Comprehensive co-expression analysis reveals candidate regulatory genes associated with carcass and meat quality traits in Neijiang and Large White pigs
Article information
Abstract
Objective
The Neijiang indigenous pig breed of China and the Western Large White pig breed have unique meat quality and carcass characteristics. However, the genetic factors and mechanisms influencing their distinct meat and carcass traits are still not well understood. Therefore, using weighted gene co-expression network analysis (WGCNA), this study aimed to identify key genes influencing these traits.
Methods
Transcriptome data from 17 Neijiang and 22 Large White pigs, along with their carcass weight, backfat thickness, eye muscle area, meat color, and muscle pH phenotypic data, were analyzed using WGCNA. A total of 9,249 genes were used to construct a weighted gene co-expression network.
Results
Twenty-two co-expression gene modules were identified. Genes in the top modules were enriched in processes relevant to carcass and meat quality, such as protein transport. Further analysis identified six key genes, including HSPH1, HSPA4, DNAJA4, MRPL3, SEC63, and SRP54, for the Neijiang breed. Also, five key genes, consisting of EP300, SETD2, NIPBL, NAT10, and VCP, were identified for the Large White population. These genes were involved in biological processes related to mitochondrial function, protein targeting, chromatin organization, and morphogenesis.
Conclusion
The findings from this study elucidate the regulatory mechanisms influencing the carcass and meat characteristics of the Neijiang and Large White pigs. The key genes could serve as potential biomarkers for enhancing breeding strategies aimed at improving pork quality.
INTRODUCTION
Pork is a source of high-quality protein, and its quality is a major economic factor that holds an important position in the food market [1]. Meat color and pH are essential determinants of meat quality, while carcass weight (CW), backfat thickness (BFT), and eye muscle area (EMA) are also important carcass characteristics. Previous studies have identified candidate genes associated with some of these traits. For instance, Wang et al [2], through a genome-wide association study, identified eight potential candidate genes (COL21A1, ZNF184, ZNF391, HMGA1, GRM4, NUDT3, PGM2L1, and PLBD2) for carcass traits in pigs.
The rapid development of high-throughput technologies and the significant decrease in sequencing costs have led to the increased use of transcriptome analysis and differential expression analysis to identify genes linked to traits [3,4]. However, differential expression analysis focuses on individual genes and may overlook important gene-gene interactions that are relevant to target traits [5]. Weighted gene co-expression network analysis (WGCNA) addresses this by identifying genes with similar functions and regulation by constructing networks based on gene expression similarities and linking them to specific traits [5,6]. This method has been used in cancer, genetics, and brain imaging studies and has led to the identification of candidate biomarkers [1]. WGCNA is particularly useful for simultaneously identifying key genes related to multiple complex traits [5]. A previous study identified six key genes (RAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF) associated with fat deposition in Songliao black and Landrace breeds using WGCNA [1]. Similarly, Wang et al [5] identified five key genes and 13 key genes influencing intramuscular fat content and meat color (CIE a*), respectively. While previous studies have applied this method to investigate gene co-expression patterns related to various traits in pigs, these studies have primarily focused on Western pigs. The Neijiang pig is a valuable genetic resource and is native to the Sichuan basin of China. The Neijiang pig is classified as a fat-type breed and is reported to have tender and flavorful meat, strong adaptability, higher disease resistance, and a slow growth rate compared to other breeds [7,8]. On the other hand, the Large White, a Western lean-type breed, is known for its high growth rate, feed conversion ratio, and lean meat percentage compared to the Neijiang pig [9]. However, the genetic mechanisms influencing carcass and meat quality traits in Chinese indigenous pig breeds like the Neijiang pig and Western breeds such as the Large White are still not well understood.
In this study, WGCNA was applied to examine gene interactions and identify key genes influencing multiple carcass and meat quality traits in Neijiang and Large White pigs. The identification of key genes will elucidate the mechanisms influencing these traits in both breeds.
