A compendium and comparative analysis of hepatic transcriptome and chromatin accessibility in primiparous lactating cows with different nitrogen utilization efficiency

A compendium and comparative analysis of hepatic transcriptome and chromatin accessibility in primiparous lactating cows with different nitrogen utilization efficiency | 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 A compendium and comparative analysis of hepatic transcriptome and chromatin accessibility in primiparous lactating cows with different nitrogen utilization efficiency Hao Li, Shaokai La, Liyang Zhang, Gaiying Li, Zhanwei Teng, Sheng Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7262943/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The liver is central to regulating nitrogen utilization efficiency (NUE), defined as the ratio of milk nitrogen yield (g/d) to nitrogen intake (g/d) in dairy cows. Identifying the regulatory elements in the liver that affect nitrogen utilization is essential for understanding the factors influencing NUE. Results This study employed RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin sequencing (ATAC-seq) to profile the liver transcriptome and chromatin accessibility in primiparous lactating cows with divergent NUE. We monitored 16 primiparous lactating cows with days in milk ranging from 95 to 115. Over a period of 7 consecutive days, we measured their nitrogen intake and milk nitrogen yield to calculate individual NUE. Based on the NUE values obtained, the cows were categorized into two groups: low NUE (LNUE) with an average NUE of 22.6 ±6.2% (n = 8) and high NUE (HNUE) with an average NUE of 33.1 ±2.2% (n = 8). Liver samples were used for RNA-Seq and ATAC-Seq analysis, identifying 213 differentially expressed genes (DEGs, |fold change| ≥ 1.5, P < 0.05) and 3716 differential accessible regions (DARs, |fold change| ≥ 1.5, P < 0.01), respectively. Among these, 109 DEGs and 1342 DARs were upregulated, while 104 DEGs and 2374 DARs were downregulated in HNUE samples compared to LNUE samples. The DEGs were significantly enriched in 126 biological processes (gene ontology), with 97 normalized enrichment scores (NES) being positive, primarily related to immune processes, while 29 NES were negative, mainly related to metabolic processes and the maintenance of liver structure and function. Promoter-annotated DAR-associated genes were significantly enriched in 173 biological processes, primarily related to the maintenance of liver structure and function. Protein-protein interaction network analysis showed that 47 DEGs generated 37 protein-protein interactions, with genes PRKG1 and HBB being central in the network. Integrated analysis of RNA-seq and ATAC-seq identified one overlapping upregulated gene, TGM5 , and one overlapping downregulated gene, ROR1 . Conclusion These findings demonstrate that hepatic transcriptome and chromatin accessibility epigenetically regulate NUE in primiparous lactating cows. Nitrogen utilization Liver Epigenetics RNA-Seq ATAC-Seq Holstein cattle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Nitrogen ( N ) is a vital nutrient for dairy cows and represents the largest expense in their diet [ 1 ]. As natural resources for feed production diminish [ 2 ], optimizing protein feed utilization has become increasingly important. Furthermore, excessive N discharge from dairy systems poses significant environmental concerns [ 3 ]. Holstein dairy cows exhibit a N utilization efficiency ( NUE , milk N yield [g/d] /N intake [g/d]) ranging from 14–45%, with an average of 24.7% in North America [ 4 ] and only 17.0% in China [ 5 ]. Low NUE leads to wastage of protein resources and contributes to excessive N emissions [ 6 ]. Accordingly, improving NUE in dairy cow production is crucial. Numerous studies have investigated ways to improve NUE in dairy cows [ 7 , 8 ], often focusing on the rumen, which is the first significant site of dietary N losses [ 9 ]. For example, reducing rumen protozoa abundance has been shown to lower rumen ammonia N levels and enhance microbial protein synthesis [ 10 ]. Protozoa play a role in microbial N recycling within the rumen, which can decrease NUE [ 10 , 11 ]. Li et al. (2022) [ 12 ] reported that cows with high NUE ( HNUE ) exhibit a lower acetate to propionate ratio and less diverse rumen bacterial communities compared to cows with low NUE ( LNUE ). However, the post-absorption stage, where the most significant nitrogen losses occur—over 50% of absorbed amino acids are not used for milk production—remains less understood and lacks effective mitigation strategies [ 9 ]. As is well known, the liver is where ammonia is used to synthesis