CircRNA for milk production in Kazakh horses

CircRNA for milk production in Kazakh horses | 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 CircRNA for milk production in Kazakh horses Xiangyun Shi, Bin Chen, Wujun Liu, Lingling Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4605638/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Kazakh horses are a basic breed of Xinjiang horses with strong lactation ability. CircRNAs can be broadly involved in bioregulatory activities through a variety of mechanisms. However, there is relatively little literature on the expression of circRNAs in the milk fat of Ka-zakh horses. Therefore, this study aimed to reveal the potential impact of circRNAs on Kazakh horses’ milk production during their mid-lactation period. To be more specific, the horses were di-vided into the higher-producing (H group) group and the lower-producing group (L group) based on their milk yield, in the middle of lactation. After 300 ml of milk was collected from each horse, RNAs from these milk samples were extracted and purified, and then analyzed with the Illumina NovaSeq 6000 platform. The processed data was compared with the equine genome to select dif-ferentially expressed circRNAs, which are subject to subsequent functional studies. In the two groups, 257 upregulation and 79 downregulation differences in circRNAs were found, and 212 target genes were predicted. The genes (circRNA.12757/CSN1S1, circRNA.9870/ACSL1, circR-NA.9457/LGB1, circRNA.567/VPS13C) enriched by GO and KEGG were located in PPAR signaling pathways, circadian rhythm, insulin signaling pathways, and lactation signaling path-ways. Circular RNA Kazakh horse Milk production RNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The Kazakh horse is an ancient local horse breed raised under rough herding conditions and found on the northern slopes of the Tian Shan [1] , the western part of the Junggar Basin, and the western part of the Altay Mountains. Kazakh horses are not only resistant to rough feeding, cold, running, and intense work but also have stronger mammary glands and more nutritious milk [2] . In general, horse milk contains water, protein, milk fat, lactose, enzymes, minerals, and trace elements, closer to human milk than other dairy products such as goat milk, cow milk, and camel milk [3, 4] . Circular RNA (circular RNA, circRNA) was discovered in 1976 by Sanger et al [5, 6] , which is a closed circular RNA molecule without the typical poly A structure at the 3' end and the cap structure at the 5' end. Playing a variety of roles in bioregulatory processes [7] , CircRNAs can be divided into exonic circRNAs, intronic circRNAs, exon-intron circRNAs, and intergenic circRNAs [8] . Moreover, they are characterized by stability, universality, variability, and conservation [9-12] . With the rapid development of high-throughput sequencing and bioinformatic analysis, thousands of circRNAs have been identified in cells and tissues of different species. Due to the growing standard of living, much attention has been paid to horse milk as a daily drink, a beauty product, and a therapeutic drink [13] . In response to the social needs for this drink, horse milk has been rationally and effectively developed and exploited and thus has a wide market. Since the expression of circRNAs in the milk fat of Kazakh horses has not been studied previously, we screened circRNAs and their target genes to identify those affecting milk production by comparing the circRNAs of Kazakh horses in the high-yielding and low-yielding groups. In our study, we researched the milk yield of Kazakh horses at the transcriptional level. 2. Materials and Methods 2.1. Ethical Statement This study was approved by the local ethics committee for animal experiments at Xinjiang Agricultural University (approval number: 2017008). 2.2. Animal Testing In Fuyun County, Altay prefecture, Xinjiang, test animals were selected and managed uniformly. To measure milk production at the mid-lactation stage, test animals were milked every morning and evening. Animals in the H group produced 7-7.5 kg of milk per day, while those in the L group produced 3.2 to 3.3 kg of milk per day. 2.3. Sample Collection Milk produced at the mid-lactation stage was collected from each horse, rapidly frozen in liquid nitrogen, and stored in the laboratory for backup. Then 50 ml at 2700 g of milk was taken and placed in the centrifuge for 10 min at 4°C to extract the top layer of milk fat and add it to the trizol at 2:8. Finally, the solution was shaken thoroughly and frozen at -80°C. 2.4. Total RNA Extraction and Purification Total RNA was extracted with the miRNeasy Mini Kit (Cat 217004, QIAGEN, Germany). The extracted samples were quantified using a NanoDrop one spectrophotometer and had their quality tested using an Agilent Bioanalyzer 2100. RNA quality control criteria were RIN>7 and 28S/18S>0.7. 2.5. CircRNA Library Building This circRNA-Seq experiment requires the double-end sequencing of 8 samples. The experimental process can be divided into the following steps: RNA hybridization with probes, RNase H digestion, DNase I digestion, magnetic bead purification of RNA fragments, linear digestion, connectors and tails addition, library amplification, and magnetic bead purification of library amplification products. 2.6. Sequencing As described in the cBot User Guide, clusters are generated and first-way sequencing primers hybridized using the cBot on the Illumina NovaSeq 6000 sequencer. Following the Illumina NovaSeq 6000 User Guide, we prepared the reagents for sequencing and then loaded the flow cell with the cluster. The paired-end program was selected for double-end (PE) sequencing. Seqtk software was used to filter the sequences. We removed sequencing primer splice sequences contained in the reads, as well as bases with a quality Q lower than 20 at the 3' end of the reads, that is, a base error rate lower than 0.01, where Q = -10log (error ratio). Also, reads with a sequence length smaller than 25 were removed. The removed raw sequences were cleaned and filtered using FASTX-Toolkit [14] software. The prediction of circRNAs was performed using CIRI [15] software. Comparisons were made with the circBase database (http://circrna.org/) to obtain existing and new circRNAs. The clean reads were compared with the equine reference genome (EquCab2.91) using Hisat2 software. Differential circRNAs were calculated using edgeR [16] . Differential expression fold change was calculated using SPRBM values. Differential screening criteria were P-value < 0.05 and fold change 2. 