# A new in vitro model of the adult pig colon microbiome: application to the study of feed ingredients

Claude Durif, Sylvain Denis, Noëmie Lemoine, Charlotte Deschamps, Ophélie

Uriot, Morgane Brun, David Guillou, Stéphanie Blanquet-Diot

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Claude Durif, Sylvain Denis, Noëmie Lemoine, Charlotte Deschamps, Ophélie Uriot, et al.. A new in vitro model of the adult pig colon microbiome: application to the study of feed ingredients. Animal Microbiome, 2025, 7 (1), pp.100. 10.1186/s42523-025-00465-1 . hal-05296094

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# A new in vitro model of the adult pig colon microbiome: application to the study of feed ingredients

Claude Durif¹, Sylvain Denis¹, Noëmie Lemoine², Charlotte Deschamps¹, Ophélie Uriot¹, Morgane Brun¹, David Guillou² and Stéphanie Blanquet-Diot¹*

**Abstract**

**Background** There is an intricate relationship between pig nutrition and gut microbiome. For technical, cost and societal reasons, in vitro gut models can be advantageously used as an alternative to in vivo experiments to perform mechanistic studies on the interactions between gut microbes and nutrients. In this context, the aim of the present study was to develop a new in vitro model of the healthy adult pig colon integrating both luminal and mucosal microenvironments. The model was further used to evaluate feed ingredients (lactose and lactose + sucrose). Gut Microbiota composition and metabolic activities were followed by 16S Metabarcoding and short chain fatty acid (SCFA) / gas measurement, respectively. Then, the effect of the both feedstuffs on skatole production, responsible for boar taint in male pigs, was also investigated.

**Results** Based on in vivo data, the new MPigut-IVM was set-up to reproduce the main physicochemical (pH, transit time, self-maintained anaerobiosis), nutritional (composition of ileal effluents, bile salts) and microbial (lumen and mucus-associated microbiota) parameters of the large intestine in healthy adult pigs. The model was validated through in vitro-in vivo comparisons regarding SCFA concentrations and bacterial profiles at the phylum and family levels. Lactose and lactose + sucrose had no significant impact on SCFAs but increased gas production (*P* < 0.01 with lactose). Both sugars, particularly lactose + sucrose, tended to reduce skatole concentrations while increasing indole levels (*P* > 0.05). This was associated to a slight reduction of the numbers of skatole-producing bacteria *Olsenella scatoligenes* (*P* > 0.05). Both feed ingredients induced a decrease in bacterial α-diversity (*P* < 0.05).

**Conclusion** Despite obvious limitations such as lack of host interactions, the adult MPigut-IVM represents a powerful platform for Microbiome studies in the pig colonic environment. In Line with the 3R regulations, this in vitro model can be useful to perform preliminary screening of innovative feed strategies to improve pig health and help to elucidate their mechanisms of action in relation with gut microbiota, taking into account inter-individual variabilities.

**Keywords** Pig, In vitro fermentation, Gut microbiota, Mucus, Feedstuff, Skatole

**Background**

There is a delicate relationship in pigs between health-nutrition, meat quality, and the resident microbiota in the digestive tract [1, 2]. Gut microbiome and its metabolites have been shown to improve pig growth performance and feed efficiency, alleviating stress and providing protection from pathogens [3]. Gut microbes are colonizing the entire pig gut, with variations in microbiota composition

<footnote>*Correspondence:
Stéphanie Blanquet-Diot
stephanie.blanquet@uca.fr
¹ Université Clermont Auvergne, UMR 454 MEDIS UCA-INRAE, 28 Place Henri Dunant, 63000 Clermont-Ferrand, France
² MiXscience, 35172 Bruz, France</footnote>
and diversity along the gastro-intestinal tract [4], from the lumen to intestinal cell vicinity covered by mucus [5] and with age [2]. The highest bacterial load is found in the pig large intestine [6] and fecal Microbiota was shown to be stable from 22 weeks of age [7]. Short chain fatty acids (SCFAs) are main fermentation end-products associated with health effect [8, 9]. Skatole, a product of microbial fermentation from tryptophan in the large intestine is considered as one of the main compounds leading to boar taint, a sex-specific odor inducing meat quality defect [10]. Among feed additives used in pigs, probiotics, dietary fibers and resistant starch can modulate the intestinal microbiota and thereby contribute to gut health status by promoting the growth of beneficial microorganism and SCFA production [11–14]. However, the effects of feeding strategies on skatole production as mediated by gut microbiota changes, have been poorly studied [15, 16].

The effect of feed additives on pig gut microbiota can ideally be investigated with living animals, but this approach remains restricted by its high cost, the need of appropriate qualified personnel and dedicated infrastructures and by ethical concerns. In line with European regulation, it seems nowadays crucial to avoid animal studies as often as possible and prioritize alternative in vitro approaches [17]. Among available strategies, in vitro gut models have been developed as effective tools to study pig microbiota in a controlled environment. Most models set up to date to reproduce the pig large intestine microbiome are single-batch fermentation systems, inoculated with fecal samples and mimicking only the luminal compartment [11, 18–23]. The advantages of batch processing are ease of operation and low cost, but such experiments are restricted by substrate depletion and changes in pH, Limiting fermentation time to 72 h or less. As an advanced option, few semi-continuous or continuous in vitro models of the pig colon have been developed [24–26] and integrate additional digestive parameters such as transit time, controlled pH and simulated ileal effluents. These models provide optimal growth conditions for gut microbes and permit a long-term experimental period (until several weeks). However, none of them reproduces physiologically anaerobic conditions, distinguishes mucus from digestive lumen-associated microbiota and has been fully validated compared to in vivo data.

In this context, the present study aimed at developing a new in vitro model of the healthy adult pig large intestine as an alternative to in vivo assays, that distinguishes lumen from mucus associated microbiota. The model was subsequently used for the testing of two simple sugars, lactose and a mix of lactose and sucrose. We first performed a large literature review to determine the

specific nutritional and physicochemical parameters of the healthy adult pig colon and accordingly adapted the Mucosal Pig gut In vitro Model (MPigut-IVM), first set-up to reproduce piglet at weaning [27] or 8-weeks piglets [27, 28]. Then, in order to validate the newly developed system and evaluate the potential of feedstuffs to reduce skatole concentrations, in vitro fermentations were carried out during 21 days after inoculation of bioreactors with two healthy adult pig stools. Microbiota composition and metabolic activities were daily monitored through 16S Metabarcoding and gut metabolites measurement (gas, SCFAs, indole and skatole), respectively.

## Methods

### Ethics declarations

No ethical approval was required since this study did not involve a prospective evaluation or laboratory animals, and only involved non-invasive procedures (fecal sample collection).

### Fecal sample collection and treatment

Fecal samples used to inoculate the adult MPigut-IVM model were collected from two healthy castrated pre-adult male pigs, aged of 24 and 29 weeks for pig 1 and pig 2, respectively. Animals were raised in a conventional pig farm located in Haute-Loire area of the Auvergne-Rhône-Alpes region in France and fed with the same conventional feed (Table 1 Additional file 6). The two pigs have no history of antibiotic treatment prior to sample collection and were fed prior to fecal collection. Immediately after defecation, fecal samples were transferred into a sterile recipient placed in an airtight anaerobic box (GENbag anaer gas pack systems, Biomerieux, France), transported at ambient temperature and processed at the laboratory within 6 h. In an anaerobic chamber (COY laboratories, Grass Lake, USA), stool samples were manually homogenized, then 5 g were ground in 100 mL of 30 mM sterile sodium phosphate buffer (pH 6.5) [29]. Feces were then Mixed and filtered using a 500 µm inox sieve before inoculation.

