiveness in multiple human gut
Bacteroides.Science 350, aac5992 (2015).
33. D. N. Crowell, M. S. Anderson, C. R. Raetz, Molecular cloning of the genes for lipid A
disaccharide synthase and UDP-N-acetylglucosamine acyltransferase in Escherichia coli.
J. Bacteriol. 168, 152–159 (1986).
34. D. Ma, D. N. Cook, M. Alberti, N. G. Pon, H. Nikaido, J. E. Hearst, Molecular cloning and
characterization of acrA and acrE genes of Escherichia coli.J. Bacteriol. 175, 6299–6313
(1993).
35. H. I. Zgurskaya, H. Nikaido, Bypassing the periplasm: Reconstitution of the AcrAB
multidrug efflux pump of Escherichia coli.Proc. Natl. Acad. Sci. U.S.A. 96, 7190–7195
(1999).
36. D. Du, Z. Wang, N. R. James, J. E. Voss, E. Klimont, T. Ohene-Agyei, H. Venter, W. Chiu,
B. F. Luisi, Structure of the AcrAB–TolC multidrug efflux pump. Nature 509, 512–515
(2014).
37. J.-S. Kim, H. Jeong, S. Song, H.-Y. Kim, K. Lee, J. Hyun, N.-C. Ha, Structure of the tripartite
multidrug efflux pump AcrAB-TolC suggests an alternative assembly mode. Mol. Cells 38,
180–186 (2015).
38. D. Ma, M. Alberti, C. Lynch, H. Nikaido, J. E. Hearst, The local repressor AcrR plays a
modulating role in the regulation of acrAB genes of Escherichia coli by global stress
signals. Mol. Microbiol. 19, 101–112 (1996).
39. A. Raczkowska, J. Trzos, O. Lewandowska, M. Nieckarz, K. Brzostek, Expression of the
AcrAB Components of the AcrAB-TolC multidrug efflux pump of Yersinia enterocolitica is
subject to dual regulation by OmpR. PLOS ONE 10, e0124248 (2015).
40. E. C. Martens, H. C. Chiang, J. I. Gordon, Mucosal glycan foraging enhances fitness and
transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe 4,
447–457 (2008).
41. T. E. Renau, R. Léger, E. M. Flamme, J. Sangalang, M. W. She, R. Yen, C. L. Gannon,
D. Griffith, S. Chamberland, O. Lomovskaya, S. J. Hecker, V. J. Lee, T. Ohta, K. Nakayama,
Inhibitors of efflux pumps in Pseudomonas aeruginosa potentiate the activity of the
fluoroquinolone antibacterial levofloxacin. J. Med. Chem. 42, 4928–4931 (1999).
42. S. I. Sayin, A. Wahlström, J. Felin, S. Jäntti, H.-U. Marschall, K. Bamberg, B. Angelin,
T. Hyötyläinen, M. Orešič, F. Bäckhed, Gut microbiota regulates bile acid metabolism by
reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist.
Cell Metab. 17, 225–235 (2013).
43. N. Dey, V. E. Wagner, L. V. Blanton, J. Cheng, L. Fontana, R. Haque, T. Ahmed, J. I. Gordon,
Regulators of gut motility revealed by a gnotobiotic model of diet-microbiome
interactions related to travel. Cell 163,95–107 (2015).
44. D. G. Thanassi, L. W. Cheng, H. Nikaido, Active efflux of bile salts by Escherichia coli.
J. Bacteriol. 179, 2512–2518 (1997).
45. F. E. Rey, J. J. Faith, J. Bain, M. J. Muehlbauer, R. D. Stevens, C. B. Newgard, J. I. Gordon,
Dissecting the in vivo metabolic potential of two human gut acetogens. J. Biol. Chem.
285, 22082–22090 (2010).
46. J. J. Faith, N. P. McNulty, F. E. Rey, J. I. Gordon, Predicting a human gut microbiota’s
response to diet in gnotobiotic mice. Science 333, 101–104 (2011).
47. N. P. McNulty, T. Yatsunenko, A. Hsiao, J. J. Faith, B. D. Muegge, A. L. Goodman,
B. Henrissat, R. Oozeer, S. Cools-Portier, G. Gobert, C. Chervaux, D. Knights,
C. A. Lozupone, R. Knight, A. E. Duncan, J. R. Bain, M. J. Muehlbauer, C. B. Newgard,
A. C. Heath, J. I. Gordon, The impact of a consortium of fermented milk strains on the gut
microbiome of gnotobiotic mice and monozygotic twins. Sci. Transl. Med. 3, 106ra106 (2011).
48. N.P.McNulty,M.Wu,A.R.Erickson,E.C.Martens,N.A.Pudlo,B.D.Muegge,
B. Henrissat, R. L. Hettich, J. I. Gordon, Effects of diet on resource utilization by a
defined model human gut microbiota containing Bacteroides cellulosilyticus WH2, a
symbiont with an extensive glycobiome. PLOS Biol. 11, e1001637 (2013).
