# Gut microbial dysbiosis associated to diarrheic irritable bowel syndrome can be efficiently simulated in the Mucosal ARTificial COLon (M-ARCOL)

Ophélie Uriot, Charlotte Deschamps, Julien Scanzi, Morgane Brun, Nicolas Kerckhove, Christian Dualé, Elora Fournier, Claude Durif, Sylvain Denis, Michel Dapoigny, et al.

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Ophélie Uriot, Charlotte Deschamps, Julien Scanzi, Morgane Brun, Nicolas Kerckhove, et al. Gut microbial dysbiosis associated to diarrheic irritable bowel syndrome can be efficiently simulated in the Mucosal ARTificial COLon (M-ARCOL). Bioengineered, 2025, 16 (1), 10.1080/21655979.2025.2458362. hal-04942045

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# Gut microbial dysbiosis associated to diarrheic irritable bowel syndrome can be efficiently simulated in the Mucosal ARtificial COLon (M-ARCOL)

Ophélie Uriotª, Charlotte Deschampsª, Julien Scanzib,c,d, Morgane Brunª, Nicolas Kerckhoveb,e, Christian Dualéf, Elora Fournierª, Claude Durifª, Sylvain Denisª, Michel Dapoignyb,c, Philippe Langellaª, Monique Alricª, Lucie Etienne-Mesminª, and Blanquet-Diot Stéphanie ª,ª

ªUMR 454 MEDIS, Microbiologie Environnement Digestif et Santé, Université Clermont Auvergne - INRAE, Clermont-Ferrand, Puy-de-Dôme, France; ᵇUMR INSERM 1107 NEURO-DOL, Université Clermont Auvergne, Clermont-Ferrand, Puy-de-Dôme, France; ᶜService de Gastroentérologie, Centre Hospitalo-Universitaire, Clermont-Ferrand, Puy-de-Dôme, France; ᵈService de Gastroentérologie, Centre Hospitalier de Thiers, Thiers, Puy-de-Dôme, France; ᵉService de Pharmacologie médicale, Centre Hospitalo-Universitaire, Clermont-Ferrand, Puy-de-Dôme, France; ᶠCIC INSERM 1405, Centre Hospitalo-Universitaire, Clermont-Ferrand, Puy-de-Dôme, France; ᶞMicalis, INRAE, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, Yvelines, France

## ABSTRACT

Irritable bowel syndrome (IBS) is a common chronic gastrointestinal disorder, with diarrheal-predominant IBS (IBS-D) as the most frequent subtype. The implication of gut microbiota in the disease's etiology is not fully understood. *In vitro* gut systems can offer a great alternative to *in vivo* assays in preclinical studies, but no model reproducing IBS-related dysbiotic microbiota has been developed. Thanks to a large literature review, a new Mucosal ARtificial COLon (M-ARCOL) adapted to IBS-D physicochemical and nutritional conditions was set-up. To validate the model and further exploit its potential in a mechanistic study, *in vitro* fermentations were performed using bioreactors inoculated with stools from healthy individuals (n = 4) or IBS-D patients (n = 4), when the M-ARCOL was set-up under healthy or IBS-D conditions. Setting IBS-D parameters in M-ARCOL inoculated with IBS-D stools maintained the key microbial features associated to the disease *in vivo*, validating the new system. In particular, compared to the healthy control, the IBS-D model was characterized by a decreased bacterial diversity, together with a lower abundance of *Rikenellaceae* and *Prevotellaceae*, but a higher level of *Proteobacteria* and *Akkermansiaceae*. Of interest, applying IBS-D parameters to healthy stools was not sufficient to trigger IBS-D dysbiosis and applying healthy parameters to IBS-D stools was not enough to restore microbial balance. This validated IBS-D colonic model can be used as a robust *in vitro* platform for studies focusing on gut microbes in the absence of the host, as well as for testing food and microbiota-related interventions aimed at personalized restoration of gut microbiota eubiosis.

## ARTICLE HISTORY

Received 8 July 2024
Revised 8 January 2025
Accepted 16 January 2025

## KEYWORDS

IBS-D; gut microbiota;
in vitro gut model;
M-ARCOL; bile acids; SCFA;
mucus

## Introduction

Irritable bowel syndrome (IBS) is one of the most prevalent chronic functional disorders of the human gastrointestinal tract [1,2]. Characterized by recurrent abdominal pain, intestinal discomfort as well as bowel transit disorders [2–5], this disease is debilitating for many people. IBS with diarrhea (IBS-D) appears as the most common subtype [2,6]. Colonic biopsies from IBS-D patients revealed an increased permeability [7] associated with a low-grade inflammation, an accelerated whole gut transit [8], a visceral colonic hypersensitivity [9,10], as well as alterations of bile acids (BA) profiles [11,12]. Of importance, studies in

the recent decades have also revealed alterations of gut microbiota in IBS-D patients. Despite inconsistencies across studies, recent findings evidenced some common trends, such as an increase in *Proteobacteria* and *Enterobacteriaceae*, and a decrease in the relative abundances of *Firmicutes*, *Bacteroidota*, *Faecalibacterium*, *Ruminococcaceae* and *Archaea* in patients with IBS-D compared to healthy individuals [6,13–17]. To date, existing treatments of IBS including dietary or lifestyle interventions, pharmacological or cognitive treatment mainly aimed at alleviating patients' symptoms [4,6], but new probiotic strategies or fecal microbiota transplantation showed promising

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effects [4,18–20], further supporting the involvement
of gut microbiota in disease etiology.

Although clinical studies are the gold standard approach, they remain limited for cost and ethical reasons. In pre-clinical studies, rodent models rely on physical interventions, chemical treatments, or microbial infection and are generally used to study IBS pathophysiology regarding host response, gut microbiota and intestinal permeability or evaluate the efficiency of treatments [21]. In accordance with the 3 R European regulation aiming at limiting animal use, *in vitro* gut models offer a valuable alternative to *in vivo* studies, due to technical, regulatory, reproducibility, ethical, and cost considerations. Unlike rodent models, they faithfully simulate human physiologically relevant diets, physicochemical digestive parameters and microbiota, and can provide mechanistic insights into microbe–microbe interactions at the gut ecosystem level [22,23]. To date, most *in vitro* models integrating the gut microbiome have been designed to reproduce digestive conditions in healthy adult individuals [24–26], with no model specifically adapted to IBS conditions. The only available *in vitro* model involves Caco-2 intestinal epithelial cells grown on extracellular matrices and exposed to IBS patient plasma [27].

Here, we aimed to develop and validate,
through comparison with *in vivo* data in
humans, a new *in vitro* model that specifically
mimics the colon microbiota of IBS-D patients
in the absence of the host, encompassing all
physiologically relevant parameters described in
the literature. This was achieved using the
dynamic Mucosal Artificial Colon (M-ARCOL)
system [28,29], which uniquely recreates both
the lumen and mucus-associated microbiota
under microbe's self-maintained anaerobiosis.
Then, the full potential of this new model was
exploited by cross-comparing gut microbiota
structure and activity when fermentations were
performed under healthy or IBS-D parameters,
using stools collected from either IBS-D patients
or healthy volunteers. This approach facilitated
the acquisition of useful mechanistic insights
into the bidirectional relationship between gut
microbes and the human colonic environment
under realistic IBS-D conditions excluding host
interactions.