### Description and set-up of the adult MPigut-IVM model

The newly developed adult MPigut-IVM model was adapted from the one-stage continuous fermentation system previously set-up for piglet [27]. This in vitro model allows to reproduce both the luminal and mucosal microenvironments of the pig large intestine, through the respective use of a bioreactor (MiniBio, Applikon, The Netherlands) and a connected airtight glass vessel containing mucin beads (Fig. 1). At the beginning of the experiment, 100 mL of fecal suspension were added per bioreactor to 200 mL of a nutritive medium simulating the composition of pig ileal effluents and containing
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Healthy adult MPigut-IVM experimental set-up, sampling procedure and analyses. Once set-up with digestive parameters specific from healthy adult pigs as described in (A) and Table 1, the adult MPigut-IVM was used to run fermentations aiming to validate the model and test two simple sugars. Two bioreactors were inoculated with each stool sample (n = 2 pig donors, Pig 1 & Pig 2) and ran in parallel for 21 days, including one-day batch amplification of Microbiota, 7 days of continuous fermentation for Microbiota stabilization and 13 days of treatment with lactose or lactose + sucrose (B). Samples were daily collected from the main bioreactor (luminal microbiota), the mucin beads compartment (mucosal microbiota) and the atmospheric phase for further analysis of microbiota composition (quantitative PCR and 16S Metabarcoding) and metabolic activities (short chain fatty acid, gas composition and total production, dosage of skatole and indole) (C). SCFA short chain fatty acids</figcaption></figure>
various sources of carbohydrates, proteins, lipids, minerals, and vitamins. After an initial sparging of oxygen-free ($O_2$) nitrogen-gas ($N_2$), anaerobiosis was maintained during the total course of the fermentation by the sole activity of the resident microbiota and through ensuring the system airtightness. Bioreactor was kept at pig body temperature. Colonic pH and redox potential were constantly recorded (Applikon, The Netherlands) and pH was adjusted with 2 M NaOH. The nutritive medium was continuously introduced into the bioreactor, while the fermentation medium was automatically withdrawn, ensuring the appropriate mean retention time and maintaining the colonic volume constant. In the present study, the in vitro colon model was adapted to reproduce the mean conditions found in the large intestine of healthy adult pigs. As Further described in the result section, temperature was set at 39 °C, residence time and pH were revised according to in vivo data and set-up at 30 h and 6.4, respectively (Fig. 1A). The composition of the nutritive medium was also adapted to the digestive physiology of adult pigs (Table 1).

## Mucin beads and mucosal compartment

The mucosal compartment was dived in a water bath maintained at pig body temperature. It was filled with mucin-alginate beads offering an overall mean surface of 723 cm². To produce mucin beads, mucin type II from porcine stomach (Sigma-Aldrich, USA) and sodium alginate (Sigma-Aldrich, USA) were diluted in sterile demineralized water, at concentrations of 5% and 2%, respectively. pH was adjusted to 6 with a sodium bicarbonate solution. The mucin/alginate solution was dropped using a peristaltic pump into a sterile solution of 0.2 M $CaCl_2$ dihydrate under agitation. Every 2 days, mucin/alginate beads remaining into the mucosal compartment were renewed by fresh sterile ones under a constant flow of $CO_2$ to avoid oxygen entrance.

## In vitro experimental design and sampling

Experimental design and sampling are presented in Fig. 1. Two sets of fermentations called Pig 1 and Pig 2 were conducted using fecal samples from the two pig donors (Fig. 1B). In each fermentation assay, two bioreactors were inoculated with a fecal sample from one donor and run in parallel for 21 days. After stool inoculation, fermentations were initially performed under batch conditions for 24 h for Microbiota amplification and then under continuous conditions for 7 days enabling Microbiota stabilization and 13 additional days of treatment with either lactose (0.40 g/day) or a 50/50 mix of lactose and sucrose (0.40 g/day of each compound). Samples were collected daily in the fermentation medium (corresponding to the luminal phase) for analysis of microbiota

<table><caption>**Table 1** Composition of the nutritive medium provided to the adult MPigut-IVM and simulating the composition of ileal effluents from adult pigs</caption><thead><tr><td>Ingredients</td><td>Concentration</td><td>Unit</td></tr></thead><tbody><tr><td>Soy Protein Acid Hydrolysate</td><td>0.5</td><td>g/L</td></tr><tr><td>Potatoes peptone</td><td>4</td><td>g/L</td></tr><tr><td>Yeast extract</td><td>0.5</td><td>g/L</td></tr><tr><td>L-Cystein HCl monohydrate</td><td>0.5</td><td>g/L</td></tr><tr><td>Mucin type II from porcine</td><td>7</td><td>g/L</td></tr><tr><td>Cellulose</td><td>4</td><td>g/L</td></tr><tr><td>Pectin from apple</td><td>2</td><td>g/L</td></tr><tr><td>Guar gum</td><td>0.5</td><td>g/L</td></tr><tr><td>Arabinogalactan</td><td>2</td><td>g/L</td></tr><tr><td>Xylan</td><td>2</td><td>g/L</td></tr><tr><td>Starch from corn</td><td>3.333</td><td>g/L</td></tr><tr><td>Tween 80</td><td>1</td><td>g/L</td></tr><tr><td>Porcine Bile Extract</td><td>0.2</td><td>g/L</td></tr><tr><td>L-alpha-Lecithin</td><td>0.4</td><td>g/L</td></tr><tr><td>D-Pantothenic acid (B vitamin)</td><td>0.01</td><td>mg/L</td></tr><tr><td>Nicotinamide (B3 Vitamin)</td><td>0.005</td><td>mg/L</td></tr><tr><td>4-aminobenzoic acid</td><td>0.005</td><td>mg/L</td></tr><tr><td>Thiamin</td><td>0.004</td><td>mg/L</td></tr><tr><td>Menadione (K3 vitamin)</td><td>0.001</td><td>mg/L</td></tr><tr><td>D-biotin (H vitamin H)</td><td>0.002</td><td>mg/L</td></tr><tr><td>K1 Vitamin</td><td>0.001</td><td>mg/L</td></tr><tr><td>B12 Vitamin</td><td>0.0005</td><td>mg/L</td></tr><tr><td>MnSO<sub>4</sub> H<sub>2</sub>O (Mn, 1 µM)</td><td>0.17</td><td>mg/L</td></tr><tr><td>ZnSO<sub>4</sub> 7 H<sub>2</sub>O (Zn, 0.5 µM)</td><td>0.144</td><td>mg/L</td></tr><tr><td>CuSO<sub>4</sub> 5 H<sub>2</sub>O (Cu, 0.1 µM)</td><td>0.025</td><td>mg/L</td></tr><tr><td>Na<sub>2</sub>SeO<sub>3</sub> (Se, 0.1 µM)</td><td>0.038</td><td>mg/L</td></tr><tr><td>NaCl</td><td>4.5</td><td>g/L</td></tr><tr><td>FeSO<sub>4</sub> 7 H<sub>2</sub>O</td><td>0.015</td><td>g/L</td></tr><tr><td>KH<sub>2</sub>PO<sub>4</sub></td><td>0.5</td><td>g/L</td></tr><tr><td>NaHCO<sub>3</sub></td><td>1</td><td>g/L</td></tr><tr><td>KCl</td><td>4.5</td><td>g/L</td></tr><tr><td>MgSO<sub>4</sub> 7 H<sub>2</sub>O</td><td>0.5</td><td>g/L</td></tr><tr><td>CaCl<sub>2</sub> 2 H<sub>2</sub>O</td><td>0.3</td><td>g/L</td></tr><tr><td>Hemin from porcine</td><td>0.005</td><td>g/L</td></tr></tbody></table>

composition (qPCR and 16S Metabarcoding) and metabolic activities through measuring of SCFA as mean fermentation end-products (Fig. 1C). The atmospheric phase was also sampled to follow anaerobiosis and evaluate gas composition and production thanks to a sampling bag connected to the condenser. Five mL of Luminal phase was also collected at days 0, 7, 10, 14 and 20, and quickly frozen at -20 °C for further skatole and indole dosage by chromatography. Every two days, mucin beads were collected for mucus-associated microbiota analysis (qPCR and 16S Metabarcoding). Mucin/alginate beads
were washed thrice in sterile phosphate buffered saline and stored at -80 °C before microbiota analysis.

## DNA extraction

Genomic DNA was extracted from fermentative medium samples and mucin/alginate beads using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Germany) following manufacturer's instructions with the following modifications. Prior to DNA extraction, 2 mL of luminal samples were centrifuged (18,000×g, 15 min, 4 °C) and the pellets were collected. Pellets and 200 mg of mucin/alginate beads samples were then incubated 10 min with sterile citrate buffer (sodium citrate 55 mM and NaCl 154 mM) at 37 °C [30], before vortexing (maximal speed, 15 s) and additional centrifugation (8,000×g, 1 min at room temperature). Then, a step of mechanical disruption using a bead beater (5 min, 20 beat/s) was made with 300 mg sterile glass beads (diameter ranging from 100 to 600 µm). After extraction, DNA quality and quantity were evaluated using the NanodropTM2000 (A260/280) and Qubit dsDNA Broad Range Assay Kit (Invitrogen, Carlsbad, USA) with a Qubit 3.0 Fluorometer (Invitrogen, Carlsbad, USA). DNA integrity was checked by 2% agarose gel electrophoresis. Samples were stored at -20 °C prior to microbiota analysis (qPCR and rRNA 16S Metabarcoding).

