Whole transcriptomic analysis revealed the effects of different dietary protein levels on the growth and development of Jersey-yak

Whole transcriptomic analysis revealed the effects of different dietary protein levels on the growth and development of Jersey-yak | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Whole transcriptomic analysis revealed the effects of different dietary protein levels on the growth and development of Jersey-yak Guowu Yang, Bin Ma, Yongfu La, Xiaoming Ma, Xiaoyun Wu, Min Chu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7466956/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Jersey-yak, a hybrid offspring of Jersey cattle and yak, exhibits characteristics such as strong adaptability, rapid growth and development, and outstanding production performance. Therefore, it is necessary to find a reasonable supplemental feeding scheme and the regulation mechanism of the growth and development basis of Jersey-yak. Results In this study, we established transcriptomic information for lncRNAs, circRNAs, miRNAs, and mRNAs in the skeletal muscle tissue of Jersey-yak, and constructed a ceRNA network using differentially expressed RNAs. We identified 429, 298, and 84 differentially expressed mRNAs, 394, 356, and 302 differentially expressed lncRNAs, 212, 234, and 20 differentially expressed circRNAs, and 6, 7, and 6 differentially expressed miRNAs in LP vs. MP, LP vs. HP, and MP vs. HP, respectively. These genes were found to be involved in skeletal muscle development, cell proliferation and differentiation, and various signaling pathways, such as metabolic processes, adipose tissue development, and regulation of peptidase activity, as well as signaling pathways including PI3K-Akt, MAPK, HIF-1, Wnt, and Notch. Subsequently, we constructed three competitive endogenous RNA (ceRNA) networks, including DEGs, DELs, DECs and DEMs. In this network diagram, it mainly includes 6 miRNAs, 6 circRNAs, 8 mRNAs and multiple lncRNAs. These interactions affect the specific biological processes during the growth and development of Jersey-yak: MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579- LASP1 , MSTRG.24136.1-bgrcirc_004446-bgr-undef-67- NAGA / PDRG1 , MSTRG.155383.1-bgrcirc_004326-bgr-undef-29- PRAG1 / ACSF3 may affect the growth and development of Jersey-yak. Conclusions The whole transcriptome sequencing of Jersey-yak screened several key genes and ceRNA regulatory networks. These genes may be involved in biological processes such as metabolic processes, cell proliferation and adipose tissue development, as well as PI3K-Akt, MAPK and Notch signaling pathways involved in regulating the growth and development of Jersey-yak. These findings are helpful to further study the muscle development mechanism of yak hybrid offspring. Whole transcriptome muscle ceRNA Jersey-yak Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Yak ( Bos grunniens ) primarily inhabits the Qinghai-Tibet Plateau and surrounding regions at elevations of 3,000–5,500 meters, exhibiting remarkable adaptability to harsh environmental conditions such as low temperatures, low oxygen levels, high altitudes, and intense ultraviolet radiation. It is a unique cattle breed resource native to China’s high-altitude, low-oxygen regions [ 1 , 2 ]. China is the primary producer of yak, accounting for over 90% of the global population, primarily distributed across provinces and regions such as Gansu, Qinghai, Sichuan, Xinjiang, Yunnan, and Tibet. Additionally, yak populations are also found in countries such as India, Mongolia, Russia, and Nepal [ 3 ]. With the development of modern industrial technology, China's yak industry has gradually matured, with a growing number of high-value yak products entering the public eye and playing a significant role in driving local economic development. However, due to the extremely harsh natural environment in which yaks live, their growth is slow, leading to significant resource wastage. People have begun to crossbreed yak with superior breeds such as Jersey cattle and Angus cattle to improve yak production performance [ 4 , 5 ]. The Jersey-yak crossbreed is the F1 generation resulting from the crossbreeding of Jersey cattle and yak, and it exhibits significant hybrid vigor in terms of production performance. As a crossbred improved offspring of yak, the pre-slaughter live weight, carcass weight, and lean meat percentage of Jersey-yak are significantly higher than those of same-age yak. Research results indicate that under the same feeding conditions, the meat performance of crossbred offspring between Simmental and yak is better, but the meat quality is poorer, while Jersey-yak beef has better color and higher protein content [ 6 ]. Since most livestock farming areas still rely on a single grazing model, the growth cycles of yak and crossbred cattle such as the Jersey-yak are relatively longer, resulting in lower meat production rates. Additionally, due to traditional constraints, there has been a lack of innovation in farming techniques and basic infrastructure during the farming process, hindering the large-scale development of the yak industry. Furthermore, the cold weather conditions result in a prolonged dry season of up to seven months on pastoral grasslands, during which single-pasture grazing cannot meet the nutritional needs of large livestock such as yak and crossbred cattle [ 7 ]. During the dry season, most livestock do not receive timely and effective supplementary feed, leading to weight loss due to insufficient nutrient intake and increasing pressure on grassland recovery. Therefore, changing livestock feeding patterns during the cold season can not only alleviate pressure on grasslands and forage shortages but also accelerate livestock weight gain and reduce economic losses for herders. Growth traits are important indicators of the economic value of livestock, primarily including body height, body weight, body length, and growth rate [ 8 ]. Growth traits are primarily manifested through the growth and development of muscles, with skeletal muscle being an important category within muscles. The regulation of skeletal muscle development is a complex biological process involving multiple stages and various regulatory factors, including transcription factors during myofibrillar differentiation, genes or proteins that regulate muscle growth, and pathways that play a crucial role in the early differentiation of muscle cells and the regulation of protein synthesis [ 9 ]. In recent years, an increasing number of researchers have utilized transcriptomics technology to explore and elucidate the molecular mechanisms underlying various growth and developmental processes in plants and animals. Dehghanian et al. employed whole-transcriptome analysis and competitive endogenous RNA (ceRNA) network analysis to identify molecular regulators involved in intramuscular fat content and fat metabolism across five beef cattle breeds, identifying a total of 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs. Functional enrichment analysis revealed that these RNAs were enriched in signaling pathways related to fat metabolism, including metabolism, calcium, cGMP-PKG, and thyroid hormone signaling pathways. Additionally, genes such as MCU , CYB5R1 , and BAG3 were identified as important candidate marker genes for fat metabolism in beef cattle [ 10 ]. Circular RNAs (circRNAs) play a regulatory role in the development of skeletal muscle in animals. Bao et al. used RNA sequencing to reveal the temporal patterns of circRNAs expression at different growth stages in Tibetan sheep and investigated the ceRNA regulatory network in the pectoralis major muscle, elucidating the role of circRNAs in skeletal muscle fiber type conversion and their impact on meat quality, thereby deepening our understanding of the role of circRNAs in Tibetan sheep muscle development [ 11 ]. Skeletal muscle is the primary site for energy conversion, where mechanical energy plays a crucial role in muscle contraction and maintaining muscle function integrity. Research indicates that the growth traits of livestock are directly related to their feeding methods, such as muscle yield, intramuscular fat, and meat quality [ 12 ]. Many factors influence the quality of animal meat, among which nutrient intake (crude protein) is one of the most important factors. Additionally, as an excellent offspring of the crossbreeding between Jersey cattle and yak, the expression characteristics of lncRNAs and circRNAs in the growth and development process of Jersey-yak have been poorly studied. Therefore, it is necessary to further analyze the expression patterns of RNAs, including coding and non-coding RNAs, in Jersey-yak fed with different dietary protein levels during their growth and development. This study utilized RNA sequencing (RNA-seq) technology to investigate the expression characteristics of mRNAs, lncRNAs, and circRNAs in Jersey-yak under different protein-level dietary feeding patterns, providing essential research insights to promote muscle development and enhance productivity in Jersey-yak. Materials and methods Experimental Animals and Management This experiment was conducted in Yangnuo Yak Breeding Professional Cooperative in Xiahe County, Gannan Tibetan Autonomous Prefecture. In this experiment, 18 healthy, 6-month-old male Jersey-yaks (bred by artificial insemination of yaks with frozen semen of the same batch of Jersey cattle) were selected. The body weight before the pre-experiment was 62.20 ± 2.64 kg. According to the weighing data before the pre-experiment, 18 Jersey-yaks were randomly divided into three groups by using R software (v.4.1.2) [ 4 ]. The treatments of the three groups were: (1) no supplementary feeding (as a control group, low protein level, LP); (2) Supplementary low-protein diet (crude protein content: 15.16%; median protein level, MP); (3) Supplementary high protein diet (crude protein content: 17.90%; high protein level, HP). The specific composition and nutritional level of the two different crude protein level supplemental diets are shown in Table S1 . Weight Data Collection and Sample Collection At the beginning of the formal experiment, 18 Jersey-yaks were weighed and the initial body weight (IBW) of all Jersey-yaks in the three treatment groups was recorded. After the formal test began, all the Jersey cattle in the three groups were weighed on the 20th of each month until the end of the formal test. The weight data recorded during the period were the fasting weight of the Jersey-yak before grazing. Total weight gain (TWG) and average daily gain (ADG) were calculated based on the body weight data recorded during the formal experiment. At the end of the feeding experiment, three Jersey-yaks were randomly selected from each group for slaughter, and the muscle histology of the longissimus dorsi muscle was studied. The samples of the longissimus dorsi muscle tissue from the 12th to 13th ribs of the left half carcass were quickly collected during slaughter, immediately stored in liquid nitrogen, and brought back to the laboratory for transcriptome sequencing and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. All samples are stored in the refrigerator (-80°C). RNA Isolation, cDNA library construction, and Illumina Sequencing Total RNA was isolated from the LD muscle samples of Jersey-yak using TRIzol reagent (Invitrogen, CA, USA). Then, total RNA was purified using DNase and RNeasy Mini Kit (Qiagen, CA, USA). By means of ion fragmentation, the RNA is fragmented into fragments with a length of about 300bp. Using RNA as a template, use the first 6 base random primers and reverse transcriptase to synthesize the first strand of cDNA, and use the first strand cDNA as a template to synthesize the second strand cDNA. The quantity and quality of extracted total RNA were measured using NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, DE, USA), Bio-Photometer (Eppendorf, Hamburg, Germany) and 1% agarose gel electrophoresis. Finally, RNA integrity was assessed using the RNA NanoSeq 6000 Assay Kit for the Agilent 2100 Bioanalyzer system (Agilent Technologies, CA, USA). After RNA extraction, purification and library construction, the samples were subjected to Next-Generation Sequencing (NGS) based on Illumina HiSeq (LC Sciences, Houston, TX, United States) sequencing platform. These libraries were then subjected to Paired-end (PE) sequencing. Data Preprocessing and Read Mapping The samples were sequenced using a sequencing platform, generating image files. These files were converted using the platform's built-in software to produce raw FASTQ data. The sequencing data contained some reads with adapters and low-quality reads, which could significantly interfere with subsequent information analysis. Therefore, Cutadapt was used to remove sequences with adapters at the 3' end and reads with an average quality score below Q20. The upgraded version of TopHat2, HISAT2 [ 13 ], was used to align the filtered reads against the yak reference genome (Bosgru_v3.0) [ 14 ]. HISAT2 uses an improved BWT algorithm, which offers faster processing speeds and reduced resource requirements [ 15 ]. A reference genome index was created using Bowtie2, and the filtered reads were then aligned to the reference genome using TopHat2. LncRNAs acquisition and splicing LncRNAs refers to long non-coding RNA with a fragment length greater than 200 nt. Based on the structure and non-coding characteristics of LncRNAs, we screened candidate LncRNAs using three strict screening criteria. Subsequent analyses were performed on genes and the strictly screened candidate LncRNAs. First, we used the Stringtie software [ 16 ] (version 2.2.0) to assemble transcripts using the alignment results from Hisat2 [ 13 ]. After removing transcripts with uncertain strand orientation, we screened for LncRNAs in the remaining assembled transcript set. (1) Screen transcripts with a length ≥ 200 bp and ≥ 2 exons; (2) Screen transcripts with Class-code x/u/i. Specifically, Anti-sense LncRNAs, Intergenic LncRNAs, and Intronic LncRNAs transcripts. (Here, x refers to transcripts on the opposite strand of the reference transcript that cover its exons; u refers to unknown transcripts in intergenic regions; i refers to transcripts entirely within an intron of the reference transcript); (3) Screen for LncRNAs with coverage > 3 in at least one sample. That is, the transcript appears at least three times in one sample. Conduct encoding potential analysis on candidate LncRNAs to determine whether these new transcripts have the ability to encode proteins, thereby further screening the new transcripts to obtain high-confidence LncRNAs. We simultaneously employed the following three methods for analysis: PLEK [ 17 ], CNCI [ 18 ], and Pfamscan [ 19 ]. New transcripts deemed by all three software tools to lack coding potential were considered high-confidence LncRNAs and used for subsequent analysis. CircRNAs Identification After aligning with the reference genome, CircRNAs is identified using unaligned reads. These reads cannot be directly aligned to the reference genome because they originate from exonic regions that are far apart, resulting in large gaps during direct alignment. A portion of the sequence from the 5' end and 3' end of the Reads that did not align with the reference genome (referred to as the 5' Anchor and 3' Anchor, respectively) is re-aligned with the reference genome. If the two sequences align in opposite positions, the Read is likely derived from a CircRNAs. Extend the Anchor sequences continuously. If the sequence matches the reference genome perfectly up to the connection point, and the splicing pattern at the connection point conforms to the AG-GT splicing pattern, it is identified as a CircRNAs. After aligning the anchor sequences of each sample with the reference