# In vitro microbiota model recapitulates and predicts individualised sensitivity to dietary emulsifier

Héloïse Rytter, Sabrine Naimi, Gary Wu, Jim Lewis, Maeva Duquesnoy, Lucile Vigué, Olivier Tenaillon, Eugeni Belda, Marta Vazquez-Gomez, Nina Touly, et al.

► To cite this version:

Héloïse Rytter, Sabrine Naimi, Gary Wu, Jim Lewis, Maeva Duquesnoy, et al. In vitro microbiota model recapitulates and predicts individualised sensitivity to dietary emulsifier. Gut, 2025, 74 (5), pp.761-774. 10.1136/gutjnl-2024-333925. hal-05102288

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Original research

# *In vitro* microbiota model recapitulates and predicts individualised sensitivity to dietary emulsifier

Héloïse Rytter,¹,² Sabrine Naimi,² Gary Wu,³ Jim Lewis,⁴ Maeva Duquesnoy,¹,²

Lucile Vigué ID,⁵ Olivier Tenaillon,⁵ Eugeni Belda,⁶,⁷ Marta Vazquez-Gomez ID,⁶,⁷

Nina Touly,⁸,⁹,¹⁰,¹¹ Djésia Arnone ID,⁸,⁹,¹⁰,¹¹ Fuhua Hao,¹² Ruth E Ley,¹³

Karine Clément ID,⁶,¹⁴ Laurent Peyrin-Biroulet ID,⁸,⁹,¹⁰,¹¹,¹⁵ Andrew D Patterson,¹²

Andrew T Gewirtz ID,¹⁶ Benoit Chassaing ID,¹,²,¹¹

► Additional supplemental material is published online only. To view, please visit the journal online (https://doi.org/10.1136/gutjnl-2024-333925).

For numbered affiliations see end of article.

**Correspondence to**
Dr Benoit Chassaing;
benoit.chassaing@pasteur.fr

Received 19 September 2024

Accepted 26 December 2024

Published Online First

27 January 2025

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## ABSTRACT

**Background** Non-absorbed dietary emulsifiers, including carboxymethylcellulose (CMC), directly disturb intestinal microbiota, thereby promoting chronic intestinal inflammation in mice. A randomised controlled- feeding study (Functional Research on Emulsifiers in Humans, FRESH) found that CMC also detrimentally impacts intestinal microbiota in some, but not all, healthy individuals.

**Objectives** This study aimed to establish an approach for predicting an individual's sensitivity to dietary emulsifiers via their baseline microbiota.

**Design** We evaluated the ability of an *in vitro* microbiota model (MiniBioReactor Array, MBRA) to reproduce and predict an individual donor's sensitivity to emulsifiers. Metagenomes were analysed to identify signatures of emulsifier sensitivity.

**Results** Exposure of human microbiotas, maintained in the MBRA, to CMC recapitulated the differential CMC sensitivity previously observed in FRESH subjects. Furthermore, select FRESH control subjects (ie, not fed CMC) had microbiotas that were highly perturbed by CMC exposure in the MBRA model. CMC-induced microbiota perturbability was associated with a baseline metagenomic signature, suggesting the possibility of using one's metagenome to predict sensitivity to dietary emulsifiers. Transplant of human microbiotas that the MBRA model deemed CMC-sensitive, but not those deemed insensitive, into IL-10⁻⁷ germfree mice resulted in overt colitis following CMC feeding.

**Conclusion** These results suggest that an individual's sensitivity to emulsifier is a consequence of, and can thus be predicted by, examining their baseline microbiota, paving the way to microbiota-based personalised nutrition.

## WHAT IS ALREADY KNOWN ON THIS TOPIC

⇒ Commonly used dietary emulsifiers can perturb intestinal microbiota, fostering chronic intestinal inflammation and metabolic deregulations.

⇒ A high level of interindividual variations has been observed, in human, regarding emulsifier-induced microbiota perturbations.

## WHAT THIS STUDY ADDS

⇒ Sensitivity to dietary emulsifiers of a given faecal sample can be predicted using *in vitro* microbiota modelling system.

⇒ Dietary emulsifier sensitivity associated with a metagenomic signature commonly observed in healthy individuals as well as in patients with chronic inflammatory conditions.

## HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

⇒ These findings highlight the need to carefully consider interindividual variations in emulsifier sensitivity status for personalised nutrition.

diabetes.³⁻⁶ Myriad environmental factors can detrimentally impact the intestinal microbiota, especially the consumption of highly processed foods, thus contributing to the pathogenesis of these disease conditions.⁷ ⁸ A common feature of processed food products is the presence of various food additives, such as emulsifiers, which improve texture and/or extend shelf-life.⁹ Dietary emulsifiers have long been considered non-toxic compounds, in part, because they pass through the intestinal tract and are largely eliminated in faeces without having been absorbed. However, such restriction to the intestinal lumen positions them to directly interact with the intestinal microbiota. Indeed, recent evidence suggests that detrimental impacts of emulsifiers on the intestinal microbiota may be linked to increased incidence of several chronic inflammatory diseases.¹⁰ ¹¹ Mechanistically, these compounds are thought to act by promoting microbiota encroachment into the normally sterile inner mucus layer,¹²⁻¹⁴ positioning bacteria to promote chronic intestinal inflammation

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**To cite:** Rytter H, Naimi S, Wu G, et al. *Gut* 2025;74:761–774.

## INTRODUCTION

The intestinal microbiota has major beneficial impacts on host physiology, including driving immune system maturation and impeding colonisation of pathogens.¹,² However, detrimental alterations in microbiota composition and/or function, referred as dysbiosis, can promote chronic diseases with an inflammatory component, including inflammatory bowel disease (IBD) and various interrelated metabolic deregulations such as obesity and
that, depending on host genetics, can manifest as colitis or meta-
bolic deregulations.<sup>15 16</sup>

The degree to which emulsifiers impact humans was recently explored via a randomised double-blind controlled-feeding study focusing on the synthetic emulsifier carboxymethyl-cellulose (CMC). We found that CMC consumption induces alteration in microbiota composition and faecal metabolome in healthy participants.<sup>17</sup> However, the extent of microbiota perturbation was not uniform. Rather, select subjects exhibited profound CMC-induced alterations in microbiota composition, accompanied by microbiota encroachment, suggesting high interindividual variability in CMC sensitivity.<sup>17</sup> Such individu-alised sensitivity to CMC was likely a consequence of baseline microbiota in that transplantation of microbiotas from individ-uals presenting strong CMC-induced microbiota alterations into germfree IL10<sup>-/-</sup> mice resulted in severe colitis following expo-sure to CMC.<sup>18</sup> In stark contrast, transplant of microbiotas from CMC-resistant individuals conferred protection against CMC-induced pathologies,<sup>18</sup> highlighting the central role played by the microbiota in driving detrimental responses to emulsifiers, as well as the strong microbiota-driven interindividual variations in emulsifier sensitivity. Consequently, there is a need for a prac-tical means to identify emulsifier-sensitive individuals, and thus alerting them that they would likely benefit from avoiding these additives.

That mice receiving microbiota transplants acquire the CMC sensitivity status of the human faecal donor demonstrates that the information needed to determine CMC sensitivity is contained within an individual's baseline microbiota. However, use of germfree mice is not a practical or scalable means of determining sensitivity to food additives. Hence, we here explored the use the MiniBioReactor Arrray (MBRA) microbiota model and metagenome-based bioinformatic modelling to predict the CMC sensitivity of a given microbiota. We found the MBRA model recapitulated the CMC sensitivity status observed in the Functional Research on Emulsifiers in Humans (FRESH) trial. Furthermore, study of faeces from FRESH control subjects in the MBRA model, and subsequently IL-10<sup>-/-</sup> germfree mice, indicated the MBRA could predict an individual's CMC sensi-tivity status. We also found that CMC sensitivity was associated with a metagenomic signature, suggesting that emulsifier sensi-tivity may ultimately be reliably predictable from one's metage-nome. These results support development of microbiome-based personalised nutrition strategies.

