Digestive parameters and gut microbiota load and composition along the in vivo piglet gastrointestinal tract

Abstract

The increased attention toward the role of the gut microbiome in health and disease for both animals and humans has fueled the demand for more relevant and accurate research models. In this study, we present an overview of biochemical and microbial parameters measured throughout the digestive tract of ten TopigsNorsvin × German Piétrain piglets to better understand the in vivo dynamics of digestive and fermentative processes in different gastrointestinal segments, as pigs are suggested to be a representative animal model for the human gastrointestinal tract. Our key findings include region-specific and significantly differing (P < 0.001) pH profiles, with the stomach having the lowest pH (3.36 ± 0.72) and the ileum the highest (7.24 ± 0.18). Dry matter content also varied significantly (P < 0.001), with the stomach having the highest (27.8 ± 2.4%) and the duodenum the lowest (10.6 ± 0.7%). The average total transit time was 12 hr and 45 min ± 1 h and 42 min. Enzyme activities (pepsin, trypsin, amylase) showed interindividual differences. Amino acid levels varied among piglets, with total concentrations averaging 7.04 × 10² ± 2.29 × 10² µg mL⁻¹ in the duodenum, 1.19 × 10³ ± 2.69 × 10² µg mL⁻¹ in the jejunum and 9.39 × 10² ± 2.54 × 10² µg mL⁻¹ in the ileum. Bile acid concentrations varied strongly between piglets, with high levels in the gall bladder and varying levels throughout the digestive tract. Short-chain fatty acid concentrations increased significantly (P < 0.001) along the digestive tract, with the highest levels in the large intestine. The microbial load increased consistently (P < 0.001) along the digestive tract, with the highest loads in the rectum (6.82 × 10¹⁰ ± 2.88 × 10¹⁰ cells mL⁻¹). The highest microbial diversity was observed in the lower intestine (i.e., caecum, colon and rectum), with significant shifts in microbial community composition, especially from the ileum to the caecum. This study provides valuable insights into the digestive and microbiological parameters of the porcine gut, confirming the pig’s relevance as a model for gastrointestinal research. The findings can inform the development of in vitro or ex vivo models, reducing ethical constraints of animal studies and aiding in the assessment of dietary interventions on gut health.

This study is a detailed description of biochemical and microbial parameters of the digestive tract of TopigsNorsvin × German Piétrain piglets. We report on transit time, enzymatic activity, amino acid profile and the composition and functional output of the gut microbiota.

Graphical Abstract

Graphical Abstract.

Introduction

Gastrointestinal disorders are highly prevalent and form a large, costly, health-care challenge (Chang, 2004; Bernstein, 2017; Peery et al., 2019). Disturbances in the composition and activity of the gut microbial community are often associated with health status and may play a large, if not crucial, role in many diseases. Despite extensive advances in the field of gut microbiome research, insight in the mechanisms behind microbiota-health or microbiota-disease relationships often remains insufficiently clear. These issues can, at least in part, be attributed to a lack of accurate and relevant research models (Rose et al., 2022) which are required to conduct mode-of-action research. Such methodological roadblocks may also slow down the development of preventive or therapeutic strategies for beneficially modulating gut health.

Animal models are generally included in the development of gastrointestinal therapeutics or during gastrointestinal research (Ziegler et al., 2016; Rose et al., 2022). Usually, rodents are the animal model of choice, due to their low cost, rapid reproduction rate, relative ease of handling, the availability of analytical tools. (Ziegler et al., 2016) However, with respect to gastrointestinal research, crucial differences in physiology and anatomy between humans and rodents frequently impede accurate mimicry of clinical and pathologic signs of human diseases (Ziegler et al., 2016). Even nonhuman primates, for example the Cebus Capuchin, have been found to harbor a gut microbial community vastly different from its human counterpart (Firrman et al., 2019). Thus, there remains a need for a relevant animal model that can be applied to study the gut microbiota and its interactions with the mammalian cells.

The pig is an animal model which is becoming increasingly important for biomedical, toxicological, and pharmaceutical research (Wylensek et al., 2020; Henze et al., 2021). Additionally, for gastrointestinal research, pigs may constitute a valuable model system, because their digestive tract is more similar to humans in terms of anatomy and physiology as compared with the rodent gut. Some of these similarities between the pig and the human gastrointestinal tract include 1) similar intestinal-length-to-body-weight ratio that approximately equals 0.1, 2) similar small intestinal villus structure and epithelial cell types, 3) similar transit time, and 4) similar colonic anatomy including the presence of taeniae coli resulting in haustra or sacculation (Ziegler et al., 2016; Rose et al., 2022). Furthermore, the porcine microbial community composition is more similar to humans as compared with rodents, showing enhanced Bacteroides and Bifidobacterium abundance, with the pig already been successfully used as a model to study human dietary interventions (as reviewed in Roura et al., 2016). This is also particularly relevant when considering engraftment success of human candidate biotherapeutic strains. However, it must be mentioned that also some important differences exist between the pig and human digestive system, such as 1) a larger porcine caecum in which significant fermentation can already take place (even though the colon remains the main site of bacterial fermentation as in humans; Roura et al., 2016), 2) the spiral conformation of the colon, and 3) the absence of the appendix in the pig (Ziegler et al., 2016; Rose et al., 2022).

In order for pigs to function as a relevant model for gastrointestinal research, all relevant features of their digestive tract should be well characterized. Several of the physiological and biochemical attributes have been described previously by several researchers (Martens et al., 2019; Roura et al., 2022,Low et al., 2020; Martens et al., 2020,Roura et al., 2022). However, a comprehensive analysis of the physicochemical, digestive, enzymatic, and microbiome properties along the entire gastrointestinal tract in one and the same study is lacking in the literature. There is for instance no account of the complete bile acid profile throughout the digestive tract, or the difference between microbiota composition from the luminal and mucosal environments in the porcine intestine (Bergamaschi et al., 2020; Wylensek et al., 2020; Luo et al., 2022). Protein digestibility, and thus amino acid levels, is usually only measured at the end of the ileum and not throughout the different small intestinal sections (Fan et al., 1994, 1995; Stein et al., 1999; Wu et al., 2018), and digestive enzyme activities are typically not measured during duodenal transit (Lindemann et al., 1986; Hedemann et al., 2006; Yu et al., 2019; Shi et al., 2022).

This study therefore aimed at addressing several of these knowledge gaps. To that end, ten 9-wk-old piglets were fed with a standard experimental diet for 3 wk, after which they were sacrificed and the entire gastrointestinal tract was sampled to determine gut transit time, residing pH-values, digestive enzyme activities, amino acid and bile acid profiles as well as the activity and composition of the gut microbiota both in the luminal and mucosal environment. Availability of these digestive and microbiological parameters will deliver crucial information to better understand the in vivo dynamics of digestive processes and microbial subpopulations in different gut microenvironments. Yet, it may also facilitate a more accurate development of ex vivo model systems using the porcine gastrointestinal tract, resulting in the possibility to conduct more detailed mode-of-action research in the field of nutritional digestion, enteropathogenic behavior, and/or the role of the endogenous microbiota for host health modulatory processes.

