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Growing evidence indicates that the human gut microbiota interacts with xenobiotics, including persistent organic pollutants and foodborne chemicals. The toxicological relevance of the gut microbiota-pollutant interplay is of great concern since chemicals may disrupt gut microbiota functions, with a potential impairment of host homeostasis. Herein we report within batch fermentation systems the impact of food contaminants (polycyclic aromatic hydrocarbons, polychlorobiphenyls, brominated flame retardants, dioxins, pesticides and heterocyclic amines) on the human gut microbiota by metatranscriptome and volatolome i.e. "volatile organic compounds" analyses. Inflammatory host cell response caused by microbial metabolites following the pollutants-gut microbiota interaction, was evaluated on intestinal epithelial TC7 cells. Changes in the volatolome pattern analyzed via solid-phase microextraction coupled to gas chromatography-mass spectrometry mainly resulted in an imbalance in sulfur, phenolic and ester compounds. An increase in microbial gene expression related to lipid metabolism processes as well as the plasma membrane, periplasmic space, protein kinase activity and receptor activity was observed following dioxin, brominated flame retardant and heterocyclic amine exposure. Conversely, all food contaminants tested induced a decreased in microbial transcript levels related to ribosome, translation and nucleic acid binding. Finally, we demonstrated that gut microbiota metabolites resulting from pollutant disturbances may promote the establishment of a pro-inflammatory state in the gut, as stated with the release of cytokine IL-8 by intestinal epithelial cells.
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SCIENtIFIC REPORTS | (2018) 8:11006 | DOI:10.1038/s41598-018-29376-9
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Food Chemicals Disrupt Human
Gut Microbiota Activity And Impact
Intestinal Homeostasis As Revealed
By In Vitro Systems
Clémence Defois1, Jérémy Ratel2, Ghislain Garrait1, Sylvain Denis1, Olivier Le Go1,
Jérémie Talvas3,4, Pascale Mosoni1, Erwan Engel2 & Pierre Peyret1
Growing evidence indicates that the human gut microbiota interacts with xenobiotics, including
persistent organic pollutants and foodborne chemicals. The toxicological relevance of the gut
microbiota-pollutant interplay is of great concern since chemicals may disrupt gut microbiota functions,
with a potential impairment of host homeostasis. Herein we report within batch fermentation systems
the impact of food contaminants (polycyclic aromatic hydrocarbons, polychlorobiphenyls, brominated
ame retardants, dioxins, pesticides and heterocyclic amines) on the human gut microbiota by
metatranscriptome and volatolome i.e. “volatile organic compounds” analyses. Inammatory host
cell response caused by microbial metabolites following the pollutants-gut microbiota interaction,
was evaluated on intestinal epithelial TC7 cells. Changes in the volatolome pattern analyzed via solid-
phase microextraction coupled to gas chromatography-mass spectrometry mainly resulted in an
imbalance in sulfur, phenolic and ester compounds. An increase in microbial gene expression related to
lipid metabolism processes as well as the plasma membrane, periplasmic space, protein kinase activity
and receptor activity was observed following dioxin, brominated ame retardant and heterocyclic
amine exposure. Conversely, all food contaminants tested induced a decreased in microbial transcript
levels related to ribosome, translation and nucleic acid binding. Finally, we demonstrated that gut
microbiota metabolites resulting from pollutant disturbances may promote the establishment of a pro-
inammatory state in the gut, as stated with the release of cytokine IL-8 by intestinal epithelial cells.
People are exposed on a daily basis to a variety of environmental pollutants arising from industries, transports,
heating or agriculture. Persistent organic pollutants (POPs), such as polycyclic aromatic hydrocarbons (PAHs),
polychlorobiphenyls (PCBs), brominated ame retardants (BFRs), polychlorinated dibenzo-p-dioxins (PCDDs)
and pesticides, are compounds that cause concern because of their toxicity, persistence in the environment,
capacity to move over very long distances and ability to accumulate in organisms1. Exposure to these pollutants
has been linked to various pathologies, including metabolic2, immune3,4 and reproductive disturbances5 and even
cancers6,7. Heterocyclic amines (HCAs) are foodborne chemicals produced by some cooking practices that are
similar to POPs in terms of structure and toxic properties. HCAs are mutagenic and characterized as possible
human carcinogens8, increasing the risk of the emergence of colorectal cancer9.
Because exposure to POPs and foodborne chemicals occurs mainly through the diet, the host gastrointesti-
nal tract (GIT) and the gut microbiota are likely to be exposed to these compounds. Recent studies have shown
that xenobiotic-microbiota interactions may lead to modications of the gut microbiota composition and func-
tions, which could then impact host homeostasis10,11. In a murine model, Zhang and colleagues have shown that
ve days of orally administered 2,3,7,8-tetrachlorodibenzofuran (TCDF) lead to dramatic modications of the
structure of the mice gut microbiota by reducing the ratio of Firmicutes to Bacteroidetes, accompanied by acti-
vation of the microbial fermentation (elevation of short chain fatty acids in feces and cecal content extracts)12.