RESULTS

MBRA model faithfully recapitulated microbiotas of the
FRESH subjects

The results of our recently reported randomised double-blind controlled-feeding study of non-metabolisable emulsifier CMC, entitled Functional Research on Emulsifiers in Humans (FRESH),¹⁷ accorded with findings in mice that CMC impacts the intestinal microbiota. However, the extent of such impacts was heterogeneous in that only two of the seven subjects fed this additive exhibited stark changes in microbiota composi-tion and microbiota encroachment in response to this additive. Such subjects were deemed CMC-sensitive. Here, we sought to develop a means of identifying CMC-sensitive microbiotas without *in vivo* studies. We employed the MiniBioReactor Arrays (MBRA),¹⁹ ²⁰ *an in vitro* microbiota modelling system that allows dynamic and stable culture of human-derived microbiota under anaerobic conditions (online supplemental figure S1A). Faecal samples from the 16 FRESH subjects (9 participants from

the control group and 7 participants from the CMC-exposed
groups) were used to inoculate triplicate MBRA chambers
(figure 1A). MBRA chambers were then sampled longitudinally
(eight times over 8 days), assaying microbiota composition via
Illumina-based 16S rRNA gene sequencing.

We first analysed the extent to which the MBRA system
could reproduce the well-appreciated interindividual hetero-
geneity in microbiota composition. Data were displayed as a
principal coordinates analysis (PCoA) plot wherein each colour
corresponds to an individual subject. This approach revealed
strong per-donor clustering (figure 1B), indicating that the
MBRA model was capable of stably cultivating donor-specific
microbiotas for up to 12 days. Moreover, taxonomic analysis
of the stabilised MBRA microbial communities showed that the
MBRA captured and maintained donor specificity at the order
and phylum levels (figure 1C and online supplemental figure
S2A). At the phylum level, *Firmicutes* was the most abundant
MBRA microbiota member, averaging 39%, followed by *Bacte-
roidetes* (35%) and *Proteobacteria* (21%) (online supplemental
figure S2A). Interindividual differences were more apparent at
the order level. For example, Verrucomicrobiales ranged from
0.009% to 7.647%, mostly driven by *Akkermansia muciniphila*,
which promotes intestinal and metabolic health.<sup>21-23</sup> We also
observed donor-dependent variations in the relative abundance
of *Faecalibacterium prausnitzii*, ranging from 0.00% to 11.00%,
a bacterium purported to protect against chronic intestinal
inflammation.<sup>24-26</sup> Both of these bacteria are strict anaerobes and
are often lost in *in vitro*-based microbiota culturing. Thus, we
viewed their presence in the MBRA as supporting its fastidious-
ness. We next assessed the extent to which the stabilised MBRA
microbiomes corresponded to the FRESH subjects from which
they were derived. PCoA plotting revealed that each donor
microbiome clustered squarely within the MBRA time point
samples derived from it (online supplemental figure S2B). More-
over, Bray-Curtis distances between each baseline MBRA sample
and all baseline donor samples indicated that baseline MBRA
microbiomes were far more similar to their cognate donor than
the other donors (online supplemental figure S2C). Comparison
of bacterial composition of the poststabilisation MBRA chambers
with baseline faeces composition showed the ability of MBRA
to reproduce donor specificity was most apparent at the order
levels (online supplemental figure S3). Thus, MBRA faithfully
reproduced the interindividual variations in microbiota compo-
sition of the FRESH cohort.

The extent to which MBRA maintained donor-specific micro-
biota metabolomes was investigated by targeted ¹H NMR-based
metabolomic analysis. Analysis of data by PCoA of the Bray-
Curtis distance found a high level of per-participant metabolome
clustering (time point 72 hours, Permanova p <0001, online
supplemental figure S4A). Moreover, we observed a strong inter-
individual variation in the concentration of various metabolites,
such as short-chain fatty acids, lactate and tryptophan (online
supplemental figure S4B). Altogether, these data support the
suitability of the MBRA to study donor-specific microbiotas ex
vivo.

MBRA reproduced the individualised CMC-sensitivity status
observed in the FRESH study

The FRESH study found that sensitivity to CMC was not
uniform. Specifically, as shown here in figure 1D, we reported
that two of the seven CMC-fed subjects exhibited marked alter-
ations in their microbiota composition over the 10-day course
of their CMC consumption.¹⁷ That these two individuals also
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**Figure 1** MBRA-based microbiota modelling accurately replicates interindividual variations in microbiota composition and captures the CMC-induced microbiota perturbations. (A) Schematic outline of the experimental plan used. Faecal samples from the 16 FRESH subjects (9 participants from the control group and 7 participants from the CMC-exposed groups) were used to inoculate triplicates MBRA chambers. Microbiota were then allowed to equilibrate for 24 hours prior flow initiation. After a 3-day microbiota stabilisation phase, chambers were treated from 0 hour to 120 hours, in triplicates for each donor, with BRM medium containing CMC at 0.1% or control sterile BRM medium. At the 120 hours time point, post-treatment phase was initiated, with CMC treatment discontinued by connecting all chambers to emulsifier-free BRM sterile medium. Samples were collected longitudinally. (B) Microbiota composition was analysed by 16S rRNA gene sequencing and principal coordinates analysis (PCoA) of the Jaccard matrix combining MBRA samples at all time points was plotted, with dots coloured by patient. (C) Taxonomic analyses were computed through QIIME2 pipeline at the order level. Data are the means±SEM, with individual data points being represented (N=3). Significance was determined using Permanova analysis. (D) Longitudinal analysis of the Bray-Curtis distance in faecal sample from CMC arm of the FRESH study. Data were normalised based on day 4 distance. (E) Longitudinal analysis of the Bray-Curtis distance in MBRA samples generated with CMC participants of the FRESH study. Data were normalised with the untreated condition and time point 0 hour (poststabilisation), both defined as 1. Grey area indicates the post-treatment phase. p<0.05 determined by a two-way ANOVA corrected for multiple comparisons or mixed-effect analysis. (D) Data from Chassain et al.¹⁷ ANOVA, analysis of variance; BRM, Bioreactor Medium; CMC, carboxymethylcellulose; FRESH, Functional Research on Emulsifiers in Humans; MBRA, MiniBioReactor Array.
developed microbiota encroachment suggests the composi-
tional changes were functionally important.¹⁷ We refer to these
two individuals, shown in orange in figure 1D, as being CMC-
sensitive and deem the other five CMC-fed subjects, shown in
blue, to be relatively CMC-insensitive individuals. Here, we
investigated if such CMC-sensitivity could be recapitulated by
the MBRA model. MBRA were inoculated with baseline faecal
samples from each FRESH subject (three replicate MBRA per
faecal sample) and allowed an 8-day stabilisation period. MBRA
microbiotas were then left untreated for eight additional days
or administered CMC (0.1% w/v) for 5 days, followed by a
post-treatment phase of 3 days (online supplemental figure
S1B). MBRA samples were collected longitudinally (online
supplemental figure S1B) and subjected to microbiome analysis
(online supplemental figure S1C). Bacterial density and Shannon
diversity were stable for all donors irrespective of CMC expo-
sure (online supplemental figure S5A,B). However, temporal
Bray-Curtis dissimilarity analysis revealed stark donor-specific
differences that corresponded to the CMC-sensitivity previously
observed in the FRESH study. Specifically, MBRA corresponding
to both CMC-sensitive subjects displayed stark CMC-induced
changes. In contrast, MBRA that had been inoculated with faeces
from the CMC-insensitive subjects remained relatively stable in
response to CMC (figure 1E and online supplemental figure
S6A). These results indicated that the MBRA had reproduced
interindividual variations in CMC-sensitivity observed in the
FRESH study.