2.7. GO Annotation and KEGG Pathway Analysis Information on the genomic location of the circRNAs on the genome can be used to determine the protein-coding genes associated with that location. We annotated genes with the Gene Ontology (GO) database (http://www.geneontology.org) and analyzed pathways with the KEGG database (Kyoto Encyclopedia of Genes and Genomes). Both GO annotation and KEGG pathway analysis were performed with a P-value lower than 0.05 as the criterion for significant enrichment. 3. Results 3.1. Library Quality Control A Qubit 3.0 Fluorometer and the Agilent 2100 were used to test the concentration and size of the libraries. The results are shown in Table 1. Table 1. Library quality test results Library Name Concentration (ng/uL) Fragment length (bp) H1 16.9 280 H2 3.02 300 H3 4.32 300 H4 18.1 290 L1 2.6 300 L2 3.32 300 L3 2.6 300 L4 2.64 300 3.2. Sequencing Quality Control Results The quality assessment of sequencing results was performed using FastQC software. The blue line is a concatenation of the mean values for each position, and as can be seen from Fig 1, the error rates for the reads are all lower than 0.01. This satisfies the subsequent analysis. Figure 1. Sequencing quality control chart. 3.3. Sequence Data Statistics The final statistics are shown in Table 2 below. Table 2. Sequence filtering statistics Sample Total Reads Comparison rate no rRNA rRNA Ratio(%) no rRNA pair H1 149147788 96.78% 140069606 2.97% 140069606 H2 107396734 92.34% 98670270 0.50% 98670270 H3 93967642 95.80% 89708646 0.34% 89708646 H4 145486526 93.28% 130786848 3.62% 130786848 L1 101161038 94.95% 95267052 0.81% 95267052 L2 104224568 95.65% 99173578 0.52% 99173578 L3 107414334 94.54% 140069606 0.19% 101359624 L4 109351008 95.29% 98670270 0.35% 103835780 3.4. CircRNA Prediction After the sequencing reads were obtained, the circRNAs were predicted using CIRI software. To distinguish the existing circRNAs from the circRNAs newly predicted, all circRNAs results were merged based on the location of the circRNAs on the chromosome, and the merged results were recoded for ID. The number of circRNAs was counted, and the results are shown in Table 3. Table 3. Summary table of circRNA statistics Sample Total circRNA H1 7082 H2 5029 H3 7614 H4 8849 L1 6194 L2 6735 L3 4282 L4 7462 3.5. Sequence Comparison The software used for the sequence alignment was Hisat2, the alignment algorithm of which was called spliced mapping algorithm, and the reference genome was EquCab2.0. The results of the alignment were tallied, and the results are shown in Table 4. Table 4. Statistics of comparison results Sample Total reads mapped reads pair mapped reads single mapped reads Contrast ratio H1 140069606 135795408 135561806 233602 96.95% H2 98670270 98067318 98004512 62806 99.39% H3 89708646 89314270 89259802 54468 99.56% H4 130786848 124035260 123705906 329354 94.84% L1 95267052 94672427 94608488 63939 99.38% L2 99173578 98650480 98593722 56758 99.47% L3 101359624 100910041 100848224 61817 99.56% L4 103835780 103340641 103240812 99829 99.52% Compared to the sequenced sequences, the comparison percentages are above 90%, which meets the experimental requirements. 3.6. CircRNA Classification We classified circRNAs based on the position of their genomic elements in relation to their position on the genome. Fig 2 shows the results. CircRNAs are mostly distributed in exonic regions, with a small proportion in intronic regions. Figure 2. CircRNA classification 3.7. Differential Expression of CircRNAs Differences in circRNAs were calculated using edgeR. CircRNAs were used to calculate the differential expression fold change using SPRBM values. The criteria for differential screening were P-value 1. Fig 3 shows the results. According to the red and blue distributions, there are 257 and 79 circRNAs showing significant up- or down-regulation in differential expression, respectively. Figure 3. Volcano map of circRNA 3.8. Functional Enrichment Analysis of Differentially Expressed CircRNAs and Their Potential Target Genes To further validated the role of these circRNAs, the GO analysis of these 212 predicted target genes was conducted, indicating that they were mainly involved in processes such as circadian rhythm regulation, steroid hormone receptor binding, and ATP binding (Fig 4). The target genes were rich in PPAR signaling, circadian rhythm, insulin signaling, and lactation-related signaling pathways (Fig. 5). Figure 4. GO functional enrichment analysis of the potential target genes for differentially expressed circRNAs in the equine breast. Figure 5. Functional enrichment analysis of KEGG, a potential target gene for differentially expressed circRNAs in the equine breast. 4. Discussion In this experiment, high-throughput transcriptome sequencing and analysis of high- and low-yielding horse samples has been conducted using RNA-seq technology and revealed a total of 336 differentially expressed circRNAs, which include 257 up-regulated and 79 down-regulated ones. The functional analysis of the parental genes of the differentially expressed circRNAs has initially demonstrated that circRNA.12757/CSN1S1, circRNA.9870/ACSL1, circRNA.9457/LGB1, circRNA.17315/PTK2, circRNA.567/ VPS13C may affect milk production at the mid-lactation stage in Kazakh horses through the PPAR signaling pathway, circadian rhythm, insulin signaling pathway, and lactation-related signaling pathways. In recent years, circRNA-Seq technology has been applied to the study of lactating mammary glands of several species [17] . In order to perform this experiment, we extracted RNAs from equine milk fat, which is present primarily as milk fat globules secreted by mammary epithelial cells [18] . Milk fat can be used as a substitute for mammary epithelial cells for RNA isolation and transcriptome sequencing, as has been demonstrated in humans and animals [19-21] . A negative Pearson correlation between αS1-casein and β-casein content in goat milk was observed in one study [22] . CSN1S1 infection via adenovirus reduced β-casein mRNA (CSN2) and protein abundance, while interference in CSN1S1 significantly increased β-casein abundance. The binding of the transcription factor STAT5A and the promoter region of β-casein promoted β-casein synthesis and thus activated the transcription of CSN2. CSN1S1 inhibited the mechanism of β-casein synthesis by repressing STAT5a. The data suggested that β-casein increased the abundance of the major milk proteins in the organism. In the study using PCR-SSCP (polymerase chain reaction-single