Materials and methods

Feces origin and treatment

Fresh fecal samples were collected from four
healthy adults (HS1, HS2, HS3 and HS4) and
four IBS-D patients (IS1, IS2, IS3, and IS4), as
described in Figure 1a, with no history of antibio-
tic 2-months prior to the beginning of the study.
IBS-D patients were selected and recruited by
University Hospital Gabriel Montpied (Clermont-
Ferrand, France). Donors all gave written,
informed consent to take part in the study. The
use of fecal microbiota of human origin was
approved by the French Ethical research commit-
tee (Comité de protection des personnes (CPP)
Sud-Est VI, Clermont-Ferrand, France) under
registration number AU1274 (IBSET, PHRC IR
2015 DAPOIGNY, EudraCT 2016 -002,110-42).
Stool samples were collected and kept anaerobi-
cally for a maximum of 12 h before inoculation.
Then, they were processed under strict anaerobic
conditions (COY Laboratory Products Inc, Grass
Lake, MI, USA) before their introduction into the
M-ARCOL system. Each stool was tenfold diluted
in 30 mm anaerobic sodium phosphate buffer (pH
6.5) supplemented with 1.9 mm cysteine, then fil-
tered through a 500 µm sieve.

M-ARCOL system

The M-ARCOL system is a one-stage fermentation
system (Figure 1) used under continuous condi-
tions which simulates, based on *in vivo* data, the
main physicochemical (pH, body temperature,
retention time), nutritional (composition of ileal
effluents used as a nutritive medium) and micro-
bial (lumen and mucus-associated microbiota)
conditions found in the human colon [24,28–30].
At the beginning of experiments, each glass vessel
containing 200 mL of nutritive medium was set
under anaerobiosis by flushing with O₂-free N₂
gas before adding the fecal suspension (100 mL).
Anaerobiosis was maintained during the total
course of fermentation by the sole activity of the
resident microbiota and through ensuring the sys-
tem airtightness [24,28–30]. Overproduced gases
were collected daily in a sampling bag connected
to the condenser. Bioreactors were kept at body
temperature using an incorporated panel heater.
<figure><img src="image_9.png" /></figure>

<table><thead><tr><th></th><th>Healthy donors</th><th>IBS-D donors</th></tr></thead><tbody><tr><td>Gender (women/men)</td><td>2/2</td><td>3/1</td></tr><tr><td>Age (years)</td><td>36.3 ± 6.1</td><td>40.0 ± 9.6</td></tr><tr><td>[mean ± SD (range)]</td><td>(22 – 52)</td><td>(25 – 66)</td></tr><tr><td>Body mass index (kg/m²)</td><td>21.7 ± 2.0</td><td>20.9 ± 0.4</td></tr><tr><td>[mean ± SD (range)]</td><td>(16.6 – 25.1)</td><td>(20.3 – 22.0)</td></tr><tr><td>Bristol stool form scale [5]</td><td>Type 3 &amp; 4</td><td>Type 5 to 7</td></tr></tbody></table>

<table><thead><tr><th></th><th>Healthy parameters</th><th>IBS-D parameters</th></tr></thead><tbody><tr><td>pH</td><td>6.3</td><td>6.3</td></tr><tr><td>Temperature</td><td>37 °C</td><td>37 °C</td></tr><tr><td>Retention time</td><td>24 hours</td><td>18 hours</td></tr><tr><td>Ileal effluents<br>composition</td><td>Adapted from [28]</td><td>Adapted from [28]<br>+ 10 % water<br>×3 total bile salts<br>BAI / BAII ratio</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Figure 1.** Experimental design in the M-ARCOL.</figcaption></figure>

(a) Eight stools were collected from four healthy adult volunteers (HS: healthy stools) and four IBS-D adult patients (IS: IBS-D stools) to inoculate the Mucosal Artificial Colon Model (M-ARCOL). Characteristics of the donors are summarized in this table. (b) Parameters used to set-up the M-ARCOL model were adapted to reproduce either healthy (HP: healthy parameters) or IBS-D (IP: IBS-D parameters) conditions. (c) Two bioreactors were inoculated with each stool and ran in parallel for 12 days, one set-up with healthy parameters, the other with IBS-D ones. (d) Samples were regularly collected from luminal and mucosal compartments of the M-ARCOL model and from its atmospheric phase to follow microbiota composition and metabolic activities. In this study, the different conditions where cross-compared: healthy stool-healthy parameters (HS-HP), healthy stool-IBS-D parameters (HS-IP), IBS-D stool-healthy parameters (IS-HP) and IBS-D stool-IBS-D parameters (IS-IP). BAI: primary bile acids, BAII: secondary bile acids, HPLC: high performance liquid chromatography, PBS: phosphate buffer saline, SCFA: short chain fatty acids.

Colonic pH and redox potential were constantly recorded (Applikon, Delft, The Netherlands) and pH was adjusted with an automatic addition of 2 M NaOH. A nutritive medium containing various sources of carbohydrates, proteins, lipids, minerals, and vitamins [28,30] was continuously introduced into the bioreactor, while the fermentation medium was automatically withdrawn, ensuring

the appropriate retention time inside bioreactors. Colonic mucosal environment was reproduced through by-passing the fermentation medium from the main vessel toward an airtight removable compartment maintained at body temperature and containing an average surface of 556 cm² mucin-alginate beads [28,31]. Mucin beads were prepared as previously described [28,31] by dropwise
<figure><img src="image_11.png" /></figure>

gelation of a 5% mucin (Mucin from porcine sto-
mach type II, Sigma-Aldrich, Saint-Louis, MO,
USA) and 2% sodium alginate (Sigma-Aldrich,
Saint-Louis, MO, USA) solution inside 0.2 M
CaCl₂.

Experimental design

Each stool was used to inoculate two fermenters
ran in parallel, one set-up with healthy parameters
and the other with IBS-D ones, based on human
*in vivo* data (Figures 1a-c). This results to different
conditions that where cross-compared during the
present study: healthy stool-healthy parameters
(HS-HP), healthy stool-IBS-D parameters (HS-IP)
, IBS-D stool-healthy parameters (IS-HP) and IBS-
D stool-IBS-D parameters (IS-IP). After a 24 hours
batch fermentation allowing microbiota amplifica-
tion, bioreactors were run under continuous con-
ditions during 11 additional days. Samples were
daily collected from the main vessel (termed lumi-
nal compartment) and centrifugated (18,000 g,
15 min, 4 °C). Pellets were kept at −80 °C for
downstream analysis of luminal gut microbiota
and supernatant were collected and stored at −20
°C for further short-chain fatty acids (SCFA) mea-
surement. Atmospheric phase was also sampled
every day to check anaerobic condition and deter-
mine gas composition. The total volume of over-
produced gases was measured daily. Every 2 days,
mucin beads were collected from external vessel
(termed mucosal compartment) and renewed by
fresh ones under a CO₂-flow. Beads were washed
thrice in sterile phosphate buffer saline (PBS) and
stored at −80 °C before mucosal microbiota ana-
lysis (Figure 1d).

