Abstract Simple Summary Intramuscular fat (IMF) is increasingly being recognized as a key meat trait in the modern pork industry. The aims of this research were to identify potential signaling pathways associated with IMF content in the longissimus dorsi (LD) muscle of different pig breeds and investigate the gene expression levels in the screened signaling pathways. Our results indicated that the AMPK signaling pathway may be related to IMF deposition in the LD muscle of pigs. The results of qRT-PCR analysis showed that the expression of ten key hub genes (AMPK, ADIPOR1, ADIPOR2, LKB1, CAMKKβ, CPT1A, CPT1B, PGC-1α, CD36, and ACC1) differed between the LD muscle of Min and Large White pigs. The protein expression levels of AMPK, LKB1, CaMKK2, CPT1A, and ACC1 were similar to the genes expression patterns in the LD muscle of Large White pigs. The results of this study provide novel insights into the regulatory function of the AMPK signaling pathway in relation to the IMF content in the LD muscle of different pigs. Abstract Intramuscular fat (IMF) is an important economic trait for pork quality and a complex quantitative trait regulated by multiple genes. The objective of this work was to investigate the novel transcriptional effects of a multigene pathway on IMF deposition in the longissimus dorsi (LD) muscles of pigs. Potential signaling pathways were screened by mining data from three gene expression profiles in the Gene Expression Omnibus (GEO) database. We designed quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) arrays for the candidate signaling pathways to verify the results in the LD muscles of two pig breeds with different IMF contents (Large White and Min). Western blot analysis was used to detect the expression levels of several candidate proteins. Our results showed that the AMPK signaling pathway was screened via bioinformatics analysis. Ten key hub genes of this signaling pathway (AMPK, ADIPOR1, ADIPOR2, LKB1, CAMKKβ, CPT1A, CPT1B, PGC-1α, CD36, and ACC1) were differentially expressed between the Large White and Min pigs. Western blot analysis further confirmed that LKB1/CaMKK2-AMPK-ACC1-CPT1A axis dominates the activity of AMPK signaling pathway. Statistical analyses revealed that AMPK signaling pathway activity clearly varied among the two pig breeds. Based on these results, we concluded that the activation of the AMPK signaling pathway plays a positive role in reducing IMF deposition in pigs. Keywords: AMPK signaling pathway, pig, intramuscular fat, data mining 1. Introduction As one of the most important domesticated animals for agricultural production, pigs provide many meat products for humans [[36]1]. In modern society, pork quality has had an increasing influence on consumer acceptance and initial purchasing decisions. Consumers are interested in several major pork quality traits, including meat color, pH value, water holding capacity, and intramuscular fat (IMF) content, which are becoming increasingly important from an economic perspective [[37]2,[38]3]. Skeletal muscle is a heterogeneous tissue that consists of different types of myofibers, connective tissue, vascular tissue, nervous tissue, and IMF [[39]4]. IMF is a major meat quality trait in pigs, and its content is directly associated with the sensory qualities, flavor, juiciness, tenderness, and nutritional quality of pork [[40]5,[41]6]. In recent decades, several studies have focused on the relationship between IMF and pork quality [[42]7,[43]8,[44]9,[45]10]. Famous lean pig breeds, such as Large White, Landrace, and Duroc, have lower IMF contents and reduced meat quality due to the intensive selection processes used to improve pork productivity. However, many excellent indigenous breeds are distributed in China, such as the Jinhua, Laiwu, Meishan, and Min, and they have higher IMF contents and better meat quality than the lean breeds [[46]11,[47]12,[48]13,[49]14,[50]15,[51]16]. Thus, it will be