strand conformation polymorphism) technology, 708 polymorphisms in the CSN1S1 gene were identified [23] . CSN1S1 primers were designed to amplify exon 9 (producing AA, AB, and BB genotypes) and intron 14 (amplifying AA and AB individuals). The genotypes FF, FN, and NN were detected in AA individuals, while the genotypes FO and NO were detected in AB individuals. A NN genotyped individual significantly lowered protein content (P＜0.01), whereas milk production was significantly higher in FF genotyped individuals than in NO genotyped individuals (P<0.05). Thus, the CSN1S1 gene could potentially be used to improve the quality and yield of milk in Chinese dairy goat breeds. In an experiment examining the effect of 11 bp insertions and deletions (II, DD, ID) in the CSN1S1 gene on milk production using three Chinese goat breeds [24] , it was found that goats with genotype II in the CSN1S1 gene had better milk production performance. In the association analysis between CSN1S1 and CSN3 genes and milk quality traits in 89 Murciano-Granadina goats, no interaction between CSN1S1 and CSN3 was found [25] . CSN1S1 genotypes did not differ significantly in protein, casein, and fat concentrations. The distribution of alleles of CSN1S1 (αs1-casein) in Indian goats was found to differ greatly from that of European goat breeds [26] . In all Indian goat breeds, since higher casein production is known to be associated with the A (gene frequency from 0.68 to 1.00) and B (0.098 to 0.23) alleles, variability in the CSN1S1 gene could be used to conserve Indian goat breeds to improve milk production and milk quality. According to the results of a study examining how STAT5 regulates αS1-casein in goat mammary epithelial cells (GMEC), two putative STAT5 binding sites were located in the core promoter region of CSN1S1 [27] . CSN1S1 expression was upregulated by the overexpression of STAT5a, and STAT5 inhibitors reduced the transcriptional activity of phosphorylated STAT5 and CSN1S1. Thus, in mammary epithelial cells, the transcription of S1-casein is dependent on the promoter activity of the CSN1S1 gene. The lactating mammary gland of the bovine represents an ideal model for synthesizing triacylglycerol [28] . Relative mRNA abundance percentages and expression fold changes of isoforms in mammary tissue of six cows at 215, 15, 60, and 240 days were analyzed by quantitative PCR, respectively. ACSL accounted for 7% of all genes measured. A seven-fold increase in ACSL mRNA abundance was seen at 60 days postpartum. The results suggested that ACSL1 regulated the passage of fatty acids to lipid synthesis in the bovine mammary gland. The LGB1 gene sequences of 12 horse breeds were analyzed by direct DNA sequencing [29] . Genetic expression in specific genotypes, as well as milk composition characteristics, were assessed. In the association study of the protein tyrosine kinase 2 (PTK2) gene, two SNPs related to milk production traits were identified [30] . Through PTK2 gene sequencing of 14 unrelated female Chinese Holsteins, 33 novel SNPs were identified. A total of 13 SNPs identified in this study were genotyped and tested to identify their association with milk production traits. Twelve of them were found to be significantly associated with over two milk production traits after Bonferroni-corrected. PTK2 variants may thus be related to milk production traits in dairy cows, based on these results. A study also showed that the VPS13C gene was related to the average daily milk yield of Laoshan dairy goats [31] . In adipocytes, VPS13C is a galactose lectin-12-binding protein involved in protein stability regulation. Thus, the VPS13C gene may be associated with milk production traits in goats. Transcriptomic analysis was conducted on Holstein cows at the mid- and late lactation stages [32] . KEGG analysis showed that the PPAR signaling pathway differed at mid- and late lactation stages. Thus, the activation of the PPAR signaling pathway may be a key factor that affects an increase in the content of milk fat. Daily chromatin shift is associated with circadian prolactin transcription, according to the study [33] . PIT-1 interacted with HLFT to produce a circadian rhythm of prolactin transcription. NONO and SFPQ (HLTF-associated proteins) were bound to the PRL promoter in circadian rhythms. NONO and SFPQ over-expression reduced prolactin promoter activity and disrupted its circadian rhythm. Thus, circadian rhythms are associated with prolactin promoter activity. Gene expression patterns associated with milk synthesis in yaks were assessed through studies. Yak mammary gland biopsies were analyzed using real-time quantitative PCR [34] . TSC1 and PRKAA2 are mTOR signaling inhibitors that have been found to be significantly upregulated during lactation among 41 genes. Thus, AA and glucose transporter proteins in yak mammary glands and insulin signaling via mTOR are essential for the regulation of milk protein synthesis in yaks. 5. Conclusion In this experiment, high-throughput transcriptome sequencing and analysis of high- and low-yielding horse samples has been conducted using RNA-seq technology and revealed a total of 336 differentially expressed circRNAs, which include 257 up-regulated and 79 down-regulated ones. The functional analysis of the parental genes of the differentially expressed circRNAs has initially demonstrated that circRNA.12757/CSN1S1, circRNA.9870/ACSL1, circRNA.9457/LGB1, circRNA.17315/PTK2, circRNA.567/ VPS13C may affect milk production at the mid-lactation stage in Kazakh horses through the PPAR signaling pathway, circadian rhythm, insulin signaling pathway, and lactation-related signaling pathways. Declarations Availability of Data and Materials NCBI: PRJNA684780 Acknowledgements The authors thank Prof. Lingling Liu's laboratory for their generous support of this research. Financial Support This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous (grant number 2019D01B14). Conflict of Interest The authors declared that there is no conflict of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4605638","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317840402,"identity":"2b22bb8f-ec6d-4fa1-bdde-da8ce2345360","order_by":0,"name":"Xiangyun Shi","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyun","middleName":"","lastName":"Shi","suffix":""},{"id":317840406,"identity":"00ad0b15-c1e3-40f9-a1b1-0811d63dea4e","order_by":1,"name":"Bin Chen","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Chen","suffix":""},{"id":317840410,"identity":"d74abe10-efc5-4266-9452-1f97f80be161","order_by":2,"name":"Wujun