Introduction

The Inner Mongolian cashmere goat is a local breed that provides both cashmere and meat, and the production of cashmere in Inner Mongolia accounts for approximately 40 % of the total output of the whole country (Su et al. 2018; 2020). Nearly 700,000 Aerbasi cashmere goats are reared per year, and the corresponding meat production is nearly 10,000 tons. Hence, the study of Aerbasi cashmere goat meat has great significance for the mutton industry. Aerbasi cashmere goat meat is well liked for their various medicinal functions as well as their characteristics of rich protein, low cholesterol, and low fat, but the meat quality of these goats has not been reported.

Meat quality was influenced by sensory properties and nutritional value. And intramuscular fat (IMF) deposit was one of the important factors which influence meat quality of flavor, juiciness, and tenderness (Hocquette et al. 2010). IMF is mainly found in the sarcolemma, including the perimysium, epimysium, and endomysium, and is mainly composed of triglycerides and phospholipids (Guo 2014). The positive effect of IMF on the sensory quality of meat has been demonstrated in pork (Su-Mei and Shi-Zheng 2009), mutton (Watkins et al. 2013), and beef (Mateescu et al. 2015). The IMF deposition capacity is regulated by 3 aspects: fatty acid transport, fat anabolism, and fat catabolism. Recent studies have demonstrated that FABP and CD36 play important roles in the process of fat up-take (He et al. 2012; Yang et al. 2015) and that DGAT1 and SCD regulate the synthesis of IMF (Philipp et al., 2011). The HSL and LPL genes regulate fat breakdown (Qiao et al. 2007). However, it is not known which proteins affect the deposition and metabolism of IMF in goat meat, so research on markers of IMF deposits in goat meat is urgently needed.

Protein abundance patterns in cashmere goats remain unclear. In recent years, proteomics has become a powerful postgenomic tool to quantify changes in protein abundance in meat samples (Gallego et al. 2015). Not only that, it has been successfully applied to compare protein changes related to IMF in meat (Hopkins et al. 2014; Du et al. 2021; Xie et al. 2021). However, it is usually difficult to verify the expression of identified biomarkers only at the level of proteomics. Therefore, the objective of our study was to identify potential biomarkers of IMF by combining methodologies of proteomics and transcriptomics and to reveal underlying biochemical events with the help of bioinformatic analyses. Our study lays a basis for understanding the molecular mechanism underlying IMF differentiation in goats and serves as a guide for molecular breeding.

Materials and Methods

Animals

Twenty wethers from Aerbasi Inner Mongolian cashmere goats were set free on the pastures with water and food adlibitum and slaughtered at the age of 2 years old with carcass weights of 25–26 kg. Slaughtering was carried out through the assembly line of a local commercial abattoir; the slaughtering method followed the traditional procedures, which include stunning. After the goats were slaughtered, longissimus dorsi and biceps femoris were isolated. Each muscle was divided into two blocks; one block was retained for meat quality analyses, and another block of meat was immediately immersed in liquid nitrogen and subsequently stored at −80 °C until proteomic analysis. All meat were collected in accordance with the International Guiding Principles for Biomedical Research involving animals and approved by the Special Committee on Scientific Research and Academic Ethics of Inner Mongolia Agricultural University, responsible for the approval of Biomedical Research Ethics of Inner Mongolia Agricultural University [Approval No. (2020) 056], as reported by Gong et al. (2022).

