Metabolomic analysis of intestinal epithelial cell maturation along the crypt–villus axis

Abstract

Epithelial cells along the crypt–villus axis (CVA) undergo continual renewal through highly coordinated proliferation, differentiation, and apoptosis; however, the changes in metabolism during maturation along the CVA are still unclear. The present study investigates the global metabolite changes in intestinal epithelial cells during maturation along the CVA. Eight 21 day-old suckling piglets were used. Intestinal epithelial cells were isolated sequentially from the villus top to the bottom of the crypt with 6 fractions (F1 to F6), and GC-MS was used to identify the metabolites, whose levels changed. Three hundred metabolites were identified. PLS-DA and OPLS-DA analyses showed that the metabolism of intestinal epithelial cells gradually changed during maturation along the CVA. These analyses were also run to distinguish between two fractions of cells, and yielded good separation of F1 and F3, F1 and F4, F1 and F5, and F1 and F6 cells. Significant differences were found in the metabolism of fatty acids, amino acids, glucose, and other metabolites between villus cells and crypt cells. These results reveal a global change in cellular metabolism during maturation along the CVA, and provide basal information for understanding the mechanism involved for specific nutrients in regulating epithelial cell renewal and identifying nutrients to regulate mucosal morphology and functions.

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Introduction

The gastrointestinal mucosa not only acts as an organ for digestion and nutrient absorption, but also plays an important role against pathogenic bacteria and toxic substances that are present in intestinal contents.¹ Thus, illuminating the mechanism and regulation of intestinal mucosal growth and maturation is a fundamental requirement in mucosal biology.² The intestinal mucosa is mainly composited of a monolayer of epithelial cells that line intestinal lumen. The epithelium can be divided into two parts: the flask-shaped submucosal invaginations, termed crypts, and the finger-like luminal protrusions, known as villi.¹ These epithelial cells undergo continual renewal through highly coordinated processes of cellular proliferation, differentiation, and apoptosis along the crypt–villus axis (CVA).³ The mature epithelial cell-covered villi arise from multipotent stem cells located near the base of crypt.^4,5 Epithelial cell maturation along CVA is accompanied by its functional specialization, and the continual renewal of epithelial cells along CVA ensures the functions of small intestine.¹ However, various factors, such as diseases, diet, weaning stress, hormones, and genetics, affect epithelial cell renewal along CVA.

Clarification of cellular metabolic alterations in epithelial cells during maturation along CVA is essential to understanding the regulation of mucosal development and function.¹ However, studies designed to test the changes in the metabolism of epithelial cells during maturation have been largely conducted using cell culture models, which cannot be used to test the effects of diet, weaning stress, and genetic programming on epithelial cell maturation, and intestinal mucosal renewal along CVA remains poorly understood.⁶ Moreover, the limited studies conducted to explore the metabolic alterations in epithelial cells during maturation along CVA were largely conducted using adult rodent models, while the epithelial cells lining the adult intestinal mucosa are different from those of neonate, and intestinal development programming in rodents is considerably different from those in man.^7–10 The intestinal system of pigs is closely comparable with that of humans, and is therefore considered as an ideal model to investigate intestinal development and function.¹⁰ Metabolomics, which determines cellular status by testing biomolecules and metabolites, has recently emerged as a complementary technology to genomic and proteomic approaches.^11,12 The status of the metabolome cumulatively reflects the status of cellular gene expression, protein expression, and environment.^11,12 Therefore, the present study was conducted to explore the changes in epithelial cell metabolism during maturation along CVA in piglets using metabolomics.

