Donkers et al. Microbiome Res Rep 2024;3:18
DOI: 10.20517/mrr.2023.79

Microbiome Research
Reports

Original Article

Open AccessA host-microbial metabolite interaction gut-on-achip model of the adult human intestine
demonstrates beneficial effects upon inulin
treatment of gut microbiome
Joanne M. Donkers1 , Maria Wiese2, Tim J. van den Broek2, Esmée Wierenga1, Valeria Agamennone2,Frank Schuren2, Evita van de Steeg1
1

Department of Metabolic Health Research, TNO, Leiden 2333 BE, the Netherlands.
Department of Microbiology & Systems Biology, TNO, Leiden 2333 BE, the Netherlands.

2

Correspondence to: Dr. Joanne M. Donkers, Department of Metabolic Health Research, TNO, Sylviusweg 71, Leiden 2333 BE, theNetherlands. E-mail: joanne.donkers@tno.nl
How to cite this article: Donkers JM, Wiese M, van den Broek TJ, Wierenga E, Agamennone V, Schuren F, van de Steeg E. A
host-microbial metabolite interaction gut-on-a-chip model of the adult human intestine demonstrates beneficial effects upon
inulin treatment of gut microbiome. Microbiome Res Rep 2024;3:18. https://dx.doi.org/10.20517/mrr.2023.79
Received: 21 Dec 2023 First Decision: 15 Jan 2024 Revised: 29 Jan 2024 Accepted: 20 Feb 2024 Published: 22 Feb 2024
Academic Editor: Marco Ventura Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Background: The gut and its microbiome have a major impact on many aspects of health and are therefore also an
attractive target for drug- or food-based therapies. Here, we report on the added value of combining a microbiome
screening model, the i-screen, with fresh intestinal tissue explants in a microfluidic gut-on-a-chip model, theIntestinal Explant Barrier Chip (IEBC).
Methods: Adult human gut microbiome (fecal pool of 6 healthy donors) was cultured anaerobically in the i-screen
platform for 24 h, without and with exposure to 4 mg/mL inulin. The i-screen cell-free culture supernatant was
subsequently applied to the luminal side of adult human colon tissue explants (n = 3 donors), fixed in the IEBC, for
24 h and effects were evaluated.
Results: The supplementation of the media with inulin promoted the growth of Anaerostipes, Bifidobacterium,
Blautia, and Collinsella in the in vitro i-screen, and triggered an elevated production of butyrate by the microbiota.
Human colon tissue exposed to inulin-treated i-screen cell-free culture supernatant or control i-screen cell-free
culture supernatant with added short-chain fatty acids (SCFAs) showed improved tissue barrier integrity measured© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, andindicate if changes were made.

www.oaepublish.com/mrr

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by a 28.2%-34.2% reduction in FITC-dextran 4000 (FD4) leakage and 1.3 times lower transport of antipyrine.
Furthermore, the release of pro-inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α was reduced under thesecircumstances. Gene expression profiles confirmed these findings, but showed more profound effects for inulintreated supernatant compared to SCFA-supplemented supernatant.
Conclusion: The combination of i-screen and IEBC facilitates the study of complex intestinal processes such as
host-microbial metabolite interaction and gut health.
Keywords: Gut-on-a-chip, microbiome, in vitro models, ex vivo tissue, host-response, microbial metabolite, shortchain fatty acids, host-microbe interaction

INTRODUCTION
Gut health, or intestinal health, has a major impact on our overall health and well-being. Not only is thegastrointestinal system the main portal for nutrients and thus for energy and building blocks for our body,
but it also acts as a barrier of defense against disease and harmful substances and is very communicativewith our brain and other organs via its own nervous system[1-3]. Furthermore, the gut hosts the largest
microbial community in the human body, with the highest microbial cell density of around 1011 CFUs/mL
being present in the proximal colon[4,5]. In return for nutrients and intestinal mucus provided by the host,the microbiota produces compounds beneficial for the intestinal cells and body, such as vitamins,
neurotransmitters, and short-chain fatty acids (SCFAs)[6-8]. By doing so, and via other mechanisms, it
supports the protection against pathogens, enhances the immune system, influences gut-brain
communication, and impacts the gut epithelial cells[6,8,9]. The eubiosis of the gut microbial community and aproper balance between the microbiota and the host are very important for good health. In fact, several
pathological diseases such as autoimmune disorders, allergies, and IBD have been associated with adisbalance in the host-microbe homeostasis[10,11]. The six main gut microbial phyla are Bacillota,
Bacteroidota, Actonomycetota, Pseudomonadota, Fusobacteriota, and Verrucomicrobiota, of which the firsttwo represent approximately 90% of the total gut microbiota[7,12]. The majority of the gut microbiota arestrict anaerobes, which dominate the facultative anaerobes and aerobes by two to three folds[4]. The
microbiome plays an important role in food fermentation, and in the proximal colon, the microbiome
mainly converts non-digestible carbohydrates and dietary fibers into SCFA[13]. The main SCFAs producedby the microbiota are acetate, propionate, and butyrate[14]. Over the recent years, SCFAs have gained
increasing attention for local beneficial effects in the gut such as improved gut barrier function and reduced
intestinal inflammation[9,14-17]. Higher intestinal levels of SCFAs may be achieved by increased consumptionof dietary fiber; however, the efficiency of fiber fermentation and the subsequent production of metabolites
depends on the composition and functional capabilities of the gut microbiota and the specific dietary fiber
or composition of non-digestible carbohydrates that are being provided[18]. Such substrates, selectively
utilized by microorganisms and leading to host health benefits, are also called prebiotics[16,19]. Inulin is awell-known example of a prebiotic; its strong bifidogenic and SCFA-inducing effects have been described byus previously[20] and many others[21-24]. Therefore, manipulation of the gut microbiome is likely to be aphysiologically adequate strategy to increase SCFA production in the gut[16,25]. In vitro or ex vivo models arevery effective to test such hypotheses[26,27]. Here, we connect the microbial component to host gut tissue in ahost-microbial metabolite interaction gut-on-a-chip model of the adult human large intestine. Fresh humanadult colon tissue explants were exposed in our microfluidic ex vivo tissue model, the Intestinal Explant
Barrier Chip (IEBC)[28], to cell-free culture supernatant from our in vitro intestinal microbiota screen, thei-screen[29], without and with inulin intervention to stimulate microbial SCFA production. This sequential
use of an in vitro model and an ex vivo model provides a unique strategy to study the interaction of
microbial metabolites, influenced by specific interventions, with the host tissue in a controlled experimental

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setting.