Meat Quality

Within 2 hours of slaughter, we calculated the water loss rate by removing muscles from LD and BF using a round sampler (diameter 2.532 cm), weighed them (W1), and wrapped them with 18-layer filter paper to calculate the water loss rate. Following pressing, the sample was held at 35 kg force and weight (W2) for 5 min with a compression machine (RH-1000, Guangzhou, China). The percentage of pressing loss was calculated as follows: [(W1−W2)/W1]. The method of color analysis was followed by Li et al. (2013). Within 2 hours of slaughter, we measured the meat color of LD and BF using a colorimeter (SMY-2000, Beijing, China). Each color parameter was graded according to its lightness, redness, and yellowness. After thawing for 24 h at 4 °C, shear force values were determined. The meat was placed in a thermostated bath at 80 °C, then heated until the meat was 70 °C in the middle. Three to five test pieces (1×1×4 cm) were taken from the heart of the muscle, with 3 to 4 repetitions of each piece using a slice shear force measurement (C-LM3, Guangzhou, China). Within 1 hour of slaughter, meat electrical conductivity was measured 3 times per sample with the electrode inserted into the muscles using the LF-STAR conductivity determinator (Matthäus). Protein contents were measured according to Chinese National Standards GB 5009.5-2016. The IMF contents were determined using the Soxtec extraction protocol following the method of Hopkins et al. (2014). Three grams of freeze-dried meat was weighed into a thimble and extracted in 85 mL of hexane for 60 min within individual extraction tins. The solvent was then allowed to evaporate for an additional 20 min. The tin was subsequently dried for 30 min at 105 °C to remove any residual solvent. The difference in meat weight before and after extraction was used to calculate the IMF content. The final value is expressed as a percentage of meat weight. The extracted fatty acids were prepared with fatty acid methyl esters. Mass spectrometry coupled with gas chromatography (GC/MS, 5977B, Agilent) was used to quantify all esters. To identify target compounds, we used MassHunter Qualitative Analysis software (Agilent, version B.07.00) to analyze the data. The fatty acid results were expressed as the percent of total fatty acids.

Protein Extraction and Trypsin Digestion

Protein extraction was performed following the procedure with minor modifications (Jia et al. 2022). Each frozen sample was ground to a powder in liquid nitrogen and dissolved in lysis buffer (8.0 M urea, 100 mM Tris-HCl, 10 mM DTT, pH 8.0) and ice ultrasonic bath (repeated 5 times, 2 s each time). Then, the samples were centrifuged at 12,000 g and 4 °C for 30 min. Then, supernatants were tested for protein concentration with a BCA kit (Tiangen, China).

The protein sample in the supernatant mixture (100 μg) was reduced by DTT (Sigma–Aldrich, Germany) at 60 °C for 1 h at a final concentration of 10 mM for 1 h. Then, the reaction proceeded at room temperature for 30 min after the addition of 200 μL of 50 mM iodoacetamide (Sigma-Aldrich, Germany) in darkness. After that, the protein solutions were centrifuged for 30 min at 12,000 g using a 10 kDa filter unit (Fluka, Germany). Next, the filters were washed three times with 100 μL buffer (8 M urea, 150 mM Tris-HCl, pH 8.0). Then, 50 μL of NH4HCO3 (Fluka, Germany) was added to wash the filtrate tube a third time. Finally, the remaining particles were digested with 20 ng/μL trypsin (Promega, Madison, USA) in 25 mM NH4HCO3 buffer at 37 °C for 16 h. In order to stop digestion, 0.1% formic acid was added. Collected peptides and desalted using C18 ZipTip pipette tips. The filtrate was collected, freeze-dried into lyophilized powder, and stored at −20 °C (Wu et al. 2019).

Generation of the Spectral Library

First, buffer solution A (0.1% formic acid in water) and buffer solution B (0.1% formic acid in acetonitrile) were prepared. Then, the lyophilized powder was dissolved with 10 μL of solution A and centrifuged at 12,000 g for 30 min at 4 °C. After that, 2 μg of supernatant was separated via LC system Ekspert nLC400 (Eksigen, Dublin, CA, USA) at a flow rate of 5 μL/min. Finally, peptides were separated in a homemade C18 Nano-Trap column (3 μm, 4.5 cm × 75 μm). The gradient consisted of 3% to 25% B for 38 min, 25% to 32% over 5 min, and 32% to 80% over 2 min; then kept at 80% for 3 min; reduced to 3% over 1 min; and kept at 3% 8 min for column equilibration, for a total time of 57 min. Isolated peptides were analyzed by MS/MS (Sci-ex, TripleTOF 5600) with the method of information-dependent acquisition (IDA). The conditions for IDA were as follows: nominal resolving power of 30,000, time-of-flight (TOF)-MS collection from 350 to 1800 m/z, and automated collision energy for MS/MS with IDA scanned from 400 to 1800 m/z. Protein Pilot 4.5 software (Sciex, Framingham, MA, USA) was used with the UniProt/SWISS-PROT/Capra hircus database (downloaded from https://www.UniProt.org; 35499 proteins, updated to 05-15-2020) to generate the reference spectral library. The results were filtered at a 1% FDR. The selected search parameters included the use of trypsin as the enzyme, allowing up to two missed cleavage sites. The peptide mass tolerance was ±15 ppm, and the fragment mass tolerance was 20 mmu.