beneficial to reveal the molecular mechanisms of IMF deposition by comparing gene expression between lean and indigenous Chinese pig breeds. With the rapid development of microarray and RNA-seq technologies in the last few decades, researchers are now able to study many differentially expressed genes (DEGs) simultaneously in a given tissue. To date, many studies concerning meat quality traits and gene expression in pigs have been reported [[52]17,[53]18,[54]19]. Fortunately, the relevant datasets have been deposited and stored in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database and are freely accessible to researchers worldwide. However, few studies have focused on integrating and reanalyzing these datasets, which contain valuable clues regarding important porcine economic traits. Thus, by integrating and reanalyzing these datasets, we can provide significant insights into the molecular changes associated with IMF deposition. In this study, we integrated and reanalyzed three original expression profiles from the GEO database based on a current popular differential gene expression analysis method. We found that the AMPK pathway plays a critical role in IMF deposition. We further validated this pathway through the use of quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) arrays in the Large White and Min pig breeds. 2. Materials and Methods 2.1. GEO Data Collection The gene expression profiles [55]GSE24192, [56]GSE75045, and [57]GSE99092 [[58]12,[59]20,[60]21] were downloaded from the GEO database. The [61]GSE24192 dataset contained six samples, which included three Large White longissimus dorsi (LD) samples and three Northeastern Indigenous (Min) LD samples. The [62]GSE75045 dataset contained six samples, which included three Large White LD samples and three Wannanhua LD samples. The [63]GSE99092 dataset contained six samples, which included three Large White LD samples and three Wei LD samples. In the present study, Large White was set as the experimental group, the indigenous Chinese pig breeds were set as the control group. 2.2. Identification of DEGs The limma package from Bioconductor and the online tool iDEP ([64]https://github.com/gexijin/iDEP) were used to identify DEGs for the selected gene expression profile datasets [[65]22]. A p-value less than 0.05 and |log[Fold Change(FC)]| ≥1 were regarded as the cutoff thresholds for DEGs. 2.3. Signaling Pathway Enrichment Analysis of DEGs To analyze the functions of the DEGs, we performed a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs using the online tool Kobas 3.0 [[66]23]. A p-value less than 0.05 was considered statistically significant. ClusterProfiler was used for the statistical analysis and visualization of the functional profiles of the DEGs in the GEO datasets and qRT-PCR arrays [[67]24]. TBtools was used to construct the Venn diagrams of the KEGG pathways for the DEGs in the three GEO datasets. 2.4. Animals and Tissue Collection Three sows each from the Min and Large White breeds were used in this study. The Min pig is an excellent indigenous breed from northeastern China, and it has an IMF content higher than that of Large White pigs [[68]12]. The pigs used in this study were obtained from the Institute of Animal Husbandry Research, Heilongjiang Academy of Agricultural Sciences (Harbin, China). The pigs were raised for 180 days under the same conditions. When the pigs were slaughtered, the LD muscle was collected between the 10th and 12th ribs from the carcasses. All tissue samples were divided into two groups; one group was quickly frozen immediately after collection and stored at −80 °C until use in qRT-PCR arrays, and the other group was stored at 4 °C for the determination of IMF content. All animal procedures were performed according to the University Committee on the Use and Care of Animals at Jilin University (approval ID: 201706030). 