DNA extraction

Genomic DNA was extracted from luminal and
mucosal samples using the QIAamp Fast DNA
Stool Mini Kit® (Qiagen, Hilden, Germany).
Samples were incubated 10 min at 37 °C with 2
mL of 55 mm citrate buffer, then vortexed 3 min at
full speed [32]. After centrifugation (8000 g, 1 min,
20 °C), pellets were subjected to mechanical dis-
ruption using a bead beater (5 min, 20 beat/s) with
300 mg of sterile glass beads (diameter ranging
from 0.1 to 0.6 mm) and 1 mL of InhibitEX® buffer

from Qiagen kit. Then, DNA was extracted follow-
ing manufacturer's instructions. DNA integrity
was verified by agarose gel electrophoresis and
quality checked by A260/280 ratio absorbance
using the NanoDropTM 2000 (Thermo Scientific,
Wilmington, DE, USA). DNA quantity was mea-
sured with the Qubit dsDNA Broad Range Assay
Kit® (Invitrogen, Carlsbad, CA, USA) on a Qubit
3.0 Fluorometer® (Invitrogen, Carlsbad, CA, USA).
DNAs were stored at -20 °C prior to quantitative
PCR analysis and 16S Illumina sequencing.

Quantitative PCR

Eight populations were quantified by qPCR: total
bacteria, *Firmicutes*, *Bacteroidetes*, *Gamma-
proteobacteria*, *Ruminococcaceae*, *Prevotellaceae*, and
*Akkermansia muciniphila*. Primers and hybridization
temperatures are listed in Supplementary Table S1.
qPCR analysis was performed on a Biorad CFX96TM
Real-Time System (Bio-Rad Laboratories, Hercules,
CA, USA) using Takyon Low ROX SYBR 2X
MasterMix blue dTTP kit (Eurogentec, Liège,
Belgium). Each reaction was performed in duplicate
in a final volume of 10 µL prepared as previously
described [28]. The qPCR reactions were carried out
using the following protocol: 1 cycle at 95 °C for
5 min, 40 cycles of 95 °C for 30 s, X °C
(Supplementary Table S1) for 30 s, and 72 °C for 30
s. A melting step was added to ensure primer specifi-
city. Standard curves were generated from 10-fold
dilutions of bacterial DNA (isolated from reference
strains), allowing the calculation of DNA concentra-
tions from extracted samples.

Gut microbiota composition by 16S rDNA
metabarcoding

The primers used for bacterial V3-V4 region of 16S
ribosomal DNA (rDNA) and for the archaeal 16S
rDNA amplification are listed in Supplementary
Table S1. Amplicons were generated using
a Fluidigm Access Array followed by high-
throughput sequencing on an Illumina MiSeq sys-
tem (Illumina, San Diego, CA, USA) performed at
the Carver Biotechnology Center of the University of
Illinois (Urbana Champaign, IL, USA). Prior to ana-
lysis, raw data were demultiplexed and quality fil-
tered using 'DADA2' R-package [33]. Reads with
<figure><img src="image_12.png" /></figure>

quality score under 2 were truncated. Reads under 100 pb length were removed as well as sequences similar to PhiX DNA used as a spike-in control for MiSeq runs. Filtered sequences were dereplicated and cleaned for chimeras (DADA2). Taxonomic classification of the sequences was then performed with 'DECIPHER' package [34]. Assignations from both SILVA 138 release [35] and GTDB_bac120_arc122 [36] databases were merged using IDTAXA with a 60% confidence cutoff. A phylogenetic tree was then reconstructed using DECIPHER. Incorrect taxonomic affiliation of *Prevotella* genus in *Bacteroidaceae* family (instead of *Prevotellaceae*) was manually corrected.

## Gas analysis by gas chromatography

Relative abundances of O₂, N₂, CO₂, CH₄, and H₂ present in the fermenter atmospheric phase was determined using a 490 Micro-gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with Molecular Sieve 5A column and PoraPlot U column coupled with thermal conductivity detector TCD detectors. Argon was used as the carrier gas. Calibration curves were made from four gases: (i) ambient air (78% N₂, 21% O₂ and 0.04% CO₂), (ii) mixture A (5% CO₂, 5% H₂ and 90% N₂), (iii) mixture B (20% CO₂ and 80% H₂), and mixture C (19.9% CO₂, 19.9% CH₄, 20% H₂ and 40% N₂). Results were expressed as relative percentages.

## SCFAs analysis

Concentration of the three main SCFAs, i.e. acetate, propionate, and butyrate, were determined in luminal samples using high-performance liquid chromatography (HPLC) (Elite LaChrom, HITACHI, San Jose, CA, USA) coupled with a diode-array detector. The HPLC column (Concise Separations, San Jose, CA, USA) was maintained at 50 °C. A mobile phase composed of sulfuric acid 0.04 M was used at a flow rate of 0.6 mL/min to separate the different SCFAs. Data were analysed by the EZChrom Elite software at 205 nm. SCFAs concentrations (expressed as mM or relative percentages) were calculated from standard curves established with known concentrations of acetate, propionate, and butyrate (0, 10, 25, and 40 mm) [24].

## Statistical analysis

Results are expressed as means ± SEM (n = 4). Statistical analyses were processed using either GraphPad Prism software version 9.3.1 (GraphPad Software, San Diego, CA, USA) or R studio. Data normal distribution was verified by combining Anderson-Darling, D'Agostino & Pearson, Shapiro-Wilk and Kolmogorov-Smirnov tests and homoscedasticity was checked using the Fisher test. Then, appropriate statistical analysis was applied (either two-way ANOVA, t-test, Mann-Whitney or Welch's tests). A Tukey's multiple comparisons test was applied after ANOVA if necessary. Beta-diversity was evaluated by principal coordinate analysis (PCoA, data not shown) and non-metric multidimensional scaling (NMDS), highlighting an important donor effect. Constraint redundancy analysis (RDA) was then performed with age, weight, sex, microenvironment, donor, and time as variables of the model, using VEGAN package [37]. The analysis was conducted first with all parameters (data not shown) and then with the removal of the 'donor' variable. Bray-Curtis distances were used for each beta-diversity analysis, and significance between groups was assessed with a one- or two-way ANOVA. Differential analyses (DESeq2, metagenomeSeq, metacoder) were performed using rANOMALY package [38,39].

## Results and discussion

### Adaptation of M-ARCOL model to the specific IBS-D colonic conditions

As an alternative to animal experiment, the first aim of this paper was to adapt the M-ARCOL model, initially developed to reproduce the main physicochemical, nutritional, and microbial parameters of the healthy human colon [28,29], to the specific IBS-D condition. A comprehensive literature review analyzing more than 100 articles was conducted to identify the physicochemical parameters specific from the colonic environment of IBS-D patients, including pH, colonic transit time, composition of ileal effluent and BA profiles. The search strategy was constructed around the following criteria: adult (18-60 years) patients suffering from IBS-D, no drug treatment (including
<figure><img src="image_13.png" /></figure>

antibiotics) nor probiotic or prebiotic supplementation, comparison between healthy volunteers and IBS-D patients under strict similar experimental conditions and colonic data prioritized over fecal ones whenever possible. Following those selection criteria, 9 studies were selected to set-up the IBS-D parameters in the M-ARCOL model, as summarized in Figure 1b. This scarcity of pertinent studies resulted from: (i) a lack of *in vivo* assays that effectively distinguished IBS-D from other subtypes, (ii) the considerable variability in techniques and experimental protocols [40], (iii) the limited number of articles that compared IBS-D patients with healthy volunteers, and (iv) the important inter-individual variabilities.

