Combined Analysis of the Transcriptome and Metabolome at Different Tissue Glycogen Levels in Yili Horses

Simple Summary

This study systematically analyzed the glycogen content across various tissues in Yili horses, conducting transcriptomic and broadly targeted metabolomics investigations on the visceral (liver) and muscle (gluteus medius) tissues, which exhibit the highest glycogen concentrations. A total of 1485 metabolites and 7366 mRNAs were identified with differential expression. Notably, these differentially expressed genes are predominantly involved in glycolysis and the metabolism of fructose and mannose. Through a multi−omics joint analysis, the mechanisms underlying glycogen synthesis and accumulation in different tissues of Yili horses were explored at a deeper and broader level, providing a theoretical foundation for elucidating the genetic mechanisms of glycogen metabolism in this breed.

Abstract

This study aimed to investigate the relationship between genes and metabolites involved in glycogen metabolism across different tissues of Yili mares using joint transcriptomic and metabolomic analyses. Glycogen content was measured in various tissues (pincer, trapezius, latissimus dorsi, gluteus medius, semitendinosus, external abdominal obliques, liver, and heart) from seven Yili mares. The liver, as the visceral tissue with the highest glycogen content, and the gluteus medius, as the muscle with the highest glycogen content, were selected for transcriptomic sequencing and metabolomic analysis. KEGG pathway analysis of differentially expressed genes and metabolites in the liver and the gluteus medius revealed several key pathways associated with glycogen metabolism, including pentose and glucuronic acid interconversion, glycolysis/gluconeogenesis, the TCA cycle, fructose and mannose metabolism, and the pentose phosphate pathway. The gluteus medius tissue exhibited differential expression of 1485 metabolites and 7366 genes compared to the liver, with correlation coefficients between some genes and metabolites in the aforementioned pathways exceeding 0.8. This study highlights the regulatory differences in glycogen synthesis between liver and muscle tissues in Yili horses from multiple perspectives. Notably, genes such as ACO1, ACLY, PCK2, and FBP1, along with metabolites like leucine, tyrosine, and valine, play significant roles in regulating glycogen synthesis in the liver. It is hypothesized that these genes and metabolites contribute to the observed differences in energy metabolism between liver and muscle tissues in Yili horses; however, further in vivo and in vitro experiments are needed to validate this hypothesis.

1. Introduction

The Yili horse, an exceptional breed developed in Xinjiang, China, is renowned for its outstanding performance in speed races. However, its athletic potential has yet to be fully explored. Glycogen, composed of individual glucose molecules and stored primarily in the liver and skeletal muscle, is more accurately described as glycogen granules. In the liver, glycogen serves as the primary reservoir for maintaining blood glucose homeostasis, while in skeletal muscle, it provides energy during high−intensity exercise [1]. Although glycogen in the liver and muscle shares some functional overlap, they are regulated differently [2].

The synthesis of glycogen is a complex, multi−step process governed by various enzymes. Research has identified two glycogen pools in the skeletal muscles of humans and rats: pre−glycogen (PG) and mature glycogen (MG). PG is a smaller molecule with an estimated molecular weight of 400 kDa, whereas MG is a larger, fully matured glycogen molecule with a maximum molecular weight of approximately 10,000 kDa [3]. As early as 1990, Whelan discovered a TCA−insoluble, glycogen−like substance (P400) in rabbit skeletal muscle, which had a molecular weight of 400 kDa. This substance could be degraded by amylase and isoamylase into intermediates of progressively decreasing molecular weights and eventually converted into glycogen, leading Whelan to coin the term PG [4]. Glycogen synthesis involves three key stages: First, glycogenin (GN) undergoes self−glycosylation to form oligosaccharide chains. Next, PG is synthesized using GN−oligosaccharide chain primers. Finally, MG is produced through the action of glycogen synthase and branching enzymes [5]. Understanding these three stages of glycogen synthesis is essential for studying a horse’s fundamental glycogen synthesis capacity. This approach allows for the identification of key rate−limiting enzymes and the regulation of gene expression, providing a framework for accurately assessing the glycogen synthesis potential of the Yili horse.