2.5. Determination of IMF Content In the present study, the IMF content was measured in each LD sample by the Soxhlet extraction method with petroleum ether [[69]25]. 2.6. RNA Extraction and Quantitative Real-Time Reverse Transcription-Polymerase Chain Reaction (Qrt-PCR) Arrays Total RNA from the LD was isolated from approximately 200 mg of frozen tissue using TRIzol-A^+ (TIANGEN, Beijing, China) following the manufacturer’s instructions. The BioRT cDNA First Strand Synthesis Kit (Bioer Technology, Hangzhou, China) was used to synthesize first-strand cDNA. Subsequently, the expression levels of the target genes were analyzed on an iQ™5 real-time PCR detection system (Bio-Rad, USA). A BioEasy SYBR Green I Real Time PCR kit (Bioer Technology) was used according to the manufacturer’s instructions to detect each sample in triplicate. The primers used for the qRT-PCR arrays are listed in [70]Table S1. The gene IDs from the selected pathways were obtained from the KEGG database ([71]Table S2). 2.7. Western Blot Analysis Protein samples from the LD of Large White (n = 3) and Min (n = 3) were separated by 10% SDS-PAGE gels and transferred electrophoretically onto polyvinylidene fluoride (PVDF) membranes (Millipore, Boston, MA, USA). Western blot was performed with rabbit anti-AMPK alpha 1 polyclonal antibody (1:1000, Bioss, Beijing, China), rabbit anti-LKB1 polyclonal antibody (1:1000, Bioss), rabbit anti-CaMKK2 polyclonal antibody (1:1000, Bioss), rabbit anti-CPT1A polyclonal antibody (1:1000, Bioss), and rabbit anti-ACC1 polyclonal antibody (1:1000, WUHAN SANYING) as the primary antibodies and Horseradish peroxidase (HRP)-conjugated goat anti-rabbit antibody as the second antibody. Rabbit anti-β-actin polyclonal antibody (1:8000, Bioss) was used as an internal control. Signals were detected using the SuperSignal WestPico Chemiluminescent Substrate Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. 2.8. Statistical Analysis of the Qrt-PCR Array Results In the present study, the Large White pigs were set as the experimental group and the Min pigs were set as the control group. GraphPad Prism 6.01 (GraphPad Software, San Diego, CA, USA) was used for analyzing our results. Student’s t-tests were used to compare the control and experimental groups. For all comparisons, * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001 were considered significant differences. The 2^−ΔCT method was used to calculate the Ct values from the qRT-PCR array data. The limma package was used to identify the significant DEGs of the qRT-PCR arrays between different groups [[72]22]. TBtools was used to draw heatmaps of the qRT-PCR arrays ([73]https://github.com/CJ-Chen/TBtools). The online tool KEGG Mapper was used to draw the colored map of DEGs ([74]https://www.kegg.jp/kegg/mapper.html). The STRING database was used to predict protein interactions and construct the network for DEGs [[75]26]. The protein-protein interaction (PPI) network was visualized by Cytoscape [[76]27]. 3. Results 3.1. Identification of DEGs in GEO Datasets According to the cutoff threshold (p < 0.05 and |log[FC]| ≥ 1), in [77]GSE24192, 1237 DEGs were identified in the LD of Large White pigs when compared with the indigenous Chinese breeds, and they included 877 upregulated genes and 360 downregulated genes. In [78]GSE75045, a total of 2582 DEGs were identified in the LD of Large White pigs, and they included 1096 upregulated genes and 1486 downregulated genes. Finally, in [79]GSE99092, a total of 1822 DEGs were identified in the LD of Large White pigs, and they included 809 upregulated genes and 1013 downregulated genes. 