Liu","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wujun","middleName":"","lastName":"Liu","suffix":""},{"id":317840413,"identity":"6a15baea-ab76-4bca-9cec-77d11b2c7436","order_by":3,"name":"Lingling Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACfvnnBx9+4LFhZmxvIFKLZENOsrGETBo7c88BIrUYNCSYSfDYHOZnn5FArBaGA8kGEjmHpXlnPt54g6HGJpqgFnPGxoMPCs6kG0vOTiu2YDiWlttASItlM0OygWSPdbLh7BwzCcaGw4S1GBxjMJPg/cdcv//mGWK1nAFq4eFxZmacwUOkFskZPMBA5kljZuwB+iWBGL/wS7DDovLwxhsfamwIa0FxpEQCKcohWkjVMQpGwSgYBSMDAABGiD05OjTh5gAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Lingling","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-19 11:39:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4605638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4605638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59989984,"identity":"27c489f1-5516-41f2-a672-c8571ffcd6d4","added_by":"auto","created_at":"2024-07-10 08:15:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38259,"visible":true,"origin":"","legend":"\u003cp\u003eSequencing quality control chart.\u003c/p\u003e","description":"","filename":"Fig1.Sequencingqualitycontrolchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4605638/v1/45b64a1188daa3e7f4f6db15.jpg"},{"id":59989986,"identity":"467982d1-61a9-4ab0-a8c1-7ed9a102c6d7","added_by":"auto","created_at":"2024-07-10 08:15:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20933,"visible":true,"origin":"","legend":"\u003cp\u003eCircRNA classification\u003c/p\u003e","description":"","filename":"Fig2.CircRNAclassification.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4605638/v1/08668a4ac3aa0ff5e6ba400f.jpg"},{"id":59989981,"identity":"1543290e-f717-4966-92c8-08c9bc18e82c","added_by":"auto","created_at":"2024-07-10 08:15:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26462,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano map of circRNA\u003c/p\u003e","description":"","filename":"Fig3.VolcanomapofcircRNA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4605638/v1/c5a5fc681463f464e80a4fe0.jpg"},{"id":59989987,"identity":"a92ae933-53d0-4be0-a163-ff217601b8d5","added_by":"auto","created_at":"2024-07-10 08:15:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56214,"visible":true,"origin":"","legend":"\u003cp\u003eGO functional enrichment analysis of the potential target genes for differentially expressed circRNAs in the equine breast.\u003c/p\u003e","description":"","filename":"Fig4.GOfunctionalenrichmentanalysisofthepotentialtargetgenesfordifferentiallyexpressedcircRNAsintheequinebreast.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4605638/v1/48939a9881dee94b88c25f94.jpg"},{"id":59989990,"identity":"1066c4ea-4e7c-4a34-938e-3f10f12eec46","added_by":"auto","created_at":"2024-07-10 08:15:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59006,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of KEGG, a potential target gene for differentially expressed circRNAs in the equine breast.\u003c/p\u003e","description":"","filename":"Fig5.FunctionalenrichmentanalysisofKEGGapotentialtargetgenefordifferentiallyexpressedcircRNAsintheequinebreast.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4605638/v1/77612f1cf6447f6acd8bad86.jpg"},{"id":59990634,"identity":"0aa15d67-84e5-4573-bddb-74f32f94974c","added_by":"auto","created_at":"2024-07-10 08:23:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":648033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4605638/v1/fe63e778-b318-49c3-bea8-ff220e658d49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CircRNA for milk production in Kazakh horses","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Kazakh horse is an ancient local horse breed raised under rough herding conditions and found on the northern slopes of the Tian Shan\u0026nbsp;\u003csup\u003e[1]\u003c/sup\u003e, the western part of the Junggar Basin, and the western part of the Altay Mountains. Kazakh horses are not only resistant to rough feeding, cold, running, and intense work but also have stronger mammary glands and more nutritious milk\u003csup\u003e[2]\u003c/sup\u003e. In general, horse milk contains water, protein, milk fat, lactose, enzymes, minerals, and trace elements, closer to human milk than other dairy products such as goat milk, cow milk, and camel milk\u0026nbsp;\u003csup\u003e[3, 4]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCircular RNA (circular RNA, circRNA) was discovered in 1976 by Sanger et al\u0026nbsp;\u003csup\u003e[5, 6]\u003c/sup\u003e, which is a closed circular RNA molecule without the typical poly A structure at the 3\u0026apos; end and the cap structure at the 5\u0026apos; end. Playing a variety of roles in bioregulatory processes\u0026nbsp;\u003csup\u003e[7]\u003c/sup\u003e, CircRNAs can be divided into exonic circRNAs, intronic circRNAs, exon-intron circRNAs, and intergenic circRNAs\u0026nbsp;\u003csup\u003e[8]\u003c/sup\u003e. Moreover, they are characterized by stability, universality, variability, and conservation\u0026nbsp;\u003csup\u003e[9-12]\u003c/sup\u003e. With the rapid development of high-throughput sequencing and bioinformatic analysis, thousands of circRNAs have been identified in cells and tissues of different species.\u003c/p\u003e\n\u003cp\u003eDue to the growing standard of living, much attention has been paid to horse milk as a daily drink, a beauty product, and a therapeutic drink\u0026nbsp;\u003csup\u003e[13]\u003c/sup\u003e. In response to the social needs for this drink, horse milk has been rationally and effectively developed and exploited and thus has a wide market. Since the expression of circRNAs in the milk fat of Kazakh horses has not been studied previously, we screened circRNAs and their target genes to identify those affecting milk production by comparing the circRNAs of Kazakh horses in the high-yielding and low-yielding groups. In our study, we researched the milk yield of Kazakh horses at the transcriptional level.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cem\u003e2.1. Ethical Statement\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the local ethics committee for animal experiments at Xinjiang Agricultural University (approval number: 2017008).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2. Animal Testing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn Fuyun County, Altay prefecture, Xinjiang, test animals were selected and managed uniformly. To measure milk production at the mid-lactation stage, test animals were milked every morning and evening. Animals in the H group produced 7-7.5 kg of milk per day, while those in the L group produced 3.2 to 3.3 kg of milk per day.