Quantification by SWATH and Data Analysis

LC was performed under previously described conditions on two micrograms of protein. Three technical replicates were conducted in each case for 12 samples drawn from two groups. The MS/MS analysis was performed with the method of sequential window acquisition of all theoretical fragment ion spectra (SWATH). The conditions for SWATH-MS were as follows: 150–1200 m/z MS1 mass range, 100–1500 m/z MS2 spectra, and nominal resolving power of 30,000 and 15,000 for MS1 and MS2, respectively. We performed the spectral alignment and the targeted data extraction using PeakView (SCIEX, Framingham, MA, USA), matching the reference library (“Generation of the spectral library”). The data were normalized using total area sums. Peptide/protein quantification was calculated by the following scheme: (1) the peak area from spectra for fragment ions, (2) adding the peak areas from the corresponding fragment ions for a given peptide (1% FDR), and (3) adding the intensity of peptides for a given protein (1%FDR), and sample coverage ≥95%. In the last, the results were interpreted and quantitatively analyzed using MarkerView software (Sciex, Framingham, MA, USA). The log2 values of the measured areas were normalized by the median values to correct for protein abundance variation. All data were log2-transformed, and the differences in protein levels between the BF and LD groups were compared using Student’s t tests with Benjamini–Hochberg correction of the p value (q-value). A resulting q-value of 0.05 was set as a cutoff (López-Pedrouso et al. 2019).

Statistical Analysis

For the data of meat color data, shear force value, meat conductivity, water loss rate, IMF, and protein content were reported as the means ± SEM. The repeat number was 3 for proteomics and western blotting and 20 for meat quality trait evaluation. Statistical analyses of differences were carried out with Student’s t test when data were from independent groups using the SPSS package (SPSS 23.0, Chicago, IL, USA).

PCA and volcano plot analysis, which combined fold-change analysis and Student’s t tests, were performed. A fold-change >2 or < 0.5 and a p value < 0.05 were used as the criteria for identifying DEPs (Ebhardt et al. 2017). The DEPs were subjected to bioinformatic analyses. g: Profiler (https://biit.cs.ut.ee/gprofiler/gost) online software was used to perform the GO and KEGG pathway analyses. Cytoscape_v3.6.1 was used to perform PPI analysis (combined score >0.4) of the DEPs. For the screening of hub proteins (the most highly connected central proteins in scale-free PPI networks), the top 10 hub nodes were ranked by MCC in cytoHubba (Yu et al. 2017).

There are three steps for correlation analysis of miRNA, transcriptome, and proteome: target gene prediction of miRNAs; integrate transcriptome and protein data according to translational relationships; integrate the three sets of data by using transcriptome data as a bridge.

Western Blotting

The homogenized denatured proteins were separated by SDS–PAGE and transferred in a semidry state to a PVDF membrane (Bio-Rad, USA) (n = 3 replicates). The PVDF membrane was cut according to the molecular weight of the marker and target protein. The PVDF membrane was blocked in blocking buffer (LI-COR, USA) for 2 h and incubated overnight with primary antibodies (HADHA rabbit polyclonal, Proteintech, USA, catalog number: 10758-1-AP; ACSL1 rabbit polyclonal, Proteintech, USA, catalog number: 13989-1-AP; FABP3 rabbit polyclonal, Abcam, USA, catalog number: ab231568; MYL2 rabbit polyclonal, Abcam, USA, catalog number: ab79935). The membrane was washed 3 times for 10 min each time and then incubated with a fluorophore-conjugated goat anti-mouse/rabbit antibody (LI-COR, USA, catalog number: 680RD) for 1 h. Finally, the membrane was rinsed with water, and the immunoreactive bands were examined using an LI-COR Odyssey (CLX-0496, USA) near-infrared imager. Finally, β-tubulin mouse monoclonal (Affinity, USA, catalog number: T0023) was used for normalization.

