Data-Independent Acquisition Method for In-Depth Proteomic Screening of Donkey Meat

Abstract

Donkey meat has gained popularity as an emerging meat product due to its superior nutritional value and distinctive flavor. Despite this, research on the molecular mechanisms that contribute to meat quality, particularly within the field of proteomics, remains limited. This study aimed to address this gap by utilizing the data-independent acquisition (DIA) technique to identify differentially expressed proteins (DEPs) in the gluteus superficialis (WG), longissimus thoracis (WLT), and semitendinosus (WS) muscles of donkeys. Our analysis revealed 189 and 384 DEPs in the WG/WLT and WS/WLT muscles, respectively. Several significant potential pathways, involving these DEPs, were found to be closely associated with donkey meat quality. These pathways include fatty acid biosynthesis, TGF-β signaling, FOXO signaling, mTOR signaling, oxidative phosphorylation, citrate cycle, alanine, aspartate, and glutamate metabolism, arginine biosynthesis, and valine, leucine, and isoleucine degradation. The identified DEPs and their regulated pathways were involved in regulating intramuscular fat deposition, protein metabolism, and amino acid metabolism in donkey muscles. These mechanisms have a direct impact on the physicochemical properties and flavor of donkey meat. Our findings contribute to a better understanding of the molecular processes influencing the quality of donkey meat. Additionally, the findings of our study may be influenced by the sample size. Therefore, further research with a larger sample is needed to provide a more comprehensive evaluation of meat quality.

1. Introduction

Donkey meat has a distinct nutritional profile that sets it apart from common red meats. It is low in fat and cholesterol, but high in protein, iron, and various unsaturated fatty acids [1,2,3,4,5]. This makes it a potential alternative for those seeking a beneficial and nutritious meat option [6]. Moreover, donkey meat is not only rich in nutrients but also offers a unique flavor [7,8,9], making it an exciting addition to the range of available meat products, alongside pork, beef, lamb, and chicken. However, due to economic and cultural factors, donkey meat may enter the food chain through adulteration to increase revenue, despite the fact that this practice jeopardizes people’s sentiments and health [10,11].

Proteins are the main component of animal meat and play a crucial role in various biochemical and physiological processes that can affect meat quality [12]. A study showed that the crude protein content was consistently highest in the triceps brachii muscle, followed by the longissimus dorsi and biceps femoris muscles, and lowest in the buttocks muscle. Conversely, the crude fat content showed the opposite trend, with better fat deposition and tenderness observed in the buttocks muscle [13]. Furthermore, compared to the rectus femoris and semimembranosus muscles, the longissimus dorsi muscle of the donkey had higher levels of aspartate, methionine, isoleucine, lysine, essential amino acids, non-essential amino acids, and total amino acids [14]. A study on the Dezhou donkey found statistical differences in hardness, adhesiveness, cooking loss, water holding capacity, pH value, and color among the gluteus maximus, longissimus thoracis, and semitendinosus muscles [15]. These studies demonstrate that different muscles of a donkey have varying qualities.

During the process of muscle growth, two important biological processes are muscle fiber development and the deposition of intramuscular fat (IMF) [16]. Muscle fiber, which makes up the majority of muscle composition, plays a crucial role in determining the quality of meat by influencing the number and type of muscle fibers. The IMF content, on the other hand, affects the juiciness and tenderness of the meat [17]. Additionally, the flavor of the meat can be influenced by flavor nucleotides, free amino acids, and fatty acids [4]. Previous studies have investigated the impact of differentially abundant proteins and differentially expressed genes on IMF content in donkey meat, examining both the transcriptome and proteome levels [18,19]. It was found that the IMF content in the longissimus dorsi muscle of the Dezhou black donkey was significantly higher compared to the gluteus maximus and biceps femoris muscles [3]. Furthermore, a clear difference in the muscle fiber type composition was observed among the neck muscle, longissimus dorsi muscle, psoas major muscle, and biceps femoris muscle [20].

Donkey meat has been examined for its characteristics through various omics approaches, including lipidomics [21], metabolomics [22], and transcriptomics [23,24,25]. Recently, proteomics has gained considerable attention for studying the sensory attributes of donkey meat [15,26]. Given the significance of proteomics in understanding the sensory attributes of meat, we employed data-independent acquisition (DIA)-based quantitative proteomics to investigate the meat of Wutou donkeys. Studies have consistently reported that DIA is superior to traditional data-dependent acquisition (DDA) in quantitative reproducibility, specificity, accuracy, and quantification of low-expression proteins, and is more precise in screening for differentially expressed proteins [27,28,29]. For this study, we collected samples from semitendinosus (WS), longissimus thoracis (WLT), and gluteus superficialis (WG) muscles of 24-month-old male Wutou donkeys, all raised under uniform conditions. Utilizing DIA-based quantitative proteomics, we examined the differentially expressed proteins (DEPs) across these muscle groups, with the aim of identifying proteins that may influence meat quality. Our findings are expected to enhance the understanding of the molecular mechanisms affecting donkey meat quality. Furthermore, donkey meat is prepared in various ways across China, with different qualities of meat being suited to specific cooking methods. As such, our study may serve as a theoretical reference for the classification and processing of donkey meat.

