Introduction

Poultry meat represents the most consumed meat product, and its consumption continues to grow in both developed and developing countries1. Compared with other livestock, broilers have lower rearing costs and higher production efficiency2. According to the growth rate of chickens, broilers can be divided into fast-growing (FG) broilers and slow-growing (SG) broilers. FG broilers are preferred by producers for their high feed efficiency, meat yield and slaughter rate3. However, SG broiler meat is considered to have a better flavor than FG broiler meat4. For consumers, the quality of broiler meat affects their purchasing decision5. With the improvement of living standards, the demand for quality meat products is also increasing. There is no doubt that it is imperative to understand the molecular mechanism of meat flavor formation.

In the last decades, proteomics has been widely used to elucidate mechanisms and explore biomarkers that determine meat quality6. Recently, protein posttranslational modification (PTM), which regulates structures and functions of proteins in living tissues, has also been reported to play important roles in meat quality4,7. Phosphorylation is one of the first reported and most commonly PTMs. Notably, it has been found that protein phosphorylation negatively affects meat tenderness, color and water holding capacity (WHC)8,9. Proteins phosphorylation in the glycolysis pathway is believed to lead to pale, soft, and exudative (PSE)-like meat in broiler10. Moreover, AMP-activated protein kinase (AMPK) phosphorylation was significantly positively correlated with AMP/ATP ratio, droplet loss, and cooking loss of meat11. Protein phosphatase magnesium/manganese-dependent 1J (PPM1J) belongs to the metal-dependent protein phosphatase family, which is well known to involved in regulating diverse cellular functions, such as cell cycle control, cell differentiation, immune responses, and cell metabolism, by modulating reversible protein phosphorylation12. Recent studies have shown that PPM phosphatase family facilitated adipogenesis and fat accumulation through dephosphorylation of PPARγ13.

In this study, metabolome based on mass spectrometry and transcriptome were performed to construct key metabolite-gene networks involved in the formation of meat flavor by using Guangming-2 (GM2, FG broilers, which is a white-feathered broiler autonomous bred by Chinese) and Xinghua chicken (XH, SG broilers, which is a Chinese native breed with yellow-feather). Based on the above results, PPM1J, which is one of the genes with the highest connectivity of meat flavor-related metabolites, was selected as a candidate. Here, to study the role of PPM1J in meat flavor formation, lentivirus-mediated PPM1J overexpression or knockdown animal model were constructed. Non-target metabolomic and phosphoproteomic analysis were further performed to explore its molecular mechanism. Our study reveals a model in which protein phosphatase regulates meat flavor formation by mediating protein dephosphorylation, and will contribute to the development of further studies.

Results

The regulatory network governing meat flavor formation in different broilers

To explore the differences in meat flavor of different broiler breeds, GM2 and XH at marketing age were selected for the further study. H&E staining analysis was performed to analyze the differences in myofiber cross-sectional area in breast muscle of GM2 (GM2-B) and of XH (XH-B). The breast muscle of GM2 exhibited a higher proportion of large myofibers (>1000 μm2) compared to XH (Fig. 1A, B).

Fig. 1: Identification of muscle characteristics in breast muscle of GM2 and XH.
figure 1

H&E staining (A) and frequency distribution of fiber cross-section area (B) in breast muscle of GM2 (GM2-B) and breast muscle of XH (XH-B) comparison. The volcano plot (C) and hierarchical clustering analysis (D) of differential metabolites in GM2-B and XH-B comparison. The volcano plot (E) and hierarchical clustering analysis (F) of DEGs in GM2-B and XH-B comparison. G Nine-quadrant diagrams show the correlation of metabolites (obtained from metabolome) and genes (identified from transcriptome) in GM2-B and XH-B comparison. H Relative meat flavor-related metabolite content in GM2-B and XH-B comparison. I Correlation heatmap of meat flavor-related differential metabolites and the top 30 differential expressed genes in GM2-B and XH-B comparison. K Regulatory network between screened differential metabolites top 30 DE genes and and the top 30 differential expressed genes in GM2-B and XH-B comparison. In panels B and H, results are shown as mean ± SEM. Statistical significance of differences between means was assessed using independent sample t-test. (*P < 0.05; **P < 0.01).