Results

Meat Quality of Cashmere Goats

To investigate meat quality traits of cashmere goats, we measured physiochemical qualities (meat color, shear force value, meat conductivity, water loss rate) and nutritional properties (IMF, protein content, and fatty acid content) from LD and BF tissues of 20 goats. There was no significant difference in physiochemical qualities between LD and BF (Table 1). However, independent samples t tests of different muscles showed that the IMF content was significantly higher in the BF than in the LD (P < 0.01) (Fig. 1). This suggests that IMF is deposited at higher levels in the BF or metabolized to greater degree in the LD. Therefore, we further determined the fatty acid species and total content in the BF and LD (Supplementary Table S1). As a result, there were 34 fatty acids in the meat of Inner Mongolian cashmere goats, and there was no significant difference in fatty acid content between LD and BF. So, we provided high-IMF (HI) group and low-IMF (LI) group as a good model for studying the regulatory mechanisms of IMF anabolism and catabolism. To explore potential reasons for these differences, we performed quantitative proteomics between the BF and LD of 6 goats for the next experiment.

Table 1 The meat quality results of cashmere goats in HI group and LI group
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Fig. 1
figure 1

IMF and protein contents of BF and LD muscle tissues (t test: **P < 0.01)

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Protein Identification and Comparative Analysis

To understand the protein composition of the tissues of these six goats, the UniProt/Swiss-Prot/Capra hircus database was used as a reference to investigate the proteome according to a label-free mass spectrometry (MS) strategy. A total of 993 and 896 proteins were identified in the HI group (Supplementary Table S2) and LI group (Supplementary Table S3), respectively, and a total of 1209 proteins, 16,669 distinct peptides, and 176,041 spectrals were identified in both muscles with a false discovery rate (FDR) ≤ 0.01 (Supplementary Table S4). Therefore, there are many proteins that have yet to be studied in Inner Mongolian cashmere goats.

To visualize and differentiate the observed sample clusters, principal component analysis (PCA) was performed to compare the HI and LI groups based on the proteins present in both groups. PCA is an unsupervised method that condenses a large number of variables (proteins) into a set of representative and uncorrelated principal components based on their variance-covariance structure. The score plot of the PCA of the HI and LI groups (Fig. 2a) showed that 57.2% of the observed variability was explained by the first two principal components, which accounted for 45.8% and 11.4% of the total variance. Samples from the two groups could be completely separated and were located in different quadrants, which indicated the existence of differentially abundant proteins between the HI and LI groups.

Fig. 2
figure 2

Identified DEPs. (a) The PCA score plots of proteins in the HI (red) and LI (blue) groups by using Markview software. (b) The volcano plots show the comparison of the HI and LI groups. The points in red located in the right plots represent the proteins with higher abundance in the HI group with a fold-change of at least 2.0 (P < 0.05), while those in blue in the left plots represent the proteins with higher abundance in the LI group

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The volcano plots include samples with fold-changes >2 or <0.5 and q-values <0.05 (Fig. 2b). In the comparison of the HI and LI groups, a total of 110 significantly differentially expressed proteins (DEPs) were identified, among which 81 proteins were upregulated in the HI group, whereas 29 proteins were upregulated in the LI group. The detailed results for the DEPs are presented in Supplementary Table S5.

Functional Analysis of DEPs

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to evaluate the potential functions of exclusively detected proteins and DEPs (110 in total). According to the GO classification statistics, the terms associated with the DEPs were divided into three main categories: biological processes (45 terms), cell components (32 terms), and molecular functions (21 terms). The DEPs were functionally classified into 100 annotation clusters. The top 3 annotated GO terms in the biological process category showed that the DEPs participated in the oxidation–reduction processes, small molecule metabolic processes, and oxoacid metabolic processes (Fig. 3a). The top 3 GO terms in the cellular component category indicated that the DEPs were mainly enriched in mitochondria, the intracellular region, and organelles. The top 3 GO terms in the molecular function category indicated that the DEPs were mainly involved in catalytic activity, ion binding, and oxidoreductase activity. Importantly, 3 of the enriched GO terms were closely associated with lipid metabolism and adipogenesis. As shown in Supplementary Table S6, in the biological process category, 8 DEPs were enriched in the fatty acid metabolic process term, 5 DEPs were enriched in “lipid oxidation,” and 5 DEPs were enriched in “fatty acid oxidation.” The GO annotations suggested that there are many regulatory mechanisms and regulatory proteins that are closely associated with IMF in the BI and HI group.

