The anti-diarrheal role of iron in piglets: Regulation of colonic goblet cell function

Abstract

Diarrhea is a common condition in piglets. The intestinal mucosal barrier is the primary protective mechanism against external challenges. Iron—an essential trace element for the growth and metabolism of living organisms—is crucial for enhancing host defense functions. Newborn piglets and porcine colonic organoids were employed as experimental models to investigate the mechanisms underlying the regulation of the intestinal mucosal barrier by iron supplementation. A total of 24 seven-d-old piglets (body weight 1.75 ± 0.20 kg) were orally administered formula milk powder supplemented with different concentrations of iron (FeSO₄) for 2 weeks. The treatment groups were low (without), middle (70 mg/kg), and high (700 mg/kg) iron (n = 8). Compared with the low group, iron supplementation significantly reduced fecal scores (P = 0.016), increased the number of colonic goblet cells (P = 0.009), and enhanced MUC2 production (P = 0.049). Additionally, fecal scores were negatively correlated with both the number of colonic goblet cells (P = 0.003, |r| = 0.772) and MUC2 production (P = 0.026, |r| = 0.636). To investigate the mechanism by which iron regulates goblet cells, a hypothesis was proposed that iron exerts its effects through both the gut microbiota and the host. Specific microbial taxa and metabolites were identified as potential regulators of goblet cell function. However, organoid-based in vitro experiments demonstrated that iron regulates goblet cells regardless of microbial metabolites. Additionally, porcine colonic organoids were treated with deferoxamine (DFO; 100 μmol/L), ammonium iron citrate (FAC; 100 or 200 μmol/L), or a mock treatment for 48 h, which was followed by gene expression profiling and transcriptome sequencing. Elevated iron concentrations increased MUC2 expression (P < 0.001). In the group with increased MUC2 expression, the MAPK and PI3K-Akt signaling pathways were downregulated (P = 0.001), whereas the mucin-type O-glycan biosynthesis pathway was upregulated (P = 0.002). Subsequent treatment of porcine colonic organoids with inhibitors specific to these pathways validated the crucial role of the PI3K-Akt signaling pathway in the iron-regulated goblet cell function. Therefore, dietary iron intake increased colonic goblet cell MUC2 production by inhibiting the PI3K-Akt signaling pathway, thereby alleviating diarrhea in piglets.

1. Introduction

Piglet diarrhea is a prevalent challenge in pig production (Han et al., 2024). The gastrointestinal tract encounters numerous challenges and requires robust defense mechanisms for protection. Tightly connected intestinal epithelial cells form a physical barrier that is further protected by mucus layers. The mucus layer primarily consists of MUC2 (The full names of the abbreviations of proteins can be found in Table S1) secreted by goblet cells (Hansson, 2020), which is essential for maintaining the function of the mucosal barrier. A key indicator of intestinal mucus layer damage is the disruption of goblet cell homeostasis, characterized by a reduction in goblet cell numbers and decreased mucus secretion. A disrupted mucus layer increases susceptibility to enteritis or infection (Gustafsson and Johansson, 2022).

Iron, an essential nutrient for both the host and gut microbiota, regulates intestinal barrier function through interconnected microbial and epithelial pathways (Liu et al., 2024). Alterations in iron homeostasis, whether owing to iron overload or deficiency, can adversely affect gut microbiota composition and compromise the integrity of the mucosal barrier. Excess dietary iron reduces the abundance of Akkermansiaceae, increases epithelial permeability, and facilitates luminal bacterial translocation (Liu et al., 2023). In contrast, iron deficiency results in a loss of beneficial taxa such as Firmicutes and Blautia and an enrichment of pathobionts, including Faecalibaculum and Escherichia-Shigella, collectively weakening the barrier (Xie et al., 2025). In addition to microbiota-mediated effects, iron imbalance affects epithelial function through distinct host mechanisms, such as enhancing cluster of differentiation 8-positive (CD8⁺) T lymphocyte differentiation under iron overload or activating the MLCK/p-MLC pathway under iron deficiency, both of which compromise mucosal barrier function (Feng et al., 2024; Liu et al., 2024). These findings highlight the dual role of iron as an essential micronutrient and a potential driver of dysbiosis-associated barrier dysfunction.

Iron deficiency is the most prevalent nutritional disorder in newborn piglets owing to insufficient iron content in maternal milk combined with the high demands of rapid growth during early development. Iron deficiency in piglets results in reduced growth rates, a compromised intestinal mucosal barrier, and may result in diarrhea (Chen et al., 2019). Although previous studies have demonstrated that iron supplementation enhances immune cytokine production and monocyte phagocytic capacity in piglets (Pu et al., 2018), whether and how iron regulates goblet cell function and mucus barrier integrity during early development remains unclear.

Therefore, this study aimed to elucidate the anti-diarrheal role of iron in piglets by investigating its regulatory effects on goblet cell differentiation and mucus secretion. Adequate iron supplementation was hypothesized to strengthen intestinal defense by improving goblet cell function through both microbiota-dependent and microbiota-independent mechanisms. Understanding these pathways will provide new insights into iron-mediated protection of the intestinal barrier in neonatal piglets.

2. Materials and methods

2.1. Animal ethics statement

The experiment was conducted in accordance with the guidelines (No. 20230008) of the Experimental Animal Ethics Committee of the Institute of Subtropical Agriculture (Changsha, Hunan, China). The experiment complied with the ARRIVE 2.0 guidelines (Percie du Sert et al., 2020).

2.2. Animal experiment

Animal experiments were conducted at the Institute of Subtropical Agriculture, Chinese Academy of Sciences (Changsha, Hunan, China). A total of 24 five-d-old piglets (males) weighing 1.75 ± 0.20 kg, from Baiyi Breeding Swine Co., Ltd. (Changsha, Hunan, China), were purchased. They were chosen for similarity in parity, genetic background, different litters, and had not received iron supplementation or any vaccinations. The sows were kept under identical conditions and fed the same diet. Prior to 5-d age, the piglets were nursed normally.

After purchase and transportation to the research institute, the environmental temperature was maintained at 30 to 35 °C with humidity controlled at 30%–40%. The piglets were fed Quanrubao milk powder, a commercial product manufactured by Chengdu Xingguang Full Nutrition Foods Co., Ltd. (Chengdu, Sichuan, China). The main ingredients of this formula included formula milk powder, whey powder, lactose, coconut oil, amino acids, probiotics, acidity regulators, enzyme preparations, oligosaccharides, and a premix of vitamins and trace elements. Each feeding consisted of 250 g milk powder mixed with 1 L purified water at 45 °C. Feeding times were 07:00, 10:00, 13:00, 16:00, 19:00, and 21:00, during which piglets had ad libitum access to feed, and their intake was recorded. At 5-d age, the milk powder was gradually diluted and then transitioned back to the standard concentration (250 g milk powder per 1 L warm water) to help the piglets adapt to the new feed.