Experimental procedures

Sequential isolation of epithelial cells along CVA

A total of eight suckling piglets (21 days old) were purchased from the Hunan Institute of Animal Husbandry and Veterinary Medicine (Changsha, China). Piglets were maintained under general anesthesia and sacrificed by intravenous (jugular vein) injection of 4% sodium pentobarbital solution (40 mg kg⁻¹ birth weight). Intestinal epithelial cells were isolated using the distended intestinal sac method as previously described¹³ with slight modifications. The divided mid-jejunum segments were rinsed thoroughly with ice-cold physiological saline solution and incubated at 37 °C for 30 min with oxygenated PBS (Sigma-Aldrich, MO, USA). Then, six “cell fractions” (designated F1 to F6 represents cell fractions from villus top to the bottom of crypt) were collected along CVA using oxygenated isolation buffer (5 mM Na₂EDTA (Sigma-Aldrich, MO, USA), 10 mM HEPES pH 7.4 (Sigma-Aldrich, MO, USA), 0.5 mM DTT (Sigma-Aldrich, MO, USA), 0.25% BSA (Sigma-Aldrich, MO, USA), 2.5 mM D-glucose (Sigma-Aldrich, MO, USA), 2.5 mM L-glutamine (Sigma-Aldrich, MO, USA), 0.5 mM dl-β-hydroxybutyrate sodium salt (J&K Chemical Ltd., USA), oxygenated with an O₂/CO₂ mixture (19 : 1, v/v)). Each of the F1 and F2 fractions were collected after a separate 20 min incubation, F3 and F4 were collected after a separate 25 min incubation, and F5 and F6 were collected after a separate 30 min incubation. Each of the cell fraction was washed twice with oxygenated cell resuspension buffer (10 mM HEPES (Sigma-Aldrich, MO, USA), 1.5 mM CaCl₂ (Sigma-Aldrich, MO, USA), 2.0 mM MgCl₂ (Sigma-Aldrich, MO, USA), pH 7.4), and centrifuged at 400 × g for 4 min at 4 °C. The isolated cells were immediately frozen in liquid nitrogen and then stored at −80 °C until analysis. The experimental design and procedures used in this study were carried out in accordance with the Chinese Guidelines for Animal Welfare and Experimental Protocols, and approved by the Animal Care and Use Committee of the Institute of Subtropical Agriculture at the Chinese Academy of Sciences.

Metabolite extraction and sample derivatization

Cells (80–85 mg wet weight) and 800 μL of HPLC grade methanol were placed into an Eppendorf tube, successively. The mixture was vortexed for 30 s, and ultrasonicated for 2 min at 4 °C. Then 200 μL of ultrapure water was spiked, and the mixture was ultrasonicated for 2 min at 4 °C, placed at −20 °C for 20 min, and again ultrasonicated for 2 min at 4 °C prior. Subsequently, the mixture was centrifuged at 16 000g and 4 °C for 15 min. In total, 400 μL of supernatant was transferred into a glass vial, containing 10 μL of 0.1 mg mL⁻¹ dulcitol (Sigma-Aldrich, MO, USA) as internal standard and dried under gentle nitrogen stream before derivatization. To the glass vial with dried cell residue, 30 μL of 20 mg mL⁻¹ methoxylamine hydrochloride (Sigma-Aldrich, MO, USA) in anhydrous pyridine was added. The resultant mixture was vortexed vigorously for 30 s and incubated at 37 °C for 90 min. Finally, 30 μL of BSTFA (with 1% TMCS; Sigma-Aldrich, MO, USA) was added to the mixture and derivatized at 70 °C for 60 min.

GC-MS analysis

Metabolome analysis was performed on an Agilent 7890A gas chromatography system coupled to an Agilent 5975C inert MSD system (Agilent Technologies Inc., CA, USA). A HP-5ms fused-silica capillary column (30 m × 0.25 mm × 0.25 μm; Agilent J&W Scientific, Folsom, CA) was utilized to separate the derivatives. Helium (>99.999%) was used as a carrier gas at a constant flow rate of 1 mL min⁻¹. Injection volume was 1 μL by splitless mode, and the solvent delay time was 6 min. The oven temperature was initially held at 70 °C for 2 min, ramped to 160 °C at a rate of 6 °C min⁻¹, to 240 °C at a rate of 10 °C min⁻¹, to 300 °C at a rate of 20 °C min⁻¹, and finally held at 300 °C for 6 min. The temperatures of injector, transfer line, and electron impact ion source were set to 250 °C, 290 °C, and 230 °C, respectively. The electron energy was 70 eV, and data were collected in the full-scan mode (m/z 50–600).