Materials and Methods

Ethics approval

The study was conducted in accordance with the ethical standards and recommendations for accommodation and care of laboratory animals covered by the European Directive 2010/63/EU on the protection of animals used for scientific purposes and the Belgian royal decree KB29.05.13 on the use of animals for experimental studies.

Animal model and sampling

Ten 9-wk-old piglets (six male and four female; TopigsNorsvin × German Piétrain) were housed in two pens (2.10 m² per pen, with full slatted floor), each consisting of five animals. Both pens contained three male and two female piglets, so that each pen had an equal number of both sexes. They were fed an experimental diet as mash (Supplementary Table S1) ad libitum for 3 wk. The start of the feeding period, at 9 wk of age, was considered day 0. On day 21, all piglets were first weighed and then euthanized by electronarcosis followed by exsanguination.

Subsequently, abdomens were opened and the gastrointestinal tract removed from stomach to rectum. The length of the small intestine (SI) was measured and the gall bladder was excised. The gastrointestinal tract was then separated into the stomach, duodenum (i.e., part between 0% and 10% of SI-length), jejunum (i.e., part between 10% and 90% of SI-length), ileum (i.e., part between 90% and 100% of SI-length), caecum, proximal colon (using a portion of 20 cm collected at 50% of proximal colon-length), distal colon (using only a portion of 20 cm collected at 50% of distal colon-length), and rectum. Digesta were collected from each of these sections, including the complete content of the gall bladder, and pH was determined. After washing the different parts of the gastrointestinal tract with 0.9% (w/v) saline, mucosal swabs were collected from the SI (at 50% and 95% of SI-length), caecum, proximal colon and distal colon, and suspended in 2-mL filter sterilized (0.22 μm) phosphate-buffered saline (PBS; 8.8 g/L K₂HPO₄, 6.8 g/L KH₂PO₄, 8.5 g/L NaCl). Cryoprotectant solution, prepared as described in Hoefman et al. (2013), was added in a 1:1 ratio and aliquots were frozen at −80 °C.

An aliquot of the digesta of each gastrointestinal region was collected for dry matter. Another aliquot was destined for transit time marker analysis following freeze-drying, whilst the remaining digesta of each section were suspended in sterile PBS. Suspended digesta were further aliquoted for different analysis purposes. Aliquots for short-chain fatty acid (SCFA) analysis were frozen at −20 °C. Aliquots for analysis of enzymatic activity, lactate levels, and ammonium concentrations were first centrifuged (5 min at 7,700 g) and supernatant frozen at −20 °C. Aliquots for bile salt and amino acid analysis were centrifuged (5 min at 7,700 g), filtered (except for gall bladder samples), and frozen at −20 °C. For 16S rRNA gene amplicon sequencing, aliquots of luminal digesta were centrifuged (5 min at 7,700 g) and the pellet flash frozen. For flow cytometry, cryoprotectant solution, prepared as described in Hoefman et al. (2013), was added in a 1:1 ratio and aliquots were frozen at −80 °C. Finally, rectal samples were cryopreserved for future microbial inoculation purposes using the same methods as described in Duysburgh et al. (2023).

Dry matter content

Dry matter content was determined by oven drying at 103 °C until constant weight (ISO 6496:1999).

Transit time measurement

On day 14 of the experiment, animals of pen 1 (pig 1 to 5) were given a fixed amount of feed containing 2% Cr₂O₃ (green color marker) from 8 a.m. onwards. The animals were then monitored for the first appearance of green-colored faeces. Time between feeding and the first appearance of green-colored faeces was considered the total transit time. The pigs in pen 2 were also provided with green marker feed. However, due to technical difficulties, it was not possible to record the initial appearance of green-colored faeces.

Biochemical analysis

Amino acid analysis

Digesta samples obtained from the duodenum, jejunum, and ileum were subjected to metabolomics analysis (Metabolon, Durham, NC, USA) in order to determine free amino acid concentrations through absolute quantification. Twenty-four amino acids, including alanine, arginine, asparagine, aspartic acid, glutamic acid, glutamine, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, trans-4-Hydroxyproline, ornithine, citrulline, cysteine, and cysteine, were determined using liquid chromatography with tandem mass spectrometry (LC-MS/MS) following the method described by Niewczas et al. (2014).

Bile acid analysis

Suspended digesta samples from the gall bladder, duodenum, jejunum, ileum, caecum, proximal colon, and distal colon were subjected to targeted metabolomics analysis (Metabolon, Durham, NC, USA) for the determination of bile acid composition. In each sample, 15 bile acids, including cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycocholic acid (GCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), glycoursodeoxycholic acid (GUDCA), taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), taurodeoxycholic acid (TDCA), taurolithocholic acid (TLCA), tauroursodeoxycholic acid (TUDCA), and glycolithocholic acid (GLCA), were determined using LC-MS/MS following the method reported by Shumar et al. (2019) and Rivera-Andrade et al. (2022) and expressed per gram of digesta fluid.

Enzymatic activity analysis

The activity of the digestive enzymes amylase, chymotrypsin, lipase, pepsin, and trypsin were measured in the digesta from the stomach, duodenum, jejunum, and ileum.

Amylase and lipase were measured using commercial kits (ab102523 for amylase and ab102524 for lipase, Abcam, Cambridge, UK) according to manufacturer’s instructions.

Pepsin activity was determined using a modified version of the haemoglobin assay as described in Anson (1938). Briefly, 500 µL of a 2% (m/v) haemoglobin solution (pH = 2) was incubated at 37 °C for 4 min. Subsequently, 100 µL of digested samples was added and the mixture was incubated for 10 min at 37 °C, after which the reaction was stopped by adding 1 mL of 5% trichloroacetic acid (TCAA) in each tube. Samples were centrifuged at 6,000 g to precipitate haemoglobin and absorbance in the supernatant was measured at 280 nm using a Synergy HT microplate reader (BioTek Instruments, Winooski, VT, USA). A standard curve and a blank, in which pepsin was added after TCAA-addition, were included to allow for calculation of pepsin activity.

$${\displaystyle \begin{array}{l}{\displaystyle \frac{\mathrm{U}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{s}}{\mathrm{m}\mathrm{L}}=\frac{[A280\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}-A280\mathrm{b}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{k}]x1000}{\left(\mathrm{\Delta }txX\right)},}\end{array}}$$ (1)

$\mathrm{\Delta }\mathrm{t}$ : duration of the reaction (10 min)

X: concentration of pepsin powder in the final reaction mixture (mg/mL)

Trypsin activity was measured using a protocol adapted from Brown and Wold (1973)and (Chen et al., 2009). First 2.6 mL of tris-acetate-EDTA (TAE) buffer (10 mL/L TAE 50×, 1.28 g/L CaCl₂) was combined with 300 µL of tosyl arginine methyl ester (TAME)-solution (3.788 g/L TAME) and incubated for 4 min at 25 °C. Next, 100 µL of digesta sample was added and dynamic absorbance at 247 nm was measured on a Synergy HT microplate reader (BioTek Instruments, Winooski, VT, USA) every 50 s for 10 min. Trypsin activity was calculated using formula 2, based on the slope of the absorption curve. A standard curve and a blank condition, without addition of trypsin, were included to allow for calculation of trypsin activity.