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To identify the differences in chemical composition of muscle of different broiler breeds, metabolomics analysis by UHPLC-Q-TOF-MS/MS technology was performed in GM2-B and XH-B. The OPLS-DA model evaluation parameters (R2Y, Q2) obtained after 7 cycles interactive verification are listed in Supplementary Table S1. Both R2Y and Q2 are greater than 0.93 (Supplementary Table S1), indicating that this model is stable and reliable. Meanwhile, as the permutation retention gradually decreased, the R2 and Q2 of the random models gradually reduced (Supplementary Fig. S1A, B), explaining that there was no over-fitting in the original model and a distinct difference in the metabolite composition between GM2-B and XH-B. We screened out differential metabolites with VIP > 1, FC > 1.5 or FC < 0.67, and P value < 0.05. A total of 254 different metabolites were identified, of which 84 metabolites were up-regulated and 170 metabolites were down-regulated in GM2-B (Fig. 1C, D; Supplementary Table S2). KEGG pathway analysis of differential metabolites was performed to identify significantly enriched metabolic pathways. A total of 77 metabolic pathways involving 184 different metabolites were identified. These metabolic pathways are mainly enriched in biosynthesis of amino acids, aminoacyl-tRNA biosynthesis, and neuroactive ligand-receptor interaction (Supplementary Fig. S2A). The differential metabolic pathways were also analyzed by differential abundance (DA) analysis. 20 metabolic pathways were captured, including 8 pathways (like beta--Alaine metabolism, aminoacyl-tRNA biosynthesis, and pyrimidine metabolism) were up-regulated (>0.5 DA score, red) (Supplementary Fig. S2B).

To further determine the genetic factors associated with muscle chemical composition, RNA-Seq was used to analyze gene expression changes between GM2-B and XH-B. Totally, 1766 (851 up-regulated while 915 were down-regulated) differential expression genes (DEGs) were identified (Fig. 1E, F; Supplementary Table S3). GO enrichment analysis were performed to determine the function of these DEGs. The top 3 enriched terms were multicellular organismal process, acid secretion, and cell periphery (Supplementary Fig. S2C). To explore the molecular pathways involved in these DEGs, KEGG enrichment analysis were also performed. The DEGs were mainly enriched in neuroactive ligand-receptor interaction, calcium signaling pathway, and inflammatory mediator regulation of TRP channels (Supplementary Fig. S2D).

Correlation analysis of the metabolome and transcriptome were performed, and the variations of differential metabolites and DEGs in GM2-B and XH-B comparison were illustrated by nine-quadrant diagrams, with |R|> 0.80 and P < 0.05 (Fig. 1G). 2538 positive metabolite-gene connections and 3647 negative metabolite-gene connections were identified. (Fig. 1H). Comparing the differences in meat flavor-related metabolite in different breeds, we found that 8 amino acids and nucleotides (Methionine, Glycine, Histidine, Leucine, Phenylalanine, Tyrosine, Hypoxanthine, Inosine, and Inosine 5’-monophosphate) were up-regulated in GM2-B, while only Lysine content was decreased (Fig. 1H). The connections between meat flavor-related differential metabolites and the top 30 DEGs were analyzed to identify the key genetic factors involved in the formation of meat flavor (Fig. 1I). The meat-flavor-related metabolite-gene networks comprised 20 gene-metabolite connections (|R|> 0.8, P < 0.05) among 8 genes and 6 metabolites was constructed (Fig. 1J). And the top 3 genes with the most connectivity were PPM1J, ELN, and CCDC92B (Fig. 1J).

PPM1J regulates muscle development and meat quality feature

PPM1J, which is one of the most differential expressed gene with the highest connectivity of meat flavor-related metabolites was found (Fig. 2A–C), implied that PPM1J is probably associated with meat flavor formation. Tissue expression profile found that PPM1J was highly expressed in breast muscle, leg muscle, and heart (Fig. 2D). To analyze the potential function of PPM1J, we used the SOPMA software to predict its secondary structure. The result showed that alpha helix, beta turn, extended strand, and random coil accounted for 24.46%, 4.24%, 14.34% and 54.95% of the PPM1J protein, respectively (Fig. 2E). In addition, molecular phylogenetic analysis revealed chicken PPM1J protein has a large genetic distance with mammals, whereas mainly conserved in Aves (such as Coturnix japonica and Meleagris gallopavo) (Fig. 2F).

Fig. 2: Identification of chicken PPM1J.
figure 2

Relative PPM1J expression in breast of Guangming-2 chicken and Xinhua chicken detected by RNA-seq (A) and qPCR (B). C Correlation analysis of PPM1J and meat flavor related metabolites. D Tissue expression profiles of PPM1J. The horizontal axis and vertical axis indicate different tissues and their relative expression values, respectively. E Secondary structure of PPM1J protein predicted by SOPMA software (https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_sopma.html). F Phylogenetic tree of chicken PPM1J aligned amino acid sequences was analyzed by using MEGA 11 software. Results are presented as mean ± SEM. In panels A and B, statistical significance of differences between means was assessed using paired t-tests. (*P < 0.05; **P < 0.01).

