Comparative phosphoproteomic analysis reveals regulatory mechanisms underlying muscle fiber density in goslings

Abstract

Muscle fiber density at hatching is closely associated with post-hatch meat yield through subsequent hypertrophy and with meat tenderness at market age. However, inter-individual variation in muscle fiber traits among goslings remains poorly understood. In this study, 129 one-day-old Yangzhou goslings were sampled and classified into three phenotypic groups based on gastrocnemius muscle weight and muscle fiber density: high meat yield-high fiber density (HH; 0.56 ± 0.01 g, 7803.88 ± 434.12 n/mm²), high meat yield-low fiber density (HL; 0.59 ± 0.03 g, 3847.01 ± 135.68 n/mm²), and low meat yield (LY; 0.46 ± 0.02 g, characterized by lower body weight and muscle mass). A 4D label-free quantitative phosphoproteomic approach was then applied to uncover phosphorylation-mediated mechanisms regulating early muscle fiber development. In total, 6,412 phosphorylation sites corresponding to 5,548 phosphopeptides and 2,519 phosphoproteins were quantified. Compared with HL goslings, HH goslings exhibited elevated phosphorylation of muscle structural proteins (e.g., MYOM2 Thr614, Lamin A/C Ser299), cytoskeletal proteins (e.g., PDLIM7 Ser126, FLN Ser1524), and MAPK signaling components (p38-MAPK Thr180), correlating with higher fiber density. Conversely, HL goslings showed increased phosphorylation of kinases and energy metabolism proteins (e.g., CaMK Thr288, PGM1 Thr507/Ser408), consistent with enhanced fiber hypertrophy. KEGG enrichment indicated involvement of actin cytoskeleton regulation, MAPK signaling, and glycolysis/gluconeogenesis pathways. Overall, these results suggest that phosphorylation-mediated regulation may contribute to inter-individual differences in muscle fiber number and size during early development.

Introduction

Skeletal muscle fibers exhibit pronounced plasticity and play a critical role in determining muscle mass, fiber composition, and ultimately meat production traits in poultry (Ismail and Joo, 2017). The total number of muscle fibers is largely established during embryogenesis and early postnatal development, whereas post-hatch muscle growth primarily depends on the hypertrophic enlargement of existing fibers (Gu et al., 2024). In our previous study, the developmental characteristics of muscle fibers in geese during embryonic stages and the early post-hatching period were characterized (Weng et al., 2025). However, whether muscle fiber traits exhibit substantial inter-individual variation at birth, and whether muscle fiber number is consistent among individual goslings, remain unclear.

The molecular regulation of skeletal muscle development involves multiple post-translational modifications (Li et al., 2021). Among these, protein phosphorylation is one of the most fundamental and pervasive mechanisms regulating protein activity and cellular signaling (Cheng et al., 2011). With the rapid advancement of quantitative phosphoproteomics, phosphorylation-mediated regulation has been widely explored in studies of muscle development and meat quality formation. For example, Xing et al. (2017) reported that elevated phosphorylation of proteins involved in glycolytic pathways induced pale, soft, and exudative (PSE)-like meat characteristics in broilers. Zhang et al. (2024) demonstrated that chlorogenic acid supplementation reduced the phosphorylation levels of myofibrillar proteins, glycolytic enzymes, and endoplasmic reticulum-associated proteins involved in muscle homeostasis, thereby contributing to improved chicken meat quality. Consistently, our previous phosphoproteomic analyses in market-age geese revealed that skeletal muscle development is coordinately regulated by phosphorylated myofibrillar proteins, glycolytic enzymes, and mitochondrial proteins (Weng et al., 2021). However, phosphorylation regulation at market age is difficult to manipulate, whereas muscle fiber number is largely determined during embryogenesis and early postnatal development. Thus, phosphorylation events during early muscle development warrant particular attention. Nevertheless, phosphoproteomic regulation of early muscle development in geese remains poorly understood, especially regarding its contribution to variation in muscle fiber number and morphology at hatching.

In the present study, leg muscle (Gastrocnemius) samples from 129 one-day-old Yangzhou goslings were used to characterize inter-individual variation in muscle fiber traits at birth. Goslings were classified into three phenotypic groups based on body weight, gastrocnemius muscle weight, and muscle fiber density. A 4D label-free quantitative phosphoproteomic approach was applied to profile phosphorylation differences among groups. Integrated differential phosphorylation, functional enrichment, and protein-protein interaction analyses were performed to elucidate the regulatory role of protein phosphorylation in early muscle fiber development in geese. These findings provide novel insights into phosphorylation-mediated regulation of early muscle development and offer a molecular basis for improving meat production traits in geese.

