Method for screening Yili horse mammary gland development key genes and application thereof

Abstract

The invention discloses a method for screening Yili horse mammary gland development key genes and application thereof, and belongs to the field of genomics and molecular breeding. The method comprises the following steps: step 1, selection and treatment of experimental Yili horses: slaughtering the Yili horses in the early lactation period and the peak lactation period, collecting breast tissue samples, putting the breast tissue samples into a cryopreservation tube, putting the cryopreservation tube into liquid nitrogen, taking the cryopreservation tube back to a laboratory, and putting the cryopreservation tube into an ultralow-temperature refrigerator; step 2, carrying out RNA extraction on the Ili horse mammary gland tissue, constructing an ATAC library, an RNA library, a CUT-Tag library and an STRIPE library, and carrying out sequencing; and step 3, carrying out conjoint analysis on the sequencing data in the step 2, and mining key genes related to lactation. The invention also provides application of the method in a horse variety breeding process. The Yili horse mammary gland development and lactation related genes are excavated through a multi-omics technology, the blank in the prior art is filled, a target spot is provided for molecular marker-assisted breeding, and breeding of high-yield and high-quality horse varieties is accelerated.

Description

Technical Field

[0001] This invention belongs to the field of genomics and molecular breeding technology, and relates to a method for screening key genes for mammary gland development in Ili horses and its application. Specifically, it relates to a method for screening key genes for mammary gland development in Ili horses based on multi-omics methods and its application. Background Technology

[0002] Mammary gland development and lactation are core biological processes in the formation of reproductive and milk production capabilities in mammals, and are finely regulated by multiple levels of factors, including genetics, hormones, and epigenetics. Current research largely relies on morphological observations, molecular biology experiments, or functional verification of single genes. While these studies have revealed some regulatory factors related to lactation, they lack a systematic analysis of the entire regulatory network, making it difficult to fully elucidate the molecular mechanisms of mammary gland development and lactation.

[0003] In epigenetic regulation, chromatin open state, transcriptional activity, and histone modifications play crucial roles in regulating gene expression. However, traditional research methods often fail to simultaneously obtain comprehensive information on chromatin structural changes, histone modifications, and transcriptional levels, limiting in-depth exploration of key regulatory factors and signaling pathways.

[0004] In recent years, the rapid development of high-throughput sequencing technology has provided new tools for studying the complex process of mammary gland development. Among them, ATAC-seq technology can efficiently identify open chromatin regions of the genome, reflecting potential regulatory elements; RNA-seq technology can comprehensively analyze gene expression profiles and reveal functional genes related to lactation; and CUT-Tag technology can precisely locate the distribution of specific histone modifications (such as H3K4me3) and reveal transcriptional activation status. By integrating these three types of technologies, key genes and their regulatory networks in the lactation process can be systematically explored at three levels: chromatin accessibility, epigenetic modifications, and transcriptional expression.

[0005] Currently, multi-omics collaborative studies on mammary gland tissue at different stages of lactation are still limited, especially in important economic animals such as horses, where systematic screening of key genes and elucidation of regulatory mechanisms are lacking. This study, through the integrated application of ATAC-seq, RNA-seq, STRIPE sequencing, and CUT-Tag technologies, can reveal core regulatory elements and signaling pathways related to mammary gland development and lactation at the genomic level. This technological breakthrough not only fills a gap in existing research but also provides potential target gene resources and scientific evidence for molecular breeding and production performance improvement aimed at achieving superior milk production.