Hexokinase (HK) is the rate−limiting enzyme that catalyzes the first step of glucose metabolism. Glycogen synthesis begins with HK, primarily HK1 and HK2, converting glucose into glucose−6−phosphate (G6P). Muscle−type phosphofructokinase (PFKM) has been shown to regulate various biological processes, including glucose metabolism and muscle maintenance, acting as a key rate−limiting enzyme in glycolysis and a decisive enzyme in glucose metabolism. To better understand equine glucose metabolism, some researchers have examined type II HK and muscle phosphofructokinase in horses. They found that in horses, HKII is mainly expressed in the cervical muscle and not significantly expressed in insulin−responsive tissues like the heart or adipose tissue. Additionally, ePFKM mRNA expression is notably higher in the heart compared to the cervical muscle, which contrasts with the expression patterns of PFKM in other mammals. These tissue expression profiles are essential for understanding glucose metabolism in horses [6], consistent with the findings of Wang J. et al. [7]. Their study on the expression of GYS and PFKM in various tissues of Yili horses also found that PFKM expression was highest in the heart and lowest in the liver, while GYS expression was highest in the heart, with no significant difference in other tissues. Sihan Meng et al. [8] conducted a muscle metabolomics study on Ningqiang ponies and Guanzhong horses, revealing that Guanzhong horses had significantly higher muscle glycogen concentrations than Ningqiang ponies. Moreover, the activities of citrate synthase and HK were greater in Guanzhong horses. Lipid metabolism and its interaction with the tricarboxylic acid (TCA) cycles were notably reduced in Guanzhong horses, with significant decreases in lipid metabolism, taurolipid and hypotaurine metabolism, as well as glutathione metabolism. These findings suggest that Guanzhong horses possess a higher capacity for energy storage and utilization, which supports their athletic performance. However, further in vivo and in vitro studies are needed to validate these findings.

This study employed a combination of broadly targeted metabolomics and mRNA transcriptome sequencing based on UPLC−MS to explore the differences in glycogen storage capacity across different tissues in Yili horses. By determining glycogen content, this study aims to further understand the molecular basis behind the differences in glycogen metabolism between liver and muscle tissues in Yili horses.

2. Materials and Methods

2.1. Experimental Animals

Seven healthy Yili mares (aged 9–12 years old, body weight: 345–400 kg) were provided by Yili West Extreme Horse Foods Co. Prior to slaughter, the mares were fasted and dehydrated. Following slaughter, eight tissues-pincer muscles, obliques, latissimus dorsi, gluteus medius, semitendinosus, external abdominal obliques, liver, and heart—were harvested, packaged in self−sealing bags with proper labeling, and transported to the laboratory for preservation using liquid nitrogen.

2.2. Experimental Methods

2.2.1. Determination of Glycogen Content

The glycogen content in different tissue samples from the test horses was determined according to the instruction manual of the Glycogen Kit (purchased from Shanghai Enzyme−linked Biotechnology Co., Ltd., Shanghai, China Lot No.: G20211026S). The measurements were performed using a multifunctional enzyme labeler (model 352, 620 nm) purchased from Labsystems Multiskan MS, Finland. The gluteus medius (muscle) and liver (visceral tissue) were selected for transcriptome sequencing and metabolomics analysis due to their high glycogen content.

2.2.2. RNA Extraction and Library Creation

Total RNA was extracted using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. The RNA molecules ranging from 18 to 30 nt were enriched using polyacrylamide gel electrophoresis (PAGE). Subsequently, 3′ adapters were added, and 36–44 nt RNAs were further enriched. The 5′ adapters were ligated to the RNAs, and the ligation products were reverse transcribed by PCR amplification. The resulting 140–160 bp PCR products were enriched to generate a cDNA library, which was sequenced using the Illumina Novaseq 6000 by Gene Denovo Biotechnology Co. (Guangzhou, China).

2.2.3. Differential Gene Screening and Functional Annotation

Differential expression analysis of the transcriptome data was performed using DESeq, with five samples from each of the two groups (a total of 10 samples). The probability of hypothesis testing (p-value) was calculated, and the p-value was corrected for multiple hypothesis testing to obtain the q-value. Differentially expressed genes were selected based on the combination of q−value and Fold Change (FC) criteria. Genes were considered differentially expressed if |log₂FC| > 1 and q−value < 0.05. The differentially expressed genes were compared and analyzed using GO and KEGG databases for enrichment annotation.

2.2.4. Real−Time Fluorescence Quantitative PCR

Five differentially expressed genes were randomly selected from the transcriptome sequencing results for qRT−PCR analysis. GAPDH was used as the internal reference gene, and primers were designed using Primer 3.0. The primer information on the primers is shown in

Table 1. Real−time fluorescent quantitative primers.