Prediction of CMC sensitivity status via metagenomic
signature and MBRA model

The CMC sensitivity of subjects in the control arm of the FRESH
study is, by definition, unknown, but by extrapolation one might
expect it would contain 2–3 CMC-sensitive subjects. Hence, we
used metagenomic analysis combined with MBRA approach to
investigate this notion.

We first performed shotgun metagenomic analysis on
faecal samples collected at baseline from the FRESH subjects
(figure 2A). Data from the CMC group were analysed at the
imputed UniRef90 level using MaAsLin2, comparing CMC-
sensitive and CMC-insensitive subjects. Such analysis revealed
217 markers significantly associated with CMC-sensitivity status
(q<0.05, figure 2B). We next analysed the abundance of these
markers in the FRESH control subjects. Microbiomes from
two of the nine FRESH control subjects displayed a pattern of
expression of these markers that was highly similar to that of
CMC-sensitive subjects, as assessed by PCoA plots and heatmap
representation (figure 2C,D), suggesting that these two individ-
uals harboured CMC-sensitive microbiotas.

We next tested the metagenomic marker-based prediction of
CMC sensitivity status via the MBRA model. Specifically, MBRA
chambers were inoculated with faecal samples from the nine
FRESH control subjects and subsequently treated, or not, with
CMC. Analysis of such MBRA microbiotas validated the metag-
enomic marker-based predictions in that the two faecal micro-
biotas predicted to be CMC- sensitive, but not the other seven,
were significantly perturbed by CMC treatment as reflected by
increases in Bray-Curtis dissimilarity distances (figure 2E and
online supplemental figure S6B). These two CMC-sensitive
microbiotas did not stand out from the others in terms of
Shannon diversity nor bacterial density at baseline or following
CMC exposure (online supplemental figure S5C,D). Moreover,
examining previously generated 16S sequencing data from the
FRESH study did not reveal any of these control subjects to have

microbiomes that were inherently unstable over the course of
the FRESH study (figure 2F).

That our metagenomic marker-based predictions matched the
MBRA results for all nine individuals from the FRESH control
group by chance was unlikely (less than 1%). Nonetheless, we
felt the need to further interrogate our approach. Hence, we
assessed whether MaAsLin2 comparisons not based on known
CMC sensitivity might also, somehow, predict CMC sensitivity.
For this, we applied the MaAsLin2 algorithm to metagenomes
of arbitrarily selected groups (2 vs 4, not based on the sensitivity
status) among the CMC arm of the FRESH study. All possible
combinations were computed, and none of them were found
to correlate with the CMC sensitivity status observed in the
MBRA. Thus, training a prediction algorithm with metagenomes
from CMC-sensitive subjects, but not arbitrary metagenomes,
predicted a microbiota's sensitivity to CMC in the MBRA model.

In vivo validation of CMC-sensitivity predictions

We next investigated the extent to which microbiotas that the
metagenomic signature and MBRA model identified as being
CMC- sensitive were truly capable of conferring a biologically
significant consequence of CMC exposure *in vivo*. Faecal micro-
biotas from two FRESH control subjects deemed CMC-sensitive
by MBRA, as well as two deemed insensitive, were transplanted
into colitis-prone IL-10⁻/⁻ germfree mice (n=2 donors per
condition and recipient mice per donor). After a 2-week period
of microbiota stabilisation, mice were, or were not, administered
CMC via their drinking water (1% w/v) for 16 weeks (figure 3A),
at which time intestinal inflammation was assessed. IL-10⁻/⁻
mice harbouring microbiotas corresponding to CMC-insensitive
MBRA lacked signs of intestinal inflammation following CMC
consumption, as evidenced by unchanged histopathological
colonic score and stable colonic infiltration with CD68⁺ macro-
phages (figure 3B-E). In stark contrast, IL-10⁻/⁻ mice admin-
istered microbiotas corresponding to CMC-sensitive MBRA
developed robust intestinal inflammation on CMC consump-
tion, as evidenced by significant colon shortening, increased
histopathological scores mostly driven by mucosal damages,
and increased CD68⁺ macrophage infiltration (figure 3B-E and
online supplemental figure S7). To identify potential key bacte-
rial taxa associated with intestinal inflammation, correlation
coefficients were calculated between inflammatory scores and
bacterial abundance at the specie level. This approach identified
bacteria, including *Adlercreutzia equolifaciens* and *Frisingicoccus
caecimuris*, that positively correlated with intestinal inflamma-
tion (online supplemental figure S8). Future work will be needed
to decipher the role played by these microbiota members in
emulsifier-driven inflammation.

One cardinal driver of an array of inflammatory diseases is encroachment of microbiota into the normally near-sterile inner mucus region.<sup>12 13 15 18 27–29</sup> Microbiota encroachment has been observed in mice consuming emulsifiers,<sup>12 13 30</sup> as well as in select FRESH participants consuming CMC.<sup>17</sup> Hence, we examined microbiota localisation via confocal microscopy in water-treated or CMC-treated IL-10<sup>-/-</sup> mice colonised with microbiota from either predicted CMC-sensitive or predicted CMC-insensitive FRESH participants. In agreement with the above measures of inflammation, CMC consumption had no impact on microbiota-epithelium distance in IL-10<sup>-/-</sup> mice colonised with microbiota predicted to be CMC-insensitive (figure 3F,G). In stark contrast, CMC consumption drove severe microbiota encroachment in IL-10<sup>-/-</sup> mice that had been colonised with both CMC-sensitive microbiotas tested. Specifically, microbiota-epithelium distance
A

<table><thead><tr><th rowspan="2">Patient ID</th><th colspan="2">Treatment groups<br>(from the FRESH clinical study)</th></tr><tr><th>CMC</th><th>Sensitive / Insensitive<br>(from the FRESH clinical study)</th></tr></thead><tbody><tr><td>Patient 2</td><td>CMC</td><td>Insensitive</td></tr><tr><td>Patient 6</td><td>CMC</td><td>Insensitive</td></tr><tr><td>Patient 10</td><td>CMC</td><td>Insensitive</td></tr><tr><td>Patient 13</td><td>CMC</td><td>Insensitive</td></tr><tr><td>Patient 16</td><td>CMC</td><td>Insensitive</td></tr><tr><td>Patient 3</td><td>CMC</td><td>Sensitive</td></tr><tr><td>Patient 12</td><td>CMC</td><td>Sensitive</td></tr><tr><td>Patient 1</td><td>Control</td><td>NA</td></tr><tr><td>Patient 4</td><td>Control</td><td>NA</td></tr><tr><td>Patient 8</td><td>Control</td><td>NA</td></tr><tr><td>Patient 9</td><td>Control</td><td>NA</td></tr><tr><td>Patient 11</td><td>Control</td><td>NA</td></tr><tr><td>Patient 14</td><td>Control</td><td>NA</td></tr><tr><td>Patient 15</td><td>Control</td><td>NA</td></tr><tr><td>Patient 5</td><td>Control</td><td>NA</td></tr><tr><td>Patient 7</td><td>Control</td><td>NA</td></tr></tbody></table>