Body temperature was not modified and kept at 37 °C under both healthy and IBS-D settings. Colonic pH was also unchanged under IBS-D parameters, since very few studies have measured the fecal pH of IBS-D patients and results were not consistent [8,41]. Residence time in the bioreactors (reflecting colonic transit time) was decreased from 24 h in healthy to 18 h in IBS-D condition, according to *in vivo* data showing around 25% drop of the mean colonic transit time in IBS-D patients [8,42,43]. An important challenge was the adaptation of the nutritive medium supplied continuously into the bioreactors. We did not uncover any literature data comparing the ileal effluents of IBS-D patients with those of healthy volunteers. Nevertheless, some studies have documented an increase in total BA concentrations in fecal samples from IBS-D patients suffering from BA malabsorption [44,45], as well as an increase in primary to secondary BA ratios [11,46]. Given that most BA are reabsorbed in the distal small intestine and part of the conversion from primary to secondary BA occurs in the colon, we inferred from those findings that both BA quantities and ratios would undergo similar modifications in the colon. Consequently, the total amount of BA was multiplied by 3 in the nutritive medium under IBS-D condition and primary to secondary BA ratio was adjusted from 15/85 in healthy condition to 40/60 in IBS-D condition. Lastly, consistent with the study of Goy et al. (1976) [47] showing a higher water concentration in the feces of IBS-D patients compared with healthy controls, 10% of water was added to the IBS-D nutrient medium.

Microbiota stabilization: from in vivo to in vitro colonic microenvironments

Fecal samples used as inoculum were first analysed to characterize both microbiota composition and activity (Figure 2). Fecal microbiota of IBS-D patients notably differed from healthy donors. In IBS-D, the fecal microbiota was characterized by a lower number of observed ASVs (126 *versus* 175 for healthy volunteers) and a lower Shannon index (*p* > 0.05) (Figure 2b), consistent with *in vivo* studies [14–17,48–53]. As expected [13,16,49,51,52,54–56], fecal bacterial profiles were different in IBS-D samples with higher *Lachnospiraceae*, *Acidaminococcaceae*, and *Akkermansiaceae*, but lower *Prevotellaceae*, *Oscillospiraceae*, and *Rikenellaceae* relative abundances. Main fecal SCFAs were lower in IBS-D donors than in healthy volunteers, particularly propionate and butyrate (Figure 2e), and total SCFAs concentrations also tended to be lower in IBS-D donors (Figure 2f). Over-time analysis of atmospheric gas (Supplementary Fig. S1), luminal SCFAs (Supplementary Fig. S2) and microbiota composition at several taxonomic levels (Supplementary Fig. S3 to S5) indicated that a stabilized state in the *in vitro* model was simultaneously reached under healthy and IBS-D parameters but differed depending on the health status of donors. RDA of bacterial beta-diversity confirmed stability and absence of discernible time effect on both luminal and mucosal microbiota collected from days 8 to 11 at the ASV level (Figure 3d). As previously outlined [28,29,57], this stabilization period is necessary for the gut microbiota to adapt and shift from *in vivo* to *in vitro* situation, as well as to facilitate the differentiations between luminal and mucosal profiles (Figure 3e). *In vivo* to *in vitro* acclimatation led to a lower diversity and an enrichment in *Bacteroidota* and *Proteobacteria* compared to the initial stool (Figures 2-5, Supplementary Fig. S3 to S5), aligning with previous *in vitro* studies in M-ARCOL [28,29], M-SHIME [57,58] and PolyFermS [59,60]. As previously demonstrated in human, piglet, and canine gut models, the addition of a mucosal compartment allows to capture greater bacterial diversity from the fecal microbiota [28,31,61,62], as shown by alpha-diversity indexes (Figures 3a,b). Such compartment however presented a lower bacterial density (7–8 Log₁₀ 16S rDNA copies/g, Figure 5c) compared to
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 2.** Fecal sample characterization.</figcaption></figure>

Eight stools were collected from four healthy volunteers (HS1, HS2, HS3, and HS4) and four IBS-D patients (IS1, IS2, IS3, and IS4). Each stool was characterized by (a) Bacterial relative abundances at the family level determined by 16S metabarcoding analysis, (b) Bacterial diversity estimated by observed ASVs and Shannon indexes (unpaired t-test), (c) Levels of selected bacterial populations quantified by qPCR, (d) Archaeal abundances measured by metabarcoding analysis, (e) Proportion of the three main short chain fatty acids (i.e. acetate, propionate, and butyrate) in relative percentages and (f) Total SCFAs concentrations. SCFA: short chain fatty acids, HS: healthy stool, IS: IBS-D stool, NS: non-significant.

the luminal one (10 Log₁₀ 16S rDNA copies/g, Figure 4c), in accordance with *in vivo* data from healthy volunteers [63]. *In vivo* studies in both healthy volunteers [52,64–67] and IBS-D patients [51,52,66,67] highlighted the presence of specific bacterial populations colonizing the lumen and mucus micro-environments (colonic biopsies compared to fecal samples). Such differences were successfully reproduced *in vitro*, with M-ARCOL's mucosal compartment enriched in *Firmicutes* including *Lachnospiraceae* and *Ruminococcaceae*, and the luminal environment presenting higher abundance of *Bacteroidaceae*, *Prevotellaceae*, and *Rikenellaceae* (Figures 4 and 5, Supplementary Fig. S3 to S5).

Interestingly, M-ARCOL captured the inter-individual variability present in stool samples, as shown by NMDS where a clear clustering of data per donor was observed (*p* < 0.05), stronger than with any other parameters (Figure 3c). This was also confirmed by analysis of *in vitro* gut microbiota profiles at both

family (Figure 2a) and genera (Supplementary Fig. S5) levels. As an example, *Butyricicoccaceae* family was exclusively detected in the bioreactors inoculated with fecal samples from donor HS2 and IS4 (raw data). *Methanobacteriaceae* (*Archaea*), identified in the stools of donors HS1, HS3 and IS1, were successfully preserved in the M-ARCOL model (Figures 2, 4 and 5). *Archaea* presence was linked to methane production in these bioreactors (Figure 6a, Supplementary Fig. S1), highlighting that our *in vitro* M-ARCOL model enabled the capture of donor inter-individuality in terms of microbiota composition and function.

