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. 2025 Aug 18;11:100286. doi: 10.1016/j.fochms.2025.100286

Comparative phosphoproteomics provides insights into the differences of porcine longissimus thoracis, semimembranosus, psoas major and semitendinosus muscles

Li Zhang a,b, Wen Luo a, Linlin Zhao a, Longyun Li a,⁎, Yizhong Huang a,⁎
PMCID: PMC12747191  PMID: 41472705

Abstract

Different pork cuts vary in muscle fiber characteristics and meat quality, significantly affecting processing properties and consumer preference. However, the role of protein phosphorylation in meat quality variation in pork cuts remains unclear. Using a 4D label-free platform, we performed quantitative phosphoproteomic analysis on longissimus thoracis (LT), semimembranosus (SMM), psoas major muscle (PS) and semitendinosus (SMT), identifying 13,232 phosphopeptides from 3137 phosphoproteins, and over 1000 differentially accumulated phosphopeptides (DAPPs) in each of six comparison groups. Enrichment analysis showed that these phosphoproteins were enriched in sarcomere organization and muscle cytoskeleton pathways. A total of 184, 53, 75, and 691 unique DAPPs were identified in LT, SMM, PS, and SMT, respectively. Protein-protein interaction networks revealed that phosphoproteins regulated meat quality differences. Several key phosphoproteins, including ATP5F1A, ATP5F1C, FLNC, MDH2, BAG3, and AKT1, demonstrated significant associations with meat quality traits. These findings provide valuable insights for phosphoproteins that regulate meat quality in pigs.

Keywords: Pork cuts, Meat quality, Phosphoproteome, Differentially accumulated phosphopeptides, Cut-specific phosphoproteins

Graphical abstract

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Highlights

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    4D label-free quantitative phosphoproteomic analysis was conducted on the four pork cuts.

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    Differentially accumulated phosphoproteins were enriched in sarcomere organization and muscle cytoskeleton pathways.

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    Cuts-specific differential abundance phosphoproteins were identified.

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    Key phosphoproteins were proved to be significantly correlated with meat quality traits.

1. Introduction

Pigs are the most important agricultural animal, producing one-third of the red meat consumed globally. China maintains a substantial domestic market for swine-derived protein, with annual per capita consumption surpassing 30 kg according to FAO statistics – constituting a critical pillar of national food security and agricultural economics (Lim & Grohn, 2021). With the outbreak of African swine fever, the adjustment of relevant national policies and the change of consumers' lifestyles, pork cut is increasingly favored by consumers (Xie et al., 2023). The optimization of techno-functional attributes in different pork cuts has emerged as a critical research priority, driven by escalating consumer expectations for pork products exhibiting superior sensory characteristics and enhanced nutritional profiles. However, meat quality traits are multifactorial and subject to intricate interactions among internal genetic determinants and external environmental influences, including breed characteristics (Deng et al., 2023), animal age (Weng et al., 2021), and nutritional management strategies (Guo et al., 2022). Among various influencing factors, pork cut position also significantly affects meat quality characteristics. Chen et al. conducted a comprehensive investigation into the correlation between muscle fiber characteristics and the quality of frozen/thawed pork meat across four distinct muscle groups: longissimus thoracis et lumborum (LTL), psoas major (PM), semimembranosus (SM), and semitendinosus (ST), demonstrating significant intermuscular variations in both physicochemical meat quality parameters and histomorphological characteristics of muscle fibers, revealing critical structure-function relationships affecting frozen meat quality retention (Cheng et al., 2021). Building on this foundation, Duan et al. implemented LC-MS/MS-based lipidomic mapping to decipher phospholipid remodeling patterns and volatile compound signatures in pork cuts (loin, belly, shoulder, hind leg), identifying cut-specific lipid oxidation gradients (Duan et al., 2024). Complementarily, Zhang et al. employed flavoromics-guided multi-omics integration in Chalu black pigs, demonstrating cut-dependent flavor precursor accumulation (longissimus thoracis vs. trapezius vs. hamstring vs. belly) through proteoglycan-mediated metabolite channeling (Zhang et al., 2025). Nevertheless, the mechanisms that govern the different meat quality traits found in pork cuts of the pig require more understanding.

The orchestration of meat quality characteristics appears fundamentally governed by dynamic post-translational regulatory mechanisms, with phosphorylation, acetylation, and ubiquitination emerging as critical modulators of myofibrillar protein functionality during postmortem metabolic processes, thus influencing the tenderness, juiciness, meat color, and flavor precursor formation (Li et al., 2021). Recent advancements in cutting-edge proteomic technologies have enabled comprehensive mapping of diverse post-translational modifications (PTMs) that critically influence meat quality attributes (Munekata et al., 2021). Phosphoproteomic studies have progressively unveiled the critical regulatory roles of phosphoproteins in determining meat quality across diverse species. Weng et al. performed comprehensive phosphoproteomic profiling in geese skeletal muscle, identifying phosphorylation signatures associated with key meat quality parameters through functional bioinformatics analysis (Weng et al., 2021). Parallel investigations in yak M. longissimus lumborum (LL, the anterior 12th rib to the last lumbar vertebrae) muscle revealed altitude-adaptive phosphorylation patterns that mediated post-translational regulation of hypoxia response pathways, suggesting evolutionary conservation of phosphorylation-mediated stress adaptation (Yang et al., 2020). Building on these findings, Li et al. established a direct mechanistic link between myoglobin phosphorylation at Ser133 and impaired color stability in ovine meat through a targeted post-translational modification study (Li et al., 2018). Contrastingly, Chen et al. identified pro-firmness phosphorylation networks in grass carp, involving collagen type I α2 remodeling, myofibrillar filament reorganization, and coordinated regulation of tight junction components with glycolytic enzymes (Chen et al., 2020). Subsequent mechanistic investigations demonstrated that postmortem phosphorylation dynamics in porcine longissimus dorsi (LD) muscle, particularly those governing glycolytic flux redirection, critically determine water retention properties through spatial-temporal modification patterns (Huang et al., 2014). Complementing these discoveries, He et al. conducted comparative phosphoproteomics in porcine fast-twitch (biceps femoris) and slow-twitch (soleus) muscles, revealing breed-specific phosphorylation features underlying muscle fiber type differentiation in Duroc × Meishan hybrids (He et al., 2022). Collectively, it can be concluded that post-translational phosphorylation serves as a central regulatory mechanism governing both the ultrastructural organization of muscle fibers and the organoleptic properties determinant of meat quality. However, the phosphoprotein profiles among more different pork cuts and the distinct phosphoprotein networks related to organoleptic quality determinants (e.g., pH, meat color) within the same individual have not been fully investigated.

The Shanxia Long Black pig, a new breed recently approved by the Ministry of Agriculture and Rural Affairs of the People's Republic of China, is bred as a paternal strain with high-quality meat and black coat color (Yan et al., 2024). We hypothesized that the abundance of the distinct phosphoproteins within four different raw pork cuts may contribute to the variation in meat quality and muscle fiber characteristics in each cut. Therefore, a comprehensive phosphoproteomic profiling was performed on four distinct pork cuts (LT, SMM, PS, and SMT) to identify phosphoproteins and characterize their phosphorylation sites. The differential abundance phosphopeptides (DAPPs) were systematically identified across the muscle groups through comparative analysis. Proteins harboring these DAPPs subsequently underwent functional annotation through GO and KEGG enrichment analyses. To elucidate interaction networks among these phosphoproteins, we further conducted protein-protein interaction (PPI) network analysis, specifically focusing on DAPP-containing phosphoproteins. This study elucidates previously undefined phosphorylation-dependent mechanisms governing muscle-type-specific quality attributes in pigs. Notably, we identified functionally conserved phosphorylation signatures with translational potential for guiding precision breeding programs, serving as potential biomarkers for meat quality enhancement through targeted genetic modulation.