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To prepare efficient PPM1J overexpression and knockdown lentivirus, PPM1J overexpression vector was constructed and specific siRNA against PPM1J were synthesized. The qPCR results showed that these vector and RNA oligonucleotide could significantly overexpress and inhibit the expression of PPM1J in CPM (Fig. 3A and Supplementary Fig. S3A), suggesting that they can be used for lentivirus production. Subsequently, lentivirus-mediated PPM1J overexpression (Lv-PPM1J) and lentivirus-mediated PPM1J knockdown (Lv-shPPM1J) animal models were constructed through targeted injection (Fig. 3B and Supplementary Fig. S3B).

Fig. 3: PPM1J regulate muscle development and meat quality feature.
figure 3

A Relative PPM1J expression with PPM1J interference in CPMs. B-E. Relative PPM1J mRNA expression (B), H&E staining (C), frequency distribution of fiber cross-section area (CSA) (D), and relative ATROGIN1 and MURF1 expression (E) in breast muscle with infection of lentivirus-mediated PPM1J knockdown (Lv-shPPM1J); MyHC immunostaining (F), myotube area (%) (G) of CPMs transduced with PPM1J interference. Shear force (H), Drop-loss (I), and IMF (J) in breast muscle with PPM1J knockdown. In panels (A, B, E and G–J), results are shown as mean ± SEM, statistical significance of differences between means was assessed using paired t-tests (A, B, and G–J) and independent sample t-tests (E). (*P < 0.05; **P < 0.01).

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Previous studies have established a strong correlation between the muscle fiber characteristics (including myofiber cross-sectional area (CSA) and diameter) and tenderness of meat14,15. Firstly, we analyzed the function of PPM1J on muscle fiber characteristics. The results shown that PPM1J overexpression increased the proportion of small myofiber ( ≤ 1000 μm2) (Supplementary Fig. S3C, D). Inversely, the proportion of large myofiber (>1000 μm2) was raised with PPM1J knockdown (Fig. 3C, D), indicating that PPM1J regulates myofiber CSA. To explore the regulatory mechanism of PPM1J on muscle development, we further detected the genes associated with ubiquitin-proteasome system (UPS) after PPM1J overexpression and knockdown. The results demonstrated that overexpression of PPM1J upregulated the expression level of ATROGIN1 and MURF1, while the opposite result occurred upon PPM1J knockdown (Fig. 3E and Supplementary Fig. S3E). Furthermore, immunofluorescence staining was also performed to investigate the potential function of PPM1J in myoblast differentiation. The results showed that suppression of PPM1J increased the total areas of myotubes, while myotube formation was reduced with PPM1J overexpression (Fig. 3F, G and Supplementary Fig. S3F, G). Given that PPM1J function in muscle development, we also analyzed the biological effect of PPM1J in meat-quality performance. Overexpression of PPM1J decreased the shear force, but significantly increased the WHC and IMF content of breast muscle (Supplementary Fig. S3H–J). On the contrary, shear force was increased while WHC and IMF content were decreased after PPM1J knockdown (Fig. 3H–J), indicating that PPM1J improves meat quality feature.

PPM1J regulates meat flavor-related metabolites

Given that PPM1J is one of the most differential expressed gene with the highest connectivity of meat flavor-related metabolites, we performed metabolome to inquire about the metabolite landscapes in PPM1J knockdown muscle. OPLS-DA models were conducted, and results of OPLS-DA analysis showed that these comparisons could be clearly distinguished, indicating that these models are stable and reliable (Fig. 4A). We screened out differential metabolites with VIP > 1, and P-value < 0.05. Respectively, 75 metabolites were found to be downregulated, while 42 were upregulated. (Fig. 4B, C and Supplementary Table S4). According to their chemical taxonomy attribution information, the categories with the most differential metabolites are lipids and lipid-like molecules (25.89%), organic acids and derivatives (20.54%) and benzenoids (15%) (Fig. 4D). Among them, the content of meat flavor related metabolites, such as 1-stearoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine, glycine and hypoxanthine were significantly up-regulated with PPM1J knockdown (Fig. 4E). Inversely, 1-o-hexadecyl-2-o-(5z,8z,11z,14z,17z-eicosapentaenoyl)-sn-glyceryl-3-phosphorylcholine and anserine were significantly downregulated in PPM1J knockdown muscle (Fig. 4E). KEGG pathway analysis was used to extract biological pathways associated with these identified differential metabolites. The results showed that differential metabolites were mainly related to the ABC transporters, valine, leucine and isoleucine biosynthesis and starch and sucrose metabolism (Fig. 4F).