Materials and methods

Animals and sample collection

The experiment was conducted at Tiange Goose Industry Co., Ltd. (Yangzhou, Jiangsu, China). All experimental procedures were performed in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Shanghai Academy of Agricultural Sciences (SAAS) and were approved under Permit Number SAASXM0525031. A total of 129 one-day-old female Yangzhou goslings (Anser cygnoides) were obtained from Tiange Goose Industry Co., Ltd. Among them, 120 goslings were healthy at hatching and exhibited similar body weights (approximately 100 g), whereas 9 goslings had relatively lower body weights (below 90 g). Body weight was individually recorded for each gosling prior to sampling. All Goslings were humanely euthanized by cervical dislocation (Jacobs et al., 2019). Immediately after euthanasia, leg muscle samples (gastrocnemius) were carefully dissected from each individual and weighed. Samples from the right side were fixed in 4% paraformaldehyde for hematoxylin and eosin (H&E) staining, whereas samples from the left side were rapidly frozen in liquid nitrogen and stored at -80°C for subsequent molecular analyses.

H&E Staining

Muscle samples were fixed in 4% formalin for 24 h, followed by routine paraffin embedding. Serial transverse sections (5 μm thick) were prepared using a Leica microtome (Leica Biosystems, Wetzlar, Germany). After overnight drying at 40°C, sections were stained with hematoxylin and eosin using an automated staining system (Leica Autostainer XL, Leica Biosystems, Wetzlar, Germany). Stained sections were digitized using a NanoZoomer slide scanner (Hamamatsu, Sydney, Australia). For each sample, three randomly selected microscopic fields without visible tissue damage were analyzed, with approximately 300 muscle fibers evaluated per field (Weng et al., 2022). Muscle fiber diameter, cross-sectional area, and fiber density were quantified using Image-Pro Plus software (Media Cybernetics, Rockville, MD, USA). The muscle fiber morphological parameters obtained from H&E-stained sections were subsequently used for sample grouping and further analyses.

Sample grouping

Goslings were classified into three groups according to body weight, gastrocnemius muscle weight, and muscle fiber density: low meat yield (LY), high meat yield-low fiber density (HL), and high meat yield-high fiber density (HH).

The HL and HH groups had comparable body weight and gastrocnemius muscle weight, but differed in muscle fiber density and fiber diameter as determined by H&E staining. The HL group exhibited lower fiber density and larger fiber diameter, whereas the HH group showed higher fiber density and smaller fiber diameter. These two groups were distinguished based on muscle fiber density among the 120 healthy goslings with similar body weights (approximately 100 g), and nine individuals with extreme fiber density values were selected for each group.

The nine goslings with lower body weight (< 90 g) were classified into the LY group. Although this group exhibited relatively high fiber density, their overall body weight and gastrocnemius muscle weight were markedly lower than those of the HH and HL groups. For subsequent analyses, nine individuals from each group were used for further phosphoproteomic studies. The detailed phenotypic characteristics are presented in the results.

4D Label-free quantitative phosphoproteomic analysis

Protein extraction, digestion, and phosphopeptide enrichment

Muscle samples were lysed using SDT buffer (4% SDS, 100 mM Tris-HCl, pH 7.6, 0.1 M DTT), and protein concentration was determined using the BCA assay (Thermo Fisher Scientific, Waltham, MA, USA). For each sample, an appropriate amount of protein was subjected to tryptic digestion using the Filter-Aided Sample Preparation (FASP) method (Wiśniewski et al., 2009). The resulting peptides were desalted with C18 cartridges, lyophilized, and resuspended in 40 μL of 0.1% formic acid for quantification.

For phosphopeptide enrichment, each peptide solution was vacuum-dried and processed using the High-Select™ Fe-NTA Phosphopeptides Enrichment Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. Enriched phosphopeptides were then vacuum-concentrated and resuspended in 20 μL of 0.1% formic acid prior to LC-MS/MS analysis.

