Integrated transcriptomic and proteomic analysis reveals molecular and morphological differences between triceps brachii and longissimus dorsi muscles in the Junggar Bactrian camel

Integrated transcriptomic and proteomic analysis reveals molecular and morphological differences between triceps brachii and longissimus dorsi muscles in the Junggar Bactrian camel | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated transcriptomic and proteomic analysis reveals molecular and morphological differences between triceps brachii and longissimus dorsi muscles in the Junggar Bactrian camel Yongbin Cai, Chen Meng, Jintao Gan, Ye Qin, Yaqi Zeng, Wanlu Ren, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8736931/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Muscle fiber type is a critical determinant of meat quality, with its phenotypic characteristics regulated by intricate biological processes encompassing gene transcription and translation. This study presents the first comprehensive integrated analysis of muscle morphology, transcriptomics, and proteomics across distinct muscle tissues in the Junggar Bactrian camel ( Camelus bactrianus ). A comparative morphological and molecular assessment was conducted between the triceps brachii (TB) and longissimus dorsi (LD) muscles to elucidate structural and functional differences in muscle fiber composition. An integrated transcriptomic and proteomic approach was employed to identify key genes and proteins associated with muscle fiber type specification. Results Comparative analysis revealed 921 differentially expressed genes (DEGs) and 79 differentially expressed proteins (DEPs) between the two muscle types. The analysis examined these DEGs and DEPs at the muscle fiber type level, focusing on their associations with key genes implicated in muscle contraction, glycolysis, and intramuscular lipid oxidation metabolism, such as TNNT1, hexokinase 2 (HK2), and fatty acid binding protein 3 (FABP3), as well as with critical proteins, including slow-twitch troponin T, actin alpha-3 chain isoform X1, monocarboxylate transporter 4, and FABP3. Notably, the coordinated expression patterns of these factors suggest their potential roles in shaping the metabolic and contractile properties specific to each fiber type. Conclusions By integrating morphological, transcriptomic, and proteomic data from the TB and LD muscles of the Junggar Bactrian camel, this study reveals significant differences at both structural and molecular levels. These findings provide novel insights into the molecular mechanisms underlying muscle fiber type determination in camels and offer potential biomarkers for meat quality improvement. Camel muscle Muscle fiber type Transcriptome Proteome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Camels exhibit exceptional survival and reproductive capabilities, along with distinctive physiological adaptations that enable them to thrive under extreme environmental conditions, such as high temperatures, prolonged water deprivation, and limited nutritional availability [ 1 ]. In arid and semi-arid regions where camels are widely distributed, their meat serves as a vital source of red meat protein for local populations [ 2 ]. Compared to other conventional red meats, camel meat is characterized by a favorable nutritional profile, including high levels of essential amino acids, minerals, vitamins, bioactive compounds, and beneficial fatty acids [ 3 , 4 ]. Current research on camel meat has primarily focused on meat quality traits [ 5 ], microbial composition in processed meat products [ 3 ], and the identification of differentially expressed proteins (DEPs) and metabolites [ 6 ]. However, integrated analyses combining muscle histomorphological characteristics with multi-omics data, particularly transcriptomic and proteomic profiles, remain limited, especially with respect to regional variations in muscle properties within the Junggar Bactrian camel ( Camelus bactrianus ). This knowledge gap hinders scientific progress and the industrial development of camel meat production in Xinjiang. Muscle fibers are the fundamental functional units of skeletal muscle tissue and are broadly classified into two main types: white (fast-twitch) and red (slow-twitch) fibers. This classification is based on myosin heavy chain (MyHC) isoforms, with Type I (slow oxidative) fibers predominant in red muscle and Type II (fast glycolytic) fibers predominant in white muscle [ 7 , 8 ]. Accumulating evidence indicates that the total number of muscle fibers is negatively correlated with fiber cross-sectional area (CSA) but positively associated with overall meat quality [ 9 ]. The proportion of red muscle fibers has been shown to positively correlate with the chromaticity value a* (a measure of meat redness) [ 10 , 11 ], suggesting that increasing red fiber content could be an effective strategy for improving meat color and sensory attributes [ 12 ]. Yan et al. [ 13 ] reported that red muscle fibers generally exhibit a smaller diameter than white fibers. Furthermore, Picard et al. [ 14 ] demonstrated a positive correlation between shear force and muscle fiber diameter, while Bakhsh et al. [ 15 ] found that fiber diameter is significantly negatively correlated with fiber density and positively correlated with shear force. Collectively, these findings suggest that increasing the proportion of red muscle fibers may contribute to enhanced tenderness. Transcriptomic analysis enables a comprehensive assessment of gene expression patterns in specific tissues under defined physiological or pathological conditions, thereby facilitating the identification of candidate genes involved in phenotypic regulation and elucidating their underlying biological functions [ 16 ]. For instance, Yan et al. [ 17 ] identified myosin heavy chain 10 (MYH10) and glutamate decarboxylase like 1 (GADL1) as potential regulators of beef quality traits through transcriptomic profiling of bovine longissimus dorsi (LD) muscle. Similarly, Wang et al. [ 18 ] revealed that fatty acid synthase (FASN) may promote intramuscular fat deposition, whereas aldolase, fructose-bisphosphate C (ALDOC), phosphofructokinase, liver type (PFKL), and serine dehydratase (SDS) could be involved in regulating citrulline metabolism. Proteomics has emerged as a powerful tool in meat science, enabling the discovery of biomarkers for meat quality evaluation and providing insights into the molecular mechanisms underlying meat traits [ 19 , 20 ]. Zhang et al. identified several DEPs associated with water-holding capacity in goose meat, including structural proteins and key metabolic enzymes [ 20 ]. Previous studies have also identified biomarker proteins related to intramuscular fat content [ 19 ], water-holding capacity [ 20 ], myoglobin peroxidase-2 for muscle color stability [ 12 ], and troponin C1 as a potential indicator of meat tenderness [ 21 ] in beef. Therefore, integrating transcriptomic and proteomic approaches provides a robust strategy for uncovering molecular differences and deciphering the complex regulatory networks underlying distinct muscle types. In this study, differentially expressed genes (DEGs) and DEPs were systematically identified in the triceps brachii (TB) and LD muscles of the Junggar Bactrian camel using RNA-Seq-based transcriptomics and data-independent acquisition (DIA) quantitative proteomics. This integrative multi-omics approach offers a more comprehensive understanding of the coordinated roles of genes and proteins in muscle fiber type specification. The primary objective of this research is to identify key molecular determinants associated with muscle fiber type differentiation in camel skeletal muscles. These findings will provide valuable molecular resources and essential data support for future studies on the genetic and biochemical mechanisms of muscle fiber variation in camels, with potential implications for improving meat quality in non-traditional livestock species. Materials and methods Animals and sample collection Twelve healthy male Junggar Bactrian camels ( Camelus bactrianus ), aged 4-5 years and weighing 450 ± 50 kg, were selected for the study. All camels were raised at Urumqi Muxingyuan Livestock Farming Farmers’ Professional Cooperative (Xingjiang, China). Camel slaughter was conducted in accordance with the guidelines specified in Technical Specification for Humane Slaughter of Livestock and Poultry (GB/T 19477–2018). There were no other animals were present at the slaughter site. Prior to exsanguination, camels were rendered insensible via electrical stunning under controlled parameters: voltage ≤ 200 V, current 1.0–1.5 A, and duration 7–30 s. Finally, exsanguination was performed by rapidly severing the carotid arteries. Muscle samples were collected from the TB and LD muscles. For histological analysis, a 1 cm³ portion of each sample was fixed in universal tissue fixative, embedded in paraffin, sectioned, and subjected to hematoxylin and eosin (H&E) staining as well as immunohistochemical (IHC) analysis to evaluate cellular and morphological characteristics. For molecular analyses, approximately 1 g of fresh tissue was immediately placed into a 5 mL cryotube and snap-frozen in liquid nitrogen for total RNA extraction. An additional 1 g aliquot was preserved under the same conditions for quantitative proteomic profiling. Muscle fiber characterization Hematoxylin and eosin (H&E) staining Fixed skeletal muscle tissues were trimmed into approximately 0.3 cm³ blocks, dehydrated in a graded ethanol series, cleared in xylene, and embedded in paraffin using a biological tissue embedding machine (AiHua BMJ-1; Tianjin AiHua Medical Instrument Co., Ltd., Tianjin, China) with the cross-sectional surface oriented downward. The tissue was sectioned serially into 5-μm sections using a rotary microtome (YiDi YD-335; Jinhua YiDi Medical Equipment Co., Ltd., Jinhua, China) and baked at 70 °C for 30 min to ensure adhesion to slides. H&E staining was performed according to the standard protocol provided by Wuhan Servicebio Technology Co., Ltd. (Wuhan, China), followed by dehydration and mounting for microscopic examination. Immunohistochemical (IHC) staining Immunohistochemical staining was conducted following the manufacturer's instructions (Wuhan Servicebio Technology Co., Ltd., Wuhan, China). In brief, paraffin-embedded tissue sections underwent deparaffinization in xylene, followed by gradual rehydration using a series of decreasing ethanol concentrations. Antigen retrieval was performed with the manufacturer-recommended retrieval solution under appropriate conditions. To block endogenous peroxidase activity, sections were treated with 3% hydrogen peroxide for 25 min at room temperature in the dark. The slides were then rinsed three times (5 min each) in phosphate-buffered saline (PBS, pH 7.4). Nonspecific binding was prevented by incubating the sections with 3% bovine serum albumin (BSA) in PBS for 1 hour at room temperature. Subsequently, sections were incubated overnight at 4°C in a humidified chamber with the primary antibody (HRP-labeled rabbit anti-goat IgG, diluted 1:200 in PBS). After thorough washing, the sections were exposed to HRP-conjugated secondary antibody (goat anti-rabbit IgG, diluted 1:200 in PBS) for 50 minutes at room temperature. Following three additional PBS washes, the immunoreaction was detected using freshly prepared 3,3′-diaminobenzidine (DAB) substrate, with development monitored microscopically until optimal brown-yellow coloration appeared. The reaction was halted by rinsing in distilled water. Nuclear counterstaining was achieved with hematoxylin for about 3 minutes, followed by differentiation in hematoxylin differentiation solution and bluing in alkaline solution. The sections were then dehydrated in a graded series of increasing ethanol concentrations, cleared in xylene, air-dried, and finally mounted using neutral balsam. Image acquisition and analysis Tissue sections were scanned using a digital slide scanning system (EasyScan 6; Motic, Xiamen, China) to obtain high-resolution whole-slide images. Under brightfield microscopy, muscle cell nuclei appeared blue (hematoxylin), while positive muscle fibers exhibited brown-yellow staining (DAB chromogen). Representative regions were imaged at 200× magnification using Motic DSAssistant software (Motic, Xiamen, China). The numbers of fast-twitch and slow-twitch muscle fibers were quantified in multiple non-overlapping fields using ImageJ software (Java 1.8.0_345, National Institutes of Health, Bethesda, MD, USA). Based on calibrated pixel-to-area measurements, the average cross-sectional area, fiber count per field, and proportional distribution of each fiber type were calculated. All measurements were performed in triplicate and averaged to minimize technical variability. Statistical analysis of phenotypic data Fiber phenotype data were analyzed using one-way analysis of variance (ANOVA) in SPSS 23.0 (IBM Corp., Armonk, NY, USA). Data are presented as mean ± standard deviation (mean ± SD). Intrasubject regional differences in fast-twitch and slow-twitch fiber composition between the TB and LD muscles were evaluated. A P-value < 0.05 was considered statistically significant, and P < 0.01 indicated highly significant differences. Transcriptome sequencing and analysis Library preparation and sequencing Total RNA was obtained from frozen muscle specimens using a commercial kit following the manufacturer's instructions. Poly(A)+ mRNA was enriched using Oligo(dT) magnetic beads. The resulting purified mRNA was subjected to controlled thermal fragmentation to generate short segments. Double-stranded cDNA was then prepared through sequential first- and second-strand synthesis. Target cDNA fragments of 370–420 bp were size-selected via AMPure XP bead-based purification. Libraries were amplified by PCR and purified again with AMPure XP beads to remove residual primers and dimers. High-throughput paired-end sequencing (150 bp) was performed on the Illumina NovaSeq platform by Beijing Novogene Co., Ltd. (Beijing, China). Data processing and alignment Raw sequencing reads were processed to remove adapter sequences, reads containing more than 10% ambiguous bases (N > 10%), and low-quality reads (those with >50% bases having Qphred ≤ 5). The clean sequencing data underwent quality evaluation, with metrics encompassing Q20, Q30, and overall GC content being calculated. Only high-quality clean reads were utilized for all further analyses. Reference genome assembly and gene annotation information were sourced from the authoritative camel genome database (https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/009/834/535/GCF_009834535.1_BCGSAC_Cfer_1.0/GCF_009834535.1_BCGSAC_Cfer_1.0_genomic.fna.gz). Clean paired-end reads were aligned to the reference genome using HISAT2 v2.0.5 after constructing a genome index. Gene-level read counts were generated using featureCounts v1.5.0-p3. Gene expression levels were normalized and reported as FPKM (Fragments Per Kilobase of transcript per Million mapped reads), accounting for both sequencing depth and transcript length. Differential gene expression analysis Differential expression analysis between groups (with biological replicates) was performed using DESeq2 v1.20.0. This method uses a negative binomial distribution to model variance and test for differential expression. Genes with an adjusted P ≤ 0.05 and |log₂ fold change| ≥ 1 were classified as DEGs. Proteomic analysis Protein extraction and digestion Muscle tissue samples were pulverized in liquid nitrogen and lysed in SDT lysis buffer (4% SDS, 100 mM Tris-HCl pH 7.6, 100 mM DTT) by sonication in an ice-water bath for 5 min. After homogenization, the lysate was cleared by centrifugation (12,000 × g, 15 min, 4 °C). The collected supernatant was boiled at 95 °C for 8–15 minutes to facilitate denaturation, chilled on ice for 2 min, and alkylated with iodoacetamide (IAM) solution in the dark for 1 h. Proteins were precipitated by adding four volumes of pre-chilled acetone and incubating at –20 °C for at least 30 min, followed by centrifugation at 12,000 × g for 15 min at 4 °C. The pellet was washed once with 1 mL of pre-chilled acetone, air-dried, and resolubilized in dissolution buffer (DB buffer: 6 M urea, 100 mM TEAB, pH 8.5). Quantification of protein content was performed with a commercial Bradford assay kit (BioVision, Shanghai, China). Protein samples were diluted to 100 μL with DB buffer and digested using trypsin (enzyme:protein ratio ≈1:50) at 37 °C for 4 h. Formic acid was added to acidify the mixture to pH < 3, and after a 5-min centrifugation at 12,000 × g, the supernatant was desalted on a C18 SPE column (washed 3× with 0.1% FA/3% ACN; eluted with 0.1% FA/70% ACN). The eluate was dried under vacuum and analyzed by liquid chromatography-mass spectrometry (LC-MS) in data-independent acquisition (DIA) mode. LC-MS/MS analysis (DIA mode) Peptide samples were analyzed via a Vanquish™ Neo UHPLC system online-coupled to an Orbitrap Astral mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-Spray source. Mobile phases were A: 0.1% formic acid in water and B: 0.1% formic acid in 80% acetonitrile. Peptides (200 ng, reconstituted in 10 μL mobile phase A after 14,000 × g centrifugation at 4 °C for 20 min) were separated on a trap column (Acclaim PepMap™ 100, cat. no. 174500) and analytical column (PepMap™ Neo, 150 μm × 15 cm, 2 μm, cat. no. ES906) at 50 °C, following the gradient in Table 1. DIA mode parameters included spray voltage 2.0 kV, ion transfer tube 290 °C, MS1 range m/z 380–980 (resolution 240,000), AGC 500%, 2 Th windows (300 total), NCE 25%, and MS2 m/z 150–2000 (resolution 80,000, max IT 3 ms). Raw files (.raw) were saved for