**IBS-D microbiota disturbances are successfully maintained in the M-ARCOL**

Physicochemical and nutritional parameters associated to IBS-D (Figure 1b) were implemented into the M-ARCOL to evaluate their capacity in maintaining the gut microbiota dysbiosis observed in
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 3.** Alpha and beta-diversity of bacterial communities during M-ARCOL experiments.</figcaption></figure>

Diversity indexes were calculated from ASV table on samples collected the last 4 days of fermentation only. Alpha-diversity was calculated in both luminal medium (a) and mucosal phase (b) and illustrated by observed ASVs and Shannon indexes, with significant differences at *p* < 0.05 (*), *p* < 0.01 (**) or *p* < 0.001 (***) , NS: non-significant. NMDS analysis based on Bray-Curtis distances with donor effect (*p* < 0.05) (c). Partial redundancy analysis (RDA) removing donor effect were then performed based on Bray-Curtis distances to explore fermentation time (d), Sample (luminal, mucosal, or stool) (e) or treatment (healthy vs IBS-D) effects, considering all donors together (f) or separated by healthy and IBS-D initial stool origin (g). All statistical ellipses were calculated based on 95% confidence interval. HS: healthy stool (in green); IS: IBS-D stool (in yellow); HP: healthy parameters (round shape); IP: IBS-D parameters (square shape).

IBS-D patients. The relevance of the *in vitro* model was investigated by comparing the results obtained using healthy stools when healthy parameters were applied (HS-HP) *versus* IBS-stools combined with IBS-D parameters (IS-IP). Regarding alpha-diversity indexes, no significant difference was

observed between the two conditions, although the number of observed ASVs tended to be lower in both luminal and mucosal compartments for IBS-D compared to healthy conditions (Figures 3a,b), in line with *in vivo* data where no consensus was found. Indeed, half of the studies
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>**Figure 4.** Cross-compared effect of IBS-D versus healthy stools and colonic parameters on bacterial and archaeal populations in the luminal environment of the M-ARCOL. M-ARCOL was inoculated with stools collected from four healthy volunteers (HS1, HS2, HS3, and HS4) and four IBS-D patients (IS1, IS2, IS3, and IS4) and set-up to reproduce either healthy (HP) or IBS-D (IP) parameters. Graphs</figcaption></figure>
reported no difference in gut microbiota diversity and richness between IBS-D patients and healthy volunteers [51,52,55,56,66-69], while others indicated lower values in IBS-D patients [14-17,48-53,66]. This suggests that a loss of gut microbial diversity seems not to be a main criterion for characterizing IBS-D pathology. RDA of microbial beta-diversity confirmed a shift between HS-HP and IS-IP conditions, mainly occurring in the luminal compartment (Figure 3f). Accordingly, bacterial profiles of each donor in the luminal and mucosal compartments as well as differential analysis (Figures 4 and 5, Supplementary Fig. S3 to S5) highlighted numerous changes between IBS-D (IS-IP) and healthy (HS-HP) conditions, such as a lower relative abundance of *Rikenellaceae* and *Prevotellaceae* and a higher level of *Proteobacteria* and *Akkermansiaceae*. Quantification of selected populations confirmed most of those results (Figures 4c and 5c). In particular, in the luminal compartment, qPCR analysis revealed a significant lower level of *Prevotella* spp. in IBS-D versus healthy situation (7 versus 8.5 Log₁₀ 16S rDNA copies/g) associated with higher amount of *A. muciniphila* (8.5 versus 6.5 Log₁₀ 16S rDNA copies/g) (Figure 4c). Regarding *Archaea*, similar levels were observed in the luminal compartment between IS-IP and HS-HP conditions, while a decrease was noted in the mucosal phase under IBS-D conditions (Figures 4e and 5e).

To faithfully validate our M-ARCOL IBS-D model, *in vitro-in vivo* comparisons were performed on the 15 most abundant microbial families found *in vitro* (Table 1). Main bacterial shifts observed in the M-ARCOL between IS-IP and HS-HP conditions were compared to those occurring between IBS-D patients and healthy volunteers (including fecal samples and biopsies). This in-depth literature analysis revealed that half of the most abundant populations exhibited similar shifts in the M-ARCOL and in humans, both in luminal [14-17,45,49-51,54-56,67,69,70] and mucosal [51,52,67,70] compartments, while

a lower number of populations exhibited opposite tendencies [13-17,49,51,52,56,67,71,72]. Notably, some *in vitro-in vivo* comparisons were limited by the scarcity of human data and the high variability between studies, even at the phylum level (e.g. *Bacteroidota*, Table 1). Unfortunately, *Enterobacteriaceae* were less abundant *in vitro* under IBS-D condition, while our literature review suggested them as a hallmark marker population for the disease [15-17,51,52,56].

At the functional level, no significant difference was observed in gas production (Figures 6a,c), except lower CO₂ percentages under IBS-D compared to healthy condition. Unfortunately, these results cannot be corroborated by *in vivo* data in IBS-D patients. Concerning SCFAs, a significantly lower percentage of butyrate was observed in IS-IP compared to HS-HP (*p* < 0.05) (Figure 6b). This reduction could be linked to the lower abundance of butyrate-producing families, such as *Ruminococcaceae* and *Lachnospiraceae* observed *in vitro* (Figures 4a,b). These results are in contradiction with two *in vivo* studies in humans [73,74], but in line with a third one [75]. Such a decrease in butyrate, a well-known anti-inflammatory compound, may contribute to IBS-D pathogenesis, as previously described in inflammatory bowel disease [76]. Additionally, a significantly higher production of propionate and acetate was observed (*p* < 0.05), in non-methane and methane producers, respectively. This lack of clear trend in the M-ARCOL is consistent with *in vivo* data showing wide variability in SCFA production and profiles between individuals and studies [8,73,75,77,78].

**IBS-D colonic parameters are insufficient to trigger significant dysbiosis in M-ARCOL bioreactors inoculated with healthy stools**

The next step of our work was to evaluate the impact of IBS-D parameters on gut microbiota when bioreactors were inoculated with fecal samples from healthy donors (HS1 to HS4, Figure 2).

focus on the last 4 days of fermentation experiments (D08 to D11). Luminal bacterial composition determined by 16S metabarcoding in bioreactors inoculated with healthy (a) or IBS-D (b) stools. (c) qPCR quantification of selected bacterial populations in the luminal medium. Results are expressed as mean 16S rRNA copies/g ± standard error of the mean (SEM, n = 4). Statistical differences between all conditions for each population as tested by two-ways ANOVA are indicated by different letters (*p* < 0.05) (d) Differential analysis performed at the family level, showing significant differences with at least one of three tested methods (DeSeq2, Metacoder, and MetagenomeSeq R-analysis). (e) *Archaea* abundances determined by metabarcoding analysis. HS: healthy stool; IS: IBS-D stool; HP: healthy parameters; IP: IBS-D parameters.
<figure><img src="image_20.png" /><figcaption>**Figure 5.** Cross-compared effect of IBS-D versus healthy stools and colonic parameters on bacterial and archaeal populations in the mucosal environment of the M-ARCOL.</figcaption></figure>