2. Materials and Methods

2.1. Sample Collection

The donkey meat samples were obtained from a commercial slaughterhouse in Dong’e, Shandong Province. The experimental procedures were reviewed and approved by the Liaocheng University Animal Care and Ethics Committee (No. LC2019-1). The slaughter procedures followed the operating procedures of livestock and poultry slaughtering for donkey in China (NY/T 3743-2020).

Given the high cost and difficulty of sampling, four 24-month-old male Wutou donkeys were selected from the same farm for the study. They had an average weight of 223 ± 19.88 kg. The donkeys were slaughtered after a 12 h fast and were provided with free access to water. Samples from the right side of the semitendinosus (WS), longissimus thoracis (WLT), and gluteus superficialis (WG) muscles were collected in sterile tubes immediately after centralized slaughter. These samples were initially placed in liquid nitrogen and subsequently transferred to −80 °C for amino acid determination and DIA procedures. Fresh muscle samples for meat quality measurement were collected at the same time and stored at 4 °C. The meat quality traits were determined after 24 h of aging.

2.2. Measurement of Quality Traits

The meat quality traits were measured according to a protocol described in our previous study [15]. The texture analyzer (BosinTech Instrument Technology Co., Ltd., Shanghai, China) was used to determine the hardness, adhesiveness, chewiness, gumminess, elasticity, cohesiveness, and resilience of the fresh donkey meat, which was cut into cuboids of 2 × 2 × 2.5 cm. Texture profile analysis was measured with a pressing speed of 1.0 mm/s and compressing to 60%. The pH of the meat samples was determined using a pH meter (Testo 205, Mettler Toledo MTCN., Ltd., Switzerland, Germany). The pH meter electrode was fully inserted into each sample in three different locations. The brightness (L*), redness (a*), and yellowness (b*) were determined at three different locations on the cross-section of each sample using a colorimeter (CR-10Plus, Konica Minolta, Inc., Tokyo, Japan) after 30 min of exposure to air. For each sample, six meat cubes weighing approximately 10 g were cut from each sample, three each for cooking loss rate and water holding capacity. The meat cubes were weighed and then cooked in a 100 °C water bath for 5 min. After drying the surface water and cooling to room temperature, the cubes were weighed again. The cooked meat rate was calculated as a percentage of the weight after cooking compared to the weight before cooking. Shear force was measured using a muscle tenderness meter (C-LM3B, Tenovo International Co., Ltd., Beijing, China). The meat cubes were placed in the middle of six layers of filter paper and squeezed under a pressure of 1 kg for 5 min. The water holding capacity was calculated based on the weight before and after squeezing. The arithmetic mean of three measurements was taken for each indicator of each sample.

2.3. Amino Acid Determination

The amino acid determination [alanine (Ala), lysine (Lys), glutamic acid (Glu), glycine (Gly), serine (Ser), asparagine (Asp), leucine (Leu), arginine (Arg), tyrosine (Tyr), proline (Pro), phenylalanine (Phe), valine (Val), isoleucine (Ile), histidine (His) and threonine (Thr), cysteine (Cys), and methionine (Met)] was performed according to a protocol adopted in previous studies [30,31]. The meat samples were processed using the acid hydrolysis method, and the amino acid determination was performed on the automatic amino acid analyzer (S-433D, Sykam Scientific Instrument Co., Ltd., Munich, Germany). The relevant amino acid content was calculated according to the following formula:

Total amino acid (TAA) = Asp + Thr + Ser + Glu + Gly + Ala + Cys + Val + Met + Ile + Leu + Tyr + Phe + His + Lys + Arg + Pro;

Essential amino acid (EAA) = Thr + Val + Met + Ile + Leu + Phe + Lys + His + Arg;

Non-essential amino acid (NEAA) = Asp + Ser + Glu + Gly + Ala + Cys + Tyr + Pro;

Branched-chain amino acid (BCAA) = Val + Ile + Leu;

Flavor amino acid (FAA) = Asp + Glu + Gly + Ala + Arg + Met.

2.4. DIA Procedures

2.4.1. Protein Preparation

The 12 samples (4 samples each for WG, WL, and WS) were frozen on ice and then treated with lysis buffer (8 mol/L urea and protease inhibitor cocktail) to extract the proteins. After sonication at 50 Hz for 2 min on ice, the samples were incubated on ice for 30 min. Next, the samples were centrifuged at 4 °C and 12,000× g for 30 min, and the resulting supernatant was transferred to a clean tube. The protein contents of the supernatant were quantified by using the BCA assay and analyzed using sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE). The detailed steps are provided in Supplementary File S1.