C

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E

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F

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B

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D

<figure><img src="image_10.png" /><figcaption>**Figure 2** Metagenomic markers exhibited a positive correlation with sensitivity status, facilitating the identification of new CMC-sensitive microbiotas through MBRA. (A) List of the FRESH participants used in the current study and their respective CMC-sensitivity status, identified in clinics. (B) Heatmap visualisation of the MaAsLin2-identified emulsifier sensitivity marker's abundance in sensitive and insensitive FRESH participants from CMC arm. (C) Heatmap visualisation of the MaAsLin2-identified emulsifier sensitivity marker's abundance in FRESH participants from control arm. (D) Longitudinal analysis of the Bray-Curtis distance in faecal sample from control arm of the FRESH study. Data were normalised based on day 4 distance. (E) Longitudinal analysis of the Bray-Curtis distance in MBRA samples generated with control participants of the FRESH study. Data were normalised with the untreated condition and time point 0 hour (poststabilisation), both defined as 1. Grey area indicates the post-treatment phase. p<0.05 determined by a two-way ANOVA corrected for multiple comparisons or mixed-effect analysis. (D) Data from Chassaing *et al.*¹⁷ ANOVA, analysis of variance; CMC, carboxymethylcellulose; FRESH, Functional Research on Emulsifiers in Humans; MBRA, MiniBioReactor Array.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 3** Microbiotas from FRESH participants predicted to be CMC-sensitive conferred severe CMC-induced colitis in germfree IL10⁻/⁻ recipient mice. (A) Germfree IL-10⁻/⁻ mice were transplanted with faecal suspension from either CMC-predicted insensitive or CMC-predicted sensitive FRESH participants. Two weeks post microbiota stabilisation, mice were treated with either water or CMC (1% w/v) for 16 weeks. (B) Colon length, (C) histopathological scoring of the colonic mucosa, (D) representative images of colonic sections immune-stained with anti-CD68 antibody, and (E) quantification of colonic CD68+cells. (F, G) Proximal colons were fixed in Carnoy solution and subjected to immunostaining paired with fluorescent in situ hybridisation (FISH), followed by confocal microscopy analysis to determine microbiota localisation. MUC2, green; actin, purple; bacteria, red and DNA, blue. (F) Representative confocal images of microbiota encroachment. (G) Averaged distances separating the microbiota from the intestinal epithelium. Data are represented as the means±SEM. Statistical analyses were performed using a one-way ANOVA or a two-way repeated measures ANOVA or a mixed-effects model (if missing values; assuming sphericity and no correction). ANOVA was followed by a Sidak post hoc test between CMC-treated group and water-treated group for each of the microbiota used for transplantation. Significant differences were recorded as follows: \*p<0.05, \*\*p<0.01, \*\*\*p<0.001, \*\*\*\*p<0.0001. n=3–4. ANOVA, analysis of variance; CMC, carboxymethylcellulose; FRESH, Functional Research on Emulsifiers in Humans.</figcaption></figure>
decreased in response to CMC by 14,81 µm and 12,04 µm compared with water-treated groups (figure 3F,G). Collectively, these results support the validity of the MBRA model as a means to identify CMC-sensitive microbiomes.

## CMC sensitivity associated with a baseline microbiota signature within the whole FRESH cohort

The identification of two additional CMC-sensitive microbiotas, and seven additional CMC-insensitive microbiotas, proved an opportunity to further investigate microbiota-mediated CMC sensitivity. An array of genomic and functional microbiota parameters was compared between the 4 CMC-sensitive and 12 CMC-insensitive donors that were identified by FRESH and/or MBRA studies (figure 4A). First, we measured, using MBRA-derived samples, the ability of the various microbiota to directly degrade/consume CMC. We found that, irrespective of donor, CMC concentrations in the MBRA effluent were only slightly decreased relative to the administered dose (online supplemental figure S9A,B). This result aligns with the view that CMC traverses the intestine without being metabolised and argues against its differential metabolism contributing to one's CMC sensitivity status. Next, we investigated the possibility that emulsifier sensitivity status reflects more general alterations in microbiota-derived metabolomes. For this purpose, we performed targeted metabolomic analysis employing ¹H NMR-based assay on MBRA samples collected in the middle of the CMC treatment phase. While this approach revealed subtle CMC-induced variations, for example, in methanol level, such alterations were independent of CMC sensitivity status (online supplemental figure S10).

We next further probed the potential of microbiomes to predict CMC sensitivity. Neither PCoA analysis of the Bray-Curtis distances using MetaPhlAn4-generated taxonomical data, nor taxonomical barplots, at phylum and order levels, revealed differential clustering based on the CMC sensitivity status (online supplemental figure S11A-C). Moreover, MaAsLin2³¹ found that none of the 461 species that were identified in these samples significantly differed in abundance between CMC-sensitive and CMC-insensitive participants. These data suggest that taxonomical composition of a given faecal sample may not be sufficient to predict emulsifier sensitivity status.

We next analysed shotgun metagenomic data at the UniRef90 level, using the HUMAnN 3.0 pipeline.³² PCoA analysis of the Bray-Curtis distances comparing the 1649 550 obtained metagenomic markers did not reveal CMC sensitivity-based clustering (figure 4B). However, MaAsLin2-based analysis revealed a specific CMC-sensitivity metagenomic signature of 78 genomic markers whose abundance strongly associated with CMC sensitivity status ($q<0.05$, figure 4C,D). The algorithm used by MaAsLin2 uses a statistical correction to correct for the high number of variables being analysed. Nonetheless, we worried that deep comparisons of metagenomes from any two groups of subjects might reveal analogous sets of distinguishing genomic markers. Hence, we applied the MaAsLin2 algorithm to metagenomes of an arbitrarily selected group, namely male versus female participants (n=7 and 9, respectively) and found 0 distinguishing metagenomic markers. We also applied this approach to the subjects that had previously been randomised to be in the CMC versus the control arm of the study (7 vs 9), as well as to randomly selected group sizes that matched the number of CMC-sensitive versus insensitive subjects (4 vs 12). Again, in both cases, we found 0 metagenomic markers that distinguished these groups. Such lack of association in these arbitrarily and randomly selected groups supports the notion

that these 78 metagenomic markers are indeed associated with emulsifier sensitivity status. Interestingly, all 78 metagenomic markers displayed increased abundance in CMC-sensitive microbiotas, suggesting that sensitivity status is driven by the presence, rather than absence, of select metabolic pathways. Most of these metagenomic markers were from *Clostridium*, *Dorea*, *Coprobacillus* and *Escherichia* genera (online supplemental figure S12A). Analysis of these markers at the protein level indicated that the majority of them encoded uncharacterised functions (online supplemental figure S12B), and future studies will be needed to decipher the exact role played by these metagenomic markers in driving sensitivity towards dietary emulsifiers. Nonetheless, these results further support the potential of metagenomic analysis as means of marking the CMC-sensitivity status of an individual's microbiota.

## Prevalence of metagenomic markers of emulsifier sensitivity in independent cohorts of healthy participants and patients

We next applied the CMC sensitivity metagenomic signature, identified in the Philadelphia PA, USA FRESH cohort, to European cohorts (figure 5A). Specifically, we defined the prevalence of each of the 78 metagenomic markers in subjects in the Metacardis cohort, which includes both healthy participants (N=458) and obese patients with (N=657) or without type 2 diabetes (T2D) (N=430).³³ We also applied this approach to Crohn's disease patients (N=199) present in Nancy Hospital's Mucosa cohort. Both of these cohorts exhibited a heterogeneous prevalence of the CMC sensitivity genomic markers independent of health status (figure 5B-D) although, as expected, a high level of interindividual variation was observed. Specifically, individual subjects harboured 0-66 (of 78) metagenomic markers, while the prevalence of each marker varied between 1% and 88%. Future studies will be needed to investigate the specificity and sensitivity of this signature in identifying CMC sensitivity in various cohorts.