MATERIALS AND METHODS
Animals, phenotypic characteristics, and sample collection
A total of 17 Neijiang and 22 Large White pigs with an average age of 180 days were used in this study. The Neijiang and Large White pigs had an average weight of 120 kg and 130 kg, respectively. The pigs were fed a standard commercial diet of corn and soybeans, formulated according to their body weight and adhering to Chinese national standard GB/T 5915-2020. They were transported to a commercial slaughterhouse and fasted overnight, with only ad libitum access to drinking water before slaughter. The animals were electrically stunned (1.3 A for 10–20 seconds) and subsequently bled. Post-slaughter and evisceration, CW and other parameters of interest were measured and recorded. The BFT and EMA (calculated as height×width×0.7) [10] were measured using a vernier caliper at the sixth and seventh ribs. Meat color lightness (L1), redness (a1), and yellowness (b1), were evaluated at 45 minutes using a Minolta CR-300 colorimeter (Minolta Camera). The muscle pH values of the longissimus dorsi muscle were recorded at 45 minutes (pH45) and 24 hours (pH24) post-mortem with a portable pH meter (model 720A; Orion Research). All assessments were conducted on the left side of the carcass. In addition, approximately 0.2 g of the longissimus dorsi muscle samples were collected and stored at −80°C for subsequent transcriptome sequencing.
Library construction and RNA sequencing
Total RNA was extracted from 39 longissimus dorsi muscle samples, and mRNA was purified with poly-T magnetic beads. The mRNA was fragmented and converted into cDNA using random hexamer primers and reverse transcriptase. The second strand was synthesized, blunt ends created, and adaptors ligated. cDNA fragments of 370–420 bp were purified and amplified using PCR. The libraries were pooled, and DNA nanoballs were generated and sequenced using the DNBSEQ-T7 platform. The library construction was done by Novogene.
RNA-Seq data analysis
Read quality control was performed on the raw data to obtain clean reads (Supplement 1). The clean reads were then mapped to the Sus scrofa reference genome (Sus scrofa11.1) using HISAT2, which was then sorted and indexed using Samtools. The genes were quantified separately for the two pig breeds using the FeatureCounts tool in the subread software, and the FPKM (fragments per kilobase per million) values were calculated [11]. Genes with FPKM values less than 1 in more than ten individuals were excluded.
Weighted gene co-expression network analysis
The weighted gene co-expression network construction was performed using the WGCNA package (v.4.3.3) [6] in R (v.4.4.1). The datasets were filtered to include only common genes between the two breeds. The phenotype data were categorized into two groups: carcass traits (CW, BFT, and EMA) and meat traits (L1, a1, b1, pH45, and pH24). This categorization enabled a more focused examination of relationships within each trait group, leading to the identification of modules associated with these traits and breeds. The analyses were performed separately for each trait group and pig breeds using the same parameters.
Genes and samples were checked for outliers using the goodSamplesGenesMS function to ensure data quality. Using the pickSoftThreshold function [12], a soft-threshold power of 10 was chosen, and the topological overlap matrix (TOM) was computed. A reference percentile of 0.95 was employed to scale TOM values with a set seed for reproducibility. Modules were identified using dynamic tree cutting with parameters set to minModuleSize = 30, deepSplit = 2, and pamRespectsDendro = FALSE, followed by merging closely related modules based on consensus eigengene dissimilarity with a cut height of 0.25. Module-trait correlations and p-values were calculated, and the results were visualized through heatmaps. Modules were screened using an absolute correlation coefficient (|r|)≥0.3 and a p-value≤0.1 in at least one trait, and the top modules were selected based on the highest total absolute correlation. Consensus module eigengenes were recalculated to assess module relationships across the breeds.
Protein-protein interaction network and identification of key genes
Protein-protein interaction network (PPI) was constructed to analyze the interactions between genes encoding proteins in candidate genes using the STRING database (v12.0) (https://cn.string-db.org/). Genes in the selected modules were screened based on module membership and gene significance (|MM|>0.8 and |GS|>0.3). The filtered genes were imported into the STRING database with a minimum required interaction score set to 0.40 (minimum confidence), and the resulting PPI network was visualized using Cytoscape software (v3.10.3) [13]. CytoHubba plugin (v0.1) [14] was used to rank the nodes in the PPI network using the Degree topological analysis method. Genes with high degree scores, ranking between one and two, were selected as the key genes.
Functional enrichment analysis
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed for genes in the top modules using the DAVID database (https://davidbioinformatics.nih.gov/), and p<0.05 was used as the threshold.