Gene	Primer Sequences	Annealing Temp (°C)	Product Size (BP)
GYS1	F: ATCCTACTCCTTCCGTGCGT	60	105
	R: GCTGGGAAGACAGCCAGTTC
GYS2	F: TTACATCGTTGACAGGCGGT	60	246
	R: TCTGTCGTTGGGTGGTGATGT
PGM2	F: GGGCTGGTGGCTCTTTACTT	60	174
	R: GCTCGGTTTCCCATCCACTT
ALDOB	F:ATGGGGCTGGTTCCCATTGTT	60	157
	R:ATGTTGGGCTTCAGCAAGGT
PPP1R3A	F: CAGTTCCCACCCAGGCAATA	60	112
	R: GTGCCTCCGCTAGTCAAGAG
GAPDH	F: TTGCCCTCAACGACCACTTT	60	139
	R: TCTTGCTGGGGTGATTGGTGGG

. The results were analyzed using the 2^−ΔΔCT method for the relative quantitative analysis of the data, and the accuracy and reproducibility of the transcriptome sequencing results were further validated.

2.2.5. Metabolome Analysis

A total of 400 μL of extraction solution (methanol: acetonitrile: water = 2:2:1 (v/v), containing an isotope−labeled internal standard mixture) was added to 10 samples from two groups. The samples were then placed in a liquid nitrogen tank for 1 min, followed by thawing and vortexing for 30 s. This process was repeated 2–3 times, after which the samples were sonicated for 10 min in an ice−water bath and rested at −40 °C for 1 h. Following this, the samples were centrifuged at 4 °C for 15 min at 12,000 rpm (centrifugal force of 13,800× g, radius 8.6 cm). The supernatant was collected in an injection vial for analysis, and an equal amount of supernatant from all samples was pooled to create QC samples for testing.

Target compounds were chromatographically separated on a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm) using a Vanquish ultra−high−performance liquid chromatograph (Thermo Fisher Scientific, Shanghai, China). Mobile phase A consisted of an aqueous solution containing 25 mmol/L ammonium acetate and 25 mmol/L ammonia, while phase B was acetonitrile. The sample plate temperature was set at 4 °C, and the injection volume was 2 μL.

Raw data were converted into mzXML format using ProteoWizard software and processed with an R package (kernel XCMS) for peak identification, extraction, alignment, and integration. The data were then matched to the secondary mass spectrometry database for substance annotation, with the algorithmic scoring cutoff set at 0.3. Multivariate statistical analyses, including Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS−DA), were conducted to identify metabolite differences between groups. Hierarchical Clustering Analysis (HCA) and metabolite correlation analysis were applied to explore relationships among samples and metabolites. Differential metabolites were selected based on the following criteria: VIP > 1.0, FC > 2 or FC < 0.5, and p-value < 0.05. Finally, the biological significance of metabolite correlations was assessed through functional analysis, including metabolic pathway analysis.

3. Results

3.1. Glycogen Content Results

The glycogen content in different tissues of Yili horses is shown in Figure 1. The liver exhibited significantly higher glycogen content than other tissues (p < 0.01), while the semitendinosus muscle had significantly lower glycogen content compared to the other tissues (p < 0.01). In contrast, the gluteus medius muscle showed significantly higher glycogen content than the other muscle tissues (p < 0.01).

3.2. Transcriptome Sequencing Results

3.2.1. RNA Sequencing Data Quality Testing

RNA sequencing data quality assessment for 10 samples—gluteus medius (T) and liver (G) groups—revealed 239,238,730 and 234,275,102 raw reads, respectively, with Q20 > 94.15% and Q30 > 87.465%. The bipartite GC content ranged from 47.43% to 52.91%, ensuring that the data quality was sufficient for subsequent analysis (See

Table 2. RNA seq data quality control results.

Sample Name	Raw Reads	Avg. Quality	Clean Bases (BP)	Q20/%	Q30/%	GC Content/%
T3	53,152,640	35.065	7.97G	95.00	89.72	50.34
T7	38,317,820	35.35	5.75G	95.96	90.94	52.32
T8	43,466,388	34.875	6.52G	94.56	88.64	52.64
T10	46,664,006	34.895	7.00G	94.67	88.65	52.79
T13	57,637,876	35.055	8.65G	95.06	89.58	49.59
G3	43,188,882	34.68	6.48G	94.15	87.47	49.16
G7	42,094,458	35.09	6.31G	95.13	89.78	48.78
G8	60,137,914	35.42	9.02G	96.17	91.28	49.32
G10	43,218,416	34.885	6.48G	94.85	88.40	51.51
G13	45,635,432	35.255	6.85G	95.82	90.37	47.43

).