urea [ 9 ]. About 57% of absorbed intestinal N is in the form of ammonia, and most of the ammonia is converted to urea in the liver. On average, only 47% of liver-synthesized urea is recycled to the intestines [ 13 , 14 ]. Alterations in dietary amino acids or energy supply could affect urea N flux in the liver [ 15 ]. Zou et al. (2023) [ 16 ] reported that supplementation of rumen-protected lysine and arginine in a low-protein diet increased the relative expression of liver urea metabolism-related genes (e.g., arginase and ornithine carbamoyltransferase ) in Holstein bulls. In our previous study, the plasma concentrations of L-arginine and L-lysine in the HNUE-cows decreased by 39.4% and 49.1%, respectively, compared with the LNUE-cows [ 17 ]. Liver non-branch-chain amino acid (except for lysine) catabolism is one of the metabolic pathways influencing mammary gland amino acid supply [ 18 , 19 ]. In addition, a recent literature suggested that supplementing rumen-protected lysine to transition dairy cows effected liver function and inflammatory status [ 20 ]. Taken together, the liver play a crucial role in regulating cow NUE [ 19 ]. Chen et al. (2023) [ 21 ] reported that the liver was the highly expressed tissue for key candidate genes, such as DGAT1 , GC , and AHSG , which are associated with the genetic mechanisms of NUE-related traits. Similarly, Liu et al. (2022) [ 22 ] reported that the liver was the most enriched tissue among cis -eQTLs for milk yield ( MY )-related SNPs, following mammary gland tissue and mammary epithelial cells. Previous research has already demonstrated that MY was a key indicator directly impacting NUE [ 5 , 17 , 19 ]. Understanding the role of the liver in regulating NUE in dairy cows is fundamental for designing new strategies to further improve NUE. Chromatin accessibility, a fundamental element of epigenomics, serves as a direct indicator of the influence exerted by chromatin structural modifications on gene transcription [ 23 ]. Among the various techniques employed in chromatin accessibility analysis, ATAC-Seq has emerged as a preferred method. Its superiority lies in its requirement for a relatively small number of starting cells and a significantly shorter sample-processing time when compared to other established techniques [ 24 – 26 ]. The integration of ATAC-Seq and RNA-Seq enables a comprehensive and intuitive visualization of the intricate relationship between chromatin openness and gene expression. This integrative analysis further allows for in-depth monitoring of the underlying regulatory mechanisms [ 27 ]. Given the advantages of these techniques, this experiment was designed to analyze and compare the hepatic transcriptome and chromatin accessibility of dairy cows with different NUEs. Specifically, we sought to identify the key genes in the liver that are involved in regulating NUE in dairy cows. The ultimate goal was to offer novel insights into the epigenetic regulatory mechanisms of different NUE in Holstein cattle. 2. Materials and Methods 2.1. Animals and management The utilization and management of cows, as well as the experimental protocols, were evaluated, approved, and monitored by the Institutional Animal Care and Use Committee (IACUC) of Henan Agricultural University (Zhengzhou, China) (HNND2021072798). The study was conducted at the ZhongLi dairy farm in Ulanhot City, Inner Mongolia Autonomous Region, spanning from October to December in the year 2021. For the experiment, 16 primiparous and non-pregnant Holstein cows with DIM ranging between 95 and 115 d, body weight ( BW) ranging between 512.9 and 643.0 kg, and an average MY over the past 7 days ranging between 20.11 and 26.87 kg/d, were selected from a group of 1,221 primiparous cows located in the same barn. All animals were adapted for 14 days after being transferred from a large herd to an individual pen measuring 2.4 m × 6 m. The diet and environmental conditions were not changed from large herd to individual pen. The diets in this experiment were formulated according to the NRC based on the actual production levels. Staff members uniformly prepared the total mixed ration ( TMR ). Each cow was individually fed and allowed ad libitum access to feed to facilitate accurate calculation of dry matter intake ( DMI ). The nutritional composition and levels of the experimental diets are listed in Table S1 . Throughout the experiment, cows were milked 3 times daily at 07:30 h, 15:00 h, and 22.30 h in the rotary milking parlor and provided with 60% TMR at 07: 00 h and 40% TMR at 19: 00 h. The cows were fed to ad-libitum intake and had unrestricted access to water. Barn cleaning and disinfection