Enrichment analyses were also performed for the key genes using the ShinyGO database (v0.81) [15]. For GO terms, significant enrichment was determined using a false discovery rate (FDR) threshold of <0.05. However, few KEGG pathways were identified using this threshold. Therefore, the FDR threshold was adjusted to <0.1 to explore additional pathways that may be associated with the key genes.
Feature selection and validation of key genes
To ensure the biological relevance and predictive power of the key genes, their normalized expression values were first correlated with the phenotypic traits using Pearson correlation analysis to explore associations between the genes and traits. The key genes were further analyzed using three feature selection models: Least Absolute Shrinkage and Selection Operator (LASSO) regression, Elastic Net regression, and Random Forest. LASSO regression is a method that adds a penalty to the absolute value of the coefficients, helping to simplify the model by forcing some coefficients to zero and selecting only the most relevant features. In this study, LASSO regression was performed using the glmnet R package (v4.1–8) with a Gaussian family for multi-response variables. Leave-one-out cross-validation (LOOCV) was used to identify the optimal penalty parameter (λ), with features showing nonzero coefficients considered significant [16]. Elastic Net regression blends the penalties of both LASSO and Ridge regression and is effective for datasets with highly correlated predictors. This model reduces overfitting while selecting a strong subset of relevant features. Similar to LASSO, the optimal λ was identified through LOOCV, and features with nonzero coefficients were retained as relevant contributors [16]. The Random Forest model was implemented using the randomForest package (v4.7–1.2). This ensemble method builds multiple decision trees and evaluates the importance of each feature by measuring how much its inclusion improves the model’s predictions. Importance scores were calculated using Percentage Increase in Mean Squared Error to identify the most influential genes [17]. The accuracy of these models was assessed by computing correlations between observed and predicted trait values.
Statistical analysis
Carcass and meat quality measurements were analyzed using a two-tailed Student’s t-test in the Statistical Package for Social Science (SPSS) program (v. 22) [18] with a p-value<0.05 considered statistically significant. Data on carcass and meat quality measurements are presented as means±standard deviations. Additional analyses, including WGCNA, were conducted using the R software (v.4.4.1).
RESULTS
Carcass and meat quality performance
Carcass and meat quality traits are important indicators of pork quality. Significant differences were observed in both breeds (Table 1). The Large White pigs had a higher CW (99.43±10.15 kg) compared to Neijiang pigs (89.78±17.24 kg). However, the Neijiang breed had a thicker backfat (45.09±8.98 mm) than the Large White breed (22.76±3.77 mm), indicating a higher fat content as was expected. In addition, the Large White pigs had considerably larger EMA (32.01±6.26 cm2), reflecting greater muscle mass and leanness than Neijiang pigs (14.69±3.24 cm2). Furthermore, while the Large White pigs displayed a slightly higher lightness (L1) (44.70±1.51) than Neijiang pigs (43.28±2.49), Neijiang pork exhibited higher redness (a1) (10.02±2.83) and yellowness (b1) (4.47±1.20) compared to Large White pork (a1: 4.13±1.24; b1:3.26±0.49). Postmortem pH45 was slightly lower in Neijiang pigs (6.28± 0.10) than in Large White pigs (6.47±0.21). However, pH24 was higher in Neijiang pigs (5.70±0.10) than in Large White pigs (5.33±0.36).
Carcass and meat quality characteristics of the Neijiang and Large White pig breeds
Weighted gene co-expression network analysis and identification of top modules
Genes with low expression levels (FPKM<1) in more than ten individuals were excluded from the analysis, leaving 9,302 genes and 10,597 genes for the Neijiang and Large White pigs, respectively. This was further filtered to include only the genes shared between both breeds, resulting in a total of 9,249 genes, with all genes and samples passing quality checks. The scale-free topological model and mean connectivity were determined (Figures 1A, 1B), and a soft-threshold of 10 was selected as the optimal value. After dynamic tree trimming, 22 co-expression modules were identified (Figure 2), with the number of genes in the modules ranging from 51 to 1,902 (Supplement 2).