2. Materials and methods

2.1. Animals and sample collection

Muscle samples used in this study were obtained from six genetically half-sibling sows with homogeneous carcass weight (mean ± SD: 93.40 ± 2.93 kg) and matched age (226 ± 5 days) derived from the Shanxia Long Black pigs. To minimize confounding effects from genetic and extraneous environmental variability, all sibling pigs were reared under standardized conditions. They were housed in solid concrete floor pens and provided with ad libitum access to a corn-soybean meal-based diet and fresh water throughout the study period. Details about the determinants of slaughtering and meat quality traits could be referred to the article from Yan et al. (Yan et al., 2024), and longissimus thoracis (LT), semimembranosus (SMM), psoas major (PS) and semitendinosus (SMT) muscle tissues aseptically harvested at precise anatomical locations from six individuals. The meat quality traits used in this study for the correlation analysis were pH, Minolta L*, a*, b* at 45 min and 24 h postmortem, and Type1, Type2a, Type2b muscle fibers, and the detection methods were referred to our previous articles (Huang et al., 2020; Huang et al., 2022). Following collection, all biological specimens were promptly immersed in liquid nitrogen for rapid cryopreservation and subsequently transferred to −80 °C ultra-low-temperature freezers for long-term storage before experimental analysis. All animal procedures were performed in strict accordance with the guidelines for the care and use of experimental animals, as approved by the Ministry of Agriculture and Rural Affairs of China. The ethics committee of Jiangxi Agricultural University and Nanchang Normal University specifically approved this study (No. JXAU2011–006).

2.2. Quantitative phosphoproteomic analysis

4D label-free quantitative phosphoproteomic analysis was performed to investigate the phosphorylation events in LT, SMM, PS, and SMT muscle samples. Each group contained six biological replicates. The experimental approach mainly included protein extraction, proteolytic desalting, modification and enrichment, LC-MS/MS (Liquid chromatography-mass spectrometry) analysis, database search and quantification, and bioinformatic analysis.

2.2.1. Protein extraction

Begin by retrieving cryopreserved samples from the −80 °C freezer and immediately homogenize the tissue into a fine powder using a pre-chilled mortar and pestle under continuous liquid nitrogen cooling. Transfer 50–100 mg of the homogenized powder into pre-labeled 1.5 mL microcentrifuge tubes. Add an adequate volume of ice-cold lysis buffer (8 M urea, 1 mM PMSF, 1 % PhosSTOP™ (Sigma-Aldrich), 2 mM EDTA). Perform ultrasonic lysis for 5 min on ice, centrifuge at 15,000 g at 4 °C for 10 min, and carefully collect the supernatant while avoiding contact with the pelleted debris. Finally, total protein concentrations were determined spectrophotometrically in triplicate using a commercially available enhanced bicinchoninic acid (BCA) assay kit (Beyotime™ #P0009) according to the manufacturer's protocol. Prepare albumin standards in parallel and measure absorbance at 562 nm with a microplate reader. Adjust sample concentrations with lysis buffer as needed for downstream applications.

2.2.2. Proteolytic desalting

Normalize to 0.5 mg total protein using the previously determined concentration and adjust the volume to 200 μL with 8 M urea, then reduce with dithiothreitol (final concentration 10 mM) at 37 °C for 45 min, and the iodoacetamide (final concentration 50 mM) was added and incubated at room temperature in the dark for 15 min to complete the alkylation. Add four times the volume of protein solution in cold acetone to the samples, precipitate at −20 °C for 2 h, and wash twice with acetone. Next, add 800 μL of 25 mM ammonium bicarbonate solution, 10 μL of 1 μg/μL trypsin (Promega), and 3 μL of 1 μg/μL Lys-C (Thermo Fisher), and digest overnight at 37 °C. After digestion is completed, adjust the pH of the hydrolyzed peptide segment to 2–3 with 20 % TFA, and centrifuge to obtain the supernatant peptide segment solution. The peptide segment concentration was determined using the Pierce™ quantitative peptide detection kit (Thermo Fisher).

2.2.3. Modification and enrichment

The obtained peptide segment was dissolved in the enrichment buffer (80 % acetonitrile / 6 % trifluoroacetic acid, saturated with glutamic acid solution). Transfer the supernatant to the pre-cleaned IMAC (Immobilized metal affinity chromatography) packing, place it on a rotary shaker, and gently shake it for incubation. After the incubation was completed, the resin was washed three times in sequence with the buffer solutions containing 50 % acetonitrile / 6 % trifluoroacetic acid / 200 mM sodium chloride and 30 % acetonitrile / 0.1 % trifluoroacetic acid, respectively. Finally, the modified peptides were eluted using a 10 % ammonia water solution, and the eluent was collected and subjected to vacuum freeze-drying. The samples were desalinated under the C18 ZipTips instruction manual (Wu et al., 2010). After vacuum freeze-drying, they were quantified and then analyzed by liquid chromatography-mass spectrometry.

2.2.4. LC-MS/MS (liquid chromatography-mass spectrometry) analysis

Liquid chromatography (LC) was conducted on a nanoElute UHPLC system (Bruker Daltonics, Germany). Peptide separation (∼200 ng) was performed on a commercial reversed-phase C18 analytical column (Aurora Series with CSI technology, 25 cm × 75 μm ID, 1.6 μm particle size; IonOpticks, Australia) using a 60 min linear gradient at a constant flow rate of 0.3 μL/min (Wu et al., 2012). The analytical column was fitted with a built-in CaptiveSpray Emitter and thermally regulated at a constant 50 °C through an integrated Toaster column oven. The mobile phases comprised (A) 0.1 % (v/v) formic acid aqueous solution and (B) 0.1 % (v/v) formic acid in acetonitrile (ACN), both acidified with equivalent modifier concentrations for enhanced ionization efficiency. The separation process was performed as follows: mobile phase B was ramped from 2 % to 22 % over the first 45 min, increased to 35 % over the next 5 min, further increased to 80 % over the following 5 min, and maintained at 80 % for the final 5 min.

The LC system was directly coupled to a timsTOF Pro 2 hybrid quadrupole-time-of-flight mass spectrometer (Bruker Daltonik GmbH) through a nanoelectrospray ionization source (CaptiveSpray™, Bruker). The timsTOF Pro2 was operated in Data-Dependent Parallel Accumulation-Serial Fragmentation (PASEF) mode, with 10 PASEF MS/MS frames per complete frame (Meier et al., 2018). The total acquisition cycle duration was 1.17 s. The capillary voltage was set to 1400 V, and MS and MS/MS spectra were acquired across the mass-to-charge ratio range of 100 to 1700 m/z. The ion mobility range (1/K0) was set from 0.7 to 1.4 Vs/cm2. Both the TIMS accumulation and ramp times were configured to 100 ms, enabling near 100 % duty cycle operation. A “target value” of 10,000 was applied in a repeated acquisition schedule, with an intensity threshold of 2500. The charge state range was set from 0 to 5. The collision energy was ramped linearly as a function of mobility, ranging from 59 eV at 1/K0 = 1.6 Vs/cm2 to 20 eV at 1/K0 = 0.6 Vs/cm2. The quadrupole isolation width was set to 2 Th for m/z values below 700 and 3 Th for m/z values above 800.

2.2.5. Database search and quantification

The original data of MS was analyzed through FragPipe (v21.1), which relied on MSFragger for qualitative analysis and used Phosopher for verification and filtering (Yu et al., 2020). The spectrogram file searched the combined.fasta database containing 46,177 sequences, which was merged with the reverse sequence database and the contaminated protein database. The search parameters were as follows: The mass tolerance of both the precursor ions and fragment ions was set at 20 ppm; trypsin and Lys-C were designated as digestive enzymes, allowing up to 2 uncut sites. The length range of the peptide segment was set at 6 to 50 amino acid residues. The carbamidomethyl modification of designated Cys was fixed, and the oxidation (M), acetyl (protein N-term), and phosphorylation (S, T, Y) were variable modifications, with a maximum of 3 variable modifications allowed. Adjusting the FDR of the search results to both protein and peptide levels to 1 % and removing the reverse sequence and contaminated proteins. For label-free quantitative analysis, the IonQuant was selected as the quantitative module and check MBR and MaxLFQ. For post-translational modification (PTM) analysis, the minimum locus localization probability was set as above 0.75. Only proteins containing more than one razor peptide were used for further quantitative analysis.

