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Article

Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Mechanisms Underlying Hepatic Differences Between Zaozhuang Heigai Piglets and Duroc×Landrace×Yorkshire Piglets

by
Caitong Wang
1,2,
Jingxuan Li
2,
Xueyan Zhao
2,
Yanping Wang
2,
Xiaodong Zhu
3,
Fuping Zhao
4,
Chuansheng Zhang
1,
Liying Geng
1,* and
Jiying Wang
2,*
1
Hebei Key Laboratory of Animal Genetic Resources Exploration and Innovation, Hebei Normal University of Science and Technology, Qinhuangdao 066600, China
2
Shandong Provincial Key Laboratory of Animal Breeding and Genetics, Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China
3
Shandong Futeng Food Co., Ltd., Zaozhuang 277599, China
4
Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
Agriculture 2026, 16(2), 241; https://doi.org/10.3390/agriculture16020241
Submission received: 15 December 2025 / Revised: 15 January 2026 / Accepted: 15 January 2026 / Published: 17 January 2026
(This article belongs to the Section Farm Animal Production)
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Versions Notes

Abstract

Piglets weaning is a critical developmental stage marked by significant metabolic and inflammatory challenges. The hepatic responses during this period may differ among pig breeds with distinct genetic backgrounds. To explore the phenotypic and molecular differences in the livers between the Zaozhuang Heigai (HG) pig and Duroc×Landrace×Yorkshire (DLY) piglets and elucidate the regulatory mechanisms of genetic background on liver function, five 35-day-old piglets from each breed were selected. Body weight and liver coefficients were measured; histological features of liver sections were observed, and the transcriptome and metabolome of the liver were determined using mRNA sequencing and non-targeted metabolomics analysis. The results showed that HG piglets had significantly lower body weight (p < 0.01) and slightly higher liver coefficients than DLY piglets. Histological examination revealed that the hepatic lobule structure was intact in both breeds, while mild hepatic congestion was observed in some DLY piglets. Transcriptome analysis identified 429 differentially expressed genes (DEGs) with criteria of FDR adjusted p-values < 0.01 and |log2(Fold Change)| > 1, and they were significantly enriched in oxidoreductase activity, peroxisome proliferator-activated receptor (PPAR) signaling, and arachidonic acid metabolism pathways. Metabolome analysis identified 169 differentially expressed metabolites (DEMs) with criteria of p < 0.05, VIP > 1, and |log2(Fold Change)| > 1, and they were significantly enriched in nucleotide metabolism, arginine biosynthesis, and arachidonic acid metabolism pathways. Integrative analysis of DEGs and DEMs showed that arachidonic acid metabolism was the common pathway. Within this pathway, key genes (GPX3, ALOX5, and CBR3) were significantly associated with specific metabolites (15-deoxy-PGJ2 and phosphatidylcholines) (FDR adjusted p < 0.05), suggesting a gene–metabolite interaction network that coordinates inflammatory regulation and oxidative stress. These findings provide molecular evidence for breed-specific hepatic metabolic regulation during the weaning period and are therefore conducive to the management of weaned piglets and the investigation of local pig characteristics.