further analysis. Protein identification and quantification Protein were identified and quantifiedusing DIA-NN software (version 1.8.1; https://github.com/vdemichev/DiaNN) against a customized camel muscle protein sequence database. This database was constructed by combining predicted tryptic peptides from the camel reference proteome (aligned to the reference genome used for transcriptomics) and filtered for muscle-expressed proteins based on prior transcriptomic data (or specify the exact source, e.g., UniProt + muscle-specific annotations). Search parameters included automatic adjustment of precursor and fragment mass tolerances (with recommended settings for Orbitrap Astral: MS1 accuracy 4 ppm, MS/MS 10 ppm), fixed modification of cysteine carbamidomethylation (+57.021 Da), variable modifications including N-terminal methionine oxidation (+15.995 Da) and up to two missed cleavages. Peptide and protein identifications were filtered at a false discovery rate (FDR) of 1% using global precursor q-value (Global.Q.Value < 0.01) and global protein group q-value (Global.PG.Q.Value < 0.01). DEPs were defined as those showing an adjusted P 1 between the TB and LD groups. Bioinformatic analysis Functional annotation and enrichment analysis of DEGs were performed using clusterProfiler v3.8.1 for Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. DEPs were annotated for GO terms and InterPro (IPR) domains, including Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER, using InterProScan (version 5.22–61.0). COG and KEGG pathway classifications were applied to categorize functional protein families and metabolic or signaling pathways [22]. Volcano plots, hierarchical clustering heatmaps, and enrichment bar charts for GO, IPR, and KEGG pathways were generated for DEPs [23]. Protein–protein interaction (PPI) networks were predicted via the STRING database (version 12.0; https://string-db.org/) using the default parameters with medium confidence score of 0.4 and all active interaction sources [24] and visualized using Cytoscape v3.10.4. Integrated transcriptomic-proteomic analysis Muscle fiber phenotype data were analyzed using one-way analysis of variance (ANOVA) in SPSS 23.0 (IBM Corp., Armonk, NY, USA), with results presented as mean ± SD. Intrasubject regional differences between the TB and LD muscles were evaluated. Differences were considered not significant at P > 0.05, statistically significant at P < 0.05, and highly significant at P < 0.01. To assess the concordance between transcriptomic and proteomic expression profiles, log₂-transformed fold changes of matched gene–protein pairs were subjected to Pearson correlation analysis [25]. A correlation coefficient (R) > 0.80 was considered indicative of strong positive consistency between the two omics layers [26]. DEGs and DEPs were integrated to construct comprehensive PPI networks using the STRING database. Hub nodes within the integrated networks were identified based on topological properties, including degree centrality, betweenness centrality, and closeness centrality. Real-time quantitative reverse transcription PCR (qRT-PCR) validation of RNA-seq results Primers specific to the selected candidate genes were generated using Oligo7.0 and Primer5 software (Table 1). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the endogenous reference gene. Total RNA was extracted from samples using TRIpure Reagent (Beijing Aidele Biotechnology Co., Ltd.), and first-strand cDNA was synthesized from 1 μg of total RNA using RevertAid Reverse Transcriptase (Thermo Fisher Scientific). Quantitative PCR was performed using AceQ Universal SYBR Green qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). The thermal cycling protocol consisted of an initial denaturation step at 95 °C for 5 min, followed by 40 cycles of amplification (95 °C for 10 s; 60 °C for 30 s), and a final melting curve analysis (60–95 °C, with increments of 0.3 °C per 15 s). All qRT-PCR reactions were run in triplicate for each gene using three independent biological replicates. Relative gene expression levels were calculated using the 2−ΔΔCt method. Table 1 . Primers used for Quantitative Real-Time PCR. Genes Primer sequences (5’-3’) Annealing temperature (℃) Product length (bp) MYL6B AGGACTACGACTCCCAGCAT 60 149bp TGATGTCCGGTAGCCAAAGG PANK1 ACAACGGCTTCCACCCAAC 60 223bp ATATCCATGCCGAACCACGG SH3KBP1 GCAGGAGAGGTTTGTTCCCT 60 222bp GCTCATCATCGTTCTGGGGT ALPK2 GTGCTCGGAAGGAGTGTCAT 60 208bp CAAGTTACCTCTGGCTCGGG ZNF385A CCTGTGCAGAAGGCTGTACT 60 167bp GCCTCGATGCCTTTGACTCT RYR3 TGGAACCCACATCAGAAGCC 60 144bp CCTTCCTTTGAAACCCTTCGC HOXA4 CGTGGTGTACCCCTGGATGAA 60 125bp ACTCCTTCTCCAACTCCAAGAC MYH8 GCTGCAGCATCAGCACATTAG 60 243bp TGTTTTGGGCTTCAATCCGC CSRP2 GCGTGGTCCAGCTTCGATTA 60 224bp CCCGTACTTCTTCCCGTAGC GAPDH CCGGCGCTCTCTGCTC 60 146bp CCAGAGTGAAAAGCAGCCCT Results Muscle fiber morphology Cross-sectional morphometric analysis of muscle fibers was quantified using ImageJ software, based on hematoxylin–eosin (HE) and immunohistochemical staining results. One-way analysis of variance (ANOVA) revealed no significant differences in total muscle fiber number, muscle fiber density, or mean cross-sectional area (CSA) of fast-twitch muscle fibers between the TB and LD muscles ( P > 0.05; Table 2). However, the TB muscleexhibited a significantly lower number and proportion of slow-twitch muscle fibers compared with the LD muscle ( P < 0.01 ). Conversely, both the number and proportion of fast-twitch muscle fibers were significantly higher in the TB than in the LD ( P < 0.01 ). Moreover, the mean CSA of slow-twitch fibers was significantly smaller in the TB relative to the LD ( P < 0.05 ). In TB, the CSA of slow-twitch fibers was also significantly smaller than that of fast-twitch fibers ( P < 0.01 ). These results demonstrate marked regional heterogeneity in muscle fiber type composition between the TB and LD muscles. Table 2 . Comparison of muscle fiber morphometric parameters between triceps brachii (TB) and longissimus dorsi (LD). Name TB LD Number of all muscle fibers (N) 27.35±5.46 25.59±6.33 Density of muscle fiber (N/mm2) 443.03±88.38 414.56±102.50 Number of slow muscle fibers (N) 9.69±2.80 B 13.98±4.07 A Number of fast muscle fibers (N) 17.65±4.67 A 11.64±5.31 B Area of slow muscle fibers (µm 2 ) 1599.14±386.49 b 2160.58±842.54 a Area of fast muscle fibers (µm 2 ) 2808.29±521.42 2730.50±720.18 Slow muscle fibers proportion (%) 24.50%±9.29% B 49.49%±17.88% A Fast muscle fibers proportion (%) 75.50%±9.29% A 50.51%±17.88% B Note: Different uppercase letters (A, B) in the same row indicate extremely significant differences ( P < 0.01) between TB and LD; different lowercase letters (a, b) indicate significant differences ( P 0.05). As shown in Fig. 1, H&E staining revealed typical cross-sectional muscle fiber morphology, with dark blue nuclei, purplish-red cytoplasm, clearly delineated cellular boundaries, and well-preserved structural integrity. Immunohistochemical (IHC) analysis demonstrated specific positive staining in the cytoplasm, appearing as dark brown precipitates in reactive fibers, while non-reactive regions showed light brown or no staining. Nuclei were counterstained dark blue, providing clear cellular demarcation. Collectively, these histological features indicate intact tissue architecture and high-quality sample preservation. Overall analysis of transcriptomics and proteomics Transcriptomic sequencing statistics As summarized in Table 3, transcriptomic sequencing was performed on 12 muscle samples (6 from TB and 6 from LD), yielding a total of 87.15 Gb of clean data. Each sample produced at least 5.94 Gb of high-quality bases, with Q20 and Q30 values exceeding 99.39% and 97.4%, respectively, confirming excellent sequencing quality. The reference genome used was that of Camelus ferus (wild Bactrian camel; NCBI assembly GCF_009834535.1_BCGSAC_Cfer_1.0). After quality control, clean reads from each sample were aligned to this reference genome, resulting in alignment rates ranging from 91.85% to 93.90%. In total, 270,771,982 clean reads were mapped from TB samples and 286,676,450 from LD samples. A total of 24,118 expressed genes were identified across all samples. Compared with LD, 921 DEGs were detected in TB, including 344 upregulated and 577 downregulated genes (Fig. 2A). Hierarchical clustering analysis of the DEGs clearly separated the two muscle types, revealing distinct transcriptional profiles (Fig. 3A). Table 3 . Summary of transcriptomic data quality. Sample Raw_reads Raw_bases Clean_reads Clean_bases Error_rate Q20 (%) Q30 (%) GC_pct TB_17 47817556 7.17G 45313218 6.8G 0.01 99.43 97.49 51.77 TB_24 49280702 7.39G 47637424 7.15G 0.01 99.44 97.51 51.35 TB_26 48554208 7.28G 47074984 7.06G 0.01 99.41 97.45 51.65 TB_27 46294612 6.94G 44622458 6.69G 0.01 99.42 97.48 51.67 TB_28 41817650 6.27G 40253898 6.04G 0.01 99.43 97.43 51.67 TB_33 48085064 7.21G 45870000 6.88G 0.01 99.43 97.5 51.82 LD_17 48188404 7.23G 46382976 6.96G 0.01 99.41 97.41 51.33 LD_24 46529606 6.98G 44367328 6.66G 0.01 99.42 97.47 50.71 LD_26 47457602 7.12G 45871330 6.88G 0.01 99.42 97.52 52.03 LD_27 57998890 8.7G 55171882 8.28G 0.01 99.39 97.4 51.99 LD_28 48834750 7.33G 46817092 7.02G 0.01 99.42 97.47 52.08 LD_33 50222006 7.53G 48065842 7.21G 0.01 99.41 97.42 51.18 Proteomic sequencing statistics In the proteomic analysis, a total of 48,140 unique peptides were identified across the 12 samples, enabling reliable quantification of 4,557 proteins. Quality assessment confirmed the robustness of the dataset: the majority of identified peptides ranged from 7 to 30 amino acids, which is within the optimal length range for LC-MS/MS detection. Retention time (RT) consistency was evaluated across 12 representative peptides distributed evenly throughout the gradient; their RTs showed excellent reproducibility among biological replicates, indicating stable instrument performance and reliable chromatographic separation. Additionally, the number of unique peptides per protein positively correlated with protein abundance, further supporting the accuracy and depth of identification. Collectively, these quality metrics demonstrate the high reliability of the proteomic dataset for subsequent quantitative analyses. Compared with the LD group, 79 proteins were identified as differentially expressed in the TB group, comprising 7 upregulated and 70 downregulated proteins (Fig. 2B). Unsupervised hierarchical clustering of these DEPs clearly separated the TB and LD samples, underscoring distinct protein expression profiles between the two muscle types (Fig. 3B). GO functional enrichment analysis of DEGs and DEPs To explore the functional implications, GO term enrichment analysis was performed on DEGs and DEPs, identifying terms with P < 0.05. At the transcriptomic level, 32 GO terms were significantly enriched among the DEGs between TB and LD (Fig. 4). These terms were classified into the three main GO domains: biological process (BP), cellular component (CC), and molecular function (MF). In the BP category, the most prominent enriched terms included protein phosphorylation, phosphorylation, and regulation of biological quality. In the CC category, significant enrichment was observed in cytoskeleton, actin cytoskeleton, and myosin complex. In the MF category, key enriched terms included motor activity and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen. At the proteomic level, 15 GO terms were significantly enriched among the DEPs between TB and LD ( P < 0.05; Fig. 5A). These terms were distributed across the three main GO categories: Biological process (BP): prominent terms included protein phosphorylation, protein modification process, and protein peptidyl-prolyl isomerization; Cellular component (CC): sarcomere, troponin complex, and actin cytoskeleton; Molecular function (MF): selenium binding, O-acyltransferase activity, and chemokine activity. Notably, the term actin cytoskeleton was significantly enriched in both the transcriptomic and proteomic datasets, indicating consistent regulation at the gene and protein levels. Furthermore, several GO terms directly related to muscle fiber structure and function, such as cytoskeleton, myosin complex, and motor activity at the transcriptomic level, and sarcomere and troponin complex at the proteomic level, highlight biological relevance to muscle fiber type specification and contractile properties. KEGG pathway enrichment analysis of DEGs and DEPs KEGG pathway enrichment analysis was performed on DEGs and DEPs to identify biological pathways potentially associated with muscle fiber type specification and function. At the transcriptomic level, 20 KEGG pathways were significantly enriched among the DEGs between the TB and LD muscles ( P < 0.05; Fig. 4B). Among these, the most significantly enriched pathways included neuroactive ligand-receptor interaction, motor proteins, cAMP signaling pathway, and pentose and glucuronate interconversions. Several of these pathways are functionally linked to muscle contraction, signaling, and energy metabolism. In particular, motor proteins and pentose and glucuronate interconversions are relevant to muscle fiber contractility and metabolic adaptation, respectively. At the proteomic level, KEGG pathway enrichment analysis of DEPs between the TB and LD muscles identified 12 significantly enriched pathways ( P < 0.05; Fig. 5B). These pathways were predominantly associated with cytoskeletal organization and muscle-related functions, including cytoskeleton in muscle cells, dilated cardiomyopathy, hypertrophic cardiomyopathy, and motor proteins. Notably, the motor proteins pathway was significantly enriched in both the transcriptomic and proteomic datasets, indicating consistent regulation across mRNA and protein levels and highlighting its potential central role in the observed differences in muscle fiber type specification and contractile properties. Domain enrichment analysis of DEPs Domain enrichment analysis of DEPs was performed using InterProScan, following the same methodology as the GO functional enrichment analysis. Significant enrichment was defined as P < 0.05. In the TB compared with the LD, 15 protein domains were significantly enriched among the DEPs (Fig. 6). These included MyBP-C (myosin-binding protein C), tri-helix bundle domain, troponin domain, EF-hand domain, titin (TTN), and Z repeat, among others. These domains are predominantly associated with sarcomeric structure, calcium binding, and contractile regulation, suggesting that domain-specific alterations may contribute to the observed differences in muscle mechanical and regulatory properties. Correlation between transcriptomics and proteomics As shown in Fig. 7A, of the 890 DEGs, 771 lacked corresponding protein detection in the proteomic dataset, likely due to the relatively lower sensitivity and coverage of proteomic profiling compared with transcriptomic sequencing. Additionally, the protein products of 99 DEGs showed no significant abundance changes between TB and LD and were therefore not classified as DEPs. Conversely, 55 DEPs were derived from genes that exhibited no significant transcriptional changes, and 51 DEPs had no detectable corresponding transcripts. A total of 20 gene–protein pairs showed concordant differential expression at both mRNA and protein levels. Overall, integrated analysis of transcriptome and proteome data revealed a weak positive correlation (Pearson r = 0.274), which is commonly observed in multi-omics studies owing to post-transcriptional regulatory mechanisms. Nevertheless, joint analysis of molecules showing consistent expression patterns across omics layers are particularly valuable for identifying functionally relevant regulators. As illustrated in Fig. 7B, transcripts and proteins in the third and seventh quadrants represent concordant trends. Specifically, the third quadrant contained three upregulated gene–protein pairs, while the seventh quadrant included 17 downregulated pairs. These 20 concordant pairs represent key candidates for further functional validation and mechanistic investigation. Integrated GO enrichment analysis of DEGs and DEPs All DEGs and DEPs were mapped to the GO database to retrieve their corresponding annotations. Intersection analysis identified GO terms significantly enriched in both the transcriptomic and proteomic datasets. Integrated multi-omics analysis revealed significant co-enrichment in three GO terms (Fig. 8): protein phosphorylation (biological process, BP), actin cytoskeleton (cellular component, CC), protein kinase activity (molecular function, MF). These shared terms highlight coordinated regulation at both transcript and protein levels, particularly in signaling and structural processes relevant to muscle fiber differences. Integrated KEGG pathway analysis of DEGs and DEPs All DEGs and DEPs were annotated using the KEGG database to assign pathway memberships. Intersection analysis identified pathways significantly co-enriched in both datasets ( P < 0.05). As shown in Fig. 9, four pathways exhibited significant co-enrichment: Motor proteins, Drug metabolism - other enzymes, Folate biosynthesis, and Metabolism of xenobiotics by cytochrome P450. Detailed examination of the Motor proteins pathway (Fig. 10) revealed differential expression patterns in key gene and protein families associated with muscle contraction and sarcomeric function: Kinesin-related genes: upregulated genes included kinesin family member 1B (KIF1B) and kinesin family member 11 (KIF11); downregulated genes included centromere protein E (CENPE) and kinesin family member 19 (KIF19); Myosin-related genes: upregulated genes included myosin XVIIIB (MYO18B), myosin heavy chain 8 (MYH8), and myosin heavy chain 1 (MYH1); downregulated genes included myosin 5 (MYO5), myosin heavy chain 7B (MYH7B), myosin heavy chain 6 (MYH6), myosin heavy chain 11 (MYH11), and myosin light chain 6B (MYL6B); troponin complex genes: only downregulated genes were observed, include troponin T1, slow skeletal type (TNNT1) and troponin I3, cardiac type (TNNI3). At the protein level, myosin-associated DEPs included myosin, MYH10, MYL6B, myosin light chain 2 (MYL2), troponin C1 (TNNC1), troponin I1 (TNNI1), and tropomyosin 3 (TPM3). Notably, MYL6B and TNNT1 were consistently downregulated at both the mRNA and protein levels. These concordantly dysregulated genes and proteins are closely linked to sarcomeric structure, motor function, and contractile regulation, providing strong evidence for coordinated molecular mechanisms underlying the observed differences in muscle fiber type composition between the TB and LD muscles. PPI network analysis To explore functional relationships among differentially expressed molecules, a PPI network was constructed using the STRING database, integrating 892 DEGs (327 upregulated and 565 downregulated) and 77 DEPs (8 upregulated and 69 downregulated). As shown in Fig. 11, node color intensity reflects the degree of connectivity (darker red indicates higher interaction degree). Among upregulated genes, the most highly connected hubs were tropomyosin 1 (TPM1) (degree = 25) and vinculin (VCL) (degree = 18). Among downregulated genes, prominent hub genes included TNNT1 (degree = 21), TNNI3 (degree = 19), MYL6B (degree = 15), and MYH11 (degree = 14). At the protein level, TTN emerged as a central hub among upregulated DEPs (degree = 11), while TNNT1 (degree = 16) and TPM3 (degree = 14) showed the highest connectivity among downregulated DEPs. These hub molecules are strongly associated with sarcomeric organization, contractile machinery, and cytoskeletal dynamics, suggesting they may serve as key regulatory nodes underlying the structural and functional differences between the TB and LD muscles. Validation of RNA-seq-based DEGs by qRT-PCR To evaluate the technical reproducibility and biological reliability of the RNA-seq data, we selected nine DEGs for validation by quantitative reverse transcription PCR (qRT-PCR), including one upregulated gene (MYH8) and eight downregulated genes (MYL6B, SH3KBP1, ALPK2, ZNF385A, RYR3, HOXA4, CSRP2, and PANK1). As shown in Figure 12, the qRT-PCR results exhibited strong concordance with the RNA-seq expression trends for all nine genes, both in direction (up- or downregulation) and in the relative magnitude. This high concordance confirms the reliability and robustness of the transcriptomic profiling results. Discussion With rising living standards, consumer demand for meat has shifted from quantity toward quality and nutritional value. Previous studies have established that smaller muscle fiber diameter is positively correlated with improved meat tenderness [ 27 ], while a higher proportion of type II (fast-twitch) fibers is negatively associated with tenderness [ 28 ]. Consequently, muscle fiber architecture serves as a key cellular indicator for evaluating meat composition and quality traits [ 29 ]. Understanding structural and molecular differences between muscle types is therefore essential for improving meat quality and optimizing livestock production systems. In this study, the CSA of slow-twitch (type I) fibers in the TB was significantly smaller than that of fast-twitch (type II) fibers ( P < 0.01). These findings are consistent with reports across various species, including humans, showing that fast-twitch fibers generally exhibit larger diameters than slow-twitch fibers [ 30 , 31 ]. Muscle fiber type and physiological properties play a central role in determining meat yield and quality [ 32 ]. Fast-twitch fibers are characterized by rapid contraction, high power output, and quick fatigue, making them well-suited for explosive movements, whereas slow-twitch fibers contract more slowly, exhibit greater fatigue resistance, and support sustained, low-intensity activity [ 33 ]. Functionally, the camel's TB acts as the primary extensor of the forelimb, facilitating powerful and rapid elbow extension during locomotion or sudden exertion, activities that predominantly recruit fast-twitch fibers (type II). In contrast, the LD runs along the vertebral column and is primarily responsible for maintaining postural stability, supporting load bearing, and contributing to prolonged, low-velocity movements such as long-distance walking, functions typically supported by a higher proportion of slow-twitch fibers (type I). Consistent with these functional demands, the number and proportion of slow-twitch fibers were significantly lower in TB than in LD ( P < 0.01 ), whereas fast-twitch fibers were significantly more abundant in TB ( P < 0.01). These results indicate that TB is enriched in fast-twitch fibers, while LD contains a relatively higher proportion of slow-twitch fibers. Based on these morphological data and previous findings by Kim et al. [ 34 , 35 ], which demonstrated a positive correlation between muscle fiber CSA and shear force, it can be inferred that LD likely exhibits superior tenderness compared with TB. This fiber-type distribution aligns with the distinct biomechanical roles of the two muscles and provides a cellular basis for potential differences in camel meat quality traits. Transcriptome sequencing (RNA-Seq) and quantitative proteomics provide high-resolution, accurate quantification and deep coverage, enabling comprehensive molecular profiling of complex tissues [ 36 , 37 ]. Moreover, constructing sample-specific protein databases from RNA-Seq data improves the accuracy of protein identification by more faithfully reflecting the tissue-specific transcriptomic landscape. In turn, proteomic data offer functional validation of transcriptomic findings, thereby increasing confidence in their biological relevance [ 38 ]. To date, integrated transcriptomic and proteomic analyses of Bactrian camel skeletal muscle have remained limited. The present study addresses this gap by employing a multi-omics approach that combines physiological, morphological, and molecular analyses to identify DEGs and DEPs), along with their associated biological pathways and functional networks. Transcriptomic analysis revealed that DEGs between the TB and LD muscles were significantly enriched ( P < 0.05) in GOterms related to cytoskeletal organization, motor activity, actin cytoskeleton, and myosin complex. KEGG pathway enrichment further identified significant associations ( P < 0.05) with Motor proteins and the cAMP signaling pathway. At the proteomic level, DEPs showed significant enrichment ( P < 0.05) in GO terms including sarcomere, troponin complex, and actin cytoskeleton. The cytoskeleton, a dynamic network composed of microtubules, actin filaments (microfilaments), and intermediate filaments [ 39 ], plays a fundamental role in muscle fiber development, structural integrity, contractile function, and fiber-type specification. Key structural components such as myosin heavy chain (MyHC) and actin filaments are central to fiber-type identity. MyHC, the major component of thick filaments, exists in multiple isoforms (e.g., MyHC-I, MyHC-IIa, MyHC-IIx/d, MyHC-IIb), whose differential expression serves as a primary molecular marker for distinguishing slow- and fast-twitch fiber types [ 40 ]. Actin filaments constitute the thin filaments and interact with MyHC to drive the sliding filament mechanism that underlies muscle contraction [ 41 , 42 ]. The troponin complex, comprising troponin C (TnC), troponin I (TnI), and troponin T (TnT), regulates actin–myosin interactions in a calcium-dependent manner, thereby modulating contraction speed and force. Isoform-specific expression of troponin subunits is closely associated with fiber-type classification and functional specialization [ 43 ]. KEGG pathway analysis at the transcriptomic level confirmed significant enrichment in Cytoskeleton in muscle cells and Motor proteins, while proteomic analysis revealed additional enrichment in Tight junction and reaffirmed Motor proteins ( P < 0.05). Motor proteins, including myosin, kinesin, and dynein, are essential for muscle contraction, intracellular transport, and cellular motility [ 44 ]. Myosin interacts with actin filaments, harnessing ATP hydrolysis to generate mechanical force and facilitate filament sliding [ 45 ]. Different MyHC isoforms determine distinct contractile properties: MyHC-I is characteristic of slow-twitch fibers, whereas MyHC-IIa, IIx, and IIb are associated with fast-twitch subtypes, which differ in contraction velocity, metabolic profile, and fatigue resistance [ 46 ]. At the transcriptomic level, upregulated DEGs in TB included hexokinase 2 (HK2), parvalbumin (PVALB), LOC102507296, and MYH8. At the proteomic level, upregulated DEPs included solute carrier family 16 member 3 (SLC16A3), also known as monocarboxylate transporter 4 (MCT4). HK2 encodes hexokinase 2, the first rate-limiting enzyme in glycolysis, which promotes the shift from oxidative phosphorylation to aerobic glycolysis, a hallmark of glycolytic metabolism [ 47 ]. Its elevated expression supports the high energetic demands of fast-twitch fibers during anaerobic, high-intensity contractions [ 48 ]. The upregulation of HK2 in TB relative to LD is consistent with the higher proportion of glycolytic (fast-twitch) fibers in this muscle. PVALB encodes parvalbumin, a calcium-binding protein that is selectively expressed in fast-twitch fibers across vertebrates [ 49 ]. Parvalbumin facilitates rapid sarcoplasmic reticulum calcium reuptake, thereby accelerating muscle relaxation after contraction [ 50 ]. SLC16A3 (MCT4) mediates proton-coupled lactate efflux, playing a critical role in clearing metabolic byproducts and preventing acidosis during intense glycolytic activity [ 51 , 52 ]. The coordinated upregulation of HK2 at the transcript level and MCT4 at the protein level in TB strongly supports the metabolic specialization of this muscle toward glycolytic energy production. Downregulated DEGs in TB included TNNT1, MYH11, and fatty acid binding protein 3 (FABP3), while downregulated DEPs included the slow-fiber marker TNNT1, tropomyosin α-3 chain isoform X1 (TPM3), FABP3, and regulatory MYL2. The troponin complex, comprising troponin T (TNNT1), troponin I (TNNI3), and troponin C (TNNC), is a key regulatory component of muscle contraction [ 53 , 54 ]. TNNT1, a specific marker of slow-twitch fibers, anchors the troponin complex to tropomyosin on the thin filament and modulates calcium sensitivity and contractile dynamics [ 45 ]. Its significant downregulation in TB confirms a reduced proportion of slow-twitch fibers in this muscle. MYH11 encodes smooth muscle myosin heavy chain, which is predominantly expressed in vascular smooth muscle cells [ 55 ]. Its presence in skeletal muscle transcriptomes is often attributed to higher capillary density around slow-twitch fibers, which require greater oxidative capacity and perfusion [ 56 – 59 ]. Thus, reduced MYH11 expression in TB is consistent with lower capillary density associated with a smaller slow-fiber population. TPM3 encodes the slow muscle-specific tropomyosin α-3 chain isoform X1 [ 60 – 62 ], which regulates contractile function in slow fibers through precise interactions with actin, troponin, and other regulatory proteins [ 60 ]. FABP3 facilitates intracellular fatty acid transport and is highly expressed in oxidative (type I) fibers, supporting β-oxidation and endurance metabolism [ 63 – 67 ]. Collectively, these findings demonstrate a lower proportion of slow-twitch fibers in TB compared with LD, as evidenced by consistent downregulation of TNNT1 and proteins involved in lipid oxidative metabolism (FABP3, TPM3) at both transcriptomic and proteomic levels. In contrast, upregulated molecules in TB are associated with fast-fiber contraction and glycolytic metabolism, including HK2 (glycolytic rate-limiting enzyme) and SLC16A3 (lactate transporter). These molecular patterns provide robust confirmation of the distinct fiber-type compositions between the TB (fast-twitch enriched) and LD (slow-twitch enriched) muscles in the Junggar Bactrian camel. Integrated transcriptome-proteome analysis revealed that 31 DEGs and 2 DEPs were absent from the combined Venn diagram, likely due to incomplete annotation or shared gene origins. Among the detected molecules, 99 DEGs showed no corresponding changes in protein abundance, while 55 DEPs originated from genes without significant transcriptional changes. Only 22 gene–protein pairs exhibited concordant differential expression across both omics layers, resulting in a weak overall correlation (Pearson r = 0.274). This modest correlation is commonly observed in multi-omics studies and can be attributed to extensive post-transcriptional regulation, including differences in translational efficiency, mRNA stability, alternative splicing, and protein turnover rates [ 68 , 69 ]. Additional biological and technical variability, such as differences in detection sensitivity between RNA-Seq and DIA proteomics, further contributes to discrepancies between mRNA and protein levels [ 70 ]. Among the concordantly differentially expressed molecules, three (TNNT1, MYH11, MYL6B) were enriched in the Motor proteins pathway, underscoring the importance of this pathway in coordinating contractile and structural differences between the two muscles. PPI network analysis using Cytoscape highlighted functional relationships among DEGs and DEPs, with motor protein-related genes emerging as central hubs. Notably, VCL stood out as a key upregulated node. VCL is a focal adhesion protein that links integrin-mediated extracellular matrix signals to the actin cytoskeleton, playing essential roles in cell adhesion, cytoskeletal organization, and mechanotransduction [ 71 ]. It stabilizes actin stress fibers and regulates cell shape and force transmission in response to mechanical cues [ 72 , 73 ]. Other hub genes, such as TNNT1, have been discussed previously and are not reiterated here. This study represents the first integrated transcriptomic and proteomic analysis of skeletal muscle in the Junggar Bactrian camel (Camelus bactrianus). While the multi-omics approach provides a comprehensive molecular framework for understanding muscle fiber type differences, this study has several limitations that should be noted. For example, the use of more specialized muscle fixatives could further minimize histological artifacts such as staining cracks or shrinkage. If resources permit, single-cell RNA sequencing of isolated slow- and fast-twitch fibers would offer higher-resolution insights into cell-type-specific gene expression. Moreover, the precise regulatory mechanisms underlying the observed expression changes, particularly at the post-transcriptional, translational, and post-translational levels, remain to be fully elucidated and represent important avenues for future research. Conclusions This study integrated transcriptomic and proteomic analyses to compare the TB and LD muscles of the Junggar Bactrian camel, revealing distinct morphological and molecular differences at the muscle fiber level between these two skeletal muscle types. A comprehensive set of DEGs and DEPs was identified, with particular emphasis on those associated with muscle fiber type specification, contractile function, and metabolic specialization. Key DEGs included TNNT1, MYH11, MYH8, and PVALB, genes involved in muscle fiber contractile dynamics, and HK2, a critical regulator of glycolysis, as well as FABP3, which plays a central role in fatty acid oxidation. Corresponding DEPs encompassed slow-twitch fiber markers such as troponin T (TNNT1) and tropomyosin α-3 chain isoform X1 (TPM3), both essential for contractile regulation; MCT4/SLC16A3, associated with lactate efflux during glycolysis; and FABP3, a key mediator of intracellular lipid transport. Notably, the Motor proteins pathway was significantly co-enriched in both DEGs and DEPs, underscoring its pivotal role in muscle contraction and fiber-type divergence. These findings provide a multi-dimensional molecular framework for understanding the functional differences between TB (fast-twitch enriched) and LD (slow-twitch enriched) muscles, spanning sarcomeric organization, anaerobic glycolytic capacity, and oxidative metabolic profiles. This work represents the first integrated transcriptomic and proteomic characterization of skeletal muscle in the Junggar Bactrian camel, significantly expanding current knowledge of skeletal muscle biology in this species and offering robust evidence for the physiological distinctions between these two muscle groups. Furthermore, the study lays a strong foundation for future research into the regulatory mechanisms governing muscle fiber type determination in camels. The candidate biomarkers identified herein, such as TNNT1, FABP3, and SLC16A3, hold potential for validation in larger populations, which could refine muscle fiber type classification and support precision breeding programs or strategies to improve camel meat quality. Abbreviations DEGs Differentially expressed genes DEPs Differentially expressed proteins TB Triceps brachii LD Longissimus dorsi GO Gene ontology KEGG Kyoto encyclopedia of genes and genomes PPI Protein-protein interaction MYH10 Myosin heavy chain 10 GADL1 Glutamate decarboxylase like 1 FASN Fatty acid synthase ALDOC Aldolase, fructose-bisphosphate C PFKL Phosphofructokinase, liver type TNNI3 Troponin I3, cardiac type SDS Serine dehydratase MYL6B Myosin light chain 6B MYL2 Myosin light chain 2 TNNC1 Troponin C1, slow skeletal and cardiac type TNNI1 Troponin I1, slow skeletal type TPM3 Tropomyosin 3 TPM1 Tropomyosin 1 VCL Vinculin TTN Titin HK2 Hexokinase 2 PVALB Parvalbumin MYH8 Myosin heavy chain 8 FABP3 Fatty acid binding protein 3 SLC16A3 Solute carrier family 16 member 3 Declarations Ethics approval The sample collection and experimental protocols were conducted in strict accordance with the approved guidelines. All experimental procedures in this study were conducted in accordance with the ARRIVE guidelines and approved by the Animal Welfare and Ethics Committee of Xinjiang Agricultural University, China (Approval Number: 2025032). All researchers named in the ethics review application form read, reviewed, and approved the manuscript. Consent for publication Not applicable. Availability of data and materials Raw reads of Transcriptomic sequencing of camel skeletal muscle are available at CNCB. GSA submission information: CRA063056. https://ngdc.cncb.ac.cn/gsa/browse/CRA063056. Raw reads of proteomic sequencing of camel skeletal muscle are available at CNCB. OMIX submission information: OMIX014968. https://ngdc.cncb.ac.cn/omix/OMIX014968 Competing interests The authors declare that they have no conflicts of interest. Funding This study was supported by the Major Science and Technology Special Project of the Xinjiang Uygur Autonomous Region (Sponsor: Jun Meng ; Grant No: 2022A02013-1), the 2024 Open Fund Program of the Xinjiang Key Laboratory of Equine Breeding and Sports Physiology (Sponsor: Wanlu Ren; Grant No: XJMFY202405) and the 2025 Central Government-Guided Local Science and Technology Development Project Mechanisms of Equine Breeding and Exercise Performance Regulation (Sponsor: Wanlu Ren, Grant No: XJMFY202405JD02). Author contributions J.M. and Y.C. Conceptualization; J.M. and Y.C. Methodology; Y.C.,J.G. and Y.Q. Software; Y.C.,J.G. and Y.Q. Validation; Y.C. and C.M. Formal analysis; Y.C. and C.M. Investigation; J.M. and C.M. Resources; Y.Z. and J.W.; Data Curation; Y.C., C.M. and J.M. Writing - Original Draft ; Y.C. and J.M. Writing - Review & Editing; Y.Z. and J.W.; Visualization; X.Y.; J.M and X.Y. Supervision ; J.M. 