Data preprocessing and statistical analysis

Extraction, alignment, deconvolution, and further processing of raw GC-MS data were conducted according to previous published protocols.¹⁴ Briefly, raw GC/MS data files were transformed to nominal CDF files using MetAlign software. Data were extracted from each nominal CDF file using an XCMS software package and in-house scripts. The resulting data of each tested sample was exported to a single text file. All data with time window 330–2940 s and mass window 80–600 m/z were imported to TagFinder04 software and retention time correction, peak alignment, mass tag correlation, clustering, and grouping were performed using TagFinder04.¹⁵ The final data were exported as a peak table file, including observations (sample name), variables (rt_mz), and summarized peak area. The peak table (named matrix X) file was imported to Simca-P (version 11.0, Umetrics AB, Umeå, Sweden), where multivariate statistical analyses, such as PCA, PLS-DA, and OPLS-DA, were performed. All data were mean-centered and unit variance (UV)-scaled prior to multivariate statistical analysis. The quality of the models is described by the R² and Q² values. R² is defined as the proportion of variance in the data explained by the models and indicates the goodness of fit. Q² is defined as the proportion of variance in the data predictable by the model and indicates the predictability of current model, calculated by cross-validation procedure. In order to avoid model over-fitting, a default 7-round cross-validation in Simca-P was performed throughout to determine the optimal number of principal components. The values of R² and Q² were used as indicatives to assess the robustness of a pattern recognition model.

Identification and structural validation of differential metabolites

The differential metabolites were determined by the combination of the VIP value (>1) and the P-values (<0.05) from two-tailed Student's t test on the normalized peak areas. Fold change was calculated as the binary logarithm of average normalized peak area ratio between Group 1 and Group 2, where the positive value means that the average mass response of Group 1 is higher than that of Group 2. Structural identification of differential metabolites was performed as follows. The AMDIS software was used to deconvolute mass spectra from raw GC-MS data, and the purified mass spectra were automatically matched with an in-house standard library including retention time and mass spectra, Golm Metabolome Database, and the Agilent Fiehn GC-MS Metabolomics RTL Library.

Amino acid analysis

The contents of amino acid in the isolated intestinal epithelial cells were measured using iTRAQ®-LC-MS/MS as described previously.¹⁶ The data were analyzed by Student's t test using the SAS version 9.2 program. A P value < 0.05 was considered statistically significant.

Results

Cell isolation and metabolomic analysis

The cell isolation procedure was validated as previously described by measuring the activity of ALP (marker of intestinal epithelial cells differentiation) and the expression of PCNA (marker of DNA synthesis) along CVA. ALP activity increased from F6 (crypt bottom) to F1 (villus tip), while the abundance of PCNA gradually decreased from F6 to F1, indicating that epithelial cells with different maturation status along CVA were successfully isolated. GC-MS analysis was performed to investigate the metabolite levels in intestinal epithelial cells. As seen in Fig. 1, the total ion chromatogram of all the samples showed very good repeatability in chromatographic retention times, which provided a foundation for analyzing metabolites in epithelial cells with different maturation status along CVA, and a total of 300 metabolites, with 162 unknown identities, were identified.

Fig. 1 The total ion chromatogram of all the samples.

Changes in metabolites in intestinal epithelial cells during maturation along CVA

The PLS-DA analysis was run to observe the changes in intestinal epithelial cells during maturation along CVA. Remarkably, the score plots with one principal component (Fig. 2A) or two principal components (one principal component was added artificially; Fig. 2B) showed that intestinal epithelial cells gradually changed during maturation along CVA. The OPLS-DA analysis also showed a gradual change in intestinal epithelial cells along CVA with very good description of data (R² = 0.905) and good predictability (Q² = 0.782; Fig. 2C). The F1 and F2 were on the left of PC1; F4, F5, and F6 were on the right of PC1; and F3 was in the transitional zone between them (Fig. 2C). PLS-DA analysis and OPLS-DA analyses were then run to discriminate between two cell fractions. The F1 were firstly compared to F2. The values of R² and Q² indicated that this model performed badly both in description and predictability. The difficulty in discriminating the two groups suggests that they produce similar metabolites (data not showed).

Fig. 2 Metabolic footprint of epithelial cells during maturation along CVA. PLS-DA analysis score plots with one principal component (A) and two principal components ((B) one principal component was added artificially), and OPLS-DA analysis score plots (C) of intestinal epithelial cells during maturation along CVA.

The PLS-DA was run to discriminate F1 and F3, and the values of R² (0.885) and Q² (0.336) suggested a separation of these two groups (Fig. 3A). OPLS-DA analysis discriminated F1 from F3 by giving very good description of data (R² = 0.965) and good predictability of data (Q² = 0.706; Fig. 4A). The loading plots of the first principal components indicated that deoxycholic acid, lyxose, and tetracosanoic acid are the main metabolites marking the difference between F1 and F3 (Table 1).