1. Introduction

In the pig production process, piglets often face the challenge of weaning stress. This stress not only impedes the growth of piglets but also causes a decline in immunity and a substantial increase in disease susceptibility [1,2,3], thereby bringing significant economic losses to the breeding industry. Alleviating weaning stress in piglets, especially during the early post-weaning period, is one of the key challenges confronting the modern pig industry. The liver, as the central hub of substance metabolism, detoxification, and immune regulation, is prone to redox imbalance under multiple factors of weaning stress (nutritional disorders, endotoxin exposure, and oxidative stress). This leads to liver oxidative damage, and such damage will further exacerbate the growth retardation and disease susceptibility of piglets [4,5]. However, currently, the specific mechanisms linking weaning stress to liver injury remain poorly elucidated. This knowledge gap severely limits the development of targeted regulatory measures to protect liver function and alleviate weaning stress, ultimately hindering the realization of healthy and efficient pig breeding.
In recent years, the rapid advancement of omics technologies, such as transcriptomics and metabolomics, has greatly promoted our understanding of the molecular mechanisms underlying the complex traits of organisms [6,7]. These technologies offer powerful means to thoroughly investigate the intricate network of gene expression, protein synthesis, and metabolite changes in organisms. With their assistance, researchers have systematically analyzed the differential mechanisms underlying livestock and poultry physiological characteristics, including growth [8,9], feed conversion rate [10,11], meat quality [12,13], reproductive ability [14,15], and disease resistance [16,17]. They have also identified genes or metabolites closely related to these traits, therefore laying a solid foundation for screening and determining biomarkers related to specific traits. As one of the vital metabolic organs in animals, the liver plays a crucial role in maintaining the organism’s internal environment stability, metabolic balance, and resistance to external aggression [18,19]. Previous studies have employed the transcriptome, metabolome, and other technologies to explore the liver’s response mechanisms to the influence of maternal, environmental, and other factors. For instance, Gao et al. [20] revealed the close correlation between endogenous oxidative stress and fatty acid metabolism disorders through the combined analysis of the liver transcriptome and metabolome of piglets with intrauterine growth restriction. Yu et al. [21] uncovered the long-term effect of fetal nutrition regulation on the offspring liver’s metabolic and immune pathways through the integration of the liver transcriptome and metabolome. Gu Xuedong et al. [22] used multi-omics technologies to explore the physiological and metabolic differences between Tibetan pigs and Yorkshire pigs in response to different altitude environments at the transcriptional, translational, and metabolic levels and found that energy metabolism is the key for the two breeds to regulate their own liver metabolism and adapt to different altitude environments. However, the current research on the impact of piglet weaning on the liver and its mechanism remains unclear. Therefore, an in-depth study of the gene transcription and metabolic levels of liver tissue is of great significance for comprehensively understanding the physiological differences between organisms and their regulatory mechanisms.
As an important local pig breed in China, the Zaozhuang Heigai (HG) pig has developed unique physiological and metabolic characteristics during the long-term process of natural and artificial selection. Compared with Duroc×Landrace×Yorkshire (DLY) pigs, which are commonly used in intensive farming, HG pigs are characterized by a slow growth rate, superior meat quality, and strong environmental adaptability [23,24]. However, the physiological and regulatory mechanisms underlying these differences remain unclear at present. Considering the pivotal role of the liver in the regulation of nutrient metabolism and immune regulation, the liver may play an essential role in breed-specific growth and disease resistance. Therefore, in this study, we selected 35-day-old HG and DLY piglets during the critical period of the post-weaning transition as the research subjects to determine the phenotypic differences (e.g., body weight, liver weight, and liver coefficients). These differences were also determined via histological observation for comprehensive analysis. Meanwhile, high-throughput transcriptome sequencing and non-targeted metabolome detection technologies were employed to screen differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs). Based on these results, the key pathways in the liver function regulation process of HG and DLY piglets were identified through joint analysis. This study will help characterize phenotypic and molecular differences between these two pig breeds, elucidate genetic background-dependent regulatory mechanisms in liver function, and promote the investment of local pig characteristics.

2. Materials and Methods

2.1. Experimental Animals

In this study, five 28-day-old weaned HG and DLY piglets from Zaozhuang Heigai Pig Breeding Co., Ltd. (Tengzhou City, Zaozhuang, Shandong Province, China) were selected based on the following criteria: (1) All selected piglets of each breed were from the same litter of sows. (2) All selected piglets were individually weighed, and the weight difference were controlled based on a coefficient of variation (CV) of 10%. (3) A clinical health assessment was conducted, including observing the mental state; checking for the absence of diarrhea, cough, or other disease-related symptoms; and measuring rectal temperature. The piglets were weaned according to the following protocol: Two weeks prior to weaning, creep feed was gradually incorporated into the piglets’ diet. At 28 days of age, the piglets underwent a 3-day gradual weaning process. During the weaning transition and acclimation period, they were provided with creep feed ad libitum.
All selected weaned piglets were reared in two pens under the same conditions for one week. The housing conditions were as follows: Each pen measured 2.0 m × 3.0 m and had a slatted floor for fecal removal. The ambient temperature was maintained at 24–26 °C, and the relative humidity was controlled at 55–65%. The composition and nutritional level of the diet provided during the experiment are detailed in the study by Li et al. [23]. No antibiotics or other drugs were provided to the experimental piglets during the experiment. When the piglets reached 35 days of age, they were fasted for 6 h, weighed, and then euthanized.

2.2. Phenotypic Determination and Sample Collection

After euthanizing the piglets, their livers were taken out from the abdominal cavity. The blood on the liver surface was washed off, and the livers were weighed. The liver coefficient was calculated using the following formula: liver coefficient = liver weight (g)/body weight (kg) × 100%. Subsequently, appropriately 1.2 g of liver tissue was collected from the apical part of the left medial lobe of the liver and immersed in liquid nitrogen for more than six hours, and then stored at −80 °C for subsequent RNA extraction. In parallel, an adjacent piece of liver tissue (approximately 2 cm × 1.5 cm × 0.8 cm) was collected, fixed in 4% paraformaldehyde, and embedded in paraffin. Next, the liver tissue was cut into 5 μm sections and stained with hematoxylin and eosin (HE). The liver sections were observed under a 400-fold optical microscope and scanned using the NDP View (v2.3.1) software (Hamamatsu Photonics K.K, Hamamatsu, Japan).

2.3. Liver Transcriptome Sequencing and Analysis

Total RNA from liver tissue was extracted using the silica gel membrane method. The integrity and purity of the RNA were evaluated via agarose gel electrophoresis and a Nanodrop (Thermo Fisher Scientific, Waltham, MA, USA), and the samples were accurately quantified using Qubit (Thermo Fisher Scientific, Waltham, MA, USA). mRNA, with RNA integrity numbers (RINs) larger than 7.0, was enriched with oligo (dT) magnetic beads, and cDNA was synthesized after lysis. The mRNA non-strand-specific library was constructed using the MGIEasy RNA Library Prep Kit (MGI Tech, Shenzhen, China). Paired-end sequencing with a read length of 150 bp was performed using the high-throughput sequencing platform DNBSEQ-T7 (MGI Tech, Shenzhen, China). All samples were sequenced in one batch to eliminate the possible batch effect.
Raw sequencing data were subjected to quality assessment using FastQC (v0.11.9), and they were filtered using fastp (v0.21.0) [25]. This involved removing reads with a N base content exceeding 5% in the raw reads, reads with low-quality (quality value less than or equal to 5) base numbers reaching 50%, reads contaminated with adapters, and duplicate sequences caused by PCR amplification to obtain clean reads for subsequent analysis. The effective sequences of the filtered transcriptome were aligned with the reference genome (Sscrofa11.1, Ensembl gene 107, https://mart.ensembl.org/Sus_scrofa/Info/Index?db=core, accessed on 2 October 2022) using Star (v2.7.9a) [26]. The number of reads mapped to each transcript of each sample was obtained using RSEM [27], and FPKM (fragments per kilobase of transcript per million mapped reads, FPKM) normalization was applied. The R package stats (v3.6.3) [28] was used to conduct principal component analysis (PCA) on the standardized gene expression data.
After removing genes with FPKM < 0.1 (present in half of the individuals) and novel genes that were unannotated, DESeq2 (V1.26.0) [29] was used to analyze the difference in expression between the HG and DLY groups. The Benjamini–Hochberg method was used to correct the original p-value for the false discovery rate (FDR), and the corrected value was used for significance judgment. Differentially expressed genes (DEGs) between the two breeds were screened with an adjusted p-value (p-adj) < 0.01 and |log2 fold change (FC)| > 1 as the criteria. For the screened DEGs, gene ontology (GO) term and pathway enrichment analysis were carried out using the clusterProfiler (v3.14.3) [30] software and the GO [31] and KEGG [32] databases. Significantly enriched GO terms and KEGG pathways were screened with an FDR-corrected p-value (p-adj) < 0.05 as the criterion.

2.4. Metabolome Sequencing and Analysis

At low temperatures, 25 mg liver samples were weighed into EP tubes. Two homogenization beads and 500 μL of the extraction solution containing an isotope-labeled internal standard (methanol/acetonitrile/water = 2:2:1, V/V) were added. The mixture was vortexed for 30 s, then placed in a homogenizer and homogenized at 35 Hz for 4 min. Next, the mixture was transferred to an ice-water bath and sonicated for 5 min, and this sonication step was repeated 3 times. The samples were then left to stand at −40 °C for 1 h. After that, the samples were centrifuged at 4 °C and 12,000 rpm for 15 min. Finally, 100 mL of the supernatant was taken and transferred to an injection vial for on-machine detection. Moreover, equal amounts of supernatant from all samples were taken and mixed to prepare pooled quality control (QC) samples.
For metabolites, a Vanquish (Thermo Fisher Scientific) ultra-high-performance liquid chromatograph (UHPLC) was employed to chromatographically separate the target compounds using a Waters ACQUITY UPLC BEH Amide (2.1 mm × 50 mm, 1.7 μm) liquid chromatography column. Liquid chromatography mobile phase A was an aqueous phase containing 25 mmol/L ammonium acetate and 25 mmol/L ammonia water, while mobile phase B contained acetonitrile. The temperature of the sample tray was set to 4 °C, and the injection volume was 2 μL. Metabolomic analyses were performed using a high-resolution Orbitrap Exploris 120 mass spectrometer (Xcalibur v4.4, Thermo Fisher Scientific, Waltham, MA, USA) coupled to a Vanquish UHPLC system (Thermo Fisher Scientific, Waltham, MA, USA) for the acquisition of both MS and MS/MS data. Data were collected in both positive and negative ion modes. MS/MS analyses employed stepped collision energies of 20, 30, and 40 eV. High-resolution mass spectrometry covered a wide m/z range to comprehensively capture metabolites with diverse molecular weights. Throughout the analysis, pooled QC samples were injected at regular intervals to monitor system stability, and metabolic features with high relative standard deviations (RSDs) in QC samples were excluded.
The original metabolomics data was converted to the mzXML format using the ProteoWizard (v3.0) software [33]. After outlier removal, missing-value filtering and imputation, and data normalization, metabolite identification and quantification were outsourced to Biotree Biomedical Technology Co., Ltd. (Shanghai, China) and determined using the BiotreeDB (V3.0) database. The qualitative method is secondary mass spectrum matching with an error of 15 ppm. Level 3 metabolites, based on the Metabolomics Standards Initiative (MSI) [34], were selected for subsequent analysis. After logarithmic transformation and centering of the data, orthogonal partial least-squares discriminant analysis (OPLS-DA) [35] was performed using the SIMCA software (v18.0.1, Sartorius Stedim Data Analytics AB, Umea, Sweden). Specifically, the fitting and prediction abilities of the metabolites were evaluated via 7-fold cross-validation and permutation tests, based on their R2Y and Q2 values. The variable importance in projection (VIP) values were obtained from the OPLS-DA model, and the p-values for the differential analysis between the two breeds were determined using a t-test. Differentially expressed metabolites (DEMs) were screened with the criteria of p < 0.05, VIP > 1, and |log2(FC)| > 2. The R package clusterProfiler (v4.0.5) [30] used the KEGG pathway database [36] to perform KEGG pathway enrichment analysis on the screened DEMs.