## Quantitative PCR

Total bacteria and *Olsenella scatoligenes* were quantified by qPCR using primers and conditions described in Table 2. Real-time PCR assays were performed in a CFX96 Touch Real-Time PCR Detection System (BIO-RAD Laboratories, California, USA) based on SYBR Green technology using Kit Takyon™ Low ROX SYBR® MasterMix dTTP Blue (Eurogentec, Seraing, Belgium). Each reaction was run in duplicate in a final volume of 10 µL with 5 µL of MasterMix, 0.45 µL of each primer

(10 µM), 1 µL of DNA sample, and 3.1 µL of ultra-pure water. Amplifications were carried out using the following ramping profile: 1 cycle at 95 °C for 5 Min, followed by 40 cycles of 95 °C for 30 s, 58 °C for 30 s, 72 °C for 30 s. A melting step from 58 to 95 °C was added to ensure primer specificity. Standard curves were generated from tenfold dilutions of bacterial DNA isolated from a bio-reactor sample or extracted from *Olsenella scatoligenes* (DSMZ 28304: SK9K4), allowing the calculation of DNA concentrations from extracted samples [31].

## 16S Metabarcoding and data analysis

Bacterial V3-V4 region of 16S ribosomal DNA and the archaeal 16S rDNA were amplified with primers described in Table 2. Amplicons were generated using a Fluidigm Access Array followed by high-throughput sequencing on an Illumina MiSeq system (Illumina, USA) performed at the Carver Biotechnology Center of the University of Illinois (Urbana, USA). Bioinformatic analysis was performed by Plateforme GeT-Biopuces / Metatoul using R software version 4.1.1 (2021-08-10) and rANOMALY package [36]. Demultiplexed raw sequences data in fastq format were quality filtered using the package rANOMALY based on DADA2 [37]. Reads with N bases or low phred quality score (less than or equal 2) were eliminated and reads under 100 pb length were removed. Decontamination steps were carried out to filter out sequences corresponding to PhiX DNA used as a spike-in control for MiSeq runs. Filtered sequences were dereplicated and cleaned for chimeras (DADA2). Remaining sequences were dereplicated and amplicon variant sequences (ASVs) were inferred using the dada function from dada2 package. A table with ASVs counts for each sample and representative sequences of ASVs in fasta format were generated. Taxonomic affiliation of all ASVs were performed with idtaxa function from DECI-PHER package [38] using SILVA release 138 [39] and

<table><caption>**Table 2** Primers used for qPCR and 16S Metabarcoding analysis of samples collected in the adult MPigut-IVM</caption><thead><tr><th>Primer Name</th><th>Sequence 5' → 3'</th><th>Target</th><th>Annealing temperature</th><th>References</th></tr></thead><tbody><tr><td colspan="5">qPCR primers</td></tr><tr><td>BAC338R</td><td>ACTCCTACGGGAGGCAG</td><td rowspan="2">Total bacteria</td><td rowspan="2">58 °C</td><td rowspan="2">[32]</td></tr><tr><td>BAC516F</td><td>GTATTACCGCGGCTGCTG</td></tr><tr><td>OscF</td><td>CTTACCAGGGCTTGACATCTTGG</td><td rowspan="2">O. scatoligenes</td><td rowspan="2">54 °C</td><td rowspan="2">[33]</td></tr><tr><td>OscR</td><td>ACGACACGAGCTGACGACAG</td></tr><tr><td colspan="5">Metabarcoding Primers</td></tr><tr><td>V3_F357_N</td><td>CCTACGGGNGGCWGCAG</td><td rowspan="2">Bacteria</td><td rowspan="2">-</td><td rowspan="2">[34]</td></tr><tr><td>V4_R805</td><td>GACTACHVGGGTATCTAATCC</td></tr><tr><td>Arch349F</td><td>GYGCASCAGKCGMGAAW</td><td rowspan="2">Archaea</td><td rowspan="2">-</td><td rowspan="2">[35]</td></tr><tr><td>Arch806R</td><td>5GGACTACVSGGGTATCTAAT</td></tr></tbody></table>
GTDB bac120_arc122 [40] databases (60% bootstrap cut-off). At this step, to improve completeness of taxonomic affiliation, we carried out alignments using BLAST [41] (98% identity and coverage) on representative sequences of unassigned ASVs or assigned with high taxonomy rank such as Genus, Family, Order and higher. A phylogenetic tree was constructed based on ASVs representative sequences using functions from phangorn package [42]. The α analysis was processed, with number of observed amplicon sequence variants (ASVs) and Shannon index. Then for β-diversity, principal coordinate analysis (PCoA) was performed followed by constraint redundancy analysis (RDA) with microenvironment, donor and time as variables.

## Gas analysis

Analysis of oxygen (O₂), nitrogen (N₂), carbon dioxide (CO₂), methane (CH₄), and dihydrogen (H₂) from the bioreactor atmospheric phase was performed using a 490 Micro-gas chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with two columns, Molecular Sieve 5A and PoraPlot U, coupled with micro thermal conductivity detector. Argon was used as the carrier gas. Gas composition was determined using calibration curves made from ambient air (78.09% N₂, 20.95% O₂, 0.04% CO₂) and three gas mixtures A (5% CO₂, 5% H₂, 90% N₂), B (19.98% CO₂, 80.02% H₂), and C (19.89% CO₂, 19.88% CH₄, 20% H₂, 40.23% N₂). Technical replicates were performed for each sample and results were expressed as relative percentages [43].

## Short-chain fatty acid analysis

Fecal samples suspended into phosphate buffer and supernatants collected from Luminal samples were centrifuged at 18,000×g for 15 Min at 4 °C. Pellets were discarded and 900 µL of supernatant were diluted at 9/10 in H₂SO₄ 0.04M and filtered (0.22 µm). Concentrations of the three major SCFAs (acetate, propionate and butyrate) were quantified using high performance liquid chromatography (HPLC) (Elite LaChrom, MERCK-HITACHI, San Jose, CA, USA) coupled with a diode-array detector. The HPLC column (Corogel 87H FA, 150 mm, Concise Separations, San Jose, CA, USA) and its guard column were maintained at 50 °C. Sulfuric acid 0.04 M was used as mobile phase and SCFAs were separated at a flow rate of 0.6 mL/min. Data were obtained and analyzed by the EZChrom Elite software at 205 nm. SCFAs concentrations were calculated from standard curves established from known concentrations of acetate, propionate and butyrate (0, 10, 25 and 40 mM) [43] and data expressed as mM or relative percentages.

## Skatole and indole dosage

Analysis of skatole and indole concentrations in the luminal samples were performed by ELFI Analytik GbR (Germany) as subcontractor. Method used is based on the stable isotope dilution analysis. Firstly, isotopically labeled standards of the target compounds were added to the samples to serve as internal references. The samples were then homogenized in water, filtered and transferred to headspace vials. Sampling was achieved using Solid-Phase Microextraction, which allows for the efficient extraction of volatile compounds from the headspace of the vials. Finally, the extracted compounds were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS: Varian 450 GC coupled to a 240 Ion trap MS).

## Statistical analyses

Regarding statistical processing of datasets, all graph showing quantity or concentration (e.g. SCFA, gas, redox, qPCR, indole/skatole) are means of the experimental values with error bars based on standard errors of the mean. For the statistical comparison between control (days 5, 6, 7 for metabolic activities or days 4, 6, 7 for microbiota activities) and treatment phases (days 18, 19, 20), we assess normal distribution of data before applying appropriate statistical analyses (either t-test or Wilcoxon test).