occurred when the cows were taken to the milk parlor for milking at 15: 00 h. 2.2. Data recording and sample collection During the final 2 days of adaptation in individual pens, the BW were measured and recorded for each cow, with the average values used for statistical analysis. The adaptation period was followed by a 7-day digestive and metabolic analysis period during which the daily feeding and leftover amounts and MY were recorded for each cow, and samples of TMR, leftover feed, and milk were collected. At the end of the experiment, the TMR and leftover samples collected over the 7 d were separately mixed for chemical analysis. Each day, a 50-mL milk sample (collected in a 4:3:3 ratio for morning, midday, and evening) per cow was mixed with potassium chromate preservative and stored at 4°C until analysis. After the digestive and metabolic analysis period, for the following two days, liver samples from 8 cows were collected each morning from 9:00 h to 11:00 h. Due to the undetermined NUE of each individual cow at the time of sample collection, the order of sample collection was randomized. Fortunately, on the first day of sampling, we obtained 4 LNUE samples (with experimental animal numbers 1, 3, 7 and 8) and 4 HNUE samples (with experimental animal numbers 10, 13, 14 and 15), while the remaining 8 samples were obtained on the second day of sampling (Fig. 1 ). The liver was biopsied under local anaesthesia by blind percutaneous needle biopsy (8 mm × 300 mm, Wuhan Kelibo Animal Technology Co., LTD.). For the local anaesthesia, 15 mL of 0.25% procaine hydrochloride solution was used: 10 mL was injected subcutaneously and 5 mL was administered deeper into the muscle layer. The procedure was initiated after waiting for about 15 minutes to ensure the anaesthetic took effect [ 28 ]. Each cow contributed approximately 150 mg of liver sample, which was divided equally into two portions for different analyses. One portion of the liver sample was directly immersed in RNAlater (Ambion, Applied Biosystems, Austin, TX) and stored at -80℃ for RNA-seq analysis. The other portion of the liver sample was rapidly frozen in liquid nitrogen immediately after collection and transferred to -80℃ after 5 minutes of freezing for ATAC-seq analysis. 2.3. Chemical analysis and calculation The content of DM in TMR and leftover samples were analyzed using the methods described in AOAC [ 29 ]. The content of N in TMR and leftover samples were determined using an automatic Kjeldahl N analyzer (SKD-2000, Shanghai Haineng Experimental Instrument Technology Co., LTD). Milk samples were sent to the DHI Determination Center in Henan Province for analysis of fat, protein, lactose, total solids, milk urea nitrogen ( MUN ), and somatic cell count ( SCC ) in milk using a MilkoScan FT + analyzer (Foss Electric A/S). The average values of these indicators over the 7-day digestive and metabolic analysis period were used for statistical analysis. The NUE of each cow was calculated using the following formula: NUE (%) = milk N yield (g/d)/NI (g/d) × 100% [ 30 ], where milk N yield (g/d) = MY (kg/d) × milk protein content (%)/6.38 × 1000 [ 31 ]. The 16 cows were retrospectively divided into 2 groups (LNUE and HNUE) based on their NUE levels. The LNUE group consisted of 8 cows with an average NUE of 22.6 ± 6.2%, while the HNUE group comprised 8 cows with an average NUE of 33.1 ± 2.2%. Due to the limited amount of liver samples contributed by each individual cow, in order to ensure the quality of RNA-Seq and ATAC-Seq, the liver samples were systematically pooled into composite samples in pairs based on the number of experimental animals. Fortunately, on the first day of sampling, we obtained 4 LNUE samples (with experimental animal numbers 1, 3, 7 and 8) and 4 HNUE samples (with experimental animal numbers 10, 13, 14 and 15), while the remaining 8 samples were obtained on the second day of sampling (Fig. 1 ). Grouping information was blinded to the experimental personnel for RNA-Seq and ATAC-Seq. 2.4. Transcriptome sequencing of liver and data analysis 2.4.1. RNA extraction, Library preparation, and Sequencing Hepatic total RNA was extracted with TRlzol Reagent (Life Technologies, California, USA). RNA concentration and purity were determined using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE). RNA integrity was evaluated using the Agilent Bioanalyzer 2100 system and RNA Nano 6000 Assay Kit (Agilent Technologies, CA, USA). All RNA samples meet the high-quality criteria (OD 260/280 ranging between 1.8 and 2.2, OD 260/230 > 2.0, RIN ≥ 8, 28S : 18S ≥ 1.0, and > 1µg). According to the instructions provided by Hieff NGS Ultima Dual-mode mRNA Library