2.5. Transcriptome-Metabolome Integration Analysis

To explore the relationship between the liver transcriptome and metabolome of HG and DLY piglets, Pearson’s correlation analysis was conducted on DEGs and DEMs. The p-values were subjected to FDR multiple correction, and a significant correlation was defined as an FDR-adjusted p-value of less than 0.05.

3. Results

3.1. Comparison of Liver Coefficients and Tissue Sections of Piglets from the Two Breeds

The body weights, liver weights, and liver coefficients of five HG and DLY piglets were determined and compared between the two groups. The averaged body weight of piglets in the HG group (5.83 ± 0.60, mean ± standard deviation, the same below) was significantly lower than that in the DLY group (7.29 ± 0.55) (p < 0.01). The averaged liver weight of HG piglets (123.83 ± 20.75) was lower than that of DLY piglets (146.76 ± 12.38), while their averaged liver coefficient (2.12 ± 0.19) was slightly higher than that of DLY piglets (2.03 ± 0.30); neither the difference in liver weight nor that in liver coefficient between the two groups reached a significant level (both p > 0.05). As shown in the liver tissue sections of piglets (Figure 1), the hepatic lobule structures of both breeds were intact and clear. The hepatic cords were regularly arranged, and the hepatocytes were polygonal, with a normal nucleus-to-cytoplasm ratio and uniformly sized, round or oval, nuclei. There was a small amount of lymphocyte infiltration in both breeds, and slight congestion was observed in three of the five DLY piglets (Figure 1A).

3.2. Comparative Analysis of the Liver Transcriptomes of Piglets from the Two Breeds

The statistics of the RNA-seq data of the liver tissues of piglets from the two breeds are shown in Supplemental File S1. After averaging, each individual yielded 70,033,626 raw reads. After quality control, an average of 70,032,400 clean reads were obtained per individual. When aligned to the reference genome, the proportion of reads with unique alignment positions ranged from 94.45% to 95.50%. The Q20 ratio values were between 98.53% and 98.79%, and the Q30 ratio values ranged from 94.78% to 95.73%. A total of 13,437 genes were aligned to the reference genome (Supplemental File S2). Among them, 13,356 genes were effectively expressed (FPKM > 0.1 in more than half of the individuals). After removing novel genes, 12,842 effectively expressed genes were retained for subsequent analysis (Supplemental File S3). The PCA results of the sequencing data (Figure 2) showed good within-group repeatability for the two groups of data, while there were substantial differences between the groups. This suggests significant differences in the liver mRNA transcriptomes of HG and DLY piglets.
Using the criteria of FDR-adjusted p-values < 0.01 and |log2(Fold Change)| > 1, a total of 429 DEGs were screened from the liver transcriptomes of HG and DLY piglets (Supplemental File S4). Among them, compared with DLY piglets, 224 DEGs were upregulated in HG piglets, while 205 DEGs were downregulated in HG piglets (Figure 3A). Cluster analysis of these DEGs showed that they had relatively consistent expression trends within their respective groups (Figure 3B). To further explore the biological functions of these DEGs in the piglets’ livers, GO and KEGG enrichment analyses were performed on the screened DEGs (Table 1). Five GO terms in the molecular function (MF) category were significantly enriched (p-adj < 0.05), and 11 KEGG pathways were also significantly enriched. These significant GO terms and KEGG pathways are crucial for maintaining the body’s normal physiological functions, ensuring cell stability, and regulating metabolism and life activities.