3.2.2. Reads Mapping to Reference Genome

The comparison of the sequencing data with the reference genome is summarized in

Table 3. Comparison of reads and reference genome.

Sample	Total Reads after Filtered	Mapped on Reference	Unmapped	Multi Map
T3	46,739,544	42,449,805	4,289,739	2,345,335
		(90.82%)	(9.18%)	(5.02%)
T7	37,735,536	32,831,646	4,903,890	1,989,190
		(87.0%)	(13.0%)	(5.27%)
T8	40,786,318	35,402,109	5,384,209	2,366,005
		(86.8%)	(13.2%)	(5.8%)
T10	45,577,966	38,666,139	6,911,827	2,990,667
		(84.84%)	(15.16%)	(6.56%)
T13	50,638,376	46,131,387	4,506,989	3,360,035
		(91.1%)	(8.9%)	(6.64%)
G3	42,107,240	35,074,791	7,032,449	2,981,636
		(83.3%)	(16.7%)	(7.08%)
G7	38,700,588	34,688,330	4,012,258	2,240,249
		(89.63%)	(10.37%)	(5.79%)
G8	54,096,344	50,499,165	3,597,179	3,054,153
		(93.35%)	(6.65%)	(5.65%)
G10	42,911,120	36,303,458	6,607,662	3,619,506
		(84.6%)	(15.4%)	(8.43%)
G13	45,042,500	40,402,148	4,640,352	2,040,538
		(89.7%)	(10.3%)	(4.53%)

. Notably, 88.11% and 88.12% of the clean reads from the two groups of samples were successfully aligned with the reference genome, with alignment rates ranging from 83.30% to 93.35%. The non−alignment rate ranged from 6.65% to 16.7%, and 4.53% to 8.43% of reads were aligned in multiple locations. These results suggest that the reads from each sample had a high alignment rate with the reference genome, confirming the appropriateness of the selected reference genome and the normal utilization rate of the data.

3.2.3. Analysis of Differentially Expressed Genes

The criteria for screening differentially expressed genes in this study were |log2FC| > 1 and padj < 0.05. A total of 7366 differentially expressed genes were identified in the gluteus medius muscle group compared to the liver group, including 3450 up−regulated and 3916 down−regulated genes (Figure 2, Some differentially expressed genes are shown in Appendix A,

Table A1. Significant differences in genes.

Gene Id	log2FC	Padj	Trend
ALB	−14.61084156	0	DOWN
MYH2	14.26117559	2.80 × 10−272	UP
LOC100061367	−11.3617582	8.27 × 10−245	DOWN
LOC100064919	−9.703437559	6.00 × 10−219	DOWN
KLHL41	12.10899662	7.30 × 10−213	UP
TNNC2	13.63502593	1.70 × 10−212	UP
MYH1	13.67749449	6.11 × 10−209	UP
FGB	−17.2250579	3.43 × 10−191	DOWN
PYGM	13.42385532	1.66 × 10−182	UP
RYR1	9.480313894	7.92 × 10−178	UP
MYOZ1	12.4849085	8.81 × 10−175	UP
ANKRD23	11.71850884	1.36 × 10−174	UP
MYH7	13.78195827	1.53 × 10−173	UP
MYL1	15.12567816	1.36 × 10−160	UP
ATP2A1	11.87092359	3.50 × 10−159	UP
CKM	13.7868656	5.07 × 10−159	UP
NEB	12.13071621	2.77 × 10−157	UP
SERPINA3	−8.347164704	5.42 × 10−156	DOWN
PDE4DIP	8.600900433	1.25 × 10−154	UP
ATP2A2	5.726153152	8.70 × 10−153	UP
PFKM	6.973953392	1.86 × 10−152	UP
MYBPC2	13.72134728	1.18 × 10−150	UP
TNNT1	14.08752755	2.03 × 10−148	UP
APOC2	−13.91835744	3.99 × 10−147	DOWN
CFB	−12.33947368	1.78 × 10−142	DOWN
PAH	−12.30635339	4.53 × 10−141	DOWN
APOA1	−11.39249981	1.42 × 10−138	DOWN
USP13	8.017592969	2.41 × 10−137	UP
MYLPF	11.77271676	4.73 × 10−137	UP

).