3.2. Pathway Enrichment of DEGs in GEO Datasets The KEGG pathway enrichment results ([80]Figure 1D,E; [81]Table 1, [82]Table 2 and [83]Table 3) yielded no shared pathways among the downregulated DEGs of Large White pigs in the three GEO datasets. In contrast, the AMPK signaling pathway (ssc04152), the peroxisome proliferator-activated receptor (PPAR) signaling pathway (ssc03320), fat digestion and absorption (ssc04975), fatty acid metabolism (ssc01212), metabolic pathways (ssc01100), and biosynthesis of amino acids (ssc01230) were among the upregulated DEGs in Large White pigs in the three GEO datasets. The AMPK signaling pathway may represent a novel pathway for regulating IMF deposition in pigs. Figure 1. [84]Figure 1 [85]Open in a new tab Data mining results for the three Gene Expression Omnibus (GEO) datasets. Scatter plot of differentially expressed genes (DEGs) in (A) [86]GES24192, (B) [87]GSE75045, and (C) [88]GSE99092. (D) Venn diagrams of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the upregulated genes in the three GEO datasets. The six shared pathways (AMPK signaling pathway, the peroxisome proliferator-activated receptor (PPAR) signaling pathway, fat digestion and absorption, fatty acid metabolism, metabolic pathways, and biosynthesis of amino acids) are listed in the figure. (E) Venn diagrams of the KEGG pathways for the downregulated genes in the three GEO datasets. Table 1. KEGG pathway enrichment of the DEGs in the Large White pigs from [89]GSE24912. Pathway ID Name Gene Count p-Value Upregulated DEGs ssc04145 Phagosome 36 1.66 × 10^−22 ssc01100 Metabolic pathways 87 2.06 × 10^−18 ssc00100 Steroid biosynthesis 6 3.08 × 10^−5 ssc00330 Arginine and proline metabolism 8 4.48 × 10^−5 ssc04062 Chemokine signaling pathway 15 5.27 × 10^−5 ssc00590 Arachidonic acid metabolism 9 5.59 × 10^−5 ssc01230 Biosynthesis of amino acids 9 8.49 × 10^−5 ssc04923 Regulation of lipolysis in adipocytes 8 1.32 × 10^−4 ssc04060 Cytokine-cytokine receptor interaction 16 1.92 × 10^−4 ssc00010 Glycolysis/gluconeogenesis 8 2.24 × 10^−4 ssc04810 Regulation of actin cytoskeleton 15 2.75 × 10^−4 ssc04390 Hippo signaling pathway 12 2.98 × 10^−4 ssc00592 alpha-Linolenic acid metabolism 5 4.30 × 10^−4 ssc03320 PPAR signaling pathway 8 4.72 × 10^−4 ssc04152 AMPK signaling pathway 10 9.76 × 10^−4 ssc04975 Fat digestion and absorption 5 2.28 × 10^−3 ssc00071 Fatty acid degradation 5 4.66 × 10^−3 ssc01212 Fatty acid metabolism 5 7.78 × 10^−3 Downregulated DEGs ssc00190 Oxidative phosphorylation 17 1.67 × 10^−13 ssc01100 Metabolic pathways 38 5.02 × 10^−10 ssc01210 2-Oxocarboxylic acid metabolism 4 5.24 × 10^−5 ssc04024 cAMP signaling pathway 10 5.68 × 10^−5 ssc01230 Biosynthesis of amino acids 5 7.29 × 10^−4 ssc04960 Aldosterone-regulated sodium reabsorption 4 9.76 × 10^−4 ssc04931 Insulin resistance 6 1.03 × 10^−3 ssc00220 Arginine biosynthesis 3 1.33 × 10^−3 ssc04923 Regulation of lipolysis in adipocytes 4 2.79 × 10^−3 ssc00400 Phenylalanine, tyrosine, and tryptophan biosynthesis 2 3.14 × 10^−3 ssc03320 PPAR signaling pathway 4 5.59 × 10^−3 ssc04920 Adipocytokine signaling pathway 4 5.86 × 10^−3 ssc00250 Alanine, aspartate, and glutamate metabolism 3 6.59 × 10^−3 [90]Open in a new tab Table 2. KEGG pathway enrichment of the DEGs in the Large White pigs from [91]GSE75045. Pathway ID Name Gene Count p-Value Upregulated DEGs ssc04922 Glucagon signaling pathway 18 2.68 × 10^−7 ssc04910 Insulin signaling pathway 20 5.21 × 10^−6 ssc00010 Glycolysis/gluconeogenesis 13 9.46 × 10^−6 ssc01230 Biosynthesis of amino acids 14 1.18 × 10^−5 ssc01200 Carbon metabolism 14 5.35 × 10^−4 ssc04152 AMPK signaling pathway 14 1.01 × 10^−3 ssc00500 Starch and sucrose metabolism 8 2.01 × 10^−3 ssc00760 Nicotinate and nicotinamide metabolism 6 2.10 × 10^−3 ssc04974 Protein digestion and absorption 10 2.29 × 10^−3 ssc04911 Insulin secretion 9 4.71 × 10^−3 ssc04931 Insulin resistance 12 4.82 × 10^−3 ssc00051 Fructose and mannose metabolism 6 6.50 × 10^−3 ssc01100 Metabolic pathways 67 8.00 × 10^−3 ssc00620 Pyruvate metabolism 6 8.50 × 10^−3 ssc04022 cGMP-PKG signaling pathway 14 9.21 × 10^−3 ssc04015 Rap1 signaling pathway 16 9.32 × 10^−3 ssc00030 Pentose phosphate pathway 5 9.47 × 10^−3 ssc01212 Fatty acid metabolism 7 1.37 × 10^−2 ssc03320 PPAR signaling pathway 6 4.81 × 10^−2 ssc04975 Fat digestion