LC-MS/MS Data acquisition

Peptides were separated using a nano-flow HPLC system with buffer A (0.1% formic acid in water) and buffer B (0.1% formic acid in acetonitrile). The analytical column (homemade, 25 cm × 75 μm ID, 1.9 μm C18 particles) was equilibrated with buffer A, and peptides were eluted at a flow rate of 300 nL/min at 50°C. Separated peptides were analyzed on a timsTOF Pro mass spectrometer (Bruker Daltonics, Bremen, Germany) in positive ion mode. The ion source voltage was set to 1.5 kV, and both MS and MS/MS data were acquired in TOF mode. The mass scan range was set to 100-1700 m/z. Data were collected using the parallel accumulation-serial fragmentation (PASEF) mode, with one MS1 scan followed by 10 PASEF MS/MS scans per cycle. Precursors with charge states 0-5 were fragmented, and the dynamic exclusion for MS/MS was set to 24 s to avoid repeated sequencing of the same ions. The total cycle time per loop was 1.17 s.

Protein identification and quantitative analysis

Raw mass spectrometry data (RAW files) were processed using MaxQuant software (version 1.5.3.17) for database search-based protein identification and quantitative analysis. The detailed search parameters and settings are provided in Table 1.

Table 1.

The parameters set in the protein identification and data analysis.

Item	Value
Enzyme	Trypsin
Max Missed Cleavages	2
Main Search	6 ppm
First Search	20 ppm
MS/MS Tolerance	±20 ppm
Fixed Modifications	Carbamidomethyl (C)
Variable Modifications	Oxidation(M), Acetyl (Protein N-term), Phospho (STY)
Database	uniprot_Anser_cygnoid_26951_20220908.fasta
Database pattern	Reverse
Include containments	True
Peptide FDR	≤ 0.01
Site FDR	≤ 0.01
Protein FDR	≤ 0.01

Note: FDR false discover rate.

Bioinformatic analysis

Quantitative data of phosphorylated peptides were normalized (-1 to 1), and hierarchical clustering of samples and proteins was performed using the ComplexHeatmap package in R (version 3.4) with Euclidean distance and average linkage. Conserved phosphorylation motifs were identified by extracting 13-amino-acid sequences centered on the modification sites and analyzing them using the MEME suite. Subcellular localization of phosphoproteins was predicted using CELLO (Yu et al., 2004). Protein domain annotation was conducted using the Pfam database through InterProScan (Finn et al., 2016), while Gene Ontology (GO) annotation and KEGG pathway analysis were performed using Blast2GO and the KEGG Automatic Annotation Server, respectively (Ashburner et al., 2000; Kanehisa et al., 2012). Functional enrichment analyses for GO terms, KEGG pathways, and protein domains were carried out using Fisher’s exact test. Protein-protein interaction networks were constructed based on information retrieved from the STRING database using a confidence score cutoff of combined score ≥ 0.7.

Statistical analysis

All statistical analyses were performed using SPSS software (version 25.0; SPSS Inc., USA). Pearson’s correlation analysis was applied to evaluate the relationships between muscle fiber characteristics and gosling body weight as well as gastrocnemius muscle weight. One-way analysis of variance (ANOVA) was used to compare body weight, gastrocnemius muscle weight, and muscle fiber traits among different groups. Data are presented as mean ± standard error (SE), and differences were considered statistically significant at P ≤ 0.05.

Results

Variation analysis of muscle fiber traits in Yangzhou goslings

To explore the variation and regulatory characteristics of muscle fiber traits in Yangzhou goslings, body weight, gastrocnemius muscle weight, muscle fiber diameter, cross-sectional area, and fiber density were quantified in 129 one-day-old individuals. A significant positive correlation was observed between body weight and gastrocnemius muscle weight (Fig. 1A), indicating that goslings with greater body weight at hatching generally exhibited higher leg muscle mass, which was consistent with expectations. Correlation analyses between muscle fiber diameter and both body weight and gastrocnemius muscle weight revealed no significant associations. These results suggest that muscle fiber size alone does not directly determine leg muscle yield, but may instead act in concert with muscle fiber number to influence muscle mass. Analysis of muscle fiber diameter distribution showed an approximately normal pattern, with the majority of fibers ranging from 10 to 14 μm, although individuals with extreme values were also observed (Fig. 1B).

Fig. 1.