MATERIALS AND METHODS
I-screen supernatantFecal collection
Fecal material was provided by six healthy adult volunteers (Caucasian, age 25-65 years, no antibiotic use in
the 3 months preceding the donation nor consumed prebiotics or probiotics the week before donation, selfassessment of health status). Collection of fecal samples was performed anonymously following TNO
standard operational procedures. The collection was approved by an internal ethical evaluation board and isin compliance with the Dutch laws on medical/scientific research. Participants gave written informed
consent. Fecal samples were collected and prepared as described[30,31] with some modifications. In brief, fecal
samples were collected by the volunteers with the FecesCatcher (fecesvanger.nl). Fecal material wastransferred into a container in an anaerobic jar equipped with an AnaeroGen sachet (Thermo Fisher
Diagnostics GMBH). The jar was kept cool and delivered to the laboratory within 24 h. The material was
introduced in an anaerobic chamber, diluted 1:3 with phosphate-buffered saline, and homogenized using a
Tissue Homogenizer Omni THQ (12-500, Omni International). Finally, 20% glycerol was added before
storing the material at -80 °C.
Anaerobic incubation: i-screen
The fecal material was incubated in the i-screen (intestinal screening), an in vitro system for the anaerobic
incubation of fecal microbiota[29]. Before starting the i-screen incubations, the six fecal samples were pooled
and pre-cultured overnight in a modified standard ileal efflux medium (SIEM) under anaerobic conditions,at 37 °C and with shaking at 300 rpm[30]. The microbiota was then transferred to a microtiter plate forincubation in SIEM medium, with and without 4 mg/mL inulin, with pH adjusted to 5.8. The incubation
started with a fecal bacterial load of approximately 109 CFU/mL. Conditions were tested in triplicate (preculture at t = 0 h), or with n = 9 or n = 6 replicates for control or inulin-treated conditions, respectively. At
the start of the incubation (t = 0, referred to in this article as pre-culture), and after 24 h of fermentation inanaerobic conditions, 100 µL and 50 µL of sample material were collected and used for DNA isolation andmetabolite analyses, respectively.
DNA isolation
Following incubation, samples were collected and DNA was isolated as described[32].
16S rRNA amplicon sequencing
Changes in the microbiota composition were analyzed by using 16S rRNA gene amplicon sequencing as
described[32].
Metabolite analyses
SCFAs acetate, propionate, and butyrate, and branched chain fatty acids (BCFA) iso-butyrate and isovalerate were analyzed as described[32].
Data analysis microbiome
Statistical analysis of the microbiome data was performed using R version 4.1.2[33]. Figures were composedusing the ggplot2 package[34]. The phyloseq package was used to manage the phylogenetic sequencing
data[35].

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Before ordination, the 16S data was filtered to include only those ASVs that contribute to the first 97.5% of
all counts in the data. The method for selection of ASVs in this way is described in detail in[32].
Principal component analysis on microbiome data was performed using the pcaMethods package[36]. For
this analysis, the data were centered log-ratio transformed to account for their compositional properties. To
calculate the weighted averages of the SCFAs in the inulin and untreated control conditions of the PCA
ordination of the microbiome data, the wascores function from the vegan package was used[37].
A linear mixed-effects model was employed to identify differences in microbial abundance, which facilitatedthe control for both fixed effects of the experimental conditions and random effects due to sample
replication. For this analysis, we used edgeR[38] alongside variancePartition[39] for data normalization andvariance modeling, respectively. This modeling approach accounts for between-replicate variability.
Visualization of the differential abundance data was conducted using a heatmap, which displayed log2 foldchanges of microbial genera across the experimental conditions.
To determine whether incubation with inulin resulted in significant changes in SCFA production comparedto the control condition, a linear model was fitted to data from each of the SCFAs using the lm function.
ANOVA testing was performed on these models to make comparisons between the treated samples and theuntreated control for each SCFA.
Intestinal explant barrier chip

Chemicals and assay buffer[14C]antipyrine was purchased from American Radiolabeled Chemicals Inc. and [3H]atenolol was purchasedfrom Moravek Biochemical Inc., all other chemicals were purchased from Sigma-Aldrich Chemie B.V.
unless stated otherwise. Williams E buffer was prepared and used according to Stevens et al.[40]. Williams E
supplemented with 1% penicillin/streptomycin was used for transport and handling of the tissue. Williams
E supplemented with 1% and 4% BSA was used to precoat the system and in the basolateral compartment
during an experiment, respectively.
Processing of i-screen culture supernatant for exposure in the IEBC model
The culture supernatant of inulin-treated and untreated conditions was processed to obtain cell-free culture
supernatant containing the microbial metabolites. Therefore, the i-screen culture supernatant was pooled
and centrifuged at 3,000 rpm for 15 min, subsequently filtered using a 0.22 µM filter, and stored at -20 °C
until use. For the IEBC experiment, this cell-free culture supernatant, from now on just called supernatant,
was set at pH 6.5 (based on[41]) and supplemented with 25 mM d-glucose, 10 mL/L Glutamax and 10 mL/L
HEPES, 50 μg/mL gentamicin, and 25 μg/mL amphotericin. Additionally, for the short-chain fatty acidtreatment, the choice of butyrate, acetate, and propionate concentrations was based on the i-screen SCFA
metabolite analysis and the SCFAs were added to the supplemented untreated control supernatant in aconcentration of 20, 50, and 10 µM, respectively.
Human intestinal tissue collection and preparation
Human intestinal proximal colon tissue was obtained from three human adult patients undergoing surgery
for colon carcinoma. Informed consent was requested from the patients and ethical approval for the use ofhuman intestinal tissue was provided by the hospital board. Collection and preparation of the tissue
explants were described previously[28].