$${\displaystyle \begin{array}{l}{\displaystyle \frac{\mathrm{U}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{s}}{\mathrm{m}\mathrm{L}}=\frac{[\mathrm{\Delta }247\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}-\mathrm{\Delta }247\mathrm{b}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{k}]\times 1000\times 0.2}{(540\times X)},}\end{array}}$$ (2)

ΔA247: slope of the initial linear portion of the curve for the test and the blank

X: concentration of trypsin powder in the final reaction mixture (mg/mL)

540: molar extinction coefficient of TAME at 247 nm

0.2: Volume (in mL) of the reaction mix

Chymotrypsin activity was determined as modified from (Hummel, 1959). First 1.5 mL of TAE buffer (10 mL/L TAE 50×, 5.56 g/L CaCl₂) was combined with 1.4 mL of benzoyl-L-tyrosine ethyl ester (BTEE) and incubated at 25 °C for 4 min. Then, 100 µL of digesta sample was added and dynamic absorbance at 256 nm was measured every 50 s for 10 min of a Synergy HT microplate reader (BioTek Instruments, Winooski, VT, USA). Chymotrypsin activity was determined using formula 3 based on the slope of the absorption curve. A standard curve and a blank condition, without addition of chymotrypsin, were included to allow for calculation of chymotrypsin activity.

$${\displaystyle \begin{array}{l}{\displaystyle \frac{\mathrm{U}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{s}}{\mathrm{m}\mathrm{L}}=\frac{[\mathrm{\Delta }256\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}-\mathrm{\Delta }256\mathrm{b}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{k}]\times 1000\times 0.2}{(964\times X)},}\end{array}}$$ (3)

ΔA256: slope of the initial linear portion of the curve for the test and the blank

X: concentration of chymotrypsin powder in the final reaction mixture (mg/mL)

964: molar extinction coefficient of BTEE at 256 nm

0.2: Volume (in mL) of the reaction mix

Microbiome analysis

Short chain fatty acid, lactate, and ammonia analysis

Samples obtained from the duodenum, jejunum, ileum, caecum, proximal colon, distal colon, and rectum were analyzed for the determination of microbial metabolic activity, including measurement of SCFA (acetate, propionate, and butyrate), branched chain fatty acids (bCFA; sum of isobutyrate, isovalerate, and isocaproate), lactate, and ammonium. SCFA and bCFA were extracted using diethylether and were subsequently detected on a GC-2014 capillary gas chromatograph (Shimadzu, Hertogenbosch, Netherlands) equipped with a GC SGE capillary column (30 mm × 0.32 mm ID-BP 21 × 0.25 μm, Achrom, Machelen, Belgium) and coupled to a flame ionization detector and split injector, as previously described in De Weirdt et al. (2010). Lactate concentrations were determined using a commercially available kit (Enzytec Liquid D-/L-Lactic acid kit; R-Biopharm, Darmstadt, Germany) and measured on an iCubio iMagic-M9 (Shenzhen iCubio Biomedical Technology Co. Ltd., Shenzhen, China) according to manufacturer’s instructions. Ammonium analysis was performed using a KjelMaster K-375 device (Büchi, Hendrik-Ido-Ambacht, The Netherlands). Briefly, addition of 32% NaOH liberated ammonium from the sample in the form of volatile ammonia, which was further distilled into a 2% boric acid solution. The ammonium in the distillate was quantified titrimetrically with a 0.02 M HCl solution (Duysburgh et al., 2019).

Bacterial load

Samples for measurement of bacterial load were collected from the duodenum, jejunum, ileum, caecum, proximal colon, distal colon, and rectum. In the intestines, both luminal and mucosal samples were collected, as described above. Total bacterial cell densities were determined using flow cytometry, as previously described by Van den Abbeele et al. (2022).

Microbial community composition

Samples for microbial profiling, using 16S rRNA gene amplicon sequencing, were collected from the duodenum, jejunum, ileum, caecum, proximal colon, distal colon, and rectum. In the intestines, both luminal and mucosal samples were collected, as described above.

Microbial community composition in the samples was investigated using 16S rRNA gene amplicon sequencing on an Illumina Miseq platform. First, DNA from all samples was extracted using the Qiagen DNeasy PowerSoil Pro kit. Barcoded PCR primers annealing to the V1-V2 region of the 16S rRNA gene (Forward: 5’-AGAGTTTGATCCTGGCTCAG-3’; Reverse: 5’-TGCTGCCTCCCGTAGGAGT-3’) were used for library preparation. PCR reactions were carried out in quadruplicate using Q5 High-Fidelity DNA Polymerase (NEB, Ipswich, MA). Each PCR reaction contained 0.5 µM of each primer, 0.34 U Q5 Pol, 1× Buffer, 0.2 mM dNTPs, and 2.5 µL DNA in a total volume of 25 µL. Cycling conditions were as follows: 1 cycle of 98 °C for 1 min; 20 cycles of 98 °C for 10 s, 56 °C for 20 s, and 72 °C for 20 s; 1 cycle of 72 °C for 8 min. After amplification, quadruplicate PCR reactions were pooled and then purified using a 1:1 volume of SPRI beads (GE Healthcare, Wilmington, DE). DNA in each sample was quantified using PicoGreen and pooled in equal molar amounts. The resulting library was sequenced on the Illumina MiSeq using 2 × 250 bp chemistry. Extraction blanks and DNA free water was subjected to the same amplification and purification procedure to allow for empirical assessment of environmental and reagent contamination. Positive controls, consisting of eight artificial 16S gene fragments synthesized in gene blocks and combined in known abundances, were also included (Kim et al., 2017; Weiss et al., 2021).

Bioinformatics and statistics

All further data processing, visualizations, and statistical analyses were performed in R version 4.4.1 (R Core Team, 2022). All data were visualized with ggplot2_3.3.5 and ggpubr_0.4.0 unless mentioned otherwise (Wickham, 2016; Kassambara, 2019).

The amplicon data were processed using QIIME2 (Bolyen et al., 2019). Read pairs were processed to identify amplicon sequence variants (ASVs) with DADA2 (Callahan et al., 2016). Taxonomic assignments were generated by comparison to the Greengenes reference database (McDonald et al., 2012), using the naive Bayes classifier implemented in scikit-bio (Bokulich et al., 2018). The obtained read count table from QIIME2 was loaded into R for data processing (v 4.4.1) (R Core Team, 2022). Singletons and ASVs present in less than 5% of the samples or with read counts not exceeding 0.5 times the number of samples were removed (McMurdie and Holmes, 2014). Relative abundances of the ASVs and genera were calculated from the read counts while using phyloseq (v 1.46.0) and were explored via bar plots using ggplot2 (v3.4.4) (McMurdie and Holmes, 2013; Wickham, 2016).