Network topology and connectivity metrics used to determine the soft-threshold power for WGCNA analysis. (A) Scale-free topology model fit. (B) Mean connectivity. (C) Median connectivity. (D) Max connectivity. The figure shows the summary of network indices (y-axes) as functions of soft-thresholding power (x-axes). Numbers in the plots indicate the corresponding powers tested. An approximate scale-free topology was achieved around a power of 10 for both datasets, based on the thresholds of 0.8 (Large White breed) and 0.7 (Neijiang breed) in panel A. Connectivity measures decrease with increasing power (panels B–D), supporting the selection of the lowest power that satisfies the scale-free criterion. Gray numbers in the plots represent the Large White breed, and black numbers represent the Neijiang breed. WGCNA, weighted gene co-expression network analysis.
Cluster dendrogram of genes and modules. Hierarchical clustering dendrogram of genes based on topological overlap matrix (TOM) dissimilarity. The top row of color bars (Unmerged) represents the initial modules identified using dynamic tree cutting. The bottom row (Merged) shows the final module assignments after merging modules whose eigengenes were highly correlated (cut height = 0.25). The y-axis represents clustering height from hierarchical clustering. Genes with similar expression patterns cluster together into branches (modules), and color annotations below indicate module membership before and after merging.
The palevioletred3 and green modules were identified as the top modules for the Neijiang dataset. The palevioletred3 module, consisting of 74 genes, showed a positive correlation with CW (r = 0.33) and BFT (r = 0.57) while exhibiting a negative correlation with EMA (r = −0.20) (Figure 3A). For meat traits, the green module, which included 1,165 genes, was negatively correlated with L1 (r = −0.17), a1 (r = −0.79), b1 (r = −0.46), and pH24 (r = −0.16), while positively correlated with pH45 (r = 0.30) (Figure 3B).
Heatmap of the correlation between modules and traits. (A) Module-trait correlation for carcass traits for the Neijiang breed. (B) Module-trait correlation for meat traits for the Neijiang breed. (C) Module-trait correlation for carcass traits for the Large White breed. (D) Module-trait correlation for meat traits for the Large White breed. Columns represent traits, and rows represent eigengene modules. The correlation coefficient values between the module eigengene and the traits, along with the p-value in parentheses, are presented in each cell. CW, carcass weight; BFT, backfat thickness; EMA, eye muscle area; L1, lightness; a1, redness; b1, yellowness; pH45, postmortem pH at 45 minutes; pH24, postmortem pH at 24 hours.
For the Large White population, the blue module, comprising 1,902 genes, was positively correlated with CW (r = 0.34), BFT (r = 0.21), and EMA (r = 0.36) (Figure 3C). The plum1 module, with 88 genes, was positively correlated with L1 (r = 0.24), a1 (r = 0.06), b1 (r = 0.26), and pH45 (r = 0.41), but negatively correlated with pH24 (r = −0.17) (Figure 3D). A comparative analysis was conducted on the consensus eigengene networks derived from the Neijiang and Large White datasets. These networks capture the relationships between consensus modules, quantified by eigengene correlations. The overall preservation of the two eigengene networks was high, with a preservation score of 0.81 (Figure 4). However, the consensus module-trait relationships across both breeds did not meet the predefined filtering criteria and were not analyzed further (Supplement 3).
Consensus eigengene networks and their differential analysis. (A, B) Dendrogram and eigengene representation of consensus eigengene networks for Neijiang and Large White breeds, respectively. (C, F) Heatmap of eigengene adjacencies in Neijiang and Large White networks. Each row and column correspond to an eigengene tagged by consensus module colors. (D) Bar plot of preservation degree of each consensus eigengene. The height of the bar (y-axis) and each bar corresponds to the eigengene of the associated consensus module. The high-density value D (preserve Neijiang and Large White) = 0.81 indicates the high overall preservation between the two networks. (E) Adjacency heatmap of the preservation network between Neijiang and Large White consensus eigengene networks. The saturation of the black or gray color indicates the correlation preservation of Neijiang and Large White module eigengenes.
Functional enrichment analysis of genes in top modules
Functional enrichment analysis was performed for genes in the top modules (Figure 5). In the Neijiang population, the palevioletred3 module genes were enriched in GO terms such as response to heat, protein refolding, and protein folding. Genes in the green module were enriched in protein transport, endoplasmic reticulum to Golgi vesicle-mediated transport, mRNA splicing via spliceosome, and intracellular protein transport.