Conversely, chronic exposure (26 weeks) of the mouse gut microbiota to 2,3,7,8-tetrachlorodibenzo-p-dioxin
1MEDIS, Université Clermont Auvergne, INRA, Clermont-Ferrand, France. 2UR370 QuaPA, MASS Group, INRA, Saint-
Genès-Champanelle, France. 3UMR 1019, Unité de Nutrition Humaine, Equipe ECREIN, CLARA, Université Clermont
Auvergne, Clermont-Ferrand, France. 4UMR 1019, Unité de Nutrition Humaine, CRNH Auvergne, INRA, Clermont-
Ferrand, France. Correspondence and requests for materials should be addressed to P.P. (email: pierre.peyret@uca.fr)
Received: 30 January 2018
Accepted: 4 July 2018
Published: xx xx xxxx
OPEN
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(TCDD) induced a major increase in the Firmicutes to Bacteroidetes ratio, while the same exposure on a short
time scale (2 days) did not appear to have any signicant eects13. To complement these observations, a recent
study showed an increase of 13 antimicrobial resistance genes and 1 mobile genetic element gene along with
a bloom of Enterobacteriaceae (bacterial groups harboring these genes) within 8 days of TCDD exposure in the
murine gut microbiome14. Finally, acute exposure to a mixture of PCBs or benzo[a]pyrene (B[a]P) induced either
a substantial decrease in the level of Proteobacteria in the mouse gut microbiota or showed no signicant impact
on the human gut microbiota but induced a shi in microbial metabolic activity, respectively15,16.
e gut microbiota may, in return, metabolize chemical compounds, which might deect their therapeu-
tic (drugs) or toxic (environmental and foodborne chemicals) properties toward the host17,18. A previous work
reported that six commonly used host-targeted drugs, induced 328 microbial genes, most of which could be asso-
ciated with drug transport or degradation19. Dichlorodiphenyltrichloroethane (DDT), an organochlorine insec-
ticide, has been found to be metabolized to dichlorodiphenyldichlorophenylethane (DDD) by rat and human
fecal microbiota, although it remains unclear whether this biotransformation corresponds to bioactivation or
detoxication because both DDT and DDD are probable endocrine disruptors in humans20. Finally, the human
gut microbiota has been shown to biotransform B[a]P and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine
(PhIP) into 7-hydroxybenzo[a]pyrene and 7-hydroxy-5-methyl-3-phenyl-6,7,8,9-tetrahydropyrido[3,2:4,5]
imidazo[1,2-a]pyrimidin-5-ium chloride (PhIP-M1) identied as B[a]P and PhIP derivatives, respectively2123.
Beside metabolomics where all metabolites produced by living organisms are analyzed, volatolomics focuses
on the study of volatile metabolites reducing the complexity of the analysis. is method has proven to be a prom-
ising omic approach to diagnose metabolism changes in response to physiological stresses induced by pathol-
ogy24 or xenobiotic exposure25. Regarding gastrointestinal or inammatory disorders, Ahmed et al. reported
that changes in fecal VOC pattern may result from changes in the microbiota and/or pathologies in the GIT26,27.
Recently, a previous work from Defois et al. showed in vitro that volatolomics along with metatranscriptomics
enable to decipher a rapid change in the gut microbiota activity following acute exposure to B[a]P while the gut
microbiota composition remained stable16.
In the present work, we characterized within in vitro systems the impact of six POPs and/or foodborne chem-
icals that are frequently found in the diet and considered them as models for the eects of various classes of
pollutants on human gut microbiota functions at the metatranscriptome and volatolome levels. We also meas-
ured the inammatory properties of the metabolites arising from this xenobiotic-gut microbiota interaction to
characterize their eects on gut homeostasis.
Results
Pollutant exposure alters the fecal microbiota volatolome pattern. Analysis of the microbial vol-
atolome is a promising approach to detect an imbalance of microbial activity. Five pollutants (TCDD, deltame-
thrin, HBCD, B[a]P and PhIP) and one mixture of pollutants (PAHs) were screened for their impact on human
fecal microbiota functions following a 24-hr exposure. Briey, the pollutants were added at dierent concen-
trations into Hungate tubes along with a fecal microbiota suspension containing human in vitro-cultured feces.
Volatolome patterns were assessed by solid-phase microextraction coupled to gas chromatography-mass spec-
trometry (SPME-GC-MS) analysis and vehicle 1 and 2 conditions (methanol and methanol:dichloromethane
mixture, respectively) were conducted to remove the eects of the vehicles on the microbial community.
More than 250 volatile organic compounds (VOCs) were detected by SPME-GC-MS following the 24 hr of
pollutant exposure. We identied 5, 2, 7 and 4 VOCs that were signicantly altered by deltamethrin, PhIP, TCDD
and PAHs compared to their respective vehicle condition (Table1). Sulfur compounds (including thioesters)
were increased in samples exposed to deltamethrin and TCDD conditions with 3 and 5 compounds, respectively.
One ketone (2,2,4,4-tetramethyl-3-pentanone) decreased in deltamethrin, PhIP and TCDD conditions and one
hydrocarbon (m- or p-xylene) decreased in deltamethrin and PhIP conditions. Following PAHs exposure, one
phenol (4-methylphenol), one ester (propylphenylacetate) and one ketone (methylacetophenone) increased while
one unknown compound decreased. Finally no signicantly altered compounds were found following B[a]P and
HBCD exposure.
Pollutant exposure alters the fecal microbiota metatranscriptome. The volatolome analysis
showed that four tested chemicals (deltamethrin, PhIP, TCDD and PAHs) signicantly shied the microbial vola-
tile pattern, suggesting that the activity of the microbiota might have been aected. us, using RNA-sequencing,
we investigated the microbial response to chemicals by analyzing the dierential metatranscriptome between
exposed and non-exposed microbiota and then deducing the metabolic pathways that may have been altered.