Principal coordinates analyses (PCoA) based on the Bray–Curtis dissimilarity measure were visualized on ASV level to explore community variation relating to different GIT regions (phyloseq_1.46.0) (McMurdie and Holmes, 2013; Wickham, 2016). The significance of the clustering of intestinal regions was tested using PERMANOVA tests, implemented with the adonis function in the vegan (v2.6-4) package (Oksanen et al., 2019).

The richness, Shannon and Simpson diversity indices for the microbial community were calculated based on the ASV-table using the estimate_richness and diversity function in the phyloseq and vegan (v2.6-4) package, respectively. Indices were plotted using ggplot2.

To evaluate which genera were differentially abundant between different intestinal regions, the DESeq2 package (v 1.38.3) was applied on the read count table at genus level. First, a genus level table was created from the read count table using the aggregate function (stats package v4.2.2). Then, a generalized and reduced model was created, on which the DESeq-function, incorporating a likelihood ratio test, was applied to test the significance of the models. The generalized model included only the factor Region, so the reduced model contained no factor. Low-count genera were subjected to an empirical Bayesian correction (Love et al., 2014). For pairwise comparison of consecutive intestinal regions, Wald tests were used after shrinkage of the Log2FoldChange (L2FC) values by means of the lfcShrink function with the adaptive shrinkage estimator from the ashr-package (Stephens, 2017). P-values were adjusted by means of a Benjamini–Hochberg procedure (Love et al., 2014). Results were visualized in volcanoplots, displaying the −log(adjusted P-value) versus the Log2FoldChange of each genus.

Kruskal–Wallis tests were performed to determine statistically significant differences between the gastrointestinal pH values, dry matter contents, SCFA concentrations, total and intact microbial cell concentrations, and proportional taxon abundances between gastrointestinal locations (stats_3.6.3). Subsequently, post hoc tests with Holm correction were used to compare gastrointestinal locations (paired two-sided Wilcoxon signed rank tests) (stats_3.6.3, ggpubr_0.6.0) (Kassambara, 2019; R Core Team, 2022). No statistical analyses were performed on the different bile acids as not all bile acids were consistently measured.

Results

Generic gastrointestinal characteristics exhibit region-specific profiles

The porcine gastrointestinal tract was characterized using ten 9-wk-old piglets that were slaughtered 21 d after onset of the study (at a total age of 12 wk). At slaughter, the mean body weight and SI-length of the piglets were 21.38 ± 3.07 kg and 14.46 ± 1.38 m, respectively (Figure 1A and B).

Figure 1.

Body weight (A) and small intestinal length (C) in ten 9-wk-old piglets after 21 d on an experimental diet. pH values (B) and dry matter content (D) in various gastrointestinal tract (GIT) regions (stomach, duodenum, jejunum, ileum, caecum, proximal colon, and distal colon). Statistically significant differences between GIT regions are indicated by different letters (P < 0.05, paired two-sided Wilcoxon signed-rank tests with Holm correction); identical letters indicate no significant differences (P > 0.05). Due to the limited availability of dry matter samples (2 for the duodenum and 4 for the ileum) in panel D, these samples were excluded from the statistical analysis.

The pH values showed region-specific and significant differences (Figure 1C, P = 1.20 × 10⁻¹⁰). The stomach had the lowest pH (3.36 ± 0.72), followed by a gradual increase in the duodenum (5.27 ± 0.32), the jejunum (6.50 ± 0.20, significantly higher than stomach, P = 0.041), and the ileum (7.24 ± 0.18). The pH then dropped in the caecum (5.90 ± 0.30) and remained stable in the proximal colon (5.98 ± 0.18, significantly higher than stomach, P = 0.041) and distal colon (6.10 ± 0.27, significantly higher than stomach, P = 0.041).

Dry matter content of the digesta significantly varied across the gastrointestinal tract (P = 1.91 × 10^-8). As shown in Figure 1D, the stomach had the highest dry matter content (27.8 ± 2.4 % (w/w)), followed by a significant decrease in the duodenum (10.6 ± 0.7 % (w/w)) and jejunum (12.7 ± 2.0 % (w/w)) (P = 0.041). The ileum and caecum had similar dry matter contents (13.9 ± 1.0 % (w/w) and 13.7 ± 0.9 % (w/w), respectively), while the proximal colon and distal colon showed a significant increase (19.1 ± 2.6 % (w/w) and 23.6 ± 3.1 % (w/w), respectively).

Average total transit time, as measured with the green chromium marker (Cr₂O₃), was 12 h 45 min ± 1 h 42 min across the five piglets of pen 1 (Supplementary Table S2). For two pigs, loose stool was observed at the moment of measurement.

Gastrointestinal digestive enzymatic activity, amino acid levels, and bile acid profiles portray interindividual differences among piglets

Digestive enzyme activity levels (pepsin, trypsin, and amylase) were measured on digesta samples throughout the upper digestive tract (Figure 2). The average pepsin activity in the stomach equaled 6.56 × 10³ ± 2.40 × 10³ U mL⁻¹_digesta. Pepsin activity varied among piglets, with a coefficient of variation of 37%, ranging from 1.43 × 10³ ± 6.55 × 10² U mL⁻¹_digesta for piglet 6 to 1.63 × 10⁴ ± 27.33 U mL⁻¹_digesta for piglet 7. The average amylase activity was 53.83 ± 61.07 U mL⁻¹_digesta in the duodenum, 169.71 ± 98.68 U mL⁻¹_digesta in the jejunum, and 98.62 ± 115.25 U mL⁻¹_digesta in the ileum. Average trypsin activity was 451.95 ± 100.58 U mL⁻¹_digesta in the duodenum, 591.78 ± 131.38 U mL⁻¹_digesta in the jejunum, and 432.93 ± 171.65 U mL⁻¹_digesta in the ileum.

Figure 2.

Activity of pepsin in the stomach (U mL⁻¹), amylase (U mL⁻¹), and trypsin (U mL⁻¹) in the duodenum, jejunum, and ileum of ten 9-wk-old piglets fed an experimental diet for 21 d. N = data not available (no sample collected).

To evaluate the amino acid levels in the digestive tract, measurements were taken from the duodenum, jejunum, and ileum of each piglet (Supplementary Figure S1). The relative amino acid profile, which reflects the composition of dietary protein, showed little fluctuations across the different intestinal regions and across piglets (Supplementary Figure S1B). However, the absolute amino acid levels varied among piglets (Supplementary Figure S1A), largely reflected by the high standard deviations obtained when averaging the total amino acid concentrations across piglets, resulting in 7.04 × 10² ± 2.29 × 10² µg mL⁻¹ in the duodenum, 1.19 × 10³ ± 2.69 × 10² µg mL⁻¹ in the jejunum, and 9.39 × 10² ± 2.54 × 10² µg mL⁻¹ in the ileum, thereby suggesting interindividual differences in the digestive and absorptive capacity of the different piglets.