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FEBS Letters. 2009 Oct 20;583(24):3966–73. http://dx.doi.org/10.1016/j.febslet.2009.10.036 Fukunaga T, Zou W, Warren JT, Teitelbaum SL. Vinculin Regulates Osteoclast Function. J Biol Chem. 2014 May;289(19):13554–64.http://dx.doi.org/10.1074/jbc.m114.550731 Garcia-Arguinzonis M, Escate R, Lugano R, Peña E, Borrell-Pages M, Badimon L, et al. Gene Expression Pattern Associated with Cytoskeletal Remodeling in Lipid-Loaded Human Vascular Smooth Muscle Cells: Crosstalk Between C3 Complement and the Focal Adhesion Protein Paxillin. Cells. 2025 Aug 12;14(16):1245. http://dx.doi.org/10.3390/cells14161245 Weiler SME, Bissinger M, Rose F, von Bubnoff F, Lutz T, Ori A, et al. SEPTIN10-mediated crosstalk between cytoskeletal networks controls mechanotransduction and oncogenic YAP/TAZ signaling. Cancer Letters. 2024 Mar;584:216637. http://dx.doi.org/10.1016/j.canlet.2024.216637 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8736931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606784085,"identity":"25a511c2-ce8f-48f5-9d3a-232c01bd55bb","order_by":0,"name":"Yongbin Cai","email":"","orcid":"","institution":"Xinjiang Agricultural University, Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yongbin","middleName":"","lastName":"Cai","suffix":""},{"id":606784086,"identity":"f823320d-246e-461a-b48c-ebac8f7104f9","order_by":1,"name":"Chen Meng","email":"","orcid":"","institution":"Xinjiang Agricultural University, Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Meng","suffix":""},{"id":606784088,"identity":"36772847-c44b-478f-8363-b1df86414408","order_by":2,"name":"Jintao Gan","email":"","orcid":"","institution":"Xinjiang Agricultural University, Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jintao","middleName":"","lastName":"Gan","suffix":""},{"id":606784090,"identity":"f0ffc4f1-058a-4286-89db-44abaf1c0715","order_by":3,"name":"Ye Qin","email":"","orcid":"","institution":"Xinjiang Agricultural University, Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Qin","suffix":""},{"id":606784092,"identity":"41dd9b18-0b10-496e-9295-f599d2b21718","order_by":4,"name":"Yaqi Zeng","email":"","orcid":"","institution":"Xinjiang Agricultural University, Xinjiang Agricultural 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04:24:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8736931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8736931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105035578,"identity":"febe298b-673c-4328-be37-2263af6fb047","added_by":"auto","created_at":"2026-03-20 07:26:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1302180,"visible":true,"origin":"","legend":"\u003cp\u003eFrom left to right, the images present HE staining of muscles from the same site, followed by staining for fast-twitch fibers (type II) and slow-twitch fibers (type I), respectively.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/3d29a9950235805db478ffe5.jpg"},{"id":105035021,"identity":"3eb06370-9781-444c-a9fb-904d13aa5024","added_by":"auto","created_at":"2026-03-20 07:25:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":741015,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes (DEGs) between the triceps brachii (TB) and longissimus dorsi (LD) muscles. The abscissa corresponds to the log₂ fold change (|log₂FC| \u0026gt; 1), and the ordinate corresponds to the negative log₁₀-transformed \u003cem\u003eP\u003c/em\u003e-value (−log₁₀ \u003cem\u003eP\u003c/em\u003e-value). Red dots: significantly upregulated genes; green dots: significantly downregulated genes.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/0531a42eece27d80e887a8ca.jpg"},{"id":105035412,"identity":"ada9cde7-afcb-405d-835b-09bb796412d3","added_by":"auto","created_at":"2026-03-20 07:26:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":360041,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering heatmap of differentially expressed genes differentially expressed genes (DEGs) between the triceps brachii (TB) and longissimus dorsi (LD) muscles. The x-axis represents individual sample names, and the y-axis represents the normalized FPKM values of the DEGs. The color gradient ranges from red (higher expression) to blue (lower expression).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/1f63bf945ab7a177b73a45f5.png"},{"id":105034965,"identity":"d32042de-a477-4c8d-a1a8-4e9534e52610","added_by":"auto","created_at":"2026-03-20 07:25:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":919506,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) and KEGG pathway enrichment visualization for differentially expressed genes (DEGs) between the triceps brachii (TB) and longissimus dorsi (LD) muscles. \u003cstrong\u003e(A)\u003c/strong\u003e Bar chart of significantly enriched GO terms. The x-axis represents the significance of enrichment (−log₁₀ P-value), and the y-axis lists the enriched GO terms. Bars are colored according to the three main GO categories: biological process (BP), cellular component (CC), and molecular function (MF). \u003cstrong\u003e(B)\u003c/strong\u003e Bubble plot of significantly enriched KEGG pathways. The x-axis shows the gene ratio (proportion of DEGs annotated to each pathway relative to the total number of annotated DEGs), and the y-axis lists the enriched pathways. The size of each bubble corresponds to the number of DEGs associated with the pathway, while the color gradient indicates the level of statistical significance (−log₁₀ P-value).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/71ce5d98f11601186239c5d7.png"},{"id":104986560,"identity":"0a92acd6-5d0f-483a-a665-11d6388cef1e","added_by":"auto","created_at":"2026-03-19 14:32:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":802262,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) and KEGG pathway enrichment visualization for differentially expressed proteins (DEPs) between the triceps brachii (TB) and longissimus dorsi (LD) muscles. \u003cstrong\u003e(A)\u003c/strong\u003e Bar chart of significantly enriched GO terms. The upper x-axis indicates the number of annotated DEPs, the lower x-axis shows the gene ratio (proportion of DEPs associated with each term relative to the total annotated DEPs), and the y-axis lists the enriched GO terms. Bars are colored according to the three main GO categories: biological process (BP), cellular component (CC), and molecular function (MF). \u003cstrong\u003e(B)\u003c/strong\u003eBubble plot of significantly enriched KEGG pathways. The x-axis shows the gene ratio (proportion of DEPs annotated to each pathway relative to the total number of annotated DEPs), and the y-axis lists the enriched pathways. The size of each bubble corresponds to the number of DEPs associated with the pathway, and the color gradient indicates the level of statistical significance (−log₁₀ P-value).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/1b1ba7803107e1d50b1b49d6.png"},{"id":105035662,"identity":"79e9e046-f83c-405e-b7e4-e8b019706a77","added_by":"auto","created_at":"2026-03-20 07:26:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":282203,"visible":true,"origin":"","legend":"\u003cp\u003eBubble plot of significantly enriched protein domains among differentially expressed proteins (DEPs). The horizontal axis represents the enrichment degree (RichFactor = number of DEPs in the domain / total number of DEPs annotated to any domain). The vertical axis lists the functional categories of the domains. Point color indicates statistical significance (\u003cem\u003eP\u003c/em\u003e-value), with a gradient from blue (less significant) to red (more significant). Point size is proportional to the number of associated DEPs.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/69bfe8ea17bb41db8c5ee6c9.png"},{"id":104986556,"identity":"80aed52f-e009-4378-80c5-3fba2a6f8267","added_by":"auto","created_at":"2026-03-19 14:32:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":283662,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of transcriptomic and proteomic datasets.\u003cstrong\u003e (A)\u003c/strong\u003e Series of Venn diagrams illustrating overlap between identified and differentially expressed molecules. The sets include all_prot (all detected proteins), all_tran (all detected genes), diff_prot (DEPs), and diff_tran (DEGs). The intersection between all_prot and all_tran represents genes/proteins with mutual annotation (transcript-protein correspondence), while overlap between diff_prot and diff_tran indicates molecules showing concordant differential expression at both levels. \u003cstrong\u003e(B)\u003c/strong\u003e Scatter plot comparing log₂ fold changes between proteomics (x-axis) and transcriptomics (y-axis). Points in the third and seventh quadrants represent concordant expression trends (upregulated and downregulated, respectively).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/27dde3b3db24c5a9000be7f5.png"},{"id":105035558,"identity":"2a58c72c-8d27-4dfa-a2cb-17290103dd4d","added_by":"auto","created_at":"2026-03-20 07:26:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":569976,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated GO enrichment analysis of co-enriched terms between DEGs and DEPs. The horizontal axis represents the enrichment ratio (Ratio = number of DEGs/DEPs annotated to the term / total annotated DEGs/DEPs). The vertical axis lists the shared GO terms. Triangles denote DEGs, circles denote DEPs. Gray bars on the right indicate the three main GO categories (biological process, cellular component, and molecular function). Count refers to the number of DEGs or DEPs enriched in each term. Color intensity reflects enrichment significance (−log₁₀ P-value), with redder shades indicating lower P-values (higher significance).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/97465bacdc7f65756da904a4.png"},{"id":105035446,"identity":"55ef893b-1d29-4ead-bc64-67ccb5cbf355","added_by":"auto","created_at":"2026-03-20 07:26:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":417694,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated KEGG pathway enrichment analysis of co-enriched pathways between DEGs and DEPs. \u003cstrong\u003e(A)\u003c/strong\u003e Bar chart showing shared pathways. The horizontal axis represents the enrichment ratio (Ratio = number of DEGs/DEPs annotated to the pathway / total annotated DEGs/DEPs). The vertical axis lists the co-enriched KEGG pathways. Orange bars indicate DEGs, blue bars indicate DEPs. \u003cstrong\u003e(B)\u003c/strong\u003eBubble plot of the same shared pathways. The abscissa shows the enrichment ratio, the ordinate lists the pathways. Triangles denote DEGs, circles denote DEPs. Count indicates the number of DEGs or DEPs enriched in each pathway. Color intensity reflects significance (−log₁₀ P-value), with redder shades indicating higher significance.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/0008f4ee93661630a064e2ba.png"},{"id":105035594,"identity":"763d3cbf-f948-4006-842a-a6c0b228633e","added_by":"auto","created_at":"2026-03-20 07:26:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":419696,"visible":true,"origin":"","legend":"\u003cp\u003ePathway map of the motor proteins KEGG pathway. Each gene node is split into: the left half represents protein-level expression changes, the right half represents transcript-level expression changes. Red indicates upregulation, green indicates downregulation, and color intensity reflects the magnitude of fold change (darker shades correspond to greater differential expression).\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/b95b1bbe7b1d425a39136015.png"},{"id":104986566,"identity":"c4d25443-09fd-4f23-b5fa-be4d5fa706d1","added_by":"auto","created_at":"2026-03-19 14:32:55","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":125805,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of integrated DEGs and DEPs. Node color intensity (ranging from light to dark red) reflects the degree of connectivity, with darker red indicating a higher number of interacting partners.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/6f3ee0dfa031924e36daaaf9.png"},{"id":104986562,"identity":"7123cc8c-386a-4896-b5a5-77fde0416699","added_by":"auto","created_at":"2026-03-19 14:32:55","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":55161,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of RNA-Seq results by qRT-PCR.Bar graph comparing log₂ fold changes between qRT-PCR (black bars) and RNA-Seq (gray bars ). The abscissa corresponds to gene names, and the ordinate corresponds tothe log₂ fold changes.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/839060491fbde9f7ab0e2506.png"},{"id":105036908,"identity":"8351df81-7a30-4068-b576-cf636aefc9ae","added_by":"auto","created_at":"2026-03-20 07:36:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7034538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8736931/v1/492bf57c-8c67-4c23-976a-e7ce8665289c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated transcriptomic and proteomic analysis reveals molecular and morphological differences between triceps brachii and longissimus dorsi muscles in the Junggar Bactrian camel","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCamels exhibit exceptional survival and reproductive capabilities, along with distinctive physiological adaptations that enable them to thrive under extreme environmental conditions, such as high temperatures, prolonged water deprivation, and limited nutritional availability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In arid and semi-arid regions where camels are widely distributed, their meat serves as a vital source of red meat protein for local populations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compared to other conventional red meats, camel meat is characterized by a favorable nutritional profile, including high levels of essential amino acids, minerals, vitamins, bioactive compounds, and beneficial fatty acids [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current research on camel meat has primarily focused on meat quality traits [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], microbial composition in processed meat products [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the identification of differentially expressed proteins (DEPs) and metabolites [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, integrated analyses combining muscle histomorphological characteristics with multi-omics data, particularly transcriptomic and proteomic profiles, remain limited, especially with respect to regional variations in muscle properties within the Junggar Bactrian camel (\u003cem\u003eCamelus bactrianus\u003c/em\u003e). This knowledge gap hinders scientific progress and the industrial development of camel meat production in Xinjiang.\u003c/p\u003e \u003cp\u003eMuscle fibers are the fundamental functional units of skeletal muscle tissue and are broadly classified into two main types: white (fast-twitch) and red (slow-twitch) fibers. This classification is based on myosin heavy chain (MyHC) isoforms, with Type I (slow oxidative) fibers predominant in red muscle and Type II (fast glycolytic) fibers predominant in white muscle [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Accumulating evidence indicates that the total number of muscle fibers is negatively correlated with fiber cross-sectional area (CSA) but positively associated with overall meat quality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The proportion of red muscle fibers has been shown to positively correlate with the chromaticity value a* (a measure of meat redness) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], suggesting that increasing red fiber content could be an effective strategy for improving meat color and sensory attributes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Yan et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that red muscle fibers generally exhibit a smaller diameter than white fibers. Furthermore, Picard et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] demonstrated a positive correlation between shear force and muscle fiber diameter, while Bakhsh et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] found that fiber diameter is significantly negatively correlated with fiber density and positively correlated with shear force. Collectively, these findings suggest that increasing the proportion of red muscle fibers may contribute to enhanced tenderness.