Fig. 4: Analysis of metabolome in muscle of PPM1J knockdown.
figure 4

A–D The OPLS-DA score scatter plots result of the positive and negative ion mode in PPM1J knockdown muscle. B–F Statistics (B), heatmap (C), chemical taxonomy (D) of differential metabolites in PPM1J knockdown muscle. E Relative meat flavor-related metabolite content in PPM1J knockdown muscle. F KEGG pathway analysis of differential metabolites in PPM1J knockdown muscle. In panel E, results are shown as mean + SEM, statistical significance of differences between means was assessed using paired t-tests. (*P < 0.05; **P < 0.01).

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PPM1J mediates protein dephosphorylation in muscle

Recently, numerous studies have found that phosphorylation of proteins affects meat quality9,16. PPM1J is well known to regulate reversible protein phosphorylation12, suggested that it may exert its biological function by mediating protein dephosphorylation. To explore the molecular mechanism of PPM1J function in meat flavor formation, quantitative phosphoproteomic analysis using 4D-labfree labeling was performed in PPM1J knockdown muscle. A total of 3372 phosphosites mapped onto 2903 phosphopeptides corresponding to 1279 phosphoproteins were identified (Fig. 5A). Among the 1279 phosphoproteins, 61.16% of the proteins have more than two phosphorylation sites (Fig. 5B). The length distribution of phosphorylated peptides and the molecular weight distribution of phosphoproteins were further analyzed (Fig. 5C, D). The results showed that the phosphorylated site distribution in PPM1J knockdown muscle is very diverse. Among the 3372 phosphorylation sites, 2692 (79.84%) occurred at Ser, 619 (18.36%) occurred at Thr, and 61 (1.79%) occurred at Tyr residues (Fig. 5E).

Fig. 5: Analysis of phosphoproteome in muscle of PPM1J knockdown.
figure 5

A Number of phosphosites, phosphopeptides, and phosphoproteins detected in PPM1J knockdown muscle. B Distribution and number of phosphosites matched to phosphoproteins in PPM1J knockdown muscle. C The length distribution of phosphorylated peptides in PPM1J knockdown muscle. D The molecular weight distribution of phosphoproteins in PPM1J knockdown muscle. E Distribution of the amino acid residues for all detected phosphosites in PPM1J knockdown muscle. Volcano plot (F), hierarchical clustering analysis (G), GO terms analysis (H) and KEGG pathways analysis (I) of DAPs in PPM1J knockdown muscle.

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To reveal the differential abundance of phosphopeptides (DAPs) in PPM1J knockdown muscle, phosphopeptides with a FC > 2 or <0.5 and a P-value < 0.05 were identified. Volcano plots and hierarchical cluster analysis were performed, and 33 phosphopeptides with differential abundance were indentified (Fig. 5F, G). Specifically, 18 phosphopeptides were up-regulated, while 15 were down-regulated. (Fig. 5F, G; Supplementary Table S5). Meanwhile, 61 phosphopeptides were found to specifically peculiar in the PPM1J knockdown group, while 46 phosphopeptides were only present in the control group (Supplementary Table S6).

Most of the up-regulated phosphopeptides are associated with actin binding and muscle development-related proteins, such as Titin (TTN), Obscurin (OBSCN) and troponin T (TNNT3) (Supplementary Table S6). Moreover, RICTOR (an important component of mTORC2 kinase) and heat shock protein family members (like HSPD1 and HSPB1) was specifically phosphorylated after PPM1J knockdown, suggesting that PPM1J may regulate meat quality feature by mediating protein phosphorylation. GO enrichment analyses were performed to identify the functional terms of these DAPs (Fig. 5H). According to biological processes, the most significantly enriched proteins were involved in positive regulation of cytokine production, regulation of cytokine production, and cytokine production (Fig. 5H). In addition, the major cellular components were related to sarcomere, contractile fiber, myofibril. Moreover, KEGG pathway analysis was performed. Vascular smooth muscle contraction, MAPK signaling pathway and tight junction were the most enriched pathways associated with the DAPs. (Fig. 5G).