3.2.4. GO Annotation of Differentially Expressed Genes

To further investigate the functional annotation of the differentially expressed genes at a significance level of padj < 0.05, the differentially expressed genes were categorized into three groups: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The top ten GO terms were significantly enriched in oxidation−reduction process and drug metabolic process (BP); cytosol and cytoplasm (CC); identical protein binding, signaling receptor binding, and protein homodimerization activity (MF). Key GO terms related to glycogen metabolism include glycogen biosynthetic process, regulation of glycogen metabolic process, regulation of glycogen (starch) synthase activity and glycogen catabolic process, etc (see Figure 3).

3.2.5. Differentially Expressed Gene KEGG Analysis

KEGG enrichment analysis was performed to further explore the functions of differentially expressed genes in different tissues of Yili horses (Figure 4). The analysis revealed significant enrichment of these genes in pathways related to lysosome function, sphingolipid metabolism, and carbon fixation in photosynthetic organisms. Additionally, five glycogen metabolism−related pathways were identified, namely starch and sucrose metabolism, pyruvate metabolism, fructose and mannose metabolism, and the pentose phosphate pathway. It is hypothesized that the genes involved in these pathways play a critical role in regulating glycogen metabolism in Yili horses.

3.2.6. Real−Time Fluorescence Quantitative Validation

To validate the expression accuracy of the differentially expressed genes identified by transcriptome sequencing, five known genes−GYS1, GYS2, PGM2, ALDOB, and PPP1R3A—were selected for qRT−PCR analysis. The RNA samples used in this experiment were the same as those used for RNA−seq sequencing, with GAPDH serving as the internal reference gene. The qRT−PCR results showed that the expression levels of these genes in different tissues of Yili horses were consistent with the RNA−seq data (Figure 5), confirming the reliability of the transcriptome sequencing results and supporting their use for further research.

3.3. Metabolomics Sequencing

3.3.1. Test Stability

The overall PCA demonstrated that the QC samples clustered closely together in both positive and negative ion modes. These results indicate that the instrument’s stability was good, and the test data were stable and reliable, reflecting the biological differences between the samples (Figure 6).

3.3.2. Multivariate Statistical Analysis

Due to the high dimensionality and correlation of metabolomic data, traditional univariate analysis is insufficient for efficiently exploring potential information. Therefore, multivariate statistical methods such as PCA and PLS−DA were applied. PLS−DA was used to observe the overall distribution trend among samples in each group, and both positive and negative ion modes showed R2Y values greater than Q2Y in all three groups, indicating that the model was well established (Figure 7).

3.3.3. Analysis of Differential Metabolites

In this study, the Variable Importance in the Projection (VIP) value of the first principal component of the PLS−DA model was used to assess the contribution of metabolite differences in different subgroups. The VIP value indicated the significance of metabolite variation across groups. Additionally, the FC was calculated as the ratio of the mean quantitative values of each metabolite in the comparison group. Differentially expressed metabolites were identified by combining the VIP value with the p-value from the t-test. The thresholds for screening were set as VIP > 1.0, FC > 2 or FC < 0.5, and p-value < 0.05 (

Table 4. Combined transcriptome and metabolome KEGG enrichment analysis of selected pathways.

Metabolic Pathway ID	Metabolic Pathway Name	Clusters	Gene Count	p-value	Number of Metabolites	p-value
map00040	Pentose and glucuronate interconversions	T vs. G	23	0.00000274	9	0.100101604
map00010	Glycolysis/Gluconeogenesis	T vs. G	47	0.0000105	4	0.488355491
map00020	Citrate cycle (TCA cycle)	T vs. G	25	0.000029	3	0.850436371
map00051	Fructose and mannose metabolism	T vs. G	23	0.001191953	11	0.034901092
map00030	Pentose phosphate pathway	T vs. G	21	0.00167807	7	0.383678739

). A total of 1485 differentially expressed metabolites were identified between the gluteus medius muscle and the liver, with 602 metabolites up−regulated and 883 down−regulated (Figure 8; Appendix A,

Table A2. Significant differences in metabolites.