M-ARCOL was inoculated with stools collected from four healthy volunteers (HS1, HS2, HS3, and HS4) and four IBS-D patients (IS1, IS2, IS3, and IS4) and set-up to reproduce either healthy (HP) or IBS-D (IS) parameters. Graphs focus on the last mucin beads samples collected (D08 and D10). Mucosal bacterial composition determined by 16S metabarcoding in bioreactors inoculated with healthy (a) or IBS-D (b) stools. (c) qPCR quantification of selected bacterial populations in the mucosal medium. Results are expressed as mean 16S rRNA copies/g ± standard error of the mean (SEM, n = 4). (d) Differential analysis performed at the family level, showing significant differences with at least one of three tested methods (DeSeq2, Metacoder and MetagenomeSeq R-analysis). (e) *Archaea* methanogens abundances as determined by metabarcoding analysis. HS: healthy stool; IS: IBS-D stool; HP: healthy parameters; IP: IBS-D parameters.
<figure><img src="image_21.png" /><figcaption>**Table 1.** Comparison of most abundant bacterial and Archaeal populations found in the IBS-D *in vitro* model and *in vivo* data in the luminal and mucosal compartments.</figcaption></figure>

<table><thead><tr><th rowspan="3">Bacterial populations</th><th colspan="3">Luminal</th><th colspan="3">Mucosal</th></tr><tr><th rowspan="2">IBS-D<br>in vitro<br>data</th><th colspan="2">Correlation with in vivo data<br>(fecal samples)</th><th rowspan="2">IBS-D<br>in vitro<br>data</th><th colspan="2">Correlation with in vivo data<br>(colonic biopsies)</th></tr><tr><th>In vivo data<br>relevance</th><th>References</th><th>In vivo data<br>relevance</th><th>References</th></tr></thead><tbody><tr><td><strong>Phylum Bacteroidota</strong></td><td>✓</td><td>★</td><td>✓ (3/10) [16,51,67]; ↗ (4/10)<br>[14,54,56,69]; ~ (3/10) [17,52,55]</td><td>✓</td><td>★★</td><td>✓ (1/3) [51]; ~ (2/3) [52,67]</td></tr><tr><td>Family Bacteroidaceae</td><td>✓</td><td>★★</td><td>✓ (2/3) [16,55]; ~ (1/3) [56]</td><td>✓</td><td>★</td><td>↗ (1/1) [70]</td></tr><tr><td>Family Muribaculaceae</td><td>✓</td><td>-</td><td>-</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>Family Prevotellaceae</td><td>✓</td><td>★</td><td>↗ (1/1) [56]</td><td>✓</td><td>★</td><td>↗ (1/1) [51]</td></tr><tr><td>Family Rikenellaceae</td><td>✓</td><td>★★</td><td>✓ (2/3) [51,56]; ↗ (1/3) [70]</td><td>✓</td><td>★</td><td>↗ (1/1) [51]</td></tr><tr><td>Family Tannerellaceae</td><td>✓</td><td>-</td><td>-</td><td>✓</td><td>-</td><td>-</td></tr><tr><td><strong>Phylum Firmicutes</strong></td><td>✓</td><td>★★★</td><td>↗ (11/13) [14,17,45,49–51,54–<br>56,67,69]; ↗ (1/13) [16]; ~ (1/13) [52]</td><td>✓</td><td>★★★</td><td>↗ (3/3) [51,52,67]</td></tr><tr><td>Family Acidaminococcaceae</td><td>↗</td><td>★</td><td>↗ (1/2) [55]; ~ (1/2) [56]</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family Lachnospiraceae</td><td>✓</td><td>★</td><td>✓ (1/4) [69]; ↗ (2/4) [16,51]; ~ (1/4)<br>[56]</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family Oscillospiraceae</td><td>✓</td><td>-</td><td>-</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family Ruminococcaceae</td><td>✓</td><td>★★★</td><td>↗ (8/8) [14–17,49,50,56,70]</td><td>↗</td><td>★</td><td>↗ (1/1) [51]</td></tr><tr><td><strong>Phylum Proteobacteria</strong></td><td>↗</td><td>★★★</td><td>↗ (2/13) [54,69]; ↗ (10/13) [15–<br>17,45,49–51,55,56,67]; ~ (1/13) [52]</td><td>↗</td><td>★★★</td><td>↗ (3/3) [51,52,67]</td></tr><tr><td>Family Burkholderiaceae</td><td>✓</td><td>-</td><td>-</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family CAG-239</td><td>✓</td><td>-</td><td>-</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family Enterobacteriaceae</td><td>✓</td><td>★★★</td><td>↗ (6/6) [15–17,51,52,56]</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family Rhodospirillales family</td><td>↗</td><td>-</td><td>-</td><td>↗</td><td>-</td><td>-</td></tr><tr><td>Family Sutterellaceae</td><td>✓</td><td>-</td><td>-</td><td>↗</td><td>-</td><td>-</td></tr><tr><td><strong>Phylum Verrucomicrobiota</strong></td><td>↗</td><td>★</td><td>↗ (2/5) [13,49]; ~ (3/5) [52,56,69]</td><td>↗</td><td>★</td><td>↗ (1/1) [51]</td></tr><tr><td>Family Akkermansiaceae</td><td>↗</td><td>★★★</td><td>↗ (3/4) [13,14,49]; ~ (1/4) [56]</td><td>↗</td><td>★</td><td>↗ (1/1) [51]</td></tr><tr><td><strong>Archaeal population</strong></td><td>~</td><td>★★★</td><td>↗ (4/4) [13,14,71,72]</td><td>↗</td><td>-</td><td>-</td></tr></tbody></table>

Changes in the 15 most abundant bacterial and archaeal populations induced by IBS-D were compared between *in vitro* results (IS-IP vs HS-HP conditions) and *in vivo* data (IBS-D patients vs healthy volunteers from the same study). Inclusive parameters for *in vivo* data are adult patients with well-identified IBS-D, with no drug, no probiotic or prebiotic treatment, and comparison with healthy volunteers in the same study. Green box: *in vitro* data obtained in the M-ARCOL are in line with *in vivo* data in human; orange box: no clear conclusion; red box: *in vitro* and *in vivo* data are contradictory. Bolded ratio: consensus for *in vivo* data, ★★★: more than 75% of *in vivo* studies from literature review are in accordance; ★★: between 50% and 75% of *in vivo* studies are in accordance; ★: less than 50% of *in vivo* studies are in accordance or only one article was found. : increase in relative abundance, : decrease in relative abundance; ~: no difference, -: no *in vivo* data. Ratio: proportion of the total number of articles in each category.

Alpha-diversity indexes were not significantly impacted when applying IBS-D parameters on healthy stools (HS-HP versus HS-IP, Figures 3a, b). Accordingly, no major shift in the luminal and mucosal microbial populations can be induced by IBS-D parameters at the family level (Figures 4a,d, and 5a,d). However, individual profiles highlighted an increase in the relative abundance of *Rhodospirillales* (donors HS1, HS3 and HS4) and *Dialisteraceae* (HS2 and HS4) in the luminal compartment (Figure 4a) and in *Prevotellaceae* (donors HS3 and HS4) and *Ruminococcaceae* (donor HS2) in the mucosal phase (Figure 5a) under HS-IP condition. This absence of major bacterial shifts was confirmed by the lack of significant changes in qPCR quantification of *Firmicutes*, *Bacteroidetes*, *Gamma-proteobacteria*, *Ruminococcaceae*, *Prevotella* spp and *A. muciniphila* (Figures 4c and 5c). RDA also confirmed that main changes associated to IBS-D (HS-IP) conditions occurred in the

luminal microbiota (Figures 3f,g). In addition, applying IBS-D parameters on healthy stools from methane-producing donors (HS1 and HS3) led to a lower relative abundance of *Archaea* in both luminal (Figure 4e) and mucosal (Figure 5e) compartments.