\u003c/p\u003e \u003cp\u003eTranscriptomic analysis enables a comprehensive assessment of gene expression patterns in specific tissues under defined physiological or pathological conditions, thereby facilitating the identification of candidate genes involved in phenotypic regulation and elucidating their underlying biological functions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, Yan et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] identified myosin heavy chain 10 (MYH10) and glutamate decarboxylase like 1 (GADL1) as potential regulators of beef quality traits through transcriptomic profiling of bovine longissimus dorsi (LD) muscle. Similarly, Wang et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] revealed that fatty acid synthase (FASN) may promote intramuscular fat deposition, whereas aldolase, fructose-bisphosphate C (ALDOC), phosphofructokinase, liver type (PFKL), and serine dehydratase (SDS) could be involved in regulating citrulline metabolism. Proteomics has emerged as a powerful tool in meat science, enabling the discovery of biomarkers for meat quality evaluation and providing insights into the molecular mechanisms underlying meat traits [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Zhang et al. identified several DEPs associated with water-holding capacity in goose meat, including structural proteins and key metabolic enzymes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Previous studies have also identified biomarker proteins related to intramuscular fat content [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], water-holding capacity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], myoglobin peroxidase-2 for muscle color stability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and troponin C1 as a potential indicator of meat tenderness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] in beef.\u003c/p\u003e \u003cp\u003eTherefore, integrating transcriptomic and proteomic approaches provides a robust strategy for uncovering molecular differences and deciphering the complex regulatory networks underlying distinct muscle types. In this study, differentially expressed genes (DEGs) and DEPs were systematically identified in the triceps brachii (TB) and LD muscles of the Junggar Bactrian camel using RNA-Seq-based transcriptomics and data-independent acquisition (DIA) quantitative proteomics. This integrative multi-omics approach offers a more comprehensive understanding of the coordinated roles of genes and proteins in muscle fiber type specification. The primary objective of this research is to identify key molecular determinants associated with muscle fiber type differentiation in camel skeletal muscles. These findings will provide valuable molecular resources and essential data support for future studies on the genetic and biochemical mechanisms of muscle fiber variation in camels, with potential implications for improving meat quality in non-traditional livestock species.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eAnimals and sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwelve healthy male Junggar Bactrian camels (\u003cem\u003eCamelus bactrianus\u003c/em\u003e), aged 4-5 years and weighing 450 \u0026plusmn; 50 kg, were selected for the study. All camels were raised at Urumqi Muxingyuan Livestock Farming Farmers\u0026rsquo; Professional Cooperative (Xingjiang, China). Camel slaughter was conducted in accordance with the guidelines specified in Technical Specification for Humane Slaughter of Livestock and Poultry (GB/T 19477\u0026ndash;2018). There were no other animals were present at the slaughter site. Prior to exsanguination, camels were rendered insensible via electrical stunning under controlled parameters: voltage\u0026nbsp;\u0026le;\u0026nbsp;200 V, current 1.0\u0026ndash;1.5 A, and duration 7\u0026ndash;30 s. Finally, exsanguination was performed by rapidly severing the carotid arteries. Muscle samples were collected from the TB and LD muscles. For histological analysis, a 1 cm\u0026sup3; portion of each sample was fixed in universal tissue fixative, embedded in paraffin, sectioned, and subjected to hematoxylin and eosin (H\u0026amp;E) staining as well as immunohistochemical (IHC) analysis to evaluate cellular and morphological characteristics. For molecular analyses, approximately 1 g of fresh tissue was immediately placed into a 5 mL cryotube and snap-frozen in liquid nitrogen for total RNA extraction. An additional 1 g aliquot was preserved under the same conditions for quantitative proteomic profiling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMuscle fiber characterization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHematoxylin and eosin (H\u0026amp;E) staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFixed skeletal muscle tissues were trimmed into approximately 0.3 cm\u0026sup3; blocks, dehydrated in a graded ethanol series, cleared in xylene, and embedded in paraffin using a biological tissue embedding machine (AiHua BMJ-1; Tianjin AiHua Medical Instrument Co., Ltd., Tianjin, China) with the cross-sectional surface oriented downward. The tissue was sectioned serially into 5-\u0026mu;m sections using a rotary microtome (YiDi YD-335; Jinhua YiDi Medical Equipment Co., Ltd., Jinhua, China) and baked at 70 \u0026deg;C for 30 min to ensure adhesion to slides. H\u0026amp;E staining was performed according to the standard protocol provided by Wuhan Servicebio Technology Co., Ltd. (Wuhan, China), followed by dehydration and mounting for microscopic examination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemical (IHC)\u0026nbsp;staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunohistochemical staining was conducted following the manufacturer\u0026apos;s instructions (Wuhan Servicebio Technology Co., Ltd., Wuhan, China). In brief, paraffin-embedded tissue sections underwent deparaffinization in xylene, followed by gradual rehydration using a series of decreasing ethanol concentrations. Antigen retrieval was performed with the manufacturer-recommended retrieval solution under appropriate conditions. To block endogenous peroxidase activity, sections were treated with 3% hydrogen peroxide for 25 min at room temperature in the dark. The slides were then rinsed three times (5 min each) in phosphate-buffered saline (PBS, pH 7.4). Nonspecific binding was prevented by incubating the sections with 3% bovine serum albumin (BSA) in PBS for 1 hour at room temperature. Subsequently, sections were incubated overnight at 4\u0026deg;C in a humidified chamber with the primary antibody (HRP-labeled rabbit anti-goat IgG, diluted 1:200 in PBS). After thorough washing, the sections were exposed to HRP-conjugated secondary antibody (goat anti-rabbit IgG, diluted 1:200 in PBS) for 50 minutes at room temperature. Following three additional PBS washes, the immunoreaction was detected using freshly prepared 3,3\u0026prime;-diaminobenzidine (DAB) substrate, with development monitored microscopically until optimal brown-yellow coloration appeared. The reaction was halted by rinsing in distilled water. Nuclear counterstaining was achieved with hematoxylin for about 3 minutes, followed by differentiation in hematoxylin differentiation solution and bluing in alkaline solution. The sections were then dehydrated in a graded series of increasing ethanol concentrations, cleared in xylene, air-dried, and finally mounted using neutral balsam.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage acquisition and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTissue sections were scanned using a digital slide scanning system (EasyScan 6; Motic, Xiamen, China) to obtain high-resolution whole-slide images. Under brightfield microscopy, muscle cell nuclei appeared blue (hematoxylin), while positive muscle fibers exhibited brown-yellow staining (DAB chromogen). Representative regions were imaged at 200\u0026times; magnification using Motic DSAssistant software (Motic, Xiamen, China). The numbers of fast-twitch and slow-twitch muscle fibers were quantified in multiple non-overlapping fields using ImageJ software (Java 1.8.0_345, National Institutes of Health, Bethesda, MD, USA). Based on calibrated pixel-to-area measurements, the average cross-sectional area, fiber count per field, and proportional distribution of each fiber type were calculated. All measurements were performed in triplicate and averaged to minimize technical variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis of phenotypic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFiber phenotype data were analyzed using one-way analysis of variance (ANOVA) in SPSS 23.0 (IBM Corp., Armonk, NY, USA). Data are presented as mean \u0026plusmn; standard deviation (mean \u0026plusmn; SD). Intrasubject regional differences in fast-twitch and slow-twitch fiber composition between the TB and LD muscles were evaluated. A P-value \u0026lt; 0.05 was considered statistically significant, and P \u0026lt; 0.01 indicated highly significant differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome sequencing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLibrary preparation and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was obtained from frozen muscle specimens using a commercial kit following the manufacturer\u0026apos;s instructions. Poly(A)+ mRNA was enriched using Oligo(dT) magnetic beads. The resulting purified mRNA was subjected to controlled thermal fragmentation to generate short segments. Double-stranded cDNA was then prepared through sequential first- and second-strand synthesis. Target cDNA fragments of 370\u0026ndash;420 bp were size-selected via AMPure XP bead-based purification. Libraries were amplified by PCR and purified again with AMPure XP beads to remove residual primers and dimers. High-throughput paired-end sequencing (150 bp) was performed on the Illumina NovaSeq platform by Beijing Novogene Co., Ltd. (Beijing, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing and alignment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw sequencing reads were processed to remove adapter sequences, reads containing more than 10% ambiguous bases (N \u0026gt; 10%), and low-quality reads (those with \u0026gt;50% bases having Qphred \u0026le; 5). The clean sequencing data underwent quality evaluation, with metrics encompassing Q20, Q30, and overall GC content being calculated. Only high-quality clean reads were utilized for all further analyses. Reference genome assembly and gene annotation information were sourced from the authoritative camel genome database (https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/009/834/535/GCF_009834535.1_BCGSAC_Cfer_1.0/GCF_009834535.1_BCGSAC_Cfer_1.0_genomic.fna.gz). Clean paired-end reads were aligned to the reference genome using HISAT2 v2.0.5 after constructing a genome index. Gene-level read counts were generated using featureCounts v1.5.0-p3. Gene expression levels were normalized and reported as FPKM (Fragments Per Kilobase of transcript per Million mapped reads), accounting for both sequencing depth and transcript length.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis between groups (with biological replicates) was performed using DESeq2 v1.20.0. This method uses a negative binomial distribution to model variance and test for differential expression. Genes with an adjusted \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026le; 0.05 and |log₂ fold change| \u0026ge; 1 were classified as DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein extraction and digestion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuscle tissue samples were pulverized in liquid nitrogen and lysed in SDT lysis buffer (4% SDS, 100 mM Tris-HCl pH 7.6, 100 mM DTT) by sonication in an ice-water bath for 5 min. After homogenization, the lysate was cleared by centrifugation (12,000 \u0026times; g, 15 min, 4 \u0026deg;C). The collected supernatant was boiled at 95 \u0026deg;C for 8\u0026ndash;15 minutes to facilitate denaturation, chilled on ice for 2 min, and alkylated with iodoacetamide (IAM) solution in the dark for 1 h. Proteins were precipitated by adding four volumes of pre-chilled acetone and incubating at \u0026ndash;20 \u0026deg;C for at least 30 min, followed by centrifugation at 12,000 \u0026times; g for 15 min at 4 \u0026deg;C. The pellet was washed once with 1 mL of pre-chilled acetone, air-dried, and resolubilized in dissolution buffer (DB buffer: 6 M urea, 100 mM TEAB, pH 8.5). Quantification of protein content was performed with a commercial Bradford assay kit (BioVision, Shanghai, China).\u003c/p\u003e\n\u003cp\u003eProtein samples were diluted to 100 \u0026mu;L with DB buffer and digested using trypsin (enzyme:protein ratio \u0026asymp;1:50) at 37 \u0026deg;C for 4 h. Formic acid was added to acidify the mixture to pH \u0026lt; 3, and after a 5-min centrifugation at 12,000 \u0026times; g, the supernatant was desalted on a C18 SPE column (washed 3\u0026times; with 0.1% FA/3% ACN; eluted with 0.1% FA/70% ACN). The eluate was dried under vacuum and analyzed by liquid chromatography-mass spectrometry (LC-MS) in data-independent acquisition (DIA) mode.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS analysis (DIA mode)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePeptide samples were analyzed via a Vanquish\u0026trade; Neo UHPLC system online-coupled to an Orbitrap Astral mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-Spray source. Mobile phases were A: 0.1% formic acid in water and B: 0.1% formic acid in 80% acetonitrile. Peptides (200 ng, reconstituted in 10 \u0026mu;L mobile phase A after 14,000 \u0026times; g centrifugation at 4 \u0026deg;C for 20 min) were separated on a trap column (Acclaim PepMap\u0026trade; 100, cat. no. 174500) and analytical column (PepMap\u0026trade; Neo, 150 \u0026mu;m \u0026times; 15 cm, 2 \u0026mu;m, cat. no. ES906) at 50 \u0026deg;C, following the gradient in Table 1. DIA mode parameters included spray voltage 2.0 kV, ion transfer tube 290 \u0026deg;C, MS1 range m/z 380\u0026ndash;980 (resolution 240,000), AGC 500%, 2 Th windows (300 total), NCE 25%, and MS2 m/z 150\u0026ndash;2000 (resolution 80,000, max IT 3 ms). Raw files (.raw) were saved for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein identification and quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein were identified and quantifiedusing DIA-NN software (version 1.8.1; https://github.com/vdemichev/DiaNN) against a customized camel muscle protein sequence database. This database was constructed by combining predicted tryptic peptides from the camel reference proteome (aligned to the reference genome used for transcriptomics) and filtered for muscle-expressed proteins based on prior transcriptomic data (or specify the exact source, e.g., UniProt + muscle-specific annotations). Search parameters included automatic adjustment of precursor and fragment mass tolerances (with recommended settings for Orbitrap Astral: MS1 accuracy 4 ppm, MS/MS 10 ppm), fixed modification of cysteine carbamidomethylation (+57.021 Da), variable modifications including N-terminal methionine oxidation (+15.995 Da) and up to two missed cleavages. Peptide and protein identifications were filtered at a false discovery rate (FDR) of 1% using global precursor q-value (Global.Q.Value \u0026lt; 0.01) and global protein group q-value (Global.PG.Q.Value \u0026lt; 0.01). DEPs were defined as those showing an adjusted \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (after multiple testing correction) and |log₂ fold change| \u0026gt; 1 between the TB and LD groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatic analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunctional annotation and enrichment analysis of DEGs were performed using clusterProfiler v3.8.1 for Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. DEPs were annotated for GO terms and InterPro (IPR) domains, including Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER, using InterProScan (version 5.22\u0026ndash;61.0). COG and KEGG pathway classifications were applied to categorize functional protein families and metabolic or signaling pathways [22]. Volcano plots, hierarchical clustering heatmaps, and enrichment bar charts for GO, IPR, and KEGG pathways were generated for DEPs [23]. Protein\u0026ndash;protein interaction (PPI) networks were predicted via the STRING database (version 12.0; https://string-db.org/) using the default parameters with medium confidence score of 0.4 and all active interaction sources [24] and visualized using Cytoscape v3.10.4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated transcriptomic-proteomic analysis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMuscle fiber phenotype data were analyzed using one-way analysis of variance (ANOVA) in SPSS 23.0 (IBM Corp., Armonk, NY, USA), with results presented as mean \u0026plusmn; SD. Intrasubject regional differences between the TB and LD muscles were evaluated. Differences were considered not significant at \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05, statistically significant at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, and highly significant at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. To assess the concordance between transcriptomic and proteomic expression profiles, log₂-transformed fold changes of matched gene\u0026ndash;protein pairs were subjected to Pearson correlation analysis [25]. A correlation coefficient (R) \u0026gt; 0.80 was considered indicative of strong positive consistency between the two omics layers [26]. DEGs and DEPs were integrated to construct comprehensive PPI networks using the STRING database. Hub nodes within the integrated networks were identified based on topological properties, including degree centrality, betweenness centrality, and closeness centrality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal-time quantitative reverse transcription PCR (qRT-PCR) validation of RNA-seq results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimers specific to the selected candidate genes were generated using Oligo7.0 and Primer5 software (Table 1). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used \u0026nbsp;as the endogenous reference gene. Total RNA was extracted from samples using TRIpure Reagent (Beijing Aidele Biotechnology Co., Ltd.), and first-strand cDNA was synthesized from 1\u0026nbsp;\u0026mu;g of total RNA using RevertAid Reverse Transcriptase (Thermo Fisher Scientific). Quantitative PCR was performed using AceQ Universal SYBR Green qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). The thermal cycling protocol consisted of an initial denaturation step at 95 \u0026deg;C for 5 min, followed by 40 cycles of amplification (95 \u0026deg;C for 10 s; 60 \u0026deg;C for 30 s), and a final melting curve analysis (60\u0026ndash;95 \u0026deg;C, with increments of 0.3 \u0026deg;C per 15 s). All qRT-PCR reactions were run in triplicate for each gene using three independent biological replicates. Relative gene expression levels were calculated using the 2\u0026minus;\u0026Delta;\u0026Delta;Ct method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Primers used for Quantitative Real-Time PCR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eGenes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003ePrimer sequences (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eAnnealing temperature (℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eProduct length (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eMYL6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eAGGACTACGACTCCCAGCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e149bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eTGATGTCCGGTAGCCAAAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003ePANK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eACAACGGCTTCCACCCAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e223bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eATATCCATGCCGAACCACGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSH3KBP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eGCAGGAGAGGTTTGTTCCCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e222bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eGCTCATCATCGTTCTGGGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eALPK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eGTGCTCGGAAGGAGTGTCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e208bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCAAGTTACCTCTGGCTCGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eZNF385A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCCTGTGCAGAAGGCTGTACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e167bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eGCCTCGATGCCTTTGACTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eRYR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eTGGAACCCACATCAGAAGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e144bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCCTTCCTTTGAAACCCTTCGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHOXA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCGTGGTGTACCCCTGGATGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e125bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eACTCCTTCTCCAACTCCAAGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eMYH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eGCTGCAGCATCAGCACATTAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e243bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eTGTTTTGGGCTTCAATCCGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eCSRP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eGCGTGGTCCAGCTTCGATTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e224bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCCCGTACTTCTTCCCGTAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCCGGCGCTCTCTGCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e146bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 256px;\"\u003e\n \u003cp\u003eCCAGAGTGAAAAGCAGCCCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMuscle fiber morphology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCross-sectional morphometric analysis of muscle fibers was quantified using ImageJ software, based on hematoxylin\u0026ndash;eosin (HE) and immunohistochemical staining results. One-way analysis of variance (ANOVA) revealed no significant differences in total muscle fiber number, muscle fiber density, or mean cross-sectional area (CSA) of fast-twitch muscle fibers between the TB and LD muscles (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05; Table 2). However, the TB muscleexhibited a significantly lower number and proportion of slow-twitch muscle fibers compared with the LD muscle (\u003cem\u003eP \u0026lt; 0.01\u003c/em\u003e). Conversely, both the number and proportion of fast-twitch muscle fibers were significantly higher in the TB than in the LD (\u003cem\u003eP \u0026lt; 0.01\u003c/em\u003e). Moreover, the mean CSA of slow-twitch fibers was significantly smaller in the TB relative to the LD (\u003cem\u003eP \u0026lt; 0.05\u003c/em\u003e). In TB, the CSA of slow-twitch fibers was also significantly smaller than that of fast-twitch fibers (\u003cem\u003eP \u0026lt; 0.01\u003c/em\u003e). These results demonstrate marked regional heterogeneity in muscle fiber type composition between the TB and LD muscles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Comparison of muscle fiber morphometric parameters between triceps brachii (TB) and longissimus dorsi (LD).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eNumber of all muscle fibers (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e27.35\u0026plusmn;5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e25.59\u0026plusmn;6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eDensity of muscle fiber (N/mm2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e443.03\u0026plusmn;88.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e414.56\u0026plusmn;102.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eNumber of slow muscle fibers (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e9.69\u0026plusmn;2.80\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e13.98\u0026plusmn;4.07\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eNumber of fast muscle fibers (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e17.65\u0026plusmn;4.67\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e11.64\u0026plusmn;5.31\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eArea of slow muscle fibers (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e1599.14\u0026plusmn;386.49\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e2160.58\u0026plusmn;842.54\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eArea of fast muscle fibers (\u0026micro;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e2808.29\u0026plusmn;521.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e2730.50\u0026plusmn;720.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eSlow muscle fibers proportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e24.50%\u0026plusmn;9.29%\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e49.49%\u0026plusmn;17.88%\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 281px;\"\u003e\n \u003cp\u003eFast muscle fibers proportion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e75.50%\u0026plusmn;9.29%\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e50.51%\u0026plusmn;17.88%\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Different uppercase letters (A, B) in the same row indicate extremely significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) between TB and LD; different lowercase letters (a, b) indicate significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05); identical letters indicate no significant difference (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 1, H\u0026amp;E staining revealed typical cross-sectional muscle fiber morphology, with dark blue nuclei, purplish-red cytoplasm, clearly delineated cellular boundaries, and well-preserved structural integrity. Immunohistochemical (IHC) analysis demonstrated specific positive staining in the cytoplasm, appearing as dark brown precipitates in reactive fibers, while non-reactive regions showed light brown or no staining. Nuclei were counterstained dark blue, providing clear cellular demarcation. Collectively, these histological features indicate intact tissue architecture and high-quality sample preservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall analysis of transcriptomics and proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptomic sequencing statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs summarized in Table 3, transcriptomic sequencing was performed on 12 muscle samples (6 from TB and 6 from LD), yielding a total of 87.15 Gb of clean data. Each sample produced at least 5.94 Gb of high-quality bases, with Q20 and Q30 values exceeding 99.39% and 97.4%, respectively, confirming excellent sequencing quality. The reference genome used was that of Camelus ferus (wild Bactrian camel; NCBI assembly GCF_009834535.1_BCGSAC_Cfer_1.0). After quality control, clean reads from each sample were aligned to this reference genome, resulting in alignment rates ranging from 91.85% to 93.90%. In total, 270,771,982 clean reads were mapped from TB samples and 286,676,450 from LD samples. A total of 24,118 expressed genes were identified across all samples. Compared with LD, 921 DEGs were detected in TB, including 344 upregulated and 577 downregulated genes (Fig. 2A). Hierarchical clustering analysis of the DEGs clearly separated the two muscle types, revealing distinct transcriptional profiles (Fig. 3A).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Summary of transcriptomic data quality.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRaw_reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eRaw_bases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eClean_reads\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eClean_bases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eError_rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eQ20\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eQ30\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003eGC_pct\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTB_17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e47817556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.17G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e45313218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.8G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTB_24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e49280702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.39G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e47637424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.15G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTB_26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48554208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.28G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e47074984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.06G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTB_27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e46294612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.94G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e44622458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.69G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTB_28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e41817650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.27G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e40253898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.04G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTB_33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48085064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.21G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e45870000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.88G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLD_17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48188404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.23G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e46382976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.96G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLD_24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e46529606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6.98G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e44367328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.66G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e50.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLD_26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e47457602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.12G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e45871330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e6.88G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e52.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLD_27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e57998890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e8.7G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e55171882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8.28G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLD_28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48834750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.33G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e46817092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.02G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e52.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eLD_33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e50222006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e7.53G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e48065842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7.21G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e99.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e97.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e51.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eProteomic sequencing statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the proteomic analysis, a total of 48,140 unique peptides were identified across the 12 samples, enabling reliable quantification of 4,557 proteins. Quality assessment confirmed the robustness of the dataset: the majority of identified peptides ranged from 7 to 30 amino acids, which is within the optimal length range for LC-MS/MS detection. Retention time (RT) consistency was evaluated across 12 representative peptides distributed evenly throughout the gradient; their RTs showed excellent reproducibility among biological replicates, indicating stable instrument performance and reliable chromatographic separation. Additionally, the number of unique peptides per protein positively correlated with protein abundance, further supporting the accuracy and depth of identification. Collectively, these quality metrics demonstrate the high reliability of the proteomic dataset for subsequent quantitative analyses. Compared with the LD group, 79 proteins were identified as differentially expressed in the TB group, comprising 7 upregulated and 70 downregulated proteins (Fig. 2B). Unsupervised hierarchical clustering of these DEPs clearly separated the TB and LD samples, underscoring distinct protein expression profiles between the two muscle types (Fig. 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO functional enrichment analysis of DEGs and DEPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the functional implications, GO term enrichment analysis was performed on DEGs and DEPs, identifying terms with P \u0026lt; 0.05. At the transcriptomic level, 32 GO terms were significantly enriched among the DEGs between TB and LD (Fig. 4). These terms were classified into the three main GO domains: biological process (BP), cellular component (CC), and molecular function (MF). In the BP category, the most prominent enriched terms included protein phosphorylation, phosphorylation, and regulation of biological quality. In the CC category, significant enrichment was observed in cytoskeleton, actin cytoskeleton, and myosin complex. In the MF category, key enriched terms included motor activity and oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen.\u003c/p\u003e\n\u003cp\u003eAt the proteomic level, 15 GO terms were significantly enriched among the DEPs between TB and LD (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 5A). These terms were distributed across the three main GO categories: Biological process (BP): prominent terms included protein phosphorylation, protein modification process, and protein peptidyl-prolyl isomerization; Cellular component (CC): sarcomere, troponin complex, and actin cytoskeleton; Molecular function (MF): selenium binding, O-acyltransferase activity, and chemokine activity.\u003c/p\u003e\n\u003cp\u003eNotably, the term actin cytoskeleton was significantly enriched in both the transcriptomic and proteomic datasets, indicating consistent regulation at the gene and protein levels. Furthermore, several GO terms directly related to muscle fiber structure and function, such as cytoskeleton, myosin complex, and motor activity at the transcriptomic level, and sarcomere and troponin complex at the proteomic level, highlight biological relevance to muscle fiber type specification and contractile properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG pathway enrichment analysis of DEGs and DEPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment analysis was performed on DEGs and DEPs to identify biological pathways potentially associated with muscle fiber type specification and function. At the transcriptomic level, 20 KEGG pathways were significantly enriched among the DEGs between the TB and LD muscles (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 4B). Among these, the most significantly enriched pathways included neuroactive ligand-receptor interaction, motor proteins, cAMP signaling pathway, and pentose and glucuronate interconversions. Several of these pathways are functionally linked to muscle contraction, signaling, and energy metabolism. In particular, motor proteins and pentose and glucuronate interconversions are relevant to muscle fiber contractility and metabolic adaptation, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the proteomic level, KEGG pathway enrichment analysis of DEPs between the TB and LD muscles identified 12 significantly enriched pathways (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 5B). These pathways were predominantly associated with cytoskeletal organization and muscle-related functions, including cytoskeleton in muscle cells, dilated cardiomyopathy, hypertrophic cardiomyopathy, and motor proteins. Notably, the motor proteins pathway was significantly enriched in both the transcriptomic and proteomic datasets, indicating consistent regulation across mRNA and protein levels and highlighting its potential central role in the observed differences in muscle fiber type specification and contractile properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDomain enrichment analysis of DEPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDomain enrichment analysis of DEPs was performed using InterProScan, following the same methodology as the GO functional enrichment analysis. Significant enrichment was defined as \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. In the TB compared with the LD, 15 protein domains were significantly enriched among the DEPs (Fig. 6). These included MyBP-C (myosin-binding protein C), tri-helix bundle domain, troponin domain, EF-hand domain, titin (TTN), and Z repeat, among others. These domains are predominantly associated with sarcomeric structure, calcium binding, and contractile regulation, suggesting that domain-specific alterations may contribute to the observed differences in muscle mechanical and regulatory properties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between transcriptomics and proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 7A, of the 890 DEGs, 771 lacked corresponding protein detection in the proteomic dataset, likely due to the relatively lower sensitivity and coverage of proteomic profiling compared with transcriptomic sequencing. Additionally, the protein products of 99 DEGs showed no significant abundance changes between TB and LD and were therefore not classified as DEPs. Conversely, 55 DEPs were derived from genes that exhibited no significant transcriptional changes, and 51 DEPs had no detectable corresponding transcripts. A total of 20 gene\u0026ndash;protein pairs showed concordant differential expression at both mRNA and protein levels. Overall, integrated analysis of transcriptome and proteome data revealed a weak positive correlation (Pearson r = 0.274), which is commonly observed in multi-omics studies owing to post-transcriptional regulatory mechanisms. Nevertheless, joint analysis of molecules showing consistent expression patterns across omics layers are particularly valuable for identifying functionally relevant regulators. As illustrated in Fig. 7B, transcripts and proteins in the third and seventh quadrants represent concordant trends. Specifically, the third quadrant contained three upregulated gene\u0026ndash;protein pairs, while the seventh quadrant included 17 downregulated pairs. These 20 concordant pairs represent key candidates for further functional validation and mechanistic investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated GO enrichment analysis of DEGs and DEPs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll DEGs and DEPs were mapped to the GO database to retrieve their corresponding annotations. Intersection analysis identified GO terms significantly enriched in both the transcriptomic and proteomic datasets. Integrated multi-omics analysis revealed significant co-enrichment in three GO terms (Fig. 8): protein phosphorylation (biological process, BP), actin cytoskeleton (cellular component, CC), protein kinase activity (molecular function, MF). These shared terms highlight coordinated regulation at both transcript and protein levels, particularly in signaling and structural processes relevant to muscle fiber differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated KEGG pathway analysis of DEGs and DEPs\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll DEGs and DEPs were annotated using the KEGG database to assign pathway memberships. Intersection analysis identified pathways significantly co-enriched in both datasets (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). As shown in Fig. 9, four pathways exhibited significant co-enrichment: Motor proteins, Drug metabolism - other enzymes, Folate biosynthesis, and Metabolism of xenobiotics by cytochrome P450. Detailed examination of the Motor proteins pathway (Fig. 10) revealed differential expression patterns in key gene and protein families associated with muscle contraction and sarcomeric function: Kinesin-related genes: upregulated genes included kinesin family member 1B (KIF1B) and kinesin family member 11 (KIF11); downregulated genes included centromere protein E (CENPE) and kinesin family member 19 (KIF19); Myosin-related genes: upregulated genes included myosin XVIIIB (MYO18B), myosin heavy chain 8 (MYH8), and myosin heavy chain 1 (MYH1); downregulated genes included myosin 5 (MYO5), myosin heavy chain 7B (MYH7B), myosin heavy chain 6 (MYH6), myosin heavy chain 11 (MYH11), and myosin light chain 6B (MYL6B); troponin complex genes: only downregulated genes were observed, include troponin T1, slow skeletal type (TNNT1) and troponin I3, cardiac type (TNNI3).\u003c/p\u003e\n\u003cp\u003eAt the protein level, myosin-associated DEPs included myosin, MYH10, MYL6B, myosin light chain 2 (MYL2), troponin C1 (TNNC1), troponin I1 (TNNI1), and tropomyosin 3 (TPM3). Notably, MYL6B and TNNT1 were consistently downregulated at both the mRNA and protein levels. These concordantly dysregulated genes and proteins are closely linked to sarcomeric structure, motor function, and contractile regulation, providing strong evidence for coordinated molecular mechanisms underlying the observed differences in muscle fiber type composition between the TB and LD muscles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore functional relationships among differentially expressed molecules, a PPI network was constructed using the STRING database, integrating 892 DEGs (327 upregulated and 565 downregulated) and 77 DEPs (8 upregulated and 69 downregulated). As shown in Fig. 11, node color intensity reflects the degree of connectivity (darker red indicates higher interaction degree). Among upregulated genes, the most highly connected hubs were tropomyosin 1 (TPM1) (degree = 25) and vinculin (VCL) (degree = 18). Among downregulated genes, prominent hub genes included TNNT1 (degree = 21), TNNI3 (degree = 19), MYL6B (degree = 15), and MYH11 (degree = 14). At the protein level, TTN emerged as a central hub among upregulated DEPs (degree = 11), while TNNT1 (degree = 16) and TPM3 (degree = 14) showed the highest connectivity among downregulated DEPs. These hub molecules are strongly associated with sarcomeric organization, contractile machinery, and cytoskeletal dynamics, suggesting they may serve as key regulatory nodes underlying the structural and functional differences between the TB and LD muscles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of RNA-seq-based DEGs by qRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the technical reproducibility and biological reliability of the RNA-seq data, we \u0026nbsp;selected nine DEGs for validation by quantitative reverse transcription PCR (qRT-PCR), including one upregulated gene (MYH8) and eight downregulated genes (MYL6B, SH3KBP1, ALPK2, ZNF385A, RYR3, HOXA4, CSRP2, and PANK1). As shown in Figure 12, the qRT-PCR results exhibited strong concordance with the RNA-seq expression trends for all nine genes, both in direction (up- or downregulation) and in the relative magnitude. This high concordance confirms the reliability and robustness of the transcriptomic profiling results.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith rising living standards, consumer demand for meat has shifted from quantity toward quality and nutritional value. Previous studies have established that smaller muscle fiber diameter is positively correlated with improved meat tenderness [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], while a higher proportion of type II (fast-twitch) fibers is negatively associated with tenderness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Consequently, muscle fiber architecture serves as a key cellular indicator for evaluating meat composition and quality traits [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Understanding structural and molecular differences between muscle types is therefore essential for improving meat quality and optimizing livestock production systems.\u003c/p\u003e \u003cp\u003eIn this study, the CSA of slow-twitch (type I) fibers in the TB was significantly smaller than that of fast-twitch (type II) fibers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These findings are consistent with reports across various species, including humans, showing that fast-twitch fibers generally exhibit larger diameters than slow-twitch fibers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Muscle fiber type and physiological properties play a central role in determining meat yield and quality [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Fast-twitch fibers are characterized by rapid contraction, high power output, and quick fatigue, making them well-suited for explosive movements, whereas slow-twitch fibers contract more slowly, exhibit greater fatigue resistance, and support sustained, low-intensity activity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Functionally, the camel's TB acts as the primary extensor of the forelimb, facilitating powerful and rapid elbow extension during locomotion or sudden exertion, activities that predominantly recruit fast-twitch fibers (type II). In contrast, the LD runs along the vertebral column and is primarily responsible for maintaining postural stability, supporting load bearing, and contributing to prolonged, low-velocity movements such as long-distance walking, functions typically supported by a higher proportion of slow-twitch fibers (type I). Consistent with these functional demands, the number and proportion of slow-twitch fibers were significantly lower in TB than in LD (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e), whereas fast-twitch fibers were significantly more abundant in TB (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results indicate that TB is enriched in fast-twitch fibers, while LD contains a relatively higher proportion of slow-twitch fibers. Based on these morphological data and previous findings by Kim et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which demonstrated a positive correlation between muscle fiber CSA and shear force, it can be inferred that LD likely exhibits superior tenderness compared with TB. This fiber-type distribution aligns with the distinct biomechanical roles of the two muscles and provides a cellular basis for potential differences in camel meat quality traits.\u003c/p\u003e \u003cp\u003eTranscriptome sequencing (RNA-Seq) and quantitative proteomics provide high-resolution, accurate quantification and deep coverage, enabling comprehensive molecular profiling of complex tissues [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, constructing sample-specific protein databases from RNA-Seq data improves the accuracy of protein identification by more faithfully reflecting the tissue-specific transcriptomic landscape. In turn, proteomic data offer functional validation of transcriptomic findings, thereby increasing confidence in their biological relevance [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To date, integrated transcriptomic and proteomic analyses of Bactrian camel skeletal muscle have remained limited. The present study addresses this gap by employing a multi-omics approach that combines physiological, morphological, and molecular analyses to identify DEGs and DEPs), along with their associated biological pathways and functional networks.\u003c/p\u003e \u003cp\u003eTranscriptomic analysis revealed that DEGs between the TB and LD muscles were significantly enriched (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in GOterms related to cytoskeletal organization, motor activity, actin cytoskeleton, and myosin complex. KEGG pathway enrichment further identified significant associations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with Motor proteins and the cAMP signaling pathway. At the proteomic level, DEPs showed significant enrichment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in GO terms including sarcomere, troponin complex, and actin cytoskeleton. The cytoskeleton, a dynamic network composed of microtubules, actin filaments (microfilaments), and intermediate filaments [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], plays a fundamental role in muscle fiber development, structural integrity, contractile function, and fiber-type specification. Key structural components such as myosin heavy chain (MyHC) and actin filaments are central to fiber-type identity. MyHC, the major component of thick filaments, exists in multiple isoforms (e.g., MyHC-I, MyHC-IIa, MyHC-IIx/d, MyHC-IIb), whose differential expression serves as a primary molecular marker for distinguishing slow- and fast-twitch fiber types [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Actin filaments constitute the thin filaments and interact with MyHC to drive the sliding filament mechanism that underlies muscle contraction [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The troponin complex, comprising troponin C (TnC), troponin I (TnI), and troponin T (TnT), regulates actin\u0026ndash;myosin interactions in a calcium-dependent manner, thereby modulating contraction speed and force. Isoform-specific expression of troponin subunits is closely associated with fiber-type classification and functional specialization [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKEGG pathway analysis at the transcriptomic level confirmed significant enrichment in Cytoskeleton in muscle cells and Motor proteins, while proteomic analysis revealed additional enrichment in Tight junction and reaffirmed Motor proteins (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Motor proteins, including myosin, kinesin, and dynein, are essential for muscle contraction, intracellular transport, and cellular motility [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Myosin interacts with actin filaments, harnessing ATP hydrolysis to generate mechanical force and facilitate filament sliding [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Different MyHC isoforms determine distinct contractile properties: MyHC-I is characteristic of slow-twitch fibers, whereas MyHC-IIa, IIx, and IIb are associated with fast-twitch subtypes, which differ in contraction velocity, metabolic profile, and fatigue resistance [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the transcriptomic level, upregulated DEGs in TB included hexokinase 2 (HK2), parvalbumin (PVALB), LOC102507296, and MYH8. At the proteomic level, upregulated DEPs included solute carrier family 16 member 3 (SLC16A3), also known as monocarboxylate transporter 4 (MCT4). HK2 encodes hexokinase 2, the first rate-limiting enzyme in glycolysis, which promotes the shift from oxidative phosphorylation to aerobic glycolysis, a hallmark of glycolytic metabolism [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Its elevated expression supports the high energetic demands of fast-twitch fibers during anaerobic, high-intensity contractions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The upregulation of HK2 in TB relative to LD is consistent with the higher proportion of glycolytic (fast-twitch) fibers in this muscle. PVALB encodes parvalbumin, a calcium-binding protein that is selectively expressed in fast-twitch fibers across vertebrates [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Parvalbumin facilitates rapid sarcoplasmic reticulum calcium reuptake, thereby accelerating muscle relaxation after contraction [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. SLC16A3 (MCT4) mediates proton-coupled lactate efflux, playing a critical role in clearing metabolic byproducts and preventing acidosis during intense glycolytic activity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The coordinated upregulation of HK2 at the transcript level and MCT4 at the protein level in TB strongly supports the metabolic specialization of this muscle toward glycolytic energy production.