3.3. Comparative Analysis of the Liver Metabolome of Two Piglet Breeds

A total of 2131 metabolites were detected in the livers of piglets of the two breeds (Supplemental File S5). These metabolites were further categorized into 14 superclasses, 149 classes, and 355 subclasses (Figure 4A). Among these 14 superclasses, four major categories dominated in terms of quantity: organic acids and their derivatives (20.37%), lipids and lipid-like molecules (19.90%), organic heterocyclic compounds (18.58%), and benzene compounds (12.34%). Collectively, these four categories accounted for 71% of the total metabolites (Figure 4B). OPLS-DA was used to comprehensively evaluate the metabolomic data on piglet livers from the two breeds (Figure 5A). The results indicated that the metabolomic data showed good aggregation within each group of samples, while the grouping between the groups was obvious. This indicates that the liver metabolites of HG and DLY piglets had good intra-breed repeatability and significant inter-group differences. The results of the cross-validation and permutation tests (Figure 5B) showed that as the correlation coefficient decreased (the permutation retention decreased), Q2 and R2 gradually declined. The Q2 regression curve intersected the vertical axis at −0.5, and both the R2Y (0.992) and Q2 values (0.719) were close to one, indicating that the constructed model was reliable and that there was no overfitting.
Using the criteria of p < 0.05, VIP > 1, and |log2(Fold Change)| > 1, 169 DEMs were screened from the liver metabolomes of HG and DLY piglets (Supplemental File S6). Specifically, compared with DLY piglets, 155 DEMs were significantly upregulated in HG piglets, while 14 were significantly downregulated (Figure 6). KEGG enrichment analysis of these screened DEMs revealed that 11 KEGG pathways were significantly enriched, such as nucleotide metabolism, arginine biosynthesis, ABC transporters, the mTOR signaling pathway, and arachidonic acid metabolism (Figure 7).

3.4. Integrated Analysis of the Transcriptome and Metabolome

To explore the connection between gene expression and metabolic data and to construct a gene–metabolite regulatory network, we performed integrated analysis of gene expression data and metabolite change data. Pearson’s correlation analysis of DEGs and DEMs revealed that among the 72,501 combinations of 429 DEGs and 169 DEMs, 20,742 (28.61%) exhibited a significant correlation (p-adj < 0.05), with an average absolute correlation coefficient of 0.6365 (Supplemental File S7). Among the DEGs, ENSSSCG00000002803 (CCDC113) and ENSSSCG00000039556 (KCNQ1) showed the strongest correlations with the DEMs and were significantly correlated with 150 and 149 DEMs, respectively, and their average absolute correlation coefficients were 0.8449 and 0.8671. Among the DEMs, Pisiferal and Juvocimene_1 displayed the strongest correlation with the DEGs and were significantly correlated with 279 and 275 DEGs, respectively, and their average absolute correlation coefficients reached 0.8446 and 0.8431. Joint KEGG analysis of DEGs and DEMs revealed enrichment in the arachidonic acid (AA) metabolism pathway, which contained a total of eight DEGs and two DEMs. Correlation analysis of the DEGs and DEMs in this pathway (Figure 8) showed that 15-Deoxy-PGJ2 was significantly correlated with ALOX5, ENSSSCG00000027013, ENSSSCG00000052916, ENSSSCG00000062325, GPX3, and CBR3 (p-adj ≤ 0.05), and PC(18:1(11Z)/15:0) was significantly negatively correlated with CBR3, GPX3, and ENSSSCG00000062325 (p-adj > 0.05).

4. Discussion

4.1. Differences in Liver Coefficients and Tissue Sections Between HG and DLY Piglets

The liver is one of the most vital metabolic organs in the body. It participates in various physiological functions, such as nutrient metabolism and immune defense, and plays a crucial role in maintaining the body’s homeostasis [37]. A higher liver coefficient and a more intact liver histological structure usually indicate better growth status of the liver and more robust physiological functions [38,39]. In this study, we measured and compared the body weight, liver weight, as well as two indicators for evaluating liver function (liver coefficient and hepatic histological section) in five HG and five DLY piglets. The average body weight of HG piglets was significantly lower than that of DLY piglets (p < 0.01), whereas their average liver coefficient (2.12) was slightly higher than that of the DLY group (2.03) (p > 0.05). By observing liver tissue sections, we found that the hepatic lobule structures of both pig breeds were complete and clear, and the hepatocytes were regularly arranged with normal structures. However, slight congestion occurred in three samples from the DLY group, whereas no such lesions were detected in any HG samples. Taken together, the evidence indicates that DLY piglets have an advantage in body weight and may prioritize rapid growth under weaning stress. HG piglets, as a local breed, have evolved to possess sound liver function, which enables them to efficiently metabolize nutrients from new diets, eliminate toxic metabolites generated under stress, and regulate the body’s immune response during the weaning period.