genome, we merged the alignment results of all samples and used find_circ (version 1.2, https://github.com/marvin-jens/find_circ/ ) to identify circRNAs [ 20 ]. We extracted the 20-bp regions at both ends of the unaligned reads from the HISAT2 alignment results as anchor sequences and re-aligned them to the genome using Bowtie2 (v 2.3.0) [ 21 ] to detect CircRNAs. We calculated the Reads Count values from the CircRNAs identification results of find_circ as the raw expression levels of CircRNAs, and then standardized the expression levels of CircRNAs using TPM [ 22 ]. Differential Expression Genes and Pathway Analysis We used Stringtie (version 2.2.0) software [ 16 ] to perform reads counting statistics on mRNAs and LncRNAs at the transcript level, obtaining the raw expression levels of mRNAs and LncRNAs. We then used Fragments Per Kilo bases per Million fragments (FPKM) [ 23 ] to normalize the expression levels and calculate their expression levels. We standardized the counts for each sample using the DESeq software [ 24 ] and calculated the fold change (FC). We used the negative binomial distribution test (NB) to assess the statistical significance of the count differences. Finally, differentially expressed mRNAs (DEGs), lncRNAs (DELs), circRNAs (DECs), and miRNAs (DEMs) were identified based on the criteria |log2FC| >1 and p < 0.05. To further understand gene function, functional pathway enrichment analysis was performed using Gene Ontology (GO, http://www.geneontology.org ) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/ ). Each GO and KEGG enrichment item were confirmed using a hypergeometric distribution test. Then, p -values were corrected using Benjamini and Hochberg multiple testing. Enrichments with p < 0.05 were considered significant. Construction of ceRNA Network Since most LncRNAs share certain sequence similarities with mRNAs, it suggests that miRNAs may negatively regulate LncRNA expression through mechanisms similar to those of mRNAs, thereby exerting a series of biological effects. LncRNAs can not only directly bind to target genes or participate in gene expression regulation through histone modifications, but also act as competitive endogenous RNAs (ceRNA) to interact with miRNAs, thereby participating in the regulation of target gene expression. Additionally, miRNAs can promote the expression of specific LncRNAs, while circRNAs can bind to miRNAs to inhibit their function. To better understand the interactions between mRNAs, circRNAs/lncRNAs, and miRNAs in the growth and development of Juanma cattle, a circRNA/lncRNA-miRNA-mRNA regulatory network was constructed. First, using miRanda to predict interactions between miRNA-mRNA, miRNA-lncRNA, and miRNA-circRNA, interactions with energy not exceeding − 20 kcal/mol were retained, and the network was visualized using Cytoscape (v.3.9.1) software. Quantitative Real-Time PCR for Validating Gene Expression Randomly selected differentially expressed RNAs were subjected to real-time quantitative PCR (qRT-PCR) experiments. Primers were designed using Primer Premier 5.0 software, with β-actin as the internal reference gene (Table S8 ). RNA was reverse transcribed into cDNA using the TransScript First-Strand cDNA Synthesis SuperMix kit. The total reaction volume for qRT-PCR was 20 µL, containing 10 µL SYBR Green Premix Pro Taq HS Qpcr kit (Aikrui Bio, Hunan, China), 0.4 µL each of forward and reverse primers, 1 µL cDNA, and 8.2 µL ddH₂O. The amplification program was set according to the kit instructions, with an annealing temperature of 58°C. The relative expression level of RNA was calculated using the 2 −△△CT method based on the cycle threshold (CT). Results Effects of Different Protein Levels on Body Weight of Jersey-yak. The effect of crude protein level in the supplementary diet on the body weight of Jersey-yak is shown in Table 1 [ 25 ]. The results showed that there was no significant difference in body weight between the initial test and the 30 th day ( P > 0.05). There were significant differences in body weight at 60 days, 92 days and 120 days ( P < 0.05). The results showed that the body weight of MP group and HP group was significantly higher than that of LP group from the 60 th day after grazing ( P < 0.05). ADG and TWG in MP group and HP group were significantly higher than those in LP group ( P 0.05). The results showed that after supplementary feeding after grazing, the growth performance of Jersey-yak was significantly improved from 60 days. Table 1 Effects of dietary levels on body weight of Jersey-yak Items (kg) Treatment group (unit: kg, Mean ± SEM) P -Value LP MP HP Pre-test weight (kg) 64.92 ± 4.73 63.84 ± 4.54 64.84 ± 5.91 0.986 Initial weight 65.80 ± 4.89 67.08 ± 5.05 70.36 ± 4.78 0.798 30 days weight 68.88 ± 3.40 74.28 ± 4.64 82.32 ± 5.03 0.155 60 days weight 72.40 ± 4.05 b 83.16 ± 4.35 ab 88.44 ± 3.83 a 0.046 90 days weight 75.34 ± 4.47 b 88.66 ± 4.67 a 94.10 ± 3.01 a 0.020 120 days weight 73.72 ± 4.76 b 91.88 ± 5.19 a 96.76 ± 3.17 a 0.008 ADG 0.07 ± 0.05 b 0.21 ± 0.05 a 0.22 ± 0.03 a 0.027 TWG 7.92 ± 5.60 b 24.80 ± 6.04 a 26.40 ± 3.57 a 0.027 Note: Different letters on the shoulder markers in the same row of data indicate a significant difference ( P < 0.05). Analysis of RNA-Seq data In order to evaluate the genes related to the growth and development of Juan cattle-yak, skeletal muscle tissues were collected and all mRNA and non-coding RNAs were analyzed by high-throughput sequencing. After screening out low-quality and redundant readings, we obtained an average of 74,483,798,72,003,778 and 76,062,278 clean readings from the skeletal muscle samples of the three groups of LP, MP and HP, respectively. Among them, Q30 was greater than 93.69%, and the successful comparison of the readings with the reference genome was about 93.57% (Table S2 ). We detected a large number of mRNAs, lncRNAs and circRNAs (Table S3 ). It was found that the length of lncRNAs was mainly concentrated at 200 bp. The proportion of lncRNAs containing two exons accounted for the majority, while most of the mRNAs had more than 10 exons. The average length of circRNAs detected was 1925 bp, and 10.48% of them were longer than 2000 bp. The length distribution of circRNAs in all samples was relatively uniform. Differentially expressed mRNAs during growth and development of Jersey-yak We used DESeq to analyze the differential expression of mRNAs. A total of 429,298 and 84 mRNAs were identified to be significantly differentially expressed in LP vs. MP, LP vs. HP and MP vs. HP groups, respectively (Table S4 ). Among these, in the LP vs. MP comparison group, 122 were downregulated and 307 were upregulated (Fig. 1 A). In the LP vs. HP comparison group, 83 were downregulated and 215 were upregulated (Fig. 1 B). In the MP vs. HP comparison group, 39 were downregulated and 45 were upregulated (Fig. 1 C). These genes include phosphoglycerate kinase 1 ( PGK1 ), myogenin ( MYOG ), cellular communication network factor 1 ( CCN1 ), enolase 1 ( ENO1 ), insulin-like growth factor binding protein 5 ( IGFBP5 ), and insulin-like growth factor binding protein 6 ( IGFBP6 ), which may be associated with muscle growth, fat deposition, and regulation of growth and development. Using a Venn diagram to highlight DEGs associated with each pair of samples (Fig. 1 D), 74 differentially expressed mRNAs were found to be shared between the LP vs. HP and LP vs. MP comparison groups. Cluster analysis indicated high reproducibility among DEGs across the three groups while revealing substantial differences between the three groups (Fig. 1 E). To gain a deeper understanding of the biological functions of DEGs, we performed GO and KEGG analyses (Fig. 2 , Table S5 ). The results showed that DEGs in the LP vs. MP group were significantly enriched in positive regulation of chondrocyte proliferation, collagen fibril organization, extracellular structure organization, connective tissue development, histone H3-K56 acetylation, and other biological processes at the cellular and tissue levels that are crucial for the regulation and formation of growth and development in Jersey-yak. They also participate in pathways involved in important physiological processes such as protein digestion and absorption, the HIF-1 signaling pathway, and the regulation of lipolysis in adipocytes, which are related to protein digestion, energy metabolism, and fat metabolism (Fig. 2 A and D). In the LP vs. HP group, the DEGs were significantly enriched in biological process entries such as NADH metabolic process, gluconeogenesis, glycolytic process, and cardiac muscle cell contraction, which play crucial roles in the growth and development of Jersey-yak. Additionally, they were enriched in biological process entries related to fat metabolism, such as adipose tissue development (adipose tissue development). They are also significantly enriched in cellular components of protein complexes with important functions in regulating gene expression, such as the PRC1 complex and the PcG Protein Complex. Furthermore, it is involved in the regulation of biological processes such as energy metabolism, nitrogen metabolism, cellular physiological functions, and growth and development processes, and plays a crucial role in maintaining normal physiological states and adapting to the environment. This includes signaling pathways and metabolic pathways such as ECM-receptor interaction, glycolysis/gluconeogenesis, glucagon signaling pathway (Fig. 2 B and E). Finally, the DEGs in the MPvsHP group were significantly enriched in biological process entries related to the regulation of developmental processes, cartilage development, and other processes crucial for maintaining the body's immune defense, resisting pathogens, and maintaining immune balance, such as the MyD88-dependent toll-like receptor signaling pathway. They were also involved in pathways such as the PI3K-Akt signaling pathway, the MAPK signaling pathway, and the Toll-like receptor signaling pathway (Fig. 2 C and F). Total lncRNA and differentially expressed lncRNA during the growth and development of Jersey-yak CNCI, PLEK and Pfam methods were used for comparative analysis to identify lncRNAs in muscle tissue RNA samples, resulting in a total of 81,032 lncRNAs detected in tissues (Fig. 3 A). Differential expression analysis showed that 394,356 and 302 lncRNAs were significantly differentially expressed in the three comparison groups of LP vs. MP, LP vs. HP and MP vs. HP, respectively. In the comparison group LP vs. MP, 178 DELs were significantly upregulated and 216 DELs were significantly downregulated (Fig. 3 B); in the comparison group LP vs. HP, 136 DELs were significantly upregulated and 220 DELs were significantly downregulated (Fig. 3 C); in the comparison group MP vs. HP, 138 DELs were significantly upregulated and 164 DELs were significantly downregulated (Fig. 3 D). Through the Wayne diagram to highlight the DELs associated with each pair of samples, it was found that 73 DELs were co-expressed between the LP vs. HP and LP vs. MP comparison groups (Fig. 3 E). In order to reveal the potential function of identified lncRNAs in the muscle growth and development of Jersey-yak, we predicted the target genes of lncRNAs (Table S6 ). GO enrichment analysis and KEGG pathway analysis revealed several important pathways associated with muscle tissue development in the three comparison groups (Fig. 4 , Table S6 ), such as protein dephosphorylation, skeletal muscle cell differentiation, protein ADP-ribosylation, regulation of epithelial cell proliferation, regulation of peptidase activity, animal organ development, cGMP-PKG, MAPK, and Wnt signaling pathway. These pathways play a key role in cell proliferation and differentiation, as well as skeletal development. It was found that the target genes of cis-acting lncRNAs were significantly enriched in the Notch signaling pathway in the MP vs. HP group. This pathway is a highly conserved intercellular signaling pathway that plays an important role in animal development, angiogenesis, skeletal development, cell fate determination, and tissue repair [ 26 , 27 ]. Differential expression of miRNAs and circRNAs during growth and development of Jersey-yak In the LP vs. MP, LP vs. HP, and MP vs. HP samples, there were fewer DEMs, with only 6 (5 upregulated, 1 downregulated), 7 (6 upregulated, 1 downregulated), and 6 (1 upregulated, 5 downregulated) DEMs, respectively (Table S4 ). GO functional enrichment analysis revealed that the target genes of these DEMs were significantly enriched in biological processes related to promoting cellular function and the animal's normal development, repair, and adaptation to the environment, such as positive regulation of cellular process and positive regulation of macromolecule biosynthetic process (Table S5 ). KEGG pathway analysis revealed that the target genes of these DEMs were significantly enriched in pathways critical for animal growth and development, such as the Apelin, PI3K-Akt, AMPK, ECM-receptor interaction, Wnt, Hippo, Ras, and MAPK signaling pathway. Among these, the Hippo, MAPK, and AMPK signaling pathway are associated with lipid metabolism in animals (Table S5 ). Among the circRNAs annotated in this study, exon-containing circRNAs accounted for 71.12%, including multiple exon circRNAs (annot) and single exon circRNAs (one), which accounted for 65.51% and 5.61%, respectively. intergenic circRNAs, intronic circRNAs, and antisense circRNAs accounted for 9.46%, 2.24%, and 4.01%, respectively (Fig. 5 E). Differential expression analysis revealed 212 (Fig. 5 A), 234 (Fig. 5 B), and 20 (Fig. 5 C) differentially expressed circRNAs in the LP vs. MP, LP vs. HP, and MP vs. HP comparison groups, respectively. Using a Venn diagram to highlight the DECs associated with each pair of samples (Fig. 5 D), it was found that there were 98 common DECs between the LP vs. HP and LP vs. MP comparison groups. Notably, among these DECs in the LP vs. HP and LP vs. MP comparison groups, bgrcirc_017953, bgrcirc_015858, and bgrcirc_003993 were simultaneously upregulated. GO functional enrichment analysis revealed that DECs in the LP vs. MP group were significantly enriched in regulation of cellular component organization (Fig. 6 A), which has important implications for animal growth and development, including cell differentiation and organ development, tissue and organ function, cell proliferation and division, cell signaling, and cell metabolism and energy conversion [ 28 ]. In the LP vs. HP group, the target genes of DECs were significantly enriched in positive regulation of catalytic activity and positive regulation of hydrolase activity (Fig. 6 B). Among these, positive regulation of catalytic activity plays a regulatory role in processes such as cell proliferation, differentiation, organ development, and cell migration during animal growth and development. However, the effects of positive regulation of hydrolase activity on animal growth and development are primarily manifested in nutrient digestion and absorption, activation of metabolic pathways, activation of growth factors, and degradation of waste products [ 29 ]. These regulatory processes are crucial for normal animal growth and development and the maintenance of organ function. Additionally, the biological process entry regulation of cellular component organization was significantly enriched in the MP vs. HP group (Fig. 6 C), involving multiple biological processes and cellular components, and playing a vital role in maintaining normal muscle function and homeostasis. KEGG pathway analysis revealed that DECs in the LP vs. MP group were significantly enriched in the ABC transporters pathway (Fig. 6 D). The ABC transporter pathway plays a crucial role in animal growth and development, including nutrient absorption and metabolism, tissue development and organ formation, as well as drug metabolism and toxin clearance. These processes are essential for normal growth, development, and health in animals [ 30 ]. Abnormal regulation of the ABC transporter pathway may lead to diseases such as growth and development abnormalities, metabolic disorders, and drug metabolism abnormalities. Additionally, DECs were significantly enriched in the inositol phosphate metabolism pathway, which is involved in various important biological processes within cells, including signal transduction, membrane phospholipid metabolism, and energy metabolism [ 31 , 32 ]. Notably, DECs in the LP vs. HP group were also significantly enriched in the ABC transporters pathway (Fig. 6 E). In contrast, the target genes of DECs in the MP vs. HP group were not enriched in any pathway. Construction of the ceRNA Coregulatory Network Based on the regulatory relationships among DEGs, DELs, DECs, and DEMs, we identified significantly differentially expressed lncRNAs, circRNAs, and mRNAs that are co-regulated by the same miRNAs. Network diagrams of circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA were constructed based on the relationships among mRNAs, circRNAs, lncRNAs, and miRNAs (Fig. 7 A and B). The circRNA-miRNA-mRNA network primarily involves 6 circRNAs, 9 miRNAs, and mRNAs such as PLTP , PRAG1 , and CXXC1 . The lncRNA-miRNA-mRNA network primarily involves 7 miRNAs, 16 lncRNAs, and mRNAs such as CDKN2C , LRWD1 , and ACSF3 . To determine the regulatory relationships between coding and non-coding RNAs during the growth and development of J Jersey-yak, we analyzed the regulatory relationships between differentially expressed RNAs through miRNA-miRNA, miRNA-circRNA, and miRNA-lncRNA. We identified significantly differentially expressed mRNAs, circRNAs, and lncRNAs co-regulated by miRNAs, thereby constructing a co-expressed lncRNA-circRNA-miRNA-mRNA network diagram between coding RNAs and non-coding RNAs (Fig. 7 C). This network diagram primarily includes 6 miRNAs, 6 circRNAs, 8 mRNAs, and multiple lncRNAs. MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579- LASP1 , MSTRG.24136.1-bgrcirc_004446-bgr-undef-67- NAGA/PDRG1 , MSTRG.155383.1-bgrcirc_004326-bgr-undef-29- PRAG1/ACSF3 , etc., may influence the growth and development of Jersey-yak. Validation of RNA-Seq results Eight differentially expressed RNAs were randomly selected from the sequencing results of the longest muscle tissue of the Jersey-yak for qRT-PCR experiments, including 4 mRNAs ( MYOG , PGK1 , IGFBP5 , and ENO1 ), 2 lncRNAs (MSTRG.2715.8 and MSTRG.54295.6), and 2 circRNAs (bgrcirc_005011 and bgrcirc_017953). The expression trends of the RNAs in the three comparison groups were found to be consistent with the qRT-PCR results (Fig. 8 ). Discussion Yaks provide the necessary means of production for the herdsmen in the Qinghai-Tibet Plateau and surrounding areas. However, yaks live in high altitude areas above 3,000 m, and extremely harsh environments such as low temperature and low oxygen lead to slow growth and poor production performance. In recent years, people have used excellent breeds such as Jersey cattle, Angus and Simmental cattle to cross with yaks to obtain hybrid advantages to improve the production performance of yaks. Jersey-yak is the offspring of people using Jersey cattle and yak for hybridization. In terms of phenotype, it combines the advantages of both parents, so that Jersey-yak shows obvious heterosis in growth rate, meat production rate and other traits. The growth and production performance of ruminants are affected by dietary protein levels. Studies have shown that adding crude protein to the diet can improve the production performance of beef cattle, and reducing or not adding crude protein will reduce the average daily gain of calves [ 33 , 34 ]. Protein in the diet is one of the primary sources of energy for animal tissues and plays a crucial role in the growth and development of ruminants. Additionally, the addition of appropriate amounts of protein to the diet significantly promotes the production performance and organ development of ruminants [ 35 ]. Due to the prolonged dry season during the cold season in the Qinghai-Tibet Plateau region, there is a seasonal imbalance in forage supply. During the winter and spring seasons, Jersey-yak experience weight loss and reduced body condition due to insufficient forage, nutrient deficiencies in pasture grass, and harsh environmental conditions such as low oxygen levels and extreme cold, which can impair subsequent fattening and result in significant economic losses. Research findings indicate that supplementing early-weaned yak with feed of varying crude protein levels after grazing can improve calf growth performance while minimizing energy deficiency in mother yaks during the winter feed scarcity period [ 36 ]. Researchers conducted supplementary feeding on 18-month-old yak during the cold season on the Qinghai-Tibet Plateau, and the results showed that the body weight and average daily weight gain of the supplementary feeding group were significantly improved [ 37 ]. Therefore, this study used diets containing 15.16% and 17.90% crude protein to supplement the Jersey-yak. The experimental results showed that the average daily weight gain and total weight gain of the HP and MP groups were significantly higher than those of the control group, consistent with the aforementioned studies. These findings indicate that during the dry season, providing yak and their hybrid offspring with appropriate supplementary feeding, combined with a reasonable crude protein level in the diet, has significant potential to enhance their productivity and can appropriately reduce economic losses for herders. In this study, using whole-transcriptome sequencing technology, an average of 66,869,478, 64,964,700, and 67,520,923 clean reads were obtained from the LP, MP, and HP groups of Jersey-yak, respectively, with Q30 base distribution ranging from 93.69–94.18%. By aligning the reads to the reference genome, the alignment rates for each sample ranged from 93.57–94.47%. Differentially expressed RNAs were detected using qRT-PCR technology, and the results showed that the quantitative results of the selected RNAs were consistent with the sequencing results, indicating the reliability of the sequencing data. Through comparative analysis, 429, 298, and 84 differentially expressed mRNAs were identified in the LP vs. MP, LP vs. HP, and MP vs. HP comparison groups, respectively. Through GO and KEGG functional annotation analysis, these DEGs were mainly involved in pathways related to enzyme activity, metabolic processes, cell contraction, and tissue development. They were also enriched in pathways related to muscle growth and development, as well as fat cell storage and release, such as PI3K-Akt, MAPK, and regulation of fat cell lipolysis. Furthermore, these DEGs were enriched in pathways related to cellular adaptation to oxygen environments, such as the HIF-1 signaling pathway, which plays a key regulatory role in the body's response to low oxygen concentrations or hypoxia and in maintaining oxygen homeostasis [ 38 ]. This is consistent with the ability of Jersey-yak to adapt to low-oxygen environments in high-altitude regions. Through the Venn diagram, it was found that there were 74 DEGs co-expressed between LP vs. HP and LP vs. MP. The analysis found that these DEGs regulated the biological processes of adipocyte differentiation and proliferation, such as adipose tissue development and adipocyte differentiation. These DEGs were also found to be associated with glycosaminoglycan biosynthesis-heparin sulfate/heparin, PPAR signaling pathway, pantothenate and CoA biosynthesis [ 39 , 40 ]. Among them, glycosaminoglycan biosynthesis-heparin sulfate / heparin plays an important role in anticoagulation, cell signal transduction, extracellular matrix function and immune regulation in vivo [ 41 ]. In addition, many of these co-expressed DEGs have been found to play an important role in regulating the growth and development of muscle tissue, including COL15A1 , FOXO1 , PYGO2 , SOX7 , PLOD3 , and NME3 , which are related to the development and regulation of muscle, bone and nervous system. The collagen type XV alpha 1 chain ( COL15A1 ) originally belonged to non-fibrous collagen and is present in many tissues [ 42 ]. Studies on neonatal heart, skeletal muscle, and smooth muscle tissues have shown that muscle RNA has high levels of type XV collagen mRNA expression, indicating that it is not only localized in fibroblasts in the endothelium but also in myoblasts. Interestingly, certain epithelial cells in the kidney, lung, pancreas, and placenta can also synthesize XV-type collagen mRNA. These findings suggest that XV-type collagen transcripts are widely distributed, with the primary producers being mesenchymal-derived cells, particularly muscle cells and fibroblasts [ 43 ]. COL15A1 encodes the α chain of collagen XV, primarily produced by mesenchymal cells, including muscle cells, adipocytes, fibroblasts, and neurons [ 44 ]. Therefore, the COL15A1 gene plays a crucial role in the growth and development of Jersey-yak and the formation of tissue structure. The Forkhead Box O1 ( FOXO1 ) gene encodes the FoxO1 protein, a member of the FoxO protein family, which plays a crucial regulatory role in various cellular processes. It exerts its functions by binding to the promoters of downstream target genes or interacting with other transcription factor proteins [ 45 ]. Studies have shown that the FOXO1 gene is highly expressed in insulin-responsive tissues, including the pancreas, liver, skeletal muscle, adipose tissue, and bone, and plays a crucial role in regulating muscle growth, metabolism, cell proliferation, and differentiation [ 46 ]. Its specific function involves inhibiting the downstream key factor Akt1/2 of the insulin signaling pathway, thereby impairing skeletal muscle development. However, knocking out FOXO1 can restore developmental capabilities in skeletal muscle and other tissues [ 47 ]. Additionally, studies have shown that certain non-coding RNAs can regulate FOXO1 expression, thereby influencing muscle cell proliferation and differentiation [ 48 ]. These findings suggest that reducing FOXO1 gene expression may promote muscle cell generation, thereby facilitating muscle growth and development. Notably, FOXO1 gene expression was downregulated in both the LP vs. HP and LP vs. MP comparison groups, consistent with its functional role. The SRY-box transcription factor 7 ( SOX7 ) gene encodes the transcription factor SOX7 and belongs to the Sox family. Satellite cells are skeletal muscle-specific stem cells that are activated upon skeletal muscle injury, initiating proliferation, differentiation, and fusion to facilitate repair. Research has shown that the SOX7 gene is expressed in quiescent satellite cells [ 49 ]. Additionally, genes such as SOD3 , LASP1 , and ACAD8 , which are associated with the development of muscle, bone, and organs in Jersey-yak, were differentially expressed and upregulated in both supplementation groups [ 50 , 51 ]. Although an increasing number of researchers are beginning to study the mechanisms related to lncRNA, the functions of lncRNA remain largely unknown. However, researchers believe that lncRNA is involved in many fundamental cellular processes [ 52 ], and the half-life of nuclear lncRNA is very short (< 2 h), making it highly susceptible to regulatory functions [ 53 ]. During biological development, lncRNA, as a non-coding RNA, acts as an important regulatory factor in gene expression during the processing of genetic information in living cells and interacts with major cellular pathways such as proliferation, differentiation, and apoptosis [ 54 ]. In this study, 356, 394, and 302 differentially expressed lncRNAs were identified in the three comparison groups of LP vs. HP, LP vs. MP and MP vs. HP, respectively. Through the Venn diagram, 73 lncRNAs were found to be differentially co-expressed between the two control groups and the supplementary feeding group. The number of exons of most lncRNAs identified in this study is 2–3, and the length of most lncRNA exons is about 200 bp, which is basically consistent with the characteristics of lncRNAs identified in other mammals [ 55 , 56 ]. GO and KEGG functional enrichment analysis showed that the target genes of these DELs are mainly involved in biological processes related to enzyme activity, cell proliferation and differentiation, and metabolism. Among these, the FGF2 gene is a key regulator of muscle cell proliferation and differentiation. Therefore, lncRNAs such as MSTRG.76433, MSTRG.76436, MSTRG.76443, and MSTRG.76478 can be studied as co-expressed lncRNAs of the FGF2 gene. From an evolutionary perspective, the Wnt signaling pathway is highly conserved and plays a significant role in embryonic development, growth and development, and the regeneration of adult tissues [ 57 ]. It is also crucial for maintaining genetic stability and plays a key role in determining cell fate, differentiation, apoptosis, cell movement, and stem cell maintenance [ 58 ]. Additionally, the Wnt signaling pathway is a critical pathway for controlling bone formation and fat generation by regulating processes such as cell fate determination, cell proliferation, and differentiation [ 59 ]. In this study, 14 target mRNAs of differentially expressed lncRNAs in the LP vs. MP group were significantly enriched in the Wnt signaling pathway, among which MSTRG.239143.1, which regulates the WNT3A gene, was upregulated in the MP group. The WNT3A gene encodes the Wnt-3a protein, which is an important member of the Wnt signaling pathway. Studies have shown that the Wnt-3a protein participates in regulating the proliferation, differentiation, and formation of skeletal cells, and the absence or mutation of WNT3A may lead to skeletal developmental abnormalities or skeletal diseases [ 60 ]. Previous studies have shown that Wnt3a can regulate the functions of MYOD and MYOG [ 61 ], which are highly expressed genes in fast-twitch and slow-twitch muscle fibers, respectively. Overexpression of WNT3A promotes the expression of myosin heavy chain type I ( MyHC-I ) in slow-twitch skeletal muscle fibers and facilitates cell differentiation toward slow-twitch fibers [ 62 ]. As research has progressed, it has been discovered that circRNAs are indispensable in muscle growth and development. circRNAs are a novel type of circular endogenous RNA formed by reverse splicing of mRNA precursors. Due to their circular closed-loop structure, they exhibit conservation and stability, unlike linear RNAs, which lack a 5' cap and 3' poly(A) tail [ 63 ]. In recent years, numerous studies have shown that circRNAs can regulate gene expression through various mechanisms, including acting as “molecular sponges” to absorb small RNA molecules or translating proteins to regulate muscle growth [ 64 , 65 ]. Recent research has further revealed that circRNAs can also regulate muscle growth and development by interacting with RNA-binding proteins (RBPs) [ 66 ]. Research has found that CDR1as (the antisense transcript of cerebellar degeneration-related protein 1, also known as ciRS-7, is a neuronal circRNA that plays a crucial role in regulating muscle differentiation) is highly expressed in the muscles of mid-embryonic goat fetuses and is activated during the initial stages of in vitro muscle differentiation. MyoD promotes CDR1as by binding to its 5′ end region. Overexpression or knockout of CDR1as significantly induces or inhibits muscle differentiation, respectively. By competitively binding to miR-7, CDR1as alleviates miR-7-mediated downregulation of IGF1R , thereby activating muscle differentiation. These findings highlight the critical role of CDR1as in muscle differentiation [ 65 ]. GO analysis