Analysis of regulatory network of PPM1J regulating meat flavor formation

To investigate whether PPM1J participates in the formation of meat flavor through mediated protein phosphorylation, we analyzed the co-enrichment pathways of DAPs and differential metabolites. The results showed that DAPs and differential metabolites are mainly co-enriched in metabolic pathways, including fructose and mannose metabolism, galactose metabolism, and methane metabolism (Fig. 6A). The correlation heat maps of DAPs and differential metabolites were further analyzed, considering only those with |R|> 0.80 and P < 0.05 (Fig. 6B). Further analysis of the nine-quadrant diagram showed the changes of DAPs and differential metabolites. 145 positive metabolite-DAPs connections and 5 negative metabolite-DAPs connections were identified (Fig. 6C).

Fig. 6: Correlation analysis of the phosphoproteome and metabolomes.
figure 6

A DAPs (identified from phosphorylomics) and metabolite differential co-enrichment pathway in PPM1J knockdown muscle. B Correlation heatmap of DAPs and differential metabolites (obtained from metabolome) in PPM1J knockdown muscle. C Nine-quadrant diagrams show the correlation of differential metabolites and DAPs in PPM1J knockdown muscle. D-E. Regulatory network between glycerophospholipids (like 1-stearoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (|R|> 0.80 and P < 0.05) (D) and 1-o-hexadecyl-2-o-(5z,8z,11z,14z,17z-eicosapentaenoyl)-sn-glyceryl-3-phosphorylcholine (|R|> 0.60 and P < 0.05) (E)) and DAPs in PPM1J knockdown muscle.

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Given that the expression of PPM1J is correlated with the content of glycerophospholipids (like 1-stearoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine and 1-o-hexadecyl-2-o-(5z,8z,11z,14z,17z-eicosapentaenoyl)-sn-glyceryl-3-phosphorylcholine), and that knockdown of PPM1J regulates the contents of these glycerophospholipids (Figs. 2C and 4E), we further scanned differentially abundant phosphoproteins which are associated with these glycerophospholipids. Excitingly, we found that MYLK4, AAK1, and SYNPO2L, which are specifically phosphorylated in PPM1J knockdown muscle, were highly correlated with the content of 1-stearoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (Fig. 6D). Moreover, DUSP27 (which is a specific phosphorylated protein in PPM1J knockdown muscle) and DDI2 (which is up-regulate phosphorylated with PPM1J knockdown) were found negatively correlated with the content of 1-o-hexadecyl-2-o-(5z,8z,11z,14z,17z-eicosapentaenoyl)-sn-glyceryl-3-phosphorylcholine (Fig. 6E). In consideration of PPM1J functions as a protein phosphatase that can catalytic protein dephosphorylation, we infer that PPM1J may regulate the composition of glycerophospholipids by modulating the phosphorylation level of MYLK4, AAK1, SYNPO2L, DUSP27 and DDI2.

Discussions

With the improvement of living standards, consumers’ demand for broiler meat flavor is increasing. Compared to yellow feather broilers, white feather broilers are FG broilers with higher breast muscle rate. However, the rapid growth of broilers has been identified as the main cause of meat quality issues17. Moreover, SG broiler is considered to have a better meat quality compare to FG broiler4,18. Skeletal muscle is the main component of poultry meat and is made up of myofibers. The diameter and density of myofibers have been reported to influence meat tenderness on a tissue level, serving as a crucial indicator for the evaluation of muscle tenderness19. In this study, we found that breast muscle in GM2 have larger myofiber diameter than XH, suspecting that the difference in cross-sectional area may contributed to variations of meat flavor between GM2 and XH.

Amino acids have many nutritional and physiological functions, which affect the nutritional value and taste of meat20. Different amino acids have different tastes, generally can be classified as sweet, sour, salty, bitter, umami, and tasteless21,22. Except for lipids and free amino acids, nucleotides also play important roles in imparting unique aroma and taste properties to meat products22,23. In this study, we found numerous bitter or sour amino acids (such as Histidine, Leucine, Methionine, Phenylalanine, and Tyrosine), hypoxanthine and inosine were increased, whereas Lysine (which is sweet) was reduced in breast muscle of GM2, indicated that there were significant differences in muscle flavor related metabolites between different breeds.