2.4.2. Trypsin Digestion

The sample solution containing 100 μg of proteins was treated with tris (2-carboxyethyl) phosphine (TCEP) and iodoacetamide (IAM) to reduce and alkylate the proteins, respectively. Acetone was then added to each sample to precipitate the proteins, which were subsequently separated by centrifugation at 10,000× g for 20 min. The supernatant was discarded, and the precipitate was dissolved in 100 mmol/L triethylammonium bicarbonate (TEAB) buffer. Trypsin Gold (Promega, Madison, WI, USA) was added to the dissolved proteins at a mass ratio of 1:50 (trypsin: protein) and the mixture was digested overnight at 37 °C. The digested proteins were then desalted and dried under vacuum. Peptide quantification was performed using a peptide quantification kit (Thermo Fisher Scientific, Waltham, MA, USA).

2.4.3. Spectral Library Construction and DIA Quantitative Detection

Equal amounts of digested peptide samples were mixed concentrated through vacuum centrifugation. The samples were then reconstituted with UPLC loading buffer and subjected to high-pH liquid-phase separation using a C18 reversed-phase column with a flow rate of 1 mL/min. Elution peaks were monitored at 214 nm, and one fraction per minute was collected. A total of 10 fractions were collected and vacuum-dried. The supernatant was obtained by centrifugation after re-dissolution, and a gradient separation was performed at a flow rate of 500 nL/min. The liquid-phase-separated peptides were ionized by a nanoESI source and introduced into a tandem mass spectrometer, Q-Exactive HF (Thermo Fisher Scientific, San Jose, CA, USA) for DDA (data-dependent acquisition)-mode detection. The main parameter settings were as follows: the ion source voltage was 1.9 kV; the primary mass spectrometry scan range was set to 350–1500 m/z; the resolution was set to 30,000; the maximum ion injection time (MIT) was 100 ms. The secondary mass spectrometry fragmentation mode was HCD with the starting m/z fixed at 100; the resolution was set to 30,000; the MIT was 100 ms; and the dynamic exclusion time was 30 s. Raw MS/MS data were processed using Proteome DiscovererTM 2.2 software (Thermo Fisher Scientific, San Jose, CA, USA) and the search results were imported into Spectronaut software (Biognosys AG, Version 14) to create a spectral library. After desalting and quantifying the peptide samples, equal amounts of peptides were dissolved and detected in DIA mode. The main parameter settings were as follows: the ion source voltage was 1.9 kV; the primary mass spectrometry scan range was set to 400–1250 m/z (divided into 45 windows); the resolution was set to 120,000; the MIT was 50 ms. The DIA raw data were then searched for quantitative information based on the spectral library generated by the DDA data. The detailed steps can also be seen in Supplementary File S1.

2.5. Statistical and Bioinformatics Analysis

We used SPSS 26.0 software (IBM Statistics, Armonk, NY, USA) for data analysis. All statistical values are presented as mean ± standard deviation (SD). To determine significant differences between means, we conducted one-way analysis of variance (ANOVA) and used either Tukey’s test (assuming equal variances) or Tambane’s T2 test (not assuming equal variances). The significance level was set at p < 0.05. Due to the relatively limited number of studies on donkeys and the lower number of annotated proteins compared to other research, DEPs were screened based on two criteria: a fold change (FC) ≥ 1.2 or ≤0.83, and a p-value < 0.05. Functional analysis of the DEPs was performed using the Majorbio Cloud platform (https://cloud.majorbio.com (accessed on 20 January 2023)), which includes gene ontology (GO) annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.

3. Results

3.1. Meat Quality Traits

The meat quality results are presented in

Table 1. Quality traits of muscles from different parts of Wutou donkey.

Item	WLT	WG	WS
Hardness (gf)	1400.61 ± 156.50 b	2225.13 ± 291.16 a	1468.88 ± 75.47 b
Adhesiveness (gf-mm)	5.27 ± 1.15 a	3.89 ± 0.72 a	1.54 ± 0.25 b
Chewiness (gf)	289.56 ± 59.70	282.15 ± 44.63	279.47 ± 12.11
Gumminess (gf)	659.70 ± 95.85	691.79 ± 147.35	541.77 ± 100.71
Elasticity (mm)	0.45 ± 0.01 ab	0.51 ± 0.05 a	0.41 ± 0.05 b
Cohesiveness	0.44 ± 0.05	0.45 ± 0.05	0.45 ± 0.05
Resilience	0.09 ± 0.01	0.10 ± 0.01	0.08 ± 0.02
Shear force (kgf)	3.34 ± 0.33	3.18 ± 0.44	3.24 ± 1.00
pH	5.47 ± 0.09 ab	5.36 ± 0.10 b	5.55 ± 0.03 a
L*	55.53 ± 0.53 b	58.58 ± 1.26 a	50.95 ± 5.25 ab
a*	17.73 ± 2.35	20.63 ± 4.59	17.75 ± 1.96
b*	12.68 ± 2.16	13.00 ± 2.62	13.50 ± 0.78
Cooked meat rate (%)	54.75 ± 0.77 a	56.50 ± 2.18 a	48.45 ± 1.39 b
Water holding capacity (%)	92.87 ± 1.37 b	96.82 ± 1.23 a	96.34 ± 0.71 a

Note: values are mean ± standard deviation (SD, n = 4). Values with different letters in the same row indicate significant differences (p < 0.05). WLT: longissimus thoracis; WG: gluteus superficialis; WS: semitendinosus; 1 gf = 0.0098067 N.