Variation analysis of muscle fiber traits in Yangzhou goslings. (A-C) Correlation analyses between hatch body weight, gastrocnemius muscle weight, and muscle fiber diameter in one-day-old goslings. (D) Distribution of muscle fiber diameter in the gastrocnemius muscle of 129 one-day-old Yangzhou goslings.

Collectively, these findings demonstrate substantial variability in muscle fiber morphological traits among one-day-old Yangzhou goslings. Muscle fiber size was not independently associated with gastrocnemius muscle weight, and leg muscle yield is likely jointly determined by muscle fiber size and fiber number. As fiber density reflects the number of muscle fibers per unit area and may indicate differences in total fiber number, it could influence muscle growth potential and ultimately contribute to leg muscle yield. Notably, goslings with comparable body weight and gastrocnemius muscle weight may exhibit distinct muscle fiber diameters and fiber densities.

Phenotypic grouping of Yangzhou goslings based on muscle fiber traits

To further clarify the regulatory mechanisms underlying muscle fiber traits in geese, goslings were classified into three groups according to body weight, gastrocnemius muscle weight, and muscle fiber density: low meat yield (LY), high meat yield-low fiber density (HL), and high meat yield-high fiber density (HH). Compared with the HL and HH groups, goslings in the LY group exhibited significantly lower body weight and gastrocnemius muscle weight (P < 0.05). No significant differences in body weight or gastrocnemius muscle weight were observed between the HL and HH groups. However, muscle fiber diameter in the HL group (14.37 μm) was significantly greater than that in the HH group (9.31 μm), whereas muscle fiber density in the HL group was markedly lower, being approximately half of that observed in the HH group (Fig. 2 and Table 2).

Fig. 2.

Phenotypic grouping of Yangzhou goslings based on muscle fiber traits. HE staining of one-day-old goose muscle fiber. The geese were divided into three groups (n = 9 per group): Low meat yield group (LY), High meat yield and Low muscle fiber density group (HL) and High meat yield and High muscle fiber density group (HH). Magnification of 400 × was used (Bar = 20 μm).

Table 2.

Comparison on body weight, gastrocnemius muscle weight and muscle morphological traits among three groups.

Groups	Body weight(g)	Gastrocnemius muscle weight (g)	Fiber diameter (μm)	Fiber cross-sectional area (μm2)	Fiber density (n/mm2)
Low meat yield	87.11±1.79b	0.46±0.02b	7.99±0.43c	86.30±7.78c	11781.36±1079.49a
High meat yield-Low fiber density	99.30±3.61a	0.56±0.00a	9.41±0.29b	128.98±7.57b	7803.88±434.12b
High meat yield-High fiber density	104.01±2.67a	0.59±0.03a	14.37±0.01a	260.60±9.38a	3847.01±135.68c

Note: ^a,b,c Means ± SE with different superscript are significantly different in the same line (P < 0.05).

Phosphoprotein identification, mapping of phosphorylation sites, and motif analysis

A 4D label-free quantitative phosphoproteomic approach was applied to systematically characterize phosphorylation in gosling muscle. In total, 6,412 quantifiable phosphorylation sites were identified, corresponding to 5,548 phosphopeptides and 2,519 phosphorylated proteins (Fig. 3). Analysis of phosphorylation site distribution showed that serine (S), threonine (T), and tyrosine (Y) residues accounted for 81.81%, 17.03%, and 1.16% of all phosphorylation events, respectively. Among the identified phosphorylated proteins, 870 contained a single phosphorylation site, whereas 1,649 proteins (65.46%) harbored two or more phosphorylation sites. Notably, nebulin (A0A8B9DDB7), a key structural protein in muscle, exhibited extensive phosphorylation, with as many as 106 modification sites detected. Protein phosphorylation is predominantly mediated by upstream kinases that recognize specific substrate consensus motifs. To explore conserved sequence patterns surrounding phosphorylation sites in gosling muscle proteins, motif analysis was conducted using the MEME suite. A total of 58 putative phosphorylation motifs were identified, including 46 serine-centered and 12 threonine-centered motifs […R.._S_P…..,……_S_PV….]., and [……_S_PT….] (where “.” denotes any amino acid) represented the most prominent conserved motifs. A strong enrichment of proline-directed kinase motifs was observed, with proline frequently occurring at the +1 position relative to the phosphorylated residue. Collectively, these findings suggest that proline-directed phosphorylation may play an important role in the regulation of muscle development in goslings.