Discovery of genetic regulatory factors involved in formation of meat flavor is of great significance to broiler production. To systematically identify the genetic regulation of differences in meat flavor among different broiler, we integrate metabolome and transcriptome data, and constructed key meat flavor-related metabolite-gene networks, 20 metabolite-gene connections among 6 metabolites and 8genes were identified. PPM1J has been reported to play important roles in metabolism and disease by mediating protein phosphorylation24,25. Here, we found that PPM1J is one of the genes with the highest connectivity of meat flavor-related metabolites. PPM1J overexpression reduced the cross-sectional area of myofibers. Ubiquitin-specific peptidase 10 (USP10) functions as a deubiquitinating enzyme that inhibits the ubiquitin-proteasome system (UPS) degradation pathway. Previous study revealed that phosphorylation of USP10 inhibits the USP degradation pathway by enhancing protein stability and inhibiting muscle atrophy26. Interestingly, we observed that USP10 is specifically phosphorylated in the breast muscles with PPM1J knockdown, indicating that PPM1J may mitigate muscle atrophy by modulating USP10 phosphorylation. The diameter and density of myofibers are closely related to muscle tenderness. In meat-quality traits, overexpression of PPM1J reduces muscle shear force, which means better tenderness. Tenderness can most likely be attributed to IMF content and WHC27. PPM1J increases the content of IMF and WHC in the muscles, suggesting that it may contribute significantly to better tenderness. Lipids could produce flavor precursor compounds through the lipolysis and oxidation28. Moreover, phospholipids are a major component of intramuscular fat, which is related to such as juiciness, flavor and tenderness. Here, we found PPM1J knockdown reduced the content of 1-o-hexadecyl-2-o-(5z,8z,11z,14z,17z-eicosapentaenoyl)-sn-glyceryl-3-phosphorylcholine, while promoted the accumulation of 1-stearoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine, explained that PPM1J enhances muscle tenderness through the regulation of glycerophospholipid composition in muscle. Previous studies indicated that water-soluble compounds with taste or tactile properties, such as hypoxanthine and certain amino acids are associated with bitterness, while anserine is known to produce umami29. Here, PPM1J knockdown significantly promoted hypoxanthine levels and decreased the content of anserine. suggested that PPM1J is helpful to improve meat flavor. The ELN gene encodes tropoelastin which is used to generate elastic fibers that insure proper tissue elasticity30. ELN has reported to be mainly expressed during early developmental stages, and its insufficiency predisposes to metabolic disease30. Coiled-coil domain containing 92B (CCDC92B) belongs to the coiled-coil domain-containing protein family. Previous research has indicated that CCDC genes are associated with metabolic disorders and lipid metabolism31. Given that the expression of ELN and CCDC92B is mainly connect to glycerophospholipids, we speculated that they may function in meat flavor formation by modulating lipid metabolism.

Recently, numerous studies have reported that the phosphorylation of certain proteins negatively influences meat tenderness, color, and WHC9,32,33,34. The heat shock protein (HSP) family is responsible for cell protection that involved in stress resistance and apoptotic signaling pathway. The high abundance of HSPs has the potential to delay meat aging, which negatively impacts meat tenderness35,36. In the study, we found HSPB1 and HSPD1 are specifically phosphorylated in PPM1J knockdown muscle. In addition, RICTOR, which plays a crucial role in mTOR kinase activity, was also found to be specifically phosphorylated after PPM1J knockdown, indicating that PPM1J may improve muscle tenderness by mediating protein dephosphorylation. As an important component of intramuscular fat, phospholipids make significant contributions to the flavor of meat. Here, we found PPM1J knockdown induced phosphorylation of MYLK4, AAK1, SYNPO2L, DUSP27, and DDI2, which are significantly correlated with the content of glycerophospholipids, suggesting that PPM1J modulates the phosphorylation level of proteins to regulate glycerophospholipids composition.

In conclusion, we demonstrated that PPM1J is one of the genes with the highest connectivity of meat flavor-related metabolites. PPM1J, which is highly expressed in muscle, regulates meat quality feature and glycerophospholipids composition by mediating protein dephosphorylation. Our study reveals a model in which protein phosphatase regulates meat quality feature and glycerophospholipids composition, and will contribute to the development of further studies.

Materials and methods

Ethics statement

All animal experimental protocols were conformed to “The Instructive Notions with Respect to Caring for Laboratory Animals” issued by the Ministry of Science and Technology of the People’s Republic of China, and approved by the Institutional Animal Care and Use Committee at the South China Agricultural University (approval ID: 2021c008).

Animals and cell

GM-2 and XH chickens were purchased from JinKai Agriculture and Animal Husbandry Co., Ltd. and ZhiCheng Poultry Breeding Co., Ltd, respectively.