49. A. L. Goodman, G. Kallstrom, J. J. Faith, A. Reyes, A. Moore, G. Dantas, J. I. Gordon,
Extensive personal human gut microbiota culture collections characterized and
manipulated in gnotobiotic mice. Proc. Natl. Acad. Sci. U.S.A. 108, 6252–6257 (2011).
50. J. G. Caporaso, J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello,
N. Fierer, A. G. Peña, J. K. Goodrich, J. I. Gordon, G. A. Huttley, S. T. Kelley, D. Knights,
J. E. Koenig, R. E. Ley, C. A. Lozupone, D. McDonald, B. D. Muegge, M. Pirrung, J. Reeder,
J. R. Sevinsky, P. J. Turnbaugh, W. A. Walters, J. Widmann, T. Yatsunenko, J. Zaneveld,
R. Knight, QIIME allows analysis of high-throughput community sequencing data.
Nat. Methods 7, 335–336 (2010).
51. R Development Core Team, R: A Language and Environment for Statistical Computing (R
Foundation for Statistical Computing, 2015).
52. B. Langmead, C. Trapnell, M. Pop, S. L. Salzberg, Ultrafast and memory-efficient alignment
of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
53. M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for
RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
54. R.Overbeek,R.Olson,G.D.Pusch,G.J.Olsen,J.J.Davis,T.Disz,R.A.Edwards,
S. Gerdes, B. Parrello, M. Shukla, V. Vonstein, A. R. Wattam, F. Xia, R. Stevens, The SEED
and the rapid annotation of microbial genomes using subsystems technology (RAST).
Nucleic Acids Res. 42, D206–D214 (2013).
55. G. Yu, L.-G. Wang, Y. Han, Q.-Y. He, clusterProfiler: An R package for comparing biological
themes among gene clusters. OMICS 16, 284–287 (2012).
56. W. Luo, M. S. Friedman, K. Shedden, K. D. Hankenson, P. J. Woolf, GAGE: Generally
applicable gene set enrichment for pathways analysis. BMC Bioinformatics 10, 161 (2009).
57. D. L. Tabb, C. G. Fernando, M. C. Chambers, MyriMatch: Highly accurate tandem mass
spectral peptide identification by multivariate hypergeometric analysis. J. Proteome Res.
6, 654–661 (2007).
58. Z.-Q. Ma, S. Dasari, M. C. Chambers, M. D. Litton, S. M. Sobecki, L. J. Zimmerman,
P. J. Halvey, B. Schilling, P. M. Drake, B. W. Gibson, D. L. Tabb, IDPicker 2.0: Improved
protein assembly with high discrimination peptide information filtering. J. Proteome Res.
8, 3872–3781 (2009).
59. Q. Xu, M. G. Resch, K. Podkaminer, S. Yang, J. O. Baker, B. S. Donohoe, C. Wilson,
D. M. Klingeman, D. G. Olson, S. R. Decker, R. J. Giannone, R. L. Hettich, S. D. Brown, L. R. Lynd,
E. A. Bayer, M. E. Himmel, Y. J. Bomble, Dramatic performance of Clostridium thermocellum
explained by its wide range of cellulose modalities. Sci. Adv. 2, e1501254 (2016).
60. T. Taverner, Y. V. Karpievitch, A. D. Polpitiya, J. N. Brown, A. R. Dabney, G. A. Anderson,
R. D. Smith, DanteR: An extensible R-based tool for quantitative analysis of -omics data.
Bioinformatics 28, 2404–2406 (2012).
61. P. S. Novichkov, D. A. Rodionov, E. D. Stavrovskaya, E. S. Novichkova, A. E. Kazakov,
M. S. Gelfand, A. P. Arkin, A. A. Mironov, I. Dubchak, RegPredict: An integrated system for
SCIENCE TRANSLATIONAL MEDICINE |RESEARCH ARTICLE
Hibberd et al., Sci. Transl. Med. 9, eaal4069 (2017) 17 May 2017 16 of 17
by guest on November 28, 2017http://stm.sciencemag.org/Downloaded from

regulon inference in prokaryotes by comparative genomics approach. Nucleic Acids Res.
38, W299–W307 (2010).
62. D. A. Rodionov, Comparative genomic reconstruction of transcriptional regulatory
networks in bacteria. Chem. Rev. 107, 3467–3497 (2007).
63. G. E. Crooks, G. Hon, J.-M. Chandonia, S. E. Brenner, WebLogo: A sequence logo generator.
Genome Res. 14, 1188–1190 (2004).
64. E. Mossessova, C. D. Lima, Ulp1-SUMO crystal structure and genetic analysis reveal
conserved interactions and a regulatory element essential for cell growth in yeast.