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Sample Collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMilk produced\u0026nbsp;at the mid-lactation stage\u0026nbsp;was collected from each horse, rapidly frozen in liquid nitrogen, and stored in the laboratory for backup.\u0026nbsp;Then\u0026nbsp;50 ml at 2700 g of milk was taken and placed in the centrifuge for 10 min at 4\u0026deg;C to extract the top layer of milk fat and add it to the trizol at 2:8. Finally, the solution was shaken thoroughly and frozen at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4. Total RNA Extraction and Purification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted with the miRNeasy Mini Kit (Cat 217004, QIAGEN, Germany). The extracted samples were quantified using a NanoDrop one spectrophotometer and had their quality tested using an Agilent Bioanalyzer 2100. RNA quality control criteria were RIN\u0026gt;7 and 28S/18S\u0026gt;0.7.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5. CircRNA Library Building\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis circRNA-Seq experiment requires the double-end sequencing of 8 samples. The experimental process can be divided into the following steps: RNA hybridization with probes, RNase H digestion, DNase I digestion, magnetic bead purification of RNA fragments, linear digestion, connectors and tails addition, library amplification, and magnetic bead purification of library amplification products.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6. Sequencing\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs described in the cBot User Guide, clusters are generated and first-way sequencing primers hybridized using the cBot on the Illumina NovaSeq 6000 sequencer. Following the Illumina NovaSeq 6000 User Guide, we prepared the reagents for sequencing and then loaded the flow cell with the cluster. The paired-end program was selected for double-end (PE) sequencing.\u003c/p\u003e\n\u003cp\u003eSeqtk software was used to filter the sequences. We removed sequencing primer splice sequences contained in the reads, as well as bases with a quality Q lower than 20 at the 3\u0026apos; end of the reads, that is, a base error rate lower than 0.01, where Q = -10log (error ratio). Also, reads with a sequence length smaller than 25 were removed. The removed raw sequences were cleaned and filtered using FASTX-Toolkit\u0026nbsp;\u003csup\u003e[14]\u003c/sup\u003e software.\u003c/p\u003e\n\u003cp\u003eThe prediction of circRNAs was performed using CIRI\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e software. Comparisons were made with the circBase database (http://circrna.org/) to obtain existing and new circRNAs.\u003c/p\u003e\n\u003cp\u003eThe clean reads were compared with the equine reference genome (EquCab2.91) using Hisat2 software. Differential circRNAs were calculated using edgeR\u003csup\u003e[16]\u003c/sup\u003e. Differential expression fold change was calculated using SPRBM values. Differential screening criteria were P-value \u0026lt; 0.05 and fold change 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.7. GO Annotation and KEGG Pathway Analysis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformation on the genomic location of the circRNAs on the genome can be used to determine the protein-coding genes associated with that location. We annotated genes with the Gene Ontology (GO) database (http://www.geneontology.org) and analyzed pathways with the KEGG database (Kyoto Encyclopedia of Genes and Genomes). Both GO annotation and KEGG pathway analysis were performed with a P-value lower than 0.05 as the criterion for significant enrichment.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1. Library Quality Control\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA Qubit 3.0 Fluorometer and the Agilent 2100 were used to test the concentration and size of the libraries. The results are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eLibrary quality test results\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLibrary Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcentration (ng/uL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragment length (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e3.2. Sequencing Quality Control Results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe quality assessment of sequencing results was performed using FastQC software.\u003c/p\u003e\n\u003cp\u003eThe blue line is a concatenation of the mean values for each position, and as can be seen from Fig 1, the error rates for the reads are all lower than 0.01. This satisfies the subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u0026nbsp;\u003c/strong\u003eSequencing quality control chart.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. Sequence Data Statistics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe final statistics are shown in Table 2 below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Sequence filtering statistics\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"698\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparison rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eno rRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e\u003cstrong\u003erRNA Ratio(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003eno rRNA pair\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e149147788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e96.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e140069606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e2.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e140069606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e107396734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e92.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e98670270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e98670270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e93967642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e95.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e89708646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e89708646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e145486526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e93.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e130786848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e3.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e130786848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e101161038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e94.