Functional enrichment analysis of genes in the selected modules. (A) Palevioletred3 module. (B) Green module. (C) Blue module. (D) Plum1 module. The top 5 significant biological processes (BP), cellular component (CC), and molecular function (MF) are presented in the plot.
In the Large White population, blue module genes were significantly enriched in biological processes, including regulation of transcription by RNA polymerase II, positive regulation of transcription by RNA polymerase II, protein phosphorylation, and negative regulation of transcription by RNA polymerase II. Furthermore, plum1 module genes were significantly enriched in processes, such as in utero embryonic development, ubiquitin-dependent protein catabolic process, ERAD pathway, and proteasome-mediated ubiquitin-dependent protein catabolic process.
The KEGG enrichment analysis was performed to explore the pathways associated with the traits (Figure 6). In the Neijiang population, the palevioletred3 module demonstrated significant enrichment in the spliceosome pathway and the protein processing in the endoplasmic reticulum pathway. The green module showed enrichment in the protein processing in the endoplasmic reticulum pathway, mRNA surveillance pathway, and spliceosome pathway. In the Large White population, the blue module exhibited significant enrichment in the insulin signaling pathway, nucleocytoplasmic transport pathway, ErbB signaling pathway, and autophagy pathway. The plum1 module demonstrated significant enrichment in the nucleocytoplasmic transport pathway, amyotrophic lateral sclerosis pathway, proteasome pathway, and mTOR signaling pathway.
Top 10 KEGG pathway enrichment analysis of genes in the selected modules. (A) Palevioletred3 module. (B) Green module. (C) Blue module. (D) Plum1 module. KEGG, Kyoto Encyclopedia of Genes and Genomes.
Protein-protein interaction network and key genes identification
A PPI network analysis was conducted using the STRING database to explore protein interactions within selected modules. Genes were initially screened using the criteria |MM|> 0.80 and |GS|>0.30, resulting in the palevioletred3, green, blue, and plum1 modules having 27, 286, 430, and 31 genes, respectively. Genes encoding proteins with high interaction with other proteins in the modules were selected as key genes. In total, six key genes, including HSPH1, HSPA4, DNAJA4, MRPL3, SEC63, and SRP54, were identified for the Neijiang breed. Furthermore, five key genes consisting of EP300, SETD2, NIPBL, NAT10, and VCP, were also identified in the Large White dataset. These key genes are important for understanding the biological processes influencing the traits of interest. Figure 7 presents the PPI network and key genes of the top modules.
PPI network interaction of modules and key genes. (A) Palevioletred3 module. (B) Green module. (C) Blue module. (D) Plum1 module. PPI, protein-protein interaction network.
The key genes in the Neijiang dataset were significantly enriched in biological processes, such as outer mitochondrial membrane organization, protein insertion into mitochondrial outer membrane, SRP-dependent cotranslational protein targeting to membrane translocation, and post-translational protein targeting to membrane translocation. Key genes in the Large White breed were enriched in the regulation of protein localization to chromatin, protein localization to chromatin, and face, head, and body morphogenesis.
KEGG pathway enrichment analysis revealed protein export, antigen processing and presentation, ribosome, and tight junction pathways for key genes in the Neijiang breed. Key genes for the Large White breed were enriched in pathways such as viral life cycle-HIV-1, Legionellosis, notch signaling pathway, and lysine degradation (Figure 8).
GO and KEGG pathway enrichment analysis of key genes associated with carcass and meat traits in the Neijiang and Large White pig breeds. (A) GO biological process enrichment analysis of key genes in the Neijiang breed. (B) GO biological process enrichment analysis of key genes in the Large White breed. (C) KEGG pathway enrichment analysis of key genes in the Neijiang breed. (D) KEGG pathway enrichment analysis of key genes in the Large White breed. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
To validate the key genes identified through WGCNA and PPI network analysis, we employed three complementary machine learning approaches: LASSO regression, Elastic Net regression, and Random Forest. These methods were applied to analyze the relationship between the key genes and traits. The key genes were first correlated with these traits, and the analysis revealed that the genes were significantly correlated with most of the traits (Supplement 4). LASSO regression, which is known for its strength in feature selection and reducing overfitting, identified five key genes (SEC63, HSPA4, DNAJA4, SRP54, and MRPL3) in the Neijiang dataset. In the Large White dataset, four key genes (NAT10, EP300, SETD2, and NIPBL) were identified. Elastic Net regression identified additional genes consisting of HSPH1 and VCP for the Neijiang and Large White datasets, respectively (Supplements 5–8). Random Forest analysis assigned importance scores for the genes to support their relevance. Genes with the highest importance score were deemed the most influential in predicting traits. Among the identified genes, SRP54, MRPL3, and HSPA4 were the top predictors of the carcass and meat traits in the Neijiang pig breed, while EP300, SETD2, NIPBL, and VCP were among the top predictors of traits in the Large White breed (Figures 9A, 9B; Supplements 9–12). The top key genes and their importance scores are presented in Table 2.