revealed that the target genes of these DECs are involved in enzyme activity regulation, cell proliferation, differentiation, and muscle contraction. Among them, the MYL1 protein encoded by myosin light chain 1 ( MYL1 ) participates in muscle fiber assembly and muscle development, promoting muscle cell growth and differentiation, and plays an important role in muscle growth and development. The bgrcirc_017953 targeting the MYL1 gene was upregulated in both the LP vs. HP and LP vs. MP groups, suggesting that bgrcirc_017953 may have a regulatory role in muscle development in Jersey-yak. Through KEGG pathway enrichment analysis, it was found that the target genes of DECs in the LP vs. HP and LP vs. MP groups were significantly enriched in the ABC transport pathway. The ABC transport pathway plays an important regulatory role in animal growth and organ development, with the ABCC1 , ABCA1 , and ABCG2 genes all enriched in this pathway. These genes are involved in the transport and metabolic regulation of substances, influencing animal growth and development processes. The expression of bgrcirc_011298 related to ABCC1 gene was downregulated in both HP and MP groups, and there may be competitive regulation between them [ 67 ].Its function research deserves further study. ceRNA is a regulatory mechanism in living organisms involving mRNA, lncRNA, miRNA, and circRNA. These RNA molecules regulate each other by competitively binding to common miRNAs, forming a complex regulatory network. The fundamental principle of this mechanism is that RNA molecules can compete for miRNA molecules by possessing identical or similar miRNA response elements (MREs), thereby influencing each other's expression and function [ 68 , 69 ]. This interaction forms the ceRNA network, which plays a role in many biological processes such as cell proliferation, differentiation, and development. In order to determine the regulatory relationship between coding RNAs and non-coding RNAs in the growth and development of Jersey-yak, based on the regulatory relationship between differentially expressed RNAs of miRNA-mRNA, miRNA-circRNA and miRNA-lncRNA, we identified significantly differentially expressed mRNAs, circRNAs and lncRNAs co-regulated by miRNAs, thus constructing a ceRNA regulatory network diagram between coding RNAs and non-coding RNAs. The ceRNA network contains 8 DEGs, 6 DECs, 6 DEMs, and multiple DELs. This study showed that MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579- LASP1 , MSTRG.24136.1-bgrcirc_004446-bgr-undef-67- NAGA / PDRG1 , MSTRG.155383.1-bgrcirc_004326-bgr-undef-29- PRAG1 / ACSF3 may be involved in the regulation of growth and development of Jersey-yak. Among them, bgr-undf-29 targeted multiple DELs such as MSTRG.155383.1, MSTRG.190135.23 and MSTRG.51284.4, as well as 4 DEGs such as OLFM1 , PRAG1 , ACSF3 and PALM . Analysis revealed that the PEAK1-related, kinase-activating pseudokinase 1 ( PRAG1 ) gene is associated with biological processes such as cell migration, metabolism, and protein phosphorylation. Studies have shown that PRAG1 induces cell protein phosphorylation by binding to the CSK tyrosine kinase [ 70 ]. Research has shown that PRAG1 is highly expressed in various tissues, including the cerebral cortex, respiratory system, and digestive tract [ 71 ]. PRAG1 is localized in the cytoplasm, cell junctions, focal adhesions, and cell nucleus, and has been reported to participate in the regulation of the cytoskeleton, leading to morphological changes in various cell types [ 72 , 73 ]. PRAG1 is primarily associated with neural cell morphology regulation, cell contraction, and cellular stress mechanisms. Although there are no studies confirming its role in animal growth and development, bone cell proliferation, and differentiation, this study suggests that it may influence the growth and development of Jersey-yak. Olfactomedin 1 ( OLFM1 ), also known as “noelin” or “pancortin,” is a secreted glycoprotein that has garnered significant attention in recent years [ 74 ]. This protein is highly expressed in brain tissue, particularly in regions closely associated with cognitive processes, such as the hippocampus and cortex [ 75 ]. Research has shown that the OLFM1 gene has an inhibitory effect on osteoclast formation in mouse cells [ 76 ]. Acyl-CoA synthetase family member 3 ( ACSF3 ) encodes an enzyme in the acyl-CoA synthetase family, which activates fatty acids by catalyzing the formation of a thioester bond between fatty acids and coenzyme A [ 77 ]. Research has shown that ACSF3 is associated with meat traits, carrying quantitative trait loci related to beef production traits. Functional analysis revealed its involvement in biologically relevant processes such as fatty acid oxidation, biosynthesis, and lipid storage [ 78 ]. The results indicate that the genes in this ceRNA network, along with their corresponding miRNAs, lncRNAs, and circRNAs, may influence the growth and development of skeletal muscle and fat formation in Jersey-yak. Conclusion In this study, whole-transcriptome sequencing was performed on Jersey-yak fed diets with different protein levels. Bioinformatics analysis identified a large number of differentially expressed coding RNAs and non-coding RNAs, which play important roles in the growth and development of Jersey-yak skeletal muscle. Analysis of the ceRNA network revealed that MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579- LASP1 , MSTRG.24136.1-bgrcirc_004446-bgr-undef-67- NAGA/PDRG1 , MSTRG.155383.1-bgrcirc_004326-bgr-undef-29- PRAG1/ACSF3 , and others may be involved in the cell proliferation, differentiation, and muscle growth and development of Jersey-yak. This study provides a theoretical foundation for further understanding the mechanisms underlying muscle development in yak hybrid offspring. Declarations Acknowledgements The authors thank the mass spectrometry analysis of Shanghai Bioprofile Technology Co., Ltd. (Shanghai, China) Author contributions GWY performed the experiments and the manuscript. BM and YFL performed the sample collection. GWY and XMM analysed the data. XYW, and MC suggested revisions to the manuscript. XG was involved in the design of the experiments. LW and CNL designed the study and provided overall guidance. All authors read and approved the final version of the manuscript. Funding This research was funded by the Modern Beef Yak Industry Technology System, grant number MATS-Beef Cattle System, CARS-37; the Hezuo City Yak Germplasm Improvement and Quality Improvement Project. Data availability RNA-seq data from this study can be read from NCBI sequences (Accession Number: PRJNA1310280). Ethics approval and consent to participate All the animal experiments were approved by Lanzhou Institute of Husbandry and Pharmaceutical Sciences of the Chinese Academy of Agricultural Sciences (CAAS) with the grant number: No. 1610322020018. All the slaughter as well as sampling procedures strictly complied with the Guidelines for Ethical Treatment of Experimental Animals of China. Competing interests The authors declare no competing interests. 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Edea Z, Jung KS, Shin S-S, Yoo S-W, Choi JW, Kim K-S: Signatures of positive selection underlying beef production traits in Korean cattle breeds . Journal of Animal Science and Technology 2020. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":149685,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of differentially expressed mRNAs among different groups of Jersey-yaks. (A) Volcano plot showing the differential expression of mRNAs in LP vs. MP. (B) Volcano plot showing the differential expression of mRNAs in LP vs. HP. (C) Volcano plot showing the differential expression of mRNAs in MP vs. HP. (D) The co-expressed DEGs in three comparison groups. (E) Heatmap showing the cascading relationship of DEGs in different comparison groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/f341e8394eaf89088de1f86c.png"},{"id":92514678,"identity":"da1870df-e61e-4104-be70-11031ddbdd90","added_by":"auto","created_at":"2025-09-30 14:00:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134651,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DEGs. (A) GO enrichment analysis of DEGs in LP vs. MP. (B) GO enrichment analysis of DEGs in LP vs. HP. (C) GO enrichment analysis of DEGs in MP vs. HP. (D) KEGG enrichment analysis of DEGs in LP vs. MP. (E) KEGG enrichment analysis of DEGs in LP vs. HP. (F) KEGG enrichment analysis of DEGs in MP vs. HP.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/b4f3d99170233366767c6956.png"},{"id":92514680,"identity":"4365433c-40dd-4219-aaf4-8843fdf6a7d9","added_by":"auto","created_at":"2025-09-30 14:00:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113155,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of differentially expressed lncRNAs among different groups of Jersey-yaks. (A) The number of lncRNAs detected by CNCI, CPC2, and PLEK software in muscle tissue. (B) Volcano plot showing the differential expression of lncRNAs in LP vs MP. (C) Volcano plot showing the differential expression of lncRNAs in LP vs HP. (D) Volcano plot showing the differential expression of lncRNAs in MP vs HP. (E) The co-expressed DELs in three comparison groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/bbf548f6dd921fd8522ca80b.png"},{"id":92515591,"identity":"a281c1e2-755d-4bad-bd3f-3bd472cbf697","added_by":"auto","created_at":"2025-09-30 14:08:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134645,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DELs. (A) GO enrichment analysis of DELs in LP vs. MP. (B) GO enrichment analysis of DELs in LP vs. HP. (C) GO enrichment analysis of DELs in MP vs. HP. (D) KEGG enrichment analysis of DELs in LP vs. MP. (E) KEGG enrichment analysis of DELs in LP vs. HP. (F) KEGG enrichment analysis of DELs in MP vs. HP.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/0693a4fe02ba7371f0ffe037.png"},{"id":92516439,"identity":"74d9623a-70f6-4961-8cb4-824ec254ff91","added_by":"auto","created_at":"2025-09-30 14:16:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101426,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of differentially expressed circRNAs among different groups of Jersey-yaks. (A) Volcano plot showing the differential expression of circRNAs in LP vs. MP. (B) Volcano plot showing the differential expression of circRNAs in LP vs. HP. (C) Volcano plot showing the differential expression of circRNAs in MP vs. HP. (D) The co-expressed DECs in three comparison groups. (E) Distribution map of circRNAs structural types.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/1fa972a7068355e1111bbe47.png"},{"id":92514688,"identity":"8a913022-2a15-42bd-ac96-88cc16fb2fa2","added_by":"auto","created_at":"2025-09-30 14:00:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":108064,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DECs. (A) GO enrichment analysis of DECs in LP vs. MP. (B) GO enrichment analysis of DECs in LP vs. HP. (C) GO enrichment analysis of DECs in MP vs. HP. (D) KEGG enrichment analysis of DECs in LP vs. MP. (E) KEGG enrichment analysis of DECs in LP vs. HP.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/5bcb023ec918641ecfb680b1.png"},{"id":92515600,"identity":"ee10385f-c02b-46c5-8c27-bbf41a5b8244","added_by":"auto","created_at":"2025-09-30 14:08:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":204413,"visible":true,"origin":"","legend":"\u003cp\u003eThe ceRNA regulatory network. (A) circRNA-miRNA-mRNA network of Jersey-yaks. (B) lncRNA-miRNA-mRNA network of Jersey-yaks. (C) lncRNA-circRNA-miRNA-mRNA network of Jersey-yaks. The colors indicate the mRNAs (blue) and miRNAs (green) lncRNAs/circRNAs (orange).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/1ebae8bdcef56582b6998245.png"},{"id":92515599,"identity":"8746641d-5837-4949-b260-1eb87ba6c732","added_by":"auto","created_at":"2025-09-30 14:08:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":68647,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of RNA-seq data using qPCR\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/43468a747342db367c386d61.png"},{"id":103982306,"identity":"8f50351e-3d8a-4eaa-bdeb-e781d6ca0457","added_by":"auto","created_at":"2026-03-05 09:43:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5141142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/ad25678e-ca98-4acd-80db-d7e53723910f.pdf"},{"id":92515595,"identity":"aefeab6c-6718-4682-b809-e6af2cfa88b4","added_by":"auto","created_at":"2025-09-30 14:08:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10411,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/c6ec13516c51a9778d16b7a8.xlsx"},{"id":92515592,"identity":"4c6d20dc-2bdb-4101-a9c8-9c9ef95af0f1","added_by":"auto","created_at":"2025-09-30 14:08:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11903,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/f584c974cacbc9d8161701f9.xlsx"},{"id":92514716,"identity":"df1464eb-1f40-4d97-aa48-3e43b324d4be","added_by":"auto","created_at":"2025-09-30 14:00:32","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19571983,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/715b1eca6c1e8c82b66a6683.xlsx"},{"id":92514689,"identity":"2012aeae-83ea-427e-be84-c4ec28fc3290","added_by":"auto","created_at":"2025-09-30 14:00:31","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":323540,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/7c60760499df4c366bf8355d.xlsx"},{"id":92516440,"identity":"e8500f42-eb8d-4e3f-a1d2-14c0c1db3dc2","added_by":"auto","created_at":"2025-09-30 14:16:31","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":225996,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/38a7c18d14da59250d52c9bc.xlsx"},{"id":92514717,"identity":"79e574d4-0816-4a61-8e4f-f35ea6765f3e","added_by":"auto","created_at":"2025-09-30 14:00:32","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21692605,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/09a48003b01bdbfa0bf9d1f1.xlsx"},{"id":92514698,"identity":"9d296656-518b-4255-8aa9-ab34982a8da0","added_by":"auto","created_at":"2025-09-30 14:00:31","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":4308510,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/988b416b0f3244a461f40409.xlsx"},{"id":92514693,"identity":"5bb287c9-2939-4282-af7c-f24bdb026c5c","added_by":"auto","created_at":"2025-09-30 14:00:31","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10159,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7466956/v1/b7509d2c49d739cda6c9a143.