. The hardness of WG was significantly higher than that of WS and WLT (p < 0.05). WS obtained the lowest adhesiveness value and cooked meat rate (p < 0.05), while WLT obtained the lowest water holding capacity (p < 0.05). The elasticity of the WG is significantly higher than that of WS (p < 0.05). The pH of WS is significantly higher than that of WG (p < 0.05). The L* value of the WG is significantly higher than that of WLT (p < 0.05). However, there were no statistically significant differences among the three muscles in terms of a*, b*, chewiness, gumminess, shear force, cohesiveness, and resilience values (p > 0.05).

3.2. Amino Acids Content

As shown in

Table 2. Content of amino acids in muscles from different parts of Wutou donkey (mg/g).

Amino Acid	WG	WLT	WS
Asp	67.09 ± 1.12	66.96 ± 0.69	90.70 ± 14.35
Thr	31.59 ± 0.75	30.88 ± 0.32	39.61 ± 5.23
Ser	25.71 ± 0.53	26.25 ± 0.34	31.44 ± 2.80
Glu	117.88 ± 2.80	117.15 ± 1.67	161.93 ± 23.38
Gly	32.85 ± 0.99 a	30.00 ± 0.33 b	33.64 ± 0.83 a
Ala	44.22 ± 0.76 b	44.44 ± 1.19 b	48.78 ± 1.99 a
Cys	11.22 ± 0.62 b	10.53 ± 0.46 b	13.77 ± 0.75 a
Val	33.78 ± 0.45 b	33.67 ± 0.58 b	36.97 ± 1.28 a
Met	18.91 ± 0.88	19.72 ± 1.03	21.17 ± 1.44
Ile	33.87 ± 0.30	33.58 ± 0.67	36.08 ± 1.47
Leu	62.55 ± 1.26 ab	61.38 ± 1.07 b	64.49 ± 1.82 a
Tyr	24.09 ± 0.40 b	25.08 ± 0.30 b	27.43 ± 1.29 a
Phe	27.65 ± 0.59 b	27.50 ± 0.51 b	31.44 ± 1.00 a
His	31.68 ± 0.49 ab	32.54 ± 0.67 a	31.03 ± 0.69 b
Lys	69.33 ± 1.38	68.77 ± 1.75	69.04 ± 3.64
Arg	45.97 ± 0.69	46.37 ± 0.45	47.38 ± 1.67
Pro	23.02 ± 0.60 b	20.98 ± 0.53 b	28.09 ± 2.52 a
TAAs	701.38 ± 8.80	695.79 ± 8.89	812.96 ± 51.57
EAAs	355.32 ± 4.58 b	354.40 ± 5.06 b	377.20 ± 11.09 a
NEAAs	346.06 ± 4.24	341.39 ± 4.04	435.76 ± 45.80
BCAAs	130.21 ± 1.79 b	128.63 ± 2.22 b	137.54 ± 4.19 a
FAAs	326.91 ± 4.24	324.64 ± 3.34	403.59 ± 39.52

Note: values are mean ± standard deviation (SD, n = 4). Values with different letters in the same row indicate significant differences (p < 0.05). WLT: longissimus thoracis; WG: gluteus superficialis; WS: semitendinosus; TAAs: total amino acid; EAAs: essential amino acid; NEAAs: non-essential amino acids; BCAAs: branched-chain amino acids; FAAs: flavor amino acids.

, the amino acid content was significantly different among WLT, WG, and WS (p < 0.05). The WS had significantly higher contents of EAAs and BCAAs compared to WG and WLT. Glu, Asp, and Lys had the highest contents in all the three muscles of donkey. There were no statistical differences in the content of Asp, Thr, Ser, Glu, Met, Ile, Lys, Arg, TAAs, NEAAs, and FAAs among the three muscles (p > 0.05). WS had the highest contents of Ala, Cys, Val, Tyr, Phe, and Pro (p < 0.05). The Leu content in WS was significantly higher than that in WLT (p < 0.05), while the His content in WLT was significantly higher than that in WS (p < 0.05). The Gly content in WG and WS was significantly higher than that in WLT (p < 0.05).

3.3. Identification and Comparison of DEPs Between WG/WLT and WS/WLT

A total of 29,256 spectra were generated from DIA proteomic analysis. Out of these, 21,918 spectra were matched and identified, resulting in 21,528 peptides being mapped to 4609 proteins. The coverage, number of peptides, and molecular weight of the identified proteins can be seen in Supplementary File S2. In the WG and WS relative to the WLT, a total of 189 and 384 DEPs were identified, respectively, with 44 DEPs being common to both comparisons. When comparing WS and WLT, 219 proteins were found to be upregulated, while 165 proteins were downregulated. Between WG and WLT, 118 upregulated proteins and 71 downregulated proteins were detected (Figure 1). Hierarchical clustering analysis based on the DEPs clearly distinguished between the WG/WLT and WS/WLT comparisons (see Figure 2). The fold change (FC) and p-value of the DEPs in donkey muscle for the WS/WLT and WG/WLT comparisons can be found in Supplementary Files S3 and S4, respectively.