Functional assignation was realized at the gene ontology (GO) slim term and at the gene family levels, and the
results are presented as copies per million units (CPM) abundances. To discard the eects of both vehicles on the
fecal microbiota, the pollutant samples were directly compared to the corresponding vehicle samples. Following
the 24 hr of exposure, variations observed at the GO level clustered the pollutant samples into two main groups
(Fig.1). e PAH, B[a]P and deltamethrin samples showed a strong downregulation of GO slim terms together
with a weak upregulation of slim terms, whereas the PhIP, TCDD and HBCD samples showed a strong upreg-
ulation of GO slim terms together with a weak downregulation of slim terms. Although each pollutant induced
specic transcriptomic responses, general trends were largely shared by the six pollutant samples, such as an
increase in transcript levels related to the lipid metabolism process, plasma membrane, periplasmic space, protein
kinase activity and receptor activity. Conversely, a decrease in transcript levels related to ribosome, translation
and nucleic acid binding was observed (Fig.2).
At the gene family level, the number and mean abundance of the dierentially expressed genes (>3-fold
change) diered among the pollutant samples (Fig.3). e number of upregulated genes varies from 157 to 456
for the B[a]P and PAH exposure, respectively. e number of downregulated genes varied from 174 to 245 for
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SCIENtIFIC REPORTS | (2018) 8:11006 | DOI:10.1038/s41598-018-29376-9
the PhIP and PAH exposure, respectively (Fig.3A). While the mean abundance of upregulated genes was within
a similar range for all samples (from 29 CPM for the B[a]P to 43 for the HBCD samples), the mean abundance
of downregulated genes was far higher for the PAH sample (from 7 CPM for the deltamethrin to 63 for the PAH
samples) (Fig.3B). e high mean abundance of downregulated genes in the PAH sample might explain the sub-
stantial downregulation observed for some GO slim terms (Fig.1).
Among the dierentially expressed genes, we identied three rubredoxin (Rdx)-coding genes that were found
in the most upregulated genes in the PhIP and PAH samples compared with their respective vehicle samples (27
to 437-fold change) (Supplementary Data1). e products of this gene family have been implicated in the alkane
degradation pathway, which is part of hydrocarbon metabolism28,29.
Some genes were found to be specically induced by the pollutants (not expressed in their respective vehicle
condition) and will be referred as “pollutant-specic genes” (Supplementary Data2). PhIP and PAHs shared the
Figure 1. Transcript levels related to microbial GO slim terms that were up and downregulated aer the 24 hr
of pollutant exposure. Analysis was performed on the pooled rRNA-depleted RNA arising from ve technical
replicates. Variations are expressed as the Z-Score. Lines represent GO slim terms, columns represent pollutant
samples.
Volatile Metabolite m/zaLRIbIDc
p-valuedRatio (Pollutant / Vehicle)
DeltaM PhIP TCDD PAHs DeltaM PhIP TCDD PAHs
Sulfur compounds
Carbon disulde 76 <600 a,b 7.4E-04 3.80
Dimethyl disulde 94 748 a,b 2.3E-03 1.8E-03 1.69 2.14
Dimethyl trisulde 126 983 a,b 2.5E-03 5.5E-05 1.78 2.39
4- or 5-methyl-2-acetylthiazole 126 1116 a,b 2.3E-03 1.51
Dimethyl tetrasulde 79 1243 a,b 1.0E-03 9.6E-04 3.68 6.21
ioesters
S-methyl 3-methylbutanethioate 85 945 a 2.3E-03 1.46
Phenols
4-methylphenol 105 1076 a,b 3.0E-04 1.30
Esters
Propylphenylacetate 91 1345 a,b 3.0E-03 2.86
Ketones
2,2,4,4-tetramethyl-3-pentanone 85 939 a,b 4.0E-06 0.0E + 00 2.0E-06 0.52 0.56 0.53
Methylacetophenone 119 1198 a,b 2.2E-04 1.61
Hydrocarbons
m- or p-xylene 91 875 a,b 4.0E-04 2.1E-04 0.38 0.38
Unknown
Unknown 57 <600 8.3E-04 0.37
Tot ale5 2 7 4
Table 1. Volatile metabolites detected in the fecal microbiota volatolome as signicantly altered by the
pollutants. DeltaM: deltamethrin. aMass fragment used for peak area determination. bLinear retention index on
a RTX-5MS capillary column. cTentative identication based on (a) mass spectrum, (b) linear retention index
from the literature. dP-values corrected for multiple testing. eTotal number of volatiles signicantly altered by
the pollutants.
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SCIENtIFIC REPORTS | (2018) 8:11006 | DOI:10.1038/s41598-018-29376-9
largest number of pollutant-specic genes (41 genes), highlighting common microbial metabolic pathways acti-
vated by the exposure (Fig.4A). Conversely, the PAH sample shared few pollutant-specic genes (7 to 11 genes)
with the other chemicals (except with PhIP). We identied 109 to 1 pollutant-specic genes shared by 2 to 6 of the
chemicals, respectively (Fig.4B and Supplementary Data2). Among the pollutant-specic genes, a Rdx-coding
gene (dierent from the three others previously described) was the most highly induced gene by the HBCD (344
CPM). Finally, a gene that was specically induced by the PhIP and PAH pollutants encodes an uncharacterized
protein harboring the Toluene_X Outer Membrane Transport family domain (UniRef50_R5K1C5). Proteins of
this family are involved in toluene catabolism and the degradation of aromatic hydrocarbons. One gene that was
specically expressed in response to the 6 pollutants was found to be an uncharacterized protein-coding gene
(UniRef50_R7EHV5) (Supplementary Data2).
Fermentation-derived supernatants do not induce TC7 cell death. As shown above, pollutant expo-
sure modied the microbial activity and thus modied the compounds produced in the fermentation-derived
supernatants (FDS). We can assume that the microbial community may also metabolize the pollutants and con-
vert them into more or less harmful compounds towards the host. ese compounds may induce cell damage in
the intestinal epithelium, potentially through the release of inammatory molecules.