The experiment began when the piglets were 7-d-old and ended when they were 21-d-old. According to their body weights, the piglets were randomly divided into three groups: Low, Middle, and High iron groups, each consisting of eight piglets. Four piglets were housed in each stainless-steel cage, with two cages per group. The Low group received basic formula milk powder, the Middle group received formula milk powder with 70 mg Fe/kg, and the High group received formula milk powder with 700 mg Fe/kg (all iron sources were FeSO₄).

2.3. Blood sampling and serum preparation

On d 14 of the experiment, eight piglets per group were used for blood collection. Blood samples were collected from the jugular vein of piglets before the morning feeding, following an overnight fast with unrestricted access to water. Approximately 10 mL of blood was collected using sterile venipuncture needles and subsequently separated into two portions. One portion was placed in vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA) as an anticoagulant and immediately used for hematological (routine blood) analysis. The remaining sample was placed in plain tubes without anticoagulant, allowed to clot at room temperature for 30 min, and then centrifuged at 3000 × g for 10 min at 4 °C to obtain the serum. The separated serum was immediately stored at −80 °C until further analysis.

2.4. Tissue collection and processing

At the end of the experiment, piglets were administered an intravenous injection of an approved anesthetic agent prior to euthanasia, in accordance with institutional animal welfare and ethical standards. Eight piglets per group were used for tissue sampling. Sterile surgical scissors, forceps, and scalpels were used to collect the spleen, liver, colonic, and ileal and fecal content samples under aseptic conditions. Portions of the tissue samples were fixed in 4% paraformaldehyde for histological examination, while the remaining samples were immediately frozen in liquid nitrogen and stored at −80 °C for subsequent molecular analyses.

2.5. Chemical analysis of milk powder chemical compositions

The milk powder, Quanrubao, was purchased from Chengdu Xingguang Full Nutrition Foods Co., Ltd. (Chengdu, Sichuan, China). The analyzed nutrient composition is presented in Table 1. Iron, calcium, and phosphorus were determined according to GB 5009.268-2016 (China National Standard, 2016a). The protein content was measured following the methods of GB 5009.5-2016 (China National Standard, 2016d) using an analytical balance (FA-1004, Shanghai Sunny Hengping Scientific Instrument Co., Ltd., Shanghai, China) and a Kjeldahl analyzer (K9840, Jinan Hanon Instruments Co., Ltd., Jinan, Shandong, China). Ash content was determined based on GB 5009.4-2016 (China National Standard, 2016c) using an analytical balance (XT220A, Precisa Gravimetrics AG, Dietikon, Switzerland) and a muffle furnace (SX2-12–10, Longkou Electric Furnace Factory, Longkou, Shandong, China). Lysine content was analyzed according to GB 5009.124-2016 (China National Standard, 2016b) and the method described by Lin et al. (2018) using an analytical balance (FA-1004), a refrigerated centrifuge (TGL-16M, Xiangyi Centrifuge Instrument Co., Ltd., Changsha, Hunan, China), and an automatic amino acid analyzer (LA8080, Hitachi High-Tech Corporation, Hitachinaka, Ibaraki, Japan).

Table 1.

Chemical composition of the milk powder (as-fed basis).

Chemical composition	Content
Moisture, g/100 g	5.86
Crude protein, g/100 g	22.80
Crude fat, g/100 g	16.30
Ash, g/100 g	3.70
Calcium, mg/g	3.90
Phosphorus, mg/g	3.54
Iron, mg/100 g	13.00
Hydrolyzed lysine, g/100 g	1.84

2.6. Porcine colonic organoids culture

The separation of intestinal crypts was performed following the method described by Miyoshi and Stappenbeck (2013), with modifications for porcine intestines. In contrast to the original procedure, which involves directly cutting and fragmenting entire intestinal segments, the porcine intestinal crypts required initial scraping of the mucosal layer from glass slides. Subsequently, the scraped mucosal layer was minced for further processing.

Porcine colonic organoids were cultured in media containing Advanced DMEM/F12 medium (PWL038, Dalian Meilun Biotech Co., Ltd., ‌Dalian, Liaoning, China) with 1 × N2 (17502048), 1 × B27 (17504044), and 50 ng/mL EGF (PHG0311) from Thermo Fisher Scientific Inc. (Waltham, MA, USA); 2 mmol/L L-glutamine (ST1441), 10 mmol/L 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic acid (HEPES; C0215), 10 mmol/L nicotinamide (Y265195), 10 μmol/L Y-27632 (SC0326), 10 μmol/L SB202190 (SC0380), 2.5 μmol/L CHIR99021 (SF2703), and 10 μmol/L A83-01 (SF7917) from Beyotime Biotechnology Inc. (Shanghai, China) in 25 μL Matrigel (356231, Corning Incorporated, Corning, NY, USA). Following the initial passage, the intestinal organoids were incubated in culture medium either without or with supplementation containing 100 μmol/L DFO (252750), 100, or 200 μmol/L FAC (F5879) from Sigma Chemical Co. (St. Louis, MO, USA) for 48 h. Additionally, following the first passage, the intestinal organoids were cultured in medium either without or supplemented with 100 μmol/L DFO, 50 μmol/L Ac5GalNTGc (HY-160109, MedChemExpress LLC., Monmouth Junction, NJ, USA), 100 μmol/L DFO combined with 10 μmol/L SB202190, or 100 μmol/L DFO combined with 10 μmol/L LY294002 (S1737, Beyotime Biotechnology Inc., Shanghai, China) for 48 h.

2.7. Hematological and iron analysis

Complete blood count (CBC) was determined using the Fully automatic veterinary hematology analyzer (BC-5000Vet, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, Guangdong, China). Inductively coupled plasma optical emission spectrometry (ICP-OES; 5110, Agilent Technologies, Inc., Santa Clara, CA, USA) was used to determine the iron content in the liver and spleen. Briefly, the dried samples were weighed in triplicate, underwent acid digestion, dried at 260 °C, and redissolved in 5 mL of 1% HNO₃. The samples were then diluted with 1% HNO₃ prior to ICP-OES analysis.