To assess the porcine bile acid metabolism, concentrations of 15 different bile acids were measured throughout the digestive tract as well as in the gall bladder for each piglet (Figure 3). It was observed that the levels of the different individual bile acids varied strongly between piglets. In the gall bladder, high bile acids’ concentrations were detected, comprising the conjugated forms of all detected primary and secondary bile acids (i.e., GCDCA, TCDCA, GCA, TCA, GDCA, TDCA, GLCA, TLCA, GUDCA, and TUDCA). Highest levels were observed for the conjugated primary bile acid GCDCA, with concentrations ranging between 5.21 × 10³ µg mL⁻¹ and 1.15 × 10⁴ µg mL⁻¹ across piglets. Throughout the digestive tract, bile acid levels remained very low in the stomach region, followed by increasing concentrations being observed in the SI. Primary bile acids CDCA and CA were mainly enriched in the jejunum region, with concentrations ranging from 38.78 to 110.14 µg mL⁻¹ and from 3.85 to 10.72 µg mL⁻¹, respectively, across piglets. In the ileum region, CDCA and CA remained high, followed by a strong reduction toward the caecum region. In the caecum, concentrations of secondary bile acids LCA and UDCA spiked, resulting in levels of 0.12 to 0.56 µg mL⁻¹ and 0.14 to 0.77 µg mL⁻¹, respectively, across piglets. Highest concentrations of the secondary bile acid DCA, on the other hand, were already observed in the jejunum (0.67 to 3.57 µg mL⁻¹ across piglets), though remained detectable throughout the entire lower gastrointestinal tract. Indeed, in the proximal colon and distal colon, mainly unconjugated secondary bile acids (i.e., DCA, LCA, and to a lower extent UDCA) were detected.

Figure 3.

Concentrations (μg mL⁻¹) of 15 different bile acids throughout the GIT of 10 piglets fed an experimental diet for 21 d. Bile acid levels (µg mL⁻¹) in the gall bladder and the different regions of the gastrointestinal tract (i.e., stomach, duodenum, jejunum, ileum, caecum, proximal colon, and distal colon) of ten 9-wk-old piglets fed an experimental diet for 21 d. Average across donors per intestinal region is depicted for each bile acid as a dotted line (n = 10). Data in the gall bladder for piglet 3 are missing due to technical difficulties during sample analysis. No statistical analyses were performed on the different bile acids as not all bile acids were consistently measured.

SCFA concentrations significantly increase along the digestive tract

The total SCFA concentrations significantly varied along the gastrointestinal tract, as shown in Figure 4A (P = 3.38 × 10^-9). The lowest concentrations were found in the first segments of the SI, namely the duodenum (3.16 ± 1.62 μmol g⁻¹) and jejunum (2.81 ± 1.93 μmol g⁻¹). The ileum exhibited a significant increase in total SCFA concentrations to 10.70 ± 7.80 μmol g⁻¹ (P = 0.005). The highest SCFA concentrations were observed in the large intestine, starting with an almost 10-fold significant increase in the caecum (98.31 ± 26.08 μmol g⁻¹, P = 6 × 10^-4). The remainder of the large intestine, i.e., proximal colon and distal colon, and the rectum sample had similar concentrations, ranging from 106.45 ± 19.53 to 117.70 ± 23.71 μmol g⁻¹, with no significant differences observed among them.

Figure 4.

Acetate, propionate, butyrate, and total SCFA concentrations (μmol g⁻¹) in different regions of the gastrointestinal tract (GIT; i.e., duodenum, jejunum, ileum, caecum, proximal colon, and distal colon) and the rectum sample of ten 9-wk-old piglets fed an experimental diet for 21 d. Statistically significant differences between GIT regions are depicted by the letters a, b, and c (paired Wilcoxon signed rank tests with Holm correction). Identical letters indicate no significant differences (P > 0.05).

The acetate concentrations followed a similar pattern as the total SCFAs along the gastrointestinal tract (Figure 4A). The duodenum and jejunum had the lowest concentrations of 2.95 ± 1.75 and 2.77 ± 1.89 μmol g⁻¹, respectively, followed by a significant increase in the ileum to 8.50 ± 3.33 μmol g⁻¹ (P = 0.005). Acetate levels significantly increased in the caecum to 60.07 ± 15.43 μmol g⁻¹ (P = 6 × 10^-4). The proximal colon and the rectum sample had similar concentrations of acetate, ranging from 61.28 ± 10.95 to 64.57 ± 11.81 μmol g⁻¹, with no significant differences found between them.

Propionate and butyrate concentrations followed a similar pattern as the total SCFA concentrations, except in the ileum, where they did not differ significantly from the duodenum and jejunum (Figure 4A). The ileal concentrations of propionate and butyrate were 1.96 ± 5.17 and 0.20 ± 0.28 μmol g⁻¹, respectively, while the duodenal and jejunal concentrations were 0.05 ± 0.15 and 0.04 ± 0.13 μmol g⁻¹ for propionate and 0.06 ± 0.12 μmol g⁻¹ and below detection limit for butyrate, respectively. The concentrations of both propionate and butyrate increased significantly from the ileum to the caecum, reaching 23.77 ± 9.36 and 11.52 ± 4.20 μmol g⁻¹, respectively (P = 0.006 and 0.005, respectively). No significant differences were observed between the caecum and the proximal colon and distal colon as well as the rectum sample for either propionate or butyrate.

When looking at the proportional level of the different SCFA (Supplementary Figure S2), it was observed that acetate was the predominant SCFA produced in the SI, which was reflected in proportions of 84.56 ± 32.43% in the duodenum, 99.10 ± 2.30% in the jejunum, and 90.53 ± 18.46% in the ileum. Propionate and butyrate were detected in lower amounts and only in a limited number of piglets. The propionate proportion equaled 6.44 ± 19.33% in the duodenum (only detected in piglet 5), 0.90 ± 2.70% in the jejunum (only in piglet 8), and 7.61 ± 17.96% in the ileum (only in piglets 2, 4, and 8). The butyrate proportion was 5.48 ± 13.91% in the duodenum (only in piglets 5 and 9), below detection limit in the jejunum and 1.48 ± 2.35% in the ileum (only in piglets 2, 4, and 8). Propionate and butyrate proportions increased and remained consistent in the remainder of the gastrointestinal tract, including the rectum sample, being detectable for all piglets. Acetate proportions varied from 55.14 ± 4.09% in the rectum sample to 61.73 ± 0.05% in the caecum, whereas propionate proportions ranged from 22.71 ± 3.84% in the rectum sample to 24.37 ± 2.89% in the proximal colon. The butyrate proportions varied from 11.87 ± 3.12% in the caecum to 14.18 ± 3.37% in the distal colon.

BCFA levels (Figure 4B) increased gradually and significantly throughout the digestive tract (P = 4.47 × 10⁻¹¹). In the SI, bCFA levels were below 3.32 × 10^-2 ± 9.40 × 10^-2 μmol g⁻¹. From the caecum to the rectum, bCFA concentrations increased gradually from 0.99 ± 0.56 to 5.95 ± 2.27 μmol g⁻¹ (P = 2.27 × 10⁻⁴). Ammonium levels (Figure 4C) also significantly increased gradually throughout the digestive tract, from 9.60 ± 3.34 μmol g⁻¹ in the duodenum to 23.72 ± 5.84 μmol g⁻¹ in the distal colon (P = 4.12 × 10⁻⁴) and 38.09 ± 10.89 μmol g⁻¹ in the rectum (P = 4.12 × 10⁻⁴).