To test these hypotheses, we exposed TC7 cells to FDS for 4 hr. Cells were analyzed by ow cytometry to
assess their viability, and the cell culture supernatant was harvested to quantify pro (IL-8, TNFα) and anti (IL-
10) inammatory cytokine release. No dierences in the proportion of necrotic or apoptotic cells were observed
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
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TCDD
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
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B[a]P
-1
-0,8
-0,6
-0,4
-0,2
0
0,2
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0,6
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PhIP
-1
-0,8
-0,6
-0,4
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0
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PAHs
-1
-0,8
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0
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HBCD
-1
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-0,2
0
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Deltamethrin
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Figure 2. Transcript levels related to microbial GO slim terms that were up and downregulated aer the 24 hr
of pollutant exposure. Variations are expressed as the log2 of the pollutant and the vehicle CPM abundance ratio
(y-axis). Analysis was performed on the pooled rRNA-depleted RNA arising from ve technical replicates. GO
slim terms are represented on the x-axis. GO: gene ontology.
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between the pollutant and vehicle samples. Only exposure to 10% DMSO, a necrotic and apoptotic reagent used
as positive control, led to a signicant increase in both necrotic and apoptotic cells, making up 25.7% and 7.9%
of the total cells, respectively (Supplementary Figure1). FDS was also compared to fecal microbiota-free colon
medium supplemented with each pollutant at its initial experimental concentration. Without the fecal microbi-
ota, a slight increase in necrotic (and apoptotic for PAHs) cells was observed for the PhIP, B[a]P and PAH samples
(Fig.5). Necrotic cells increased from 0.72, 0.72 and 0.76% to 1.40, 1.31 and 1.49% in the PhIP, B[a]P and PAH
samples, respectively. Apoptotic cells in the PAH sample increased from 0.64 to 1.14%.
Fermentation-derived supernatants alter IL-8 production in TC7 cells. IL-8, TNFα and IL-10
release was measured in TC7 cell supernatants to highlight a putative pro- or anti-inammatory cell response.
e vehicle samples did not induce the release of IL-8 compared with DMEM (Fig.6). However, the control
condition induced a slight signicant increase in IL-8 compared with the vehicle 1 condition (62.1 to 101.5 ng/mL).
e deltamethrin, HBCD and PAH samples showed induction of a signicant increase in IL-8 compared with
their associated vehicle sample. e strongest increase was observed for the PAH sample, which exhibited a
3.85-fold increase (from 62.1 to 239 ng/mL). e responses to PAHs and the IL-1β (pro-inammatory control)
were similar, with 239 and 298 ng/mL of IL-8 release. Compared with the microbiota-free conditions, signicant
variations were observed for the deltamethrin and PAH samples, with, on average, a 2-fold increase. Interestingly,
the PhIP and B[a]P samples showed induction of less IL-8 release compared with the microbiota-free condition,
but these variations were not signicant. In contrast to IL-8, TNFα and IL-10 were not detected in any of the
culture cell supernatants.
Discussion
Food pollution by chemicals is of great concern due to the toxicity of the compounds that accumulate in the food
chain and because the exposure occurs chronically throughout life. While the toxicity toward the host has been
evaluated for some representatives of the main groups of contaminants, their impact on the gut microbiota has
received less attention. In this work, we investigated the impact of six pollutants on human gut microbiota activity
at the volatolome and metatranscriptome levels. e microbial- and chemical-derived compounds generated
0100 200300 400500 600700 80
0
B[a]P
Deltamethrin
TCDD
HBCD
PhIP
PAHs
Number of genes
Pollutants
upregulated genesdownregulated genes
0
10
20
30
40
50
60
70
B[a]P
Deltamethrin
TCDD
HBCD
PhIP
PAHs
upregulated genesdownregulated genes
A
B
Number of genes
Figure 3. Dierentially expressed microbial genes aer the 24 hr of pollutant exposure. (A) Number of
dierentially expressed genes. (B) Mean abundances in CPM of the dierentially expressed genes. Analysis was
performed on the pooled rRNA-depleted RNA arising from ve technical replicates. Only genes with at least
a 3-fold change are represented, and values were derived from a comparison between the pollutant and the
vehicle condition.
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SCIENtIFIC REPORTS | (2018) 8:11006 | DOI:10.1038/s41598-018-29376-9
during acute (24 hr) exposure of the microbiota to the pollutants were then assessed in TC7 cell monolayers to
characterize their toxicity and inammatory properties in the host.
e doses used in this work are lower or in the range of previous studies carried out to examine the impact
of TCDD, PhIP and B[a]P on the gut microbiota, allowing us to make comparisons and generate discus-
sions13,14,16,21,22. e doses used for PAH, HBCD and deltamethrin exposures were far lower than those used for
toxicity studies in mice3032. e doses used in this study are still higher than the expected daily consumption.
However, the purpose of this study was to investigate a unique exposure to pollutants that might provide initial
insights into their eects on the human gut microbiota. Of course, further studies will be necessary to mimic the
chronic exposure that occurs throughout a person’s life.
e study of the physiology of the GIT and the role of the gut microbiota is dicult in humans due to ethical
(e.g. exposure to pathogens, chemicals, pharmaceuticals) and technical (diculty to access the GIT) aspects,
most oen limiting studies to fecal analysis (non-invasive, cheap). Our work was based on in vitro systems thus
allowing us to decipher the direct impact of the pollutants on the gut microbiota. Such systems have been used
A
B
Pollutant specific response Pollutant shared response
940
109
35
15
4
1
1 2 3 4 5 6
NUMBER OF GENES
NUMBER OF POLLUTANTS
TCDD HBCD Deltamethrin B[a]P PhIP PAHs
31 28 24 24 8 TCDD
39 30 33 8 HBCD
29 27 11 Deltamethrin
19 7 B[a]P
41 PhIP
PAHs
Figure 4. Microbial genes specically induced by pollutants aer the 24 hr of exposure. (A) Number of
pollutant-specic genes shared between each couple of pollutants. (B) Representation of the core pollutant-
specic genes. Analysis was performed on the pooled rRNA-depleted RNA arising from ve technical replicates.