Chicken primary myoblasts (CPMs) were isolated from E11 chicken leg muscles as previously described37, and cultured in Roswell Park Memorial Institute (RPMI)-1640 medium (Gibco, USA) with 20% fetal bovine serum (Gibco, USA).

RNA isolation, complementary DNA (cDNA) synthesis, and real-time (RT) PCR analysis

Total RNA was extracted from tissues or cells using RNAiso plus reagent (TaKaRa, Japan). cDNA synthesis was obtained by using a PrimeScript RT Reagent Kit with gDNA Eraser (Perfect Real Time) (Takara, Japan). Real-time quantitative PCR (qRT-PCR) reactions were performed on a QuantStudio 5 Real-Time PCR Systems (Thermo Fisher, Waltham, MA, USA) by using a ChamQ Universal SYBR qPCR Master Mix (Vazyme, China). All primers for RT-PCR and real-time qPCR are listed in Supplementary Table S7.

Plasmid construction and RNA oligonucleotides

For viral vectors constructed, the full-length sequence of PPM1J was amplified and then cloned into a pLVX-mCMV-ZsGreen-IRES-Puro vector (Addgene, USA).

The small interfering RNAs (siRNA) that were used for the specific knockdown of PPMIJ were designed and synthesized by Guangzhou RiboBio Biotechnology Co., Ltd. In order to produce a large number of interfering fragments to construct PPMIJ knockdown animal model, specific siRNA sequence was cloned into the pLVX-shRNA2-Puro vector (Addgene, USA).

Primer pairs for vector construction and the sequence of oligonucleotides are presented in Supplementary Tables S7 and S8.

All transient transfections were performed using Lipofectamine 3000 Reagent (Invitrogen, USA) according to the manufacturer’s instructions.

Lentivirus production and transduction

Lentivirus productions were performed as described before37. Thirty Xinhua chickens of 14 weeks were randomly divided into two groups (n = 15) (1) experimental group: Lv-PPM1J and control group: Lv-NC, (2) experimental group: Lv-shPPM1J and control group: Lv-shNC. Chickens received thrice intramuscular doses of lentivirus (106 titers) in the breast muscle (at the age of 14, 15, and 16 weeks). Chickens at 17 weeks were euthanized according to the Chinese National Standard (GB/T 19478-2018). Briefly, before slaughter, chickens were fasted for 12 h but had free access to water. Subsequently, chickens were unconscious by electric shock and euthanized using exsanguination method. Muscles were collected after rapid dissection, then immediately frozen in liquid nitrogen and stored at -80°C.

Hematoxylin and eosin (HE) staining

HE staining were performed as described before37.

Immunofluorescence

Immunofluorescence was performed using anti-MyHC (B103; DHSB, USA; 2.5 mg/mL), and images were captured using a fluorescence microscope (DMi8; Leica, Germany). The area of cells labeled with anti-MyHC was measured and calculated as previously described37.

Meat-quality traits determination

Meat tenderness was measured by shear force using a C-LM4 digital tenderness meter (Tenovo, China). The muscles were cut into three pieces (2.5 × 1.0 × 3.0 cm3) to measure shear force at a speed of 1 mm/s. Each piece of data was measured three times, and the average value was taken as final value.

WHC was measured by drip loss. After removing the excess fat and fascia, muscles were cut into three pieces (2.0 × 1.0 × 3.0 cm3) to be weighed and stored in a plastic bag at 4 °C for 24 h. The pieces were wiped dry and weighted to calculate drip loss.

Intramuscular fat (IMF) content was measured using a Soxtec automated analyzer (FOSS Analytical, Hillerod, Denmark). Grind 30 g breast muscle into meat batters and remove moisture by oven-drying at 110 °C for 5 h. The routine Soxhlet extraction method was conducted to extract the total fat with petroleum ether, and IMF content was calculated. Each data was measured three times to take the average value.

RNA sequencing (RNA-seq)

For RNA-seq, total RNA was extracted from the breast tissue using TRIzol® Reagent according the manufacturer’s instructions (Magen). RNA samples were detected based on the A260/A280 absorbance ratio with a Nanodrop ND-2000 system (Thermo Scientific, USA), and the RIN of RNA was determined by an Agilent Bioanalyzer 4150 system (Agilent Technologies, USA). Qualified samples were further used for library construction. Paired-end libraries were prepared using an ABclonal mRNA-seq Lib Prep Kit (ABclonal, China) following the manufacturer’s instructions. The library preparations were sequenced on an Illumina Novaseq 6000 and 150 bp paired-end reads were generated. The data generated from Illumina platform were used for bioinformatics analysis.