Fig. 3
figure 3

Function of DEPs in the skeletal muscle of the HI and LI groups. (a) GO analysis. The numbers in blue represent DEPs. (b) KEGG pathway analysis. Only the top 20 enriched pathways are presented here

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KEGG analysis was performed to evaluate the potential functions of exclusively detected proteins and DEPs. The top 20 enriched KEGG terms of these proteins are described in Fig. 3b. The results showed that the 110 investigated proteins were related to 34 KEGG pathways. In total, three of the pathways were related to IMF metabolism and deposition, including fatty acid metabolism, fatty acid degradation, and fatty acid elongation. Seven proteins were associated with these three pathways, including acetyl-CoA acyltransferase 2 (ACAA2), cholesterol acyltransferase 1 (ACAT1), and enoyl-CoA hydratase (HADHA) (Supplementary Table S7). The KEGG analysis results provide further insight into IMF anabolism and catabolism.

Protein–protein interaction (PPI) network analyses were performed to construct the specific molecular network involving the key DEPs between the HI group and the LI group related to IMF anabolism and catabolism. To search for hub proteins among the DEPs, we used the cytoHubba software. One PPI network including 33 DEPs and 147 edges was constructed (Fig. 4). In addition, the top 10 DEPs in the network were identified, as ranked by the maximal clique centrality (MCC) method. The results showed that the 33 DEPs cooperated to form an IMF anabolism and catabolism PPI network centered on ACAT1, ACAA2, succinate–CoA ligase [ADP/GDP-forming] subunit alpha (SUCLG1), succinate–CoA ligase [GDP-forming] subunit beta (SUCLG2), trifunctional enzyme subunit beta (HADHB), citrate synthase (CS), dihydrolipoyllysine-residue succinyl transferase component of 2-oxoglutarate dehydrogenase complex (DLST), enoyl-CoA hydratase (ECHS1), aconitate hydratase (ACO2), and HADHA. Among these 10 hub proteins, ACAT1 and ACAA2 targeted 9 proteins; SUCLG1 and SUCLG2 targeted 8 proteins (except HADHA); HADHB targeted 8 proteins (except ACO2); CS and DLST targeted 7 proteins (except HADHA and ECHS1); ECHS1 targeted 7 proteins (except CS and DLST); ACO2 targeted 7 (except HADHA and HADHB); and HADHA targeted 5 proteins (except DLST, SUCLG1, SUCLG2, and ACO2). These 10 hub proteins play a vital role in regulating IMF anabolism and catabolism. Both functional analysis and PPI network analysis showed that HADHA, ACAA2, and ACAT1 might play an important role in regulating IMF anabolism and catabolism in Inner Mongolian cashmere goats.

Fig. 4
figure 4

PPI network of DEPs. The circle of blue denotes downregulated proteins, and the arrow denotes upregulated proteins. The circle of red indicates that there is an interaction between the source protein and the target protein. A ranking indicates a greater contribution of the protein to network regulation. The PPI network and DEP ranking were produced by Cytoscape and cyto-Hubba software, respectively

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mRNA–miRNA–Protein Interaction Network

To gain further insight into the molecular network of IMF deposition, we compared their mRNA and miRNA profiles based on PPI network analyses (Fig. 5). There were 10 genes, 52 miRNAs and 9 proteins in the mRNA–miRNA–protein interaction network, and 3 subnetworks related to IMF deposition. In the first subnetwork, 4 protein-coding genes (ECSH1, ACSL1, HADHB, and ACAA2) were regulated by 27 miRNAs. In the second subnetwork, 2 protein-coding MYH4 and HADHA genes were regulated by 8 miRNAs. In the third subnetwork, 1 protein-coding ACADS gene was regulated by 2 miRNAs. Our previous research on microRNA-mRNA regulatory networking fine-tunes polyunsaturated fatty acid synthesis and metabolism in the Inner Mongolia cashmere goat revealed 9 miRNAs that negatively regulate the ACSL1 gene in cashmere goats, including chi-miR-10a-5p, chi-miR-10b-5p, chi-miR-130b-5p, chi-miR-15a-5p_R-1, chi-miR-15b-5p, chi-miR-16a-5p, chi-miR-16b-5p, chi-miR-181c-5p_R + 1, and chi-miR-26b-5p. chi-miR-361-3p negatively regulates ACADS genes (Xie et al., 2021). These analyses together showed that the ACADS and ACSL1 proteins have positive contributions to the regulatory machinery of the IMF position.