Importance scores of key genes and model comparison of LASSO, Elastic Net, and Random Forest models. (A) Importance scores of key genes for the Neijiang breed. (B) Importance scores of key genes for the Large White breed. (C) Model comparison of LASSO, Elastic Net, and Random Forest models for the Neijiang dataset. (D) Model comparison of LASSO, Elastic Net, and Random Forest models for the Large White dataset.
Top key genes and their importance scores based on Random Forest analysis
The predictive performance of the models was evaluated by calculating the correlation coefficients between predicted trait values and observed values. The LASSO model demonstrated an accuracy ranging from 0.37 to 0.77 and 0.13 to 0.60 for the Neijiang and Large White datasets, respectively. Elastic Net showed moderate predictive accuracy, with correlation coefficients ranging from 0.39 to 0.77 and 0.13 to 0.61 for the Neijiang and Large White breeds, respectively. The Random Forest model demonstrated the highest accuracy, with correlation coefficients ranging from 0.94 to 0.96 and 0.87 and 0.95 for the Neijiang and Large White breeds, respectively, indicating a strong predictive power (Figures 9C, 9D, Supplement 13).
DISCUSSION
Meat quality and carcass characteristics are important factors and are largely influenced by consumer demand for high-quality and affordable products. This demand has led to the integration of these traits into breeding programs to improve pork quality [19]. This study observed differences in the carcass and meat quality characteristics between Neijiang and Large White pigs. The Large White pigs had a higher CW and EMA, which indicates a high meat yield and leanness. Also, Neijiang pigs had thicker backfat, indicating higher fat content, which can influence meat flavor and juiciness. Higher redness and yellowness, which are often associated with perceived freshness and quality, were observed in the Neijiang pork. The Neijiang pigs also had higher pH values, which can positively impact meat tenderness. Similar phenotypic distinctions between indigenous and commercial pig breeds, such as Ghungroo and Large White pigs, have been previously reported, with indigenous breeds generally exhibiting superior meat quality traits but lower lean yield [20]. However, comprehending the biological and molecular regulatory mechanisms influencing these unique traits is essential. Therefore, this study utilized WGCNA to identify key genes and molecular mechanisms regulating meat quality and carcass traits in the Neijiang and Large White pig breeds.
This study identified 22 consensus modules through WGCNA analysis. In the Neijiang population, the palevioletred3 module demonstrated significant enrichment in biological processes related to stress responses, including response to heat. Other processes included protein refolding and protein folding. These processes are involved in maintaining normal cellular function and preserving protein integrity, especially under stressful conditions. Heat shock proteins (HSPs) assist cells to recognize and refold damaged proteins or target them for degradation, effectively removing proteins that can no longer function properly. Under stress, HSP levels rise, promoting the synthesis and maturation of new proteins to replace those affected by stress. This increase not only aids in protein repair but also helps maintain cell viability by inhibiting apoptosis [21]. The presence of genes involved in these processes in Neijiang pigs also suggests possible adaptive mechanisms that may help maintain or enhance meat quality traits, particularly under stressful conditions. Additionally, the green module’s enrichment in protein transport and mRNA splicing via spliceosome may be important in regulating the molecular mechanisms behind muscle development and quality. The efficient transport of proteins and the precise regulation of gene expression are necessary for the development of desirable carcass traits, including backfat and muscle area [22].