## DISCUSSION

The increased prevalence of an array of chronic inflammatory diseases, particularly as world food supplies increasingly industrialise, suggests that components of modern diets may be environmental (ie, non-genetic) disease determinants. An example of this notion is dietary emulsifiers, a ubiquitous class of food additives, which, in mice, promotes inflammation that can manifest in a variety of forms depending on the genetics of the host. Emulsifiers promote/exacerbate colitis in an array of mouse models, primarily by altering intestinal microbiota composition and/or gene expression.¹² ¹³ That these findings are conceptually relevant to humans is supported by observations that 'exclusion diets', which lack food additives, including emulsifiers, show efficacy in maintaining remission in paediatric IBD.³⁴⁻³⁶ Furthermore, the FRESH study, which directly examined the impacts of the synthetic emulsifier CMC on healthy humans, supported the notion that this additive alters the intestinal microbiota. However, the extent of CMC's impact was highly variable, suggesting that only some individuals are CMC-sensitive. Mechanism that drove interindividual variations in CMC sensitivity, as well as whether this parameter can be predicted without exposing a host to this additive, remained to be elucidated.

We, here, report that the MBRA *in vitro* microbiota modeling system, previously shown to maintain key features of human faecal microbial communities,²⁰ ³⁷ ³⁸ reproduced the clinically observed intermicrobiota variations in emulsifier sensitivity observed in the FRESH study. Further, MBRA studies suggested
A

<table><thead><tr><th>Patient ID</th><th>Treatment groups<br>(from the FRESH clinical study)</th><th>Sensitive / Insensitive<br>(from the FRESH clinical study)</th><th>Sensitive / Insensitive<br>(from the current study)</th></tr></thead><tbody><tr><td>Patient_2</td><td>CMC</td><td>Insensitive</td><td>Insensitive</td></tr><tr><td>Patient_6</td><td>CMC</td><td>Insensitive</td><td>Insensitive</td></tr><tr><td>Patient_10</td><td>CMC</td><td>Insensitive</td><td>Insensitive</td></tr><tr><td>Patient_13</td><td>CMC</td><td>Insensitive</td><td>Insensitive</td></tr><tr><td>Patient_16</td><td>CMC</td><td>Insensitive</td><td>Insensitive</td></tr><tr><td>Patient_3</td><td>CMC</td><td>Sensitive</td><td>Sensitive</td></tr><tr><td>Patient_12</td><td>CMC</td><td>Sensitive</td><td>Sensitive</td></tr><tr><td>Patient_1</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_4</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_8</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_9</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_11</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_14</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_15</td><td>Control</td><td>NA</td><td>Insensitive</td></tr><tr><td>Patient_5</td><td>Control</td><td>NA</td><td>Sensitive</td></tr><tr><td>Patient_7</td><td>Control</td><td>NA</td><td>Sensitive</td></tr></tbody></table>

B

<figure><img src="image_12.png" /></figure>

C

<figure><img src="image_13.png" /></figure>

D

<figure><img src="image_14.png" /><figcaption>**Figure 4** Identification of a metagenomic signature associated with CMC-sensitivity status. (A) List of the FRESH participants used in the current study and their respective CMC-sensitivity status, identified either in clinics or using *in vitro* microbiota modelisation (B–D) Shotgun metagenomic approach was performed on baseline faecal samples from FRESH participants. (B) PCoA plot of the Bray-Curtis distance matrix generated on metagenomic data from FRESH study participants at the functional level using HUMAnN 3.0 pipeline and the UniRef90 database. (C) Heatmap visualisation of the MaAsLin2-identified emulsifier sensitivity marker's abundance in sensitive and insensitive FRESH participants. (D) Abundance of the MaAsLin2-identified emulsifier sensitivity marker's abundance in sensitive and insensitive FRESH participants. CMC, carboxymethylcellulose; FRESH, Functional Research on Emulsifiers in Humans; PCOA, principal coordinates analysis.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>**Figure 5** Presence of metagenomic markers associated to CMC-sensitivity status among other cohorts. (A) Overview of the cohorts used in the current study. After use of the FRESH cohort to identify the metagenomic signature of CMC sensitivity, the prevalence of these markers was searched across various cohorts, including healthy participants and patients. (B–D) Shotgun metagenomic approach was performed on faecal samples from Mucosa (IBD patients) and Metacardis (healthy participants and obese±T2D patients) cohorts. Data are represented as PCoA of the Euclidean distance matrix generated using the relative abundance of markers in the Mucosa (B) or (C, D) the Metacardis cohort. Dots are coloured by number of markers present (B, C) or patients health status (D). CMC, carboxymethylcellulose; FRESH, Functional Research on Emulsifiers in Humans; IBD, inflammatory bowel disease; PCoA, principal coordinates analysis; T2D, type 2 diabetes.</figcaption></figure>

that some subjects in the control arm of the FRESH study were also CMC-sensitive. These microbiotas were confirmed to indeed be CMC-sensitive via faecal transplant studies. Specifically, germfree IL-10⁻/⁻ mice transplanted with faeces from microbiota MBRA studies deemed CMC-sensitive, developed severe colitis following CMC feeding while similarly treated recipient microbiotas MBRA deemed CMC-insensitive displayed only mild inflammation. Thus *in vitro* microbiota modelling enables a means to predict whether a given microbiota would be prone to promoting inflammation on exposure to CMC without performing any *in vivo* studies.

Our results suggest that prediction of CMC sensitivity may also be predictable from an individual's metagenome. Indeed, we found that training the MaAsLin2 algorithm with metagenomes of the CMC arm of the FRESH study enabled it to correctly stratify the CMC sensitivity status of the nine FRESH control subjects. However, using the resulting refined signature generated from the full FRESH cohort to predict CMC sensitivity in new cohorts proved less straightforward. Specifically, training MaAsLin2 with the metagenomes of the 4 CMC-sensitive and 12 CMC-insensitive FRESH subjects identified 78 functional markers as being positively correlated with emulsifier
sensitivity. While these markers could be found in European cohorts, the frequency at which they were present resulted in poor ability to stratify CMC-sensitivity status. Such inapplicability of the FRESH cohort-derived signature to distinct cohorts may reflect the need for more extensive sets of training data and/or that some predictive microbiome signatures are inherently cohort-specific.

Analysis of the metagenomic signature as means of inputting mechanisms that mediate CMC sensitivity did not prove informative. Many of these markers (47%) coded for unidentified function while others were assigned to general functional categories, for example, 15% were related to transcription. Thus, further research is needed to determine how these markers might mediate CMC sensitivity. In any event, that all identified markers were positively associated with CMC sensitivity suggests that this phenotype is driven by the presence of select microbiome genes rather than the absence of factors that provide emulsifier resistance. Lastly, the identification of sensitivity markers at the functional level, but not at the taxonomical levels, suggests that a given—or association of—microbiota member(s) is not sufficient to drive emulsifier deleterious effects in all susceptible individuals. Accordingly, presence of these metagenomic markers was heterogeneous in independent cohorts in which we'd expect some individuals would be CMC-sensitive.