and absorption 4 4.83 × 10^−2 Downregulated DEGs ssc01100 Metabolic pathways 127 1.26 × 10^−9 ssc00071 Fatty acid degradation 16 1.62 × 10^−7 ssc01212 Fatty acid metabolism 16 2.91 × 10^−7 ssc03320 PPAR signaling pathway 16 2.16 × 10^−5 ssc00190 Oxidative phosphorylation 23 2.84 × 10^−5 ssc04146 Peroxisome 18 5.44 × 10^−5 ssc04260 Cardiac muscle contraction 15 1.18 × 10^−4 ssc00640 Propanoate metabolism 9 1.86 × 10^−4 ssc00620 Pyruvate metabolism 10 3.69 × 10^−4 ssc00280 Valine, leucine, and isoleucine degradation 11 5.68 × 10^−4 ssc01200 Carbon metabolism 17 1.05 × 10^−3 ssc00480 Glutathione metabolism 10 1.14 × 10^−3 ssc00561 Glycerolipid metabolism 10 2.15 × 10^−3 ssc00270 Cysteine and methionine metabolism 9 4.87 × 10^−3 ssc00250 Alanine, aspartate, and glutamate metabolism 8 5.33 × 10^−3 ssc00062 Fatty acid elongation 5 7.17 × 10^−3 ssc04261 Adrenergic signaling in cardiomyocytes 16 1.20 × 10^−2 ssc04810 Regulation of actin cytoskeleton 21 1.53 × 10^−2 ssc00061 Fatty acid biosynthesis 4 1.53 × 10^−2 ssc04920 Adipocytokine signaling pathway 10 1.82 × 10^−2 ssc00400 Phenylalanine, tyrosine and tryptophan biosynthesis 3 1.88 × 10^−2 ssc01230 Biosynthesis of amino acids 9 4.78 × 10^−2 [92]Open in a new tab Table 3. KEGG pathway enrichment of the DEGs in the Large White pigs from [93]GSE99092. Pathway ID Name Gene count p-Value Upregulated DEGs ssc01200 Carbon metabolism 17 5.04 × 10^−7 ssc01100 Metabolic pathways 68 3.05 × 10^−6 ssc00071 Fatty acid degradation 8 1.72 × 10^−4 ssc01212 Fatty acid metabolism 8 2.37 × 10^−4 ssc00280 Valine, leucine, and isoleucine degradation 8 3.73 × 10^−4 ssc00640 Propanoate metabolism 6 5.48 × 10^−4 ssc01210 2-Oxocarboxylic acid metabolism 5 8.14 × 10^−4 ssc00630 Glyoxylate and dicarboxylate metabolism 5 2.26 × 10^−3 ssc00350 Tyrosine metabolism 5 6.61 × 10^−3 ssc00620 Pyruvate metabolism 5 1.08 × 10^−2 ssc00061 Fatty acid biosynthesis 3 1.32 × 10^−2 ssc03320 PPAR signaling pathway 6 2.76 × 10^−2 ssc00190 Oxidative phosphorylation 9 3.24 × 10^−2 ssc04920 Adipocytokine signaling pathway 6 3.37 × 10^−2 ssc00360 Phenylalanine metabolism 3 3.52 × 10^−2 ssc01230 Biosynthesis of amino acids 6 3.82 × 10^−2 ssc04152 AMPK signaling pathway 9 3.83 × 10^−2 ssc04975 Fat digestion and absorption 3 4.38 × 10^−2 ssc00380 Tryptophan metabolism 4 4.55 × 10^−2 Downregulated DEGs ssc04666 Fc gamma R-mediated phagocytosis 11 1.61 × 10^−3 ssc04130 SNARE interactions in vesicular transport 7 2.63 × 10^−3 ssc04512 ECM-receptor interaction 9 4.05 × 10^−3 ssc00100 Steroid biosynthesis 5 4.94 × 10^−3 ssc04974 Protein digestion and absorption 9 7.22 × 10^−3 ssc04810 Regulation of actin cytoskeleton 15 2.87 × 10^−2 ssc00310 Lysine degradation 6 3.41 × 10^−2 ssc00260 Glycine, serine, and threonine metabolism 5 4.09 × 10^−2 ssc00410 beta-Alanine metabolism 4 4.29 × 10^−2 ssc04330 Notch signaling pathway 5 5.34 × 10^−2 ssc00230 Purine metabolism 12 4.38 × 10^−2 ssc00280 Valine, leucine, and isoleucine degradation 5 4.77 × 10^−2 ssc00330 Arginine and proline metabolism 5 4.77 × 10^−2 [94]Open in a new tab 3.3. IMF Content of LD Muscles in the Two Pigs As shown in [95]Figure 2, the Min pigs had significantly higher IMF content than the Large White pigs. Figure 2. [96]Figure 2 [97]Open in a new tab Detection of the IMF content in the LD muscles of Large White and Min pigs. Data represent the means ± SEM (n = 3), *** p < 0.001. 