## Results

### Characterization of pig stools used to inoculate the adult MPigut-IVM

Stool samples from two adult pigs (castrated males) aged 24 and 29 weeks were collected to inoculate the adult MPigut-IVM bioreactors (Fig. 1). Fecal inoculum was characterized in terms of microbiota composition and main SCFAs concentrations (Fig. 2). Metabarcoding analysis revealed similar bacterial patterns between the two pigs at the phylum level (Fig. 2A). Fecal microbiota was dominated by *Bacteroidota* (50–55% abundance) and *Firmicutes* (30%). At the family level (Fig. 2B), inter-individual differences were evidenced, with higher relative abundances of *Prevotellaceae* and *Lactobacillaceae* in Pig 1, and higher proportions of *Spirochaetaceae* and *Streptococcaceae* in Pig 2. Very few sequences associated to *Archaea* were quantified in the fecal sample of Pig 1 (<1000 sequences), compared to around 8,500 sequences in Pig 2 (Fig. 2C). *Archaea* were mostly identified as members of *Methanobacteriaceae* and *Methanomethylophilaceae* families. Regarding bacterial diversity (Fig. 2D), a higher number of observed ASVs was quantified in Pig 2 compared to Pig 1 (388 *versus* 431), but similar Shannon
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Characterization of pig stool samples used to inoculate the adult MPigut-IVM. Stool samples were collected from two healthy adult pigs (Pig 1 & 2). Microbiota composition was assessed by 16S Metabarcoding. Relative abundance of the main bacterial populations was determined at the phylum (A) and family (B) levels. *Archaea* methanogens were also followed and results expressed as OTU numbers (C). Observed ASVs and Shannon index were presented as alpha-diversity indexes (D). The three main short-chain fatty acids, i.e., acetate, propionate and butyrate, were measured as end-products of microbial fermentation in stool samples and given as relative percentages (E)</figcaption></figure>

index values were found (around 5). Lastly, relative abundances of major SCFA (i.e., acetate, propionate and butyrate) were quite similar between the two pigs,
except a higher percentage of propionate to the expense of acetate in Pig 2 compared to Pig 1 (Fig. 2E).

### Adaptation of the in vitro colon model to the specific adult pig digestive conditions

Based on a large literature survey on healthy adult pig physiology (around 100 publications), we adapted the physicochemical parameters of the in vitro colon model, initially developed to reproduce piglets' digestive conditions, to the specific digestive conditions of adult pigs. A search by target keywords was carried out on PubMed (https://pubmed.ncbi.nlm.nih.gov/) and Google Scholar (https://scholar.google.com/) to collect in vivo data regarding pig colonic physiology (temperature, pH, transit time and ileal content). This Literature research was not restricted in terms of body weight or feed. In the MPigut-IVM set-up to reproduce healthy adult pig conditions, body temperature was kept at 39 °C. Based on averaged in vivo colonic pH values found for adult pigs in the literature [21, 25, 44–59], pH was set-up at 6.4, compared to pH 6 for piglets [27]. Based on our literature review [44, 47, 52, 59–72], we also calculated an average large intestinal transit time of 60 h in adult pigs. When applied to the in vitro model, this led to a residence time (time for half-renewal of fermentation medium) of 30 h (compared to 20 h or 24 h for piglets [27, 28], in accordance with a larger size and higher feed volume ingested. The nutritive medium simulating porcine ileal effluents (Table 1) was adapted from that previously developed for piglets [27, 28]. Its composition was determined to fit porcine adult diet and ensure 4% Lipids, 18% proteins and 48% carbohydrates. Quantity of nutrients supplied in vitro to the bioreactors correspond to the fraction that has been not digested and absorbed in the upper pig gut. Consequently, digestibility indices of each ingredient were also considered when designing the nutritive medium, based on in vivo data [18, 44, 73, 74]. In practice, the amounts of nutrients delivered into the adult MPigut-IVM were estimated as the difference between feed intake and ileal digestibility. Porcine mucins were also added to the nutritive medium to simulate this endogenous intake of proteins and sugars. Compared to the piglet model, the proportion of insoluble fibers (cellulose, arabinogalactan, xylan) was increased to fit with feeding requirements of adult pig [75].

### In vitro stabilization of colonic microbiota composition and activities

Two bioreactors from MPigut-IVM model set-up with healthy adult pig physicochemical and nutritional parameters were inoculated in parallel with a pig fecal sample (two biological replicates). Gut Microbiota activity and composition were followed daily to determine the time

period necessary for Microbiota stabilization. The stabilization state was reached at day 5–6 based on individual SCFAs percentages and concentrations (Fig. S1, Fig. 3A and B), gas (Fig. S2, Fig. 3C and D) profiles and volume. After stabilization, similar percentages of the three main SCFAs, i.e., acetate, propionate and butyrate were found in Pig 1 and Pig 2, with around 60, 25 and 15%, respectively. As expected, anaerobiosis was efficiently supported by the sole activity of resident microbiota with percentages of O₂ maintaining below 2% throughout continuous fermentations (Fig. 3C). Stabilized gas profiled were slightly different between the two pigs, with more CH₄ in Pig 2 than in Pig 1 (around 50% versus 30%), but less CO₂ (around 20% versus 50%). Redox potential showed a quick decrease during the first 24 h of fermentation (data not shown) and stabilized from day 4–6 at lower values in Pig 2 (-275 mV) compared to Pig 1 (-250 mV) (Fig. 3E). Total bacteria quantification stabilized from day 2 at around 10¹⁰ copies of 16S rDNA/g in the Luminal compartment and 10⁸ copies of 16S rDNA/g in the mucosal phase (Fig. S3, Fig. 4A). At the phylum and family levels (Fig. S4, Fig. 4B and C), Microbiota composition stabilized respectively at day 2 and day 6–7 in the Luminal compartments. In the mucosal phase, stabilization was more difficult to reach, with still variations in population abundances between day 4 and day 6. However, RDA of bacterial β-diversity indicated that stabilization was reached from day 4 in the in vitro colon model at the ASV level in both luminal and mucosal phases (Fig. 5C). Microbial stabilization in the MPigut-IVM model was also associated to a decrease in microbial richness and evenness (Fig. 5A) compared to the initial stool (Fig. 2D), as previously reported in other in vitro models [31, 76]. The stabilization was also associated with a shift from fecal (Fig. 2A and B) to colonic profiles (Fig. 4B and C), mainly characterized by a decrease in *Bacteroidota* and *Spirochaetota* and an increase in *Proteobacteria* in accordance with in vivo data [77, 78]. At the family level, an increase in *Succinivibrionaceae* and *Lachnospiraceae* relative abundances was also observed compared to fecal profiles, in both luminal and mucosal compartments (Fig. 4C). Of interest, we also showed that both colonic microenvironments were colonized by methanogenic *Archaea*, mostly represented by *Methanobacteriaceae* and *Methanomethylophilaceae* families in Pig 1 and Pig 2, respectively (Fig. 6). Higher *Archaea* ASVs numbers were found in the luminal compared to mucosal compartment (around 8,000 versus 6,000 ASVs in Pig 1 and Pig 2 founded). When detected, *Archaea* populations were stabilized from day 6 (Fig S5). Taken together, all those data showed that 6 to 7 days are necessary to stabilize the pig microbiota composition and metabolic activities in the MPigut-IVM. Lastly, comparison of the two technical
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Stabilization of microbial metabolic activities in the healthy adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run under continuous fermentation for microbiota stabilization (up to day 7). Samples were regularly collected during fermentations in the bioreactors and in the atmospheric phase. Results of the 3 last days of the two technical replicates are represented as a mean (days 5, 6 and 7). The three main SCFAs, i.e., acetate, propionate and butyrate, were measured in the luminal phase and results expressed as relative percentages and concentrations (A and B). Composition of atmospheric gases is given as relative percentages (C) and total gas production was also measured and provided in mL (D). Redox potential (continuously monitored) is represented in mV (E). Bars (B, D and E) represent standard error of the mean of the two bioreactors. SCFA short chain fatty acid. CH₄ methane, CO₂ carbon dioxide, H₂ hydrogen, N₂ dinitrogen, O₂ dioxygen</figcaption></figure>
replicates (up to day 7) in Pig 1 and Pig 2 revealed very
consistent results between replicates regarding SCFA
(Fig. S1), gas (Fig. S2) and total bacteria (Fig. S3) but
slight differences in bacterial composition (Fig. S4). The
highest variations between technical replicates were seen
for *Archaea* populations, certainly due to a low num-
ber of reads in some samples (Fig. S5). The repeatability
between technical replicates proves the validity of our
experimental setup and enables further testing of simple
sugars in two bioreactors in parallel after the stabilization
phase.