0,00
0,50
1,00
1,50
2,00
2,50
3,00
3,50
4,00
% of cells
necrosis apoptosis
* * *
*
Figure 5. Percentage of necrotic and apoptotic TC7 cells aer 4 hr of FDS and microbiota-free medium
exposure. Values are the mean of the three replicates ± SEM. Signicant variations were assessed using the
Mann-Whitney test (p-value < 0.05). mf: microbiota-free.
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in various applications such as the fermentation of food components3335, the impact of antibiotics on the micro-
biota of patients with Crohn’s disease36, the metabolism of gut methanogenic archaea37 or the degradation of
toxic compounds such as PAHs by the gut microbial metabolism21. Meanwhile these models are missing the host
counterpart, thus the results arising from this work should be considered carefully until in vivo experiments are
conducted.
VOCs are small molecules produced by living organisms that play an important role in chemical ecology,
specically in the biological interactions between organisms38. During the past decade, VOCs have gained rec-
ognition in the health care eld because they are presently used as biomarkers to detect various human diseases,
including cancers39,40. In this work, we analyzed the volatolome pattern of the fecal microbiota aer 24 hr of pol-
lutant exposure. Overall, a partially shared response between deltamethrin and TCDD exposure was observed,
with an enrichment of the microbial volatolome with sulfur compounds and a decrease in one ketone compound
(2,2,4,4-tetramethyl-3-pentanone) while following PhIP and PAHs exposure the volatolome patterns appeared
more specic (Table1). Surprisingly we observed no signicant shi in the VOC pattern following HBCD and
B[a]P exposure contrary to a previous work in which two human fecal microbiota were exposed to B[a]P at three
gradual concentrations16. More precisely, in the rst study, at the same concentration that was used in the present
work, a decrease in 1 ketone and 1 ester compound was observed. However, while not signicant (p-value< 0.05
but >0.05/ni), similar variations have been highlighted in the present work following B[a]P exposure including
the decrease of 1 ketone and 9 ester compounds (data not shown). Until now, studies examining microbial VOCs
have been scarce, and only a few available databases have described VOCs, their emitting organisms and their
biological activities41,42. Despite limited information on VOC biological activities, some links between microbial
VOC patterns and many physiological and pathological states have been reported, including gastrointestinal met-
abolic disorders43,44. A decrease in volatolome ketones has recently been reported in patients with an inamed gut
(Crohns disease, ulcerative colitis or pouchitis)44. Moreover, patients with Crohn’s disease showed increased pro-
duction of phenol, as observed herein following PAHs exposure. However, sulfur compounds, which increased
in this study, decreased in patients with Crohns disease. As alterations in the microbial VOC patterns are a con-
sequence of the disruption of the normal bacterial ecology in pathologies such as inammatory bowel disease
(IBD), it can be hypothesized that similar disturbances (shi in VOC proles) are triggered by chemical agents,
such as environmental pollutants.
To obtain a more detailed characterization of the altered metabolic pathways, we analyzed gut microbiota gene
expression using RNA sequencing. e variations observed at the GO level were pollutant-dependent; however,
general trends emerge from the analysis, showing an increase in transcript levels related to the lipid metabolism
process, plasma membrane and periplasmic space following dioxin, brominated ame retardant and heterocyclic
amine exposure. A previous study from the laboratory also identied an increase in these metabolic pathways
aer a 24-hr B[a]P exposure16, indicating a clear impact of such hydrophobic compounds on cell membranes. It
has been proposed that lipophilic compounds, when crossing membranes, increase the membrane uidity, lead-
ing to a loss of membrane functionality45. One of the major adaptive mechanisms of bacteria cells to counteract
this eect is to increase their membrane rigidity (enhance membrane lipid saturation) to prevent compound
accumulation within the cell46.
We also identied an increase in transcript levels related to protein kinase activity and receptor activity mainly
for TCDD, HBCD and deltamethrin exposure. Such variations have never been identied elsewhere following
environmental toxicant exposures. However, the increase in the transcript levels of these two GO slim terms
might indicate an increase in the cell signaling network and thus an activation or repression of cellular responses
due to the presence of the pollutants.
0
50
100
150
200
250
300
350
Il-8 concentraon (pg/mL)
*
*
*
*
*
*
*
Figure 6. IL-8 release in the TC7 cell culture supernatants. TC7 cells were exposed to FDS and microbiota-free
medium for 4 hr. Values represent the mean of three replicates ± SEM. Signicant variations were assessed using
the Mann-Whitney test (p-value < 0.05). DMEM was a negative control for toxicity and inammation, whereas
IL-1β was a positive control for inammation in TC7 cells. Control: no pollutant and no vehicle; Vehicle 1:
methanol; Vehicle 2: methanol:dichloromethane 1:1; mf: microbiota-free.
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A decrease in transcript levels related to the GO slim terms ribosome, translation and nucleic acid binding
was observed. In a recent study, we identied a reduction in energy metabolism following B[a]P exposure and
proposed that the bacterial cells might engage adaptive processes to manage stressful events. Microorganisms
may thus engage energy for adaptation mechanisms to ensure necessary physiological functions prior to energy
expenditure for growth46. Ribosome assembly and protein synthesis processes are major targets for a large num-
ber of antibiotics47. is observation potentially raises the hypothesis that POPs may act as antibiotics target-
ing the bacterial translation apparatus. Interestingly, a > 2-fold increase in 13 antimicrobial resistance genes
was observed in the murine gut microbiome within 8 days of TCDD exposure14. Among these genes, some
(multidrug-resistant genes) were associated with resistance to antibiotics targeting bacterial protein synthesis
(aminoglycoside, glycylcycline, macrolide and erythromycin).