2.8. Real-time quantitative PCR (RT-qPCR)

Total RNA was extracted using AG RNAex Pro Reagent (AG21101, Accurate International Biotechnology [Guangzhou] Co., Ltd., Changsha, Hunan, China) from pig tissues (ileum and colon) and porcine colonic organoids. The RNA was reverse-transcribed to complementary DNA (cDNA) using the RT-qPCR Evo M-MLV for qPCR (AG11708, Accurate Biology AG, Changsha, Hunan, China). The RT-qPCR reactions were set up in three technical replicates for each sample by combining equal concentrations of cDNA, gene-specific forward and reverse primers, and 2 × SYBR Green Pro Taq HS Premix (AG11701, Accurate Biology AG, Changsha, Hunan, China), and run on a Real-time PCR System (LightCycler 480 II, Roche Diagnostics GmbH, Mannheim, Baden-Württemberg, Germany). The fold change of the genes was calculated using the 2^−ΔΔCt method with GAPDH as housekeeping gene. The used primer sequences are listed in Table S2. The full names of the abbreviations of all genes can be found in Table S1.

2.9. Histological staining

Intestine and spleen tissues were fixed in 4% paraformaldehyde (G1101, Wuhan Servicebio Technology Co., Ltd., Wuhan, Hubei, China), paraffin-embedded, and sectioned at 5 to 7 μm for hematoxylin and eosin (HE) staining. Intestinal tissues were fixed in Carnoy's fluid (G1120, Wuhan Servicebio Technology Co., Ltd., Wuhan, Hubei, China), paraffin-embedded, and sectioned at 5 to 7 μm for Alcian blue-Periodic Acid Schiff (AB-PAS) staining. Pathological scoring of the ileum and colon was based on the degree of inflammatory cell infiltration, intestinal mucosal injury, and damage to intestinal crypts.

2.10. Immunohistochemistry

Immunohistochemistry was employed to assess the expression levels of goblet cell-associated proteins in the colon. The experimental procedure was conducted as follows: deparaffinization, paraffin-embedded sections were sequentially immersed in xylene (5 min), 100% ethanol (3 min), and 70% ethanol (3 min). Sections were washed 2 to 3 times in 0.05% Tween 20 solution. Subsequently, sections were boiled in citrate buffer (0.01 mol/L, pH 6.0) for 30 min. After cooling to room temperature in cold water, sections were treated with 3% hydrogen peroxide for 10 min at room temperature, followed by another wash with 0.05% Tween 20. Tissue blocking was performed using 5% non-fat milk at room temperature for 30 min. Sections were washed four times with phosphate-buffered saline (PBS) buffer (5 min each) after blocking. Sections were then incubated overnight at 4 °C in a humidified chamber with primary antibody diluted in 5% non-fat milk: anti-MUC2 (1:1000, Santa Cruz Biotechnology, Inc., Dallas, TX, USA). The following day, sections were equilibrated to room temperature and washed 2 to 3 times with 0.05% Tween 20. Subsequently, sections were incubated for 1 h at room temperature in a humidified chamber with secondary antibody (1:1000 dilution, Boster Biological Technology Co., Ltd., Pleasanton, CA, USA). Following another 2 to 3 washes with 0.05% Tween 20, sections were incubated with 3,3′-diaminobenzidine (DAB) chromogen for 3 to 5 min. Sections were observed under a microscope, and the reaction was stopped by rinsing with ultrapure water when brown coloration developed. Counterstaining was performed using hematoxylin (15–60 s), followed by rinsing with tap and ultrapure water. Sections were dehydrated by sequential immersion in 70% ethanol (3 min, once), 100% ethanol (3 min, twice), and xylene (5 min, twice). Mounting and photography: neutral gum was applied to the tissue and air-dried in a fume hood for at least 30 min. Subsequently, sections were observed and photographed under a microscope. The expression level of MUC2 protein was quantified in ImageJ software (version 1.54t, National Institutes of Health, Bethesda, MD, USA) by calculating the ratio of optical density to tissue area.

2.11. 16S rRNA sequencing

The V3–V4 region of the bacterial 16S rRNA gene was amplified using primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR reactions (10 μL) contained 0.3 μL each of forward and reverse primers (10 μM), 5 μL KOD FX Neo buffer, 2 μL dNTPs (2 mM each), 0.2 μL KOD FX Neo polymerase, and 5 to 50 ng genomic DNA. The amplification program was: 95 °C for 5 min; 25 cycles of 95 °C for 30 s, 50 °C for 30 s, and 72 °C for 40 s; followed by 72 °C for 7 min.

The PCR products were purified using magnetic beads (VAHTS DNA Clean Beads, Nanjing Vazyme Biotech Co., Ltd., Nanjing, Jiangsu, China) at a 1:1 ratio, following the manufacturer's protocol. Briefly, 10 μL PCR products were mixed with 10 μL magnetic beads, incubated for 5 min, washed twice with 80% ethanol, air-dried, and eluted with 8 to 10 μL ddH₂O. The purified products were analyzed on a 1.8% agarose gel. Sequencing libraries were prepared using the TruSeq DNA PCR-Free Kit (20015963, Illumina, Inc., San Diego, CA, USA), and library quality was assessed using the Qsep-400 system (BiOptic Inc., New Taipei City, Taiwan, China).

Paired-end sequencing (2 × 250 bp) was performed on an Illumina NovaSeq 6000 platform (Biomarker Technologies, Beijing, China). Raw image data were converted to FASTQ files using standard Illumina base-calling pipelines. Raw reads were quality-filtered using Trimmomatic v0.33, and primer sequences were removed with Cutadapt v1.9.1. A total of 1,920,605 paired reads were obtained across all samples; after quality control and merging, 1,917,087 clean reads remained (≥79,356 per sample).

Denoising, merging of paired-end reads, and chimera removal were performed using the DADA2 plugin in QIIME2 2020.6, generating high-resolution amplicon sequence variants (ASVs). Taxonomic assignment was conducted using a naïve Bayes classifier trained on the SILVA database. Community composition was summarized at multiple taxonomic levels (phylum to species). The ASV abundance tables were exported for downstream statistical analysis using R.

2.12. Transcriptome sequencing

Transcriptome sequencing was conducted by Biomarker Technologies (Beijing, China). Briefly, the procedure included RNA extraction and quality assessment from samples, followed by library construction.