Fig. 3.

Identification of phosphoproteins and mapping of phosphorylation sites in goose muscle. (A-C) Numbers of identified phosphopeptides, phosphorylation sites, and phosphoproteins in the goose muscle phosphoproteome. (D) Amino acid residue distribution of all identified phosphorylation sites. (E) Distribution of phosphoproteins according to the number of phosphorylation sites per protein. (F-G) Significantly conserved motifs surrounding the phosphorylation sites. The height of each residue represents its frequency at the corresponding position, and residue colors indicate their physicochemical properties.

Identification and comparison of differential abundance phosphopeptides

To identify differentially phosphorylated proteins among the three gosling groups, quantitative comparisons were performed at the phosphopeptide level, as individual proteins often harbor multiple phosphorylation sites with distinct regulatory patterns. Differentially phosphorylated peptides were defined using the criteria of a fold change (FC) > 2.0 or < 0.5 with P < 0.05. In addition, peptides detected in at least two biological replicates of one group but absent in the other group were also included for subsequent functional analyses.

In the HH versus HL comparison, 329 phosphopeptides were significantly upregulated or uniquely detected in the HH group (Fig. 4A and 4C). These peptides were primarily derived from muscle-related and cytoskeletal proteins, including MYOM2, MLCK2, FOXO1, and components of the MAPK signaling pathway (MAPK and MAP3K3). Conversely, 392 phosphopeptides were significantly downregulated or specifically detected in the HL group, predominantly associated with energy metabolism-related proteins (NDUFB1, PDHA1, PGM1) and kinase-related proteins (CaMK, PP, PHKA). In the LY versus HH comparison, 513 phosphopeptides were significantly upregulated or uniquely present in the LY group, whereas 237 phosphopeptides were downregulated or specifically detected in the HH group. These peptides were mainly associated with muscle structural proteins (e.g., NEB, MSN), cytoskeletal organization, and lipid metabolism (e.g., PLIN). Notably, multiple phosphorylation sites of nebulin exhibited distinct regulatory trends, highlighting site-specific phosphorylation patterns. Hierarchical clustering analysis showed clear group separation with high similarity among biological replicates (Fig. 4, Fig. 4), supporting the reliability and reproducibility of the observed phosphorylation differences.

Fig. 4.

Identification and comparison of differentially abundant phosphopeptides. (A,C) Volcano plots showing differentially abundant phosphopeptides identified in the HH vs. HL (A) and LY vs. HH (C) comparisons. (B,D) Hierarchical clustering analysis of differentially abundant phosphopeptides in the HH vs. HL (B) and LY vs. HH (D) comparisons.

Overall, these results indicate that phosphorylation differences at the phosphopeptide level are closely associated with muscle fiber phenotypic variation at hatching. Enhanced phosphorylation of muscle structural proteins, cytoskeletal components, and MAPK signaling proteins in the HH group may contribute to increased muscle fiber density, whereas elevated phosphorylation of kinases and metabolic enzymes in the HL group may be associated with larger muscle fiber size.

Functional enrichment analysis of differential abundance phosphoproteins

GO enrichment, KEGG pathway, and protein-protein interaction network analyses (Fig. 5) were performed to identify the functional terms of differentially abundant phosphopeptides. Subcellular localization analysis using CELLO showed that proteins corresponding to differentially abundant phosphopeptides were predominantly localized in the nucleus, followed by the cytoplasm, mitochondria, plasma membrane, and extracellular matrix (Fig. S1). GO annotation revealed distinct functional enrichment patterns between groups. In the HH vs. HL comparison (Fig. 5A), differentially phosphorylated proteins were mainly enriched in cellular components related to the actin cytoskeleton, contractile fibers, and protein kinase complexes. Enriched biological processes included cytoskeleton organization and regulation of actin filament dynamics, while molecular functions were dominated by actin binding and protein kinase-related activities. In contrast, in the LY vs. HH (Fig. 5B) comparison, enriched cellular components were primarily associated with sarcomeric structures, including the I band, myofibrils, and Z disc. These proteins were mainly involved in transporter regulation, nuclear localization, and modulation of the ERK1/ERK2 cascade, with actin filament binding and ATPase regulator activity as the major enriched molecular functions.

Fig. 5.