Rubredoxin-coding genes have been found to be highly induced in PhIP and PAH samples and found specif-
ically (absence of the vehicle condition) in the HBCD sample. Rdxs are essential electron transfer components of
bacterial membrane-bound alkane hydroxylases found in aerobic n-alkane degradation pathways28,48. However,
in anaerobic organisms, Rdxs have been identied as crucial for oxidative stress responses (reduction of oxygen
or reactive oxygen species)49.
Interestingly, a gene encoding an unknown protein carrying a Toluene_X Outer Membrane Transport family
domain was identied as specic to the PhIP and PAH samples. is protein domain is found in membrane pro-
teins with an uncharacterized function that are involved in toluene catabolism and the degradation of aromatic
hydrocarbons50,51. Hence, our result highlights the potential metabolism of PhIP and PAH compounds by the
human gut microbiota. is result is in agreement with previous studies showing that the gut microbiota is capa-
ble of metabolizing these chemical compounds into 1-hydroxypyrene, 7-hydroxybenzo[a]pyrene and PhIP-M1
as pyrene, benzo[a]pyrene and PhIP metabolites, respectively2123.
Gut microbial fermentation products play a major role in human health and disease52. An imbalance of these
compounds (mediated by altered microbial activity) may directly or indirectly impact the gut environment and
the host. However, although the microbial metabolism of xenobiotics, such as POPs, has been demonstrated,
the toxicity of their metabolites remains unknown. us, we investigated the impact of FDS on the TC7 cell
line by measuring the induction of the necrotic and apoptotic processes, along with the release of inammatory
cytokines aer 4 hr of contact.
e FDS did not specically induce necrosis or apoptosis in the TC7 cell line under these experimental con-
ditions. Moreover, the microbiota seemed to limit the necrotic eect of PhIP, B[a]P and PAHs. us, the gut
microbiota could play a protective role for the host against some toxic compounds. At this stage, it is not possi-
ble to precisely identify the protective mechanism and discriminate between a potential pollutant degradation
or sequestration by the microbiota. No necrotic or apoptotic eects have been observed for the other pollut-
ants under our experimental conditions. We cannot exclude toxic eects aer prolonged contact periods and/
or with other cell lines. Concerning the inammatory response, FDS from deltamethrin, HBCD and PAH sam-
ples induced a signicant increase in IL-8 production by TC7 cells. Our assumption is that pollutants induce a
functional dysbiosis that modies the balance between anti-inammatory and pro-inammatory metabolites in
the supernatant. By contrast, we also observed that the microbiota that were in contact with pollutants, such as
PhIP and B[a]P, could slightly reduce the release of IL-8. In this case, the microbiota could also play a protective
role against inammatory induction by chemical compounds, but as mentioned previously, we could not deter-
mine the involved mechanisms (pollutant degradation and/or sequestration, production of anti-inammatory
metabolites). However, it must be considered that previous in vivo studies showed the establishment of a
pro-inammatory intestinal environment following chronic B[a]P exposure at high doses53,54. B[a]P has also
been shown to potentiate murine intestinal inammation caused by a high-fat diet55.
TNFα and IL-10 cytokines were not detected in any of the culture cell supernatants. ese two cytokines have
been previously detected in Caco-2 experiments; however, immune cells56,57 and/or bacterial cells58,59 seem to be
obligate partners to induce their expression and release. Other experimental conditions, such as cellular dier-
entiation and incubation time, must be taken into account because the cytokine expression varies depending on
these parameters. As an example, the general inammatory response is higher in proliferating cells compared
with fully dierentiated cells60,61.
Chronic pollutant expositions may progressively induce a low-grade inammatory status. To our knowl-
edge, no data are available regarding the inammatory properties induced by POPs in intestinal epithelial cells.
However, environmental chemicals have been characterized as triggering a pro-inammatory response mediated
by the aryl hydrocarbon receptor (AhR) in dierent cell types6264. e AhR is a cytosolic transcription factor
that is activated by numerous environmental hydrophobic chemicals, leading to their metabolic clearance and
detoxication. While the AhR is oen regarded as a xenobiotic receptor, it is becoming increasingly clear that this
receptor exhibits activity that inuences numerous endogenous functions, including immune functions65,66. For
example, Kobayashi and colleagues have shown that environmental exposure to TCDD, through binding to AhR,
exacerbates rheumatoid arthritis pathophysiology via stimulation of the NF-κB and ERK signaling pathways67.
e rodent AhR homolog is known to bind TCDD and PAHs with ~10-fold higher anity than the human
AhR68. ese results highlight the functional disparities that may exist in rodent model systems, resulting in a
failure to accurately predict human AhR function in response to given ligands.
Finally, intestinal epithelial cells are simultaneously exposed to (i) gut microbiota-derived compounds, (ii) gut
microbiota metabolites generated by the presence of the chemicals and (iii) the pollutant along with its poten-
tial degradation compound(s). is result indicates that pollutant exposure risks, based primarily on human
biotransformation enzymes, may also take into account gut microbial processes, leading to more or less toxic
compounds and/or microbial pro-inammatory molecules. Depending on the pollutant and the intensity and fre-
quency of exposure, gut microbiota could either protect host cells or enhance toxic and inammatory responses.