Prep Kit for Illumina (Yeasen Biotechnology Co., Ltd, Shanghai, China), 1 µg of total RNA was used to generate sequencing libraries. Index codes were included to attribute sequences to individual samples. The main procedure is as follows: (1) Eukaryotic mRNA was enriched using magnetic beads with Oligo(dT) to capture the poly(A) tail; (2) Fragmentation Buffer was added to randomly break the mRNA into shorter fragments; (3) The first-strand cDNA and second-strand cDNA were synthesized using the mRNA as a template, followed by cDNA purification; (4) The purified double-stranded cDNA underwent end repair, A-tailing, and sequencing adapter ligation. Fragment size selection was performed using AMPure XP beads; (5) Finally, the cDNA library was enriched through PCR amplification. After library construction, an initial quantification was performed using the Qubit 3.0 Fluorometer. The concentration of all cDNA librarys was higher than 1 ng/µL. Subsequently, the insert fragments in the library were analyzed using the Qsep400 high-throughput analysis system. Once the insert fragments met the expected criteria (Fig. S1 a), the effective concentration of the library (library effective concentration > 2 nM) was accurately quantified using qPCR to ensure library quality. After passing the library quality control, the PE150 mode sequencing was performed on the Illumina NovaSeq6000 sequencing platform. 2.4.2. Data analysis After the sequencing data was generated from the sequencing platform, the raw reads were further processed with a bioinformatic pipeline tool, BMKCloud ( https://www.biocloud.net/ ) online platform. Initially, reads containing adapters, ploy-N, and those of low quality were removed to obtain clean data (clean reads). Simultaneously, the clean data underwent calculations for Q20, Q30, GC content, and sequence duplication level. All subsequent analyses relied on the high-quality clean data. These clean reads were subsequently mapped to the reference genome sequence (ARS_UCD1.3) using the Hisat2 tools software. Only reads with a perfect match or a single mismatch were subject to further analysis and annotation, based on the reference genome. For the quantification of gene expression levels, fragments per kilobase of transcript per million fragments mapped ( FPKM ) were utilized. The formula for estimating gene expression levels is as follows: FPKM = cDNA Fragments / over [Mapped Fragments (Millions) × TranscriptLength (kb)]. Genes with a P -value < 0.05 and |(fold change)| ≥ 1.5 found by DESeq2_edgeR were assigned as differentially expressed. To analyze the enrichment of differentially expressed genes ( DEG ) in Gene Ontology ( GO ) biological processes ( BP ), we performed a gene-set enrichment analysis ( GSEA ). The sequences of DEG were aligned (blastx) to the genomes of relevant species to obtain predicted protein-protein interactions ( PPI ) for these genes. The STRING database ( http://string-db.org/ ) [ 32 ] was used to explore the protein-protein interaction data. Subsequently, the PPI network of DEGs was visualized using Cytoscape software [ 33 ]. To investigate the co-expression relationship between NUE, MY, and BW with liver genes, we performed Weighted Gene Co-Expression Network Analysis ( WGCNA ). 2.4.3 Validation of RNA-Seq results analyzed by quantitative PCR (qPCR) In order to validate the gene expression data obtained from RNA-Seq, 6 DEG were randomly selected to perform qPCR. According to the instructions of HiScript III RT SuperMix for qPCR (+ gDNA wiper) (R323, Vazyme Biotech Co., Ltd, Nanjing, China), 1 µg total RNA was reverse transcribed to generate cDNA. After a 20 fold dilution, 2 µL of cDNA was used for qPCR, which was performed in an LightCycler®96 (Roche, CH). ChamQ Universal SYBR qPCR Master Mix (Q711, Vazyme Biotech Co., Ltd, Nanjing, China) was used for qPCR analysis. Gene ACTB was selected as a reference gene that was unaffected by experimental factors. All the primers, listed in Table S2, were synthesized by Shangya Biotechnology Co., Ltd. (Zhengzhou, China). 