Fig. 3 Differences between F1 epithelial cells and other cell fractions. PLS-DA analysis score plots of F1 vs. F3 (A), F1 vs. F4 (B), F1 vs. F5 (C), and F1 vs. F6 (D).

Fig. 4 Differences between F1 epithelial cells and other cell fractions. OPLS-DA analysis score plots of F1 vs. F3 (A), F1 vs. F4 (B), F1 vs. F5 (C), and F1 vs. F6 (D).

Table 1 Main metabolites marking the difference between F1 and other fractions^a

Assignments	F2 vs. F1	F3 vs. F1	F4 vs. F1	F5 vs. F1	F6 vs. F1
a Differential metabolites of F2 vs. F1 were determined by the P-values (<0.05) using two-tailed Student's t test on the normalized peak areas; others were determined using a combination of the VIP value (>1) and the P-values (<0.05) by two-tailed Student's t test on the normalized peak areas. Fold change was calculated as binary logarithm of average normalized peak area ratio between two cell fractions.
Phosphoric acid monomethyl ester			−1.98	−3.39	−3.63
Lyxose	−0.76	−0.90	−1.57	−2.01	−2.73
9-Tetradecenoic acid					−2.06
Dodecanoic acid					−2.03
Guanosine			−1.70		−1.94
N-Acetylgalactosamine			−1.58	−2.00	−1.92
Xylitol			−1.48	−1.51	−1.75
Ribose			−1.14	−1.38	−1.72
Sedoheptulose			−1.08	−1.29	−1.67
Pantothenic acid			−1.44	−1.59	−1.65
Glyceric acid				−1.33	−1.54
Arabinose			−1.43	−1.90	−1.50
Cadaverine			−1.20	−1.38	−1.42
Lactate			−0.85	−0.99	−1.16
Arachidonic acid					−0.94
cis-5,8,11,14,17-Eicosapentaenoic acid					−0.94
Deoxycholic acid		−1.58	−2.29	−2.95
Chenodeoxycholic acid				−0.78
Phenylalanine					0.83
Eicosanoic acid				1.09	0.99
Pyroglutamic acid			0.72	0.76	1.00
Glutamate				0.69	1.25
1-O-Hexadecylglycerol					1.31
2-Aminoadipic acid				1.56	1.35
Inositol				1.21	1.54
Mannose 6-phosphate				1.13	1.56
Creatinine			1.55	1.60	1.86
Fructose 6-phosphate				1.72	2.19
Tetracosanoic acid	0.74	1.21	1.65	2.00	2.26
Citric acid					5.01
Oleic acid				0.96
Stearic acid				0.50
Mannitol				0.63

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The PLS-DA analysis showed a significant difference in the metabolites between F1 and F4, with R² = 0.939 and Q² = 0.124 (Fig. 3B). OPLS-DA analysis discriminated F1 from F4 by giving very good description of data (R² = 0.940) and good predictability (Q² = 0.684; Fig. 4B). The loading plots of the first principal components indicated that deoxycholic acid, phosphoric acid monomethyl ester, guanosine, N-acetylgalactosamine, lyxose, xylitol, pantothenic acid, arabinose, cadaverine, ribose, sedoheptulose, lactate, aspartic acid, pyroglutamic acid, creatinine, and tetracosanoic acid were the main metabolites marking the difference between F1 and F4 (Table 1).

The PLS-DA analysis was performed to discriminate F1 and F5, and the values of R² (0.999) and Q² (0.934) suggested good separation of these two groups (Fig. 3C). OPLS-DA analysis discriminated F1 from F5 by giving very good description of data (R² = 0.947) and good predictability (Q² = 0.809; Fig. 4C). The loading plots of the first principal components indicated that phosphoric acid monomethyl ester, deoxycholic acid, lyxose, N-acetylgalactosamine, arabinose, pantothenic acid, xylitol, ribose, cadaverine, glyceric acid, sedoheptulose, lactate, chenodeoxycholic acid, stearic acid, mannitol, glutamate, pyroglutamic acid, oleic acid, eicosanoic acid, mannose-6-phosphate, inositol, 2-aminoadipic acid, creatinine, fructose-6-phosphate, and tetracosanoic acid were the main metabolites marking the difference between F1 and F5 (Table 1).