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SCIENtIFIC REPORTS | (2018) 8:11006 | DOI:10.1038/s41598-018-29376-9
Conclusion
e present work highlights, for the rst time, the impact of a panel of POPs and foodborne chemical fam-
ilies on human gut microbiota functions. We identified microbial volatiles and metabolic pathways that
shied aer chemical exposure, leading to an imbalance in microbial activity. Finally, we showed that this gut
microbiota-pollutant interplay might potentially lead to the establishment of a pro-inammatory state in the gut.
erefore, chemical exposure risk assessment based primarily on human biotransformation enzymes might be
underestimated.
Materials and Methods
Chemicals. Deltamethrin (pyrethroid insecticide), α-hexabromocyclododecane (α-HBCD), γ-hexabromo-
cyclododecane (γ-HBCD) (BFRs), B[a]P (PAH), PAH Mix 3, methanol and dichloromethane were purchased
from Sigma Aldrich (Saint-Quentin Fallavier, France). TCDD (PCDD) and PhIP (HCA) were purchased from
LGC Standards (Molsheim, France). To avoid solubility issues in batches, all pollutants were dissolved in meth-
anol (vehicle 1) except PAH Mix 3, which was purchased already dissolved in a methanol:dichloromethane (1:1)
(v/v) solution (vehicle 2). e HBCD solution was an equal mixture of the α and γ isomers. e composition
of the PAH Mix 3 solution is detailed in Supplementary Table1. Chemicals and all contaminated euents and
materials were handled in an advised and safe manner with all necessary precautions.
Experimental design. e pollutants were added at dierent concentrations (see below) into Hungate tubes
along with a fecal microbiota suspension sampled from the continuous fermentor Mini Bioreactor Applikon®
(Applikon, e Netherlands) following seven days of microbial stabilization. e fecal microbiota suspension
contained in vitro-cultured feces collected from a human volunteer donor. Informed consent was obtained from
the healthy volunteer. is was a non-interventional study with no additions to usual clinical care. According to
French Health Public Law (CSP Art L 1121-1.1), such a protocol does not require approval of an ethics commit-
tee. e incubation volume (10 mL) was composed of one-fourth fecal microbiota suspension and three-fourths
colon medium as previously described16.
e pollutant concentrations in batches were 0.005, 0.90, 2.60, 5, 21 and 38 µg/mL for TCDD, PhIP, HBCD,
B[a]P, deltamethrin, and PAHs, respectively. Vehicle 1 and vehicle 2 were added at 0.5% (v/v) in batches. e
concentration of methanol was kept below 1% (v/v) in medium to avoid potential microbial growth inhibition69.
To take into account the impact of the vehicles on the fecal microbiota, batches with only vehicle 1 or 2 (vehicle 1
condition and vehicle 2 condition) at 0.5% (v/v) and batches with no pollutant and no vehicle (control condition)
were added to the experimental design. Five replicates were assessed for all control and experimental conditions.
At the end (T24 hr) of the incubation step, samples dedicated to RNA extractions were immediately cen-
trifuged at 2000 × g for 8 min. e pellets were then resuspended in 5 volumes of RNAlater® (Fisher Scientic,
Illkirch, France) and maintained at 80 °C until extraction. e remaining incubation medium was either directly
maintained at 20 °C for SPME-GC-MS analysis or centrifuged at 2000 × g for 10 min (referred as FDS). e ve
FDS replicates were pooled and maintained at 20 °C until used to challenge TC7 cells.
Microbial volatolome analysis. e volatolome analysis was performed as previously described by Defois
et al.16. Briey, the volatile compounds in the samples were analyzed via SPME-GC-MS. A volatile compound
analysis was performed by GC-full scan MS (GC6890, MS5973N, Agilent). e volatiles were tentatively identi-
ed according to a comparison between their mass spectra and the NIST 14 mass spectral library and between
published retention index (RI) values and the RI values of an internal databank. Peak areas of the volatile com-
pounds were determined with a home-made automatic algorithm developed in Matlab R2014b (e MathWorks,
Natick, USA). e data were processed using Statistica soware (v.10) (StatSo, Maisons-Alfort, France). T-tests
(p < 0.05) were applied to datasets corresponding to each case-control study (exposed group vs vehicle group). In
order to limit the risk of false positive results, the p-values of compounds selected by t-tests were then corrected
for multiple testing: aer the pre-selection of the ni responding compounds (p < 0.05), a Bonferroni correction
(p < 0.05/ni) was applied for each of the 6 case-control comparisons.
RNA extraction, sequencing and analysis. RNA extraction and rRNA depletion were performed as pre-
viously described16. e metatranscriptome analysis was performed on the pooled rRNA-depleted RNA arising
from ve technical replicates.
Library construction (following TruSeq Stranded mRNA Sample Preparation, Illumina) and paired-end
sequencing (MiSeq, 2 × 250 bp) were performed at Fasteris (Plan-les-Ouates, Switzerland). e paired-end
sequences were assessed for quality with Trimmomatic70 and PRINSEQ71. and joined with fastq-join from the
ea-utils soware package72. e remaining rRNA sequences were removed from the data set using SortMeRNA
(v. 2.0) soware73. e UniRef50 gene family and GO slim term relative abundances in CPM were obtained using
the HMP Unied Metabolic Analysis Network2 (HUMAnN2) soware (v0.5)74. GO slim term-derived heatmaps
were created using Shinyheatmap soware75. Only genes with at least a 3-fold change between the pollutant and
the vehicle conditions were analyzed.
TC7 cell culture. TC7 cells (clone of the parenteral Caco-2 epithelial cell line) were kindly provided by Dr.