4.2. Differences in Liver Transcriptome and Metabolome Between HG and DLY Piglets

All life activities of organisms, including metabolism, the stress response, growth, and development, rely on gene expression products such as RNA, metabolites, and proteins. Variations in the expression of the liver transcriptome and metabolome may reflect potential differences in adaptability and metabolic patterns among different breeds. In this study, 429 DEGs were identified in the liver transcriptome of HG and DLY piglets, and 169 DEMs were identified in the metabolome. These widespread molecular differences preliminarily reveal distinct hepatic metabolic and regulatory patterns between the two breeds during the weaning period.
DEGs were significantly enriched in GO terms, such as iron ion binding, monooxygenase activity, and oxidoreductase activity, as well as pathways, including the PPAR signaling pathway, arachidonic acid metabolism, and the AMPK signaling pathway. These significantly enriched GO terms and pathways are closely associated with energy metabolism, the inflammatory response, and oxidative stress [40,41,42]. Moreover, previous studies indicated that multiple DEGs, such as ALOX5, GPX3, and CBR3, are related to oxidative stress, lipid metabolism, and immunity. ALOX5 encodes 5-lipoxygenase (5-LOX), a key enzyme in the synthesis of leukotrienes in the inflammatory pathway. It can catalyze arachidonic acid to generate pro-inflammatory lipid mediators such as LTB4 and LTC4 [43]. Upregulation of ALOX5 expression in HG may increase the protein level of this enzyme, thereby enhancing arachidonic acid metabolism to generate pro-inflammatory mediators (e.g., LTB4 and LTC4) and participating in the regulation of the local inflammatory environment [44]. Conversely, CBR3 expression was downregulated in HG piglets. This gene encodes an NADPH-dependent reductase, which can reduce molecules, like prostaglandin (e.g., PGE2), to their corresponding alcohols, thereby terminating their inflammatory signal effects [45]. Additionally, studies have shown that the expression of this gene can be activated by TNF-α, IL-1β, and LPS, indicating its role in the early stage of inflammation [46]. Moreover, studies have indicated that CBR3 affects the lipid metabolism pathway related to PGE2 conversion, and it is associated with insulin resistance and the risk of metabolic diseases [47]. GPX3 encodes the main extracellular antioxidant enzyme and was significantly downregulated in HG piglets. This enzyme can scavenge hydrogen peroxide and prevent harmful lipid peroxides from damaging liver cells [48]. Meanwhile, studies have shown that its expression is regulated by PPARγ and is closely related to lipid metabolism and the anti-inflammatory response [49]. The downregulation of GPX3 expression in HG piglets suggests that HG pigs may rely on other pathways or non-enzymatic antioxidant molecules to compensate when facing oxidative stress.
DEMs were primarily enriched in select pathways, such as nucleotide metabolism, arginine biosynthesis, the mTOR signaling pathway, arachidonic acid metabolism, the sulfur transfer system, and choline metabolism in cancer. These pathways encompass multiple aspects such as substance metabolism, signal transduction, and disease occurrence and are of great significance in the physiological function and pathological processes of pigs [50,51,52]. Moreover, previous studies have shown that DEMs, such as SM(d18:1/17:0), Stachydrine, and Ectoine, play important roles in liver oxidative stress, necrotic inflammation, and other forms of damage. SM is a short-chain sphingomyelin, and it has been found to have a negative correlation with liver necrotic inflammation in the serum of patients with chronic hepatitis B [53]. Additionally, it has a significant protective effect on liver lipid clearance and inflammation control in high-fat diet-fed mice [54]. Stachydrine demonstrated the ability to mitigate inflammation and oxidative stress in a CCl4-induced liver fibrosis model [55]. It also showed moderate anti-inflammatory and anti-fibrotic effects by reducing ERK activation and the release of pro-inflammatory cytokine in airway epithelial cells [56]. Ectoine, a type of osmoprotectant, has been shown to exert antioxidant, anti-inflammatory, and membrane structure-protective effects in skin and somatic cell models [57,58]. Its significant upregulation in the HG group provides metabolic-level evidence that the livers of HG piglets may possess a relatively greater capacity for cytoprotection and homeostasis maintenance when coping with oxidative stress or environmental fluctuations.