2.5. ATAC sequencing of liver and data analysis 2.5.1. DNA extraction, Library preparation and Sequencing Approximately 50 mg of liver sample was homogenized into a fine powder using liquid nitrogen. The pulverized liver tissue was then suspended in 1 mL of ice-cold PBS. Approximately 50,000 cells were centrifuged at 4℃, 500 g for 5 minutes, and the supernatant was carefully removed. The cells were then washed once with cold PBS using the same centrifugation conditions. After removing the supernatant, the cells were resuspended in a cold lysis buffer according to the protocol described by Zhao et al. (2021) [ 34 ]. The suspension was centrifuged again at 4℃ for 10 minutes at 500 g, and the supernatant was carefully removed. For the transposing reaction system was prepared by combining 10 µL of ATAC-Seq’d cells, 10 µL of 5× TTBL, 5 µL of TTE Mix V50, and 25 µL of nuclease-free water from the TruePrep® DNA Library Prep Kit V2 for Illumina (TD501-TD503). The Tn5 transposase was added to the cell nuclei suspended in the transposing reaction system, and the DNA was purified after being incubated at 37℃ for 30 minutes. The resulting purified DNA was subsequently used as a template for PCR amplification. The final DNA libraries were prepared and sequenced on an Illumina platform after purification. 2.5.2. Data analysis The raw reads were filtered using the Cutadapt software [ 35 ] to remove adapters and reads shorter than 35 bp in length. Additionally, low-quality reads were eliminated, including reads with an N ratio greater than 10% and reads where bases with a quality value Q ≤ 10 accounting for more than 50% of the entire Read. Subsequently, high-quality clean reads in FASTQ format were obtained for further analysis. High-quality reads obtained from sequencing each sample were compared to the reference genome (ARS_UCD1.3) using the Bowtie2 software [ 36 ]. This comparison allowed for the determination of alignment efficiency of the sample reads as well as the position information of the reads on the genome. Subsequent analysis was performed using only the uniquely mapped reads aligned to the reference genome. The coverage of bases on the reference genome and the length of insert fragments were calculated and recorded. The density distribution of sequencing reads in the 3 kb interval upstream and downstream of the transcription start site ( TSS ) of each gene was determined using DeepTools v2.07. The results were visualized using heat maps. The process of peak extraction was performed using MACS2 v2.1.1 software [ 37 ]. MACS2 entailed four essential steps: (1) removal of redundant reads; (2) adjustment of read positions; (3) calculation of peak enrichment; and (4) estimation of the empirical false detection rate ( FDR ). Peaks were identified based on an FDR threshold of < 0.05. The ChIPseeker software package [ 38 ] was utilized for annotating the distribution of the detected peaks across the entire genome. Based on the distance relationship between the peak regions and various genomic functional elements, the peak regions were annotated to determine the proportion of peaks falling into different genomic functional elements. The DiffBind package was utilized for conducting difference peak (ie, differentially accessible region [ DAR ]) analysis. This analysis involved calculating the read count supported by each peak in each sample and deriving an affinity score based on the count (referred to as standardized read count). The generated affinity scores were then used as input for DESeq2 software [ 39 ] to perform differential screening among samples within each group. The criteria for differential screening were set as follows: |fold change| ≥ 1.5, P-value ≤ 0.01. The MA diagram was employed to visually assess the overall distribution of differential fold changes of DARs between the two groups. According to the distance relationship between the DARs and the functional elements of each gene on the genome, the DARs was annotated. The R package clusterProfiler [ 40 ] was used to respectively perform enrichment analysis of BP of genes associated with DARs enriched in the promoter region. 2.6. Comparison of differentially expressed genes in RNA-Seq and differentially accessible region in ATAC-Seq We performed a statistical analysis on genes that exhibited differential accessibility in ATAC-Seq (genes represented by the nearest TSS to the center of the DAR) and differential expression levels. These genes were categorized into 4 types: DAR gain and DEG up, DAR gain and DEG down, DAR loss and DEG up, and DAR loss and DEG down. 2.7. Statistical analysis Throughout the entire experimental period, none of the test animals presented any abnormal conditions, and no outliers (± 3 standard deviations from the mean) were detected in the data from them, which were all used for statistical analysis. The data obtained from the qPCR experiment were analyzed using the 2 −ΔΔCt method to calculate the relative gene expression levels. Animal characteristics, production performance, and relative gene expression levels in the liver of LNUE (n = 8) and HNUE (n = 8) cows were analyzed using the t-test in the SAS. A significant trend was considered if 0.05 < P < 0.10, and a significant difference was defined as P < 0.05. 