Adeline Sivignon (M2iSH, Clermont-Ferrand, France). Caco-2 and TC7 cell lines are popularly used for studies
on intestinal metabolism and absorption of various pharmaceutical and nutritional compounds76,77. TC7 cell
line is a Caco-2 clone thus presenting numerous properties found in enterocytes such as cell polarization, brush
border, cell junctions as well as cell metabolism enzymes. However, clone TC7 shows better homogeneity than the
parental Caco line as well as more developed intercellular junctions. Also, the TC7 line has a faster metabolism
than the Caco line leading to a complete dierentiation of the cells under 14 days of culture compared to 21 days
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10
SCIENtIFIC REPORTS | (2018) 8:11006 | DOI:10.1038/s41598-018-29376-9
as required by the Caco line61. e cell culture medium consisted of Dulbecco’s Modied Eagle Medium with
high glucose (4.5 g/L) supplemented with 10% fetal bovine serum, 1% glutamine, 1% non-essential amino acids
(100X) and 1% penicillin-streptomycin (100X). DMEM and the added components were purchased from Gibco®
(Fisher Scientic).
TC7 cells were maintained in 75-cm² asks (Falcon, Fisher Scientic) at 37 °C in a humidied atmosphere
of 5% CO2. When the cells reached 80% conuence, the medium was removed from the dish, and cells were
washed twice with 10 mL of PBS solution (Gibco, Fisher Scientic). e cells were then supplemented with 0.25%
trypsin-EDTA (Gibco, Fisher Scientic) and le in the incubator for 5 min. Trypsin action was stopped by addi-
tion of 10 mL of complete medium, and the cell suspension was centrifuged for 5 min at 900 g. Aer removing the
supernatant, the cell pellet was resuspended in complete medium, seeded directly into 12-well plates Nunclon
(Fisher Scientic) (2.105 cells/well) and maintained in culture for 14 days to allow complete cell dierentiation
before challenge61. e medium was changed three times a week beginning at the rst week and every other day
until day 14. TC7 cells were used between passages 30 and 41.
Exposure of TC7 cells to fermentation-derived supernatants. Cells were treated for 4 hr with the
FDS from each pollutant, vehicle and control conditions. Furthermore, TC7 cells were also exposed to fecal
microbiota-free colon medium supplemented with each pollutant at its initial experimental concentration
(microbiota-free condition). e supernatants were diluted 1:4 in DMEM because this dilution of the colon
medium showed no cytotoxic eect on TC7 cells (results not shown). Finally, cellular control conditions (DMEM,
dimethyl sulfoxide (DMSO) 10% (Eurobio, Les Ulis, France) and Interleukin (IL) 1 beta (IL-1β) (Sigma Aldrich)
at 25 ng/mL) were included in the experimental design, and each condition was assessed in triplicate. DMSO
and IL-1β were used as positive controls for the induction of toxicity (apoptosis and necrosis) and inammation
(cytokine release) in TC7 cells, respectively.
Following a 4-hr of exposure, the cellular supernatants were stored at 80 °C for cytokine release measure-
ments, and the cell monolayers were harvested for apoptosis/necrosis detection. e results are the mean of three
replicates, and statistical analyses were conducted using the Mann-Whitney U-test with GraphPad Prism 5 so-
ware (San Diego, CA, USA). e statistical signicance was set at p < 0.05.
Apoptosis detection. TC7 cell monolayers were washed twice with Dulbecco’s Phosphate-Buffered
Saline (Gibco, Fisher Scientific) and treated with trypsin-EDTA 0.25%. Cells were recovered with cold
Phosphate-Buffered Saline (Gibco, Fisher Scientific), centrifuged (500 g, 4 °C, 5 min) and subjected to the
Annexin A5-FITC Kit (Beckman Coulter, Villepinte, France) following the manufacturer’s instructions. e
assay combines Annexin A5 and Propidium Iodide staining, distinguishing viable cells from apoptotic cells and
necrotic cells, respectively. Cells were then analyzed with a Cytomics FC 500 MPL ow cytometer.
TC7 cytokine quantication. TC7 cell supernatants were centrifuged (1,000 g, 4 °C, 12 min), and the
amount of released IL-8, Tumor Necrosis Factor alpha (TNFα) and IL-10 was determined using the Human IL-8
/ CXCL8 ELISA Kit, Human TNF-Alpha ELISA Kit and Human IL-10 ELISA Kit (Sigma Aldrich), respectively.
Cytokine concentrations were assessed according to the manufacturer’s instructions.
Accession codes. All sequence data produced via RNA sequencing are available in the NCBI Sequence Read
Archive, BioProject PRJNA416988, under accession no. SRP124200.
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Acknowledgements
CD received a graduate grant from the “Ministère de l’Enseignement Supérieur et de la Recherche” of France. We
thank Magaly Angénieux and Frédéric Mercier for providing technical assistance in the SPME-GC-MS analysis.
Author Contributions
C.D., P.P., P.M., G.G., J.R. and E.E. conceived the study and design the experiments. C.D. and J.R. performed
experimental procedures. C.D., J.R., E.E., and P.P. analyzed data. S.D. and O.L. provided support for continuous
fermentation and G.G. and J.T. for cell culture and cytometry experiments. C.D., P.P., J.R. and E.E. interpreted
data and wrote the manuscript. All authors reviewed and approved the manuscript. All authors agree to be
accountable for all aspects of the work.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-29376-9.
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Background Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, especially across single-cell sequencing studies. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Also, shinyheatmap features a built-in high performance web plug-in, fastheatmap, for rapidly plotting interactive heatmaps of datasets as large as 10⁵—10⁷ rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed. Conclusions shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap. Users can access fastheatmap directly from within the shinyheatmap web interface, and all source code has been made publicly available on Github: https://github.com/Bohdan-Khomtchouk/fastheatmap.
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