In the Large White population, genes in the blue module were enriched in transcriptional regulation by RNA polymerase II. Previous studies on meat quality have identified transcription regulation processes, including negative and positive regulation by RNA polymerase II [23,24]. Positive regulation enhances gene expression through the activation of transcription factors that bind to promoters or enhancers, while negative regulation acts to suppress gene activity. This balance is necessary for maintaining cellular homeostasis and preventing overactive or inappropriate gene expression [25]. These regulatory mechanisms likely function in controlling gene expression within muscle tissues and influence meat quality and carcass traits. Additionally, the plum1 module was involved in in-utero embryonic development and ubiquitin-dependent protein catabolic process, pointing to their potential in early growth and protein turnover [26].
The key genes further elucidate the molecular mechanisms influencing carcass and meat traits in the two breeds. The six key genes from the Neijiang dataset were predominantly involved in mitochondrial function and protein targeting, indicating their role in maintaining cellular homeostasis and energy metabolism. Mitochondria play a role in oxygen consumption, energy metabolism, and apoptotic processes. These processes influence myoglobin levels, oxidative stress, meat tenderness, fat oxidation, and protein oxidation, which affect traits such as meat color, tenderness, and flavor [27]. The presence of heat shock proteins (HSPs) like HSPH1 and HSPA4 suggests a protective stress response in muscle tissues. This response helps preserve muscle cell integrity under various physiological conditions, which are essential for desirable meat traits [28].
The five key genes identified in the Large White breed were enriched in chromatin organization and morphogenesis processes. The regulation of protein localization to chromatin reveals the importance of transcriptional regulation in the development of the traits. These genes are involved in functions such as DNA repair, gene expression regulation, and cellular signaling pathways, which are important for the proper development of physical traits such as body and head morphology and may influence the carcass and meat quality characteristics in the Large White pigs [27–29].
LASSO, Elastic Net regression, and Random Forest analyses were performed to validate and prioritize the key genes. LASSO selected five genes for the Neijiang and four for Large White, while Elastic Net identified six and five genes, respectively, for the two breeds, which further strengthens the relevance of the identified key genes. The Random Forest model was then used to assess the importance of the key genes in predicting traits. In the Nejiang pig breed, SRP54 was the top predictor for CW. SRP54 is involved in targeting secretory proteins to the endoplasmic reticulum, which is linked to protein synthesis and secretion [30]. Similarly, MRPL3, a mitochondrial ribosomal protein essential for mitochondrial protein synthesis, was identified as a key gene associated with multiple traits, including BFT and meat color (a1 and b1). These findings are consistent with a previous study suggesting that higher mitochondrial activity is associated with increased BFT and higher pH [31]. Mitochondrial activity has been reported to influence beef color through oxygen consumption and metmyoglobin reduction [32,33]. HSPA4 was also associated with multiple traits (EMA, L1, pH45, and pH24). HSPA4’s function in stress response, protein folding, and maintenance of muscle function through regulation of skeletal muscle autophagy and apoptosis may regulate meat quality characteristics [34]. Higher expression of heat shock proteins has been reported to be associated with better meat water-holding capacity and resistance to stress-induced quality deterioration in indigenous pigs than in exotic pigs [20].
In the Large White breed, the EP300 gene was associated with CW. EP300 functions as a histone and lysyl acetyltransferase that influences adipocyte differentiation, lipid storage, and lipid metabolism. Research has shown that EP300 regulates lipid metabolism and energy homeostasis in pigs [35]. High expression of EP300 has also been linked to increases in body weight, body mass index, total cholesterol levels, low-density lipoprotein cholesterol levels, and triglyceride levels [36]. SETD2, the top predictor for BFT, EMA, and L1, is involved in DNA repair and gene regulation and contributes to metabolic reprogramming during myogenesis. Its role in muscle energy metabolism may influence meat quality [37,38]. The NIPBL gene was the main predictor of a1 and b1. NIPBL facilitates interactions between enhancers and promoters and is involved in embryo development and embryonic morphogenesis. It has also been linked with adipogenesis, limb development, BFT, and other traits in livestock, suggesting that it can contribute to carcass composition and meat quality [39]. In addition, VCP was identified as the primary predictor for pH45 and pH24. As a highly conserved AAA-ATPase, VCP is integral to ubiquitin-dependent protein quality control pathways. Its multifaceted roles in protein degradation and various cellular processes indicate that VCP may be important for regulating muscle development [40].