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e95267052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e95267052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e104224568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e95.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e99173578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e99173578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e107414334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e94.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e140069606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e101359624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003e109351008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e95.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e98670270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003e0.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e103835780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e3.4. CircRNA Prediction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter the sequencing reads were obtained, the circRNAs were predicted using CIRI software. To distinguish the existing circRNAs from the circRNAs newly predicted, all circRNAs results were merged based on the location of the circRNAs on the chromosome, and the merged results were recoded for ID. The number of circRNAs was counted, and the results are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Summary table of circRNA statistics\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal circRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e7082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e5029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e7614\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e8849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e6194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e6735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e4282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e7462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e3.5. Sequence Comparison\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe software used for the sequence alignment was Hisat2, the alignment algorithm of which was called spliced mapping algorithm, and the reference genome was EquCab2.0. The results of the alignment were tallied, and the results are shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eStatistics of comparison results\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"698\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003cstrong\u003emapped reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003epair mapped reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e\u003cstrong\u003esingle mapped reads\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eContrast ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e140069606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e135795408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e135561806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e233602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e96.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e98670270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e98067318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e98004512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e62806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e99.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e89708646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e89314270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e89259802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e54468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e99.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e130786848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e124035260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e123705906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e329354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e94.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e95267052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e94672427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e94608488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e63939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e99.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e99173578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e98650480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e98593722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e56758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e99.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e101359624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e100910041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e100848224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e61817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e99.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e103835780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e103340641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e103240812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e99829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e99.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eCompared to the sequenced sequences, the comparison percentages are above 90%, which meets the experimental requirements.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.6. CircRNA Classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe classified circRNAs based on the position of their genomic elements in relation to their position on the genome. Fig 2 shows the results. CircRNAs are mostly distributed in exonic regions, with a small proportion in intronic regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e CircRNA classification\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.7. Differential Expression of CircRNAs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDifferences in circRNAs were calculated using edgeR. CircRNAs were used to calculate the differential expression fold change using SPRBM values. The criteria for differential screening were P-value \u0026lt; 0.05 and |log2fold change|\u0026gt;1. Fig 3 shows the results.\u003c/p\u003e\n\u003cp\u003eAccording to the red and blue distributions, there are 257 and 79 circRNAs showing significant up- or down-regulation in differential expression, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3.