The concept that selects components of diet selectively impact disease is not restricted to IBD, but rather can be extended to numerous other chronic inflammatory diseases, especially the large subset of such diseases in which gut microbiota composition is thought to be a disease determinant. Indeed, in the case of emulsifiers, epidemiological studies have recently highlighted the association between dietary emulsifier consumption and an increased risk of various cancer,³⁹ T2D,⁴⁰ as well as cardiovascular diseases.⁴¹ Moreover, we previously reported that the impact of various food additives on microbiota is compound specific,¹⁴ indicating that CMC is not the only food additive able of inducing deleterious effect on health. Our general hypothesis holds that the extent to which a given individual to develop deleterious response following chronic exposure to a particular food additive will likely be dependent on its intestinal microbiota.

The food industry has commonly added specific compounds, such as artificial sweeteners and stabilisers, to meet societal demands at lower costs. Several studies have reported their effects on human health and their interactions with the microbiota.⁴²⁻⁴⁴ Moreover, a recently published clinical study suggests that non-nutritive sweeteners may induce glycaemic alterations that are person-specific and dependent on the composition of the microbiome.⁴⁵ Our recent findings, along with other studies using *in vitro* microbiota systems, highlight the potential of MBRA to predict sensitivity to such compounds.¹⁴ ³⁷ ⁴⁶ Further analyses are needed to elucidate correlations between sensitivity to specific dietary compounds and microbiota composition, enabling the prediction of an individual's response based on microbiota profiling. In conclusion, we report the use of *in vitro* microbiota modelling and sequence-based microbiome analysis to predict whether an individual's intestinal ecosystem will be perturbed, and consequently their proneness to disease enhanced, by consumption of a specific food additive. Extending this approach to larger cohorts and additional food additives will advance implementation of personalised nutritional approaches potentially enabling individuals to avoid specific food additives that might pose a danger to them.

# MATERIALS AND METHODS

## Reagents

Sodium CMC (E466, average MW~250000) was purchased from (Sigma, St. Louis, Missouri, USA).

## Faecal sample collection

Fresh faecal samples were collected from participants of a randomised, controlled-feeding study that took place at the University of Pennsylvania's Center for Human Phenomic Science, as described previously¹⁷ (figure 2A and ClinicalTrials.gov, number NCT03440229). The study design and the experimental protocol were previously detailed in.¹⁷ Stool samples were collected without preservatives or stabilisers at the time of inclusion, and aliquoted and frozen at -80°C.

## Setup and use of the *in vitro* MBRA system and treatment with CMC

The MBRA system was prepared and used as described previously.¹⁹ ²⁰ Briefly, the MBRA system is housed in an anaerobic chamber and consists, per system, of 24 individual chambers, as presented online supplemental figure S1A. MBRA chambers were held on a magnetic stand for continual homogenisation and were connected to two 24-channel peristaltic pumps with low flow rate capabilities (205S peristaltic pump with 24-channel drive, Watson-Marlow). After autoclaving, the prepared MBRA system was placed in an anaerobic chamber for at least 72 hours. MBRA chambers were then filled with 15 mL of Bioreactor Medium (BRM) and subsequently inoculated with freshly prepared faecal slurry. For this, faecal samples were resuspended at 10% w/v in anaerobic phosphate buffered saline (D-PBS) (Gibco-Life Technologies) within an anaerobic environment, vortexed for 5 min and centrifuged at 800 rpm for 5 min at 20°C. Supernatant was subsequently within an anaerobic environment and filtered through a 100 µm filter to remove any residual particles. Finally, 3.8 mL of this faecal slurry was used to inoculate each MBRA chambers, with six independent chambers inoculated per donor. Twenty-four hours after inoculation, constant flow was initiated at 1.875 mL/hour, corresponding to an 8-hour retention time. After a 72-hour microbiota stabilisation phase, obtained chambers were treated, in triplicates for each donor, with BRM medium containing CMC at 0.1% (w/v, added prior autoclaving) or control sterile BRM medium. At the 120-hour time point, CMC treatment was discontinued by connecting all chambers to emulsifier-free BRM sterile medium. Samples were collected longitudinally (400 µL), as presented online supplemental figure S1B, with CMC treatment occurring between 0 hour and 120 hours postinoculation. Samples were then stored at -80°C until analysis (online supplemental figure S1C).

## Bacterial DNA extraction

As described previously,¹⁴ DNA was extracted from frozen MBRA suspension (50 µL) using a QIAamp 96 PowerFecal QIAcube HT kit (Qiagen Laboratories) with mechanical disruption (Qiagen TissueLyser II). Briefly, 650 µL of prewarmed buffer PW1 was added to 50 µL of each sample. Samples were thoroughly homogenised using bead-beating with a TissueLyser before centrifuging the plate at 4000 rpm for 5 min at 20°C in order to pellet beads and particles. 400 µL of supernatant was added into a new 96-well plate containing 150 µL of Buffer C3. After mixing and incubation on ice for 5 min, centrifugation was performed at 4000 rpm for 5 min at 20°C. 300 µL of each supernatant was added to a new 96-well S-block plate, and 20 µL of Proteinase K was added and incubated for 10 min at
room temperature. The following steps were next performed on a QIAcube high-throughput robot: addition of 500 µL of Buffer C4, DNA binding to a QIAamp 96 plate, column wash using AW1 (800 µL), AW2 (600 µL) and ethanol (400 µL) and DNA elution using ATE buffer (100 µL).

## Bacterial density quantification through 16S rRNA qPCR

Extracted DNAs were diluted 1/10 with sterile DNA-free water and subjected to quantitative PCR using the 16S V4 specific primers 515F 5'-GTGYCAGCMGCCGCGGTAA-3' and 806R 5'-GGACTACNVGGGTWTCTAAT-3' on a LightCycler 480 (Roche) using QuantiFast SYBR Green PCR Kit (Qiagen). Data were analysed by The LightCycler 480 Software (Roch molecular system), and results were expressed as relative values.

## Microbiota analysis by 16S rRNA gene sequencing using Illumina technology

16S rRNA gene amplification and sequencing were performed using the Illumina MiSeq technology following the protocol described previously.⁴⁷ ⁴⁸ The 16S rRNA genes, region V4, were PCR amplified from each sample using a composite forward primer and a reverse primer containing a unique 12-base barcode, designed using the Golay error-correcting scheme, which was used to tag PCR products from respective samples.⁴⁷ The forward primer 515F was used: 5'-AATGATACGGCGACCA CCGAGATCTACACGCTXXXXXXXXXXXXXTATGGTAA TT GTGTGYCAGCMGCCGCGGTAA-3': the italicised sequence is the 5' Illumina adapter, the 12 X sequence is the Golay barcode, the bold sequence is the primer pad, the italicised and bold sequence is the primer linker and the underlined sequence is the conserved bacterial primer 515F. The reverse primer 806R used was 5'-CAAGCAGAAGACGGCATACGAGATAGTCAG- CCAGCCGGACTACNVGGGTWTCTAAT-3': the italicised sequence is the 3' reverse complement sequence of Illumina adapter, the bold sequence is the primer pad, the italicised and bold sequence is the primer linker and the underlined sequence is the conserved bacterial primer 806R. PCR reactions consisted of 5PRIME HotMasterMix (Quantabio, Beverly, Massachusetts, USA), 0.2 µM of each primer, 10-100 ng template and reaction conditions were 3 min at 95°C, followed by 30 cycles of 45 s at 95°C, 60 s at 50°C and 90 s at 72°C on a Biorad thermocycler. PCRs products were visualised by gel electrophoresis and purified with Ampure magnetic purification beads (Agencourt, Brea, California, USA). Products were then quantified (Quant-iT PicoGreen dsDNA assay), and a master DNA pool was generated from the purified products in equimolar ratios. The pooled products were quantified using the Quant-iT PicoGreen dsDNA assay and sequenced using an Illumina MiSeq sequencer (paired-end reads, 2×250 bp) at the Genom'IC sequencing platform from Institut Cochin, Paris, France.