[0006] The Ili horse is a dual-purpose breed, and traditional breeding methods have limited efficiency, necessitating an analysis of the genetic mechanisms of lactation at the molecular level. Lactation involves the synergistic effects of multiple genes, including mammary gland development, lipid metabolism, and hormonal regulation. Single-omics analyses (such as genomics or transcriptomics) are insufficient to fully reveal its complex regulatory network. Multi-omics combined analysis (genomics, transcriptomics, metabolomics, etc.) can systematically integrate genetic variation, gene expression, and metabolic phenotypic data, overcoming the limitations of traditional research and providing a more comprehensive perspective for identifying key candidate genes (such as LALBA and DGAT1) and pathways (such as mTOR signaling). Summary of the Invention

[0007] The purpose of this invention is to provide a method for screening key genes related to mammary gland development in Ili horses and its application. By using multi-omics technologies to mine genes related to mammary gland development and lactation in Ili horses, not only can the gaps in the molecular mechanisms of lactation in equines be filled, but also targets can be provided for molecular marker-assisted breeding, accelerating the selection of high-yielding and high-quality horse breeds.

[0008] The specific technical solution is as follows:

[0009] First, this invention provides a method for screening key genes for mammary gland development in Ili horses, comprising the following steps:

[0010] Step 1: Selection and treatment of experimental Ili horses: After slaughtering Ili horses in the pre-lactation and peak lactation stages, mammary gland tissue samples were collected, placed in cryovials in liquid nitrogen, and brought back to the laboratory and placed in an ultra-low temperature freezer.

[0011] Step 2: RNA was extracted from the mammary gland tissue of Ili horses, and ATAC, RNA, CUT-Tag, and STRIPE libraries were constructed and sequenced.

[0012] Step 3: Perform joint analysis on the sequencing data from Step 2 to identify key genes related to lactation.

[0013] Furthermore, the Ili horses mentioned in step 1 were purchased from Zhaosu County, Xinjiang Uygur Autonomous Region. Six 8-year-old Ili horses were purchased, three of which were in the pre-lactation stage of the first month of lactation, and three were in the peak lactation stage of the third month of lactation. The ultra-low temperature was -80℃.

[0014] Furthermore, in step 3, the sequencing data of the ATAC library is used to identify chromatin accessibility in the mammary gland tissue of Ili horses and to analyze genes encoded by open regions; the sequencing data of the RNA library is used to identify differentially expressed genes in the mammary gland tissue of Ili horses at different stages; the sequencing data of the CUT-Tag library is used to screen for histone modification-specific genes; and the STRIPE sequencing is mainly used to identify differentially expressed transcription start sites and their associated key genes at different stages. Venn diagrams are used to find differentially expressed genes related to lactation in Ili horses that are screened by the four methods.

[0015] Secondly, this invention provides the application of the method for screening key genes for mammary gland development in Ili horses in the process of horse breed selection.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] This invention uses four methods to identify candidate genes related to mammary gland development and lactation in Ili horses, and their expression was verified. This provides important candidate genes for screening high-yielding lactating Ili horses and has good application value. Attached Figure Description

[0018] Figure 1 For ATAC data quality control chart;

[0019] Figure 2 The image shows the genomic signal enrichment map of breast tissue ATAC-seq at different stages; where 1 represents the pre-lactation S1 group and 3 represents the peak lactation S2 group.

[0020] Figure 3 This is a distribution diagram of Peak in gene functional elements; where 1 represents the pre-lactation S1 group and 3 represents the peak lactation S2 group.

[0021] Figure 4 GO and KEGG plots of peak-associated genes;

[0022] Figure 5 Transcriptome volcano plot;

[0023] Figure 6 For whole-genome CUT & Tag analysis of histone H3K4me3, (A) the distribution of S1 group sequencing reads in different regions of the genome, (B) the distribution of S2 group sequencing reads in different regions of the genome, (C) the peak length distribution of S1 group, and (D) the peak length distribution of S2 group.

[0024] Figure 7 Genomic localization and functional annotation analysis of H3K4me3 peaks, including (A) peak distribution characteristics of S1 and S2 groups across the whole genome, (B) distribution ratio of S1 group peaks in different functional genomic regions, and (C) distribution ratio of S2 group peaks in different functional genomic regions.