\u0026nbsp;\u003c/strong\u003eVolcano map of circRNA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.8. Functional Enrichment Analysis of Differentially Expressed CircRNAs and Their Potential Target Genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo further validated the role of these circRNAs, the GO analysis of these 212 predicted target genes was conducted, indicating that they were mainly involved in processes such as circadian rhythm regulation, steroid hormone receptor binding, and ATP binding (Fig 4).\u003c/p\u003e\n\u003cp\u003eThe target genes were rich in PPAR signaling, circadian rhythm, insulin signaling, and lactation-related signaling pathways (Fig. 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e GO functional enrichment analysis of the potential target genes for differentially expressed circRNAs in the equine breast.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5.\u0026nbsp;\u003c/strong\u003eFunctional enrichment analysis of KEGG, a potential target gene for differentially expressed circRNAs in the equine breast.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this experiment, high-throughput transcriptome sequencing and analysis of high- and low-yielding horse samples has been conducted using RNA-seq technology and revealed a total of 336 differentially expressed circRNAs, which include 257 up-regulated and 79 down-regulated ones. The functional analysis of the parental genes of the differentially expressed circRNAs has initially demonstrated that circRNA.12757/CSN1S1, circRNA.9870/ACSL1, circRNA.9457/LGB1, circRNA.17315/PTK2, circRNA.567/ VPS13C may affect milk production at the mid-lactation stage in Kazakh horses through the PPAR signaling pathway, circadian rhythm, insulin signaling pathway, and lactation-related signaling pathways.\u003c/p\u003e\n\u003cp\u003eIn recent years, circRNA-Seq technology has been applied to the study of lactating mammary glands of several species\u003csup\u003e[17]\u003c/sup\u003e. In order to perform this experiment, we extracted RNAs from equine milk fat, which is present primarily as milk fat globules secreted by mammary epithelial cells\u003csup\u003e[18]\u003c/sup\u003e. Milk fat can be used as a substitute for mammary epithelial cells for RNA isolation and transcriptome sequencing, as has been demonstrated in humans and animals\u003csup\u003e[19-21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA negative Pearson correlation between \u0026alpha;S1-casein and \u0026beta;-casein content in goat milk was observed in one study\u003csup\u003e[22]\u003c/sup\u003e. CSN1S1 infection via adenovirus reduced \u0026beta;-casein mRNA (CSN2) and protein abundance, while interference in CSN1S1 significantly increased \u0026beta;-casein abundance. The binding of the transcription factor STAT5A and the promoter region of \u0026beta;-casein promoted \u0026beta;-casein synthesis and thus activated the transcription of CSN2. CSN1S1 inhibited the mechanism of \u0026beta;-casein synthesis by repressing STAT5a. The data suggested that \u0026beta;-casein increased the abundance of the major milk proteins in the organism. In the study using PCR-SSCP (polymerase chain reaction-single strand conformation polymorphism) technology, 708 polymorphisms in the CSN1S1 gene were identified\u003csup\u003e[23]\u003c/sup\u003e. CSN1S1 primers were designed to amplify exon 9 (producing AA, AB, and BB genotypes) and intron 14 (amplifying AA and AB individuals). The genotypes FF, FN, and NN were detected in AA individuals, while the genotypes FO and NO were detected in AB individuals. A NN genotyped individual significantly lowered protein content (P＜0.01), whereas milk production was significantly higher in FF genotyped individuals than in NO genotyped individuals (P\u0026lt;0.05). Thus, the CSN1S1 gene could potentially be used to improve the quality and yield of milk in Chinese dairy goat breeds. In an experiment examining the effect of 11 bp insertions and deletions (II, DD, ID) in the CSN1S1 gene on milk production using three Chinese goat breeds\u003csup\u003e[24]\u003c/sup\u003e, it was found that goats with genotype II in the CSN1S1 gene had better milk production performance. In the association analysis between CSN1S1 and CSN3 genes and milk quality traits in 89 Murciano-Granadina goats, no interaction between CSN1S1 and CSN3 was found\u003csup\u003e[25]\u003c/sup\u003e. CSN1S1 genotypes did not differ significantly in protein, casein, and fat concentrations. The distribution of alleles of CSN1S1 (\u0026alpha;s1-casein) in Indian goats was found to differ greatly from that of European goat breeds\u003csup\u003e[26]\u003c/sup\u003e. In all Indian goat breeds, since higher casein production is known to be associated with the A (gene frequency from 0.68 to 1.00) and B (0.098 to 0.23) alleles, variability in the CSN1S1 gene could be used to conserve Indian goat breeds to improve milk production and milk quality. According to the results of a study examining how STAT5 regulates \u0026alpha;S1-casein in goat mammary epithelial cells (GMEC), two putative STAT5 binding sites were located in the core promoter region of CSN1S1\u003csup\u003e[27]\u003c/sup\u003e. CSN1S1 expression was upregulated by the overexpression of STAT5a, and STAT5 inhibitors reduced the transcriptional activity of phosphorylated STAT5 and CSN1S1. Thus, in mammary epithelial cells, the transcription of S1-casein is dependent on the promoter activity of the CSN1S1 gene. The lactating mammary gland of the bovine represents an ideal model for synthesizing triacylglycerol\u003csup\u003e[28]\u003c/sup\u003e. Relative mRNA abundance percentages and expression fold changes of isoforms in mammary tissue of six cows at 215, 15, 60, and 240 days were analyzed by quantitative PCR, respectively. ACSL accounted for 7% of all genes measured. A seven-fold increase in ACSL mRNA abundance was seen at 60 days postpartum. The results suggested that ACSL1 regulated the passage of fatty acids to lipid synthesis in the bovine mammary gland. The LGB1 gene sequences of 12 horse breeds were analyzed by direct DNA sequencing\u003csup\u003e[29]\u003c/sup\u003e. Genetic expression in specific genotypes, as well as milk composition characteristics, were assessed. In the association study of the protein tyrosine kinase 2 (PTK2) gene, two SNPs related to milk production traits were identified\u003csup\u003e[30]\u003c/sup\u003e. Through PTK2 gene sequencing of 14 unrelated female Chinese Holsteins, 33 novel SNPs were identified. A total of 13 SNPs identified in this study were genotyped and tested to identify their association with milk production traits. Twelve of them were found to be significantly associated with over two milk production traits after Bonferroni-corrected. PTK2 variants may thus be related to milk production traits in dairy cows, based on these results. A study also showed that the VPS13C gene was related to the average daily milk yield of Laoshan dairy goats\u003csup\u003e[31]\u003c/sup\u003e. In adipocytes, VPS13C is a galactose lectin-12-binding protein involved in protein stability regulation. Thus, the VPS13C gene may be associated with milk production traits in goats.