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole transcriptomic analysis revealed the effects of different dietary protein levels on the growth and development of Jersey-yak","fulltext":[{"header":"Introduction","content":"\u003cp\u003eYak (\u003cem\u003eBos grunniens\u003c/em\u003e) primarily inhabits the Qinghai-Tibet Plateau and surrounding regions at elevations of 3,000\u0026ndash;5,500 meters, exhibiting remarkable adaptability to harsh environmental conditions such as low temperatures, low oxygen levels, high altitudes, and intense ultraviolet radiation. It is a unique cattle breed resource native to China\u0026rsquo;s high-altitude, low-oxygen regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. China is the primary producer of yak, accounting for over 90% of the global population, primarily distributed across provinces and regions such as Gansu, Qinghai, Sichuan, Xinjiang, Yunnan, and Tibet. Additionally, yak populations are also found in countries such as India, Mongolia, Russia, and Nepal [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With the development of modern industrial technology, China's yak industry has gradually matured, with a growing number of high-value yak products entering the public eye and playing a significant role in driving local economic development. However, due to the extremely harsh natural environment in which yaks live, their growth is slow, leading to significant resource wastage. People have begun to crossbreed yak with superior breeds such as Jersey cattle and Angus cattle to improve yak production performance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Jersey-yak crossbreed is the F1 generation resulting from the crossbreeding of Jersey cattle and yak, and it exhibits significant hybrid vigor in terms of production performance. As a crossbred improved offspring of yak, the pre-slaughter live weight, carcass weight, and lean meat percentage of Jersey-yak are significantly higher than those of same-age yak. Research results indicate that under the same feeding conditions, the meat performance of crossbred offspring between Simmental and yak is better, but the meat quality is poorer, while Jersey-yak beef has better color and higher protein content [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Since most livestock farming areas still rely on a single grazing model, the growth cycles of yak and crossbred cattle such as the Jersey-yak are relatively longer, resulting in lower meat production rates. Additionally, due to traditional constraints, there has been a lack of innovation in farming techniques and basic infrastructure during the farming process, hindering the large-scale development of the yak industry. Furthermore, the cold weather conditions result in a prolonged dry season of up to seven months on pastoral grasslands, during which single-pasture grazing cannot meet the nutritional needs of large livestock such as yak and crossbred cattle [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. During the dry season, most livestock do not receive timely and effective supplementary feed, leading to weight loss due to insufficient nutrient intake and increasing pressure on grassland recovery. Therefore, changing livestock feeding patterns during the cold season can not only alleviate pressure on grasslands and forage shortages but also accelerate livestock weight gain and reduce economic losses for herders.\u003c/p\u003e\u003cp\u003eGrowth traits are important indicators of the economic value of livestock, primarily including body height, body weight, body length, and growth rate [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Growth traits are primarily manifested through the growth and development of muscles, with skeletal muscle being an important category within muscles. The regulation of skeletal muscle development is a complex biological process involving multiple stages and various regulatory factors, including transcription factors during myofibrillar differentiation, genes or proteins that regulate muscle growth, and pathways that play a crucial role in the early differentiation of muscle cells and the regulation of protein synthesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In recent years, an increasing number of researchers have utilized transcriptomics technology to explore and elucidate the molecular mechanisms underlying various growth and developmental processes in plants and animals. Dehghanian et al. employed whole-transcriptome analysis and competitive endogenous RNA (ceRNA) network analysis to identify molecular regulators involved in intramuscular fat content and fat metabolism across five beef cattle breeds, identifying a total of 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs. Functional enrichment analysis revealed that these RNAs were enriched in signaling pathways related to fat metabolism, including metabolism, calcium, cGMP-PKG, and thyroid hormone signaling pathways. Additionally, genes such as \u003cem\u003eMCU\u003c/em\u003e, \u003cem\u003eCYB5R1\u003c/em\u003e, and \u003cem\u003eBAG3\u003c/em\u003e were identified as important candidate marker genes for fat metabolism in beef cattle [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Circular RNAs (circRNAs) play a regulatory role in the development of skeletal muscle in animals. Bao et al. used RNA sequencing to reveal the temporal patterns of circRNAs expression at different growth stages in Tibetan sheep and investigated the ceRNA regulatory network in the pectoralis major muscle, elucidating the role of circRNAs in skeletal muscle fiber type conversion and their impact on meat quality, thereby deepening our understanding of the role of circRNAs in Tibetan sheep muscle development [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSkeletal muscle is the primary site for energy conversion, where mechanical energy plays a crucial role in muscle contraction and maintaining muscle function integrity. Research indicates that the growth traits of livestock are directly related to their feeding methods, such as muscle yield, intramuscular fat, and meat quality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Many factors influence the quality of animal meat, among which nutrient intake (crude protein) is one of the most important factors. Additionally, as an excellent offspring of the crossbreeding between Jersey cattle and yak, the expression characteristics of lncRNAs and circRNAs in the growth and development process of Jersey-yak have been poorly studied. Therefore, it is necessary to further analyze the expression patterns of RNAs, including coding and non-coding RNAs, in Jersey-yak fed with different dietary protein levels during their growth and development. This study utilized RNA sequencing (RNA-seq) technology to investigate the expression characteristics of mRNAs, lncRNAs, and circRNAs in Jersey-yak under different protein-level dietary feeding patterns, providing essential research insights to promote muscle development and enhance productivity in Jersey-yak.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExperimental Animals and Management\u003c/h2\u003e\u003cp\u003eThis experiment was conducted in Yangnuo Yak Breeding Professional Cooperative in Xiahe County, Gannan Tibetan Autonomous Prefecture. In this experiment, 18 healthy, 6-month-old male Jersey-yaks (bred by artificial insemination of yaks with frozen semen of the same batch of Jersey cattle) were selected. The body weight before the pre-experiment was 62.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64 kg. According to the weighing data before the pre-experiment, 18 Jersey-yaks were randomly divided into three groups by using R software (v.4.1.2) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The treatments of the three groups were: (1) no supplementary feeding (as a control group, low protein level, LP); (2) Supplementary low-protein diet (crude protein content: 15.16%; median protein level, MP); (3) Supplementary high protein diet (crude protein content: 17.90%; high protein level, HP). The specific composition and nutritional level of the two different crude protein level supplemental diets are shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWeight Data Collection and Sample Collection\u003c/h3\u003e\n\u003cp\u003eAt the beginning of the formal experiment, 18 Jersey-yaks were weighed and the initial body weight (IBW) of all Jersey-yaks in the three treatment groups was recorded. After the formal test began, all the Jersey cattle in the three groups were weighed on the 20th of each month until the end of the formal test. The weight data recorded during the period were the fasting weight of the Jersey-yak before grazing. Total weight gain (TWG) and average daily gain (ADG) were calculated based on the body weight data recorded during the formal experiment.\u003c/p\u003e\u003cp\u003eAt the end of the feeding experiment, three Jersey-yaks were randomly selected from each group for slaughter, and the muscle histology of the longissimus dorsi muscle was studied. The samples of the longissimus dorsi muscle tissue from the 12th to 13th ribs of the left half carcass were quickly collected during slaughter, immediately stored in liquid nitrogen, and brought back to the laboratory for transcriptome sequencing and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. All samples are stored in the refrigerator (-80\u0026deg;C).\u003c/p\u003e\n\u003ch3\u003eRNA Isolation, cDNA library construction, and Illumina Sequencing\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated from the LD muscle samples of Jersey-yak using TRIzol reagent (Invitrogen, CA, USA). Then, total RNA was purified using DNase and RNeasy Mini Kit (Qiagen, CA, USA). By means of ion fragmentation, the RNA is fragmented into fragments with a length of about 300bp. Using RNA as a template, use the first 6 base random primers and reverse transcriptase to synthesize the first strand of cDNA, and use the first strand cDNA as a template to synthesize the second strand cDNA. The quantity and quality of extracted total RNA were measured using NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, DE, USA), Bio-Photometer (Eppendorf, Hamburg, Germany) and 1% agarose gel electrophoresis. Finally, RNA integrity was assessed using the RNA NanoSeq 6000 Assay Kit for the Agilent 2100 Bioanalyzer system (Agilent Technologies, CA, USA).\u003c/p\u003e\u003cp\u003eAfter RNA extraction, purification and library construction, the samples were subjected to Next-Generation Sequencing (NGS) based on Illumina HiSeq (LC Sciences, Houston, TX, United States) sequencing platform. These libraries were then subjected to Paired-end (PE) sequencing.\u003c/p\u003e\n\u003ch3\u003eData Preprocessing and Read Mapping\u003c/h3\u003e\n\u003cp\u003eThe samples were sequenced using a sequencing platform, generating image files. These files were converted using the platform's built-in software to produce raw FASTQ data. The sequencing data contained some reads with adapters and low-quality reads, which could significantly interfere with subsequent information analysis. Therefore, Cutadapt was used to remove sequences with adapters at the 3' end and reads with an average quality score below Q20. The upgraded version of TopHat2, HISAT2 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], was used to align the filtered reads against the yak reference genome (Bosgru_v3.0) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. HISAT2 uses an improved BWT algorithm, which offers faster processing speeds and reduced resource requirements [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A reference genome index was created using Bowtie2, and the filtered reads were then aligned to the reference genome using TopHat2.\u003c/p\u003e\n\u003ch3\u003eLncRNAs acquisition and splicing\u003c/h3\u003e\n\u003cp\u003eLncRNAs refers to long non-coding RNA with a fragment length greater than 200 nt. Based on the structure and non-coding characteristics of LncRNAs, we screened candidate LncRNAs using three strict screening criteria. Subsequent analyses were performed on genes and the strictly screened candidate LncRNAs. First, we used the Stringtie software [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (version 2.2.0) to assemble transcripts using the alignment results from Hisat2 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. After removing transcripts with uncertain strand orientation, we screened for LncRNAs in the remaining assembled transcript set. (1) Screen transcripts with a length\u0026thinsp;\u0026ge;\u0026thinsp;200 bp and \u0026ge;\u0026thinsp;2 exons; (2) Screen transcripts with Class-code x/u/i. Specifically, Anti-sense LncRNAs, Intergenic LncRNAs, and Intronic LncRNAs transcripts. (Here, x refers to transcripts on the opposite strand of the reference transcript that cover its exons; u refers to unknown transcripts in intergenic regions; i refers to transcripts entirely within an intron of the reference transcript); (3) Screen for LncRNAs with coverage\u0026thinsp;\u0026gt;\u0026thinsp;3 in at least one sample. That is, the transcript appears at least three times in one sample. Conduct encoding potential analysis on candidate LncRNAs to determine whether these new transcripts have the ability to encode proteins, thereby further screening the new transcripts to obtain high-confidence LncRNAs. We simultaneously employed the following three methods for analysis: PLEK [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], CNCI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and Pfamscan [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. New transcripts deemed by all three software tools to lack coding potential were considered high-confidence LncRNAs and used for subsequent analysis.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCircRNAs Identification\u003c/h2\u003e\u003cp\u003eAfter aligning with the reference genome, CircRNAs is identified using unaligned reads. These reads cannot be directly aligned to the reference genome because they originate from exonic regions that are far apart, resulting in large gaps during direct alignment. A portion of the sequence from the 5' end and 3' end of the Reads that did not align with the reference genome (referred to as the 5' Anchor and 3' Anchor, respectively) is re-aligned with the reference genome. If the two sequences align in opposite positions, the Read is likely derived from a CircRNAs. Extend the Anchor sequences continuously. If the sequence matches the reference genome perfectly up to the connection point, and the splicing pattern at the connection point conforms to the AG-GT splicing pattern, it is identified as a CircRNAs. After aligning the anchor sequences of each sample with the reference genome, we merged the alignment results of all samples and used find_circ (version 1.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/marvin-jens/find_circ/\u003c/span\u003e\u003cspan address=\"https://github.com/marvin-jens/find_circ/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify circRNAs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We extracted the 20-bp regions at both ends of the unaligned reads from the HISAT2 alignment results as anchor sequences and re-aligned them to the genome using Bowtie2 (v 2.3.0) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to detect CircRNAs. We calculated the Reads Count values from the CircRNAs identification results of find_circ as the raw expression levels of CircRNAs, and then standardized the expression levels of CircRNAs using TPM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferential Expression Genes and Pathway Analysis\u003c/h3\u003e\n\u003cp\u003eWe used Stringtie (version 2.2.0) software [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to perform reads counting statistics on mRNAs and LncRNAs at the transcript level, obtaining the raw expression levels of mRNAs and LncRNAs. We then used Fragments Per Kilo bases per Million fragments (FPKM) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to normalize the expression levels and calculate their expression levels. We standardized the counts for each sample using the DESeq software [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and calculated the fold change (FC). We used the negative binomial distribution test (NB) to assess the statistical significance of the count differences. Finally, differentially expressed mRNAs (DEGs), lncRNAs (DELs), circRNAs (DECs), and miRNAs (DEMs) were identified based on the criteria |log2FC| \u0026gt;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To further understand gene function, functional pathway enrichment analysis was performed using Gene Ontology (GO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Each GO and KEGG enrichment item were confirmed using a hypergeometric distribution test. Then, \u003cem\u003ep\u003c/em\u003e-values were corrected using Benjamini and Hochberg multiple testing. Enrichments with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\n\u003ch3\u003eConstruction of ceRNA Network\u003c/h3\u003e\n\u003cp\u003eSince most LncRNAs share certain sequence similarities with mRNAs, it suggests that miRNAs may negatively regulate LncRNA expression through mechanisms similar to those of mRNAs, thereby exerting a series of biological effects. LncRNAs can not only directly bind to target genes or participate in gene expression regulation through histone modifications, but also act as competitive endogenous RNAs (ceRNA) to interact with miRNAs, thereby participating in the regulation of target gene expression. Additionally, miRNAs can promote the expression of specific LncRNAs, while circRNAs can bind to miRNAs to inhibit their function. To better understand the interactions between mRNAs, circRNAs/lncRNAs, and miRNAs in the growth and development of Juanma cattle, a circRNA/lncRNA-miRNA-mRNA regulatory network was constructed. First, using miRanda to predict interactions between miRNA-mRNA, miRNA-lncRNA, and miRNA-circRNA, interactions with energy not exceeding \u0026minus;\u0026thinsp;20 kcal/mol were retained, and the network was visualized using Cytoscape (v.3.9.1) software.