The total RNA from organoids was extracted using TRIzol reagent (15596026, Thermo Fisher Scientific Inc., Waltham, MA, USA). The RNA concentration was measured using a NanoDrop 2000 spectrophotometer (ND-2000C, Thermo Fisher Scientific Inc., Waltham, MA, USA), and RNA integrity was assessed using an Agilent 2100 Bioanalyzer and a LabChip GX system (PerkinElmer, Inc., Hopkinton, MA, USA). The RNA samples met the requirements for library construction, including a total amount ≥ 2 μg (sufficient for at least two libraries), concentration ≥ 40 ng/μL, volume ≥ 10 μL, OD_260/280 ratio between 1.7 and 2.5, OD_260/230 ratio between 0.5 and 2.5, and a normal absorption peak at 260 nm. For RNA integrity number (RIN), values ≥ 7 were required for non-plant samples. When the GX value ranged from 6.0 to 6.4, the baseline was further examined to ensure that the 28S/18S ratio was ≥ 1 and that the baseline was flat or only slightly elevated. Samples exhibiting viscosity, discoloration, turbidity, or containing insoluble material were considered unsuitable.

For library preparation, mRNA Capture Beads were mixed on a 4D rotator and equilibrated for 30 min before use. The beads were added to the total RNA samples and incubated at 65 °C to denature RNA, followed by 5 min at room temperature to enable mRNA binding. Following magnetic separation, washing with Wash Buffer, and elution with Tris Buffer at 80 °C, the eluate was re-bound to beads using Beads Binding Buffer and subsequently fragmented with Frag/Prime Buffer. The fragmented mRNA was immediately chilled on ice. First- and second-strand cDNA was synthesized sequentially using reverse transcription reagents in a PCR thermocycler (Veriti 96-Well Thermal Cycler, Applied Biosystems, Inc., Foster City, CA, USA). Adaptors were then ligated to the double-stranded cDNA using ligase in a metal bath, and the ligation products were purified and size-selected through magnetic bead binding, followed by washing and elution. The PCR amplification was performed on the purified products, followed by magnetic bead purification to remove small fragments and primer dimers.

The quality of the constructed libraries was evaluated using the Qsep-400 system, ensuring appropriate fragment size distribution and concentration. Qualified libraries were subjected to high-throughput sequencing on the Illumina NovaSeq 6000 platform following the manufacturer's standard protocols.

2.13. Data analysis

The 16S rRNA sequencing data analysis: β-diversity was evaluated in QIIME using the binary Jaccard distance, and principal coordinate analysis (PCoA) was performed to compare microbial community structures among the Low, Middle, and High groups, with 95% confidence ellipses indicating group clustering. Alpha diversity indices, including Chao1 (richness) and Shannon (diversity), were calculated using QIIME to assess within-sample diversity. Functional gene prediction was conducted using PICRUSt2 by aligning ASV representative sequences to the IMG reference database, inferring Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway abundances, and comparing functional differences between the low and high groups using pairwise t-tests. Differential taxa were identified using the linear discriminant analysis (LDA) effect size (LEfSe) pipeline, which uses the Kruskal–Wallis test followed by LDA to estimate effect sizes. Cluster heatmaps were generated in R using the “pheatmap” package based on Euclidean distance and complete linkage clustering, and Spearman correlation heatmaps were produced using the “pheatmap” and “psych” packages.

Transcriptome data analysis: Rw sequencing data were processed using the Biomarker Cloud Platform (Biomarker Technologies, Beijing, China). Following the removal of adapter sequences and low-quality reads, clean reads were obtained and mapped to the Sus scrofa reference genome using HISAT2. Gene expression levels were normalized and quantified as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) between groups were identified using DESeq2, with thresholds set at |log₂ (fold change)| ≥ 1 and P < 0.05. To assess global expression variation among samples, principal component analysis (PCA) was performed based on normalized expression profiles, enabling visualization of intergroup and intragroup clustering. Functional enrichment analyses of DEGs were conducted using gene set enrichment analysis (GSEA) and KEGG pathway enrichment using the Biomarker Cloud integrated tools. Enriched pathways were determined according to a false discovery rate (FDR) < 0.05, and biological significance was interpreted in combination with known intestinal epithelial and iron-metabolism–related signaling pathways.

Data are presented as mean and standard error of the mean (SEM). Statistical analyses were conducted using one-way analysis of variance (ANOVA) with Tukey's multiple comparisons test or Kruskal-Wallis test with Dunn's multiple comparisons test, as appropriate. For ANOVA, the statistical model used was:

$$Y_{ij}=\mu +J_i+e_{ij}\text{,}$$

where Y_ij is the dependent variable; μ is the overall mean; J_i is the fixed effect of treatment; and e_ij is the random residual error. Pearson correlation coefficients were used to assess associations between variables, and correlations with P < 0.05 and |r| > 0.3 were considered statistically significant. To assess dose-dependent trends in gradient experiments, polynomial regression analysis was performed using second-order models:

$$Y=B_0+B_{1}X+B_2{X}^2\text{,}$$

both linear (B₁) and quadratic (B₂) terms and their corresponding P values are presented in Tables S3 and S4. Data were analyzed using SPSS 22.0 (IBM Inc., Armonk, NY, USA), and figures were plotted with GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). Statistical significance is set at P < 0.05.

3. Results

3.1. Iron supplementation improves the growth performance and iron levels in piglets

Piglets were fed formula milk with different iron levels for a two-week duration. The Middle and High groups exhibited increased average daily gain (ADG), with the High group showing a significantly higher ADG than that of the Low group (P = 0.014; Table 2). Serum iron and hemoglobin (HGB) levels did not differ significantly among the three groups (P > 0.05; Table 3). However, the mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) in piglets were significantly higher in the Middle and High groups than those in the Low group, with these values notably higher in the High group than in the Low group (P = 0.002); no significant differences were observed between the Middle and High groups (P > 0.05). Iron levels in the liver (P = 0.019) and spleen (P = 0.004) were significantly higher in the Middle and High groups than in the low group, with no significant differences observed between the Middle and High groups (P > 0.05).

Table 2.

Effects of dietary iron on growth performance of piglets.

Items	Groups1			SEM	P-value
	Low	Middle	High		ANOVA	Linear	Quadratic
IBW, g	1.95	2.05	1.81	0.052	0.123	0.248	0.108
FBW, g	2.54	2.75	2.65	0.061	0.433	0.248	0.148
ADG, g/d	0.04b	0.05ab	0.06a	0.003	0.014	0.004	0.829
Fecal score2	1.73a	1.61ab	1.35b	0.043	0.016	0.001	0.034

IBW = initial body weight; FBW = final body weight; ADG = average daily gain; SEM = standard error of the mean.

Within a row, means with different lowercase superscript letters represent significant differences among groups at P < 0.05, n = 8.

¹Low group, basal diet; Middle and High groups, basal diet with 70 and 700 mg/kg FeSO₄, respectively.