[0025] Figure 8 Venn diagram of upregulated genes and differentially expressed genes in the transcriptome from ATAC-seq analysis;

[0026] Figure 9 Venn diagram of downregulated genes and differentially expressed genes in the transcriptome from ATAC-seq analysis;

[0027] Figure 10 ATAC_down_RNA_down GO enrichment results;

[0028] Figure 11 ATAC_up_RNA_up GO enrichment results;

[0029] Figure 12 The results are for ATAC_down_RNA_down KEGG enrichment.

[0030] Figure 13 The results of KEGG enrichment for ATAC_up_RNA_up;

[0031] Figure 14 For the functional enrichment analysis of overlapping genes, (A) Venn diagram of upregulated differentially expressed genes and CUT&Tag peak upregulated target genes obtained from RNA-seq analysis, and (B) Venn diagram of downregulated differentially expressed genes and CUT&Tag peak downregulated target genes obtained from RNA-seq analysis.

[0032] Figure 15 For the STRIPE-seq analysis of transcription initiation regions (TSRs) in breast tissue;

[0033] Figure 16 Four methods were used to screen for common differences, including (A) Venn diagram of differentially expressed genes and (B) common signaling pathway diagram.

[0034] Figure 17 The relative expression levels of the PTGES gene in different tissues at two stages are shown in (A) S1 group and (B) S2 group. Detailed Implementation

[0035] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] This application provides a method for mining candidate genes related to mammary gland development and lactation in Ili horses based on multi-omics joint analysis technology. This method uses ATAC-seq (Assay for Transposase Accessible Chromatin using sequencing) technology, CUT-Tag technology and RNA-seq (RNA sequencing) technology for joint analysis. It analyzes mammary gland tissue samples from the first and third months of lactation to identify key candidate genes related to lactation in Ili horses. The specific steps include the following.

[0037] Example 1: ATAC-seq analysis of mammary gland tissue from Ili horses at different stages

[0038] 0.1g of breast tissue was collected and dispersed using a micro-pestle and filter. A transposable reaction mixture was prepared, and the PCR reaction was set up. The products were digested and incubated with enzymes. The library was purified using the QiagenMinElute PCR Purification Kit, and finally eluted with Elution Buffer. The library concentration was detected using Qubit 4.0, and the fragment size was detected using QSep400. The library was sequenced using an Illumina platform. NaOH was added to the qualified library to denature it into single strands. The library was diluted to a specific concentration according to the expected data volume, and the denatured and diluted library was added to FlowCell for hybridization with adapters. Bridge PCR amplification was then performed on the cBot cluster generation platform, and finally, sequencing was performed.

[0039] The raw sequencing data underwent quality control filtering to remove adapter sequences, contaminating sequences, and low-quality bases. After quality control, all reads met the requirements. Figure 1 The filtered data were aligned to the horse reference genome (EquCab 3.0). Trimmomatic (version 0.36) was used for data quality control. Hisat2 software (version 2.0.2-beta) was used to align to the reference genome. MACS was used to perform peak calling on the mammary tissue data from two different time periods to assess the differences in chromatin accessible regions of Ili horse mammary tissue samples at different time periods. The significance criteria were: FDR < 0.05 and Fold > 0. Peak annotation was performed using the annotatePeak function in the ChIPseeker package. The clusterProfiler package was used to perform GO and KEGG analyses on the annotated differentially expressed genes. The significance enrichment criterion was p-value < 0.05.

[0040] Results of chromatin accessibility analysis based on ATAC-seq technology showed that ATAC-seq reads from the pre-lactation period showed a stronger enrichment of TSS sites than those from the pre-lactation period. Figure 2 Peak scanning was performed across the entire genome using MACS (2.1.0), and the distribution of peaks across gene functional elements was plotted using the ChIPseeker R package. The results showed that peaks were more prevalent in intergenic regions at different time points. Figure 3Based on the DiffBind analysis package (version 1.16.3), the number of reads in the peak region was counted, and differential analysis was performed on two different groups of samples to determine the intervals of up- and down-regulated differential modifications between samples. The standard conditions for screening differential peaks were: FDR value less than 0.05 and Fold greater than 0 (down-regulation was less than 0). From the 5kb upstream and downstream of the peak, the gene corresponding to the nearest transcription start site was selected for GO analysis. The results showed that differentially expressed genes in the pre-lactation and peak lactation periods were mainly enriched in cellular biological processes, metabolic processes, and developmental processes. At the same time, KEGG analysis of differentially expressed genes revealed that they were mainly enriched in signaling pathways such as lipid metabolism, energy metabolism, and amino acid metabolism. Figure 4 ).