*In vitro-in vivo* comparisons highlighted that the implementation of IBS-D parameters themselves on healthy stools did not induce a gut microbial dysbiosis as observed in IBS-D patients (Table 2). This suggests a crucial role of host interactions, not reproduced in the M-ARCOL, in driving microbial profiles from a healthy to a diseased state in IBS-D. Specifically, alterations in gut barrier function occurring in parallel with changes in gut microbiota, have been widely described in IBS-D and are thought to play a pivotal role in generating and sustaining host immune responses [79]. Only certain populations, such as *Archaea* [13,14,71,72], *Bacteroides* [13,48,55,68] and
<figure><img src="image_22.png" /><figcaption>**Figure 6.** Cross-compared effect of IBS-D versus healthy stools and colonic parameters on gut microbiota activities in the M-ARCOL. M-ARCOL was inoculated with stools collected from four healthy volunteers (HS1, HS2, HS3, and HS4) and four IBS-D patients (IS1, IS2, IS3, and IS4) and set-up to reproduce either healthy (HP) or IBS-D (IP) parameters. Microbiota activity was investigating by gas and short chain fatty acid (SCFA) measurements during fermentation. Data represents the four last days of experiments. (a) Gas composition of the atmospheric phase of M-ARCOL bioreactors, in methane (HS1, HS3, IS1, and IS2) and non-methane (HS2, HS4, IS3, and IS4) producing donors. (b) Relative abundance of the three main SCFAs (acetate, propionate and butyrate) in the luminal medium depending on donors' methane status. (c) Daily amount of gas volume produced in mL and (d) total SCFAs concentrations in the luminal medium in mM. Results were expressed as means ± standard error of the mean (SEM, *n* = 4). Significant statistical differences between all conditions, as based on two-ways ANOVA, are indicated by different letters (*p* < 0.05), non-underlined letter and underline letters for methane and non-methane producing donors, respectively. CH₄: methane, CO₂: carbon dioxide, H₂: dihydrogen, N₂: nitrogen, O₂: dioxygen. HS: healthy stool; IS: IBS-D stool; HP: healthy parameters; IP: IBS-D parameters.</figcaption></figure>
<figure><img src="image_23.png" /><figcaption>**Table 2.** *In vitro-in vivo* comparisons of microbiota composition and metabolic activities between HS-IP or IS-HP conditions in the M-ARCOL and human data in IBS-D patients.</figcaption></figure>

<table><thead><tr><th rowspan="3"></th><th colspan="10">In vitro data</th><th colspan="4">IBS-D microbiota in vivo data<sup>c</sup></th></tr><tr><th colspan="4">Healthy stools + IP</th><th colspan="4">HS + IP + Mucosal</th><th colspan="4">HS + IP + Mucosal</th><th rowspan="2">In vivo data relevance</th><th rowspan="2">Less abundant</th><th rowspan="2">More abundant</th><th rowspan="2">No difference</th></tr><tr><th>HS1</th><th>HS2</th><th>HS3</th><th>HS4</th><th>HS1</th><th>HS2</th><th>HS3</th><th>HS4</th><th>IS1</th><th>IS2</th><th>IS3</th><th>IS4</th><th>IS1</th><th>IS2</th><th>IS3</th><th>IS4</th></tr></thead><tbody><tr><td colspan="17"><strong>Microbiota populations</strong></td></tr><tr><td>Archaea</td><td>▲</td><td>-</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>p_Verrucomicrobiota</td><td>~</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>g_Akkermansia</td><td>~</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>p_Bacteroidota</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>f_Bacteroidaceae</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>~</td></tr><tr><td>g_Bacteroides</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>~</td></tr><tr><td>f_Prevotellaceae</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>g_Prevotella</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td></tr><tr><td>f_Rikenellaceae</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>p_Firmicutes</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>f_Acidaminococcaceae</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>-</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>f_Christensenellaceae</td><td>~</td><td>~</td><td>~</td><td>-</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>f_Lachnospiraceae</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>f_Ruminococcaceae / Oscillospiraceae</td><td>~</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>~</td><td>~</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>g_Faecalibacterium</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>g_Oscillospira</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>g_Ruminococcus</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>f_Veillonellaceae</td><td>~</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>p_Proteobacteria</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>f_Enterobacteriaaceae</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>p_Actinobacteriaota</td><td>-</td><td>-</td><td>~</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>g_Bifidobacterium</td><td>-</td><td>-</td><td>~</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td colspan="17"><strong>Microbiota composition</strong></td></tr><tr><td></td><td colspan="4">In vitro data</td><td colspan="4">HS + IP + Mucosal</td><td colspan="4">HS + IP + Mucosal</td><td colspan="4">IBS-D microbiota in vivo data<sup>c</sup></td></tr><tr><td></td><td colspan="4">Healthy stools + IP</td><td colspan="4">HS + IP + Mucosal</td><td colspan="4">HS + IP + Mucosal</td><td colspan="4">IBS-D microbiota in vivo data<sup>c</sup></td></tr><tr><td colspan="17"><strong>Microbiota activity</strong></td></tr><tr><td colspan="17"><strong>SCFA concentrations</strong></td></tr><tr><td>Total</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>Acetate</td><td>~</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>Propionate</td><td>▲</td><td>~</td><td>~</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td></tr><tr><td>Butyrate</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td colspan="17"><strong>SCFA proportions</strong></td></tr><tr><td>Acetate</td><td>▲</td><td>~</td><td>~</td><td>~</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>Propionate</td><td>~</td><td>~</td><td>▲</td><td>~</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr><tr><td>Butyrate</td><td>~</td><td>~</td><td>▲</td><td>~</td><td>~</td><td>▲</td><td>▲</td><td>~</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td><td>▲</td></tr></tbody></table>

a: For each healthy donor (HS1, HS2, HS3, and HS4), bacterial composition and activity of HS-IP condition was compared to its HS-HP counterpart.

b: For each IBS-D donor (IS1, IS2, IS3, and IS4), bacterial composition and activity of IS-HP was compared to its IS-IP counterpart. : increase in relative abundance or concentration; : decrease in relative abundance or concentration; ~: no difference; -: non detected.

c: Inclusive parameters for *in vivo* data are adult patients with well-identified IBS-D, with no drug, no probiotic or prebiotic treatment, and comparison with healthy volunteers in the same study. *In vivo* data on fecal samples were mainly available and used for comparison with *in vitro* results in the M-ARCOL.

Ratio: proportion of the total number of articles in each category (less abundant/more abundant/no difference); Bolded ratio: consensus for *in vivo* data, ★★★: more than 75% of *in vivo* studies from literature review are in accordance; ★★: between 50% and 75% of *in vivo* studies are in accordance; ★: less than 50% of *in vivo* studies are in accordance or only one article was found. Green box: *in vitro* data obtained in the M-ARCOL are in line with *in vivo* data in human from literature; orange box: no clear conclusion mainly due to lack of *in vivo* consensus; red box: *in vitro* and *in vivo* data are contradictory.