The concentration of lactate, a key intermediate of metabolite fermentation, was also measured in different regions of the gastrointestinal tract (Figure 4D). Lactate was below detection limit in the duodenum and jejunum, but increased to 1.20 ± 3.41 μmol g⁻¹ in the ileum. Lactate levels remained stable in the large intestine, ranging from 0.87 ± 1.33 to 1.31 ± 1.42 μmol g⁻¹, followed by a decrease to 0.09 ± 0.24 μmol g⁻¹ in the rectum.

Microbial load consistently increased along the digestive tract

The microbial load was measured in the luminal (digesta) and mucosal (retrieved with swabs after washing the GIT) environment throughout the GIT, as well as in the rectum sample (Figure 5A). The highest loads were noted in the luminal fraction, which significantly increased alongside the tract from 1.99 × 10⁸ ± 1.88 × 10⁸ in the duodenum to 6.82 × 10¹⁰ ± 2.88 × 10¹⁰ cells mL⁻¹ in the rectum (P = 5.34 × 10⁻¹¹, Figure 5A). The microbial load in the mucosal fraction was more than 100-fold lower than in the luminal fraction. Excluding the single measurement in the ileum, the microbial load ranged from 1.84 × 10⁷ ± 1.22 × 10⁷ cells mL⁻¹ in the proximal colon to 8.27 × 10⁶ ± 6.73 × 10⁶ cells mL⁻¹ in the distal colon. However, in the mucosal fraction, most microbial loads remained below the detection limit. No significant mucosal differences could therefore be found between gastrointestinal regions (Figure 5B).

Figure 5.

Bacterial cell concentrations (cells mL⁻¹) in luminal (A) and mucosal (B) suspensions from various GIT regions and rectum samples. Statistically significant differences between GIT regions are indicated by letters a–f (P < 0.05, paired two-sided Wilcoxon signed-rank tests with Holm correction); identical letters indicate no significant differences (P > 0.05). Due to the availability of only one mucosal ileum sample, it was excluded from the statistical analysis. (C) Principal Coordinate Analysis (PCoA) of ASV-level 16S rRNA gene amplicon sequencing data (Bray–Curtis dissimilarity) from lumen (left) and mucosa (right) samples across gastrointestinal tract (GIT) regions (duodenum, jejunum, ileum, caecum, proximal colon, distal colon, rectum) of ten 9-wk-old piglets after 21 d on an experimental diet (Supplementary Table S1). (D) Alpha-diversity (Chao1 and Shannon diversity index) of luminal samples in different GIT regions from the same piglets.

Microbial community composition shows highest microbial diversity in the lower porcine intestine

Based on 16S rRNA gene amplicon sequencing, 1,366 ASVs were identified in all samples, belonging to 90 genera, 48 families, and 12 phyla. PCoA plots based on the Bray–Curtis similarity showed that the porcine intestinal communities changed in composition throughout the gastrointestinal tract (Figure 5C). Especially for the luminal samples, a segregation between the different intestinal regions could be observed (P_permanova = 0.001). For the mucosal region, clustering was less obvious but nevertheless significant (P_permanova = 0.001). For both the luminal and mucosal samples, piglet 3 clustered separately from the other piglets.

The alpha-diversity was evaluated using the Chao1 richness and Shannon’s diversity index (Figure 5D). Overall, the highest alpha-diversity was observed in rectum samples. When focusing on the Chao1 index, a gradual increase in richness was observed throughout the gastrointestinal tract, with the lower intestine (i.e., caecum, proximal colon, and distal colon) showing higher bacterial diversity and richness compared with the different regions in the SI.

The microbial community composition was further assessed in the digestive samples obtained from the 10 piglets. The main phyla that were detected were Firmicutes, Proteobacteria, Bacteroidetes, Spirochaetes, Actinobacteria, and Tenericutes (Supplementary Figure S3). The main detected families were Lactobacillaceae, Ruminococcaceae, Streptococcaceae, Prevotellaceae, Lachnospiraceae, and unclassified Clostridiales (Supplementary Figure S4). When focusing on the 15 most abundant genera obtained in the microbiome of the different piglets (Figure 6), it was observed that the Lactobacillus genus took up the largest fraction of the microbial community for all piglets in every intestinal region, except for the rectum. An unclassified member of the Ruminococcaceae family and the Streptococcus genus were the second and third most prevalent genera, with the latter mainly residing in the different small intestinal regions, while the Ruminococcaceae member was more highly abundant in the large intestine. Furthermore, Prevotella was another bacterial group that was highly abundant in the rectum. Upon visualizing shifts in the microbial community composition along the digestive tract for both the luminal (Supplementary Figure S5) and mucosal (Supplementary Figure S6) environment using volcano plots, it was observed that the largest shift in microbiome composition occurred in the transition from the ileum to the caecum (i.e., significant enrichment of 43 genera in the caecal lumen and 58 genera in the caecal mucus).

Figure 6.

Proportional abundances of the 15 most abundant genera detected by 16S rRNA gene amplicon sequencing in luminal and mucosal (A) and rectum (B) samples along the different gastrointestinal tract (GIT) regions (i.e., duodenum, jejunum, ileum, caecum, proximal colon, distal colon, and rectum) of ten 9-wk-old piglets fed an experimental diet for 21 d (n = 10, with n = 1 per piglet). Higher level taxa are to be interpreted as unclassified genus belonging to the respective taxon.

Discussion

Here, we performed a thorough physicochemical, digestive, and microbial characterization of the porcine digestive tract. Our study was driven by the importance of the pig as a preclinical in vivo model for addressing research questions surrounding processes in the human gastrointestinal tract; yet, it is also of important consideration in the field of animal production where post-weaning stress may put burden on the developing animal by compromising gastrointestinal digestion efficiency and increasing susceptibility to enteropathogens.

In the current study, an average SI-length of 14.46 m was obtained across 10 piglets at 12 wk of age, which corresponds to generic gastrointestinal data reported in the literature for young pigs. Indeed, Laerke et al. described that the SI-length of a weaned piglet is on average 6.5-m long, while that of a fully grown pig is between 16- and 21-m long (Laerke and Hedemann, 2012). When comparing the SI-length of the piglets to that of neonatal infants, a longer absolute SI-length is obtained in the porcine gastrointestinal tract, though with a similar SI-length-to-body weight ratio as compared with neonatal infants. Indeed, in humans, SI-length-to-body weight ratio develops from ~0.7 m/kg in infants to 0.1 m/kg in adults (Bardwell et al., 2022; Rose et al., 2022). As we found an average ratio of 0.66 m/kg in piglets during the current study, it can be concluded that similar SI-length-to-body weight ratios were obtained.