Fig. 5
figure 5

mRNA–miRNA–protein network of fatty acid metabolism. The larger the point, the greater the P value

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Protein Determination by Western Blot Analysis

Western blot analysis was performed to validate the MS-derived data (Fig. 6). This method was used to detect the expression levels of three DEPs (HADHA, fatty acid-binding protein (FABP3), myosin regulatory light chain 2 (MYL2), and AMP-binding domain-containing protein (ACSL1)) from the HI and LI groups. All four proteins were upregulated in the BF group. The band intensities of these four proteins were significantly higher in the BF group than in the LD group (P < 0.05). The average band intensities of HADHA, FABP3, MYL2, and ACSL1 (normalized to β-tubulin) presented similar variation tendencies according to the results of MS analyses.

Fig. 6
figure 6

Western blot profiles of ACSL1, HADHA, and FABP3 in the BF and LD. (a) Western blot results; (b) histograms for Western blot results. Three biological replicates of BF and LD tissues. The relative protein intensity is expressed as the mean ± standard deviation. Asterisks represent levels of significance (t test: *P < 0.05, **P < 0.01)

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Discussion

The accumulation and decomposition of IMF have certain effects on muscle juiciness, tenderness, meat color, flavor, and overall muscle palatability (Lorenzo et al. 2014; Hwang and Joo 2017). A gradually reduced level of the special smell of mutton was observed from the ram to the ewe, to the wether, and ewes are generally slower growing (that is, older at the time of slaughter) and will mature earlier than rams (Daz et al. 2003). Therefore, we selected BF and LD of 20 3-year-old castrated goats to determine their physical properties and nutritional components to preliminarily understand the muscle quality of different parts of Inner Mongolia cashmere goats on the premise of reducing individual differences. IMF is formed by the deposition of fat in muscle, which is composed of IMF and myofibrils. A previous study showed that the IMF content ultimately depends on fatty acid transport, IMF anabolism, and IMF catabolism (Dariusz et al. 2009; Paulus et al. 2017). Fatty acid transport involves fatty acids entering intramuscular cells to provide the necessary substrates for IMF synthesis; IMF anabolism includes the synthesis, elongation, or desaturation of fatty acid chains and the synthesis of triglycerides; and IMF catabolism includes the mobilization of fat in intramuscular cells and the hydrolysis of triglycerides in lipoproteins. Interestingly, we found that the IMF content was significantly higher in the BF than in the LD. Therefore, we chose the IMF of the meat quality index to continue the study. On the basis of meat quality measurement, we provided high-IMF (HI) group and low-IMF (LI) group as a new model for subsequent screening of key proteins in IMF metabolism regulation and construction of the network.

IMF anabolism and catabolism are closely associated with many critical cellular functions and biological processes. For example, IMF or fatty acid triglycerides are metabolized in the mitochondrial matrix in a process known as fatty acid beta-oxidation (Rogers et al. 2014). Fatty acid beta-oxidation generally includes four steps: oxidation, hydration, oxidation, and cleavage (Yani et al. 2018). In our study, the GO classification statistics of 110 DEPs showed that the top biological process was the oxidation–reduction process, which included 36 DEPs. In the cellular component analysis, the most enriched cellular component was the mitochondrion, which included 39 DEPs. In the molecular function analysis, 65 DEPs were mainly enriched in catalytic activity. Overall, the DEPs were mostly located in mitochondria, showed catalytic activity, and participated in oxidation processes. In summary, the GO analysis results were consistent with the functions of fatty acid beta-oxidation steps, and DEPs involved in these processes were the key proteins contributing to the differences in fatty acid beta-oxidation, further influencing IMF metabolism.