²Fecal score: 0 = normal, well-formed feces; 1 = soft feces; 2 = loose or mildly diarrheic feces; 3 = watery or severe diarrheic feces.

Table 3.

Effects of dietary iron on anemia status and iron levels.

Items	Groups1			SEM	P-value
	Low	Middle	High		ANOVA	Linear	Quadratic
Anemia status
HGB, g/L	80.00	87.25	79.00	1.871	0.098	0.771	0.025
MCV, fL	46.93b	49.89ab	52.89a	0.811	0.002	<0.001	0.774
MCH, pg	17.80b	19.15a	19.94a	0.292	0.002	<0.001	0.383
Iron levels
Serum, μmol/L	23.04	19.20	21.48	1.129	0.617	0.690	0.880
Liver, mg/kg	249.00b	787.50a	679.58ab	99.638	0.019	0.071	0.116
Spleen, mg/kg	452.33b	785.83a	643.20ab	46.549	0.004	0.010	0.029
Colonic, mg/kg	106.8c	490.13b	2250.00a	240.622	<0.001	<0.001	0.040

HGB = hemoglobin; MCV = mean corpuscular volume; MCH = mean corpuscular hemoglobin; SEM = standard error of the mean.

Within a row, means with different lowercase superscript letters represent significant differences among groups at P < 0.05, n = 8.

¹Low group, basal diet; Middle and High groups, basal diet with 70 and 700 mg/kg FeSO₄, respectively.

3.2. Iron supplementation reduces diarrhea in piglets

The fecal scores of the Middle and High groups were significantly lower than those of the Low group (P = 0.016; Table 2), with no significant difference observed between the Middle and High groups (P > 0.05). The morphological results of the ileum and colon of the three groups are shown in Fig. 1. Pathological assessments of the ileum and colon using HE staining revealed that variations in dietary iron levels did not affect the pathological scores of the ileum (Fig. 1A and B). However, a trend toward decreased pathological scores was observed in the colon of the Middle and High groups compared with that of the Low group, with the High group showing a significant reduction compared with that of the Low group (P = 0.047; Fig. 1C and D).

Fig. 1.

Changes in intestinal pathology scores of piglets owing to different dietary iron contents. Morphological and histopathological scores of the ileum (A and B) and colon (C and D). Scale bars, 100 μm. Low group, basal diet; Middle and High groups, basal diet with 70 and 700 mg/kg FeSO₄, respectively. The ns indicates a statistically significant difference between groups at P > 0.05. ∗ indicates a statistically significant difference between groups at P < 0.05, n = 4.

3.3. Iron supplementation improved the colonic mucosal barrier function in piglets

To further elucidate how iron supplementation alleviates diarrhea, the expression of marker genes related to mucosal barrier function in the ileum and colon was evaluated. No significant differences were observed among the three groups in the relative mRNA expression of ZO1, occludin, LYZ, or MUC2 in the ileum, or of ZO1, occludin, or LYZ in the colon (P > 0.05; Fig. 2A and B). However, the relative mRNA expression of MUC2 in the chemical barrier of the colon was significantly higher in the Middle (P = 0.016) and High groups (P = 0.031) than in the Low group. Additionally, immunohistochemical analysis demonstrated that the expression level of MUC2 protein in the colon was significantly higher in the High group than in the Low and Middle groups (P = 0.049; Fig. 2C). As both the gene and protein expression levels of MUC2 were significantly higher in the High group than in the Low group, the number of goblet cells was quantified in the colon using AB–PAS staining. The goblet cell counts in the High group were significantly higher than those in the Low group (P = 0.009; Fig. 2D). Additionally, a significant negative correlation was observed between the goblet cell counts (P = 0.003 and |r| = 0.772) and MUC2 expression (P = 0.026 and |r| = 0.636) in the colon and piglet diarrhea scores (Fig. 2E).

Fig. 2.

Iron supplementation may alleviate piglet diarrhea by promoting colonic goblet cell function. (A) Ileal mucosal marker gene expression (Low, n = 4, 7, or 8; Middle, n = 4, 6, or 7; High, n = 4, 5, or 7). (B) Colonic mucosal marker gene expression (Low, n = 4 or 7; Middle, n = 4, 5, or 7; High, n = 4, 6, or 8). (C) MUC2 immunohistochemistry (Low, n = 4; Middle n = 5; High, n = 6). (D) Goblet cell AB–PAS staining (n = 7). (E) Correlation analysis between goblet cell function and fecal score. Scale bars, 20 μm. Low group, basal diet; Middle and High groups, basal diet with 70 and 700 mg/kg FeSO₄, respectively. The ns indicates a statistically significant difference between groups at P > 0.05. ∗ indicates a statistically significant difference between groups at P < 0.05.

3.4. Iron supplementation may regulate the function of goblet cells through colonic microbiota

The gut microbiota is essential for MUC2 production by goblet cells. To determine whether changes in colonic goblet cells induced by iron intervention were associated with the gut microbiota, iron concentration in colonic contents was measured and colonic microbial 16S rDNA sequencing was performed. The iron concentration in the colonic contents increased with the increase of iron concentration in the diet (P < 0.001; Table 3). The 16S sequencing results indicated that the iron concentration in the colonic contents altered the composition of the gut microbiota through PCA, and the increased iron content improved microbial diversity and abundance, specifically between the Low and High groups (P > 0.05; Fig. 3A and B). Subsequently, the Kruskal-Wallis test was used to analyze the abundance of different microbiota (ASV) between the Low and High groups (Fig. 3C). Specifically, the enrichment of Muribaculaceae ASVs, such as ASV160, ASV67, ASV143, ASV53, and ASV153, Ruminococcaceae_ASVs, such as ASV147, ASV573, ASV627, ASV595, and ASV387, and Phascolarctobacterium_ASV103 and _ASV90, and the depletion of Prevotella_sp._DJF_LS16 ASVs, such as ASV73, ASV31, ASV35, ASV84, Butyricicoccus_ASV419 and _ASV475, and Lactobacillus_ASV4 with high dietary iron.

Fig. 3.

Iron supplementation, which improves mucin production, may be involved in modulating intestinal microbiota. (A) β-Diversity of colonic microbes (PCA). (B) α-Diversity of colon microbes (Shannon and Chao1 indexes). The ns indicates a statistically significant difference between groups at P > 0.05. ∗ indicates a statistically significant difference between groups at P < 0.05, ∗∗ indicates a higher statistically significant difference between groups at P < 0.01; n = 8. (C) Heat map of differential microbes (ASV) between low and high groups (Kruskal–Wallis). (D) Marker microbes of low and high groups, respectively (LEfSe analysis). (E) The relationship between marker microorganisms and goblet cell function (Spearman correlation analysis). The color scale represents correlation coefficients, with the gradient ranging from negative to positive values and 0.0 indicating no correlation. Asterisks denote significant correlations (∗ P < 0.05, ∗∗ P < 0.01). Low group, basal diet; Middle and High groups, basal diet with 70 and 700 mg/kg FeSO₄, respectively. PCA = principal component analysis; ASV = amplicon sequence variant; LEfSe = linear discriminant analysis effect size.