Enrichment analysis

The Gene Ontology (GO) analysis and Genes and Genomes (KEGG) enrichment analysis of differential genes were performed to explain the functional enrichment of differential genes and clarify the differences between samples at the gene function level. ClusterProfiler R software package was used for GO function enrichment and KEGG pathway enrichment analysis.

Metabolomics analysis by UHPLC-Q-TOF-MS/MS and data processing

Metabolomics analysis was performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600) in Shanghai Applied Protein Technology Co.,Ltd. For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUITY UPLC BEH Amide 1.7 μm column (Waters, Ireland). The mobile phase in both ESI positive and negative modes consisted of A = 25 mM ammonium acetate and 25 mM ammonium hydroxide in water, along with B=acetonitrile. The gradient was 95% B for 0.5 min and was linearly reduced to 65% in 6.5 min, and then was reduced to 40% in 1 min and kept for 1 min, and then increased to 95% in 0.1 min, with a 3 min re-equilibration period employed.

The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60 psi, Ion Source Gas2 (Gas2) as 60 psi, curtain gas (CUR) as 30 psi, source temperature is 600 °C, IonSpray Voltage Floating (ISVF) ± 5500 V. In MS only acquisition, the instrument was set to acquire over the m/z range 60–1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25–1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan was acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 V with ±15 eV; declustering potential (DP), 60 V (+) and −60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle is 10.

Protein extraction and digestion

The protein extraction (4%SDS, 100 mM Tris-HCl, 1 mM DTT, pH7.6) buffer was used for sample lysis and protein extraction. The amount of protein was quantified with the BCA Protein Assay Kit (Bio-Rad, USA). Protein digestion by trypsin was performed according to filter-aided sample preparation (FASP) procedure described by Matthias Mann.

Phosphopeptide enrichment and LC-MS/MS analysis

The enrichment of phosphopeptides was carried out using High-SelectTM Fe-NTA Phosphopeptides Enrichment Kit according to the manufacturer’s instructions (Thermo Scientific). After lyophilized, the phosphopeptides peptides were resuspended in 20 µL loading buffer (0.1% formic acid). A reverse phase trap column (Thermo Scientific Acclaim PepMap100, 100 μm × 2 cm, nanoViper C18) was used to separate the samples, and the peptide fractions were analyzed using a Q Exactive HF mass spectrometer (Thermo Fisher Scientific, Waltham, USA).

LC-MS/MS analysis was performed on a timsTOF Pro mass spectrometer (Bruker, tims tof pro, Germany) that was coupled to Nanoelute (Bruker Daltonicsr, Germany) for 60 min. The peptides were loaded on a C18-reversed phase analytical column (homemade, 25 cm long, 75 μm inner diameter, 1.9 μm, C18) in buffer A (0.1% Formic acid) and separated with a linear gradient of buffer B (84% acetonitrile and 0.1% Formic acid) at a flow rate of 300 nl/min. The mass spectrometer was operated in positive ion mode. The mass spectrometer collected ion mobility MS spectra over a mass range of m/z 100–1700 and 1/k0 of 0.6 to 1.6, and then performed 10 cycles of PASEF MS/MS with a target intensity of 1.5k and a threshold of 2500. Active exclusion was enabled with a release time of 0.4 min.

Identification and quantitation of Phosphorylated proteins

The MS raw data for each sample were combined and searched using the MaxQuant software for identification and quantitation analysis. Trypsin with [D|P] was chosen as the specific enzyme with a maximum of two missed cleavages per peptide. The precursor and fragment mass tolerances were set to 20 ppm and 0.05 Da, respectively. The database search parameters were oxidation (methionine), acetyl (protein N-terminus), and phosphorylation (serine, threonine, tyrosine) as variable modifications; carbamidomethyl (cysteine) as fixed modifications. The false discovery rate (FDR) was set at 1%. The NCBl_Gallus_gallus_159905_20230726.fasta was employed for this analysis.

Statistical analysis

In this study, at least three biological replicates were used for all experiments, and data were presented as means ± standard error (SE). Where applicable, the statistical significance of the data was tested using independent sample t-test or paired t-test by using SPSS (version 19.0; SPSS, Chicago IL, USA), and P < 0.05 was considered statistically significant. Pearson’s correlation analysis was given for correlation analysis, and |R|> 0.80 and P < 0.05 was considered statistically significant. The types of tests and the P-values, when applicable, were indicated in the Figure legends. The programs used for graphics and figures were GraphPad Prism (GraphPad Software Inc., USA).