2.2.6. Bioinformatic analysis

The motifs were analyzed using motfix (v5.5.0; Parameters: –width 13 –score-threshold 1e-06). Gene ontology (GO) terms were mapped, and sequences were annotated using the software program, topGO (v2.48.0). The GO annotation results were plotted using the ggplot2 (v3.3.6). Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/kegg) pathway analysis was implemented via clusterProfiler (v4.4.4) for orthology assignment and pathway mapping (Yu et al., 2012). STRING database (v12.0; http://string-db.org/) was used to identify protein–protein interactions among all differentially expressed protein entries.

2.3. Statistical analysis

Means were evaluated by an analysis of univariate ANOVA test for multiple comparisons of the abundance of DAPPs at a significance level of P < 0.05 in R software (v1.4.17). PCA and correlation analyses were also conducted in R software (v1.4.17).

3. Results

3.1. Phosphoprotein identification, mapping of phosphorylation sites, and motif analysis

To unveil the roles and phosphorylation levels of regulatory proteins and their impact on the meat quality within the four pork cuts, including the longissimus thoracis (LT), semimembranosus (SMM), psoas major muscle (PS) and semitendinosus (SMT), the quantitative phosphoproteomic analysis using a 4D-label-free platform was performed. All the data were integrated, and a total of 16,822 phosphosites were identified, corresponding to 13,232 phosphopeptides and 3137 phosphoproteins (Fig. 1A, Supplementary Table S1). Among the 3137 phosphoproteins, 957 were phosphorylated once, 602 were phosphorylated twice, 415 were phosphorylated thrice, 246 were phosphorylated quadrice and 917 had more than four phosphorylation sites. Notably, NEB (Nebulin, A0A287B5G8) had 365 phosphorylation sites (Fig. 1B), which indicated that the phosphorylated site distribution within the four pork cut proteins was highly diverse. Statistical analysis of the 16,822 identified phosphorylation sites revealed that 12,734 (75.70 %) occurred at phosphoserine (Ser), 3346 (19.89 %) occurred at phosphothreonine (Thr), and 742 (4.41 %) occurred at phosphotyrosine (Thy) residues (Fig. 1C). The length distribution of phosphorylated peptides was illustrated in Fig. 1D. In addition, 7135 phosphosites were shared among the four pork cuts, and there were 498, 641, 506, and 1098 phosphosites that were unique in LT, SMM, PS and SMT, respectively (Fig. 1E). Similarly, 2259 phosphoproteins were shared among the four pork cuts, and there were 31, 62, 59, and 148 phosphoproteins that were unique in LT, SMM, PS and SMT, respectively (Fig. 1F). These results showed that the protein phosphorylation level of SMT was higher than that of three other pork cuts.

Fig. 1.

Fig. 1

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Phosphoprotein identification and mapping of phosphorylation sites in the four pork cuts. (A) Number of phosphoproteins, phosphopeptides, and phosphosites detected in the four pork cuts in SXLB pigs. (B) Distribution and number of phosphosites in each phosphoprotein. (C) Distribution of amino acid residues in the identified phosphosites. (D) The length distribution of phosphopeptides. (E) Venn diagram analysis of phosphosites in the four pork cuts. (F) Venn diagram analysis of phosphoproteins in the four pork cuts. (G) Phosphorylation-associated sequence motifs encompass the phosphorylation site along with six amino acids on either side. The height of each letter in the motif corresponds to the frequency of that amino acid occurring at that specific position. The central position represents the phosphorylation site itself. LT: Longissimus thoracis, SMM: Semimembranosus, PS: Psoas major muscle, SMT: Semitendinosus. The same as below.

Post-translational modification (PTM) generally relies on specific catalytic enzymes to recognize distinct substrate motifs for site-specific phosphorylation events. To investigate conserved consensus motifs among the four muscle phosphoproteins, bioinformatic analysis was conducted using MEME (Multiple Em for Motif Elicitation) software. 69 putative phosphorylation motifs were identified, including 56 serine motifs and 13 threonine motifs (Fig. 1G). Among the phosphorylation site motifs analyzed, three highly conserved patterns emerged as particularly prominent: [....P.SP.....], [......SP.....] and […RR.S......] (‘.’ could be any amino acid), which demonstrated significant evolutionary conservation across a substantial number of phosphopeptides. A predominant enrichment of proline-directed kinase motifs was observed, characterized by conserved proline residues at the +1 position relative to phosphorylation sites (pSer / Thr-Pro consensus pattern). Overall, it was found that the phosphorylated proteins played a role in the differences among the four pork cuts.

3.2. Identification and comparison of differential abundance phosphopeptides among four pork cuts

To identify and compare the differential abundance phosphopeptides (DAPPs) among four pork cuts. We first conducted the PCA analysis, and the results showed that there were significant differences in phosphorylation levels of proteins among LT, SMM, PS and SMT pairwise, especially LT vs PS and LT vs SMT (Fig. 2A-2F). DAPPs were defined by statistically significant changes (P ≤ 0.05) with fold change thresholds ≥1.5 (upregulated) or ≤ 0.667 (downregulated), meeting both statistical and biological significance criteria in the LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT and PS vs SMT group. A total of 1002 phosphopeptides were significantly different in abundance between the LT and SMM, of which 879 phosphopeptides were up-regulated and 103 were down-regulated in the SMM group compared to the LT group (Fig. 2G). Most of the up-regulated phosphopetides were related to the muscle contraction related proteins (including alpha-actinin-2 (ACTN2), troponin T (TNNT1), myomesin 3 (MYOM3) and others), glycolytic enzymes (L-lactate dehydrogenase A chain (LDHA), pyruvate dehydrogenase E1 component subunit alpha (PDHA1) and et al), and oxidative phosphorylation proteins (Ubiquinol-cytochrome c reductase core protein 1 and 2 (UQCRC1, UQCRC2), cytochrome c oxidase subunit 4 and 5B (COX4I1, COX5B), and NADH dehydrogenase related proteins (NDUFB5, NDUFB7, NDUFB10) and others). Among the 103 downregulated phosphopetides, Ral guanine nucleotide dissociation stimulator-like 2 (RGL2), immunoglobulin-like and fibronectin type III domain-containing 1 (IGFN1), and myomesin 1 (MYOM1) were the most differentially expressed. Detailed information is provided in Supplementary Table S2.

Fig. 2.