Name	FC	p-value	VIP	Trend
N1−(4−Chlorophenyl)−2−({4−methyl−5−[4−(trifluoromethyl)−3−pyridyl]−4H−1,2,4−triazol−3−yl}thio)acetamide	0.2110	0.000102622	1.5369	DOWN
5′−Thymidylic acid, disodium salt	0.4383	0.000103095	1.4914	DOWN
3−Deoxy−lyxo−heptulosaric acid	0.0619	0.000103825	1.5467	DOWN
1−Arachidonoylglycerol	0.1398	0.000106028	1.5186	DOWN
1−(4−fluorophenyl)−2−(4−methoxyphenyl)−4−(2−naphthyl)butane−1,4−dione	0.0422	0.000106538	1.5637	DOWN
Tetrahydropteridine	0.1934	0.000107993	1.4868	DOWN
Spermidine trihydrochloride	15.5185	0.000109036	1.5677	UP
N−Butylbenzenesulfonamide	0.1507	0.000112625	1.5202	DOWN
11−Dehydro−thromboxane B2	9.1606	0.000114289	1.5195	UP
Luteolin 7−O−beta−D−glucoside	0.2052	0.000118061	1.5075	DOWN
NP−004526	0.0701	0.000122924	1.5628	DOWN
5′−Phosphoribosyl−N−formylglycinamide	0.1010	0.000125147	1.5367	DOWN
Levamisole	2.4722	0.000129771	1.4914	UP
Indomethacin	0.0637	0.000130089	1.5565	DOWN
Dehydroepiandrosterone	4.5293	0.000131885	1.5047	UP
Dimethylglycine	0.0150	0.000136809	1.5661	DOWN
CHLORMEZANONE	0.0637	0.000148028	1.5488	DOWN
4−tert−Butylphenol	0.0702	0.000148926	1.5614	DOWN
Rutarin	0.0271	0.000153552	1.5642	DOWN
NCGC00381160−01_C14H12O6_Spiro[cyclopent−4−ene−1,1′(3′H)−isobenzofuran]−3,3′−dione, 2,4′−dihydroxy−6′−methoxy−5−methyl	0.1334	0.000155415	1.5577	DOWN
Dapsone	0.0392	0.000164596	1.5619	DOWN
2−(1h−1,2,4−triazol−5−yl)−1h−isoindole−1,3(2h)−dione	0.0278	0.000172773	1.5597	DOWN
Glucaric acid	0.0943	0.000181643	1.5206	DOWN
5−[(E)−2−(4−hydroxy−3−methoxyphenyl)ethenyl]benzene−1,3−diol	0.0262	0.000182892	1.5614	DOWN
7alpha,8alpha−Dihydroxycalonectrin	30.6432	0.000191152	1.5527	UP
Sulfinpyrazone	0.0146	0.00019317	1.5594	DOWN
Lamiide	0.0392	0.000202854	1.5592	DOWN

).

3.3.4. Differential Metabolite KEGG Enrichment Analysis

As shown in Figure 9, the differential metabolites from both the gluteus medius and the liver were involved in 236 metabolic pathways. Notably, these metabolites were significantly enriched in several key pathways, including cutin, suberine, and wax biosynthesis, amino sugar and nucleotide sugar metabolism, flavonoid biosynthesis, glucosinolate biosynthesis, fructose and mannose metabolism, purine metabolism, and mineral absorption.

3.3.5. Joint Analysis of Transcriptome and Metabolome

To investigate the molecular mechanisms underlying different glycogen content levels in various tissues of Yili horses, and to identify related genes and metabolites, a KEGG pathway co−analysis of differentially expressed genes and metabolites was performed. The analysis revealed a total of 109 enriched pathways in the semitendinosus muscle and liver groups. There were 6 common enriched pathways between the gluteus medius muscle and the semitendinosus muscle, and 118 enriched pathways between the gluteus medius muscle and the liver. Among the pathways related to glycogen metabolism in the T vs. G groups, significant pathways included pentose and glucuronic acid interconversion, glycolysis/gluconeogenesis, TCA cycle, fructose and mannose metabolism, and the pentose phosphate pathway. A total of 34 differential metabolites and 139 differentially expressed genes were enriched in these five pathways, as shown in Table 4.

3.3.6. Correlation Analysis of Differentially Expressed Genes and Differentially Expressed Metabolites

Correlation analysis of differentially expressed genes and metabolites with a correlation coefficient greater than 0.8 in the six KEGG pathways revealed significant associations. Genes and metabolites with r > 0.8 were identified in pentose and glucuronic acid interconversion, glycolysis/de novo glucose, the TCA cycle, fructose and mannose metabolism, and the pentose phosphate pathway. Correlation network diagrams were generated to visualize the relationships between metabolites and genes (e.g., Figure 10). Notably, PFKM and FBP1 were involved in the pentose and glucuronic acid interconversion pathway, as well as the pentose phosphate pathway.