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Intestinal colon tissue in the intestinal explant barrier chip
The design and fabrication of the IEBC is described by Eslami Amirabadi et al.[28]. Experiment preparationand execution were as described previously[28,42] with two modifications: no dose-replacement at t = 20 h,and supernatant from i-screen supplemented with FD4, [14C]antipyrine, and [3H]atenolol was used as anapical medium during the experiment.
Assessment of tissue viability
To assess the viability of the ex vivo intestinal segments, the cytosolic enzyme lactate dehydrogenase (LDH)
was measured in the apical and basolateral supernatants of the two-compartmental model, andhomogenized tissue segments, using an LDH kit (Sigma-Aldrich) as described previously[28,40,43]. The
acceptance criterion for this parameter is leakage < 3% per hour of total LDH under control conditions.
Assessment of tissue integrity
Tissue barrier integrity was determined using FITC Dextran 4000 (FD4) as described previously[28]. The
acceptance criterion for this parameter is FD4 leakage < 0.5% per hour under control conditions.
Assessment of tissue functionality/permeability
Tissue functionality was calculated as described[42], here using [3H]atenolol (low permeability) and[14C]antipyrine (high permeability) as reference markers for the paracellular and transcellular transportroute, respectively. The transcellular over paracellular apparent permeability (Papp) ratio was calculated as
Papp antipyrine/Papp atenolol.
Determination of cytokines
After 24 h of incubation, IL-6, IL-8, IL-1β, IL-10, IL-12p70, IL-13, IL-2, IL-4, IFN-γ, TNF-α cytokine release
into the apical and basolateral compartments by the intestinal tissue in the IEBC was determined byapplying V-PLEX Proinflammatory panel 1 (K15049D) according to the manufacturer’s instructions.
Cytokine concentration levels were determined using a Meso Scale Discovery (MSD) Sector Imager 2400
instrument equipped with discovery workbench software (version 3).
RNA isolation and RT-qPCR
Total RNA was isolated from approximately 50 mg of human colon tissue with RNAqueous™ Total RNA
Isolation Kit (Invitrogen). RNA integrity was assessed spectrophotometrically at 260 nm using a Platereader
Synergy H1 (Biotek). Five hundred nanograms of total RNA was used to synthesize first-strand cDNA with
iScript™ Reverse Transcription Supermix for RT-qPCR (Bio-Rad). RT-qPCR was carried out in a
Quantstudio 6 flex (Applied Biosystems) using iQ" SYBR Green Supermix (Bio-Rad) and was analyzed
using Quantstudio Real-Time PCR software. Expression levels in each sample were normalized for the
expression level of housekeeping gene 36B4. Relative expression of genes of interest was calculated using the
ΔΔCt method. Primer sequences are noted in Table 1.
Data analysis IEBC data
Statistical analysis of the microbiome data was performed using R version 4.1.2[33]. Figures were composedusing the ggplot2 package[34].
Statistical analysis of tissue integrity, functionality & viability data, cytokines, and gene expression was
performed using the lme4 and lmerTest packages, with the emmeans package for post hoc analysis[34,39,44].
Estimated marginal means were transformed back to their original scale in the case of models with logtransformed variables.

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Table 1. Primer sequences

Gene

Forward primer

Reverse primer

36B4

TCATCAACGGTACAAACGAGCCTTGACCTTTTCAGCAAG

ZO-1

GCACAGCAATGGAGGAAACAG

CCAAATCCAGGAGCCCTGT

CLDN-1

CTTGGAAGACGATGAGGTGCA

CCAGACCTGCAAGAAGAAATATCG

CLDN-2

CTCCTGGGATTCATTCCTGTT

TCAGGCACCAGTGGTGAGTAGAOCLNGCTACGGAAGTGGCTATGG

GCGGCAATGAAACAAAAG

IL-8

AGTTTTTGAAGAGGGCTGAGATGCTTGAAGTTTCACTGGCATC

TNSF10CGTCAGCTCGTTAGAAAGATGATTTGGTCCCAGTTATGTGAGCTG

CCL20

CAAGAGTTTGCTCCTGGCTGCAAAGTTGCTTGCTGCTTCTHDAC3AGTTCTGCTCGCGTTACACACAGAAGCCAGAGGCCTCAAALBPCAAGGGCATCAGCATTTCGGTTCAACAGCCACCCCAAGTC

MUC2

TaqMan primer probes; Assay ID Hs03005103_g1

MUC5B

TaqMan primers probes; Assay ID Hs00861595_m1

Three separate IEBC experiments were performed, each with multiple replicates. In the case of antipyrine
and atenolol, data were collected at multiple time points during a given experiment. Each model has its own
random effects structure so that the effects of the experimental condition could be estimated independentlyof the random variation introduced by the different experimental occasions replicates and sampling time
points.
In the case of LDH, we used a hierarchical random effects structure where replicate is uniquely nested
within each experiment. For FD4, atenolol, antipyrine, and the atenolol/antipyrine ratio, the time factor was
accounted for with an additional separate random effects term. Cytokines and gene expression were
measured in a single experiment; the random effects structure was adjusted accordingly with only the
replicate factor as a random effect.
The FD4 model used a Gamma distribution with a square-root link function to accommodate the
distribution of the underlying data. All models except for FD4 data used log-transformed values to ensure
heteroscedastic residuals. Samples were excluded from statistical analysis when their absolute standardized
residuals exceeded 3 standard deviations.

RESULTS
Impact of inulin on microbial diversity and short-chain fatty acid production

The effect of the inulin intervention on the gut microbial community structure and function in vitro wasstudied after 24 h of incubation. We investigated the inulin-induced change in alpha and beta diversity inthe microbial community composition [Figure 1]. A significant increase in the Shannon diversity index was
detected after 24 h of fermentation in the i-screen when SIEM media was supplemented with 4 mg/mL
inulin (P = 0.014) [Figure 1A]. Supplementation of SIEM media with 4 mg/mL of inulin led to an increase in
Alpha diversity to 2.60 in Shannon index compared to 2.52 in the control condition which resembled thediversity in the pre-culture at t = 0. Furthermore, the inulin treatment led to an increase in the relative
abundance of the genera Anaerostipes, Bifidobacterium, Blautia, and Collinsella, as detected and displayed in
the PCA plot [Figure 1B and C]. Inulin supplementation also promoted the growth of Coprococcus to alarger extent than the control fermentation, while suppressing the relative abundance of Escherichia andShigella, Allisonella, Bacteroides, Bilophila, Clostridium; these genera were higher in relative abundance inthe control condition than in the inulin and pre-culture condition [Figure 1D].