The LEfSe analysis to explore the discriminative microbiota between the Low and High groups. Muribaculaceae, UCG_002, Eubacterium_coprostanoligenes_group, Oscillospirales, and Clostridia were enriched markers in the high group, whereas Veillonellaceae, Megasphaera, Prevotellaceae, Prevotella_sp._DJF_LS16, Acidaminococcaceae, and Acidaminococcales were downregulated markers in the low group (Fig. 3D). To investigate the association between microbial changes and mucin production, Spearman correlation analysis between the relative abundance of these marker taxa and goblet cell counts (P = 0.007 and |r| = 0.683) and MUC2 expression (P = 0.041 and |r| = 0.552). A significant positive correlation was observed, specifically between the relative abundance of Muribaculaceae enriched in the High group and both goblet cell counts and MUC2 expression (Fig. 3E).

3.5. Iron supplementation alters colonic microbial metabolites independent of goblet cell function

To identify metabolic phenotypes associated with the gut microbiota that may contribute to alterations in goblet cell function, untargeted metabolomic profiling of colonic contents from piglets fed either a low- or high-iron diet was conducted. Orthogonal partial least squares discriminant analysis (OPLS-DA) analysis revealed distinct metabolic profiles between the two groups, indicating that dietary iron levels significantly influenced the colonic metabolome (Fig. 4A). Notably, metabolites such as pipecolic, mandelic, ortho-hydroxyphenylacetic, and 3-hydroxyphenylacetic acids were enriched in the High group, whereas caproic acid, malic acid, alpha-linolenic acid, and arginine were enriched in the Low group (Fig. 4B).

Fig. 4.

Iron supplementation does affect colonic goblet cell function through gut microbial metabolites. (A) Orthogonal partial least squares discriminant analysis (OPLS-DA) of colonic metabolites from piglets fed low- and high-iron diets. (B) Heatmap showing differential metabolites between the low and high groups. Correlation analyses between representative differential metabolites and goblet cell numbers (C) or MUC2 mRNA expression (D). (E) Concentrations of malic acid and arginine in the low and high groups. ∗ indicates a statistically significant difference between groups at P < 0.05, ∗∗ indicates a higher statistically significant difference between groups at P < 0.01. (F) Pearson correlation analysis between malic acid or arginine levels and the abundance of microbial taxa enriched in either the low or high group. Asterisks denote significant correlations (∗ P < 0.05, ∗∗ P < 0.01). (G) Morphology and MUC2 mRNA expression in porcine colonic organoids treated with different concentrations of malic acid or arginine. Scale bars, 50 μm. Low group, basal diet; Middle and High groups, basal diet with 70 and 700 mg/kg FeSO₄, respectively. In figure A–F, Low, n = 7; High, n = 8; In figure G, n = 3. UCA = ursocholic acid; wMCA = ω-muricholic acid; 7-DHCA = 7-dehydrocholic acid; bMCA = β-muricholic acid; GABA = gamma-aminobutyric acid; EPA = eicosapentaenoic acid. ∗ indicates a statistically significant difference between groups at P < 0.05, ∗∗ indicates a higher statistically significant difference between groups at P < 0.01.

To explore the potential association between metabolic alterations and goblet cell phenotypes, correlation analyses were performed to assess the relationships of differentially abundant metabolites with goblet cell numbers and MUC2 mRNA expression. Malic acid was significantly negatively correlated with goblet cell number (P = 0.012 and |r| = 0.696), and arginine was strongly negatively correlated with MUC2 expression (P = 0.001 and |r| = 0.836) (Fig. 4C and D). Consistently, the concentrations of malic acid (P = 0.005) and arginine (P = 0.022) were significantly lower in the High group than in the Low group (Fig. 4E).

To further explore the potential associations between iron-induced microbial shifts and metabolic alterations, microbe-metabolite correlation analysis was conducted. Both malic acid and arginine were significantly negatively correlated with iron-associated taxa such as Muribaculaceae, UCG_002, and the Eubacterium_coprostanoligenes_group, and positively correlated with taxa enriched in the low group, including Veillonellaceae, Megasphaera, and Prevotellaceae (P < 0.05; Fig. 4F).

To empirically evaluate the potential influence of these metabolites on goblet cell function, colonic organoids from piglets were treated with varying concentrations of malic acid or arginine. Neither compound affected MUC2 expression in colonic organoids (P > 0.05; Fig. 4G).

3.6. Iron supplementation promotes MUC2 mRNA expression in colonic organoids

To investigate the regulatory effect of iron on the function of goblet cells, different concentrations of iron were administered to porcine colonic organoids. Significant alterations in MUC2 mRNA expression were observed in iron-deficient (DFO), mock-treated (MOCK), and iron-supplemented (FAC) groups (Fig. 5A). Notably, MUC2 expression increased significantly with increasing iron treatment concentrations in the DFO, MOCK, and FAC200 groups (P < 0.001), with no difference observed between the MOCK and FAC100 groups. Ferritin expression was significantly lower in the DFO100 group than in the other three groups (P = 0.001), with no significant differences among the latter three groups (Fig. 5B). Subsequent Pearson correlation analysis indicated a highly significant positive correlation between MUC2 expression in porcine colonic organoids and Ferritin expression (P = 0.002 and |r| = 0.797; Fig. 5C).

Fig. 5.

Iron promotes the mRNA expression of MUC2 in porcine colonic organoids. (A) Different concentrations of iron-treated porcine colonic organoids for 48 h. (B) The mRNA expression of the MUC2 and ferritin in organoids. (C) The Pearson correlation analysis of the mRNA expression of MUC2 and ferritin in organoids. Scale bars, 100 μm. MOCK = basal culture medium; DFO = deferoxamine; FAC = ferric ammonium citrate. Different lowercase letters above columns represent significant differences among treatments at P < 0.05 (n = 3).