Fig. 2

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Identification of differential abundance phosphopeptides (DAPPs) in the four port cuts. (A-F) Principal component analysis (PCA) score of identified phosphopeptides in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT. (G-L) Volcano plot showing DAPPs in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT. DAPPs which are significantly up-regulated or down-regulated are indicated in red and green dots. DAPPs without statistical significance are indicated by gray dots. (M-R) Heatmap showing the abundance patterns of the DAPPs in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT. (S) PCA score of all DAPPs among the four pork cuts. (T) Number of total, up- and down-regulated DAPPs in the six compare groups. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

1912 DAPPs were identified in the LT vs PS group, including 1675 up-regulated and 237 down-regulated phosphopeptides in the PS group compared to the LT group (Fig. 2H). Except for the proteins that were mentioned in the LT vs SMM group, the up-regulated phosphopetides were also related to the TCA cycle (succinate dehydrogenase (SDHA), malate dehydrogenase (MDH1, MDH2), isocitrate dehydrogenase (IDH1, IDH2) and others), fatty acid biosynthesis (long-chain-fatty-acid–CoA ligase (ACSL1), fatty acid synthase (FASN), very-long-chain (3R)-3-hydroxyacyl-CoA dehydratase (HACD1) and others), purine metabolism (adenylate kinase (AK2, AK3, AK4), phosphodiesterase (PDE4A, PDE4B, PDE4C, PDE8A) and others), MAPK signaling pathway (non-specific serine/threonine protein kinase (PAK1), filamin (FLNA, FLNB, FLNC) and others), and calcium signaling pathway (sarcoplasmic/endoplasmic reticulum calcium ATPase (ATP2A2, ATP2A3), myosin light chain kinase (MYLK, MYLK2) and others). The detailed information of DAPPs in this group was shown in Supplementary Table S3. The number of DAPPs in the LT vs SMT group was the largest compared with that in the other five groups, revealing that the SMT muscle had more phosphoprotein changes than the other three pork cuts. 3791 phosphopeptides were different in abundance between the LT vs SMT groups, of which 2817 phosphopeptides were up-regulated and 974 were down-regulated in the SMT group when compared to the LT group (Fig. 2I). Detailed information was provided in Supplementary Table S4.

There were 1441 DAPPs in the SMM vs PS group, including 869 up-regulated phosphopeptides and 572 down-regulated phosphopeptides (Fig. 2J). Detailed information in this group was provided in Supplementary Table S5. A total of 2459 DAPPs in the SMM vs PS group were identified, of which 1432 phosphopeptides were up-regulated and 1027 were down-regulated in the SMT group compared to the SMM group (Fig. 2K). Detailed information in this group was provided in Supplementary Table S6. It was worth noting that the number of up-regulated and down-regulated phosphopeptides in PS vs SMT group was relatively close (Fig. 2L). Detailed information in this group was provided in Supplementary Table S7. To enhance the visualization of phosphopeptide abundance variations across experimental groups, hierarchical clustering analysis was subsequently performed on DAPPs. The heatmap analysis revealed high reproducibility among biological replicates, as evidenced by consistent color patterns within six replicate samples, indicating stable group-specific expression profiles of differential phosphoproteins (Fig. 2M–2R). Moreover, the PCA analysis of all the DAPPs also confirmed this result (Fig. 2S). The quantities of all DAPPs in the six compared groups were summarized in Fig. 2T. Collectively, these findings revealed marked differences in phosphoproteins profiles across the four different pork cuts.

3.3. GO analysis of differential abundance phosphopeptides

GO analyses were conducted to explore the functional terms of phosphoproteins corresponding to the DAPPs. All the phosphoproteins were categorized by biological process (BP), cellular component (CC), and molecular function (MF). In LT vs SMM, we annotated the phosphoproteins into 149 GO terms, comprising 87 BP terms, 26 CC terms, and 36 MF terms (Fig. 3A). The GO term that contained the largest number of phosphoproteins in BP was actin cytoskeleton organization, phosphorylation, and sarcomere organization. The largest number of phosphoproteins for GO terms in CC and MF were plasma membrane and actin binding, respectively (Supplementary Table S8). In LT vs PS, a total of 101 subcategories were identified, including 43 BP terms, 34 CC terms and 24 MF terms (Fig. 3B). The largest number of phosphoproteins in BP were enriched in actin cytoskeleton organization, cell adhesion and sarcomere organization. Mitochondrion and actin binding terms had the largest number of phosphoproteins in CC and MF terms, respectively (Supplementary Table S9). In LT vs SMT, 25 BP terms, 29 CC terms, and 25 MF terms were identified (Fig. 3C). Major biological processes in this group were involved in muscle movement (sarcomere organization, muscle contraction, muscle organ development and others), metabolic processes (gluconeogenesis, glycolytic process, AMP metabolic process, glycogen biosynthetic process and others), and signal transduction. For the CC terms, the significantly enriched phosphoproteins were mainly myofibrils (Z disc, M band, actin cytoskeleton and others), junction proteins (focal adhesion and adherens junction), and organelle proteins (mitochondrial outer membrane, mitochondrial inner membrane, intracellular organelle, nuclear matrix and others). For the MF terms, ATP binding had the most aggregated phosphoproteins (Supplementary Table S10).

Fig. 3.

Fig. 3

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Gene ontology (GO) enrichment analyses of proteins with DAPPs. (A-F) Top 15 significantly enriched GO terms in biological process, cellular component, and molecular function in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively. The x-axis represents the number of phosphoproteins, and the y-axis represents the GO term.

The DAPPs in SMM vs PS group were mainly enriched in the CC terms of myofibrils (Z disc, actin cytoskeleton, sarcolemma, microtubule) and adhesion and junction (focal adhesion, cell junction), while relatively few DAPPs were involved in the BP and MF terms (ubiquitin-dependent protein catabolic process, muscle contraction, actin binding, actin filament binding and others) (Fig. 3D & Supplementary Table S11). In the SMM vs SMT group, the GO terms with the highest protein enrichment in BP, CC and MF were protein phosphorylation, plasma membrane, and ATP binding, respectively (Fig. 3E & Supplementary Table S12). A total of 111 GO terms, including 53 BP terms, 26 CC terms and 32 MF terms, were annotated in PS vs SMT. For the cellular component, the significantly enriched phosphoproteins were mainly in myofibrils (Z disc, M band, actin cytoskeleton and others) and membrane (plasma membrane, filopodium membrane, mitochondrial outer membrane, sarcoplasmic reticulum membrane and others). Major biological processes of the enriched phosphoproteins were involved in the muscle movement (actin cytoskeleton organization, muscle contraction, sarcomere organization and others). For the molecular function, most of the phosphoproteins were enriched in the structural molecule activity (ATP binding, metal ion binding, cadherin binding and others) (Fig. 3F & Supplementary Table S13).

3.4. KEGG enrichment analysis of differential abundance phosphopeptides

KEGG analyses were conducted to further unveil the biological pathways associated with the identified DAPPs. The top five significantly enriched pathways were cytoskeleton in muscle cells, oxidative phosphorylation, diabetic cardiomyopathy, citrate cycle (TCA cycle), and carbon metabolism in the LT vs SMM group (Fig. 4A & Supplementary Table S14). 43 pathways were enriched in the LT vs PS group (P < 0.05), and cytoskeleton in muscle cells, oxidative phosphorylation, diabetic cardiomyopathy, dilated cardiomyopathy, and thermogenesis were the top five significantly enriched pathways (Fig. 4B & Supplementary Table S15). The enriched pathways in the LT vs SMT were primarily related to muscle contraction (cytoskeleton in muscle cells, hypertrophic cardiomyopathy, dilated cardiomyopathy, diabetic cardiomyopathy) and signaling pathways (adrenergic signaling in cardiomyocytes, AMPK signaling pathway, glucagon signaling pathway, cAMP signaling pathway, cGMP-PKG signaling pathway and calcium signaling pathway) (Fig. 4C & Supplementary Table S16). The pathways significantly enriched in SMM vs PS group were the fewest among the six compared groups, and the 18 KEGG pathways could roughly be divided into three major categories, namely muscle contraction (cytoskeleton in muscle cells, dilated cardiomyopathy, hypertrophic cardiomyopathy, cardiac muscle contraction and others), signaling pathways (cGMP-PKG signaling pathway, cAMP signaling pathway, calcium signaling pathway, VEGF signaling pathway and others) and metabolic pathways (pyruvate metabolism) (Fig. 4D & Supplementary Table S17). Most of the DAPPs in the SMM vs PS group were enriched in the cytoskeleton in muscle cells, calcium signaling pathway, adrenergic signaling in cardiomyocytes, motor proteins, and others (Fig. 4E & Supplementary Table S18). In the PS vs SMT group, the pathways showing higher levels of significance were mainly cytoskeleton in muscle cells, dilated cardiomyopathy, hypertrophic cardiomyopathy, and cAMP signaling pathway (Fig. 4F & Supplementary Table S18). Overlapping analysis showed that there were 8, 14, 10, 2, 6, and 4 unique KEGG pathways in the six compared groups (Fig. 4G). Notably, three common KEGG pathways, namely cytoskeleton in muscle cells, dilated cardiomyopathy, and hypertrophic cardiomyopathy were identified, and there were 32, 10, and 9 phosphoproteins were shared in all compared groups, which indicated the core role of phosphoproteins involved in the three pathways (Fig. S1).