The PLS-DA analysis showed a significant difference in the metabolites between F1 and F6, with R² = 0.994 and Q² = 0.823 (Fig. 3D). OPLS-DA analysis discriminated F1 from F4 by giving a very good description of data (R² = 0.972) and good predictability (Q² = 0.806; Fig. 4D). The loading plots of the first principal components indicated that phosphoric acid monomethyl ester, lyxose, 9-tetradecenoic acid, dodecanoic acid, guanosine, N-acetylgalactosamine, xylitol, ribose, sedoheptulose, pantothenic acid, glyceric acid, arabinose, cadaverine, lactate, arachidonic acid, cis-5,8,11,14,17-eicosapentaenoic acid, phenylalanine, eicosanoic acid, pyroglutamic acid, glutamate, 1-O-hexadecylglycerol, 2-aminoadipic acid, inositol, mannose-6-phosphate, creatinine, fructose-6-phosphate, tetracosanoic acid, and citric acid were the main metabolites marking the difference between F1 and F6 (Table 1).

Confirmation of metabolite changes

To confirm the changes in metabolites in intestinal epithelial cells, the amino acid contents in F1 and F6 were measured. As showed in Fig. 5A, the contents of glutamate in F6 were significantly greater (P < 0.05) than that in F1. Moreover, the contents of phenylalanine in F6 were also enhanced (P < 0.05) compared with F1 (Fig. 5B). These results were consistent with the results of metabolomic analysis.

Fig. 5 Amino acid contents in villus cells and crypt cells. The glutamate (A) and phenylalanine (B) contents in F1 and F6 cells were measured by iTRAQ®-LC-MS/MS.

Discussion

In the present study, we examined the metabolic changes that occur as intestinal epithelial cells mature along CVA in piglets. GC-MS was used to analyze the metabolic status of intestinal epithelial cells during maturation along CVA. Intestinal epithelial cells extracts were studied at distinct stages of maturation (from the crypt bottom to villus tip). Careful analysis of metabolites at various stages of maturation revealed that the metabolism of intestinal epithelial cells gradually change during maturation along CVA. It is difficult to discriminate F2 from F1 because the cells in F2 were located very close to F1 in intestinal villi, and they may have similar metabolic status. The differences between F1 and cells of other fractions increased with increasing distance from F1. These results indicated that intestinal epithelial cells at different maturation status have different metabolic status and nutrient requirements.

Although the levels of most metabolites remained largely unchanged during maturation, the levels of some metabolites changed dramatically along CVA. Consistent with previous reports that lipid metabolite contents are greater in villi than in the crypt,⁹ this study also found that the levels of lipid metabolites (such as 9-tetradecenoic acid, dodecanoic acid, arachidonic acid, phosphoric acid monomethyl ester, and cis-5,8,11,14,17-eicosapentaenoic acid) were higher in villus cells than in crypt cells. Mariadason et al. (2005) showed that the mRNA expression of genes involved in lipid uptake and transport was greater in villus cells than in crypt cells in the small intestine of mouse.⁷ Moreover, the expression of proteins related to lipid, fatty acid, and steroid metabolism was also higher in villus cells than in crypt cells in the small intestine of mouse.⁸ This increase in lipid metabolites in villus cells may result from the increase in lipid or fatty acid uptake and metabolism in villus cells. However, the levels of some lipid metabolites, such as oleic acid, stearic acid, and 1-O-hexadecylglycerol, were greater in crypt cells than in villus cells, suggesting that crypt cells and villus cells may have different fatty acid requirements.

The small intestine is not only the primary organ responsible for digestion and absorption of nutrients, but it also plays important roles in the metabolism of dietary amino acids.¹⁷ Stoll et al. (1998) showed in piglets that about one-third of dietary essential amino acids was absorbed by the intestine in the first-pass metabolism, and mucosal cells catabolized more amino acids than incorporating them into mucosal protein.¹⁸ Windmueller and Spaeth (1980) reported that glutamate, glutamine, and aspartate were the major contributors to oxidative energy generation in the small intestinal mucosa of animals.¹⁹ Although glutamate could be produced from glutamine in intestinal mucosa, more than 90% of the dietary glutamate was absorbed by the intestine in first-pass metabolism.¹⁸ Glutamate is considered as the single most important source of energy for the portal-drained viscera (PDV; the intestines, pancreas, spleen, and stomach) because it accounted for approximately 15% of total CO₂ production by the PDV.²⁰ In the present study, we showed that the glutamate content was greater in crypt cells than in villus cells, suggesting that the metabolism of glutamate changed during epithelial cell maturation along CVA and that the requirement of glutamate was different among cells at different maturation status. Further studies will be needed to determine the effects of glutamate on the renewal of intestinal epithelial cells and the underlying mechanisms. Moreover, the phenylalanine contents also changed in the epithelial cells during maturation along CVA. Stoll et al. (1998) showed that about 35% of dietary phenylalanine was absorbed by the intestine in first-pass metabolism.¹⁸ However, the effect of phenylalanine on the physiology and functions of small intestine is still unclear, and further studies will be needed to clarify this.