4. Discussion

Glycogen aggregates are deposited in various organs, influenced by factors such as supply routes and the organism’s nutritional status [9]. In the present study, glycogen content was analyzed in different tissues of Yili horses, revealing the highest glycogen levels in the liver. Among the skeletal muscles, the gluteus medius muscle exhibited the highest glycogen content, while the semitendinosus muscle showed the lowest. This indicates that although glycogen’s primary role as an energy reserve remains consistent across cellular tissues, its function can vary between tissues [10]. Such differences may be attributed to variations in energy metabolism characteristics, such as glycolysis dependence, across different tissues. The main function of glycogen is to store glucose, which can be rapidly converted into usable energy when required.

Glycogen synthesis is a complex process in animals influenced by various factors. Studies in humans and rodents have shown that factors affecting the rate of glycogen synthesis in skeletal muscle after exercise include initial muscle glycogen content, carbohydrate availability, glucose transport into the muscle, and glycogen synthase activity [11]. In the present study, many genes showing significant differences between T and G were significantly enriched in signaling pathways such as propanoate metabolism, glycosaminoglycan degradation, pyruvate metabolism, the pentose phosphate pathway, and fructose and mannose metabolism. Several genes were identified to play key regulatory roles in glycogen metabolism and gluconeogenesis in Yili horses, including PCK2, FBP1, FBP2, PFKM, ALDOA, ALDOB, ALDOC, HK1, HK2, and HK3. Fructose−1,6−bisphosphatase (EC 3.1.3.11) (FBPase) is a critical regulatory enzyme in gluconeogenesis, catalyzing the hydrolysis of fructose−1,6−bisphosphate to produce fructose−6−phosphate and inorganic phosphate. This enzyme is found across a wide range of species, including bacteria, fungi, plants, and animals [12,13]. In mammals, two genes, FBP1 (liver FBPase) and FBP2 (muscle FBPase), encode this enzyme. FBP1 is primarily expressed in the liver, kidney, lung, and monocytes [14], while FBP2 is predominantly expressed in muscle tissue [13]. In bovines, FBP1 is mainly expressed in the testis, liver, and kidney, with lower expression in the intestine, rumen, and lung, and minimal expression in other tissues such as the spleen, thymus, lymph nodes, heart, and muscle [15]. FBP2 plays a regulatory role in glycogen/glucose synthesis and is involved in regulating the cell cycle and apoptosis [16]. Michal Pirog’s research [17] indicated that muscle FBPase is significantly different from liver FBPase, suggesting that FBP2 is not only associated with glycogen storage but also functions as a regulator of contracting muscle mitochondria. The findings in this study align with these results, showing that FBP1 is more highly expressed in the liver than in the gluteus medius, while FBP2 is more abundant in the gluteus medius than in the liver.

Glucose is rapidly phosphorylated to G6P upon entry into the cell, where it is stored and serves as an important precursor metabolite in various metabolic pathways, including gluconeogenesis and glycolysis [18]. In humans, insulin regulates gluconeogenesis in the liver by controlling the transcription of key gluconeogenic genes, such as PCK1 and G6PC [19]. The aldolase family, comprising three isoforms—ALDOA, ALDOB, and ALDOC—plays a role in metabolism and glycolysis. These aldolases are differentially expressed in human tissues, with ALDOB (aldolase B), also known as hepatic aldolase, being primarily expressed in the liver and kidney [20]. Aldolase A catalyzes the conversion of fructose−1,6−bisphosphate into glyceraldehyde−3−phosphate and dihydroxyacetone phosphate in the glycolytic pathway. Fructose−bisphosphate aldolase (aldolase A), encoded by the ALDOA gene, is a glycolytic enzyme that catalyzes the reversible conversion of fructose−1,6−bisphosphate to glyceraldehyde−3−phosphate. In this study, ALDOA expression in the gluteus medius was significantly higher than in the liver, suggesting that ALDOA plays a major role in glucose metabolism in muscle tissue. A transcriptomic study of skeletal muscles from different parts of Texas black asses found significantly higher mRNA levels of ALDOA, ENO3, and PGK1 in the gluteus medius compared to the dorsal longissimus and popliteal muscles, indicating that these proteins regulate muscle glucose metabolism through distinct pathways [21]. However, further in vivo and in vitro studies are needed to validate the molecular mechanisms of ALDOA.

During glycogen synthesis, G6P is isomerized to glucose−1−phosphate by phosphoglucose isomerase, and then catalyzed by UDP−glucose pyrophosphorylase to form UDP−glucose (uridine diphosphate glucose), which serves as the direct glucose donor for glycogen synthesis [22]. In the present study, G6P levels were higher in the gluteus medius than in the liver, suggesting a lack of glucose−6−phosphatase activity in myocytes.