The KEGG enrichment analyses indicated that there were 3 terms (fatty acid metabolism, fatty acid degradation, and fatty acid elongation) related to IMF, and 7 of the DEPs were associated with these three pathways. The seven proteins included in these three pathways were ACAA2, short-chain acyl-coenzyme A dehydrogenase (ACADS), ACAT1, ACSL1, short-chain enoyl-CoA hydratase (ECHS1), HADHA, and HADHB. Fatty acids can be classified as long-chain (containing more than 12 carbon atoms), medium-chain (containing 6–12 carbon atoms), or short-chain (containing less than 6 carbon atoms) fatty acids (Tvrzicka et al. 2011). Short-chain fatty acids can directly cross the outer mitochondrial membrane and enter the mitochondrial matrix to undergo fatty acid oxidation, but long-chain and medium-chain fatty acids need to be transported through the inner mitochondrial membrane via a mechanism catalyzed by carnitine acyltransferase 1, which is located on the outer mitochondrial membrane. Among the 7 DEPs that were enriched according to KEGG analysis, ACSL1, HADHA, and HADHB are mitochondrial membrane proteins that act on long-chain fatty acids. ACSL1 catalyzes the first step (oxidation) in the activation of long-chain fatty acids by converting them into long-chain acyl-CoA thioesters for channeling toward chain elongation, triacylglyceride synthesis, or fatty acid oxidation (Mashek et al. 2007). ACSL1 is necessary for the synthesis of long-chain acyl-CoA esters, fatty acid degradation, and phospholipid remodeling (Philipp et al. 2011). HADHA and HADHB, which breakdown fatty acids into acetyl-CoA, are specific for long-chain fatty acids. HADHA is involved in fatty acid beta-oxidation, which is a component of lipid metabolism. Research has shown that HADHA overexpression significantly inhibits cell growth, induces cell apoptosis, and decreases the formation of cytoplasmic lipid droplets (Mashek et al. 2007). ACADS is an acyl-CoA dehydrogenase that catalyzes the first step (oxidation) of mitochondrial fatty acid beta-oxidation, an aerobic process that breaks down fatty acids into acetyl-CoA. It acts specifically on acylCoAs with saturated 4- to 6-carbon-long primary chains. Studies have shown that ACADS not only plays a vital role in free fatty acid β-oxidation but also regulates energy homeostasis (Chen et al. 2015). ECHS1, similar to ACADS in function, also participates in the metabolism of fatty acyl-coenzyme A esters and is an important mitochondrial fatty acid beta oxidase but instead acts on straight-chain enoyl-CoA thioesters that have 4 to at least 16 carbons (Alice and Matthew 2018). ACAT1 and ACAA2 are involved in lipid metabolism. They are two key enzymes of the fatty acid oxidation pathway that catalyze the last step (cleavage) in mitochondrial beta-oxidation. They use free coenzyme A/CoA and catalyze the thiolytic cleavage of medium-to-long-chain unbranched 3-oxoacyl-CoAs to acetyl-CoA and fatty acyl-CoA, which are shorter by two carbon atoms, thus playing an important role in fatty acid metabolism. GO and KEGG analyses showed that in the mitochondrion, the DEPs HADHA, HADHB, and ACSL1 participate in fatty acid metabolism, fatty acid degradation, and fatty acid elongation pathways to influence long-chain fatty acid metabolism and synthesis, further influencing IMF anabolism and catabolism. Differentially expressed ACADS affects fatty acid metabolism, fatty acid degradation, and fatty acid elongation, influencing short-chain fatty acid metabolism and synthesis and further influencing IMF anabolism and catabolism. The DEPs ACAT1 and ACAA2 play roles in fatty acid metabolism, fatty acid degradation, and fatty acid elongation, influencing long-chain and medium-long-chain fatty acid metabolism and synthesis and further influencing IMF anabolism and catabolism.