3.7. Iron supplementation alters transcriptome profiles in colonic organoids

To further investigate the mechanism by which iron facilitates MUC2 production in colonic goblet cells, it was conducted transcriptome sequencing on the DFO, MOCK, and FAC200 groups, which exhibited significant differences in MUC2 mRNA expression. As data from the Low group piglets indicated iron deficiency anemia, the analysis used the DFO group as a control, while the MOCK and FAC200 groups were iron-supplemented groups. Principal coordinate analysis revealed highly significant differences in the transcriptomes of porcine colonic organoids treated with varying iron concentrations (P = 0.005; Fig. S1A). The KEGG pathway enrichment analysis identified that both the iron supplementation groups showed enrichment of differential genes in the PI3K-Akt and MAPK signaling pathways compared to that of the DFO group (Fig. S1B). In addition, transcriptomic sequencing of piglet colons in this study revealed enrichment of the PI3K-Akt and MAPK signaling pathways in the high group (Fig. S2). Gene set enrichment analysis (GSEA) of colonic organoid transcriptome data showed significant downregulation of the PI3K-Akt (MOCK, P = 0.001; FAC200, P = 0.001) and MAPK (MOCK, P = 0.003; FAC200, P = 0.001) signaling pathways in the iron supplementation groups than in the DFO group (Fig. S1C–E and Fig. 6B). The GSEA revealed significant upregulation of the mucin O-glycan biosynthesis pathway in the iron supplementation groups than in the DFO group (MOCK, P = 0.002; FAC200, P = 0.009), highlighting the role of O-glycan synthesis for MUC2 production (Fig. S1F and G).

Fig. 6.

The PI3K-Akt pathway regulates MUC2mRNA expression under iron-deficient conditions. (A) Principal coordinate analysis (PCoA) comparing the DFO and MOCK groups. (B) Gene set enrichment analysis (GSEA) with the PI3K-Akt signaling pathway. (C) Representative morphology and MUC2 mRNA expression of colonic organoids from the MOCK, DFO, and DFO + PI3K-Akt inhibitor (LY294002) groups. Scale bars, 100 μm. MOCK = basal culture medium; DFO = deferoxamine. The ns indicates a statistically significant difference between groups at P > 0.05. ∗∗ indicates a higher statistically significant difference between groups at P < 0.01; n = 3.

3.8. The PI3K-Akt signaling pathway is crucial in iron-mediated regulation of MUC2mRNA expression

Owing to the consistent MUC2 mRNA expression and pathway alterations between the MOCK and FAC200 groups, subsequent experiments focused solely on the DFO and MOCK groups. To determine whether iron intervention regulates MUC2 production through mucin O-glycan biosynthesis, PI3K-Akt signaling, and MAPK signaling, porcine colonic organoids were treated with DFO or MOCK in the presence or absence of Ac5GalNTGc, LY294002, or SB202190. The MUC2 mRNA expression was significantly reduced in the MOCK + Ac5GalNTGc group than in the MOCK group (P = 0.025). However, MUC2 mRNA expression was also significantly increased in the MOCK + Ac5GalNTGc group than in the DFO group (P < 0.001). The MUC2 mRNA expression was significantly upregulated in the DFO + SB202190 group than in the DFO group (P = 0.021), but significantly downregulated in the DFO + SB202190 group than in the MOCK group (P < 0.001; Fig. S3A and B). However, compared with that in the DFO group, MUC2 mRNA expression was highly upregulated in the DFO + LY294002 group (P = 0.004), and MUC2 mRNA expression in the DFO + LY294002 group was restored to levels similar to those in the MOCK group (Fig. 6C).

4. Discussion

Diarrhea in piglets is a frequently encountered challenge in pig production (Han et al., 2024). The intestinal mucosal barrier plays a critical defense role in protecting against external pathogens and maintains intestinal homeostasis (Leonardi et al., 2022; Sylvestre et al., 2023). Iron, a key nutrient involved in growth metabolism, regulates the intestinal mucosal barrier through both host and gut microbiota (Chen et al., 2020; Dong et al., 2023; Spiga et al., 2023). However, the underlying mechanism of this phenomenon remains unclear. In the present study, iron-deficient neonatal piglets were fed formula milk containing varying iron concentrations, and a porcine colonic organoids model treated with different iron concentrations was employed to investigate the regulatory mechanisms of iron on the intestinal mucosal barrier.

Iron supplementation in neonatal piglets increased the average daily weight gain, consistent with previous findings (Williams et al., 2020). Although HGB levels did not differ significantly among groups, the Middle and High groups exhibited higher average HGB than that of the Low group. The slightly lower HGB in the High group may be due to increased hepcidin, which is induced by high iron intake (Pasricha et al., 2021) and negatively correlates with HGB (Basseri et al., 2013). The MCV and MCH were significantly higher in the Middle and High groups, indicating improved anemia, as low MCV and MCH are typical of iron deficiency anemia (Tkaczyszyn et al., 2018). The hematological parameter values were generally within the normal range (Ventrella et al., 2017). Liver and spleen iron levels were also significantly increased in the Middle and High groups, indicating enhanced tissue iron storage, consistent with previous studies (Huang et al., 2023). Hepcidin-mediated inhibition of intestinal iron absorption may account for the lack of a difference between the middle and high groups. Collectively, these results indicate that iron supplementation improved growth, anemia, and iron storage in piglets.

Furthermore, iron supplementation alleviated diarrhea in piglets, with the most pronounced effect observed in the high group, as exhibited by a significant reduction in colonic pathological scores. This is consistent with previous studies demonstrating that iron deficiency in neonatal piglets increases their susceptibility to pathogens and results in an increased incidence of diarrhea (Zhang et al., 2020). By contrast, post-weaning piglets that were provided lower doses of dietary iron (<700 mg/kg) exhibited increased diarrheal rates (Chen et al., 2020; Lee et al., 2008), likely because they were typically not iron-deficient, and excess iron may have facilitated the proliferation of intestinal pathogens. No significant differences in ileal pathological scores were observed among groups; however, iron supplementation notably reduced colonic pathology. The aforementioned results indicate that iron supplementation ameliorated colonic pathological alterations and alleviated the occurrence of diarrhea in piglets.

To further investigate regional differences in mucosal barrier function, the expression of barrier-related and antimicrobial genes, including ZO1, occludin, LYZ, and MUC2. Specifically, colonic MUC2 mRNA expression was significantly upregulated following iron supplementation, whereas the expression levels of ZO1, occludin, and LYZ remained unchanged. This was followed by increased MUC2 protein levels and goblet cell numbers in the colon, consistent with previous findings (Zhang et al., 2022). Correlation analysis demonstrated strong negative associations between fecal scores and both goblet cell abundance and MUC2 expression, indicating a protective role of mucin production against diarrhea. Lower levels of MUC2 compromise the structural integrity of the mucus barrier, thereby increasing epithelial permeability and intensifying mucosal inflammatory responses (Sharpen et al., 2022; Xiao et al., 2021; Zhang et al., 2024). Collectively, these results suggest that iron supplementation may alleviate diarrhea by enhancing goblet cell differentiation and stimulating MUC2 production, thereby strengthening the colonic mucosal barrier.