In this study, the content of gluconeogenic amino acids (L−tyrosine, L−valine, L−leucine, and L−tryptophan) was significantly higher in the liver compared to the gluteus medius muscle, likely due to the liver’s central role in gluconeogenesis. These amino acids are converted into their corresponding keto acids through deamination reactions, and these keto acids are subsequently converted into pyruvate via the TCA cycle. Leucine regulates the oxidative utilization of glucose in skeletal muscle by stimulating the glucose−alanine cycle. It also acts as an insulinotropic agent, promoting glycogen synthesis in response to hormonal signaling. Additionally, leucine plays a role in the initiation of protein translation, which is involved in glucose homeostasis, and can stimulate the translocation of glucose transporter 4 (Glut4) to the plasma membrane, further enhancing glycogen synthesis [23]. Insulin primarily regulates glucose metabolism by stimulating glycogen synthesis, and members of the insulin receptor substrate (IRS) family can be phosphorylated by tyrosine, playing a key regulatory role in glycogen synthesis and metabolism [24]. Pre−exercise supplementation with valine can prevent the reduction of hepatic glycogen or blood glucose in mice, helping to maintain physical performance and reduce fatigue during exercise, indicating a connection between valine and glycogen metabolism [25]. In the present study, leucine, tyrosine, and valine played a significant role in regulating glycogen synthesis in the liver of Yili horses, which is critical for glucose homeostasis in these horses.

Glycogen is a polyglucose molecule that stores energy, and when its levels rise, the body promotes glycogenolysis to release glucose, which is then metabolized into carbon dioxide and water via glycolysis and the TCA cycle, releasing energy [26]. The TCA cycle is the final common pathway for the metabolism of sugars, lipids, and amino acids, and its intermediates also serve as precursors for many biosynthetic processes, positioning it as the central hub of energy metabolism in the body [27]. Combined transcriptomic and metabolomic analysis in this study revealed that three genes—ACO1, ACLY, and PCK2—were positively associated with the TCA cycle in the gluteus medius muscle and liver comparison group. These genes showed a positive correlation with fumaric acid, a metabolite in the TCA cycle [28].

The first intermediate in the TCA cycle is citrate, a key metabolic regulator involved in glycolysis, the TCA cycle, gluconeogenesis, and fatty acid synthesis. In the cytoplasm, ATP−citrate lyase (ACLY) does not catalyze the synthesis of TCA intermediates from citric acid. Instead, it facilitates the degradation of excreted citric acid by cleaving it into oxaloacetate (OAA) and acetyl−CoA, both of which are critical for initiating the TCA cycle [29]. OAA can be used for de novo gluconeogenesis or in the TCA cycle, while acetyl−CoA serves as a precursor for several biosynthetic processes, including fatty acid synthesis. This highlights the essential role of the ACLY gene in the TCA cycle [30]. Each step in the TCA cycle is enzyme−catalyzed, and in the cytosol, ACO1 encodes aconitase 1, which catalyzes the interconversion of citric acid and isocitric acid via aconitate [31].

Phosphoenolpyruvate carboxykinase (PEPCK) exists in two isoforms: the cytoplasmic isoform PCK1 and the mitochondrial isoform PCK2. As a key gluconeogenic enzyme, PEPCK regulates cellular metabolic flexibility in response to energy stress. The expression of PCK2 is upregulated under low−glycemic conditions, promoting anabolism. PCK2 directly modulates the TCA cycle and facilitates gluconeogenesis by catalyzing the production of OAA from glutamine, indicating its involvement in multiple energy metabolic pathways [32]. Transcriptome analysis revealed that the genes ACO1, ACLY, and PCK2 were upregulated in the liver group, with the TCA cycle metabolite fumaric acid also upregulated. This suggests that Yili horses may enhance hepatic energy metabolism through these genes to better maintain blood glucose homeostasis.

5. Conclusions

Joint KEGG pathway analysis of differentially expressed genes and metabolites across groups revealed differences in glycogen synthesis and energy metabolism between the muscle and the liver of Yili horses. Yili mares rely on gluconeogenesis to supply precursors for hepatic glycogen synthesis. PCK2 and FBP1 are key regulators of hepatic gluconeogenesis, while ACO1, ACLY, and PCK2 are positively correlated with the TCA cycle and are all upregulated in the liver. Leucine, tyrosine, and valine also play a significant role in regulating liver glycogen synthesis in Yili horses. It is hypothesized that the genes and metabolites mentioned above contribute to the differences in energy metabolism between the liver and muscle tissues of Yili mares, but further in vivo and in vitro validation is necessary.