4.3. The Gene–Metabolism Regulatory Role of the Arachidonic Acid Metabolism Pathway in HG Piglets

The transcriptome and metabolome integrated analysis results indicated that DEGs and DEMs were co-enriched in the pathway of AA metabolism. Previous studies have shown that AA metabolites can modulate key inflammatory signaling pathways (e.g., NF-κB and the production of inflammatory cytokines) and affect oxidative stress levels in hepatic cells, thereby directly linking AA metabolism to hepatic immune responses and cellular redox balance [59,60,61]. These pathway may serve as potential molecular markers for breeding programs. Specifically, selecting breeds with favorable arachidonic acid pathway characteristics could help cultivate varieties with enhanced hepatic health and improved oxidative stress tolerance, thereby promoting overall animal health and production performance.
Moreover, in this pathway, 15-Deoxy-PGJ2 and PC(18:1/15:0) were significantly upregulated in HG piglets, while GPX3 and CBR3 were significantly downregulated in HG piglets, and a significant negative correlation was observed between them. 15-Deoxy-PGJ2, an endogenous anti-inflammatory lipid molecule produced in this pathway, can inhibit the release of inflammatory cytokines and alleviate tissue damage. It achieves this by activating the peroxisome proliferator-activated receptor γ (PPARγ) signal, suppressing the activity of nuclear factor κB (NF-κB), and inducing the expression of heme oxygenase-1 (HO-1) [62,63,64]. PC(18:1/15:0) is essential for membrane fluidity, signal transduction, and the inflammatory response [65]. This may suggest that when the enzymatic antioxidant ability declines (i.e., GPX3 and CBR3 are downregulated), HG piglets achieve non-enzymatic compensation by upregulating the anti-inflammatory lipid molecule 15-Deoxy-PGJ2 and PC-class phospholipids, which indirectly reduces the level of reactive oxygen species (ROS) and alleviates oxidative stress [66].
Notably, ALOX5 was significantly upregulated in HG piglets and showed a significant positive correlation with 15-Deoxy-PGJ2. ALOX5 can catalyze arachidonic acid to produce pro-inflammatory mediators such as LTB4 and LTC4 [43]. In contrast, 15-Deoxy-PGJ2 can inhibit excessive inflammation by activating PPARγ in the late stage of inflammation, thus establishing a dynamic balance between pro-inflammatory and anti-inflammatory responses [67]. Based on the results of this study, it is hypothesized that in the early stage of the inflammatory response, HG piglets may rapidly generate pro-inflammatory leukotrienes via ALOX5 to initiate the immune response. Subsequently, they rely on the PPARγ signal that is mediated by 15-Deoxy-PGJ2 to inhibit excessive inflammation, achieving an “early start-quick braking” inflammatory regulation strategy. This mode promotes the balance between immune defense and tissue protection during infection or tissue damage.

5. Conclusions

Weaned HG piglets displayed significantly lower body weight, a relatively higher liver coefficient, and a more intact liver histological structure compared to DLY piglets. The DEGs and DEMs identified in the liver transcriptome and metabolome of the two pig breeds were significantly enriched in several important functional pathways. The DEGs and DEMs were co-enriched in the arachidonic acid metabolic pathway. In this pathway, the interactive regulation of DEGs, such as GPX3, ALOX5, and CBR3, and DEMs such as PC(18:1(11Z)/15:0) and 15-Deoxy-PGJ2 may play a crucial role in the adaptability and maintenance of metabolic homeostasis of weaned HG piglets. Given the small sample size and since no key findings were validated, future studies should be conducted to verify these results. The findings of this study not only facilitate our understanding of the hepatic regulatory mechanisms during the early post-weaning period but also provide a preliminary reference for uncovering the molecular mechanism underlying the germplasm characteristics of local pig breeds.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture16020241/s1, File S1: Statistics of the liver transcriptome sequencing data of piglets from two breeds. File S2: Summary statistics for all genes aligned to the reference genome. File S3: Effectively expressed genes retained after filtering. File S4: Summary statistics for differentially expressed genes (DEGs) between two breeds. File S5: Summary for metabolites of Metabolomics Standards Initiative (MSI) levels 1, 2, and 3 detected in liver tissue. File S6: Summary statistics for differentially expressed metabolites (DEMs) between two breeds. File S7: Pearson’s correlation analysis between differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs).

Author Contributions

C.W.: sampling, formal analysis, visualization, and writing—original draft. J.L. and X.Z. (Xueyan Zhao): methodology and formal analysis. Y.W. and X.Z. (Xiaodong Zhu): sampling, methodology, and editing. F.Z. and C.Z.: sampling and editing. J.W. and L.G.: formal analysis, writing (review and editing), and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key R&D Program of Shandong Province, China (2024LZGC017); Shandong Swine Industry Technology System Innovation (SDAIT-08-03); and the Zaozhuang Talent Project (2024–2026).

Institutional Review Board Statement

The animal study protocol was approved by the Animal Experimental Ethical Inspection Form of the Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences (protocol code IASVM-2024-006).

Data Availability Statement

The transcriptomic data presented in this study are openly available in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1397935 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1397935, accessed on 17 January 2026). The metabolomic data presented in this study are openly available in the OMIX database of the National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences, under accession numbers OMIX014158 and OMIX014157 (https://ngdc.cncb.ac.cn/omix, accessed on 6 January 2026).