Capture of in vivo inter-individual variability and colonic region-specificity

The MPigut-IVM was inoculated with stool samples from
two different pigs (Pig 1 and Pig 2) in order to capture
interindividual variabilities in gut microbes. Principal
Coordinates Analysis (PCoA) of bacterial β-diversity
confirmed that a strong donor effect was kept in the
in vitro gut model in both the luminal and mucosal
compartments (Fig. 5B). At the family level, as an exam-
ple, *Hungateiclostridiaceae* population was only found
in Pig 1, while *Rhodobacteraceae* and *Rhodocyclaceae*
ones were specifically present in Pig 2. About methano-
genic *Archaea*, they were detected only in the stool of
Pig 2 (Fig. 2C) and a higher number of *Archaea* ASVs
was accordingly found in the MPigut-IVM at the end of
stabilization in the Luminal phase of Pig 2 *versus* Pig 1
(Fig. 6C). This is fully in accordance with a higher pro-
portion of CH₄ in atmospheric gases in Pig 2 compared
to Pig 1, reaching more than 50% *versus* around 30% at
day 6–7 (Fig. 3C). Fermentation activities seemed also
to differ between Pig 1 and Pig 2 at the end of stabiliza-
tion, as shown through differences in total SCFA concen-
trations (Fig. 3B), total gas volume (Fig. 3D) and redox
potential (Fig. 3E). Interestingly, the presence of a mucin
beads compartment connected to the main bioreactors
in the MPigut-IVM allowed to keep a significant differ-
ence (*P* < 0.001) between lumen and mucus-associated
gut microbes (Fig. 5D), both in Pig 1 and in Pig 2 (data
not shown). At the phylum level, *Bacteroidota* were less
abundant in the mucosal phase compared to the luminal
one in both pigs, for the benefit of *Spirochaetota* in Pig 1
and *Proteobacteria* in Pig 2 (Fig. 4B). At the family level,

*Spirochaetaceae*, *Fibrobacteraceae*, *Megasphaeraceae* and *Muribaculaceae* were enriched in the mucosal phase compared to the Luminal compartment in both Pig 1 and Pig 2 (Fig. 4C). Regarding methanogenic *Archaea*, clear different abundances of *Methanobacteriaceae* and *Methanomethylophilaceae* were observed between the two colonic Microenvironments, but only in Pig 2.

Validation of the newly developed in vitro model through in vitro-in vivo comparisons

To validate the newly developed in vitro MPigut-IVM
model, comparisons were performed between our
in vitro results (the last days of the stabilization phase)
and in vivo data collected in the colon of healthy adult
pigs. Key parameters reflecting both microbial metabolic
activities and composition were compared in Table 3 and
Table 4, respectively. Our in vitro model demonstrated a
high degree of correlation with in vivo findings regard-
ing metabolic activities, in terms of SCFA composition
and concentrations and redox potential values (Table 3).
No data was found in the literature on gas composition
and volume in healthy adult pigs, preventing any in vitro-
in vivo comparison. Regarding microbiota composition
(Table 4), the 20 most abundant populations found in
the MPigut-IVM at the phylum and family levels were
compared to the 20 most abundant described in vivo in
pig colonic samples. All the most abundant phyla found
in vitro were also described in vivo, i.e., *Firmicutes*, *Bac-
teroidota*, *Proteobacteria*, *Synergistota*, *Spirochaetota*,
*Fibrobacterota*, *Fusobacteriota* and *Elusimicrobiota*. At
the family level, among the 20 most abundant bacterial
populations found in vitro, 6 were recovered in more
than 50% of in vivo studies (*Lachnospiraceae*, *Bacteroi-
daceae*, *Lactobacillaceae*, *Enterobacteriaceae*, *Clostridia
family* and *Prevotellaceae*), 10 were found in at least one
in vivo publication (e.g., *Acidaminococcaceae*, *Rikenel-
laceae*, *Succinivibrionaceae* and *Spirochaetaceae*) and 4
described in none of the articles (e.g., *Fibrobacteraceae*,
*Elusimicrobiaceae*). Unfortunately, up to now, no in vivo
study has followed methanogenic *Archaea* in the colon of
healthy adult pigs and we found only one paper describ-
ing the porcine mucus-associated microbiota. Despite
this lack of in vivo information, our in vitro-in vivo com-
parisons allowed us to confirm most of the parameters

(See figure on next page.)

**Fig. 4** Stabilization of lumen and mucus-associated bacteria in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run under continuous fermentation for microbiota stabilization (up to day 7). Samples were regularly collected in both the main bioreactor ('luminal') and mucin bead compartment ('mucosal'). Results of the 3 last days of the two technical replicates are represented as a mean (days 5, 6 and 7). Total bacteria A were quantified by qPCR and results expressed in mean log₁₀16S rDNA copies/g. Bars represent standard error of the mean of the two bioreactors. Mean relative abundances of the main bacterial populations, as determined by 16S Metabarcoding, are given at the phylum B and family C levels
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4** (See legend on previous page.)</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 5** Diversity of bacterial communities in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run under continuous fermentation for microbiota stabilization (up to day 7). Samples were regularly collected in both the Luminal and mucosal compartments. Results obtained at days 4, 6 and 7 are represented. Composition of lumen and mucus-associated Microbiota were determined by 16S Metabarcoding and α-diversity indexes, i.e., observed ASVs and Shannon, were calculated at the ASV level (A). Median values are given as box plots. PCoA analysis of bacterial diversity revealed a significant donor effect (B). Redundancy analysis (RDA) two-dimension plot visualizations of β-diversity highlights day (C) and colonic microenvironment effects (D).</figcaption></figure>

(See figure on next page.)

**Fig. 6** Stabilization of archaeal composition of luminal and mucosal compartments in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run under continuous fermentation for microbiota stabilization (up to day 7). Samples were regularly collected in both the Luminal and mucosal compartments. Results of the 3 last days of the two technical replicates are represented as a mean (days 5, 6 and 7). Mean relative abundances of main Archaeal populations at the phylum (A) and family (B) levels and mean ASV numbers (C) were determined by 16S Metabarcoding

followed in the MPigut-IVM, showing its potential for studying adult pig microbial colonic processes.

In vitro results obtained in the adult MPigut-IVM the 3 last days of the stabilization phase were compared to colonic in vivo data in pigs, for both luminal and mucus-associated Microbiota. This comparison was made with publications using Illumina sequencing and published after 2015 and considered both the number of available data and the correspondence between in vitro and in vivo data for the 20 most abundant populations. A color code

was used to indicate the level of concordance between in vitro and in vivo results, from high (green; concordance in more than 50% of available articles), to medium (orange; concordance of at least one article) and low (red; no concordance). ND = no data (grey).

### Impact of simple sugars on colonic microbiota activities and skatole/indole production

Once developed and validated, the adult M-Pigut-IVM was used to assess the effects of two simple sugars,
<figure><img src="image_10.png" /><figcaption>**Fig. 6** (See legend on previous page.)</figcaption></figure>
<table><caption>**Table 3** In vitro-in vivo comparisons of gut microbiota metabolic activity</caption><thead><tr><th>Units</th><th>Parameters</th><th>In vitro</th><th>In vivo</th><th>In vivo references</th></tr></thead><tbody><tr><td colspan="5">Atmospheric gases</td></tr><tr><td>%</td><td>CH<sub>4</sub></td><td><strong>43.1 ± 3.1</strong></td><td>ND</td><td>ND</td></tr><tr><td></td><td>CO<sub>2</sub></td><td><strong>36.6 ± 3.6</strong></td><td>ND</td><td>ND</td></tr><tr><td></td><td>H<sub>2</sub></td><td><strong>0.05 ± 0.01</strong></td><td>ND</td><td>ND</td></tr><tr><td colspan="5">Short Chain Fatty Acid</td></tr><tr><td>%</td><td>Acetate</td><td>59.8 ± 0.6</td><td>60</td><td>[20, 21, 25, 45, 57–59, 77, 89, 89–91]</td></tr><tr><td></td><td>Propionate</td><td>26.4 ± 0.4</td><td>22</td><td></td></tr><tr><td></td><td>Butyrate</td><td>13.8 ± 0.4</td><td>11</td><td></td></tr><tr><td></td><td>Other</td><td>Not measured</td><td>7</td><td></td></tr><tr><td>mM/day</td><td>Acetate</td><td>80 ± 16</td><td>68</td><td>[20, 45, 57, 59, 90]</td></tr><tr><td></td><td>Propionate</td><td>35 ± 6</td><td>26</td><td></td></tr><tr><td></td><td>Butyrate</td><td>18 ± 1</td><td>14</td><td></td></tr><tr><td></td><td>Total</td><td>133 ± 21</td><td>108</td><td></td></tr><tr><td colspan="5">Redox potential</td></tr><tr><td>mV</td><td></td><td>-255 ± 13</td><td>-285</td><td>[25, 51, 57, 58, 89, 92]</td></tr></tbody></table>