## 16S rRNA gene sequences analysis

16S rRNA sequences were analysed using QIIME2—version 2022.⁴⁹ Sequences were demultiplexed and quality filtered using Dada2 method⁵⁰ with QIIME2 default parameters in order to detect and correct Illumina amplicon sequence data, and a table of Qiime 2 artefact was generated. A tree was next generated, using the align-to-tree-mafft-fasttree command, for phylogenetic diversity analyses, and alpha and beta diversity analyses were computed using the core-metrics-phylogenetic command. For taxonomy analysis, features were assigned to operational taxonomic units with a 99% threshold of pairwise identity to the Greengenes reference database V.13.8.⁵¹ Unprocessed sequencing

data are deposited in the European Nucleotide Archive under accession number PRJEB84075.

## ¹H NMR-based metabolomic analysis

MBRA samples collected during the pretreatment phase (−24 hours time point), during the treatment phase (72 hours time point) and during the post-treatment phase (192 hours time point) were used for metabolomic analysis. Samples preparation for NMR were performed as previously described.⁵² ¹H NMR spectra were acquired on a Bruker Avance NEO 600 MHz spectrometer equipped with an inverse cryogenic probe (Bruker Biospin, Germany) at 298 K. A typical 1D NMR spectrum named NOESYPR1D was acquired for each sample. Metabolites were assigned and confirmed with a series of 2D NMR spectra, as described previously.⁵³ All ¹H NMR spectra were adjusted for phase and baseline using Chenomx. The chemical shift of ¹H NMR spectra were referenced to sodium 3-trimethylsilyl [2,2,3,3-d4] propionate (TSP) at δ 0.00. The relative contents of metabolites were calculated by normalising to the total sum of the spectral integrals. The quantification of metabolites, including CMC, was calculated as NMR peak area against TSP using Chenomx.

## Microbiota analysis by shotgun metagenomic using Illumina technology

DNAs were extracted from faecal samples of the 16 FRESH donors used in the MBRA system using the QIAamp Fast DNA Stool Mini Kit (Qiagen). Extracted DNAs were fragmented by sonication (six cycles of 90 s) using a bath sonicator (Bioruptor Plus sonication device, Diagenode) at 4°C. Library preparation was performed using the Invitrogen Colibri PS DNA Library Prep Kit for Illumina (ThermoFisher) according to manufacturer's recommendations. The purified library was then subjected to sequencing using an Illumina NextSeq500 (paired-end reads, 2×250 bp) at the Genom'IC sequencing platform from Institut Cochin, Paris, France. Obtained sequences were quality filtered and sequencing adapters were removed from the resulting sequences *via* cutadapt.⁵⁴ These quality-filtered reads were then grouped via MetaPhlAn V.2.0⁵⁵ into taxonomical categories and via HUMaNN3²² into functional categories.

The metagenomes from two following additional cohorts were used:

* The MUCOSA cohort (Development of a Non-Invasive Mucosal Healing Index in Crohn's Disease), from Nancy, Besançon and Saint-Étienne, France and includes 199 patients with Crohn's disease, aged 18–75 years.

* The MetaCardis cohort (http://www.metacardis.net/) from various European countries with a total of 1545 participants, including 458 healthy individuals, 430 obese individuals without T2D and 657 obese individuals with T2D. Recruitment for the MetaCardis cohort occurred between 2013 and 2015 across several clinical institutions in Europe: Pitié-Salpêtrière Hospital, the Center of Research for Clinical Nutrition and the Institute of Cardiometabolism and Nutrition in France; the Integrated Research and Treatment Center (IFB) for Adiposity Diseases in Leipzig, Germany; and the Novo Nordisk Foundation Center for Basic Metabolic Research in Copenhagen, Denmark.

## Mice experiments

Germ-free C57BL/6 IL-10⁻/⁻ male mice (C57BL/6N-Tac-I10em⁸Tac; Taconic model GF-16006) were ordered at 4–6 weeks of age and immediately subjected to faecal microbiota
transplantation. For this purpose, mice were orally adminis-
tered with 400–500 µL of the faecal suspension from CMC-
predicted insensitive or CMC-predicted sensitive FRESH
participants collected at the time of inclusion (eight mice per
donor). Animals were maintained in isolated ventilated caging
system (Isocages from Techniplast, West Chester, Pennsylvania,
USA, 1–2 mice per cage) to protect them from environmental
contamination⁵⁶ and in a controlled environment (12 hours day/
night cycle, lights off at 7:00PM, 21°C±2°C, 42°C±13% of
humidity) at Institut Cochin (Paris, France). Experimentations
were under institutionally approved protocol (APAFIS#24788-
2019102806256593 v8) and mice were provided unlimited
access to autoclaved food (Purina Rodent Chow #5021) and
autoclaved water (Cristaline) ad libitum. After 2 weeks of
acclimatisation post microbiota transplantation, mice received
either autoclaved water (Cristaline) or autoclaved CMC solu-
tion (average MW~250 000, Sigma, St. Louis, Missouri, USA)
at 1% diluted in water for 16 weeks, resulting in eight groups
of mice (n=4, online supplemental figure S2). Water and CMC
solution were autoclaved and changed every other week. Body
weights over time were recorded, and fresh faeces samples were
collected at various time points. After 16 weeks of treatment,
mice were euthanised and periepididymal fat pad, spleen and
caecum were weighed, and colon length was measured. Colon
samples were collected for further analysis.

Histopathological analysis of colonic tissue and
immunochemistry

Mouse proximal colons were placed in methanol-Carnoy's fixative solution (60% methanol, 30% chloroform, 10% glacial acetic acid) for a minimum of 3 hours at room temperature and stored at 4°C. Tissues were then washed in methanol 2×30 min, absolute ethanol 2×15 min, ethanol/xylene (1:1) 15 min and xylene 2×15 min, followed by embedding in Paraffin with a vertical orientation and tissues were then sectioned at 6 µm thickness. Slides were stained with H&E using standard protocols for histological scoring, which was blindly determined on each colon as previously described.⁵⁷ ⁵⁸ Briefly, each colon was assigned four scores based on the degree of epithelial damage and inflammatory infiltrate in the mucosa, submucosa and muscularis/serosa.⁵⁷ Each of the four scores was multiplied by a coefficient 1 if the change was focal, 2 if it was patchy and 3 if it was diffuse 25 and the 4 individual scores per colon were added. For immunochemistry, stainings were performed on the automaton Leica Bond RX. Slides were unmasked at pH6 and incubated 30 min with an anti-CD68 antibody (abcam, EPR23917-164, 462 ab283654) 1:500 diluted and washed. The revelation system ('Bond Polymere Refine' kit, DS9800, Leica) included a secondary antibody HRP conjugated and revealed by DAB. High-resolution images were finally acquired using a Lamina Slide Scanner (Perkin Elmer) at the Hist'IM platform (INSERM U1016, Institut Cochin, Paris, France). Cell-based quantification of CD68+interstitial cells was performed by counting. Four images per mice were analysed, and positively immune-stained cells were counted in a blinded manner, using the ImageJ V.1.45 software and the 'cell-counter' plug-in.