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Figure 1. Microbiome data of the i-screen pre-culture (t = 0 h, n = 3), the i-screen control, and inulin-supplemented fermentations withpooled microbiota samples (both t = 24 h, n = 9 or n = 6, respectively). (A) Shannon index showing a significant increase in microbialalpha diversity after incubation with inulin. Black error bars with points represent estimated marginal means with standard errors,
obtained from linear mixed-effects models; (B) PCA plot showing the relationship between the microbiota samples and the mostabundant bacterial genera identified. The darker dots represent the average per sample type (pre-culture t = 0 h, control t = 24 h, andinulin supplementation t = 24 h), and the lighter dots represent replicates. The position of the dots in the PCA plot is indicative of the
microbiota composition of the corresponding samples. Hence, dots that are closer to each other represent samples that are more similar
to each other in microbiota composition. The clustering of dots based on color indicates that the microbiota composition of the control
samples is distinct from that of the inulin-supplemented samples and that both are different from the pre-culture at t = 0 h; (C)Differential abundance of microbial genera between control and inulin-supplemented fermentations. The heatmap illustrates the log2
fold change (Log2FC) in the abundance of microbial genera between inulin treated and untreated conditions: after 24 h of fermentationin the i-screen. Each row represents a genus, reordered based on the magnitude of Log2FC. Color intensity indicates the degree ofincrease in relative abundance (yellow) or decrease (blue) relative to the control condition. White circles highlight genera withstatistically significant changes (P ≤ 0.01). The analysis accounts for variability within replicate samples using a mixed model framework;(D) Relative abundance (%) of the 20 most abundant taxa on genus level for all experimental replicates per condition (pre-culture t =0 h, control i-screen t = 24 h, inulin supplemented i-screen t = 24 h).

The supplementation of SIEM media with inulin led to a significantly elevated level of butyrate production
by the microbiota, with 16.14 ± 0.83 mmol being detected in the supernatant of the inulin-supplemented
conditions compared to 6.96 ± 0.22 mmol detected in the control supernatant (P < 0.001) [Figure 2]. Acetatelevels were only slightly higher in the inulin-supplemented condition with 46.52 ± 0.98 mmol compared tothe control with 41.02 ± 2.78 mmol, and propionate levels were slightly lower in the inulin-treated condition
with 8.98 ± 0.35 vs. 13.51 ± 0.94 mmol in the control condition. In addition to these individual differences,
total SCFA levels were significantly higher upon inulin supplementation with 72.56 ± 1.38 mmol vs. 63.87 ±Page 8 of 20

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Figure 2. SCFA and BCFA concentration in i-screen. SCFA and BCFA were measured in absolute amounts (mmol/L) in the pre-culture
at t = 0 h, or after 24 h of fermentation in the i-screen. The black bars represent statistical comparisons between the different
conditions. They indicate where a significant (P < 0.05) difference in short-chain fatty acid concentrations was detected. SCFA: Shortchain fatty acid; BCFA: branch-chain fatty acid.

3.32 mmol in the control condition. The relative contributions of each individual SCFA were alsosignificantly different between these two groups, with the most dominant shifts being a 2.1-fold increase forbutyrate and a 1.7-fold reduction for propionate.
Effects of increased butyrate concentrations on the integrity, functionality and viability of humancolon tissue

We investigated the potential beneficial effects of the changed SCFA composition upon inulin treatment of
the microbiome, with a shift towards increased butyrate concentrations, by exposing fresh human colon
tissue explants, obtained from three different donors, to i-screen cell-free culture supernatant collected from
untreated control microbiome or microbiome incubated with inulin. Additionally, in one experiment, athird condition was included, which mimicked the SCFA composition of the inulin-stimulated microbiota
by adding a mix of butyrate (20 mM), acetate (50 mM), and propionate (10 mM) to untreated control
supernatant. Effects on the tissue explants were evaluated between 20-24 h of incubation [Figure 3]. The
ameliorating effect of supernatant containing higher levels of butyrate on epithelial barrier function wasmonitored by measuring the permeability of a large inert molecule, FD4. In line with our previously defined
cut-off value of 1%/h for proper barrier integrity[28,40], FD4 permeability was low, with values between
0.05%/h and 0.77%/h [Figure 3A]. Although not significantly, FD4 permeability decreased on average by
34.2% or 28.2% upon inulin treatment of the microbiome or SCFA supplementation, respectively.
Permeability of two smaller molecules, antipyrine and atenolol, followed the same trend with lower 20-24 haverage Papp values upon inulin treatment or SCFA supplementation [Figure 3B and C]. Significant results
were observed for antipyrine transport by a decrease from 37.5 × 10-6 cm/s under control conditions to 28.3
× 10-6 cm/s and 28.1 × 10-6 cm/s upon inulin treatment or with SCFA supplementation, respectively. As FD4
and small molecule transport data for both the inulin treatment and SCFA supplementation show similar
observations, these decreases are likely attributed to the different SCFA composition in these conditions
compared to the control condition. By taking the different transport characteristics of antipyrine andatenolol into account, tissue functionality can be assessed[28,40,42]. Antipyrine is a highly permeable drug
[fraction absorbed (FA) of 100%] that translocates transcellularly and atenolol is a moderately permeable
drug (50% FA) that translocates paracellularly. Consequently, tissue with good functionality will show 2-foldDonkers et al. Microbiome Res Rep 2024;3:18