3.4. Validation of the AMPK Signaling Pathway in the LD Muscles of the Two Pig Breeds The AMPK signaling pathway (ssc04152) consists of 117 genes ([98]Table S2). In this study, 114 of these genes were validated via qRT-PCR array. Of them, 40 genes were differentially expressed in the Large White LD; 22 were upregulated and 18 were downregulated. The qRT-PCR results for the AMPK signaling pathway are shown in [99]Figure 3, [100]Figure 4 and [101]Figure 5, [102]Table 4 and [103]Table 5. A heatmap of the AMPK signaling pathway is presented in [104]Figure 3A, indicating that the expression patterns of the DEGs in the AMPK signaling pathway among the Large White and Min breeds show significant differences. [105]Figure 3B shows a colored map of the AMPK signaling pathway in the LD of Large White pigs. [106]Figure 4 shows a PPI network of 14 upregulated genes in the LD muscle of Large White pigs, and it contains 14 nodes and 36 edges. The most significant 10 node degree genes are SLC2A4, PGC-1α, LKB1, AMPK, LIPE, CD36, CaMKK2, FOXO3, FOXO1, and CPT1B ([107]Table 5). Moreover, based on the results of the colored map and PPI network, 10 genes in the AMPK signaling pathway (AMPK, ADIPOR1, ADIPOR2, LKB1, CaMKKβ, CPT1A, CPT1B, PGC-1α, CD36, and ACC1) were selected as key hub genes, and their expression patterns are presented in [108]Figure 5. These hub genes are associated with fatty acid oxidation, lipid oxidation, and fatty acid metabolic processes. In addition, the protein expression levels of AMPK, LKB1, CaMKK2, CPT1A, and ACC1 were examined in the LD muscle of the Min and Large White pigs by Western blot analysis. The protein expression levels of AMPK, LKB1, CaMKK2, and CPT1A were higher in the Large White group than that the Min group, ACC1 showed low expression level in the LD muscle of Large White pigs ([109]Figure 6). Taken together, these results show that the AMPK signaling pathway is more active in the Large White breed than the Min breeds. Figure 3. [110]Figure 3 [111]Open in a new tab qRT-PCR array results in two pig breeds. (A) Heatmap of all qRT-PCR array genes. (B) Colored map of the AMPK signaling pathway. Upregulated and downregulated genes are indicated by red and blue, respectively. Figure 4. [112]Figure 4 [113]Open in a new tab PPI network for the upregulated genes in the AMPK signaling pathway. The nodes represent the proteins (genes); the edges represent the interaction of proteins (genes). Figure 5. [114]Figure 5 [115]Open in a new tab Relative expression levels of ten key hub genes in the AMPK signaling pathway in two pig breeds. All data are shown as the means ± SEM (n = 3), *** p < 0.001. Table 4. qRT-PCR array results for the AMPK signaling pathway (Large White-Min). Gene Symbol Fold Change p-Value Regulation CD36 3.189283629 7.67 × 10^−3 Up PGC-1A 11.14850152 4.46 × 10^−2 Up AKT2 3.156554305 3.62 × 10^−3 Up AKT1 7.868832108 3.43 × 10^−5 Up CPT1B 8.890764573 3.93 × 10^−2 Up ADIPOR2 8.509277729 7.67 × 10^−3 Up PPP2R1A 6.599066891 1.04 × 10^−2 Up CPT1A 6.846403313 8.26 × 10^−4 Up FOXO1 5.397575689 3.18 × 10^−2 Up LIPE 6.924041605 1.87 × 10^−2 Up LKB1 4.901757799 7.41 × 10^−3 Up FOXO3 3.639771136 1.49 × 10^−4 Up MTOR 4.068698015 2.35 × 10^−2 Up ADIPOR1 2.245141118 3.62 × 10^−3 Up RPS6KB2 3.361252687 4.11 × 10^−2 Up AKT3 5.250417157 2.42 × 10^−2 Up AMPK 6.699341927 4.11 × 10^−2 Up PDPK1 1.981200539 7.58 × 10^−3 Up TSC2 2.740754696 7.67 × 10^−3 Up SLC2A4 9.440463793 1.01 × 10^−2 Up CAMKK2 5.359797456 2.16 × 10^−2 Up TSC1 8.141086731 1.08 × 10^−2 Up PFKM −5.001312186 5.39 × 10^−3 Down FBP2 −5.808487321 1.08 × 10^−2 Down ACC1 −2.601477756 1.86 × 10^−2 Down RAB2A −2.652531932 3.07 × 10^−3 Down PFKFB1 −6.096200009 1.47 × 10^−2 Down PPP2R2A −4.510811417 3.62 × 10^−3 Down IRS1 −8.500540176 1.87 × 10^−2 Down CREB3 −5.026439516 1.03 × 10^−2 Down PPP2R5A −5.729220232 1.53 × 10^−2 Down PPP2R5B −3.011595315 7.67 × 10^−3 Down PFKL −2.467192832 2.29 × 10^−2 Down PIK3R2 −3.841154966 2.35 × 10^−2 Down PPP2R5E −1.634192954 3.77 × 10^−2 Down CREB5 −1.953383525 1.08 × 10^−2 Down G6PC3 −2.621247554 3.18 × 10^−2 Down RAB11B −4.129252447 3.58 × 10^−2 Down PIK3CB −2.137748295 4.37 × 10^−2 Down PIK3CD −3.600875922 4.88 × 10^−2 Down [116]Open in a new tab Table 5. Summary for the PPI network of 14 upregulated genes in the AMPK signaling pathway. Gene Symbol Degree SLC2A4 9 PGC-1α 9 LKB1 6 AMPK 6 LIPE 6 CD36 5 CAMKK2 5 FOXO3 5 FOXO1 5 CPT1B 5 CPT1A 4 ADIPOR2 3 ADIPOR1 3 PPP2R1A 1 [117]Open in a new tab Top ten degree genes are shown in bold. Figure 