[0041] Example 2: Transcriptome analysis of breast tissue at different time periods

[0042] RNA from mammary gland tissues extracted at different stages was used to construct libraries and sequence the genomes. The raw sequencing data underwent quality control filtering. The hisat2 software was used to align the RNA to the equine reference genome; the alignment results are shown in Table 1, with an alignment rate exceeding 97%. FeatureCounts (version: v1.6.0) was used to count the number of reads that fell on genes based on the genome annotation file.

[0043]

[0044] To compare gene expression differences among different samples, we performed differential expression analysis using the R package edgeR. Genes with a p-value less than 0.05 and an absolute log2FC value greater than 1 were considered significantly differentially expressed genes. A scatter plot was created for all differentially expressed genes, and a volcano plot was plotted with red indicating upregulation and blue indicating downregulation. Figure 5 By comparing differentially expressed genes at different stages, a total of 393 differentially expressed genes were identified, of which 326 were upregulated and 67 were downregulated. The top ten upregulated genes during the peak lactation period are shown in Table 2.

[0045] Example 3: Analysis of H3K4me3 Modification Changes in Ili Horses During Two Lactation Stages

[0046] CUT & Tag analysis was performed on mammary gland tissues from two lactation stages of Ili horses to investigate the role of H3K4me3 histone modification during peak lactation. After cell pretreatment and library quality assessment, sequencing was performed on the Illumina platform. A total of 193,674,036 high-quality reads were obtained from the six samples, with an average Q30 of 94.6% and an average alignment rate of 99.43%. H3K4me3 signal was significantly enriched near the transcription start site, while the enrichment outside this region was weaker. Notably, the signal intensity in group S1 was higher than that in group S2 (…). Figure 6 AB). The number of peaks is highest in the 100-300 bp range, with approximately 80% of the peaks distributed in the 1-1,000 bp range. Among them, the number of peaks at 100 bp is higher in group S2 than in group S1. Figure 6 The predominance of narrow peaks indicates that H3K4me3 is mainly enriched in gene promoter regions, supporting its role in transcriptional activation.

[0047] The H3K4me3 peaks identified at different stages of lactation were widely distributed across the equine genome, with signals detected on all chromosomes. Figure 7 A). The genomic localization of these peaks was classified into eight categories: intergenic regions, introns, exons, 3' untranslated regions (3'UTR), 5' untranslated regions (5'UTR), promoter regions (≤1 kb), promoter regions (1–2 kb), and promoter regions (2–3 kb). In groups S1 and S2, H3K4me3 signals were mainly enriched in the first intron, intergenic regions, first exons, and promoter regions (≤1 kb). Figure 7 B–C).

[0048] A total of 32,457 peaks were identified in group S1 and 31,644 peaks in group S2. Computational analysis revealed 4,146 upregulated H3K4me3 peaks and 4,959 downregulated H3K4me3 peaks (selection criterion: | p < 0.0001). Gene annotation results for these differentially regulated peaks showed that they corresponded to 2,352 upregulated genes and 2,595 downregulated genes.

[0049] Example 4: Combined analysis of transcriptome and ATAC-seq results

[0050] By comparing the peak-associated genes of ATAC-seq with differentially expressed genes of RNA-seq, we can explore genes whose target genes show differential expression in ATAC-seq, thereby discovering candidate genes related to equine mammary gland development and lactation.