3.4. GO Classification of DEPs from WG/WLT and WS/WLT of Wutou Donkeys

To understand the biological significance of the DEPs, we conducted a gene ontology (GO) analysis. In both the WS/WLT and WG/WLT comparisons, we identified 36 GO terms that were regulated by these DEPs (see Supplementary Files S5 and S6). The most-enriched terms are shown in Figure 3. For example, in terms of biological processes, 262 proteins (WS/WLT) and 125 proteins (WG/WLT) were involved in cellular processes. Similarly, 176 proteins (WS/WLT) and 94 proteins (WG/WLT) were associated with metabolic processes. Furthermore, 118 (WS/WLT) and 70 (WG/WLT) proteins were linked to biological processes. Regarding molecular functions, 214 (WS/WLT) and 123 (WG/WLT) proteins were classified as binding proteins. Consistently, 182 (WS/WLT) and 90 (WG/WLT) proteins were categorized as having catalytic activity. In relation to cellular components, 294 (WS/WLT) and 135 (WG/WLT) proteins were associated with cellular anatomical entities. Additionally, 140 (WS/WLT) and 43 (WG/WLT) proteins were associated with containing protein complexes.

3.5. KEGG Pathway Analysis of DEPs from WG/WLT and WS/WLT of Wutou Donkeys

A KEGG pathway enrichment analysis was performed on the DEPs (Figure 4). The comparison between WG and WLT revealed that the identified DEPs regulated 175 pathways related to meat quality traits (Supplementary File S7). These pathways included ribosome (13 proteins), alanine, aspartate, and glutamate metabolism (four proteins), arginine biosynthesis (three proteins), valine, leucine, and isoleucine degradation (five proteins), and butanoate metabolism (five proteins). Notably, proteins such as ACY1, ASS1, ACAT, MUT, and ALDH6A1 were associated with meat quality. Moreover, the comparison between WS and WLT indicated that the DEPs were mapped to 280 pathways (Supplementary File S8), identifying potential pathways linked to meat quality. These pathways included fatty acid biosynthesis (four proteins), the TGF-β signaling pathway (five proteins), PI3K-Akt signaling pathway (14 proteins), adipocytokine signaling pathway (five proteins), arginine biosynthesis (three proteins), FOXO signaling pathway (six proteins), and the mTOR signaling pathway (six proteins). Interestingly, proteins enriched in these pathways included SMAD4, AKT, AMPK, ACSL, ERK, EIF4B, and others (

Table 3. The DEPs regulating pathways linked to meat quality traits in Wutou donkeys.