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative Real-Time PCR for Validating Gene Expression\u003c/h2\u003e\u003cp\u003eRandomly selected differentially expressed RNAs were subjected to real-time quantitative PCR (qRT-PCR) experiments. Primers were designed using Primer Premier 5.0 software, with \u003cem\u003eβ-actin\u003c/em\u003e as the internal reference gene (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). RNA was reverse transcribed into cDNA using the TransScript First-Strand cDNA Synthesis SuperMix kit. The total reaction volume for qRT-PCR was 20 \u0026micro;L, containing 10 \u0026micro;L SYBR Green Premix Pro Taq HS Qpcr kit (Aikrui Bio, Hunan, China), 0.4 \u0026micro;L each of forward and reverse primers, 1 \u0026micro;L cDNA, and 8.2 \u0026micro;L ddH₂O. The amplification program was set according to the kit instructions, with an annealing temperature of 58\u0026deg;C. The relative expression level of RNA was calculated using the 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e method based on the cycle threshold (CT).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eEffects of Different Protein Levels on Body Weight of Jersey-yak.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe effect of crude protein level in the supplementary diet on the body weight of Jersey-yak is shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The results showed that there was no significant difference in body weight between the initial test and the 30 th day (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). There were significant differences in body weight at 60 days, 92 days and 120 days (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results showed that the body weight of MP group and HP group was significantly higher than that of LP group from the 60 th day after grazing (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ADG and TWG in MP group and HP group were significantly higher than those in LP group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and there was no significant difference in ADG and TWG between MP group and HP group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The results showed that after supplementary feeding after grazing, the growth performance of Jersey-yak was significantly improved from 60 days.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of dietary levels on body weight of Jersey-yak\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems (kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eTreatment group (unit: kg, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-test weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInitial weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.80\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.08\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30 days weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.32\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 days weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90 days weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e120 days weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Different letters on the shoulder markers in the same row of data indicate a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of RNA-Seq data\u003c/h2\u003e\u003cp\u003eIn order to evaluate the genes related to the growth and development of Juan cattle-yak, skeletal muscle tissues were collected and all mRNA and non-coding RNAs were analyzed by high-throughput sequencing. After screening out low-quality and redundant readings, we obtained an average of 74,483,798,72,003,778 and 76,062,278 clean readings from the skeletal muscle samples of the three groups of LP, MP and HP, respectively. Among them, Q30 was greater than 93.69%, and the successful comparison of the readings with the reference genome was about 93.57% (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). We detected a large number of mRNAs, lncRNAs and circRNAs (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). It was found that the length of lncRNAs was mainly concentrated at 200 bp. The proportion of lncRNAs containing two exons accounted for the majority, while most of the mRNAs had more than 10 exons. The average length of circRNAs detected was 1925 bp, and 10.48% of them were longer than 2000 bp. The length distribution of circRNAs in all samples was relatively uniform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDifferentially expressed mRNAs during growth and development of Jersey-yak\u003c/h2\u003e\u003cp\u003eWe used DESeq to analyze the differential expression of mRNAs. A total of 429,298 and 84 mRNAs were identified to be significantly differentially expressed in LP vs. MP, LP vs. HP and MP vs. HP groups, respectively (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Among these, in the LP vs. MP comparison group, 122 were downregulated and 307 were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In the LP vs. HP comparison group, 83 were downregulated and 215 were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the MP vs. HP comparison group, 39 were downregulated and 45 were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These genes include phosphoglycerate kinase 1 (\u003cem\u003ePGK1\u003c/em\u003e), myogenin (\u003cem\u003eMYOG\u003c/em\u003e), cellular communication network factor 1 (\u003cem\u003eCCN1\u003c/em\u003e), enolase 1 (\u003cem\u003eENO1\u003c/em\u003e), insulin-like growth factor binding protein 5 (\u003cem\u003eIGFBP5\u003c/em\u003e), and insulin-like growth factor binding protein 6 (\u003cem\u003eIGFBP6\u003c/em\u003e), which may be associated with muscle growth, fat deposition, and regulation of growth and development. Using a Venn diagram to highlight DEGs associated with each pair of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), 74 differentially expressed mRNAs were found to be shared between the LP vs. HP and LP vs. MP comparison groups. Cluster analysis indicated high reproducibility among DEGs across the three groups while revealing substantial differences between the three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo gain a deeper understanding of the biological functions of DEGs, we performed GO and KEGG analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The results showed that DEGs in the LP vs. MP group were significantly enriched in positive regulation of chondrocyte proliferation, collagen fibril organization, extracellular structure organization, connective tissue development, histone H3-K56 acetylation, and other biological processes at the cellular and tissue levels that are crucial for the regulation and formation of growth and development in Jersey-yak. They also participate in pathways involved in important physiological processes such as protein digestion and absorption, the HIF-1 signaling pathway, and the regulation of lipolysis in adipocytes, which are related to protein digestion, energy metabolism, and fat metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and D). In the LP vs. HP group, the DEGs were significantly enriched in biological process entries such as NADH metabolic process, gluconeogenesis, glycolytic process, and cardiac muscle cell contraction, which play crucial roles in the growth and development of Jersey-yak. Additionally, they were enriched in biological process entries related to fat metabolism, such as adipose tissue development (adipose tissue development). They are also significantly enriched in cellular components of protein complexes with important functions in regulating gene expression, such as the PRC1 complex and the PcG Protein Complex. Furthermore, it is involved in the regulation of biological processes such as energy metabolism, nitrogen metabolism, cellular physiological functions, and growth and development processes, and plays a crucial role in maintaining normal physiological states and adapting to the environment. This includes signaling pathways and metabolic pathways such as ECM-receptor interaction, glycolysis/gluconeogenesis, glucagon signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and E). Finally, the DEGs in the MPvsHP group were significantly enriched in biological process entries related to the regulation of developmental processes, cartilage development, and other processes crucial for maintaining the body's immune defense, resisting pathogens, and maintaining immune balance, such as the MyD88-dependent toll-like receptor signaling pathway. They were also involved in pathways such as the PI3K-Akt signaling pathway, the MAPK signaling pathway, and the Toll-like receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTotal lncRNA and differentially expressed lncRNA during the growth and development of Jersey-yak\u003c/h2\u003e\u003cp\u003eCNCI, PLEK and Pfam methods were used for comparative analysis to identify lncRNAs in muscle tissue RNA samples, resulting in a total of 81,032 lncRNAs detected in tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Differential expression analysis showed that 394,356 and 302 lncRNAs were significantly differentially expressed in the three comparison groups of LP vs. MP, LP vs. HP and MP vs. HP, respectively. In the comparison group LP vs. MP, 178 DELs were significantly upregulated and 216 DELs were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); in the comparison group LP vs. HP, 136 DELs were significantly upregulated and 220 DELs were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC); in the comparison group MP vs. HP, 138 DELs were significantly upregulated and 164 DELs were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Through the Wayne diagram to highlight the DELs associated with each pair of samples, it was found that 73 DELs were co-expressed between the LP vs. HP and LP vs. MP comparison groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). In order to reveal the potential function of identified lncRNAs in the muscle growth and development of Jersey-yak, we predicted the target genes of lncRNAs (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGO enrichment analysis and KEGG pathway analysis revealed several important pathways associated with muscle tissue development in the three comparison groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e), such as protein dephosphorylation, skeletal muscle cell differentiation, protein ADP-ribosylation, regulation of epithelial cell proliferation, regulation of peptidase activity, animal organ development, cGMP-PKG, MAPK, and Wnt signaling pathway. These pathways play a key role in cell proliferation and differentiation, as well as skeletal development. It was found that the target genes of cis-acting lncRNAs were significantly enriched in the Notch signaling pathway in the MP vs. HP group. This pathway is a highly conserved intercellular signaling pathway that plays an important role in animal development, angiogenesis, skeletal development, cell fate determination, and tissue repair [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDifferential expression of miRNAs and circRNAs during growth and development of Jersey-yak\u003c/h2\u003e\u003cp\u003eIn the LP vs. MP, LP vs. HP, and MP vs. HP samples, there were fewer DEMs, with only 6 (5 upregulated, 1 downregulated), 7 (6 upregulated, 1 downregulated), and 6 (1 upregulated, 5 downregulated) DEMs, respectively (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). GO functional enrichment analysis revealed that the target genes of these DEMs were significantly enriched in biological processes related to promoting cellular function and the animal's normal development, repair, and adaptation to the environment, such as positive regulation of cellular process and positive regulation of macromolecule biosynthetic process (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). KEGG pathway analysis revealed that the target genes of these DEMs were significantly enriched in pathways critical for animal growth and development, such as the Apelin, PI3K-Akt, AMPK, ECM-receptor interaction, Wnt, Hippo, Ras, and MAPK signaling pathway. Among these, the Hippo, MAPK, and AMPK signaling pathway are associated with lipid metabolism in animals (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong the circRNAs annotated in this study, exon-containing circRNAs accounted for 71.12%, including multiple exon circRNAs (annot) and single exon circRNAs (one), which accounted for 65.51% and 5.61%, respectively. intergenic circRNAs, intronic circRNAs, and antisense circRNAs accounted for 9.46%, 2.24%, and 4.01%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Differential expression analysis revealed 212 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), 234 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and 20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) differentially expressed circRNAs in the LP vs. MP, LP vs. HP, and MP vs. HP comparison groups, respectively. Using a Venn diagram to highlight the DECs associated with each pair of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), it was found that there were 98 common DECs between the LP vs. HP and LP vs. MP comparison groups. Notably, among these DECs in the LP vs. HP and LP vs. MP comparison groups, bgrcirc_017953, bgrcirc_015858, and bgrcirc_003993 were simultaneously upregulated.