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Figure 3. Tissue integrity, functionality and viability of the human colon tissue explants in the IEBC exposed to supernatant from i-screen(untreated control, inulin-treated, and untreated control with added SCFA) for 24 h (n = 5-13/group). For i-screen ctrl and i-screen
inulin-treated, data were collected from 3 independent experiments; for i-screen ctrl with added SCFA data, were collected from one
experiment (donor 3). (A) Average FD4 permeability, expressed as leakage (%)/h, between 20-24 h; (B and C) The average Papp of
antipyrine (10 μM). (B) and atenolol (10 μM), (C) was calculated between 20-24 h; (D) Ratio of transcellular transport (Papp antipyrine)
to paracellular transport (Papp atenolol); (E) Cumulative LDH release into the apical and basolateral compartments and (F) intracellular
LDH were determined after 24 h and compared to the level of intracellular LDH at t = 0 h. Black error bars with points represent
estimated marginal means with standard errors, obtained from linear mixed-effects models. *P < 0.05; **P < 0.01. IEBC: Intestinal
explant barrier chip; SCFA: short-chain fatty acid; FD4: FITC Dextran 4000; Papp: apparent permeability; LDH: lactate dehydrogenase.

higher Papp values for antipyrine than for atenolol[28]. Here, adequate tissue functionality was shown for all
three test conditions with an antipyrine/atenolol ratio not different from 2 and no differences between thetest conditions [Figure 3D]. Next, we evaluated tissue viability by measuring LDH secretion into the apicaland basolateral compartments over time and determined the intracellular LDH levels at the end of theexperiments. Comparable with our previous reports on LDH secretion by human or porcine colon tissue inthe IEBC[28,42], the cumulative LDH release was 15.6% under control conditions [Figure 3E]. Comparable
levels were observed upon incubation with the inulin-treated i-screen supernatant, whereas significantly
lower levels were detected upon the addition of SCFA to control i-screen supernatant. Endpoint
intracellular LDH levels were highest for the inulin-treated condition, but not significantly different fromcontrol, thus indicating comparable levels of tissue viability after 24 h of incubation [Figure 3F].

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Human colon tissue secretes less pro-inflammatory cytokines when exposed to i-screen
supernatant with (added) increased butyrate concentrations

SCFAs, particularly butyrate, are known for having anti-inflammatory effects in the gut[45,46]. Thus, weevaluated the secretion of a broad panel of pro- and anti-inflammatory cytokines by the human colon tissue
explants, as their levels can be indicative of changes in the inflammatory state of the tissue. Communicationof the epithelial cells to immune cells occurs predominantly at the basolateral side of the tissue.
Correspondingly, all cytokines were detected at higher concentrations in the basolateral medium than in theapical medium [Figure 4A and B, Supplementary Figure 1]. The cytokines for which the highest
concentrations were detected were IL-1β, IL-6, IL-8, and TNF-α. For the other six cytokines, IFN-γ, IL-10,
IL-12p70, IL-13, IL-2, and IL-4 values were low (< 10 pg/mL) and therefore they might be considered to beless relevant. At the basolateral side, both inulin and SCFA treatments show a trend to decrease theconcentration of IL-1β, IL-6, IL-8, and TNF-α. At the apical side, the concentrations of the same four
cytokines were significantly decreased for the inulin-treated condition, but not for the SCFA treatment.
These observations might indicate that although the SCFAs that are present in both conditions have apotentially beneficial effect on the release of pro-inflammatory cytokines at the basolateral side, anothercomponent is likely responsible for the apical decrease of the release of these cytokines in the inulin-treatedcondition. Additionally, the concentration of 5 of the other 6 cytokines tested, namely IFN-γ, IL-10,
IL-12p70, IL-2, and IL-4, were also lower upon exposure to the inulin-treated i-screen supernatant or
SCFA-supplemented i-screen control supernatant compared to the control condition.
mRNA gene expression profiles confirm increased barrier function and anti-inflammatory effectupon inulin treatment or SCFA supplementation

To establish whether the protective effects of the microbial inulin treatment or SCFA supplementation on
tissue barrier integrity and inflammation were caused by genetic changes, we assessed the mRNA expression
profiles of several genes involved in these processes. The transmembrane proteins Occludin, Claudin-1, andClaudin-2, and the cytoplasmic scaffolding protein ZO-1 are the main components of the tight junction
complex of intestinal epithelial cells[47]. Exposure of human colon tissue segments to the inulin-treatedi-screen supernatant significantly increased the mRNA expression of the major stabilizing factor of the tightjunction complex occludin[48] [Figure 5A], as did SCFA supplementation in a non-significant way.
Claudin-1 and Claudin-2 expression were also both non-significantly higher in the segments exposed to theinulin-treated supernatant, whereas zonulin-1 showed a decreasing trend [Figure 5A]. Although a decreaseof the latter sounds counterintuitive in order to protect the barrier integrity, high expression levels of
zonulin-1 correlate with increased intestinal permeability as demonstrated in patients suffering fromintestinal permeability disorders such as celiac disease[49,50]. SCFA supplementation also showed a trend toincrease claudin-2 expression, but did not alter mRNA levels of claudin-1 or zonulin-1, indicating that othercomponents than SCFA in the inulin-treated i-screen supernatant are likely at play in regulating theexpression of these two genes. In addition to tight junction proteins that can be considered parts of themechanical barrier, intestinal mucus, including mucins, adds to the chemical barrier of the gut[51]. Gene
expression levels of the two predominantly secreted mucins in the colon, MUC2 and MUB5B[52], indicatedopposite effects for the inulin-treated i-screen supernatant condition compared to SCFA supplementation[Figure 5B]. MUC5B significantly decreased and MUC2 showed a trend to decrease upon exposure to theinulin-treated i-screen supernatant, whereas both genes showed a trend to increase upon exposure tocontrol supernatant supplemented with SCFA. Mucin changes do not only alter the physical intestinal
barrier, but will also impact the innate immune response[53]. To further map genetic changes related toinflammation, we first assessed the mRNA gene expression level of IL-8, the cytokine with the highest
concentrations measured in the apical and basolateral supernatants [Figure 4]. In line with the cytokine
release, mRNA expression of IL-8 was significantly reduced in the condition exposed to the inulin-treatedi-screen supernatant [Figure 5C]. Inulin-treated i-screen supernatant also significantly reduced TNFSF10

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Figure 4. Cytokine release by human colon tissue explants (donor 3) in the IEBC exposed to i-screen supernatant (untreated control,
inulin-treated, and untreated control with added SCFA) for 24 h (n = 5-6). (A) Secretion of IL-1β , IL-6 , IL-8 , and TNF-α into the apicalcompartment; (B) Secretion of IL-1β , IL-6 , IL-8 , and TNF-α into the basolateral compartment. Black error bars with points represent
estimated marginal means with standard errors, obtained from linear mixed-effects models. *P < 0.05; **P < 0.01. IEBC: Intestinal
explant barrier chip; SCFA: short-chain fatty acid.