KEGG Pathway	DEPs	Enrichment Ratio
WS/WLT
Fatty acid biosynthesis	long-chain-fatty-acid–CoA ligase 6 isoform X1 (ACSL, fadD), malonyl-CoA-acyl carrier protein transacylase, mitochondrial (fabD, MCAT, MCT1), 3-oxoacyl-[acyl-carrier-protein] synthase, mitochondrial (fabF, OXSM, CEM1), 3-oxoacyl-[acyl-carrier-protein] synthase, mitochondrial (fabF, OXSM, CEM1)	0.4
TGF-β signaling pathway	fibrillin-1 isoform X2 (FBN1), cullin-1 (CUL1, CDC53), mothers against decapentaplegic homolog 4 (SMAD4), mitogen-activated protein kinase 3 (ERK, MAPK1_3), serine/threonine-protein phosphatase 2A catalytic subunit beta isoform (PPP2C)	0.36
PI3K-Akt signaling pathway	5′-AMP-activated protein kinase catalytic subunit alpha-2 isoform X2 (PRKAA, AMPK), glycogen [starch] synthase, muscle (GYS), RAC-beta serine/threonine-protein kinase (AKT), integrin alpha-6 isoform X1 (ITGA6, CD49f), laminin subunit alpha-5 isoform X1 (LAMA3_5), eukaryotic translation initiation factor 4B isoform X1 (EIF4B), mitogen-activated protein kinase 3 (ERK, MAPK1_3), serine/threonine-protein phosphatase 2A catalytic subunit beta isoform (PPP2C), heat shock protein HSP 90-alpha (HSP90A, htpG), heat shock protein HSP 90-beta (HSP90A, htpG), hsp90 co-chaperone Cdc37 (CDC37), guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-7 (GNG7), 14-3-3 protein eta (YWHAG_H), vitronectin (VTN)	0.22
Adipocytokine signaling pathway	RAC-beta serine/threonine-protein kinase (AKT), long-chain-fatty-acid-CoA ligase 6 isoform X1 (ACSL, fadD), 5′-AMP-activated protein kinase catalytic subunit alpha-2 isoform X2 (PRKAA, AMPK), protein kinase C theta type isoform X1 (PRKCQ), tyrosine-protein phosphatase non-receptor type 11 isoform X1 (PTPN11)	0.29
Arginine biosynthesis	aminoacylase-1 (ACY1), alanine aminotransferase 2 isoform X1 (GPT, ALT), argininosuccinate synthase (argG, ASS1)	0.3
FOXO signaling pathway	5′-AMP-activated protein kinase catalytic subunit alpha-2 isoform X2 (PRKAA, AMPK), RAC-beta serine/threonine-protein kinase (AKT), mothers against decapentaplegic homolog 4 (SMAD4), mitogen-activated protein kinase 3 (ERK, MAPK1_3), mitogen-activated protein kinase 12 isoform X1 (P38), homer protein homolog 2 isoform X1 (HOMER)	0.23
mTOR signaling pathway	5′-AMP-activated protein kinase catalytic subunit alpha-2 isoform X2 (PRKAA, AMPK), RAC-beta serine/threonine-protein kinase (AKT), eukaryotic translation initiation factor 4B isoform X1 (EIF4B), mitogen-activated protein kinase 3 (ERK, MAPK1_3), ras-related GTP-binding protein A (RRAGA_B), ras-related GTP-binding protein C (RRAGC_D)	0.21
WG/WLT
Ribosome	40S ribosomal protein S3a (RP-S3Ae, RPS3A), 40S ribosomal protein S23 (RP-S23e, RPS23), 60S ribosomal protein L10 isoform X1 (RP-L10e, RPL10), 60S ribosomal protein L13 (RP-L13e, RPL13), 60S ribosomal protein L37a isoform X1 (RP-L37Ae, RPL37A)	0.16
Alanine, aspartate, and glutamate metabolism	glutamate dehydrogenase 1, mitochondrial (GLUD1_2, gdhA), glutamine–fructose-6-phosphate aminotransferase [isomerizing] 1 (glmS, GFPT), argininosuccinate synthase (argG, ASS1), omega-amidase NIT2 (NIT2, yafV)	0.24
Arginine biosynthesis	aminoacylase-1 (ACY1), glutamate dehydrogenase 1, mitochondrial (GLUD1_2, gdhA), argininosuccinate synthase (argG, ASS1)	0.3
Valine, leucine, and isoleucine degradation	succinyl-CoA:3-ketoacid coenzyme A transferase 1, mitochondrial (OXCT), acetyl-CoA acetyltransferase, mitochondrial isoform X1 (ACAT, atoB), methylmalonate-semialdehyde dehydrogenase [acylating], mitochondrial isoform X1 (mmsA, iolA, ALDH6A1), methylmalonyl-CoA mutase, mitochondrial (MUT) aldehyde dehydrogenase family 16 member A1 isoform X1 (ALDH)	0.15
Butanoate metabolism	succinyl-CoA:3-ketoacid coenzyme A transferase 1, mitochondrial (OXCT), acetyl-CoA acetyltransferase, mitochondrial isoform X1 (ACAT, atoB)	0.17

WLT: longissimus thoracis; WG: gluteus superficialis; WS: semitendinosus.

).

4. Discussion

The main edible qualities of meat are color, flavor, tenderness, and juiciness. Extensive studies have been conducted to understand the factors that influence these traits. Muscles with a high myoglobin content and a high percentage of oxidative muscle fibers appear brighter in color compared to those with a high percentage of glycolytic muscle fibers [32]. In this study, WG was found to be the brightest, followed by WLT and WS, which could be attributed to the varying proportions of muscle fiber types. Cooking leads to the denaturation of actomyosin resulting in changes to the structure and contraction of myofibrils. These changes ultimately affect the water content in the meat, thus impacting its juiciness [33]. Cooking loss is an important indicator for evaluating the water retention of meat and is closely related to juiciness [34,35]. In our study, WG had the highest cooked meat rate and water holding capacity, indicating that it had the best juiciness. Meat tenderness depends on composition (IMF, water, proteins, and collagen concentration), state of collagen (solubility/solubilization), and myofibrillar factors (myofibril diameter, spacing, and their interactions, as well as shortening of myofibrils) [36,37,38,39]. Consistently, studies have shown that during meat aging, the degradation of muscle proteins (such as titin, nebulin, and desmin) by endogenous enzymes (including calpain) and the weakening of the binding between myosin and actin contribute to the destabilization of myofibril structure [38]. Additionally, the solubility of collagen increases, and the structural integrity of connective tissue is compromised [40]. Furthermore, the depletion of ATP and the degradation of glycogen lead to a decrease in pH, which further impacts the breakdown of myofibrillar proteins and the activity of endogenous enzymes [39]. These interconnected processes collectively enhance meat tenderness. In our research, WLT had the highest pH value and the lowest elasticity and adhesiveness values, which indicates that it is a tender meat, consistent with previous findings [3,15].