In vitro results obtained in the adult MPigut-IVM the 3 last days of the stabilization phase were compared to colonic in vivo data in pigs. Redox potential is the value measured with the redox sensor (AppliSens, Z061017080). Italic values indicate a good correspondence between in vitro and in vivo data, while no data are indicated by ND (bold)

namely lactose and lactose + sucrose, on main fermentation end-products (SCFAs and gases) and skatole/indole production. The last three days of the control period ('control') were compared to the last three days of the supplemented phase ('treatment'). These sampling points were considered as stable based on β-diversity analysis (lack of time effect). The two sugars had no significant effect on SCFA profiles and concentrations (Fig. 7A and B). On the contrary, lactose and lactose + sucrose both induced a raise in total gas produced (Fig. 7D), which reached significance for lactose only (307 mL under treatment compared to 148 mL under control conditions, *P* < 0.01). The two feedstuffs also led to a decrease in CH₄ percentages (35% versus 45% for lactose and 35% versus 38% for lactose + sucrose) to the benefit of CO₂ (Fig. 7C). Of interest, both sugars also induced a significant decrease in redox potential values (*P* < 0.001) (Fig. 7E). Addition of lactose and lactose + sucrose resulted in a drop in skatole concentrations, associated with an increase in indole (Fig. 8A). Even if non-significant, the most marked effect was the reduction of skatole levels by lactose + sucrose (2,972 ng/g under treatment versus 4,264 ng/g under control conditions). Treatment with simple sugars was associated with a slight decrease in *Olsenella scatoligenes* levels (*P* > 0.05), only in the luminal phase (Fig. 8B).

## Impact of simple sugars on colonic microbiota composition and diversity

In addition to microbial activities, we also followed the impact of the two simple sugars on microbiota composition in the MPigut-IVM. Lactose and lactose + sucrose led to a reduction in bacterial diversity, as evidenced by significantly lower observed ASVs (*P* < 0.05) both in the luminal and mucosal phases (Fig. 9A). The highest decrease (up to two-fold with 200 ASVs under treatment compared to 400 ASVs under control condition) was found with lactose + sucrose treatment in the mucin beads. Similar effects were observed on Shannon index, but reaching significance only with lactose treatment (Fig. 9B). RDA corroborated those results, with a clear separation between the control and treatment groups (Fig. 9C), showing the impact of the two feedstuffs on bacterial diversity. Lactose and lactose + sucrose had no significant effect on total bacterial both in the luminal and mucosal compartments (Fig. 10A), but impacted bacterial profiles at both the phylum (Fig. 10B) and family (Fig. 10C) levels. Those changes were confirmed by differential analysis of bacterial composition between control and treatment (Fig. 11). At the phylum level, the two sugars induced a significant increase in *Synergistota* both in the luminal and mucosal compartments. In addition, in mucosal phase only, lactose led to a significant drop in *Spirochaetota* and *Fusobacteriota*, together with an increase in *Patescibacteria* (Fig. 11A). Lactose + sucrose decreased *Fibrobacterota* in mucin beads but increased *Elusimicrobiota* in the luminal compartment (Fig. 11B). At the family level, lactose and lactose + sucrose treatments were associated to significant increases in *Acidaminococcaceae*, *Dethiosulfovibrionaceae*, *Enterobacteriaceae*, *Rhodospirillales* family and decreases in *CAG-74* family in both luminal and mucosal phases (Fig. 11). In both colonic microenvironments, we also observed a reduction of *Succinivibrionaceae* abundance with lactose (Fig. 11A) and decrease in *Oscillospiraceae* with lactose + sucrose (Fig. 11B). Lastly, our results indicated a slight impact of lactose and lactose + sucrose on Archaeal abundances both at the phylum (Fig. 10D) and family (Fig. 10E) levels. Differential analysis confirmed the lack of effects of feed sugars tested on Archaea methanogens, showing no population significantly affected by both treatments (data not shown).

## Discussion

Following the guidelines of the “3Rs” rule [93], which promotes the reduction of animal used for research purposes and the development of in vitro alternative strategies, this study aims to set-up and validate through in vitro-in vivo comparisons, a dynamic in vitro model of the healthy adult pig colonic ecosystem (adult MPigut-IVM)
<table><caption>**Table 4** In vitro-in vivo comparisons of bacterial and archaeal pig populations</caption><thead><tr><th>Microbial population<br>(Old nomenclature)</th><th colspan="2">In vivo data</th><th>In vivo references<br>(colonic compartment, pig)</th></tr><tr><th></th><th>Luminal<br>Mucosal</th><th></th><th></th></tr></thead><tbody><tr><td><strong>Archaea</strong></td><td></td><td></td><td></td></tr><tr><td><em>p_Methanobacteriota</em><br><em>f_Methanobacteriaceae</em></td><td>ND</td><td>ND</td><td>Found in none of the publications</td></tr><tr><td><em>p_Thermoplasmatota</em><br><em>f_Methanomethylophilaceae</em></td><td>ND</td><td>ND</td><td>Found in none of the publications</td></tr><tr><td><strong>Bacteria</strong></td><td></td><td></td><td></td></tr><tr><td><em>p_Bacillota</em> (Firmicutes)<br><em>f_Lachnospiraceae</em></td><td></td><td></td><td>[14, 77–87]</td></tr><tr><td><em>f_Acidaminococcaceae</em></td><td></td><td>ND</td><td>[77–86]</td></tr><tr><td><em>f_Oscillospiraceae</em></td><td></td><td></td><td>[80]</td></tr><tr><td><em>f_Megasphaeraceae</em></td><td></td><td></td><td>[14, 77, 78, 87]</td></tr><tr><td><em>f_Acutalibacteraceae</em></td><td></td><td></td><td>[80]</td></tr><tr><td><em>f_Clostridia_UGC-014_family</em></td><td></td><td></td><td>[14, 77–85, 87]</td></tr><tr><td><em>f_Lactobacillaceae</em></td><td></td><td>[93]</td><td>[14, 77–81, 83–85, 87]</td></tr><tr><td><em>f_Selenomonadaceae</em></td><td></td><td>ND</td><td>Found in none of the publications</td></tr><tr><td><em>p_Bacteroidota</em> (Bacteroidetes)<br><em>f_Bacteroidaceae</em></td><td></td><td></td><td>[14, 77–87]</td></tr><tr><td><em>f_Prevotellaceae</em></td><td></td><td>ND</td><td>[14, 78, 81–83, 85]</td></tr><tr><td><em>f_Rikenellaceae</em></td><td></td><td></td><td>[79–81]</td></tr><tr><td><em>f_Muribaculaceae</em></td><td></td><td></td><td>[80]</td></tr><tr><td><em>p_Pseudomonadota</em> (Proteobacteria)<br><em>f_CAG-239</em></td><td></td><td></td><td>[14, 77, 78, 81, 83–87]</td></tr><tr><td><em>f_Enterobacteriaceae</em></td><td></td><td>ND</td><td>[14, 77, 78, 83, 84, 87]</td></tr><tr><td><em>f_Succinivibrionaceae</em></td><td></td><td></td><td>[78]</td></tr><tr><td><em>p_Synergistota</em> (Synergistetes)<br><em>f_Dethiosulfovirionaceae</em></td><td></td><td>ND</td><td>[77, 87]</td></tr><tr><td><em>p_Spirochaetota</em> (Spirochaetes)<br><em>f_Spirochaetaceae</em></td><td></td><td>ND</td><td>[77–86]</td></tr><tr><td><em>p_Fibrobacterota</em> (Fibrobacteres)<br><em>f_Fibrobacteraceae</em></td><td></td><td>ND</td><td>[77, 79–86]</td></tr><tr><td><em>p_Fusobacteriota</em> (Fusobacteria)<br><em>f_Fusobacteriaceae</em></td><td></td><td>ND</td><td>[78, 85]</td></tr><tr><td><em>p_Elusimicrobiota</em> (Elusimicrobia)<br><em>f_Elusimicrobiaceae</em></td><td></td><td>ND</td><td>[14, 77, 78]</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>**Fig. 7** Impact of simple sugars on microbial metabolic activities in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run for 20 days under continuous fermentation, including 7 days of Microbiota stabilization, followed by a 13 days treatment with lactose or lactose + sucrose (from day 8 to day 20). Samples were regularly collected during fermentations in the main bioreactors and the atmospheric phase. Results obtained for the two pigs (Pig 1 & 2) the 3 last days of stabilization ('control') and 3 last days of treatment with simple sugars ('treatment') were averaged. The three main SCFAs, i.e., acetate, propionate and butyrate, were measured in the luminal phase and results expressed as mean relative percentages (A) and mean concentrations in mM (B). Composition of atmospheric gases is given as mean relative percentages (C) and mean total gas production was provided in mL (D). Mean redox potential values are given in mV (E). Bars represent standard error of the mean (3 days for the 2 pig donors). Statistical differences were tested by Wilcoxon test and indicated by ** (P < 0.01) and **** (P < 0.0001), ns non-significant, SCFA short chain fatty acid, CH4 methane, CO2 carbon dioxide, H2 dihydrogen, N2 dinitrogen, O2 dioxygen, Lact lactose</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 8** Impact of simple sugars on skatole/indole production in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run for 20 days under continuous fermentation, including 7 days of Microbiota stabilization, followed by a 13 days treatment with lactose or lactose + sucrose (from day 8 to day 20). Samples were regularly collected during fermentations in the main bioreactors. Results obtained at Days 0 and 7 ('control') and Days 10, 14 and 20 ('treatment') are represented as a mean for the two pigs (Pig 1 & 2). Mean concentrations in indole and skatole are given in ng/g (A). *Olsenella scatoligenes* was quantified by qPCR and results expressed as mean log₁₀ 16S rDNA copies/g (B). Bars represent standard error of the mean (2 or 3 days for the 2 pig donors). Statistical differences were tested by Wilcoxon test and indicated by ns = non-significant, *Lactose*</figcaption></figure>