(TRAIL) mRNA gene expression, another key mediator of the innate immune response[54,55]. IL-8
predominantly targets neutrophil attraction in the innate immune response[56], whereas TNFSF10 (TRAIL)
is involved in regulating controlled cell death by apoptosis and its downregulation is associated with
reduced inflammation[54,55]. Neither IL-8 nor TNFSF10 were affected by SCFA supplementation. In contrast,
the mRNA expression of LBP, an acute-phase protein of the innate immune response[57], and CCL20, a
factor involved in the adaptive immune response by recruiting dendritic and Th2 cells[58,59], was lower, but
not significantly different, for both the inulin-treated i-screen supernatant and SCFA supplementation
conditions [Figure 5B], hinting towards shared mechanisms between these conditions to reduce
inflammation in the gut. As an altered SCFA profile with increased amounts of butyrate is shared between
the inulin-treated i-screen supernatant condition and SCFA-supplemented condition, we assessed themRNA gene expression levels of a commonly known gene targeted by butyrate, HDAC3[60,61]. In line withthe expectation that increased levels of butyrate inhibit HDAC3, both inulin-treated i-screen supernatant
and SCFA-supplemented conditions showed a trend to decrease HDAC3 gene expression levels[Figure 5D], thereby confirming a common initiating factor being present in these two conditions.

DISCUSSION
In this study, we demonstrated the implementation of our i-screen[29] and IEBC technologies[28] in sequence
as a novel efficient approach to study the interaction of microbial metabolites with the host gut tissue ex
vivo. Over recent years, there has been an increasing incidence and emerging healthcare costs to treat andalleviate symptoms of patients with common diseases of modern society that have been associated with adysregulation of the host-bacteria homeostasis, like obesity, metabolic syndrome, diabetes, allergies,
autoimmune disorders, colorectal cancer, and IBD[62-65]. This means that there is a big window ofopportunity to investigate novel treatment strategies based on the modulation of host-microbe interactionsin the gut. Although modulation of the gut microbiome to improve health and even prevent or reduce
disease, such as through pre-/pro-/antibiotics or fecal microbiota transplantation (FMT), is not new,

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Figure 5. mRNA expression levels of 11 genes determined by RT-qPCR in human colon tissue explants (donor 3) in the IEBC exposed to iscreen supernatant (untreated control, inulin-treated, and untreated control with added SCFA) for 24 h (n = 5-6). (A) Tight junction
complex genes OCLN, CLDN-1, CLDN-2, and ZO-1; (B) Mucus genes mucin MUC2 and mucin MUC5B; (C) Genes related to the
inflammatory response: cytokine IL-8, TNFSF10, LBP, and CCL20; (D) Chromatin remodeler HDAC3. Target gene expression isexpressed relative to the reference gene 36B4 and normalized to control. Error bars with points represent estimated marginal means
with standard errors, obtained from linear mixed-effects models. *P < 0.05; **P < 0.01. RT-qPCR: Reverse transcription quantitative PCR;
IEBC: intestinal explant barrier chip; SCFA: short-chain fatty acid; OCLN: occludin; CLDN-1: claudin-1; CLDN-2: claudin-2; ZO-1:
zonulin-1; IL-8: interleukin-8; TNFSF10: TNF superfamily member 10; LBP: LPS binding protein; CCL20: C-C motif chemokine ligand 20;
HDAC3: histone deacetylase 3.

evidence on the exact mechanism at the local interaction site is still scarce[66]. In vitro or ex vivo co-culture
models to study the mode of action of intestinal host-microbe interactions are hardly established, hamperedby the inevitable need for an anaerobic environment to culture the full gut microbiome in all its complexity,
and the rapid overgrowth of intestinal cells by bacteria in static culture systems[67]. Using cell-free culture
supernatant, also called bacteria-free supernatant, fecal supernatant, or simply supernatant, has proven to bean effective way to study host-microbe, or in fact host-microbial metabolite, interactions. For example, afew studies report on the protective effect of probiotic culture supernatants against invasion of pathogenic
Escherichia coli (E. coli) strains or against 5-fluorouracil (5-FU)-induced intestinal epithelial cell
damage[68-70]. The latter study used an intestinal epithelial cell line derived from rat intestine[68], wherefore
results might be less relevant for humans, while the models that applied an E. coli challenge used either thehuman intestine-derived HT-29 cell line[70] or Caco-2 and T84 cell lines[69]. One step further towards thephysiological resemblance of the human intestinal tissue structure goes a Caco-2/dendritic cell co-culture
model that studies the innate immune response towards a pathogenic challenge with Salmonella typhi[71].
Still, by using cell lines and supernatants of single probiotic strains, these in vitro models have a rather
limited representation of the complex situation in vivo with respect to both the gut tissue and themicrobiome. Both aspects were grasped in their full complexity in a recent study by Gonzales et al., where
they showed impaired intestinal digestive and barrier function upon transferring fecal supernatant of
human Autism Spectrum Disorder patients into mice[72]. Although mouse models are frequently employedin biomedical research and allow for studying host-microbe interactions in a controlled setting, using amouse model for human gut microbiota research has often limited translatability[73]. Therefore, wedeveloped a co-culture model in which both the human microbiome and the human gut tissue architecture