The flavor of food is determined by the combination of taste (attributed to non-volatile compounds), aroma (related to volatile compounds), and chemesthetic sensations [41]. Lipid oxidation and degradation reactions, as well as Maillard reactions involving amino acids from protein degradation, produce various volatile compounds such as aliphatic aldehydes, ketones, alcohols, acids, esters, sulfur-containing compounds, nitrogen-containing heterocyclic compounds, and oxygen-containing heterocyclic compounds [42]. Changes in the fatty acid composition and lipid distribution, influenced by IMF content, can lead to differences in volatile odors, ultimately affecting the flavor of donkey meat [16]. Previous studies have shown that longissimus dorsi has the highest IMF content and is dominated by monounsaturated fatty acid, while gluteus maximus and biceps femoris are dominated by saturated fatty acid [3]. Meat with higher levels of amino acids is generally considered to have better nutritional value (EAAs/TAAs) and flavor characteristics (FAAs). Among the amino acids, Ala, Ser, Gly, Pro, and Thr are sweet, while Asp, Glu, and His are sour [43]. The highest content of EAAs, BCAAs, and sweet amino acids (Ala, Gly, and Pro) was found in the WS muscle, indicating that WS had a higher flavor and nutritional value compared to WG and WLT. In particular, Lys was the most abundant EAA in all three donkey muscles, followed by Leu, which is consistent with previous research findings [14]. Generally, free amino acids are closely associated with the flavor of meat. However, donkey meat must be cooked to maturity before consumption. During cooking, free amino acids are lost with the release of water, while protein degradation increases the concentration of free amino acids. The extent of protein degradation, influenced by different cooking methods, ultimately alters the content of free amino acids [44]. Therefore, the ratio of free amino acids (FAAs) to total amino acids (TAAs) in this study serves as a reference. To fully explore the flavor characteristics of the three muscles, further measurements of free amino acids in both raw and cooked meat are necessary.

The increase in fiber diameter and length is caused by a faster rate of protein synthesis compared to degradation [45]. Protein metabolism involves several pathways, including the mTOR signaling pathway, PI3K-Akt signaling pathway, and FOXO signaling pathway [46]. These pathways were found to be enriched in our study, where AMPK and AKT were downregulated in the WLT muscle. AKT and AMPK are Ser/Thr protein kinases that play important roles in protein and energy metabolism. Previous studies suggest that AKT phosphorylates FOXOs, leading to their transfer to the cytoplasm and inactivation. This suppresses the expression of FOXO-mediated ubiquitin ligase (Atrogin-1) and inhibits protein degradation. Additionally, AKT phosphorylates and activates mammalian target of rapamycin (mTOR) and S6K to promote protein synthesis, which results in muscle fiber hypertrophy [47]. However, a recent study indicates that AKT primarily controls muscle mass through muscle protein synthesis, with no significant changes in muscle proteolysis and atrogene gene expression [48]. Activated AMPK stimulates myofibrillar protein degradation by increasing the FOXO transcription factors in C2C12 myotubes [49]. AMPK may also inhibit protein synthesis by suppressing the mTOR signaling pathway [50]. Additionally, AMPK and AKT are known to regulate the activity of mammalian target of rapamycin complex 1 (mTORC1), which in turn modulates downstream targets such as EIF4B. Phosphorylation of EIF4B by mTORC1 enhances EIF4F complex stability and DEAD-box helicase activity, both of which are critical for initiating translation [51]. In our study, the downregulation of AMPK and AKT, alongside the upregulation of EIF4B downstream of mTOR in the WLT muscle, suggests a shift in the protein biosynthesis. This imbalance may influence myofiber hypertrophy by altering the net protein turnover, which ultimately affects muscle growth and development.

As mentioned earlier, the composition of myofibers has an impact on the color and tenderness of meat. Myofibers are categorized into red (slow-twitch, type I) and white (fast-twitch, type II) fibers based on their color and contractile properties. Furthermore, they can also be classified as oxidative or glycolytic fibers based on their metabolic properties [52]. The analysis of myosin heavy chain (MyHC) isoforms and myofibrillar protein adenosine triphosphatase (mATPase) activity can help differentiate the contractile and metabolic properties of the myofiber types [53]. Furthermore, myosins are a family of ATP-dependent motor proteins, which are primarily responsible for the production of contractile force in skeletal muscles [53]. Previous studies have indicated that the phosphorylation of myosin light chains plays a role in regulating the interaction between actin and myosin, thereby influencing muscle contraction [54]. In this study, the upregulation of myosin light chain kinase 2 (MLCK2) and myosin light chain 4 (MYL4) was observed in WLT. Additionally, there was an enrichment in oxidative phosphorylation and citrate cycle, suggesting variations in energy metabolism. It is possible that different muscles in donkeys regulate the assembly of myofibers by modulating the phosphorylation of myosin light chains, which in turn affects muscle contraction. However, further experiments are needed to determine the differences in muscle histology (such as fiber type frequency and diameter) among the three muscles.