Mol. Cell 5, 865–876 (2000).
65. C. Chang, C. Tesar, X. Li, Y. Kim, D. A. Rodionov, A. Joachimiak, A novel transcriptional
regulator of L-arabinose utilization in human gut bacteria. Nucleic Acids Res. 43,
10546–10559 (2015).
66. D. A. Ravcheev, X. Li, H. Latif, K. Zengler, S. A. Leyn, Y. D. Korostelev, A. E. Kazakov,
P. S. Novichkov, A. L. Osterman, D. A. Rodionov, Transcriptional regulation of central
carbon and energy metabolism in bacteria by redox-responsive repressor Rex. J. Bacteriol.
194, 1145–1157 (2012).
Acknowledgments: We thank D. O’Donnell, M. Karlsson, and S. Wagoner for their assistance
with gnotobiotic mouse husbandry; B. Mickelson at Envigo for her guidance in designing
experimental diets; and A. Hsaio and E. Martens for providing suggestions and reagents for
genetic manipulation of B. vulgatus.Funding: This work was supported in part by a grant from
the NIH (DK30292). D.A.R. was supported by the Russian Science Foundation (grant 14-14-
00289). Author contributions: M.C.H., A.L.O., and J.I.G. designed the experiments.
Experiments were performed by M.C.H. (generation of gnotobiotic mouse model; COPRO-Seq
and microbial RNA-seq analyses of microbial communities harvested from these mice; and
growth, INSeq, MS, and pharmacologic studies of B. vulgatus and B. dorei in vitro), M.W.
(construction of B. vulgatus INSeq library), D.A.R. and X.L. (expression and purification of
B. vulgatus and B. dorei AcrR expressed in E. coli and in vitro assays of AcrR binding to target
DNA), R.J.G. (MS-based proteomic studies), and J.C. (targeted MS of retinol in efflux assays).
M.C.H., M.W., D.A.R., N.W.G., R.J.G., R.L.H., A.L.O., and J.I.G. analyzed the data. M.C.H., M.J.B,
A.L.O., and J.I.G. wrote the paper. Competing interests: J.I.G. is a co-founder of Matatu Inc.,
a company characterizing the role of diet-by-microbiota interactions in animal health. The
other authors declare that they have no competing interests. Data and materials availability:
COPRO-Seq, microbial RNA-seq, and INSeq data sets have been deposited in the European
Nucleotide Archive (accession number PRJEB15673). All of the bacterial strains used in this
work were purchased from American Type Culture Collection (ATCC) or Deutsche Sammlung
von Mikroorganismen und Zellkulturen GmbH (DSMZ).
Submitted 15 November 2016
Accepted 14 March 2017
Published 17 May 2017
10.1126/scitranslmed.aal4069
Citation: M. C. Hibberd, M. Wu, D. A. Rodionov, X. Li, J. Cheng, N. W. Griffin, M. J. Barratt,
R. J. Giannone, R. L. Hettich, A. L. Osterman, J. I. Gordon, The effects of micronutrient deficiencies
on bacterial species from the human gut microbiota. Sci. Transl. Med. 9, eaal4069 (2017).
SCIENCE TRANSLATIONAL MEDICINE |RESEARCH ARTICLE
Hibberd et al., Sci. Transl. Med. 9, eaal4069 (2017) 17 May 2017 17 of 17
by guest on November 28, 2017http://stm.sciencemag.org/Downloaded from

microbiota
The effects of micronutrient deficiencies on bacterial species from the human gut
Richard J. Giannone, Robert L. Hettich, Andrei L. Osterman and Jeffrey I. Gordon
Matthew C. Hibberd, Meng Wu, Dmitry A. Rodionov, Xiaoqing Li, Jiye Cheng, Nicholas W. Griffin, Michael J. Barratt,
DOI: 10.1126/scitranslmed.aal4069
, eaal4069.9Sci Transl Med
the human host and the gut microbiota they possess.
system. These results suggest that micronutrient imbalances should be considered from the perspective of both
fitness through the activity of the bacterial AcrAB-TolC effluxB. vulgatusindicated that retinol treatment affected
vitamin A deficiency, exhibiting an increase in its abundance. Genetic, multi-omic, and pharmacologic analyses
growth in gnotobiotic mouse models of postnatal human microbiota development, had the biggest response to
, a bacterial species positively correlated with hostBacteroides vulgatusbacterial community and gene expression.
harboring bacterial strains common in the human gut. Vitamin A had the greatest effect on the structure of the
. compare the effects of acute dietary deficiency of vitamin A, folate, iron, or zinc in gnotobiotic miceet alHibberd
Deficiencies in vitamins and minerals (micronutrients) are a global health challenge. In a new study,
A gut bacterial view of micronutrient deficiency
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