and apply it for the testing of two simple sugars. This
was accomplished through a comprehensive literature
review (around 100 publications), aiming to define phys-
icochemical (pH and transit time), nutritional (composi-
tion of simulated ileal effluents containing nutrients and
bile acids), and microbial (composition and functionali-
ties of gut microbes) parameters specific to the healthy
adult pig colon. To date, most of the systems (7 out of 14)
developed to reproduce the porcine colonic environment
are static batch models [11, 18–23]. In comparison to the
adult MPigut-IVM, such systems show the advantages to
be easy to use and inexpensive, but they are significantly
simplified related to physiological conditions, excluding
digestive regionalization and dynamism (Table 2-Addi-
tional file 7).

Moreover, experiments are limited in time (up to 72 h),
restricting assays to short-term analysis and prevent-
ing in vitro simulation of chronic ingestion of any com-
pound of interest. Other dynamic models are currently
available with various levels of complexity, Cositec [25],
BioFlo 110 [94], PolyFermS [95], SLIM [26] and Baby-
SPIME [96]. Compared to CoMiniGut and Baby-SPIME,
the adult MPigut-IVM has the limitation to reproduce
only the colonic compartments and does not integrate
the pig upper digestive tract (Table 2-Additional file 7).

In addition, it does not simulate, contrarily to the SLIM,
the passive absorption of fermentation products, which
is a key feature in gut homeostasis. However, our new
in vitro colon model exhibits the unique feature to main-
tain anaerobiosis by the sole activity of resident micro-
biota, allowing to be closer to the in vivo situation and
enabling follow-up of atmospheric gases. Moreover, our
colonic pig model is the only one to distinguish lumen
from mucus-associated microbiota, aiming to recreate
the colonic microenvironments described in vivo [97].
As previously observed in human and canine in vitro
gut models, adding a mucosal compartment allows to
capture a higher bacterial diversity from the fecal micro-
biome [27, 29, 31, 98]. It was particularly helpful in main-
taining taxa from *Fibrobacteraceae* and mucin-degrading
bacteria from *Muribaculaceae* families [99]. Interestingly,
the adult MPigut-IVM allows to capture specific bacte-
rial populations found in individual pig microbiome, by
using different and un-pooled porcine fecal samples. This
advantage is lost in many pig models where fecal samples
used for inoculation are pooled [26, 94]. Keeping varia-
bilities associated to an individual (e.g., age, weight) or its
breed is of high importance, since they are acknowledged
as important features in pig gut microbiota and health
[10]. However, using pooled fecal inoculum can have
<figure><img src="image_13.png" /><figcaption>**Fig. 9** Impact of simple sugars on bacterial diversity in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run for 20 days under continuous fermentation, including 7 days of Microbiota stabilization, followed by a 13 days treatment with lactose or lactose + sucrose (from day 8 to day 20). Samples were regularly collected during fermentations in the luminal and mucosal compartments. Composition of lumen and mucus-associated Microbiota were determined by 16S Metabarcoding and alpha-diversity indexes, i.e., observed ASVs (A) and Shannon (B) were calculated at the ASV level. Results obtained for the two pigs (Pig 1 & 2) the 3 last days of stabilization ('control') and 3 last days of treatment with simple sugars ('treatment') were averaged. Bars represent standard error of the mean. Significant differences are indicated (*, P < 0.05) as assessed by Wilcoxon test, ns = non-significant. Redundancy analysis (RDA) two-dimension plot visualizations of bacterial beta-diversity highlights treatment effects (C). *Lactose*</figcaption></figure>

methodological advantages, leading to model broader
population-representative trends. To fully ensure the rel-
evance of the new adult MPigut-IVM, our in vitro results
on colonic microbiome were compared with pig in vivo
data extracted from the literature. Such in vitro-in vivo

comparisons have never been made in previous studies
aiming to develop pig colonic model. Regarding Microbi-
ota structure, the adult MPigut-IVM allowed to maintain
in vitro the dominant bacterial phyla inhabiting the pig
colon, and only 4 among the 20 most abundant families
detected in vitro were found in none of the in vivo studies (Table 4). However, the paucity of in vivo data on mucus-associated microbes in pigs [88] hampered the ability to fully validate our model regarding complex interactions at the mucosal interface. At the functional level, in vitro-in vivo comparisons revealed very relevant results regarding both main SCFAs concentrations and profiles (Table 3). Of interest, this in vitro model also provided unique data on methanogenic *Archaea* and atmospheric gases in the large intestine of pigs, which have not been yet described in vivo in the literature. Especially, methanogenic *Archaea* represented by *Methanobacteriaceae* and *Methanomethylophilaceae* families were found in our adult MPigut-IVM. By forming symbiotic relationships with bacteria, *Archaea* are able to regulate the energy metabolism and digestive functions of the host. A recent paper showed a strong correlation between the presence of *Archaea* in fecal samples and economically important traits in swine [100].

Once developed and validated, the adult MPigut-IVM was applied to assess the effect of feed ingredients by testing two simple sugars, namely lactose and lactose + sucrose, on colonic microbiota composition and metabolic activities, as well as their ability to modify skatole levels in the context of boar taint reduction [10]. These feedstuffs were selected based on their low cost, simple production process and previous evidences regarding their effects on piglet or pig growth performance [101]. In the present study, lactose and lactose + sucrose impacted pig colonic microbiota composition at both the phylum and family levels, without affecting the total bacterial load. Most changes in microbiota profiles were common to the two feedstuffs (e.g., increase in *Acidominococaceae*, *Dethiosulfovibrioaceae*, *Enterobacteriaceae* and *Rhodospirillales*), but others were specific, such as a decrease in *Succinivibrioaceae* with lactose and an increase of *Clostridia* families with lactose + sucrose. Those variations were most of the time observed both in the luminal and mucosal compartments of the MPigut-IVM. Up to now, no study has evaluated the effect of lactose and/or sucrose on mucus-associated microbes in vivo in the adult pig. However, some previous works have assessed the impact of sugar

beet pulp (containing sucrose but also various sources of fibers) on colonic microbiota profiles in pigs [81, 86, 87, 102, 103], highlighting a decrease in *Enterobacteriaceae* abundance, whereas this population increased in our in vitro model. Even if this family contains pathogenic members, it is also composed of many commensal strains that can compete with pathogens by occupying similar ecological niches in the gut [104]. In addition to Microbiota structure, we also investigated the effect of the two sugars on metabolic activities, showing no effect on SCFAs concentrations, but an increase in gas production. Contrarily to our data, Diao et al. showed in growing pigs that a diet containing 25% beet pulp increased the SCFA content from ileum to colon (e.g. acetate, butyrate) [102]. It is important to emphasize that the increased SCFA concentrations observed in previous in vivo studies using sugar beet pulp should likely be attributed to its fiber content rather than to sucrose alone. This may explain the observed differences in metabolic outputs between our in vitro data and the results from Diao et al. Regarding gases, a batch fermentation assays inoculated with pig fecal samples and supplemented with beet pulp showed a similar increase in gas volume after 24 h [105].