Fig. 4.

Fig. 4

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KEGG enrichment analyses of proteins with DAPPs. (A-F) Top 15 significantly enriched KEGG pathways in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively. The x-axis represents the number of phosphoproteins, and the y-axis represents the KEGG pathways. (G) Venn diagram showing common and unique KEGG pathways of the six comparison groups. The three pathways marked in red are KEGG pathways shared among the six groups.

3.5. Identification of pork cuts-specific differential abundance phosphopeptides

We next focused on the pork cuts-specific DAPPs, and the Upset Venn diagram analysis was performed to identify unique DAPPs for the four pork cuts (Fig. 5A). Nine DAPPs were shared in the six compared groups, corresponding to seven phosphoproteins, namely exocyst complex component 7 (EXOC7), caveolae associated protein 3 (CAVIN3), microtubule associated Serine/Threonine kinase 2 (MAST2), proline rich basic protein 1 (PROB1), glutamic-oxaloacetic transaminase 1 (GOT1), ATP2A2 and ENSSSCG00000038561. The phosphorylation abundance of the nine DAPPs was shown in Fig. S2, and all showed a trend of high abundance of SMT and the lowest in LT. A total of 184, 53, 75, and 691 unique phosphopeptides were revealed in LT, SMM, PS, and SMT, respectively. These findings were also visualized as heatmaps (Fig. 5B-5E). Most of the DAPPs (90.22 %) were downregulated in LT, while 90.57 % and 84.00 % DAPPs were upregulated in SMM and PS, respectively. The number of DAPPs in the SMT was the largest, and the proportions of upregulated and downregulated DAPPs were equal (55.14 % vs 44.86 %). Results from Fig. 5B-5E demonstrated that the SMT exhibited the most significant variation, whereas the SMM showed the least difference when compared with the remaining three pork cuts.

Fig. 5.

Fig. 5

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Identification of cut-special phosphopeptides. (A) Upset Venn diagram analysis of the DAPPs in the six compared groups. The clustering heatmap in the upper right corner is based on the shared 9 proteins in the six groups. (B- E) Clustering heatmap of the cut-special DAPPs in LT, SMM, PS and SMT, respectively.

To study the potential regulatory mechanisms by which pork cut-specific phosphoproteins regulate muscle specification through specific intermolecular interactions, we systematically constructed a protein-protein interaction (PPI) network centered on phosphoproteins using the STRING database (Fig. 6). In the PPI network of Fig. 6A, there were 133 phosphoproteins that had 403 interactions. Two clusters were notable, namely aerobic respiration/ oxidative phosphorylation, and the sarcomere, which was closely related to the muscle function and had more phosphoproteins enriched. The network suggested that mitochondrial ATP synthase (ATP5F1A, ATP5F1C, ATP5PO, and ATP5PB), cytochrome C oxidase subunit 4I1 (COX4I1) were key proteins as they bound to more than 20 proteins (eg, NDUFB5, SDHA, UQCRC1, GOT2). In SMM, 45 phosphoproteins that had 50 interactions, and Z disc and glycogen granule cluster were significantly enriched (Fig. 6B). The FLNC had the most interactions in this network, including myotilin (MYOT), heat shock protein family B (Small) member 8 (HSPB8), synaptopodin 2 like (SYNPO2L), palladin, cytoskeletal associated protein (PALLD), myeloid leukemia factor 1(MLF1), MYOM1, myopalladin (MYPN), NEB, and (myosin binding protein C1) MYBPC1. Similarly, Z disc cluster was also enriched in PS, with 62 phosphoproteins and 53 interactions, and BAG cochaperone 3 (BAG3), myosin-7 (MYH7) had the most interactions in this network (Fig. 6C). Compared with the LT, SMM, and PS, SMT has 389 phosphoproteins and 1859 interactions. Three clusters were worth noting, namely myofibril assembly, pyruvate metabolism, and I band (Fig. 6D). AKT Serine/Threonine kinase 1 (AKT1) protein in this network had 60 interactions.

Fig. 6.

Fig. 6

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Protein-protein interaction regulatory network of cut-special phosphopeptides in four pork cuts. (A-D) PPI analysis of the cut-special DAPPs in LT, SMM, PS and SMT, respectively. Each node represents a phosphoprotein. Each edge represents an interaction between phosphoproteins.

Overall, the functional enrichment analysis of DAPPs collectively demonstrated that the observed variations among the four pork cuts were linked to phosphorylation-mediated modulation of key biological pathways. These changes appeared to be coordinately regulated through dynamic interactions between myofibrillar proteins, signaling pathway components, mitochondrial proteins, and other phosphoproteins.

3.6. Correlations between differential abundance phosphoproteins and meat quality traits

Based on the key phosphoproteins identified in the functional enrichment analysis, we further explored the relationship between the abundance of these phosphoproteins and meat quality-related traits. As shown in Fig. 7, it was notable that the abundance of 14 phosphoproteins (eg, ATP5F1A, MYH7, MDH2 and others) was almost all positively correlated with characteristics of oxidative muscle fibers and Minolta a*, and significantly negatively correlated with glycolytic muscle fibers and Minolta L*. We also found some phosphoproteins closely related to the pH24h (eg, HSPB8, BAG3, and PALLD). These results demonstrated that phosphoproteins played a critical role in meat quality, predominantly affecting meat color and pH value in the current study.

Fig. 7.

Fig. 7

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Pearson correlation between identified key phosphoproteins and representative meat quality traits. *P < 0.05, **P < 0.01, ***P < 0.001.

4. Discussion

Several previous studies have identified phosphorylation sites and their proteins in pigs based on phosphoproteomic approaches (He et al., 2022; Huang et al., 2014; Zou et al., 2020), while the number of phosphoproteins is relatively small. In this study, we identified over 3000 phosphoproteins, which indicates that the identified phosphorylation sites in porcine muscle tissue remain incomplete. Notably, the NEB protein had the most amount of phosphorylation sites. Li et al. revealed that conditional NEB knockout mice exhibited a marked elevation in slow-oxidative fiber composition, suggesting NEB-derived phosphorylation signatures may orchestrate the metabolic transition from slow-oxidative to fast-glycolytic phenotypes (Li et al., 2015), which may further alter meat quality, such as drip loss and pH value that were closely related to muscle fiber types. Among the identified phosphorylation sites, serine (S) residues accounted for 75.70 %, followed by threonine (T) (19.89 %) and tyrosine (T) (4.41 %). Comparative analysis of multiple species phosphoproteomic shows that the phosphorylation frequencies of these three amino acid residues do not change much across vertebrates, as evidenced by studies in pig hybrids (Duroc × Meishan) (He et al., 2022), avian species (chicken, goose) (Weng et al., 2021; Weng et al., 2022), and ruminants (sheep, yak) (Chen et al., 2019; Yang et al., 2020) . As expected, the SMT muscle underwent more dramatic changes in tissue-specific phosphopeptides and phosphoproteins than other pork cuts. Considering that SMT has more oxidative muscle fibers and better meat quality, which further confirms the influence of phosphorylation modification on the meat quality traits (Cheng et al., 2021).