[0051] By performing Venn analysis on differentially upregulated and downregulated genes from ATAC-seq and RNA-seq, respectively, six genes were found to be upregulated. Figure 8 ), 16 genes were downregulated ( Figure 9 GO and KEGG enrichment analyses of the associated genes revealed that RGS2, NFATC4, PTGES, and EQMHCB24 genes may be key genes related to mammary gland development and lactation. Figures 10-13 ).

[0052] Example 5: Combined analysis of transcriptome and CUT-Tag H3K4me3 results

[0053] We characterized H3K4me3 binding sites across the entire genome of the Ili horse using CUT&Tag technology. However, this technology alone cannot fully reveal the regulatory landscape of H3K4me3. Therefore, we integrated CUT&Tag and RNA-seq data for joint analysis to explore the association between H3K4me3 occupancy and target gene expression. By performing intersection analysis between differentially expressed genes screened by RNA-seq and genes with H3K4me3 signaling changes detected by CUT&Tag, we ultimately identified 16 upregulated H3K4me3 target genes and 32 downregulated H3K4me3 target genes. Figure 14 A, B).

[0054] Functional enrichment analysis showed that the upregulated H3K4me3 target genes were mainly enriched in biological functions such as enzyme activity, unsaturated fatty acid biosynthesis, and nuclear envelope lumen, as well as metabolic pathways such as arachidonic acid metabolism, glutathione metabolism, and cytochrome P450 metabolism of exogenous substances. The downregulated H3K4me3 target genes were mainly involved in immune responses and antigen presentation, and were significantly enriched in pathways related to oxytocin signaling, cellular senescence, antigen processing and presentation, and type 1 diabetes.

[0055] Example 6: Screening for genes with differential transcription start sites in two stages using STRIPE-seq technology

[0056] Six samples from two lactation stages underwent TSS-Atlas library construction and sequencing, yielding high-quality raw sequencing data. The number of raw reads ranged from 19,099,688 (M3) to 41,893,642 (M5), with a total base count exceeding 5 billion (Table 4). Data quality assessment showed that the proportion of Q20 (error rate <1%) bases in all samples was higher than 97.47%, and the proportion of Q30 (error rate <0.1%) bases was higher than 90.52%. Sample M6 had the highest Q30 proportion at 93.72%, indicating that the sequencing data had extremely high base identification accuracy, meeting the requirements for subsequent analysis.

[0057] A total of 137,208 transcription start sites (TSSs) were detected in the Equus caballus 3.0 reference genome using STRIPE-seq. The vast majority of TSSs were located in coding sequences (CDS) and promoter regions, followed by 3′ untranslated regions (3′ UTRs) and intergenic regions. Other detectable TSSs were distributed in introns, proximal upstream regions, 5′ UTRs, antisense regions, and other exon elements. These results indicate that active transcription initiation in equine mammary glands mainly occurs in the gene body and proximal regulatory elements of the promoter. Figure 15 A). Dinucleotide preference analysis showed a strong preference for GG, GC, and GA; the sequence logo showed significant enrichment of sequence information outside the TSS region. In contrast, the +1 position showed significant G enrichment, accompanied by C / G enrichment at the -1 and +2 positions, forming typical Inr (Initiator) core promoter characteristics, consistent with the mammalian RNA polymerase II-mediated transcription initiation pattern. Figure 15 B–C). Polymerization analysis of the STRIPE-seq signal showed sharp enrichment in the annotated TSSs (−2 kb to +2 kb), confirming the precise capture of the promoter proximal transcription initiation event. Figure 15 D). These TSSs defined a total of 11 TSRs, covering 936 genes, of which 40 were upregulated and 896 were downregulated (|log2FC| ≥ 1, FDR < 0.05). Figure 15 E–F).

[0058] Example 7: Combining four methods to identify common differentially expressed genes

[0059] By co-enriching the differentially expressed genes screened in the above examples, one gene, PTGES, was found to be enriched in all four methods. Figure 16 A), and simultaneously identified 8 commonly regulated pathways in the KEGG pathway using 4 different methods ( Figure 16 B).