The identified key genes reveal the potential mechanisms influencing the meat and carcass traits in the Neijiang and Large White pigs. Integrating them into genomic selection, marker-assisted breeding programs, and nutritional interventions that influence the activity of these genes could further improve carcass and meat quality characteristics in pigs.
CONCLUSION
This study revealed differences in carcass and meat quality traits between the Neijiang and Large White pigs. Through WGCNA analysis, we identified six key genes in the Neijiang breed and five key genes in the Large White breed, which are involved in processes such as mitochondrial function, protein targeting, chromatin organization, and morphogenesis. The identified key genes elucidate the molecular mechanisms underlying the carcass and meat characteristics of both breeds and could serve as potential biomarkers.
Notes
CONFLICT OF INTEREST
No potential conflict of interest relevant to this article was reported.
AUTHORS’ CONTRIBUTION
Conceptualization: Tecku PKM, Chen D, Tang G.
Software: Tecku PKM.
Investigation: Wang K, Yu S, Chen J.
Writing - original draft: Tecku PKM.
Writing - review & editing: Tecku PKM, Chen D, Wang K, Yu S, Chen J, Tang G.
FUNDING
The study was supported by grants from the Sichuan Science and Technology Program (2020YFN0024, 2021ZDZX0008, and 2021YFYZ0030), the earmarked fund for the China Agriculture Research System (CARS-35-01A), the National Key R&D Program of China (2018YFD0501204), the National Natural Science Foundation of China (C170102), the Sichuan Innovation Team of Pig (sccxtd-2021-08) and the National Center of Technology Innovation for Pigs (NCTIP-XD/B01).
ACKNOWLEDGMENTS
The authors thank Sichuan New Hope Liuhe Pig Breeding Technology Co., Ltd. for their contribution to this study.
DATA AVAILABILITY
All relevant data generated or analyzed during this study are included in this article and its supplementary information files. The RNA sequencing data underlying this article are publicly available at the NCBI Sequence Read Archive (SRA) repository under BioProject ID PRJNA1224508.
ETHICS APPROVAL
All experimental procedures were performed in accordance with the Institutional Review Board (IRB14044) and the Institutional Animal Care and Use Committee of the Sichuan Agricultural University under permit number DKY-B20140302.
DECLARATION OF GENERATIVE AI
During the preparation of this work, the authors used ChatGPT and Grammarly to refine the language and improve grammar during the drafting process. After using these tools, the authors reviewed and edited the content as needed and will take full responsibility for the content of the publication.
SUPPLEMENTARY MATERIAL
Supplementary file is available from: https://doi.org/10.5713/ab.25.0259
Supplement 1. Summary of RNA-seq data.
ab-25-0259-Supplementary-1.pdfSupplement 2. Modules and gene numbers.
ab-25-0259-Supplementary-2.pdfSupplement 3. Consensus relationship of consensus module eigengenes and traits across the Neijiang and Large White dataset.
ab-25-0259-Supplementary-3.pdfSupplement 4. Correlation analyses between key genes and traits.
ab-25-0259-Supplementary-4.pdfSupplement 5. Genes selected by LASSO regression analysis for the Neijiang dataset.
ab-25-0259-Supplementary-5.pdfSupplement 6. Genes selected by LASSO regression analysis for the Large White dataset.
ab-25-0259-Supplementary-6.pdfSupplement 7. Genes selected by Elastic Net analysis for the Neijiang dataset.
ab-25-0259-Supplementary-7.pdfSupplement 8. Genes selected by Elastic Net analysis for the Large White dataset.
ab-25-0259-Supplementary-8.pdfSupplement 9. Genes selected by Random Forest analysis for the Neijiang dataset.
ab-25-0259-Supplementary-9.pdfSupplement 10. Genes selected by Random Forest analysis for the Large White dataset.
ab-25-0259-Supplementary-10.pdfSupplement 11. Importance score for Random Forest model - Neijiang dataset.
ab-25-0259-Supplementary-11.pdfSupplement 12. Importance score for Random Forest model - Large White dataset.
ab-25-0259-Supplementary-12.pdfSupplement 13. Model comparison: Observed vs Predicted correlation.
ab-25-0259-Supplementary-13.pdf