HS: healthy stool, IS: IBS-D stool, HP: healthy parameters, IP: IBS-D parameters.

*Proteobacteria* [15–17,45,49–52,55,56], exhibited similar trends *in vitro* and *in vivo* (highlighted in green in Table 2). Regarding microbial metabolites, applying IBS-D parameters resulted in lower methane but higher dihydrogen relative percentages (Figure 6a). This is consistent with the lower *Archaea* relative abundance previously observed (Figures 4e and 5e) and results by

Pimentel *et al* [80,81] and Villanueva-Millan *et al* [82] who showed an inverse relationship between CH₄ excretion in breath testing and stool frequency in IBS patients. Lastly, simulating IBS-D conditions on healthy stools induced no significant change in total gas volume and SCFA production (Figures 6c,d), meanwhile it was observed in methane-producers HS1 and
HS3 a significant reduction in butyrate relative percentages (Figure 6b). As shown in Table 2, drawing conclusions regarding the relevance of our data on microbiota activities was challenging due to the considerable variability observed in SCFA production among IBS-D patients [8,73,75,77,78].

**Healthy colonic parameters alone are insufficient to restore gut microbial eubiosis in M-ARCOL inoculated with IBS-D fecal samples**

Lastly, to assess if applying healthy parameters on IBS-D stools can restore bacterial profiles in M-ARCOL, we cross-compared IBS-D stools/healthy parameters (IS-HP) to IBS-D stools/IBS-D parameters (IS-IP) conditions. Analysis of bacterial alpha-diversity (Figures 3a,b) and beta-diversity (Figure 3g) revealed no difference between the two conditions. These results were reinforced by differential analysis of 16S Metabarcoding data, which revealed no significant change in bacterial populations when all donors were pooled, except for *Rhodospirillales*, whose relative abundance increased under IS-HP condition in the mucosal compartment (Figures 4d and 5d). It is worth noting that no publication has yet focused on this specific population in IBS-D patients. However, at the individual level, analysis of microbial profiles revealed that certain populations, typically less abundant in IBS-D patients, increased when healthy parameters were applied to IBS-D stools (Figures 4b and 5b). This was particularly evident for *Archaea* [13,14,71,72], *Rikenellaceae* [51,56] (donors IS3 and IS4), *Christensenellaceae* [14,50,51] (donors IS1 and IS4), and *Bacteroides* [13,48,55,68] in the luminal compartment, and *Ruminococcus* [49–51] in the mucosal phase (Table 2). In addition, in accordance with *in vivo* data [15–17,45,49–52,55,56], *Proteobacteria* decreased when applying healthy parameters on IBS-D stools. Regarding microbial metabolites, no significant difference was noticed between IS-IP and IS-HP conditions, except a decrease in CO₂ percentage in methane-producers (IS1 and IS2) and a higher level of propionate in non-methane producers (IS3 and IS4) with healthy parameters (Figure 6). Taken together, these findings indicate that applying physicochemical parameters associated with a healthy state to IBS-D stools was not sufficient to restore microbial eubiosis. Most probably, only external

factors such as diet (e.g. low FODMAP diet) or supplementation with probiotics would be able to improve microbiota composition, since clinical studies have already shown they are able to alleviate symptoms in IBS-D patients [83–87].

**Potential, applications, and limitations of the IBS-D in vitro model**

In line with 3R European rules strongly encouraging the development of alternative *in vitro* methods to reduce as much as possible animal experiments [88], we developed and validated an innovative *in vitro* model of the IBS-D colon, capturing the main disturbances in microbial profiles and functionalities associated to the disease. In an innovative approach, we also distinguished mucus from lumen-associated microbiota, providing valuable insights into shifts in mucin-degrader and mucin-adherent populations such as *Akkermansiaceae*. Thanks to the flexibility of such an *in vitro* approach, we also performed a mechanistic study on gut microbes in the context of IBS-D, by cross-comparing results obtained between healthy *versus* IBS-D parameters and healthy *versus* IBS-D stools. We conclude that inoculation with IBS-D stools was necessary to obtain a relevant IBS-D model, and that simply applying healthy parameters did not lead to reversible profiles. Of course, as in all *in vitro* gut systems more focused on the human digestive lumen, the IBS-D M-ARCOL model do not include host interactions and obviously cannot reproduce several key features of the pathology, such as visceral hypersensitivity, impaired intestinal barrier function or immune dysregulation [7,9,10,79,89]. To address some of these limitations, further studies will aim to couple the newly developed model with intestinal cell lines (including immune or epithelial cells) or intestinal organoids. The new *in vitro* model emerges as a promising platform to perform mechanistic studies on gut microbes in the context of IBS-D, helping to decipher the relative importance of microbiota itself. Furthermore, it can be also advantageously used to evaluate the impact of various nutritional strategies (e.g. dietary ingredients, prebiotics, and probiotics) or microbiota-
related interventions (such as fecal microbiota transplantation) on the microbiome of IBS-D patients, with the aim to identify compounds able to restore gut microbial eubiosis. Obviously, the *in vitro* model is not suitable for testing interventions focusing on other aspects of IBS-D pathophysiology, such as drugs targeting gut motility, immune modulation, or visceral hypersensitivity. Importantly, our IBS-D model successfully captured the gut microbiota inter-individuality, allowing a better understanding of responders and non-responders to dietary or microbial interventions [84,86,90–92] in preclinical studies, thereby facilitating progress toward personalized nutrition for IBS-D patients [86,93,94].

## Acknowledgments

We thank all the partners from PSPC RESTORBIOME project for fruitful scientific discussions and the IBS-D patients and healthy volunteers for their participation in the study. We would like also to thank Etienne Rifa from GeT-Biopuces Plateform (Toulouse, France) for assistance with Illumina sequencing analysis.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

This study was performed in the frame of the PSPC RESTORBIOME (2019–2023). This project was supported by the Program Investissement Avenir operated by BpiFrance, General Mills/Yoplait and Greencell industrial partners, Clermont Auvergne Metropole and Région Auvergne Rhône Alpes.

## Authors contributions

SBD and OU contributed to the conceptual design of this project and provided the study design with contributions from SD, MA, PL and LEM. JS, NK, CDua and MD enrolled the patients and obtained the informed consent. OU, CDe, MB, EF and CDu performed the experiments. OU, CDe, MB and CDu performed data analysis. OU, CDe, MB and CDu provided data visualizations. OU, CDe and SBD wrote the manuscript and revised the manuscript with contributions from LEM, EF, PL, NK and JS. SBD supervised the project. SBD, PL and MA performed the project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.

## Data availability statement

Raw sequencing data are available at NCBI at PRJNA1114942.

## Ethics approval and consent to participate

Donors all gave written, informed consent to take part in the study. The use of fecal microbiota of human origin was approved by the French Ethical research committee (Comité de protection des personnes Sud-Est VI, CPP Sud-Est VI, Clermont-Ferrand, France) under registration number AU1274 (IBSET, PHRC IR 2015 DAPOIGNY, EudraCT 2016 -002,110-42).

## ORCID

Blanquet-Diot Stéphanie http://orcid.org/0000-0003-0692-8726

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