Functional enrichment analysis of differentially abundant phosphoproteins. (A, B) Gene Ontology enrichment analysis of differentially phosphorylated proteins in goose muscle for the HH vs. HL (A) and LY vs. HH (B) comparisons. (C, D) KEGG pathway enrichment analysis of differentially phosphorylated proteins in goose muscle for the HH vs. HL (C) and LY vs. HH (D) comparisons. (E, F) Protein-protein interaction networks of differentially phosphorylated proteins in the HH vs. HL (E) and LY vs. HH (F) comparisons.

KEGG pathway analysis further highlighted group-specific signaling pathways. In the HH vs. HL comparison (Fig. 5C), significantly enriched pathways included regulation of the actin cytoskeleton, glycolysis/gluconeogenesis, AMPK, FoxO, and MAPK signaling pathways. In the LY vs. HH comparison (Fig. 5D), enriched pathways were mainly related to ferroptosis, oxidative phosphorylation, amino acid metabolism, and actin cytoskeleton regulation. PPI network analysis using STRING based on high-confidence interactions revealed coherent interaction modules among differentially phosphorylated proteins. In the HH vs. HL group (Fig. 5E), the dominant clusters consisted of glycolytic enzymes and muscle structural proteins. In the LY vs. HH group (Fig. 5F), interaction networks were enriched for muscle contraction-related proteins and stress-associated proteins, including heat shock proteins.

Overall, these results indicate that functional enrichment of differentially abundant phosphoproteins differs markedly among groups, with prominent involvement in cytoskeletal dynamics, metabolic processes, and signal transduction pathways.

Discussion

Considering that muscle fiber number influences both meat tenderness and meat yield through subsequent hypertrophy, we first characterized muscle fiber phenotypes in one-day-old goslings. The total number of muscle fibers in whole muscle is difficult to determine directly. Therefore, fiber density is commonly used as an indirect indicator. It reflects the number of fibers per unit area and may indicate variation in total fiber number. Together with fiber diameter, it contributes to differences in muscle growth potential and leg muscle yield. Based on gastrocnemius muscle weight and muscle fiber density, goslings were classified into three groups: HH, HL and LY. No significant differences in body weight or gastrocnemius muscle weight were observed between the HH and HL groups; however, muscle fiber diameter in the HL group was significantly larger than that in the HH group, while muscle fiber density was markedly lower. These results indicate that muscle fiber density and diameter vary among one-day-old Yangzhou goslings, suggesting that early muscle fiber traits are potentially amenable to selection or regulation. Similar findings have been reported in poultry and livestock (Dransfield and Sosnicki, 1999; Wang et al., 2024), showing that muscles with a higher number of fibers tend to exhibit slower fiber growth, whereas muscles with fewer fibers display faster fiber hypertrophy during postnatal development. In contrast, the LY group exhibited significantly lower muscle fiber diameter, body weight, and gastrocnemius muscle weight compared with the HH and HL groups.

The widespread application of advanced proteomic technologies in meat science has facilitated the identification of biological mechanisms underlying muscle traits and potential biomarkers (Huang et al., 2020). Therefore, a 4D label-free quantitative phosphoproteomic analysis was performed to explore the regulatory mechanisms among the three groups. In the HH/HL comparison, 329 differentially abundant phosphopeptides were identified as significantly upregulated or uniquely present in the HH group. Peptides uniquely detected in one group may be associated with low-abundance phosphorylation events that fall below detection thresholds (Derks et al., 2023). These peptides are mainly corresponding to muscle-related and cytoskeletal scaffold proteins, including MYOM2 (Thr614), MLCK2 (Ser786), TNS (Ser4273), Lamin A/C (Ser299), FOXO1 (Ser309), and FLN (Ser1524). MYOM2, a member of the myomesin family, is a major structural component of the M-band in vertebrate myofibrils and interacts with myosin, titin, and light meromyosin. Previous studies have demonstrated its important role in muscle contraction and its close association with various human diseases, as well as its regulatory potential in improving meat quality in livestock and poultry, making it a candidate gene for enhancing meat tenderness (Wu et al., 2014). MLCK2 is involved in regulating overall muscle contraction and cardiac function by phosphorylating a specific serine residue at the N-terminus of the myosin light chain (Davis et al., 2001). Tensin functions as a scaffold protein that may act as a protein and/or lipid phosphatase, participating in focal adhesion formation (Davis et al., 2001), cell polarization, and migration (Hall et al., 2009), and linking signaling pathways to the cytoskeleton (Davis et al., 2001). Collectively, these myofibrillar and cytoskeletal proteins are critical components of muscle contraction and structural integrity, and their phosphorylation may contribute to the regulation of contractile protein stability. Chen et al. (2020) reported that increased phosphorylation of major myosin proteins may protect muscle fibers from proteolytic degradation, thereby increasing muscle fiber density and muscle firmness. Another study demonstrated that phosphorylation of tropomyosin at Ser283 slowed myofibrillar relaxation and played a critical role in calcium-dependent regulation of muscle contraction (Nefedova et al., 2021). In addition, MAPK-related proteins, including p38-MAPK (Thr180) and mitogen-activated protein kinase kinase kinase 3 (Ser186), were significantly upregulated in the HH group. Previous studies have shown that the MAPK signaling pathway is activated during early muscle development and promotes myoblast proliferation and differentiation. This activation can be induced by extracellular stimuli, such as growth factors, or by intracellular signals, such as muscle-specific transcription factors (Keren et al., 2006). These findings suggest that phosphorylated proteins may contribute to the formation of a greater number of muscle fibers during early muscle development in geese.