Conflicts of Interest

Author Xiaodong Zhu was employed by the company Shandong Futeng Food Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

References

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Agriculture 16 00241 g001
Figure 1. Liver tissue sections of piglets from two breeds ((A): DLY and (B): HG).
Figure 1. Liver tissue sections of piglets from two breeds ((A): DLY and (B): HG).
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Figure 2. PCA chart of the liver transcriptome of piglets from two breeds.
Figure 2. PCA chart of the liver transcriptome of piglets from two breeds.
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Figure 3. DEGs between the liver transcriptome sequencing data of piglets from two breeds. (A) Volcano plot of differentially expressed genes. (B) Clustering heatmap of DEGs.
Figure 3. DEGs between the liver transcriptome sequencing data of piglets from two breeds. (A) Volcano plot of differentially expressed genes. (B) Clustering heatmap of DEGs.
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Figure 4. Plots of metabolite classification. (A) Donut plot of metabolite classification. (B) Pie chart of 14 metabolite superclasses.
Figure 4. Plots of metabolite classification. (A) Donut plot of metabolite classification. (B) Pie chart of 14 metabolite superclasses.
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Figure 5. OPLS-DA plot of the metabolome of piglets from two breeds. (A) OPLS-DA score plot; (B) OPLS-DA validation plot.
Figure 5. OPLS-DA plot of the metabolome of piglets from two breeds. (A) OPLS-DA score plot; (B) OPLS-DA validation plot.
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Figure 6. Volcano plot of the DEMs of piglets from two breeds. Note: The top 10 significant DEMs are marked in the figure.
Figure 6. Volcano plot of the DEMs of piglets from two breeds. Note: The top 10 significant DEMs are marked in the figure.
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Figure 7. Bubble chart of KEGG enrichment analysis of DEMs.
Figure 7. Bubble chart of KEGG enrichment analysis of DEMs.
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Figure 8. Clustering heatmap of correlation analysis between DEGs and DEMs in the arachidonic acid metabolism pathway. * denotes a significant correlation at the 0.05 level after FDR correction.
Figure 8. Clustering heatmap of correlation analysis between DEGs and DEMs in the arachidonic acid metabolism pathway. * denotes a significant correlation at the 0.05 level after FDR correction.
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Table 1. The significantly enriched GO terms and pathways for DEGs.
Table 1. The significantly enriched GO terms and pathways for DEGs.
CategoryItemIDDescriptionAdjusted p-ValueEnriched Gene CountFold
Enrichment
GOMFGO:0005506Iron ion binding9.77 × 10−51547.95
GO:0016705Oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen9.77 × 10−51647.95
GO:0004497Monooxygenase activity1.16 × 10−41347.95
GO:0020037Hheme binding3.50 × 10−31247.95
GO:0046906Tetrapyrrole binding4.43 × 10−31247.95
KEGG-map04212Longevity-regulating pathway—worm8.89 × 10−8234.83
map00830Retinol metabolism2.32 × 10−5126.75
map03410Base excision repair1.79 × 10−8164.58
map03320PPAR signaling pathway5.42 × 10−5125.88
map00900Terpenoid backbone biosynthesis4.18 × 10−4611.88
map00500Starch and sucrose metabolism5.91 × 10−478.62
map04210Apoptosis9.71 × 10−4183.05
map01100Metabolic pathways2.63 × 10−3591.61
map00590Arachidonic acid metabolism3.50 × 10−394.71
map05204Chemical carcinogenesis via DNA adducts7.35 × 10−384.73
map04152AMPK signaling pathway1.39 × 10−2103.50
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Wang, C.; Li, J.; Zhao, X.; Wang, Y.; Zhu, X.; Zhao, F.; Zhang, C.; Geng, L.; Wang, J. Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Mechanisms Underlying Hepatic Differences Between Zaozhuang Heigai Piglets and Duroc×Landrace×Yorkshire Piglets. Agriculture 2026, 16, 241. https://doi.org/10.3390/agriculture16020241

AMA Style

Wang C, Li J, Zhao X, Wang Y, Zhu X, Zhao F, Zhang C, Geng L, Wang J. Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Mechanisms Underlying Hepatic Differences Between Zaozhuang Heigai Piglets and Duroc×Landrace×Yorkshire Piglets. Agriculture. 2026; 16(2):241. https://doi.org/10.3390/agriculture16020241

Chicago/Turabian Style

Wang, Caitong, Jingxuan Li, Xueyan Zhao, Yanping Wang, Xiaodong Zhu, Fuping Zhao, Chuansheng Zhang, Liying Geng, and Jiying Wang. 2026. "Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Mechanisms Underlying Hepatic Differences Between Zaozhuang Heigai Piglets and Duroc×Landrace×Yorkshire Piglets" Agriculture 16, no. 2: 241. https://doi.org/10.3390/agriculture16020241

APA Style

Wang, C., Li, J., Zhao, X., Wang, Y., Zhu, X., Zhao, F., Zhang, C., Geng, L., & Wang, J. (2026). Integrative Analysis of Transcriptome and Metabolome Reveals Molecular Mechanisms Underlying Hepatic Differences Between Zaozhuang Heigai Piglets and Duroc×Landrace×Yorkshire Piglets. Agriculture, 16(2), 241. https://doi.org/10.3390/agriculture16020241

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