[0060] Example 8: Validation of the PTGES gene in different equine tissues

[0061] Three horses each from early lactation (first month of lactation) and peak lactation (third month of lactation) were selected from Zhaosu County as research subjects. After slaughter, 15 tissues were collected: heart, liver, spleen, lungs, kidneys, brain, cerebellum, hypothalamus, duodenum, cecum, ileum, rectum, mammary glands, ovaries, and uterus. These tissues were placed in cryovials, stored in liquid nitrogen, and then transported back to the laboratory for storage at -80°C. Primers were designed using Primer Premier 5 software based on the equine gene sequence (Gene ID: 100034143) from NCBI, with GAPDH and β-actin selected as internal reference genes. The primers were synthesized at Shanghai Sangon Biotech. Sequence information is shown in Table 5. RNA was extracted from different tissues using the FOREGENE animal tissue total RNA extraction kit and analyzed using 2×RT OR-Easy. TM The Mix kit was used to synthesize cDNA from total RNA. Then, using the reverse transcription product as a template, real-time quantitative PCR was performed using Real-Time PCR Easy™–SYBR Green I. The reverse transcription system is shown in Table 6, and the PCR reaction system and steps are shown in Tables 7 and 8. The relative expression levels of the PTGES gene in different tissues at the two stages are as follows: Figure 17 As shown.

[0062]

[0063]

[0064]

[0065] The results were statistically analyzed using the 2-ΔΔCt method, and significance analysis was performed using t-tests with GraphPad Prism 10.1 software. ** indicated extremely significant differences. The results showed that the PTGES gene was expressed in 15 tissues at both stages, with the highest expression level in uterine tissue during peak lactation. Comparison of expression levels between the two stages revealed significant and extremely significant differences in expression in the hypothalamus, ileum, mammary gland, and uterine tissue, respectively. Therefore, it is speculated that the PTGES gene is related to lactation in Ili horses.

[0066] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited thereto. Any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the scope of the technology disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for screening key genes for mammary gland development in Ili horses, characterized in that, Includes the following steps: Step 1: Selection and treatment of experimental Ili horses: After slaughtering Ili horses in the pre-lactation and peak lactation stages, mammary gland tissue samples were collected, placed in cryovials in liquid nitrogen, and brought back to the laboratory and placed in an ultra-low temperature freezer. Step 2: RNA was extracted from the mammary gland tissue of Ili horses, and ATAC, RNA, CUT-Tag, and STRIPE libraries were constructed and sequenced. Step 3: Perform joint analysis on the sequencing data from Step 2 to identify key genes related to lactation.

2. The method for screening key genes for mammary gland development in Ili horses according to claim 1, characterized in that, The Ili horses mentioned in step 1 were purchased from Zhaosu County, Xinjiang Uygur Autonomous Region. Six 8-year-old Ili horses were purchased, three of which were in the pre-lactation stage of the first month of lactation, and three were in the peak lactation stage of the third month of lactation. The ultra-low temperature was -80℃.

3. The method for screening key genes for mammary gland development in Ili horses according to claim 1, characterized in that, In step 3, sequencing data from the ATAC library were used to identify chromatin accessibility in the mammary gland tissue of Ili horses and to analyze genes encoded by open regions. Sequencing data from the RNA library were used to identify differentially expressed genes in the mammary gland tissue of Ili horses at different stages. Sequencing data from the CUT-Tag library were used to screen for histone modification-specific genes. Sequencing data from the STRIPE library were used to identify differentially expressed transcription start sites and their associated key genes at different stages. Venn diagrams were used to identify differentially expressed genes related to lactation in Ili horses that were screened by the four sequencing methods.

4. The application of the method for screening key genes for mammary gland development in Ili horses as described in any one of claims 1-3 in the process of horse breed selection.

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