Next, we conducted a pairwise DAPPs analysis among the four pork cuts, and the results showed that there were the most DAPPs in LT vs SMT, indicating a great difference between the two muscles. Nine differentially phosphoproteins, including EXOC7 (Thr 623), CAVIN3 (Ser195), MAST2 (Ser806, Ser939, Ser1363), PROB1 (Ser392), GOT1 (Ser312), ATP2A2 (Ser580), and ENSSSCG00000038561(Ser278) were shared in the six compared groups. These genes have also been reported in studies related to meat quality, such as the GOT1 gene, which has been identified as a key gene regulating meat quality characteristics and energy metabolism. By integrating carbohydrate and amino acid metabolic pathways, it forms a cellular metabolic regulatory network that maintains energy homeostasis and meets the requirements of cell proliferation (Valdés-Hernández et al., 2024). Another example is the ATP2A2 gene, which is a member of the ATP2A gene family, encoding one of the calcium-transporting ATPase enzymes in the sarcoplasmic (endoplasmic) reticulum, namely SERCA2, and plays a crucial role in calcium ion-related signaling pathways. Wei et al. reported that ATP2A2 could regulate the expression of slow muscle genes (Wei et al., 2015). In addition, SERCA2 phosphorylation at serine 663 has been reported as a key regulator of Ca2+ homeostasis in heart diseases (Gonnot et al., 2023). More functional tests on the effects of phosphorylation levels of these key genes on meat quality deserve to be carried out.

GO enrichment analysis of the DAPPs demonstrated pronounced enrichment of biological process terms related to sarcomere organization, muscle structure (Z disc, M band, and stress fiber) and actin cytoskeleton in cellular component, and actin binding and kinase activity in molecular function. The observed elevation in phosphorylation levels of major myosin heavy chains (MyHCs) may serve as a protective mechanism against proteolytic degradation in postmortem muscle, potentially preserving sarcomeric ultrastructural integrity. This biochemical modification appears to enhance myofibril packing density while reducing inter-fiber spacing, thereby contributing to improved muscle compaction and elevated tissue rigidity – key determinants of meat texture parameters including firmness and water-holding capacity (Chen et al., 2016; Chen et al., 2020). KEGG analysis identified specific pathways within each group and simultaneously highlighted three common pathways worthy of attention, including cytoskeleton in muscle cells, dilated cardiomyopathy, and hypertrophic cardiomyopathy. It has been reported that cytoskeletal proteins can influence meat quality by allowing the cytoskeleton to interact with myocyte and organelle membranes, which is conducive to the structure and maintenance of the cytoskeleton postmortem (Leal-Gutiérrez et al., 2020). Weng et al. also identified that DAPPs in the leg muscle between 70 and 120-days geese were enriched in the dilated cardiomyopathy and hypertrophic cardiomyopathy pathways (Weng et al., 2021). Moreover, lots of muscle contraction-related differentially phosphoproteins (Myosin-1 (MYH1), MYH7, MYBPC1, Myozenin 1 (MYOZ1), Myozenin 2 (MYOZ2), ACTN2, Actinin Alpha 3 (ACTN3), and et al) were significant enriched in cytoskeleton in muscle cells pathway, which was consistent with the GO analysis. Recent studies have uncovered the significance of PTMs in regulating MyHCs function and shown how these PTMs might provide additional modulation of contractile processes (Landim-Vieira et al., 2022; Morales et al., 2024). Genetic ablation of MYOZ1 in murine models induces a marked fiber-type transition characterized by elevated type I (slow-oxidative) fiber prevalence, demonstrating MYOZ1's critical role in maintaining fast-glycolytic fiber identity (Frey et al., 2008). The sarcomeric α-actinin isoforms α-actinin-2 and α-actinin-3, encoded by ACTN2 and ACTN3 genes respectively, constitute essential structural components within the Z-line architecture of striated muscle (Mills et al., 2001). These spectrin-family proteins perform critical biomechanical functions by both crosslinking adjacent actin filaments to maintain myofibrillar array integrity and anchoring these filaments to stabilize the contractile apparatus (Lee et al., 2016). Collectively, these results elucidate the influence of phosphoproteins on the meat quality differences.

Lastly, we focused on the cut specific DAPPs and their function. PPI showed that most of the downregulated DAPPs in LT were aggregated in the two networks of oxidative phosphorylation and the sarcomere. Mitochondrial ATP synthase (ATP5F1A, ATP5F1C, ATP5PO, and ATP5PB) and NADH-ubiquinone oxidoreductase complex-related phosphoproteins (NDUFA13, NDUFB10, NDUFB5, NDUFB7) had the largest number of interactions, which indicated these key proteins for regulating the meat quality difference within the LT and three other cuts. ATP5F1A is the biomarker of meat quality traits from Longissimus thoracis muscle of goats (Capra hircus) (Lamri et al., 2023). Li et al. had identified that NDUFB10 and NDUFB7 were key genes closely related to intramuscular fat in Jiangquan black pigs (Li et al., 2025). In addition, NDUFB7 is highest expressed in adipose tissue compared to other tissues in the case of human (Fagerberg et al., 2014). In the SMM and PS, FLNC and BAG3 had the most interactions in the corresponding network, respectively. FLNC is located in Z-disks (van der Ven et al., 2000), exhibiting significant positive correlations with intramuscular fat deposition, which was identified as a potential characteristic protein that could be used as a meat quality biomarker (Yu et al., 2023). Ma et al. found that the BAG3 gene was over-abundant in callipyge mutation (+/C) lambs, which could result in delayed apoptosis and possibly contribute to tougher meat in callipyge lambs (Ma et al., 2020), but its effect on pork quality required further verification. There were the most DAPPs in the SMT group compared to the other three pork cuts, and the AKT1 had the most interactions in the network. In vitro analyses demonstrated that phosphorylation of AKT1 triggers activation of the mTOR signaling pathway, which in turn up-regulated sterol regulatory element-binding protein 1 (SREBP1) transcriptional activity, enhancing adipogenesis and promoting the accumulation of intracellular triglycerides (Zhang et al., 2018), and it was ultimately possible to enhance the meat tenderness. Although a direct causal link between the phosphorylation levels of the above-mentioned proteins and meat quality characteristics has not yet been experimentally confirmed, these proteins are likely to modulate muscle fiber composition and fat deposition, suggesting an indirect but significant association with meat quality traits. Our results of the correlation analysis between partially phosphorylated proteins and meat quality traits further indicated that phosphorylation modification had a significant impact on meat quality (e.g., pH, meat color). These findings provide a compelling theoretical foundation for future studies exploring phosphorylation-driven meat quality modulation. Building upon our current findings, we will endeavor to elucidate the functional significance of individual phosphorylation sites on DAPPs through systematic in vitro validation. Subsequently, we will establish a dynamic regulation system by administering site-specific phosphatases and kinases in vivo to precisely modulate phosphorylation states. Future investigations should further incorporate the impact of additional modifications (e.g., protein acetylation and ubiquitination) in relation to meat quality attributes. The advanced proteomic profiling will be applied to explain and improve the meat quality traits.

5. Conclusion

In this study, a phosphoproteomic analysis was carried out comparing the LT, SMM, PS, and SMT pork cuts in pigs. Our phosphoproteomic data identified 16,822 phosphosites, corresponding to 13,232 phosphopeptides and 3137 phosphoproteins. More than 1000 DAPPs were identified in all six comparison groups. The functional enrichment analysis of DAPPs identified three common pathways in the six comparison groups, namely cytoskeleton in muscle cells, dilated cardiomyopathy, and hypertrophic cardiomyopathy. A total of 184, 53, 75, and 691 unique DAPPs were revealed in LT, SMM, PS, and SMT, respectively. Integrative phosphoproteomic-PPI network analysis revealed networks of phosphorylated protein-protein regulation for the four pork cuts' differences. Some key phosphoproteins, including mitochondrial ATP synthase subunits (ATP5F1A, ATP5F1C, ATP5PO, ATP5PB), redox modulator MDH2, cytoskeletal scaffold FLNC, chaperone regulator BAG3, and signaling kinase AKT1 were identified, exhibiting robust correlations with the representative meat quality traits. These results provide new insights into the role of the post-translational modification landscape in the differences of porcine muscle fiber types and meat quality among the four pork cuts.