6. [118]Figure 6 [119]Open in a new tab Relative expression levels of AMPK, LKB1, CaMKK2, CPT1A, and ACC1 proteins in the AMPK signaling pathway in two pig breeds. All data are shown as the means ± SEM (n = 3), * p < 0.05, ** p < 0.01. Representative western blots (A) and quantitative densitometry analysis (B) of AMPK, LKB1, CaMKK2, CPT1A, and ACC1 protein levels are shown in the LD muscle of Large White and Min pigs. 3.5. GO Enrichment of DEGs in Qrt-PCR Arrays The biological processes encoded by upregulated genes were involved in fatty acid oxidation, lipid oxidation, and fatty acid metabolic processes, while the biological processes encoded by the downregulated genes were involved in carbohydrate metabolic processes, including glucose, hexose, and monosaccharide metabolism, as well as hexose and monosaccharide catabolism ([120]Figure 7). These results indicate that compared with the LD of Min pigs, the LD of Large White pigs consumes more fat for energy metabolism than carbohydrates. Figure 7. [121]Figure 7 [122]Open in a new tab GO terms include three complementary biological roles, Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), for the DEGs in the qRT-PCR array. BP for upregulated (A) and downregulated (D) genes. CC for upregulated (B) and downregulated (E) genes. MF for upregulated (C) and downregulated (F) genes. 4. Discussion In the modern pork industry, the IMF content is an important trait that is positively associated with pork quality and in demand by consumers. As a complex meat trait, IMF deposition in the LD muscle is regulated by multiple genes and pathways. In this study, by integrating and reanalyzing three gene expression profiles, we compared the pathways related to IMF deposition in the LD muscle of Large White pigs with those of indigenous breeds. Several candidate signaling pathways were found, and the expression patterns of genes in the AMPK pathway in pigs were validated by qRT-PCR arrays in subsequent experiments. The AMPK signaling pathway plays critical roles in controlling both glucose and lipid metabolism. AMPK is the central gene of the AMPK signaling pathway and a heterotrimeric enzyme with α, β, and γ subunits. Once activated, AMPK promotes lipid oxidation and glucose uptake, inhibits lipid synthesis and decreases IMF contents [[123]28]. Accordingly, the activity of AMPK is inversely correlated with IMF accumulation. In the present study, AMPK was highly expressed in the LD muscle of Large White pigs, which has a lower IMF content than Min pigs. This result is consistent with several previous reports demonstrating that the expression levels of AMPK are higher in the low-IMF-content skeletal muscle of cattle and sheep [[124]29,[125]30,[126]31], suggesting that AMPK plays a positive role in reducing the IMF content in pigs. As a member of the adipocytokines, adiponectin plays crucial roles in whole-body energy homeostasis by stimulating AMPK. ADIPOR1 and ADIPOR2 are two major receptors for adiponectin and play key roles in metabolic pathways that regulate glucose and lipid metabolism, inflammation, and oxidative stress [[127]32]. ADIPOR1 and ADIPOR2 mediate the metabolic actions of adiponectin by activating AMPK and PPARα, respectively. This activation leads to increased fatty acid oxidation and glucose uptake in mice [[128]33,[129]34,[130]35]. Moreover, muscle-specific disruption of ADIPOR1 inhibits the increase in intracellular Ca^2+ concentration and reduces the activation of calmodulin-dependent kinase β (CaMKKβ) and AMPK by adiponectin. Consistent with these previously reported results, ADIPOR1 and ADIPOR2 were both upregulated in the LD muscle of Large White pigs in the present study, suggesting that these two genes may account for the low IMF accumulation in pigs. Interestingly, two upstream kinases of AMPK, the tumor suppressor LKB1 and Ca^2+/CaMKKβ, which participate in the phosphorylation and activation of AMPK, were simultaneously highly expressed