\u003c/p\u003e\u003cp\u003eGO functional enrichment analysis revealed that DECs in the LP vs. MP group were significantly enriched in regulation of cellular component organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), which has important implications for animal growth and development, including cell differentiation and organ development, tissue and organ function, cell proliferation and division, cell signaling, and cell metabolism and energy conversion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the LP vs. HP group, the target genes of DECs were significantly enriched in positive regulation of catalytic activity and positive regulation of hydrolase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Among these, positive regulation of catalytic activity plays a regulatory role in processes such as cell proliferation, differentiation, organ development, and cell migration during animal growth and development. However, the effects of positive regulation of hydrolase activity on animal growth and development are primarily manifested in nutrient digestion and absorption, activation of metabolic pathways, activation of growth factors, and degradation of waste products [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These regulatory processes are crucial for normal animal growth and development and the maintenance of organ function. Additionally, the biological process entry regulation of cellular component organization was significantly enriched in the MP vs. HP group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), involving multiple biological processes and cellular components, and playing a vital role in maintaining normal muscle function and homeostasis. KEGG pathway analysis revealed that DECs in the LP vs. MP group were significantly enriched in the ABC transporters pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The ABC transporter pathway plays a crucial role in animal growth and development, including nutrient absorption and metabolism, tissue development and organ formation, as well as drug metabolism and toxin clearance. These processes are essential for normal growth, development, and health in animals [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Abnormal regulation of the ABC transporter pathway may lead to diseases such as growth and development abnormalities, metabolic disorders, and drug metabolism abnormalities. Additionally, DECs were significantly enriched in the inositol phosphate metabolism pathway, which is involved in various important biological processes within cells, including signal transduction, membrane phospholipid metabolism, and energy metabolism [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Notably, DECs in the LP vs. HP group were also significantly enriched in the ABC transporters pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). In contrast, the target genes of DECs in the MP vs. HP group were not enriched in any pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the ceRNA Coregulatory Network\u003c/h2\u003e\u003cp\u003eBased on the regulatory relationships among DEGs, DELs, DECs, and DEMs, we identified significantly differentially expressed lncRNAs, circRNAs, and mRNAs that are co-regulated by the same miRNAs. Network diagrams of circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA were constructed based on the relationships among mRNAs, circRNAs, lncRNAs, and miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and B). The circRNA-miRNA-mRNA network primarily involves 6 circRNAs, 9 miRNAs, and mRNAs such as \u003cem\u003ePLTP\u003c/em\u003e, \u003cem\u003ePRAG1\u003c/em\u003e, and \u003cem\u003eCXXC1\u003c/em\u003e. The lncRNA-miRNA-mRNA network primarily involves 7 miRNAs, 16 lncRNAs, and mRNAs such as \u003cem\u003eCDKN2C\u003c/em\u003e, \u003cem\u003eLRWD1\u003c/em\u003e, and \u003cem\u003eACSF3\u003c/em\u003e. To determine the regulatory relationships between coding and non-coding RNAs during the growth and development of J Jersey-yak, we analyzed the regulatory relationships between differentially expressed RNAs through miRNA-miRNA, miRNA-circRNA, and miRNA-lncRNA. We identified significantly differentially expressed mRNAs, circRNAs, and lncRNAs co-regulated by miRNAs, thereby constructing a co-expressed lncRNA-circRNA-miRNA-mRNA network diagram between coding RNAs and non-coding RNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). This network diagram primarily includes 6 miRNAs, 6 circRNAs, 8 mRNAs, and multiple lncRNAs. MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579-\u003cem\u003eLASP1\u003c/em\u003e, MSTRG.24136.1-bgrcirc_004446-bgr-undef-67-\u003cem\u003eNAGA/PDRG1\u003c/em\u003e, MSTRG.155383.1-bgrcirc_004326-bgr-undef-29-\u003cem\u003ePRAG1/ACSF3\u003c/em\u003e, etc., may influence the growth and development of Jersey-yak.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eValidation of RNA-Seq results\u003c/h2\u003e\u003cp\u003eEight differentially expressed RNAs were randomly selected from the sequencing results of the longest muscle tissue of the Jersey-yak for qRT-PCR experiments, including 4 mRNAs (\u003cem\u003eMYOG\u003c/em\u003e, \u003cem\u003ePGK1\u003c/em\u003e, \u003cem\u003eIGFBP5\u003c/em\u003e, and \u003cem\u003eENO1\u003c/em\u003e), 2 lncRNAs (MSTRG.2715.8 and MSTRG.54295.6), and 2 circRNAs (bgrcirc_005011 and bgrcirc_017953). The expression trends of the RNAs in the three comparison groups were found to be consistent with the qRT-PCR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eYaks provide the necessary means of production for the herdsmen in the Qinghai-Tibet Plateau and surrounding areas. However, yaks live in high altitude areas above 3,000 m, and extremely harsh environments such as low temperature and low oxygen lead to slow growth and poor production performance. In recent years, people have used excellent breeds such as Jersey cattle, Angus and Simmental cattle to cross with yaks to obtain hybrid advantages to improve the production performance of yaks. Jersey-yak is the offspring of people using Jersey cattle and yak for hybridization. In terms of phenotype, it combines the advantages of both parents, so that Jersey-yak shows obvious heterosis in growth rate, meat production rate and other traits. The growth and production performance of ruminants are affected by dietary protein levels. Studies have shown that adding crude protein to the diet can improve the production performance of beef cattle, and reducing or not adding crude protein will reduce the average daily gain of calves [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Protein in the diet is one of the primary sources of energy for animal tissues and plays a crucial role in the growth and development of ruminants. Additionally, the addition of appropriate amounts of protein to the diet significantly promotes the production performance and organ development of ruminants [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Due to the prolonged dry season during the cold season in the Qinghai-Tibet Plateau region, there is a seasonal imbalance in forage supply. During the winter and spring seasons, Jersey-yak experience weight loss and reduced body condition due to insufficient forage, nutrient deficiencies in pasture grass, and harsh environmental conditions such as low oxygen levels and extreme cold, which can impair subsequent fattening and result in significant economic losses. Research findings indicate that supplementing early-weaned yak with feed of varying crude protein levels after grazing can improve calf growth performance while minimizing energy deficiency in mother yaks during the winter feed scarcity period [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Researchers conducted supplementary feeding on 18-month-old yak during the cold season on the Qinghai-Tibet Plateau, and the results showed that the body weight and average daily weight gain of the supplementary feeding group were significantly improved [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, this study used diets containing 15.16% and 17.90% crude protein to supplement the Jersey-yak. The experimental results showed that the average daily weight gain and total weight gain of the HP and MP groups were significantly higher than those of the control group, consistent with the aforementioned studies. These findings indicate that during the dry season, providing yak and their hybrid offspring with appropriate supplementary feeding, combined with a reasonable crude protein level in the diet, has significant potential to enhance their productivity and can appropriately reduce economic losses for herders.\u003c/p\u003e\u003cp\u003eIn this study, using whole-transcriptome sequencing technology, an average of 66,869,478, 64,964,700, and 67,520,923 clean reads were obtained from the LP, MP, and HP groups of Jersey-yak, respectively, with Q30 base distribution ranging from 93.69\u0026ndash;94.18%. By aligning the reads to the reference genome, the alignment rates for each sample ranged from 93.57\u0026ndash;94.47%. Differentially expressed RNAs were detected using qRT-PCR technology, and the results showed that the quantitative results of the selected RNAs were consistent with the sequencing results, indicating the reliability of the sequencing data. Through comparative analysis, 429, 298, and 84 differentially expressed mRNAs were identified in the LP vs. MP, LP vs. HP, and MP vs. HP comparison groups, respectively. Through GO and KEGG functional annotation analysis, these DEGs were mainly involved in pathways related to enzyme activity, metabolic processes, cell contraction, and tissue development. They were also enriched in pathways related to muscle growth and development, as well as fat cell storage and release, such as PI3K-Akt, MAPK, and regulation of fat cell lipolysis. Furthermore, these DEGs were enriched in pathways related to cellular adaptation to oxygen environments, such as the HIF-1 signaling pathway, which plays a key regulatory role in the body's response to low oxygen concentrations or hypoxia and in maintaining oxygen homeostasis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This is consistent with the ability of Jersey-yak to adapt to low-oxygen environments in high-altitude regions. Through the Venn diagram, it was found that there were 74 DEGs co-expressed between LP vs. HP and LP vs. MP. The analysis found that these DEGs regulated the biological processes of adipocyte differentiation and proliferation, such as adipose tissue development and adipocyte differentiation. These DEGs were also found to be associated with glycosaminoglycan biosynthesis-heparin sulfate/heparin, PPAR signaling pathway, pantothenate and CoA biosynthesis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Among them, glycosaminoglycan biosynthesis-heparin sulfate / heparin plays an important role in anticoagulation, cell signal transduction, extracellular matrix function and immune regulation in vivo [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, many of these co-expressed DEGs have been found to play an important role in regulating the growth and development of muscle tissue, including \u003cem\u003eCOL15A1\u003c/em\u003e, \u003cem\u003eFOXO1\u003c/em\u003e, \u003cem\u003ePYGO2\u003c/em\u003e, \u003cem\u003eSOX7\u003c/em\u003e, \u003cem\u003ePLOD3\u003c/em\u003e, and \u003cem\u003eNME3\u003c/em\u003e, which are related to the development and regulation of muscle, bone and nervous system. The collagen type XV alpha 1 chain (\u003cem\u003eCOL15A1\u003c/em\u003e) originally belonged to non-fibrous collagen and is present in many tissues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Studies on neonatal heart, skeletal muscle, and smooth muscle tissues have shown that muscle RNA has high levels of type XV collagen mRNA expression, indicating that it is not only localized in fibroblasts in the endothelium but also in myoblasts. Interestingly, certain epithelial cells in the kidney, lung, pancreas, and placenta can also synthesize XV-type collagen mRNA. These findings suggest that XV-type collagen transcripts are widely distributed, with the primary producers being mesenchymal-derived cells, particularly muscle cells and fibroblasts [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. \u003cem\u003eCOL15A1\u003c/em\u003e encodes the α chain of collagen XV, primarily produced by mesenchymal cells, including muscle cells, adipocytes, fibroblasts, and neurons [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, the \u003cem\u003eCOL15A1\u003c/em\u003e gene plays a crucial role in the growth and development of Jersey-yak and the formation of tissue structure. The Forkhead Box O1 (\u003cem\u003eFOXO1\u003c/em\u003e) gene encodes the FoxO1 protein, a member of the FoxO protein family, which plays a crucial regulatory role in various cellular processes. It exerts its functions by binding to the promoters of downstream target genes or interacting with other transcription factor proteins [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Studies have shown that the \u003cem\u003eFOXO1\u003c/em\u003e gene is highly expressed in insulin-responsive tissues, including the pancreas, liver, skeletal muscle, adipose tissue, and bone, and plays a crucial role in regulating muscle growth, metabolism, cell proliferation, and differentiation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Its specific function involves inhibiting the downstream key factor Akt1/2 of the insulin signaling pathway, thereby impairing skeletal muscle development. However, knocking out \u003cem\u003eFOXO1\u003c/em\u003e can restore developmental capabilities in skeletal muscle and other tissues [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, studies have shown that certain non-coding RNAs can regulate \u003cem\u003eFOXO1\u003c/em\u003e expression, thereby influencing muscle cell proliferation and differentiation [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These findings suggest that reducing \u003cem\u003eFOXO1\u003c/em\u003e gene expression may promote muscle cell generation, thereby facilitating muscle growth and development. Notably, \u003cem\u003eFOXO1\u003c/em\u003e gene expression was downregulated in both the LP vs. HP and LP vs. MP comparison groups, consistent with its functional role. The SRY-box transcription factor 7 (\u003cem\u003eSOX7\u003c/em\u003e) gene encodes the transcription factor \u003cem\u003eSOX7\u003c/em\u003e and belongs to the Sox family. Satellite cells are skeletal muscle-specific stem cells that are activated upon skeletal muscle injury, initiating proliferation, differentiation, and fusion to facilitate repair. Research has shown that the \u003cem\u003eSOX7\u003c/em\u003e gene is expressed in quiescent satellite cells [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, genes such as \u003cem\u003eSOD3\u003c/em\u003e, \u003cem\u003eLASP1\u003c/em\u003e, and \u003cem\u003eACAD8\u003c/em\u003e, which are associated with the development of muscle, bone, and organs in Jersey-yak, were differentially expressed and upregulated in both supplementation groups [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough an increasing number of researchers are beginning to study the mechanisms related to lncRNA, the functions of lncRNA remain largely unknown. However, researchers believe that lncRNA is involved in many fundamental cellular processes [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and the half-life of nuclear lncRNA is very short (\u0026lt;\u0026thinsp;2 h), making it highly susceptible to regulatory functions [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. During biological development, lncRNA, as a non-coding RNA, acts as an important regulatory factor in gene expression during the processing of genetic information in living cells and interacts with major cellular pathways such as proliferation, differentiation, and apoptosis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In this study, 356, 394, and 302 differentially expressed lncRNAs were identified in the three comparison groups of LP vs. HP, LP vs. MP and MP vs. HP, respectively. Through the Venn diagram, 73 lncRNAs were found to be differentially co-expressed between the two control groups and the supplementary feeding group. The number of exons of most lncRNAs identified in this study is 2\u0026ndash;3, and the length of most lncRNA exons is about 200 bp, which is basically consistent with the characteristics of lncRNAs identified in other mammals [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. GO and KEGG functional enrichment analysis showed that the target genes of these DELs are mainly involved in biological processes related to enzyme activity, cell proliferation and differentiation, and metabolism. Among these, the \u003cem\u003eFGF2\u003c/em\u003e gene is a key regulator of muscle cell proliferation and differentiation. Therefore, lncRNAs such as MSTRG.76433, MSTRG.76436, MSTRG.76443, and MSTRG.76478 can be studied as co-expressed lncRNAs of the \u003cem\u003eFGF2\u003c/em\u003e gene. From an evolutionary perspective, the Wnt signaling pathway is highly conserved and plays a significant role in embryonic development, growth and development, and the regeneration of adult tissues [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. It is also crucial for maintaining genetic stability and plays a key role in determining cell fate, differentiation, apoptosis, cell movement, and stem cell maintenance [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Additionally, the Wnt signaling pathway is a critical pathway for controlling bone formation and fat generation by regulating processes such as cell fate determination, cell proliferation, and differentiation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In this study, 14 target mRNAs of differentially expressed lncRNAs in the LP vs. MP group were significantly enriched in the Wnt signaling pathway, among which MSTRG.239143.1, which regulates the \u003cem\u003eWNT3A\u003c/em\u003e gene, was upregulated in the MP group. The \u003cem\u003eWNT3A\u003c/em\u003e gene encodes the Wnt-3a protein, which is an important member of the Wnt signaling pathway. Studies have shown that the Wnt-3a protein participates in regulating the proliferation, differentiation, and formation of skeletal cells, and the absence or mutation of \u003cem\u003eWNT3A\u003c/em\u003e may lead to skeletal developmental abnormalities or skeletal diseases [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Previous studies have shown that Wnt3a can regulate the functions of \u003cem\u003eMYOD\u003c/em\u003e and \u003cem\u003eMYOG\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], which are highly expressed genes in fast-twitch and slow-twitch muscle fibers, respectively. Overexpression of \u003cem\u003eWNT3A\u003c/em\u003e promotes the expression of myosin heavy chain type I (\u003cem\u003eMyHC-I\u003c/em\u003e) in slow-twitch skeletal muscle fibers and facilitates cell differentiation toward slow-twitch fibers [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs research has progressed, it has been discovered that circRNAs are indispensable in muscle growth and development. circRNAs are a novel type of circular endogenous RNA formed by reverse splicing of mRNA precursors. Due to their circular closed-loop structure, they exhibit conservation and stability, unlike linear RNAs, which lack a 5' cap and 3' poly(A) tail [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In recent years, numerous studies have shown that circRNAs can regulate gene expression through various mechanisms, including acting as \u0026ldquo;molecular sponges\u0026rdquo; to absorb small RNA molecules or translating proteins to regulate muscle growth [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Recent research has further revealed that circRNAs can also regulate muscle growth and development by interacting with RNA-binding proteins (RBPs) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Research has found that CDR1as (the antisense transcript of cerebellar degeneration-related protein 1, also known as ciRS-7, is a neuronal circRNA that plays a crucial role in regulating muscle differentiation) is highly expressed in the muscles of mid-embryonic goat fetuses and is activated during the initial stages of in vitro muscle differentiation. \u003cem\u003eMyoD\u003c/em\u003e promotes CDR1as by binding to its 5\u0026prime; end region. Overexpression or knockout of CDR1as significantly induces or inhibits muscle differentiation, respectively. By competitively binding to miR-7, CDR1as alleviates miR-7-mediated downregulation of \u003cem\u003eIGF1R\u003c/em\u003e, thereby activating muscle differentiation. These findings highlight the critical role of CDR1as in muscle differentiation [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. GO analysis revealed that the target genes of these DECs are involved in enzyme activity regulation, cell proliferation, differentiation, and muscle contraction. Among them, the MYL1 protein encoded by myosin light chain 1 (\u003cem\u003eMYL1\u003c/em\u003e) participates in muscle fiber assembly and muscle development, promoting muscle cell growth and differentiation, and plays an important role in muscle growth and development. The bgrcirc_017953 targeting the \u003cem\u003eMYL1\u003c/em\u003e gene was upregulated in both the LP vs. HP and LP vs. MP groups, suggesting that bgrcirc_017953 may have a regulatory role in muscle development in Jersey-yak. Through KEGG pathway enrichment analysis, it was found that the target genes of DECs in the LP vs. HP and LP vs. MP groups were significantly enriched in the ABC transport pathway. The ABC transport pathway plays an important regulatory role in animal growth and organ development, with the \u003cem\u003eABCC1\u003c/em\u003e, \u003cem\u003eABCA1\u003c/em\u003e, and \u003cem\u003eABCG2\u003c/em\u003e genes all enriched in this pathway. These genes are involved in the transport and metabolic regulation of substances, influencing animal growth and development processes. The expression of bgrcirc_011298 related to \u003cem\u003eABCC1\u003c/em\u003e gene was downregulated in both HP and MP groups, and there may be competitive regulation between them [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].Its function research deserves further study.\u003c/p\u003e\u003cp\u003eceRNA is a regulatory mechanism in living organisms involving mRNA, lncRNA, miRNA, and circRNA. These RNA molecules regulate each other by competitively binding to common miRNAs, forming a complex regulatory network. The fundamental principle of this mechanism is that RNA molecules can compete for miRNA molecules by possessing identical or similar miRNA response elements (MREs), thereby influencing each other's expression and function [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. This interaction forms the ceRNA network, which plays a role in many biological processes such as cell proliferation, differentiation, and development. In order to determine the regulatory relationship between coding RNAs and non-coding RNAs in the growth and development of Jersey-yak, based on the regulatory relationship between differentially expressed RNAs of miRNA-mRNA, miRNA-circRNA and miRNA-lncRNA, we identified significantly differentially expressed mRNAs, circRNAs and lncRNAs co-regulated by miRNAs, thus constructing a ceRNA regulatory network diagram between coding RNAs and non-coding RNAs. The ceRNA network contains 8 DEGs, 6 DECs, 6 DEMs, and multiple DELs. This study showed that MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579-\u003cem\u003eLASP1\u003c/em\u003e, MSTRG.24136.1-bgrcirc_004446-bgr-undef-67-\u003cem\u003eNAGA\u003c/em\u003e/\u003cem\u003ePDRG1\u003c/em\u003e, MSTRG.155383.1-bgrcirc_004326-bgr-undef-29-\u003cem\u003ePRAG1\u003c/em\u003e/\u003cem\u003eACSF3\u003c/em\u003e may be involved in the regulation of growth and development of Jersey-yak. Among them, bgr-undf-29 targeted multiple DELs such as MSTRG.155383.1, MSTRG.190135.23 and MSTRG.51284.4, as well as 4 DEGs such as \u003cem\u003eOLFM1\u003c/em\u003e, \u003cem\u003ePRAG1\u003c/em\u003e, \u003cem\u003eACSF3\u003c/em\u003e and \u003cem\u003ePALM\u003c/em\u003e. Analysis revealed that the PEAK1-related, kinase-activating pseudokinase 1 (\u003cem\u003ePRAG1\u003c/em\u003e) gene is associated with biological processes such as cell migration, metabolism, and protein phosphorylation. Studies have shown that \u003cem\u003ePRAG1\u003c/em\u003e induces cell protein phosphorylation by binding to the CSK tyrosine kinase [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Research has shown that \u003cem\u003ePRAG1\u003c/em\u003e is highly expressed in various tissues, including the cerebral cortex, respiratory system, and digestive tract [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. \u003cem\u003ePRAG1\u003c/em\u003e is localized in the cytoplasm, cell junctions, focal adhesions, and cell nucleus, and has been reported to participate in the regulation of the cytoskeleton, leading to morphological changes in various cell types [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. \u003cem\u003ePRAG1\u003c/em\u003e is primarily associated with neural cell morphology regulation, cell contraction, and cellular stress mechanisms. Although there are no studies confirming its role in animal growth and development, bone cell proliferation, and differentiation, this study suggests that it may influence the growth and development of Jersey-yak. Olfactomedin 1 (\u003cem\u003eOLFM1\u003c/em\u003e), also known as \u0026ldquo;noelin\u0026rdquo; or \u0026ldquo;pancortin,\u0026rdquo; is a secreted glycoprotein that has garnered significant attention in recent years [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This protein is highly expressed in brain tissue, particularly in regions closely associated with cognitive processes, such as the hippocampus and cortex [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Research has shown that the \u003cem\u003eOLFM1\u003c/em\u003e gene has an inhibitory effect on osteoclast formation in mouse cells [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Acyl-CoA synthetase family member 3 (\u003cem\u003eACSF3\u003c/em\u003e) encodes an enzyme in the acyl-CoA synthetase family, which activates fatty acids by catalyzing the formation of a thioester bond between fatty acids and coenzyme A [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Research has shown that \u003cem\u003eACSF3\u003c/em\u003e is associated with meat traits, carrying quantitative trait loci related to beef production traits. Functional analysis revealed its involvement in biologically relevant processes such as fatty acid oxidation, biosynthesis, and lipid storage [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The results indicate that the genes in this ceRNA network, along with their corresponding miRNAs, lncRNAs, and circRNAs, may influence the growth and development of skeletal muscle and fat formation in Jersey-yak.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, whole-transcriptome sequencing was performed on Jersey-yak fed diets with different protein levels. Bioinformatics analysis identified a large number of differentially expressed coding RNAs and non-coding RNAs, which play important roles in the growth and development of Jersey-yak skeletal muscle. Analysis of the ceRNA network revealed that MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579-\u003cem\u003eLASP1\u003c/em\u003e, MSTRG.24136.1-bgrcirc_004446-bgr-undef-67-\u003cem\u003eNAGA/PDRG1\u003c/em\u003e, MSTRG.155383.1-bgrcirc_004326-bgr-undef-29-\u003cem\u003ePRAG1/ACSF3\u003c/em\u003e, and others may be involved in the cell proliferation, differentiation, and muscle growth and development of Jersey-yak. This study provides a theoretical foundation for further understanding the mechanisms underlying muscle development in yak hybrid offspring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the mass spectrometry analysis of Shanghai Bioprofile Technology Co., Ltd. (Shanghai, China)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWY performed the experiments and the manuscript. BM and YFL performed the sample collection. GWY and XMM analysed the data. XYW, and MC suggested revisions to the manuscript. XG was involved in the design of the experiments. LW and CNL designed the study and provided overall guidance. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Modern Beef Yak Industry Technology System, grant number MATS-Beef Cattle System, CARS-37; the Hezuo City Yak Germplasm Improvement and Quality Improvement Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq data from this study can be read from NCBI sequences (Accession Number: PRJNA1310280).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the animal experiments were approved by Lanzhou Institute of Husbandry and Pharmaceutical Sciences of the Chinese Academy of Agricultural Sciences (CAAS) with the grant number: No. 1610322020018. All the slaughter as well as sampling procedures strictly complied with the Guidelines for Ethical Treatment of Experimental Animals of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGao J, Yang D, Sun Z, Niu J, Bao Y, Liu S, Tan Z, Hao L, Cheng Y, Liu S: \u003cstrong\u003eChanges in Blood Metabolic Profiles Reveal the Dietary Deficiencies of Specific Nutrients and Physiological Status of Grazing Yaks during the Cold Season in Qinghai Province of China\u003c/strong\u003e. \u003cem\u003eMetabolites\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(8).\u003c/li\u003e\n \u003cli\u003eGuo S, Cao M, Wang X, Xiong L, Wu X, Bao P, Chu M, Liang C, Yan P, Pei J\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eChanges in Transcriptomic Profiles in Different 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Whole transcriptome, muscle, ceRNA, Jersey-yak","lastPublishedDoi":"10.21203/rs.3.rs-7466956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7466956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe Jersey-yak, a hybrid offspring of Jersey cattle and yak, exhibits characteristics such as strong adaptability, rapid growth and development, and outstanding production performance. Therefore, it is necessary to find a reasonable supplemental feeding scheme and the regulation mechanism of the growth and development basis of Jersey-yak.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, we established transcriptomic information for lncRNAs, circRNAs, miRNAs, and mRNAs in the skeletal muscle tissue of Jersey-yak, and constructed a ceRNA network using differentially expressed RNAs. We identified 429, 298, and 84 differentially expressed mRNAs, 394, 356, and 302 differentially expressed lncRNAs, 212, 234, and 20 differentially expressed circRNAs, and 6, 7, and 6 differentially expressed miRNAs in LP vs. MP, LP vs. HP, and MP vs. HP, respectively. These genes were found to be involved in skeletal muscle development, cell proliferation and differentiation, and various signaling pathways, such as metabolic processes, adipose tissue development, and regulation of peptidase activity, as well as signaling pathways including PI3K-Akt, MAPK, HIF-1, Wnt, and Notch. Subsequently, we constructed three competitive endogenous RNA (ceRNA) networks, including DEGs, DELs, DECs and DEMs. In this network diagram, it mainly includes 6 miRNAs, 6 circRNAs, 8 mRNAs and multiple lncRNAs. These interactions affect the specific biological processes during the growth and development of Jersey-yak: MSTRG.1705804.4-bgrcirc_016930-bgr-undef-579-\u003cem\u003eLASP1\u003c/em\u003e, MSTRG.24136.1-bgrcirc_004446-bgr-undef-67-\u003cem\u003eNAGA\u003c/em\u003e/\u003cem\u003ePDRG1\u003c/em\u003e, MSTRG.155383.1-bgrcirc_004326-bgr-undef-29-\u003cem\u003ePRAG1\u003c/em\u003e/\u003cem\u003eACSF3\u003c/em\u003e may affect the growth and development of Jersey-yak.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe whole transcriptome sequencing of Jersey-yak screened several key genes and ceRNA regulatory networks. These genes may be involved in biological processes such as metabolic processes, cell proliferation and adipose tissue development, as well as PI3K-Akt, MAPK and Notch signaling pathways involved in regulating the growth and development of Jersey-yak. These findings are helpful to further study the muscle development mechanism of yak hybrid offspring.\u003c/p\u003e","manuscriptTitle":"Whole transcriptomic analysis revealed the effects of different dietary protein levels on the growth and development of Jersey-yak","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 14:00:26","doi":"10.21203/rs.3.rs-7466956/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4c5840d-6353-41b5-82c5-cece9d56b7b6","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T09:42:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 14:00:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7466956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7466956","identity":"rs-7466956","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}