This study identified enrichment of fatty acid biosynthesis, the TGF-β signaling pathway, and adipocytokine signaling pathway, all of which play a role in lipid metabolism [55]. Changes in proteins within these pathways can impact lipid synthesis and degradation, consequently affecting IMF content. In the WLT muscles, SMAD4 was upregulated, while AMPK and ACSL6 were downregulated, which may be related to its higher IMF content. SMAD4 is a protein that acts as a common mediator for TGF-β and BMP signaling, transferring extracellular signals to the nucleus [56]. Research has shown that miR-146a-5p inhibits SMAD4 formation, thus blocking the transfer of TGF-β signals to the nucleus and the proliferation of porcine intramuscular preadipocytes [57]. AMPK activation inhibits ACC, a crucial enzyme in lipid synthesis, promoting glucose uptake and reducing intracellular lipid synthesis [58]. Furthermore, AMPK activation increases the expression of uncoupling protein-3 and the activity of mitochondrial enzymes, enhancing lipid oxidation [59]. ACSLs are crucial enzymes in mammals that play a key role in utilizing long chain fatty acids (LCFAs) within cells. They are responsible for the initial activation of LCFAs and help determine their metabolic pathways, including lipogenesis, fatty acid β-oxidation, and signal lipids [60]. ACSL6 plays a role in activating and guiding fatty acids towards the anabolic pathway in skeletal myotubes derived from C2C12 cells. Conversely, when ACSL6 is inhibited, phosphorylation of AKT and insulin uptake by glucose are reduced. It is believed that ACSL6 is potentially involved in the glucose and fatty acid cycles by aiding in the re-esterification process of fatty acids in skeletal muscle [61].

Several studies have demonstrated that amino acids can influence certain metabolic processes, potentially impacting meat quality. Specifically, L-Lys and L-Arg play crucial roles in enhancing the solubility of myosin by preventing its aggregation and interacting with hydrophobic amino acid residues [62]. A study investigating chicken breast tenderness revealed that both L-Lys and L-Arg can break down troponin-T by maintaining calpain activities. Additionally, Arg can decrease Ca²⁺/Mg²⁺-ATPase activities and facilitate actomyosin dissociation [63]. Another study on the meat quality of Shaziling pigs identified the metabolism of aspartate, alanine, D-glutamine, glutamate, and D-glutamate as the primary metabolic pathways influencing meat flavor [64]. Leucine, valine, and isoleucine are essential branched-chain amino acids that cannot be synthesized in the body and must be obtained through food. Research has indicated that dietary supplementation of this combination of amino acids promotes lipid accumulation in skeletal muscle, stimulates protein metabolism, and enhances muscle growth in growing pigs [65]. Under adequate nutritional conditions, leucine and growth factors work together to coordinate net protein synthesis in skeletal muscle through the mTORC1 [45]. Valine and isoleucine also have significant effects on backfat thickness, water distribution patterns, and the solubility of myofibrillar proteins in pork [66]. In our study, we observed that certain DEPs in three muscles of donkey were enriched in the arginine biosynthesis pathway, alanine, aspartic acid, and glutamate metabolism pathway, and valine, leucine, and isoleucine degradation pathway. ASS1, the rate-limiting enzyme responsible for catalyzing the penultimate step in arginine biosynthesis, collaborates with argininosuccinate lyase (ASL) to synthesize Arg from Asp, citrulline, and ATP [67]. It has been explored that BCAAs play a crucial role in muscle energy metabolism. Muscle tissue utilizes BCAAs, particularly leucine (Leu), to generate acetyl-CoA and methylmalonyl-CoA through the activity of enzymes including branched-chain amino acid aminotransferase (BCAT), ACAT, and ALDH. Methylmalonyl-CoA is subsequently converted to succinyl-CoA by MUT. This succinyl-CoA can then participate in either the citrate cycle or the electron transport chain [68]. Moreover, the oxidation of branched-chain amino acids in muscle can impact glycogen accumulation and fatty acid oxidation, both of which influence the glycogen content and ultimately, the pH of meat [68,69]. In the WLT muscle, we observed upregulation of ASS1, while MUT, ACAT, and ALDH6A1 were downregulated. These alterations may influence meat quality through the regulation of amino acid metabolism. Compared to other studies, our findings were limited by the sample size; however, the results revealed significant differences among the three muscles. Therefore, future experiments with additional samples will be valuable for the comprehensive evaluation of meat quality.

5. Conclusions

This study utilized the DIA technique to identify the DEPs in WG, WLT, and WS muscles of donkeys. Several DEPs, including AKT, AMPK, SMAD4, ACSL6, MLCK2, MYL4, ASS1, ACAT, MUT, and ALDH6A1 were discovered to be associated with fatty acid biosynthesis, the FOXO signaling pathway, TGF-β signaling pathway, mTOR signaling pathway, oxidative phosphorylation, citrate cycle, arginine biosynthesis, alanine, aspartate, and glutamate metabolism, valine, leucine, and isoleucine degradation, and other pathways. AKT, AMPK, MLCK2, and MYL4 are likely to impact meat tenderness and juiciness by influencing muscle fiber structure through the regulation of protein metabolism and energy metabolism. SMAD4, AMPK, and ACSL6 may affect IMF content by influencing lipid synthesis and degradation metabolism, which ultimately affects meat tenderness and flavor. ASS1, MUT, ACAT, and ALDH6A1 may regulate amino acid metabolism, thereby influencing protein metabolism, myofibrillar protein solubility, energy metabolism, fatty acid oxidation, and glycogen content, which can result in variations in meat quality traits. Further investigation with more samples is required to confirm the predictive role of these identified proteins in meat quality. Altogether, our findings contribute to a deeper understanding of the molecular mechanisms underlying donkey meat quality.