The integrity of the intestinal mucus layer is closely dependent on the gut microbiota. Therefore, the role of the intestinal microbiota and their metabolites in mediating iron-induced regulation of goblet cell function. Increasing dietary iron levels notably modified the gut microbial community, with the most pronounced differences observed between the Low and High groups. The High group was characterized by enrichment of potentially pathogenic taxa, such as Clostridia, and depletion of beneficial bacteria, including Lactobacillus. This pattern likely indicates the ecological pressure created by excessive unabsorbed luminal Fe²⁺/Fe³⁺, which promotes microbiomes, such as Clostridia, with efficient iron-uptake systems and strong oxidative stress tolerance (Pi et al., 2023). By contrast, Lactobacillus, which lacks iron-dependent metabolic pathways and is highly sensitive to oxidative stress, exhibits reduced ecological fitness in such environments, resulting in decreased abundance (Loveikyte et al., 2023). Although Lactobacillus promotes MUC2 expression (Luo et al., 2023), LEfSe did not identify it as a significant biomarker for the Low group. Among all discriminative taxa between the Low and High groups, only Muribaculaceae was significantly correlated with both goblet cell counts and MUC2 expression. This taxon has also been linked to mucus secretion and epithelial barrier maintenance (Bu et al., 2023; Wang et al., 2023), supporting its functional relevance in these findings. Based on these results, iron was hypothesized to regulate goblet cell function through microbiota-mediated pathways.

Untargeted metabolomic profiling of colonic contents was conducted to explore the involvement of microbial metabolites. Distinct metabolite profiles were observed between the high- and low-iron groups. Correlation analysis identified malic acid and arginine as key metabolites negatively associated with goblet cell number and MUC2 mRNA expression, respectively. Notably, both metabolites were inversely correlated with iron-enriched microbes such as Muribaculaceae and positively correlated with microbes enriched under low-iron conditions, including Prevotellaceae.

These results indicate that microbial metabolites may act as intermediaries in the iron-mediated regulation of goblet cell function. However, functional validation using pig colonic organoid models demonstrated that neither malic acid nor arginine significantly affected MUC2 mRNA expression. Similarly, previous studies have reported that antibiotic-induced disruption of the gut microbiota does not impair the regulatory effect of iron on colonic goblet cells (Liu et al., 2023). Together, these results indicate that, in addition to microbiota-derived signals, iron can influence goblet cell function either directly or through host-mediated pathways using alternative mechanisms.

To elucidate the mechanisms by which iron regulates colonic goblet cell function, porcine colonic organoids were cultured in media containing different iron concentrations. Iron deficiency suppressed MUC2 mRNA expression, consistent with in vivo observations. The KEGG pathway enrichment analysis in groups with differential MUC2 mRNA expression identified significant enrichment of the MAPK, PI3K-Akt, and mucin-type O-glycan biosynthesis pathways. The MAPK and PI3K-Akt signaling pathways have been reported to regulate goblet cell function and thereby modulate intestinal barrier integrity and inflammation (Kabiri et al., 2018). The MAPK pathway finely tunes goblet cell differentiation and MUC2 expression, enhancing the mucosal barrier under moderate activation but causing mucus layer imbalance and promoting intestinal inflammation when excessively activated (Heuberger et al., 2014; Szandruk-Bender et al., 2025; Yang et al., 2022). Evidence regarding the PI3K-Akt pathway remains contradictory: certain studies indicate that its inhibition increases goblet cell numbers and improves barrier function (Li et al., 2023; Sharma et al., 2021), whereas other studies suggest that activation enhances goblet cell differentiation and MUC2 expression, reducing colitis susceptibility (Schumacher et al., 2021). Additionally, impaired O-glycan biosynthesis, owing to the deletion of key glycosyltransferases, compromises the mucus layer and increases susceptibility to colitis (Bergstrom et al., 2017; Fu et al., 2011).

To further investigate the regulatory role of iron on these pathways, GSEA analysis showed that iron deficiency upregulated the MAPK and PI3K-Akt signaling pathways and downregulated the mucin-type O-glycan biosynthesis pathway. This is consistent with previous findings: DFO treatment activates MAPK signaling in vitro (Xue et al., 2021), whereas in vivo iron accumulation inhibits MAPK signaling (Ikeda et al., 2019). Similarly, Xia et al. (2019) reported that iron accumulation inhibits the PI3K-Akt pathway in vitro, consistent with the present results. To date, no studies have explored the relationship between iron and mucin-type O-glycan biosynthesis. To functionally validate the involvement of these pathways in iron-regulated MUC2 expression, colonic organoids were treated with specific pathway inhibitors under iron-deficient conditions. Inhibition of the PI3K-Akt pathway significantly increased MUC2 expression in the DFO group, with levels comparable to those of the MOCK group. These results indicate that the PI3K-Akt signaling pathway plays a key role in mediating the effects of iron on MUC2 production in colonic goblet cells.

5. Conclusions

The study findings demonstrated that dietary iron supplementation improves systemic iron status and growth performance, enhances intestinal mucosal barrier integrity through increased MUC2 mRNA production, and alleviates diarrhea in piglets. Additionally, the findings suggest that iron may facilitate goblet cell differentiation and MUC2 mRNA expression through inhibition of the PI3K-Akt signaling pathway, rather than via modulation of the gut microbiome. The findings demonstrate that appropriate dietary iron supplementation increases growth performance and preserves mucosal barrier integrity in suckling piglets. Collectively, this study provides novel evidence proving that modulation of trace element iron levels is an effective strategy for regulating intestinal barrier function.

Credit Author Statement

Shuan Liu: Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization. Chengyan Gong: Investigation. Ziyue Bai: Investigation. Lingzhi Zhu: Investigation. Lan Li: Writing – review & editing. Dan Wan: Resources, Funding acquisition, Conceptualization. Yulong Yin: Supervision, Resources, Funding acquisition, Conceptualization.

Declaration of competing interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper. Yulong Yin is an Editorial Board Member for Animal Nutrition and was not involved the editorial review or the decision to publish this article. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lan Li is currently employed by Beijing Dabeinong Technology Group Co., Ltd. (Beijing, China).

Appendix A. Supplementary data

The following is the Supplementary data to this article.