Immunostaining of mucins and localisation of bacteria by
fluorescent in situ hybridisation

Mucus immunostaining was paired with fluorescent in situ
hybridisation, as previously described,⁵⁹ in order to analyse
bacteria localisation at the surface of the intestinal mucosa.
Briefly, colonic human biopsies (approximately 15 cm from the

anal verge, correlating approximately with the rectosigmoid
junction) and mouse tissues (proximal colon, 2nd cm from the
cecum) containing faecal material were placed in methanol-
Carnoy's fixative solution (60% methanol, 30% chloroform
and 10% glacial acetic acid) for a minimum of 3 hours at room
temperature and stored at 4°C. Tissues were then washed in
methanol 2×30 min, absolute ethanol 2×15 min, ethanol/
xylene (1:1) 15 min and xylene 2×15 479 min, followed by
embedding in Paraffin with a vertical orientation. Five-to-six
mm sections were performed and dewaxed by xylene 60°C for
10 min, xylene for 10 min and 99.5% ethanol for 5 min. Hybris-
disation step was performed at 50°C overnight with EUB338
probe (50-GCTGCCTCCCGTAGGAGT-30, with a 5' label-
ling using Alexa 647) diluted to a final concentration of 10 mg/
mL in hybridisation buffer (20 mM Tris-HCl, pH 7.4, 0.9 M
NaCl, 0.1% SDS, 20% formamide). After washing 10 min in
wash buffer (20 mM Tris-HCl, pH 7.4, 0.9 M NaCl) and a
quick wash in PBS, slides were incubated in block solution (5%
fetal bovine serum in PBS) in darkness at 4°C for 30 min. Slides
were then gently dried and PAP pen (Sigma-Aldrich) was used
to mark around the section. Mucin-2 primary antibody (rabbit
MUC2 antibody (C3), C-term, Genetex, GTX100664) was
diluted 1:100 in block solution and applied overnight at 4°C.
After washing 3×10 min in PBS, block solution containing anti-
rabbit Alexa 488 secondary antibody diluted 1:300, Phalloidin-
Tetramethylrhodamine B isothiocyanate (Sigma-Aldrich) at
1 mg/mL and Hoechst 33258 (Sigma-Aldrich) at 10 mg/mL was
applied to the section for 2 hours. After washing 3×10 min in
PBS, the slides were mounted using Prolong anti-fade mounting
media (Life Technolog×ies, Carlsbad, California, USA) and kept
in the dark at 4°C. Images, observations and measurement of
the distance between bacteria and epithelial cell monolayer were
performed with a Spinning Disk IXplore using the Olympus cell-
Sens imaging software 421 (V.2.3) at a frame size of 2048×2048
with 16-bit depth. A 405 nm laser was used to excite the 422
Hoechst stain (epithelial DNA), 488 nm for Alexa Fluor 488
(mucus), 488 nm for TRITC (actin), 423 and 640 nm for Alexa
Fluor 647 (bacteria). Samples were imaged with a 20× objective
immersion objective.

Statistical analysis

Significance was determined using t-tests, Mann-Whitney test, one-way analysis of variance (ANOVA) corrected for multiple comparisons with a Bonferroni post-test, two-way ANOVA corrected for multiple comparisons with a Bonferroni, Šidák or Dunnett post-test (or mixed-effect analysis when some values were missing). Differences were noted as significant if $p \le 0.05$.

Patient and public involvement

Patients were not actively involved in this research.

Author affiliations

<footnote n="1">¹Microbiome-Host Interactions, INSERM U1306, CNRS UMR6047, Institut Pasteur, Université Paris Cité, Paris, France</footnote>

<footnote n="2">²INSERM U1016, CNRS UMR8104, Mucosal Microbiota in Chronic Inflammatory Diseases, Université de Paris, Paris, France</footnote>

<footnote n="3">³Division of Gastroenterology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA</footnote>

<footnote n="4">⁴Center for Clinical Epidemiology and Biostatistics, Division of Gastroenterology and Hepatology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA</footnote>

<footnote n="5">⁵Robustness and Evolvability of Life, CNRS UMR10 8104, INSERM U1016, Université Paris Cité, Paris, France</footnote>

<footnote n="6">⁶Inserm, Nutrition and Obesities: Systemic Approaches Research Unit, NutriOmics, Sorbonne University, Paris, France</footnote>
⁷Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, UMMISCO, IRD, Sorbonne Université, Bondy, France

⁸INFYI Institute, Nancy University Hospital, Vandœuvre-lès-Nancy, France

⁹INSERM, NGERE, University of Lorraine, Nancy, France

¹⁰FHU-CURE, Nancy University Hospital, Vandoeuvre les Nancy, France

¹¹CHRU Nancy, IHU Infiny, Vandoeuvre-les-Nancy, France

¹²Center for Molecular Toxicology and Carcinogenesis, Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA

¹³Microbiome Science, Max-Planck-Institute for Biology, Tübingen, Germany

¹⁴Assistance Publique Hôpitaux de Paris, Nutrition Department, Pitié-Salpêtrière Hospital, Paris, France

¹⁵Department of Gastroenterology, Nancy University Hospital, Vandoeuvre-lès-Nancy, France

¹⁶Center for Inflammation, Immunity and Infection, Digestive Disease Research Group, Department of Biology, Georgia State University, Atlanta, Georgia, USA

**Acknowledgements** The authors thank the Hist'IM and the Genom'IC platforms (INSERM U1016, Paris, France) for their help. Schematics were created with Biorender. (https://BioRender.com/w84e441).

**Contributors** HR, SN, ATG and BC contributed to the design of the work. HR, SN, FH and BC performed the experiments. HR, SN, GW, JL, MD, LV, OT, EB, ADP and BC contributed to the analysis and interpretation of data for the work. LP-B, MV-G, KC, NT and DA provided cohort's samples and data. HR, BC, LP-B, KC, ATG and BC contributed to drafting the work and critically revising it. BC acted as the guarantor of the study.

**Funding** This work was supported by a Starting Grant (grant agreement Invaders No. ERC-2018-StG-804135) and a Consolidator Grant (grant agreement InterBiome No. ERC-2024-CoG-101170920) from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program, ANR grants EMULBIONT (ANR-21-CE15-0042-01) and DREAM (ANR-20-PAMR-0002), grant for the AFA Crohn RCH France and the national program 'Microbiote' from INSERM. This work was also supported by the French government through the France 2030 investment plan managed by the National Research Agency (ANR), as part of the ANR 23 IAHU 0012. Héloïse Ryter is supported by a fellowship from Paris Région—DIM One Health-DOH 2.0.

**Disclaimer** No funders had any role in the design of the study and data collection, analysis, and interpretation, nor in manuscript writing.

**Competing interests** LP-B received consulting and lecture fees as well as a grant from Takeda. None of the other authors declare competing interests.

**Patient and public involvement** Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

**Patient consent for publication** Not applicable.

**Ethics approval** Mouse experiments were approved and conducted in accordance with the guidelines of the local ethics committee for animal care at the Institut Cochin. All procedures adhered to the institutionally approved protocol (APAFIS#24788-2019102806256593 v8). This study also involved human faecal samples obtained from a previous study approved by the Institutional Review Board of the University of Pennsylvania (IRB#828422). The MUCOSA and MetaCardis cohort studies were approved by the local ethics committees at their respective institutions. All participants provided informed consent prior to their participation in the study.

**Provenance and peer review** Not commissioned; externally peer reviewed.

**Data availability statement** All data relevant to the study are included in the article or uploaded as supplementary information.

**Supplemental material** This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

**Open access** This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

ORCID iDs

Lucile Viguë http://orcid.org/0000-0002-0571-1990
Marta Vazquez-Gomez http://orcid.org/0000-0002-8916-2086

Djésia Arnone http://orcid.org/0000-0002-5892-9387
Karine Clément http://orcid.org/0000-0002-2489-3355
Laurent Peyrin-Biroulet http://orcid.org/0000-0003-2536-6618
Andrew T Gewirtz http://orcid.org/0000-0002-6338-7578
Benoit Cassaing http://orcid.org/0000-0002-4285-769X

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