CRediT authorship contribution statement

Li Zhang: Writing – original draft, Visualization, Investigation, Data curation. Wen Luo: Investigation. Linlin Zhao: Investigation. Longyun Li: Writing – review & editing, Resources, Funding acquisition. Yizhong Huang: Writing – review & editing, Resources, Methodology, Funding acquisition, Conceptualization.

Funding

This work was funded by the National Natural Science Foundation of China (Grant No. 32360822), Nanchang Normal University 2024 Youth Science and Technology Talent Training Project (Grant No. 24XJQN04), Nanchang Normal University Doctoral Research Start-up Fund (Grant No. NSBSJJ2023003), Ganpo Talent Support Program – Main Discipline Academic and Technical Leader Training Project - Young Talent (Industry-University-Research Cooperation Category) (20243BCE51129).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochms.2025.100286.

Contributor Information

Longyun Li, Email: lilongyun5816@outlook.com.

Yizhong Huang, Email: hyzjxlab@163.com.

Appendix A. Supplementary data

Supplementary material 1: Venn diagram analysis of the phosphoproteins in cytoskeleton in muscle cells, dilated cardiomyopathy, and hypertrophic cardiomyopathy, respectively. The shared phosphoproteins in the six compared groups were highlighted.

mmc1.pdf (115.4KB, pdf)

Supplementary material 2: Comparative analysis of nine shared DAPPs in the six compared groups with the four pork cuts.

mmc2.pdf (723.2KB, pdf)

Supplementary material 3: Table S1 The information of phosphopeptides and their abundance in each sample monitored in this study. Table S2-S7 The DAPPs details in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively. Table S8-S13 GO enrichment analyses of proteins with DAPPs in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively. Table S14-S19 KEGG enrichment analyses of proteins with DAPPs in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively.

mmc3.xlsx (8MB, xlsx)

Data availability

Data will be made available on request.