Our findings of pH levels along the piglet gastrointestinal tract are following a similar pattern, yet somewhat lower, than a previous study that reported average pH-values of 4.4 in the stomach, 6.1 to 6.5 in the duodenum, 6.3 to 6.6 in the jejunum, 6.5 to 6.7 in the ileum, 6.1 in the caecum, 6.1 to 6.5 in the proximal colon, and 6.3 to 6.6 in the distal colon (Merchant et al., 2011). Our findings were more comparable with what has previously been described for piglets (Henze et al., 2021) and humans (Minekus, 2015; Delbaere et al., 2023). Interestingly, Henze et al. (2021) succeeded in measuring gastrointestinal pH-values from Landrace pigs using a swallowed telemetric capsule. The gastric pH values from our study (3.36 on average) are comparable to the initial gastric pH from pigs at the start of gastric digestion before values rapidly decrease (Henze et al., 2021). The fact that the piglets in our study had ad libitum access to feed prior to sacrifice may explain this slightly higher value. The increase in pH values from 5.2 in the duodenum to 7.2 in the ileum was quite consistent with the pH dynamics as observed by Henze et al. (2021). In terms of pH values in the lower gut, we found a fairly narrow pH range ranging from 5.9 to 6.1 going from caecum to proximal colon and distal colon, respectively. This is different from Henze et al. (2021) who found a larger range for colonic pH (5.2 to 7.8), though showing a similar profile as obtained in Michiels et al. (2023) where pH values ranged from 5.3 to 5.9 in the large intestine. In comparison with human pH values in the lower gut, the obtained distal colon pH of 6.1 was lower as compared with the human benchmark (6.1 vs 6.6 to 6.9) (Esra Ilhan et al., 2017).

The observed total transit time, i.e., 12 h 45 min on average, was substantially shorter than the range observed in adult pigs. Indeed, Wilfart et al. (2007) describe mean transit times of 1, 4, and 38 h in the stomach, SI, and colon of adult pigs, resulting in an average total transit time of 43 h. Mosenthin (1998) found porcine transit times of 2 to 16 h for stomach plus SI and 20 to 40 h for passage through the colon, indicating whole gut transit times of 22 to 56 h. It thus looks like the transit time in the piglets was shorter than described in literature, which could be due to their young age (Snoeck et al., 2004), composition of the administered feed (Martens et al., 2019) or gastrointestinal distress, which was observed for two piglets. The feed composition in our study, being mostly easily digestible, likely contributed to the shorter transit time. Previous studies have already established the significant impact of diet on the microbiome and transit time, particularly in humans (Basit et al., 2001; Frese et al., 2015; De Paepe et al., 2018; Minnebo et al., 2023). The lower outcome from our data compared with the other studies may also be explained by the fact that we worked with younger animals that were just weaned and had a lower body weight (i.e., 21.38 kg on average) compared with the 63 kg pigs from Low et al. (2020). However, it must be mentioned that compared with humans, the obtained total transit time remains quite short. Humans gut retention times range between 12 and 90 h, with an average of 24 h being typically accounted for (Minnebo et al., 2023).

Enzyme activity levels were difficult to compare to activity levels reported in literature for pigs (Gorrill and Friend, 1970; Lindemann et al., 1986; Morales et al., 2016; Liu et al., 2021), mainly due to the large variation in different enzyme assays, different activity unit definitions, and different methods of measurement (per gram protein, per gram digesta, or per gram pancreatic tissue). Furthermore, Lindemann et al. (1986) showed that diet has a direct impact on the secretion of enzymes within the gastrointestinal tract of pigs, which can introduce another confounding factor when comparing enzyme activity levels between studies. Indeed, during our current study, the piglets had ad libitum access to feed, which may have been responsible for the high degree of digestive enzyme activity observed. On the other hand, highly similar patterns with respect to bile acid metabolism were obtained within the current study as compared with previous reports. First, high levels of conjugated bile acids were detected in the gall bladder, which is the storage location of bile acids in the piglet body following synthesis in the liver (Eggink et al., 2018). Along the intestine, high levels of primary bile acids were observed during small intestinal transit, while total bile acids levels in the digesta plummeted from the caecum onwards, since most bile acids are reabsorbed at the end of the ileum (Guzior and Quinn, 2021). The fraction of primary bile acids that escapes reabsorption and enters the colonic regions is transformed into secondary bile acids through bacterial metabolization by specific microbial groups (Ipharraguerre et al., 2018; Sun et al., 2022). This is highly similar to humans, where a limited amount of bacterial species, such as Clostridium scindens and Clostridium hiranosis, can able to transform the primary bile salts into secondary bile salts through 7α-dehydroxylation resulting in the production of deoxycholic acid (from cholic acid) and lithocholic acid (from chenodeoxycholic acid) (Ridlon et al., 2016).

Many studies have reported the pivotal role of the gut microbiome and associated SCFA production for the modulation of host’ health, mainly through the diet (Yang and Zhao, 2021). In pigs, stimulation of microbial SCFA production has for instance been associated with improvement of body weight and feed intake (Wang et al., 2020), as well as affect fatty acid composition (Cristina et al., 2023). Acetate, propionate, and butyrate comprise the main SCFA produced by the gut microbial community in the lower gut of pigs, showing an abundance of 55% to 62%, 22% to 25%, and 11% to 15%, respectively, during the current study. These data correspond to the ones discussed by den Besten et al. (2013), which reported an acetate:propionate:butyrate ratio of 67:22:11 in the caecum of pigs (Den Besten et al., 2013). Furthermore, absolute SCFA concentrations obtained during the current study, corresponded with levels reported by Nakatani et al. (2018) (Nakatani et al., 2018), except for significantly lower acetate concentrations in the latter (i.e., maximum caecal levels of around 35 μmol g⁻¹, while levels of around 60 μmol g⁻¹ were obtained during the current study). However, younger animals were used during the study of Nakatani et al. (2018), which could explain the lower production. Overall, our study confirms that pigs exhibit significant caecal fermentation, which is in strong contrast with humans due to the absence of a distinct caecum. Indeed, up to 30% of the energy requirement for maintenance is obtained from SCFA production in the lower gut for adult pigs, while this is only ~7% for humans (Heinritz et al., 2013) However, as both pigs and humans show distinct microbial fermentation in the colonic regions, comparable dietary influences on butyrate production, the pig has been suggested as an interesting animal model to investigate effects on intestinal health for human applications (Heinritz et al., 2013).

Upon assessing microbial load along the gastrointestinal segments of 10 piglets, we observed a gradual increase in small intestinal bacterial cell density from the duodenum (5.16 × 10⁷ cells mL⁻¹_digesta) to the ileum (2.17 × 10⁹ cells mL⁻¹_digesta). This aligns with Gresse et al. (2019), who reported an increase from 2.00 × 10⁷ copies g⁻¹_digesta in the stomach to 1.26 × 10⁹ copies g⁻¹_digesta in the ileum. Although there is a difference in the units used for microbial load measurements (cells mL⁻¹ vs copies g⁻¹), the differences between GIT regions are still present and therefore comparable, providing consistent insights. In the lower digestive tract, bacterial densities significantly increased, with the highest loads in the distal colon (1.55 × 10¹⁰ cells mL⁻¹_digesta) and rectum (6.82 × 10¹⁰ cells mL⁻¹_digesta), consistent with previous studies (Holman et al., 2017; Gresse et al., 2019; Luo et al., 2022). Comparable patterns are seen in humans, with lower microbial loads in the duodenum (10¹ to 10³ CFU g⁻¹), jejunum (10³ to 10⁵ CFU g⁻¹), and ileum (10³ to 10⁸ CFU g⁻¹), and higher loads in the colon (10¹⁰ to 10¹¹ CFU g⁻¹) (Cresci and Izzo, 2019; Martinez-Guryn et al., 2019; Flint, 2020; Paone and Cani, 2020). In the mucus layer, bacterial concentrations are typically lower than in the lumen due to a range of physical and immunological defences (Tropini et al., 2017). Despite this, the mucosal microbiome’s proximity to the epithelial layer can significantly impact the host. In the current study, around 10⁷ cells mL⁻¹ were detected in the porcine mucosal environment across the intestine, while Gresse et al. (2019) reported a bacterial cell density of 10⁹ copies g⁻¹ in the mucus layer of the proximal colon of pigs.