In the construction of the PPI network, a total of 10 hub proteins were found. Among these proteins, ACAT1, ACAA2, HADHB, ECHS1, and HADHA were also identified by GO and KEGG analyses. The other 5 interacting proteins were SUCLG1, SUCLG2, CS, DLST, and ACO2, all of which are involved in the tricarboxylic acid cycle (TCA). Succinate-CoA ligase (SUCL) is a heterodimer consisting of an alpha subunit encoded by SUCLG1 and a beta subunit encoded by SUCLG2, which catalyze ATP- and GTP-forming reactions, respectively (Li et al. 2013; Chinopoulos et al. 2019). SUCL is at the intersection of several metabolic pathways (Tretter et al. 2016). For example, SUCLA2 rebound increases pleiotropically affect metabolic pathways associated with SUCL (Kacso et al. 2016). CS synthesizes isocitrate from oxalo-acetate, and Tereza Škorpilová et al. showed that specific limits could be designated for CS activity in chilled and frozen/thawed meats (Korpilová et al. 2019). DLST catalyzes the overall conversion of 2-oxoglutarate to succinyl-CoA and CO2 (Koike et al. 2010). ACO2 catalyzes the isomerization of citrate to isocitrate, and Aco2 is necessary for mitochondrial translation (Jung et al. 2015). The analysis of the PPI network showed that in addition to the 5 key enzymes and proteins involved in the fatty acid synthesis and metabolism affecting the final accumulation of IMF in different muscle tissues of Inner Mongolian cashmere goats, there are five expressed TCA cycle proteins affecting the accumulation of IMF.

The great majority of proteomic experiments have aimed to understand underlying biological processes and identify biomarkers for meat processing (Picard et al. 2012). An analysis of the proteomes of LD muscle tissues from two different breeds of pigs revealed that lipid deposition is regulated by LPL, APOA1, APOC3, ACADM, FABP3, ACADVL, ACAA2, ACAT1, HADH, and PECI (Wang et al. 2017). SRSF10, CSRP3, APOH, PPP3R1, CRTC2, FOS, SER-PINE1, and AIF1L have been identified as candidates for further elucidating the molecular mechanism of IMF differentiation in goats (Du et al. 2021). In our study, FABP3, HADHB, HADHA, ACSL1, ACAA2, ACAT1, and ACADS were highly correlated with IMF metabolism. FABP3 and ACAT1 are detected in both pig meat and goat meat. IMF is a major focus in meat production. The proteomic approach has demonstrated a strong correlation between proteins and meat quality traits. Collectively, there are totally 3 pathways (fatty acid metabolism, fatty acid degradation, and fatty acid elongation) and 6 DEPs (HADHA, HADHB, ACSL1, ACADS, ACAT1, and ACAA2) related to IMF metabolism.

Our previous results show that 9 and 1 miRNAs negatively regulate the ACSL1 gene and ACADS gene in cashmere goats, respectively (Xie et al. 2021). To further screen markers associated with IMF, the mRNA–miRNA–protein regulatory network of IMF was constructed. The result shows that ACSL1 has a targeting pair with AOA452FP4 (the protein of ACSL1), and ACADS has a targeting pair with AOA452ED98 (the protein of ACADS). Therefore, we hypothesized that the following two interactions affect fatty acid metabolism: Through the fatty acid metabolism signaling pathway, 1–9 miRNAs act on ACSL1 alone or together to cause it to be degraded or repressed, reducing the amount of ACSL1 protein expression, thereby affecting the metabolism of long-chain fatty acids, and ultimately leading to a lower content of IMF in the LD; and chi-mir-361-3p_ R+1 through fatty acid metabolism signals acts on ACADS, causing it to be degraded or inhibited, decreasing the amount of ACADS protein expressed, which in turn affects the metabolism of short-chain fatty acids, ultimately leading to a lower content of IMF in the LD. We identified ACSL1 and ACADS as biomarkers which play important roles in goat IMF metabolism. However, further research needs to be done to verify these two hypotheses. Our bioinformatic analyses provided a clear starting point for further study.

Conclusions

We studied the differences in intramuscular fat between the BF and LD of cashmere goats and obtained 110 differentially expressed proteins (DEPs). GO and KEGG analyses showed that there are 3 pathways (fatty acid metabolism, fatty acid degradation, and fatty acid elongation) and 6 DEPs (HADHA, HADHB, ACSL1, ACADS, ACAT1, and ACAA2) related to IMF metabolism. PPI and mRNA–miRNA–protein network analyses showed that the ACADS and ACSL1 proteins positively contribute to the regulatory machinery of the IMF position.