\u003c/p\u003e \u003cp\u003eDownregulated DEGs in TB included TNNT1, MYH11, and fatty acid binding protein 3 (FABP3), while downregulated DEPs included the slow-fiber marker TNNT1, tropomyosin α-3 chain isoform X1 (TPM3), FABP3, and regulatory MYL2. The troponin complex, comprising troponin T (TNNT1), troponin I (TNNI3), and troponin C (TNNC), is a key regulatory component of muscle contraction [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. TNNT1, a specific marker of slow-twitch fibers, anchors the troponin complex to tropomyosin on the thin filament and modulates calcium sensitivity and contractile dynamics [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Its significant downregulation in TB confirms a reduced proportion of slow-twitch fibers in this muscle. MYH11 encodes smooth muscle myosin heavy chain, which is predominantly expressed in vascular smooth muscle cells [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Its presence in skeletal muscle transcriptomes is often attributed to higher capillary density around slow-twitch fibers, which require greater oxidative capacity and perfusion [\u003cspan additionalcitationids=\"CR57 CR58\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Thus, reduced MYH11 expression in TB is consistent with lower capillary density associated with a smaller slow-fiber population. TPM3 encodes the slow muscle-specific tropomyosin α-3 chain isoform X1 [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], which regulates contractile function in slow fibers through precise interactions with actin, troponin, and other regulatory proteins [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. FABP3 facilitates intracellular fatty acid transport and is highly expressed in oxidative (type I) fibers, supporting β-oxidation and endurance metabolism [\u003cspan additionalcitationids=\"CR64 CR65 CR66\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Collectively, these findings demonstrate a lower proportion of slow-twitch fibers in TB compared with LD, as evidenced by consistent downregulation of TNNT1 and proteins involved in lipid oxidative metabolism (FABP3, TPM3) at both transcriptomic and proteomic levels. In contrast, upregulated molecules in TB are associated with fast-fiber contraction and glycolytic metabolism, including HK2 (glycolytic rate-limiting enzyme) and SLC16A3 (lactate transporter). These molecular patterns provide robust confirmation of the distinct fiber-type compositions between the TB (fast-twitch enriched) and LD (slow-twitch enriched) muscles in the Junggar Bactrian camel.\u003c/p\u003e \u003cp\u003eIntegrated transcriptome-proteome analysis revealed that 31 DEGs and 2 DEPs were absent from the combined Venn diagram, likely due to incomplete annotation or shared gene origins. Among the detected molecules, 99 DEGs showed no corresponding changes in protein abundance, while 55 DEPs originated from genes without significant transcriptional changes. Only 22 gene\u0026ndash;protein pairs exhibited concordant differential expression across both omics layers, resulting in a weak overall correlation (Pearson r\u0026thinsp;=\u0026thinsp;0.274). This modest correlation is commonly observed in multi-omics studies and can be attributed to extensive post-transcriptional regulation, including differences in translational efficiency, mRNA stability, alternative splicing, and protein turnover rates [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Additional biological and technical variability, such as differences in detection sensitivity between RNA-Seq and DIA proteomics, further contributes to discrepancies between mRNA and protein levels [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Among the concordantly differentially expressed molecules, three (TNNT1, MYH11, MYL6B) were enriched in the Motor proteins pathway, underscoring the importance of this pathway in coordinating contractile and structural differences between the two muscles.\u003c/p\u003e \u003cp\u003ePPI network analysis using Cytoscape highlighted functional relationships among DEGs and DEPs, with motor protein-related genes emerging as central hubs. Notably, VCL stood out as a key upregulated node. VCL is a focal adhesion protein that links integrin-mediated extracellular matrix signals to the actin cytoskeleton, playing essential roles in cell adhesion, cytoskeletal organization, and mechanotransduction [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. It stabilizes actin stress fibers and regulates cell shape and force transmission in response to mechanical cues [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Other hub genes, such as TNNT1, have been discussed previously and are not reiterated here.\u003c/p\u003e \u003cp\u003eThis study represents the first integrated transcriptomic and proteomic analysis of skeletal muscle in the Junggar Bactrian camel (Camelus bactrianus). While the multi-omics approach provides a comprehensive molecular framework for understanding muscle fiber type differences, this study has several limitations that should be noted. For example, the use of more specialized muscle fixatives could further minimize histological artifacts such as staining cracks or shrinkage. If resources permit, single-cell RNA sequencing of isolated slow- and fast-twitch fibers would offer higher-resolution insights into cell-type-specific gene expression. Moreover, the precise regulatory mechanisms underlying the observed expression changes, particularly at the post-transcriptional, translational, and post-translational levels, remain to be fully elucidated and represent important avenues for future research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study integrated transcriptomic and proteomic analyses to compare the TB and LD muscles of the Junggar Bactrian camel, revealing distinct morphological and molecular differences at the muscle fiber level between these two skeletal muscle types. A comprehensive set of DEGs and DEPs was identified, with particular emphasis on those associated with muscle fiber type specification, contractile function, and metabolic specialization. Key DEGs included TNNT1, MYH11, MYH8, and PVALB, genes involved in muscle fiber contractile dynamics, and HK2, a critical regulator of glycolysis, as well as FABP3, which plays a central role in fatty acid oxidation. Corresponding DEPs encompassed slow-twitch fiber markers such as troponin T (TNNT1) and tropomyosin α-3 chain isoform X1 (TPM3), both essential for contractile regulation; MCT4/SLC16A3, associated with lactate efflux during glycolysis; and FABP3, a key mediator of intracellular lipid transport. Notably, the Motor proteins pathway was significantly co-enriched in both DEGs and DEPs, underscoring its pivotal role in muscle contraction and fiber-type divergence.\u003c/p\u003e \u003cp\u003eThese findings provide a multi-dimensional molecular framework for understanding the functional differences between TB (fast-twitch enriched) and LD (slow-twitch enriched) muscles, spanning sarcomeric organization, anaerobic glycolytic capacity, and oxidative metabolic profiles. This work represents the first integrated transcriptomic and proteomic characterization of skeletal muscle in the Junggar Bactrian camel, significantly expanding current knowledge of skeletal muscle biology in this species and offering robust evidence for the physiological distinctions between these two muscle groups. Furthermore, the study lays a strong foundation for future research into the regulatory mechanisms governing muscle fiber type determination in camels. The candidate biomarkers identified herein, such as TNNT1, FABP3, and SLC16A3, hold potential for validation in larger populations, which could refine muscle fiber type classification and support precision breeding programs or strategies to improve camel meat quality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eDEPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eDifferentially expressed proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eLongissimus dorsi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eGene ontology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eKyoto encyclopedia of genes and genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 455px;\"\u003e\n \u003cp\u003eProtein-protein interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMYH10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eMyosin heavy chain 10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eGADL1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eGlutamate decarboxylase like 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFASN\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eFatty acid synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eALDOC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eAldolase, fructose-bisphosphate C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePFKL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003ePhosphofructokinase, liver type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTNNI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eTroponin I3, cardiac type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eSerine dehydratase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMYL6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eMyosin light chain 6B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMYL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eMyosin light chain 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTNNC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eTroponin C1, slow skeletal and cardiac type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTNNI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eTroponin I1, slow skeletal type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTPM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eTropomyosin 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTPM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eTropomyosin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eVCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eVinculin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eTTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eTitin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eHK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eHexokinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePVALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eParvalbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMYH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eMyosin heavy chain 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFABP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eFatty acid binding protein 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSLC16A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 455px;\"\u003e\n \u003cp\u003eSolute carrier family 16 member 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample collection and experimental protocols were conducted in strict accordance with the approved guidelines. All experimental procedures in this study were conducted in accordance with the ARRIVE guidelines and approved by the Animal Welfare and Ethics Committee of Xinjiang Agricultural University, China (Approval Number: 2025032). All researchers named in the ethics review application form read, reviewed, and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw reads of Transcriptomic sequencing of\u0026nbsp;camel skeletal muscle\u0026nbsp;are available at CNCB. GSA submission information: CRA063056. https://ngdc.cncb.ac.cn/gsa/browse/CRA063056.\u0026nbsp;Raw reads of proteomic \u0026nbsp; sequencing of camel skeletal muscle are available at CNCB. OMIX submission information: OMIX014968. \u0026nbsp;https://ngdc.cncb.ac.cn/omix/OMIX014968\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Major Science and Technology Special Project of the Xinjiang Uygur Autonomous Region (Sponsor: Jun Meng ; Grant No: 2022A02013-1), the 2024 Open Fund Program of the Xinjiang Key Laboratory of Equine Breeding and Sports Physiology (Sponsor: Wanlu Ren; Grant No: XJMFY202405) and the 2025 Central Government-Guided Local Science and Technology Development Project Mechanisms of Equine Breeding and Exercise Performance Regulation (Sponsor: Wanlu Ren, Grant No: XJMFY202405JD02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.M. and Y.C. Conceptualization; J.M. and Y.C. Methodology; Y.C.,J.G. and Y.Q. Software; Y.C.,J.G. and Y.Q. Validation; Y.C. and C.M. Formal analysis; Y.C. and C.M. Investigation; J.M. and C.M. Resources; \u0026nbsp;Y.Z. and J.W.; Data Curation; Y.C., C.M. and J.M. Writing - Original Draft ; Y.C. and J.M. Writing - Review \u0026amp; Editing; Y.Z. and J.W.; Visualization; X.Y.; J.M and X.Y. Supervision ; J.M. \u0026nbsp;Y.Z. and J.W.; Project administration; W.R. and J.M.; Funding acquisition; All authors read, reviewed, and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the College of Animal Science at Xinjiang Agricultural University and the Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology for providing experimental facilities and support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollege of Animal Science, Xinjiang Agricultural University / Equine Industry Research Institute, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China.\u003c/p\u003e\n\u003cp\u003eXinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi 830052, Xinjiang, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKadim IT, Mahgoub O, Purchas RW. 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Cancer Letters. 2024 Mar;584:216637. http://dx.doi.org/10.1016/j.canlet.2024.216637\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Camel muscle, Muscle fiber type, Transcriptome, Proteome","lastPublishedDoi":"10.21203/rs.3.rs-8736931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8736931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMuscle fiber type is a critical determinant of meat quality, with its phenotypic characteristics regulated by intricate biological processes encompassing gene transcription and translation. This study presents the first comprehensive integrated analysis of muscle morphology, transcriptomics, and proteomics across distinct muscle tissues in the Junggar Bactrian camel (\u003cem\u003eCamelus bactrianus\u003c/em\u003e). A comparative morphological and molecular assessment was conducted between the triceps brachii (TB) and longissimus dorsi (LD) muscles to elucidate structural and functional differences in muscle fiber composition. An integrated transcriptomic and proteomic approach was employed to identify key genes and proteins associated with muscle fiber type specification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eComparative analysis revealed 921 differentially expressed genes (DEGs) and 79 differentially expressed proteins (DEPs) between the two muscle types. The analysis examined these DEGs and DEPs at the muscle fiber type level, focusing on their associations with key genes implicated in muscle contraction, glycolysis, and intramuscular lipid oxidation metabolism, such as TNNT1, hexokinase 2 (HK2), and fatty acid binding protein 3 (FABP3), as well as with critical proteins, including slow-twitch troponin T, actin alpha-3 chain isoform X1, monocarboxylate transporter 4, and FABP3. Notably, the coordinated expression patterns of these factors suggest their potential roles in shaping the metabolic and contractile properties specific to each fiber type.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBy integrating morphological, transcriptomic, and proteomic data from the TB and LD muscles of the Junggar Bactrian camel, this study reveals significant differences at both structural and molecular levels. These findings provide novel insights into the molecular mechanisms underlying muscle fiber type determination in camels and offer potential biomarkers for meat quality improvement.\u003c/p\u003e","manuscriptTitle":"Integrated transcriptomic and proteomic analysis reveals molecular and morphological differences between triceps brachii and longissimus dorsi muscles in the Junggar Bactrian camel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 14:32:50","doi":"10.21203/rs.3.rs-8736931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-25T13:55:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265118633138964395344257489894182405083","date":"2026-04-24T00:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T09:47:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T06:49:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T02:33:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-02-10T02:25:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e2cfc98-dc9b-4841-b2e9-28ed247d5fd9","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T14:32:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 14:32:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8736931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8736931","identity":"rs-8736931","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}