in the LD muscle of Large White pigs. AMPK can be activated via two distinct mechanisms, LKB1 encodes a serine-threonine kinase that directly phosphorylates and activates AMPK, and CaMKKβ can form a complex with and activate AMPK through their kinase domains in skeletal muscle [[131]36,[132]37,[133]38,[134]39]. Consistent with these previous findings, our results suggest that LKB1 and CaMKKβ play critical roles in reducing the IMF content in pigs by activating the expression of AMPK. Furthermore, AMPK plays a central role in controlling lipid metabolism by regulating the downstream acetyl-CoA carboxylase (ACC1) and carnitine palmitoyltransferase 1 (CPT1) pathways. CPT1, a rate-limiting enzyme of mitochondrial fatty acid β-oxidation, is closely associated with fat deposition. Additionally, CPT1A and CPT1B, two common CPT1 subtypes in mammals, play prominent roles in fatty acid oxidation and lipid accumulation in humans, chickens, and pigs [[135]40,[136]41,[137]42]. Moreover, according to the KEGG database, CPT1A and CPT1B are involved in the AMPK signaling pathway, implying that these genes participate in the mediation of fatty acid oxidation. In addition, the ACC1 gene encodes acetyl-CoA carboxylase (ACC), which is the rate-limiting enzyme responsible for the de novo synthesis of fatty acids [[138]43]. As a target gene of the AMPK signaling pathway, the activity of ACC1 is inhibited by AMPK. Similarly, an increase in activity of ACC1 can also inhibit the expression of CPT1 and fatty acid oxidation in skeletal muscles [[139]44,[140]45,[141]46]. Consistent with these previous results, both CPT1A and CPT1B were highly expressed and ACC1 was significantly downregulated in the LD muscle of Large White pigs, suggesting that the AMPK-ACC1-CPT1 pathway was positively associated with a decrease in fatty acid synthesis and IMF deposition in pigs. In addition to CPT1, the fatty acid transporter fatty acid translocase/cluster of differentiation 36 (FAT/CD36) has also been found to regulate FA oxidation in skeletal muscle of human and mice [[142]47,[143]48]. FAT/CD36 has been identified as contributing to fatty acid transport and oxidation in mice [[144]49,[145]50,[146]51]. In our results, FAT/CD36 was highly expressed in the LD muscle of Large White pigs, implying that the expression of this gene has a negative effect on IMF deposition in Large White pigs. Interestingly, the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A, PGC-1α) was also involved in the activation of the CPT1 gene [[147]52]. PGC-1α plays an important role in glucose and fatty acid metabolism and has a negative relationship with the IMF content in pigs [[148]53,[149]54,[150]55]. In this study, PGC-1α presented a high expression level in the LD of Large White, suggesting that this gene exerts a negative effect on IMF deposition in pigs. Additionally, the protein expression of AMPK, LKB1, CaMKK2, CPT1A, and ACC1 were further analyzed with Western blot. In our results, four proteins (AMPK, LKB1, CaMKK2, and CPT1A) showed higher expression in the LD muscle of Large White pigs than that in Min pigs, and ACC1 had a low protein expression level in the Large White group, indicating that the AMPK signaling pathway is more active at the protein level and that the oxidative degradation of fatty acids is stronger in the Large White group. In summary, the above results suggest that the activation of the AMPK signaling pathway reduces the IMF content in the LD muscle of Large White pigs. 5. Conclusions In conclusion, this study illustrates that the accumulation of IMF in Large White pigs is related to activation of the AMPK signaling pathway. The relatively high expression of genes in the AMPK pathway may represent one of the more significant features of pigs with artificially lean meat. Our results are also helpful for interpreting the different molecular mechanisms of IMF deposition between lean and fat pig breeds. Acknowledgments