Linked with the microbial load, bacterial diversity and richness proved to be considerably enriched in the colonic environment as compared with the different regions in the porcine SI, which corresponds to different observations reported in the literature for pigs (Holman et al., 2017; Crespo-Piazuelo et al., 2018; Yu et al., 2019) as well as humans (Martinez-Guryn et al., 2019). PCoA-analysis showed that the different intestinal regions clustered separately, indicating that microbial composition is significantly different between the different gastrointestinal segments, again confirming earlier literature findings (Crespo-Piazuelo et al., 2018). The main bacterial phyla that were observed, in general, included Firmicutes, Proteobacteria, and Bacteroidetes. In the SI, Firmicutes dominated together with some smaller portions of Proteobacteria especially in the mucosal environment, while in the colon, Firmicutes remained the highest abundant bacterial phylum with Bacteroidetes taking up a significantly larger portion as compared with the SI. These observations were in agreement with several other researchers (Crespo-Piazuelo et al., 2018; Gresse et al., 2019; Bergamaschi et al., 2020), who showed that the porcine small intestinal community is mainly dominated by Firmicutes, with the colonic microbiome becoming more diverse including high levels of Bacteroidetes species as well. Furthermore, the stronger representation of Proteobacteria in the porcine mucosal layer was also confirmed (Gresse et al., 2019). At a lower phylogenetic level, an overabundance of Lactobacillus was observed throughout all intestinal regions of the porcine gut during the current study, compared with typical levels reported in the literature. While Lactobacillus is known to be part of the core microbiome of pigs, the observed abundances in this study were higher than those reported in earlier findings (Luo et al., 2022). Indeed, while literature has described high levels of Lactobacillus in the small intestine of piglets (Crespo-Piazuelo et al., 2018; Gresse et al., 2019), this is typically not a bacterial group which is highly abundant in the lower porcine intestine (Gresse et al., 2019; Luo et al., 2022). It was hypothesized that the high concentration of lactose in the experimental diet, together with the short gut transit time of the piglets, boosted Lactobacillus abundance during the current study, which should be critically considered. However, upon investigating the other abundant genera in the gastrointestinal tract of the piglets, a specific colonization of Streptococcus and Ruminococcaceae were observed in the small intestine and colon, respectively, which nicely corresponds with findings from other researchers investigating the gut microbial community of pigs (Crespo-Piazuelo et al., 2018; Gresse et al., 2019). However, Prevotella species are typically also reported to be highly abundant in the colonic region of pigs (Crespo-Piazuelo et al., 2018; Luo et al., 2022), which in the current study could only be confirmed for the rectum sample, probably due to the overgrowth of Lactobacillus in the other gastrointestinal regions.

While the current study was able to address some of the knowledge gaps with relation to characterization of the digestive and microbiological parameters in the porcine gastrointestinal tract, it should be emphasized that the characterization in the current study was done with piglets with particular genetics and age (i.e., 9-wk-old piglets; TopigsNorsvin × German Piétrain), which were fed a specific experimental diet. As these factors can have an impact on all measured parameters, this must be critically considered when validating the pig as a model for detailed mode-of-action research in the field of nutrition and digestion.

In conclusion, this study provided evidence on different digestive and microbiological parameters in the porcine gut, which can be used to better understand in vivo dynamics of the different intestinal processes. This may facilitate the development of in vitro or ex vivo model systems of the porcine gastrointestinal tract, thereby removing the ethical constraints of animal studies. These model systems could help assess the impact of dietary interventions on intestinal health, focusing on parameters such as microbial composition and activity, rather than digestibility and fermentation directly.

Glossary

Abbreviations

SCFA

short-chain fatty acid

bCFA

branched chain fatty acids

CA

Cholid acid

CDCA

chenodeoxycholic acid

DCA

deoxycholic acid

GCA

glycocholic acid

GCDCA

glycochenodeoxycholic acid

GDCA

glycodeoxycholic acid

GLCA

glycolithocholic acid

GUDCA

glycoursodeoxycholic acid

LCA

lithocholic acid

PCoA

Principal coordinates analyses

SI

Small intestine

TAME

tosyl arginine methyl ester

TCA

taurocholic acid

TCAA

trichloroacetic acid

TCDCA

taurochenodeoxycholic acid

TDCA

taurodeoxycholic acid

TLCA

taurolithocholic acid

TUDCA

tauroursodeoxycholic acid

UDCA

ursodeoxycholic acid

Author contributions

Chloë Rotsaert (Methodology, Lab analysis, Data analysis and interpretation, Data visualization, Writing—original draft, Writing—review and editing), Yorick Minnebo (Data analysis and interpretation, Data visualization, Writing—original draft, Writing—review and editing), Cindy Duysburgh (Data analysis and interpretation, Writing—original draft, Writing—review and editing), LinShu Liu (Conceptualization, Methodology, Writing—review and editing), Karley K. Mahalak (Methodology, Writing—review and editing), Jenni Firrman (Methodology, Writing—review and editing), Lisa M. Mattei (Methodology, Lab analysis), Ahmed M. Moustafa (Methodology, Lab analysis), Kyle Bittinger (Data analysis and interpretation, Writing—review and editing), Weiming Hu (Data analysis and interpretation, Writing—review and editing), Massimo Marzorati (Conceptualization, Writing—review and editing), Joris Michiels (Conceptualization, Methodology, Writing—review and editing), and Tom Van de Wiele (Conceptualization, Methodology, Writing—review and editing).

Ethics approval

The study was conducted in accordance with the ethical standards and recommendations for accommodation and care of laboratory animals covered by the European Directive 2010/63/EU on the protection of animals used for scientific purposes and the Belgian royal decree KB29.05.13 on the use of animals for experimental studies.

Conflict of interest statement

Chloë Rotsaert, Cindy Duysburgh and Massimo Marzorati are employees of ProDigest. Lin Shu Liu, Karley K. Mahalak and Jenni Firrman are employees of USDA.

Funding

This work was supported by the United States Department of Agriculture In-House Project 8072-41000-108-00-D, “In Vitro Human Intestinal Microbial Ecosystems: Effect of Diet.”

Data availability

The data supporting the findings of this study are available from the corresponding author on reasonable request.