References

  1. Chen L., Li X., Ni N., et al. Phosphorylation of myofibrillar proteins in post-mortem ovine muscle with different tenderness. Journal of the Science of Food and Agriculture. 2016;96(5):1474–1483. doi: 10.1002/jsfa.7244. [DOI] [PubMed] [Google Scholar]
  2. Chen L., Li Z., Everaert N., et al. Quantitative phosphoproteomic analysis of ovine muscle with different postmortem glycolytic rates. Food Chemistry. 2019;280:203–209. doi: 10.1016/j.foodchem.2018.12.056. [DOI] [PubMed] [Google Scholar]
  3. Chen L., Liu J., Kaneko G., et al. Quantitative phosphoproteomic analysis of soft and firm grass carp muscle. Food Chemistry. 2020;303 doi: 10.1016/j.foodchem.2019.125367. [DOI] [PubMed] [Google Scholar]
  4. Cheng H., Song S., Kim G.D. Frozen/thawed meat quality associated with muscle fiber characteristics of porcine longissimus thoracis et lumborum, psoas major, semimembranosus, and semitendinosus muscles. Scientific Reports. 2021;11(1):13354. doi: 10.1038/s41598-021-92908-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Deng L., Li W., Liu W., et al. Integrative metabolomic and transcriptomic analysis reveals difference in glucose and lipid metabolism in the longissimus muscle of Luchuan and Duroc pigs. Frontiers in Genetics. 2023;14 doi: 10.3389/fgene.2023.1128033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Duan S., Tian Z., Zheng X., et al. Characterization of flavour components and identification of lipid flavour precursors in different cuts of pork by phospholipidomics. Food Chemistry. 2024;458 doi: 10.1016/j.foodchem.2024.139422. [DOI] [PubMed] [Google Scholar]
  7. Fagerberg L., Hallström B.M., Oksvold P., et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics*. Molecular & Cellular Proteomics. 2014;13(2):397–406. doi: 10.1074/mcp.M113.035600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Frey N., Frank D., Lippl S., et al. Calsarcin-2 deficiency increases exercise capacity in mice through calcineurin/NFAT activation. The Journal of Clinical Investigation. 2008;118(11):3598–3608. doi: 10.1172/JCI36277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Gonnot F., Boulogne L., Brun C., et al. SERCA2 phosphorylation at serine 663 is a key regulator of Ca2+ homeostasis in heart diseases. Nature Communications. 2023;14(1):3346. doi: 10.1038/s41467-023-39027-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Guo Z., Chen X., Huang Z., et al. Dihydromyricetin improves meat quality and promotes skeletal muscle fiber type transformations via AMPK signaling in growing–finishing pigs. Food & Function. 2022;13(6):3649–3659. doi: 10.1039/D1FO03391C. [DOI] [PubMed] [Google Scholar]
  11. He Y., Tan X., Li H., et al. Phosphoproteomic analysis identifies differentially expressed phosphorylation sites that affect muscle fiber type in pigs. Frontiers in Nutrition. 2022;9 doi: 10.3389/fnut.2022.1006739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Huang H., Larsen M.R., Palmisano G., et al. Quantitative phosphoproteomic analysis of porcine muscle within 24h postmortem. Journal of Proteomics. 2014;106:125–139. doi: 10.1016/j.jprot.2014.04.020. [DOI] [PubMed] [Google Scholar]
  13. Huang Y., Cai L., Duan Y., et al. Whole-genome sequence-based association analyses on an eight-breed crossed heterogeneous stock of pigs reveal the genetic basis of skeletal muscle fiber characteristics. Meat Science. 2022;194 doi: 10.1016/j.meatsci.2022.108974. [DOI] [PubMed] [Google Scholar]
  14. Huang Y., Zhou L., Zhang J., et al. A large-scale comparison of meat quality and intramuscular fatty acid composition among three Chinese indigenous pig breeds. Meat Science. 2020;168 doi: 10.1016/j.meatsci.2020.108182. [DOI] [PubMed] [Google Scholar]
  15. Lamri M., della Malva A., Djenane D., et al. Towards the discovery of goat meat quality biomarkers using label-free proteomics. Journal of Proteomics. 2023;278 doi: 10.1016/j.jprot.2023.104868. [DOI] [PubMed] [Google Scholar]
  16. Landim-Vieira M., Childers M.C., Wacker A.L., et al. Post-translational modification patterns on β-myosin heavy chain are altered in ischemic and nonischemic human hearts. Elife. 2022;11 doi: 10.7554/eLife.74919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Leal-Gutiérrez J.D., Elzo M.A., Carr C., et al. RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef. PLoS One. 2020;15(11) doi: 10.1371/journal.pone.0240895. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Lee F.X.Z., Houweling P.J., North K.N., et al. How does α-actinin-3 deficiency alter muscle function? Mechanistic insights into ACTN3, the ‘gene for speed’. Biochimica et Biophysica Acta. 2016;1863(4):686–693. doi: 10.1016/j.bbamcr.2016.01.013. [DOI] [PubMed] [Google Scholar]
  19. Li F., Buck D., De Winter J., et al. Nebulin deficiency in adult muscle causes sarcomere defects and muscle-type-dependent changes in trophicity: Novel insights in nemaline myopathy. Human Molecular Genetics. 2015;24(18):5219–5233. doi: 10.1093/hmg/ddv243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Li W., Yang S., Liu H., et al. Identification of key LncRNAs and mRNAs associated with intramuscular fat in pig via WGCNA. BMC Genomics. 2025;26(1):233. doi: 10.1186/s12864-025-11427-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li X., Zhang D., Ren C., et al. Effects of protein posttranslational modifications on meat quality: A review. Comprehensive Reviews in Food Science and Food Safety. 2021;20(1):289–331. doi: 10.1111/1541-4337.12668. [DOI] [PubMed] [Google Scholar]
  22. Li Z., Li M., Li X., et al. Quantitative phosphoproteomic analysis among muscles of different color stability using tandem mass tag labeling. Food Chemistry. 2018;249:8–15. doi: 10.1016/j.foodchem.2017.12.047. [DOI] [PubMed] [Google Scholar]
  23. Lim M.S.M., Grohn Y.T. Comparison of China’s and the European Union’s approaches to antimicrobial stewardship in the pork industry. Foodborne Pathogens and Disease. 2021;18(8):567–573. doi: 10.1089/fpd.2020.2887. [DOI] [PubMed] [Google Scholar]
  24. Ma D., Yu Q., Hedrick V.E., et al. Proteomic and metabolomic profiling reveals the involvement of apoptosis in meat quality characteristics of ovine M. longissimus from different callipyge genotypes. Meat Science. 2020;166 doi: 10.1016/j.meatsci.2020.108140. [DOI] [PubMed] [Google Scholar]
  25. Meier F., Brunner A.-D., Koch S., et al. Online parallel accumulation–serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer*. Molecular and Cellular Proteomics. 2018;17(12):2534–2545. doi: 10.1074/mcp.TIR118.000900. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Mills M., Yang N., Weinberger R., et al. Differential expression of the actin-binding proteins, alpha-actinin-2 and -3, in different species: Implications for the evolution of functional redundancy. Human Molecular Genetics. 2001;10(13):1335–1346. doi: 10.1093/hmg/10.13.1335. [DOI] [PubMed] [Google Scholar]
  27. Morales P.N., Coons A.N., Koopman A.J., et al. Post-translational modifications of vertebrate striated muscle myosin heavy chains. Cytoskeleton (Hoboken) 2024;81(12):832–842. doi: 10.1002/cm.21857. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Munekata P.E.S., Pateiro M., López-Pedrouso M., et al. Foodomics in meat quality. Current Opinion in Food Science. 2021;38:79–85. doi: 10.1016/j.cofs.2020.10.003. [DOI] [Google Scholar]
  29. Valdés-Hernández J., Folch J.M., Crespo-Piazuelo D., et al. Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis. Genetics, Selection, Evolution. 2024;56(1):12. doi: 10.1186/s12711-024-00882-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. van der Ven P.F.M., Obermann W.M.J., Lemke B., et al. Characterization of muscle filamin isoforms suggests a possible role of γ-filamin/ABP-L in sarcomeric Z-disc formation. Cell Motility. 2000;45(2):149–162. doi: 10.1002/(SICI)1097-0169(200002)45:2&#x0003c;149::AID-CM6&#x0003e;3.0.CO;2-G. [DOI] [PubMed] [Google Scholar]
  31. Wei H., Li Z., Wang X., et al. microRNA-151-3p regulates slow muscle gene expression by targeting ATP2a2 in skeletal muscle cells. Journal of Cellular Physiology. 2015;230(5):1003–1012. doi: 10.1002/jcp.24793. [DOI] [PubMed] [Google Scholar]
  32. Weng K., Huo W., Gu T., et al. Quantitative phosphoproteomic analysis unveil the effect of marketable ages on meat quality in geese. Food Chemistry. 2021;361 doi: 10.1016/j.foodchem.2021.130093. [DOI] [PubMed] [Google Scholar]
  33. Weng K., Li Y., Huo W., et al. Comparative phosphoproteomic provides insights into meat quality differences between slow- and fast-growing broilers. Food Chemistry. 2022;373(Pt A) doi: 10.1016/j.foodchem.2021.131408. [DOI] [PubMed] [Google Scholar]
  34. Wu J., An Y., Pu H., et al. Enrichment of serum low-molecular-weight proteins using C18 absorbent under urea/dithiothreitol denatured environment. Analytical Biochemistry. 2010;398(1):34–44. doi: 10.1016/j.ab.2009.10.047. [DOI] [PubMed] [Google Scholar]
  35. Wu J., Xie X., Liu Y., et al. Identification and confirmation of differentially expressed fucosylated glycoproteins in the serum of ovarian cancer patients using a lectin array and LC-MS/MS. Journal of Proteome Research. 2012;11(9):4541–4552. doi: 10.1021/pr300330z. [DOI] [PubMed] [Google Scholar]
  36. Xie L., Qin J., Rao L., et al. Genetic dissection and genomic prediction for pork cuts and carcass morphology traits in pig. Journal of Animal Science and Biotechnology. 2023;14(1) doi: 10.1186/s40104-023-00914-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Yan M., Li L., Huang Y., et al. Investigation on muscle fiber types and meat quality and estimation of their heritability and correlation coefficients with each other in four pig populations. Animal Science Journal. 2024;95(1) doi: 10.1111/asj.13915. [DOI] [PubMed] [Google Scholar]
  38. Yang Y., Han L., Yu Q., et al. Phosphoproteomic analysis of longissimus lumborum of different altitude yaks. Meat Science. 2020;162 doi: 10.1016/j.meatsci.2019.108019. [DOI] [PubMed] [Google Scholar]
  39. Yu F., Haynes S.E., Teo G.C., et al. Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Molecular & Cellular Proteomics. 2020;19(9):1575–1585. doi: 10.1074/mcp.TIR120.002048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Yu G., Wang L.G., Han Y., et al. Clusterprofiler: An R package for comparing biological themes among gene clusters. Omics : A Journal of Integrative Biology. 2012;16(5):284–287. doi: 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Yu Q., Li S., Cheng B., et al. Investigation of changes in proteomes of beef exudate and meat quality attributes during wet-aging. Food Chemistry: X. 2023;17 doi: 10.1016/j.fochx.2023.100608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Zhang T., Huang J., Yi Y., et al. Akt serine/threonine kinase 1 regulates de novo fatty acid synthesis through the mammalian target of rapamycin/sterol regulatory element binding protein 1 Axis in dairy goat mammary epithelial cells. Journal of Agricultural and Food Chemistry. 2018;66(5):1197–1205. doi: 10.1021/acs.jafc.7b05305. [DOI] [PubMed] [Google Scholar]
  43. Zhang Y., Diao Y., Raza S.H.A., et al. Flavor characterization of pork cuts in Chalu black pigs using multi-omics analysis. Meat Science. 2025;219 doi: 10.1016/j.meatsci.2024.109668. [DOI] [PubMed] [Google Scholar]
  44. Zou B., Zhao D., He G., et al. Acetylation and phosphorylation of proteins affect energy metabolism and pork quality. Journal of Agricultural and Food Chemistry. 2020;68(27):7259–7268. doi: 10.1021/acs.jafc.0c01822. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1: Venn diagram analysis of the phosphoproteins in cytoskeleton in muscle cells, dilated cardiomyopathy, and hypertrophic cardiomyopathy, respectively. The shared phosphoproteins in the six compared groups were highlighted.

mmc1.pdf (115.4KB, pdf)

Supplementary material 2: Comparative analysis of nine shared DAPPs in the six compared groups with the four pork cuts.

mmc2.pdf (723.2KB, pdf)

Supplementary material 3: Table S1 The information of phosphopeptides and their abundance in each sample monitored in this study. Table S2-S7 The DAPPs details in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively. Table S8-S13 GO enrichment analyses of proteins with DAPPs in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively. Table S14-S19 KEGG enrichment analyses of proteins with DAPPs in LT vs SMM, LT vs PS, LT vs SMT, SMM vs PS, SMM vs SMT, and PS vs SMT, respectively.

mmc3.xlsx (8MB, xlsx)

Data Availability Statement

Data will be made available on request.

Articles from Food Chemistry: Molecular Sciences are provided here courtesy of Elsevier

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