The next step of this work was to investigate the effects of the feedstuffs on skatole production in the in vitro colon model. Of note, we used in this study castrated males that seem to exhibit similar level of fecal skatole than entire pig males, but obvious different microbiota profiles [106]. The pig colonic microbiota plays a crucial role in the metabolism of dietary tryptophan into indole or skatole (3-methyl-indole) [10]. The formation of skatole is undesirable due to its recognized role in boar taint [10], while indole production appears as beneficial since it has anti-inflammatory and antimicrobial properties [107, 108]. Whereas many bacteria, like members from *Enterobacteriaceae* family [109], are able to metabolize tryptophan into indole or indole acetic acid (the key precursor of skatole), only a few specialized gut bacteria, mainly from the *Clostridium* and *Bacteroides* genera, catalyze the steps from indole acid acetic to skatole [10]. Consequently, as evaluated here, one of the possible strategies to reduce skatole production is to redirect these bacteria towards consuming lactose

(See figure on next page.)

**Fig. 10** Impact of simple sugars on microbiota composition in the adult MPigut-IVM. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run for 20 days under continuous fermentation, including 7 days of Microbiota stabilization, followed by a 13 days treatment with lactose or lactose + sucrose (from day 8 to day 20). Samples were regularly collected during fermentations in the luminal and mucosal compartments. Results obtained for the two pigs (Pig 1 & 2) the 3 last days of stabilization ('control') and 3 last days of treatment with simple sugars ('treatment') were averaged. Total bacteria were quantified by qPCR and results given in mean log₁₀ 16S rDNA copies/g (A). Mean relative abundances of the main Microbial populations, as determined by 16S Metabarcoding, are given at the phylum and family levels, for bacteria (B and C, respectively) and Archaea (D and E). Statistical differences were tested by Wilcoxon test, ns = non-significant. Bars represent standard error of the mean. *Lac* lactose
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>**Fig. 10** (See legend on previous page.)</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Fig. 11** Differential analysis of microbiota composition between control and treatment with feed additives. Adult MPigut-IVM was inoculated with stool samples from two healthy adult pigs (Pig 1 & 2) and run for 20 days under continuous fermentation, including 7 days of Microbiota stabilization, followed by a 13 days treatment with lactose or lactose + sucrose (from day 8 to day 20). Samples were regularly collected during fermentations in the luminal and mucosal compartments. Results obtained for the two pigs (Pig 1 & 2) the 3 last days of stabilization ('control') and 3 last days of treatment with feed additives ('treatment') were averaged. Differential analysis highlights bacterial populations significantly impacted by treatment with lactose (A) or lactose + sucrose (B), with at least one method among the three used (DeSeq2, Metacoder and MetagenomeSeq R-analysis). *Treat* treatment; *Ctrl* control; *Lact* lactose</figcaption></figure>

rather than tryptophan [110]. The second approach, that was tested in the present study was to assess potential synergic effects between lactose and sucrose, based on

two previous works showing that beet pulp containing sucrose decreased skatole concentration and production rate by microbiota in pig colonic content [111, 112]. Here,
both feedstuffs moderately reduced skatole and increased indole concentrations (without reaching significance), with greater effect with lactose + sucrose. Our results are in line with the studies from Li et al. and Xu et al. showing that sugar beet pulp, chicory roots and fructo-oligosaccharides increased indole production at the expense of skatole [15, 111, 113]. Mechanisms of action are not clearly described, but they are most probably related to changes in microbiota compositions that favorably impact metabolic pathways leading to a decrease in skatole production (e.g., decrease in *Clostridium* with fructo-oligosaccharides). In our study, changes in indole/ skatole concentrations were associated with a slight reduction in the levels of the skatole-producing bacteria *O. scatoligenes* [114]. This diminution is most probably not linked to a direct effect of the two sugars on the bacteria, but rather to changes in available niches due to the bloom in other populations such as *Enterobacteriaceae*, *Dethiosulfovibrionaceae* or *Clostridia*. Of note, *O. scatoligenes* is able to use as substrates both lactose and sucrose [114], doesn't produce indole [114] and skatole production by the bacteria should be suspended with a sufficient supply of fermentable carbohydrates [15]. Skatole can be produced by other bacteria from *Clostridium* and *Lactobacillus* genus [114] that have not been specifically quantified in the present study, such as *Clostridium drakei* and *Clostridium scatologenes* which produce skatole directly from tryptophan [115]. Here, lactose + sucrose led to the highest decrease in skatole concentrations, but also to a significant increase in *Clostridia* and *Enterobacteriaceae* family relative abundance, which suggest that further investigations are needed to better understand, at the species level (not reached by our sequencing technology), which bacteria are responsible for the observed effects. Beside modifications in microbiota composition, another possible explanation for the effects of lactose + sucrose might be that resident bacteria would preferentially use those simple sugars instead of tryptophan, leading to a reduction in skatole production [110].

## Conclusions

To conclude, the present study reported the development and validation of a new in vitro model simulating the main physicochemical, nutritional and microbial parameters of the healthy adult pig colon, with unique features such as distinct lumen and mucus microenvironments and physiological anaerobiosis. Our research also provided novel insights on the effect of two simple sugars, namely lactose and lactose + sucrose, on pig colonic microbiota composition and metabolic activities, as well as skatole production, which is an important marker in meat quality. Despite obvious limitations, such as lack of host immune responses

and epithelial cells interactions, but also the low number of biological replicates performed in this study, the adult MPigut-IVM represents a powerful platform for nutritional studies in the pig digestive environment. The in vitro model can be useful to perform preliminary screening of innovative feed strategies to improve pig health and help to elucidate their mechanisms of action in relation with colonic microbiota composition and metabolic activities, by offering a controlled and reproducible environment. Different feed formulations after appropriate upper gut in vitro digestion, as well as bioactive compounds such as prebiotics, probiotics or synbiotics, can be advantageously tested in the MPigut-IVM. Such kind of comparative feed studies will be highly relevant to the feed and livestock industry. This system will also facilitate progress towards personalized nutrition by capturing interindividual and breed-specific variations in the pig gut microbiome.

## Abbreviations

<table><tr><td>ASV</td><td>Amplicon sequence variant</td></tr><tr><td>CH<sub>4</sub></td><td>Methan</td></tr><tr><td>CO<sub>2</sub></td><td>Carbon dioxide</td></tr><tr><td>DNA</td><td>Deoxy ribonucleic acid</td></tr><tr><td>GC-MS</td><td>Gas chromatography-mass spectrometry</td></tr><tr><td>H<sub>2</sub></td><td>Dihydrogen</td></tr><tr><td>MPigut-IVM</td><td>Mucosal pig gut in vitro model</td></tr><tr><td>N<sub>2</sub></td><td>Nitrogen</td></tr><tr><td>O<sub>2</sub></td><td>Oxygen</td></tr><tr><td>PCoA</td><td>Principal coordinates analysis</td></tr><tr><td>RDA</td><td>Redundancy analysis</td></tr><tr><td>SCFA</td><td>Short chain fatty acid</td></tr><tr><td>qPCR</td><td>Quantitative polymerase chain reaction</td></tr></table>

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s42523-025-00465-1.

Additional file 1.

Additional file 2.

Additional file 3.

Additional file 4.

Additional file 5.

Additional file 6.

Additional file 7.

## Acknowledgements

We acknowledge MEDIS technical team for their assistance and Mr RONGIER's farm where stool sampling was performed.

## Author contributions

CDU: Conceptualization, methodology, formal analysis, writing of the original manuscript, prepared all figures, writing—review and editing, visualization.

SD: Conceptualization, methodology, supervision. NL: Conceptualization, methodology, supervision. CDE: writing—review and editing, visualization.

OU: Training and technical help. MB: Training and technical help. DG: Conceptualization, methodology, supervision. SBD: Conceptualization, methodology, writing—review and editing, supervision. All the authors reviewed and approved the final manuscript.
## Funding

This research was funded by MIXSCIENCE Company.

## Availability of data and material

The datasets generated and/or analysed during the current study are available in the Sequence Read Archive database with accession number no. https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1127821. Data is provided within the manuscript or supplementary information files.

## Declarations

### Ethics approval and consent to participate

The experimental procedures were performed in accordance with the guidelines established by the European Community Council under the Directive 2010/63/EU. Ethical review and approval were not required for the animal experimentation due to the non-invasive nature of the procedures described in this study (fecal sampling). All procedures were made in compliance with relevant animal welfare regulations.

### Consent for publication

Not applicable.

### Competing interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Noëmie Lemoine and David Guillou are employees from Mixscience company.

Received: 1 April 2025 Accepted: 1 September 2025

Published online: 01 October 2025

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