Meanwhile, 392 differentially abundant phosphopeptides were identified as significantly downregulated or uniquely present in the HL group, mainly associated with energy metabolism, including PGM1 (Thr507 and Ser408) and NDUFB1 (Ser42). PGM1 is a key enzyme in glycogen metabolism, catalyzing the interconversion between glucose-1-phosphate and glucose-6-phosphate via phosphorylation of serine residues, thereby activating glycolysis (Stiers et al., 2017). Phosphorylation of PGM1 has been closely associated with meat tenderness and has been proposed as a biomarker for beef tenderness (Anderson et al., 2012; Anderson et al., 2014). Moreover, PGM1 phosphorylation accelerates glycolytic rate and postmortem pH decline and increases sarcomere length (Rodrigues et al., 2017; Silva et al., 2019). In addition, kinase-related proteins such as PP (Thr241) and PHKA (Ser892) were significantly upregulated in the HL group. Generally, higher phosphorylation levels reflect increased kinase activity, which may result from autophosphorylation, a fundamental regulatory mechanism in eukaryotic cells (Beenstock et al., 2016). Phosphorylases and glycolytic enzymes can interact as functional clusters; for example, phosphorylation of PHKB activates glycogen phosphorylase, producing glucose-1-phosphate and initiating glycolysis (Xing et al., 2020). These results suggest that enhanced kinase activity and glycolytic metabolism in the HL group may be associated with accelerated muscle fiber growth and increased fiber cross-sectional area. In the LY/HH comparison, Nebulin was found to contain multiple phosphorylation sites with divergent regulatory trends, highlighting the complexity of site-specific phosphorylation.

KEGG pathway enrichment analysis further revealed that differentially phosphorylated proteins in the HH/HL comparison were significantly enriched in pathways related to regulation of the actin cytoskeleton, glyoxylate and dicarboxylate metabolism, tight junctions, FoxO signaling, glycolysis/gluconeogenesis, and the MAPK signaling pathway. Among these, the MAPK pathway plays a crucial role in transducing extracellular signals into intracellular and nuclear responses, thereby regulating cell proliferation, differentiation, transformation, and apoptosis (Trempolec et al., 2013). These findings suggest that phosphorylation-dependent regulation is closely associated with variation in muscle fiber number during the perinatal period in geese. Key phosphosites, such as p38-MAPK (Thr180), may contribute to the formation of a higher number of finer muscle fibers, thereby potentially influencing early muscle architecture. However, further studies are required to validate the long-term effects of these early phosphorylation patterns on subsequent meat production traits. Overall, this study provides novel molecular insights into the epigenetic regulation of early skeletal muscle development, offering a foundational framework for targeted interventions aimed at improving growth efficiency and meat quality in geese.

CRediT authorship contribution statement

Kaiqi Weng: Writing – original draft, Software, Investigation, Formal analysis, Data curation. Yi Liu: Software, Methodology, Investigation. Huiying Wang: Validation, Supervision, Formal analysis. Qi Xu: Visualization, Resources, Project administration. Daqian He: Writing – review & editing, Supervision, Resources, Conceptualization.

Disclosures

The authors declare that they have no competing interests.