\u003c/p\u003e\n\u003cp\u003eTranscriptomic analysis was conducted on Holstein cows at the mid- and late lactation stages\u003csup\u003e[32]\u003c/sup\u003e. KEGG analysis showed that the PPAR signaling pathway differed at mid- and late lactation stages. Thus, the activation of the PPAR signaling pathway may be a key factor that affects an increase in the content of milk fat. Daily chromatin shift is associated with circadian prolactin transcription, according to the study\u003csup\u003e[33]\u003c/sup\u003e. PIT-1 interacted with HLFT to produce a circadian rhythm of prolactin transcription. NONO and SFPQ (HLTF-associated proteins) were bound to the PRL promoter in circadian rhythms. NONO and SFPQ over-expression reduced prolactin promoter activity and disrupted its circadian rhythm. Thus, circadian rhythms are associated with prolactin promoter activity. Gene expression patterns associated with milk synthesis in yaks were assessed through studies. Yak mammary gland biopsies were analyzed using real-time quantitative PCR\u003csup\u003e[34]\u003c/sup\u003e. TSC1 and PRKAA2 are mTOR signaling inhibitors that have been found to be significantly upregulated during lactation among 41 genes. Thus, AA and glucose transporter proteins in yak mammary glands and insulin signaling via mTOR are essential for the regulation of milk protein synthesis in yaks.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this experiment, high-throughput transcriptome sequencing and analysis of high- and low-yielding horse samples has been conducted using RNA-seq technology and revealed a total of 336 differentially expressed circRNAs, which include 257 up-regulated and 79 down-regulated ones. The functional analysis of the parental genes of the differentially expressed circRNAs has initially demonstrated that circRNA.12757/CSN1S1, circRNA.9870/ACSL1, circRNA.9457/LGB1, circRNA.17315/PTK2, circRNA.567/ VPS13C may affect milk production at the mid-lactation stage in Kazakh horses through the PPAR signaling pathway, circadian rhythm, insulin signaling pathway, and lactation-related signaling pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNCBI: PRJNA684780\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Prof. Lingling Liu\u0026apos;s laboratory for their generous support of this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous (grant number 2019D01B14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003econceptualization, L.L. and W.L.; data curation, B.C. and L.L.; funding acquisition, L.L. and W.L.; writ-ing\u0026mdash;original draft, B.C. and X.S.; writing\u0026mdash;review and editing, B.C. and X.S. 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J Dairy Sci 101(12):11150\u0026ndash;11158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3168/jds.2018-14715\u003c/span\u003e\u003cspan address=\"10.3168/jds.2018-14715\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"mammalian-genome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mage","sideBox":"Learn more about [Mammalian Genome](http://link.springer.com/journal/335)","snPcode":"335","submissionUrl":"https://submission.nature.com/new-submission/335/3","title":"Mammalian Genome","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Circular RNA, Kazakh horse, Milk production, RNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-4605638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4605638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKazakh horses are a basic breed of Xinjiang horses with strong lactation ability. CircRNAs can be broadly involved in bioregulatory activities through a variety of mechanisms. However, there is relatively little literature on the expression of circRNAs in the milk fat of Ka-zakh horses. Therefore, this study aimed to reveal the potential impact of circRNAs on Kazakh horses\u0026rsquo; milk production during their mid-lactation period. To be more specific, the horses were di-vided into the higher-producing (H group) group and the lower-producing group (L group) based on their milk yield, in the middle of lactation. After 300 ml of milk was collected from each horse, RNAs from these milk samples were extracted and purified, and then analyzed with the Illumina NovaSeq 6000 platform. The processed data was compared with the equine genome to select dif-ferentially expressed circRNAs, which are subject to subsequent functional studies. In the two groups, 257 upregulation and 79 downregulation differences in circRNAs were found, and 212 target genes were predicted. The genes (circRNA.12757/CSN1S1, circRNA.9870/ACSL1, circR-NA.9457/LGB1, circRNA.567/VPS13C) enriched by GO and KEGG were located in PPAR signaling pathways, circadian rhythm, insulin signaling pathways, and lactation signaling path-ways.\u003c/p\u003e","manuscriptTitle":"CircRNA for milk production in Kazakh horses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-10 08:15:11","doi":"10.21203/rs.3.rs-4605638/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-06-23T09:08:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T12:09:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mammalian Genome","date":"2024-06-19T11:38:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mammalian-genome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mage","sideBox":"Learn more about [Mammalian Genome](http://link.springer.com/journal/335)","snPcode":"335","submissionUrl":"https://submission.nature.com/new-submission/335/3","title":"Mammalian Genome","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f400fecd-2e95-4b72-a2bc-ba46ce7252a3","owner":[],"postedDate":"July 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-10T08:15:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-10 08:15:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4605638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4605638","identity":"rs-4605638","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}