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were fully represented. In fact, to the best of our knowledge, this is the first gut-on-a-chip model with fresh
tissue explants that studied intestinal host-microbial metabolite interactions. Furthermore, this is the only
gut-on-a-chip model where the intestinal compartment is directly exposed to the supernatant of thecomplete microbiome, without, for example, a membrane for separation of the microbial and intestinal
chambers[74,75]. Although we also have a static model with fresh intestinal tissue explants, the InTESTine[40,43],
we chose to use our IEBC gut-on-a-chip model as the microfluidic flow enhances tissue viability and mucus
formation[28,67]. These advantages and the two-compartmentalized set-up have popularized the gut-on-achip technology for intestinal host-microbe interaction studies over the recent years, yielding a range ofdifferent designs. The establishment of an aerobic-anaerobic interface seems to be the most sought-after
design and would be even more physiologically relevant than using culture supernatant, as such an interface
would enable the co-culture of the anaerobic microbiome with aerobic gut cells or tissue. However, thevariety of technologies and low number of publications show that this is a complicated research area[67,76].
The model presented here might bridge the gap until such an aerobic-anaerobic interface platform issuccessfully developed and can fulfill the need to study host-microbe, or host-microbial metabolites,
interactions in the adult human intestine. Still, we realize that even the current set-up might need adaptions
to further improve its physiological resemblance, e.g., by decreasing the apical pH from 6.5 to 6.0, lowering
the glucose concentration in the media, and removing or studying the effect of the antimicrobials in thei-screen supernatant on the existing microbiome of the tissue explants. Nevertheless, by varying the sourceof the microbiome, e.g., from diseased populations, or fresh tissue explants, e.g., from a young patient, thismodel can be adapted to fit a variety of research questions.
The major findings of the data presented here are that the prebiotic fiber inulin shifted the microbiome
composition towards a more butyrogenic composition after 24 h of incubation, and that the consequently
elevated butyrate levels likely played an active role in the increase of the intestinal barrier function andreduced inflammation in human colon tissue explants. Bacterial fermentation of the prebiotic fiber inulin
leads to the generation of SCFAs[13] and can promote the growth of, e.g., Bifidobacterium and butyrateproducing species such as Faecalibacterium, Roseburia, and Anaerostipes[19,24,77,78]. Here, we used aconcentration of 4 mg/mL because the expected concentration of prebiotic products (oral dose 1-10 g) in
the colon is likely between 1-10 mg/mL and previous in vitro studies have found clear prebiotic effects withthis concentration[30]. We found that supplementation of culture media with inulin shifted the gut microbialcommunity towards a higher relative abundance of Bifidobacterium, Anaerostipes, Blautia, and Collinsella.
These bacterial groups are generally more abundant in the microbiomes of healthy people than in those of
diseased people and are known for their capacity to produce SCFAs[79]. Furthermore, the addition of inulin
to the microbiome culture medium increased the total level of SCFAs and generated an increase in thecontribution of butyrate to the total SCFA pool, as we have seen before[20]. In fact, the relative contributionof butyrate more than doubled from 10% to 22%. The contribution of propionate decreased from 20% to12%, and the acetate ratio remained more or less the same (63% vs. 64%) and in line with the average level ofacetate in the human gut[14,17]. Although most gut microbiota species can produce acetate, the production
pathways for propionate and butyrate are more conserved[14,17]. Propionate is produced via the succinate
pathway, used by Bacteroidota and many Negativicutes, or via the acrylate and propanediol pathways
restricted to the Lachnospiraceae and Negativicutes[14,17]. Butyrate production occurs via butyrate kinase or
butyryl-CoA:acetate-CoA transferase, the latter being the major pathway and needing the presence of
acetate[14]. The main butyrate producers belong to Faecalibacterium, Eubacterium, Roseburia, Coprococcus,
Anaerostipes, Subdoligranulum, and Anaerobutyricum genera[14,17,80]. Indeed, significant changes in
microbiome composition caused by inulin supplementation in the i-screen affected most of these genera,
with Anaerostipes and Coprocococcus showing the highest increase in relative abundance. Along with anincrease in butyrogenic bacteria, the relative abundance of putative propionate-producing bacteria such as
Bacteroidota was reduced after 24 h, reflecting our findings in the SCFA composition shift. In addition, thePage 14 of 20

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relative abundance of Bifidobacterium increased significantly, whereas Escherichia and Shigella decreased.
Although most species of the latter two genera are harmless, Escherichia or Shigella intestinal overgrowth isassociated with diarrhoeal disease and thus their reduction can be seen as beneficial[81,82]. Bifidobacteria are
generally viewed as having health benefits[83,84]. An increase in bifidobacteria is often associated with anincrease in butyrate, not through butyric acid production by bifidobacteria themselves but rather inassociation with cross-feeding mechanisms in co-culture with other bacteria[85]. This will be an interestingmechanism to study in future studies. Our observations are in line with human clinical trial data for inulin,which show a comparable change in microbiome composition towards increased Bifidobacterium,
Anaerostipes, and Faecalibacterium, and decreased Bacteroides, but could not always confirm that thesechanges were associated with increased SCFA levels[21-24].
In vivo, SCFAs are readily absorbed by the host. In total, 90%-95% of SCFA are absorbed by the gut
epithelial cells, enter the systemic circulation, and exert their effects via different signaling pathways, or, inthe case of butyrate, can also be used as an energy source for colonocytes[15,16]. Studying the effects of
microbial metabolites such as SCFAs can be facilitated using in vitro or ex vivo model technologies such as
the IEBC gut-on-a-chip model presented in this study. The lower permeability of the human colon tissue
for both the large molecule FD4 and the small molecules antipyrine and atenolol hint towards a tighter
epithelial barrier. This effect was observed after both the inulin treatment and SCFA supplementation, andthus is likely attributed to the shift in SCFA composition compared to the control situation. Gene
expression data of major tight junction proteins confirmed the presence of a tighter epithelial barrier withan upregulation of OCLN and CLDN-2, but also highlighted that the i-screen supernatant induced
additional beneficial effects for CLDN-1 and ZO-1 compared to SCFA supplementation alone. With
increased butyrate levels as a common denominator, the shared effects of the two exposure conditions arelikely attributed to it. Indeed, butyrate is well known for its stimulating effects on intestinal epithelial barrier
function and immune function and, therefore, can be considered a therapeutic intervention for IBD orother gastrointestinal diseases with a hampered barrier or immune activity[47,86-89]. Several in vitro studies
using Caco-2, HT-29, or other human-derived cell lines support the barrier-strengthening effect ofbutyrate[90-92], but so far, this has not yet been confirmed in ex vivo human tissue. The only other study todate identified by these authors employing fresh human gut tissue explants to study the effect of butyrate on
intestinal barrier function, could not see any difference of a 5 or 25 mM butyrate treatment on paracellularor transcellular permeability, nor at the gene expression level of tight junction proteins[93]. The higher
concentration of 25 mM is comparable to the butyrate concentration in the inulin-treated and SCFAsupplemented conditions in this study. Of note, the incubation time in that study was only 1 h, a known
limitation of the Ussing technology[94], and might have been too short for butyrate to exert its effects.
Additionally, butyrate was applied together with a stressor that caused a leaky gut, Compound 48/80[93],which is an important difference from the set-up of this study in which the tissue explants were notchallenged by a hyperpermeability inducer. So even though we could already demonstrate the ameliorating
effect of the butyrate-dominated SCFA-composition shift on intestinal barrier function, demonstrating a
preventative or treatment effect of the enriched i-screen supernatants when applying a challenge to theintestinal tissue explants in the IEBC system would be a very interesting topic for future experiments.
Butyrate is also well-known for its anti-inflammatory effects[45,46,87,88,95]. Butyrate can modulate the innate andadaptive immune system by influencing neutrophils, macrophages, and T-cells[17] and via downregulation ofhistone deacetylase (HDAC)[96-99], leading to lower TNF-α levels[98], higher IL-10 levels[99,100], and increased
numbers of regulatory T-cells[101]. Here, we measured cytokine levels in the tissue supernatant as cytokine
levels can indicate changes in the inflammation state of the tissue explants. Furthermore, in the tissue itself,
we measured mRNA gene expression levels of several genes related to inflammation. We showed reduced
release of TNF-α, and of other pro-inflammatory cytokines, at the basolateral (systemic) side of theintestinal tissue explants as well as downregulated HDAC3 gene expression upon exposure to the inulin-