In addition to amino acids, glucose plays an important role in providing energy in intestine. Glucose accounted for approximately 30% of total CO₂ production by the PDV.²⁰ Moreover, the pattern of intestinal amino acid and glucose oxidation can be altered by protein (or amino acids) restriction in pigs, because glucose oxidation increased to 50% of the total visceral CO₂ production when pigs were fed a low protein diet.²⁰ Chang et al. (2008) showed that a number of proteins involved in glycolysis were coordinately up-regulated in villus cells, and the contents of lactate and pyruvate in villus cells were also higher from that in crypt cells in mouse.⁸ The expression of proteins in glycolysis was also elevated in villus cells in piglets (unpublished data). Moreover, lactate content was greater in villus cells than in crypt cells, while citric acid lactate content was greater in crypt cells than villus cells in piglets. These results suggest that villus epithelial cells have greater glycolysis than crypt epithelial cells in piglets. More studies will be needed to test the effects of fatty acids, amino acids, glucose, and their metabolites on the renewal of intestinal epithelial cells and their interactions.

Various gastrointestinal diseases are associated with mucosal impairment, which results in electrolyte and mineral imbalance.^21,22 Therefore, the improvement of mucosal morphology and functions plays an important role in recovery from these diseases.²³ Although the intestinal mucosal could get nutrients from both blood and intestinal lumen, the importance of enteral nutrition in stimulating intestinal mucosa recovery from impairment has been confirmed by many studies.^24–27 Thus, illuminating nutrients metabolism and the regulation of intestinal mucosal growth and maturation is a fundamental requirement in mucosal biology. However, the specific luminal nutrients and the underlying mechanism that food induces mucosa recovery from impairment are not well understood. Only glutamine, free fatty acids, and short-chain triglycerides were improved to be of help.²⁸ The present study showed a global change in cellular metabolites in intestinal epithelial cells during maturation along CVA. These results may provide basal information for understanding the mechanism that specific nutrients involve in improving mucosal growth and functions and identifying novel nutrients to regulate mucosal morphology and functions, such as fatty acids and glutamine. The fatty acids and glutamine are involved in improving mucosal growth and functions, but the metabolites of fatty acids and glutamine (glutamate) were greater in villus and crypt epithelial cells, respectively. These results indicate that fatty acids may via affecting differentiated epithelial cells, while glutamine may via affecting proliferating epithelial cells to affect mucosal growth and functions.

Weaning in piglets is an abrupt process of replacing milk feeding with formulated feed, which usually results in intestinal dysfunction that is one of the major challenges in swine production all over the world.²⁹ The small intestinal villus height was decreased and the crypt depth was increased in post-weaning piglets as the proliferation of epithelial cells in crypt and the apoptosis of epithelial cells in villi was altered during weaning.^29,30 Therefore, the regulation of intestinal epithelial cells renewal is very important. Although various nutrients, such as glutamine, citric acid, and butyric acid, were proved to be helpful in improving intestinal morphology and functions of weaning piglets, more works were needed to be conducted to reveal the mechanism of nutrients in improving intestinal health and find more functional nutrients to improve the intestinal functions of piglets.^31–34 The results of the present study provide a global metabolites in intestinal epithelial cell, which may also be of importance in swine production.

In conclusion, the results of the present experiment showed a gradual change in the metabolism of intestinal epithelial cells during maturation along CVA. Metabolism of fatty acids, amino acids, and glucose was significantly different between villus and crypt cells. These results provide basal information for understanding the mechanism that specific nutrients involve in regulating epithelial cells renewal and identifying nutrients to regulate mucosal morphology and functions.

Acknowledgements

The authors would like to thank Dr Chengbo Yang for superb technical assistance.

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