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treated and SCFA-supplemented supernatants, thereby likely confirming the anti-inflammatory role ofbutyrate. However, for the condition exposed to the inulin-treated i-screen supernatant, additional andsignificant anti-inflammatory effects on the apical (luminal) pro-inflammatory cytokine release and mRNA
gene expression were observed. The expression of MUC genes was also only affected by this condition.
These observations imply that a factor other than butyrate is at play, uniquely present in the inulin-treatedi-screen supernatant. Of all metabolites produced by the microbiome, we only measured the SCFA and
BCFA concentrations in this study. As BCFA concentrations could not be matched in the SCFAsupplemented condition (as they were lower in the inulin-treated condition and could not be removed fromthe supernatant), the significantly lower concentrations of iso-butyrate and iso-valerate concentrations in
the inulin-treated condition could have contributed to its observed beneficial effects. Indeed, it is describedthat IBD patients have a 25% higher production of these BCFA[102] and in vitro experiments with highconcentrations of iso-valerate stimulated the release of pro-inflammatory cytokines[103]. However, not manyother (in vitro) studies have been performed with these BCFA and additionally iso-butyrate is poorly
metabolized by enterocytes[104,105]; thus, the potential effect of BCFA remains rather speculative here. Likely,
another metabolite, or group of metabolites, contributes to the additional positive effects of the inulintreated supernatant on intestinal barrier function and inflammation. In the future, therefore, we might
apply untargeted metabolomics to identify the different metabolites produced by the microbiome inresponse to inulin. Studying the metabolome, the collection of metabolites with usually a low molecularweight (< 2,000 Da), has become a popular analytical approach to identify and quantify novel biomarkers
for human health and disease, to detect responses to drug interventions or (environmental) stressors, and tocharacterize microbial metabolism[106,107]. It is a sport in itself to perform metabolomic research using
complicated techniques such as nuclear magnetic resonance (NMR) spectroscopy or mass spectroscopy(MS), often coupled to liquid chromatography (LC) or other chromatography techniques forseparation[108,109]. Therefore, doing this body work goes beyond the scope of the current study, but would be
one of the first steps to take in the future.
In conclusion, our findings are in line with in vitro, ex vivo, and in vivo literature and show that the
combination of i-screen and IEBC technologies provides a novel and effective way to study complex
intestinal host-microbe interactions and the impact of these interactions on gut health and host wellness.

DECLARATIONS
Acknowledgments

The authors thank Willem de Jong and Nicole Plomp for collecting the i-screen supernatant and Elwin
Verheij for the SCFA analysis. The authors also thank Angelique Speulman, Lisanne Pieters, Birol Usta,
Hans Verplancke, and Hossein Eslami Amirabadi for their help in the IEBC experiments, Petra van Duijn
for helping with the cytokine analysis, and Linda de Rooij for performing the RNA isolation and qPCR on
the tissue segments.
Authors’ contributions

Carried out experiments: Donkers JM, Agamennone V, Wierenga E
Performed mathematical and statistical analyses on the microbiome and tissue experiments: van den Broek
TJ
Developed the study concept and design: Donkers JM, Wiese M, Agamennone V, Schuren F, van de Steeg E
Drafting and initial review of the manuscript: Donkers JM, Wiese M, van den Broek TJ, Agamennone V,
Wierenga E , Schuren F, van de Steeg E
All authors were involved in the analysis and interpretation of data and have read and approved thefinal manuscript.

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Availability of data and materials

Raw data files are available upon request.
Financial support and sponsorship

None.
Conflicts of interest

All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate

Collection of fecal samples was performed anonymously following TNO standard operational procedures.
The collection was approved by an internal ethical evaluation board and is in compliance with the Dutch
laws on medical/scientific research. Because samples were collected only once and anonymously, they are
not subjected to the Medical Research Involving Human Subjects Act (WMO) in the Netherlands.
Human intestinal proximal colon tissue was obtained from three human adult patients undergoing surgery
for colon carcinoma. Ethical approval for the use of human intestinal tissue was obtained from the hospitalboard. Because samples were collected only once and anonymously, they are not subjected to the WMO in
the Netherlands. Prior to surgery, informed consent was obtained from the patients.
Consent for publication

Not applicable.
Copyright© The Author(s) 2024.

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