3. Results 3.1. Animal characteristics, Milk yield and composition The NUE of 16 experimental cows ranged from 11.9–36.9%, averaging 27.9% (Fig. 1 ). There was a significant difference in NUE between cows in the HNUE and LNUE groups, with a difference of 10.6% units ( P 0.05), the HNUE group cows produced an additional 38.8 g of milk N per day compared to the LNUE group cows ( P = 0.010). Additionally, the HNUE group cows exhibited significantly higher MY, as well as higher yield of milk protein, milk fat, and lactose ( P 0.05). Table 1 Animal characteristics and production performance of lower milk nitrogen utilization efficiency cows and high milk nitrogen utilization efficiency cows Item Group 1 SEM 2 P -value LNUE HNUE Days in milk (d) 99.6 101.0 1.235 0.573 Body weight (kg) 592.6 a 548.7 b 9.342 0.034 Dry matter intake (kg/d) 17.5 16.7 0.485 0.396 Nitrogen intake (g/d) 441 425 13.01 0.547 NUE (%) 22.6 b 33.2 a 1.170 <0.001 Yield (kg/d) Milk 18.7 b 25.4 a 1.441 0.036 Protein 0.631 b 0.879 a 0.041 0.009 Fat 0.768 b 1.012 a 0.064 0.047 Lactose 0.980 b 1.344 a 0.079 0.037 Milk nitrogen yield (g/d) 101.8 b 140.6 a 6.550 0.010 Milk composition (%) Protein 3.50 3.48 0.089 0.939 Fat 4.17 4.02 0.224 0.751 Lactose 5.15 5.29 0.054 0.198 Total solids 13.2 13.2 0.305 0.984 Milk urea nitrogen content (mg/dL) 14.6 14.3 0.328 0.615 Somatic cell counts (×10 − 3 /ml) 37.8 12.0 8.861 0.168 1 LNUE = lower milk nitrogen utilization efficiency (NUE = 22.6 ± 6.2%, n = 8); HNUE = higher milk nitrogen utilization efficiency (NUE = 33.1 ± 2.2%, n = 8). 2 SEM = Standard error of mean. a, b Means with different superscripts in each row differ significantly ( P < 0.05). 3.2. Liver RNA-Seq Data Analysis 3.2.1. Identification of Differentially Expressed Genes The RNA-Seq analysis of 8 liver samples obtained a total of 4,105.93 million raw reads. After quality filtering, a total of 3,958.45 million clean reads were obtained (Table S3). Among the 8 samples, 95.88–96.60% of the clean reads were successfully mapped to the reference genome. The insert fragments conformed to the expected standards (Fig. S1 a) and the sequencing depth was sufficient to achieve transcriptome coverage (Fig. S1 b). The DEGs were identified based on the criteria of P -value < 0.05 and |(fold change)| ≥ 1.5, resulting in 213 DEGs, with 104 downregulated and 109 upregulated genes in the liver of HNUE-group cows compared to LNUE-group cows (Fig. 2 a and 2 b). The qPCR analysis of 6 differential DEGs was conducted to validate the RNA-Seq data. Figure 2 c shows the qPCR results, which were consistent with the RNA-Seq results. 3.2.2. Identification of Biological Processes Associated with Liver Regulation of NUE The GSEA analysis revealed significant enrichment ( P -value < 0.05) of 126 GO BP terms in the comparisons between the HNUE group and LNUE group. Among these, 97 terms had a positive Normalized Enrichment Score ( NES ), while 29 terms had a negative NES. Table 2 presented the top 10 significantly enriched BP terms with positive NES and the top 10 with negative NES, along with the number of genes assigned to each term. The BP terms with positive NES were mainly associated with immune processes, with the most significant examples including immune response, inflammatory response, immune response-activating cell surface receptor signaling pathway, immune response-regulating cell surface receptor signaling pathway, and immune response-activating signal transduction. The BP terms with negative NES were mainly associated with metabolic processes, such as cellular response to amino acid stimulus, acyl-CoA metabolic process, positive regulation of glucose import, and regulation of protein processing, and the maintenance of liver structure and function, such as positive regulation of cell-substrate adhesion, extracellular matrix organization, endodermal cell differentiation, collagen fibril organization, cell-cell adhesion via plasma-membrane adhesion molecules, and positive regulation of BMP signaling pathway. Table 2 Enriched gene ontology (GO) biological process terms associated with differentially expressed genes in the liver GO biological process term 1 Count P-value NES 2 immune response 49 0.0015 1.580 inflammatory response 41 0.0016 1.984 regulation of cell shape 20 0.0016 1.710 multi-organism process 14 0.0016 2.017 response to external biotic stimulus 10 0.0017 2.248 immune response-activating cell surface receptor signaling pathway 6 0.0017 2.071 immune response-regulating cell surface receptor signaling pathway 7 0.0017 2.094 response to other organism 10 0.0017 2.229 response to external stimulus 14 0.0017 1.852 immune response-activating signal transduction 8 0.0017 2.142 cellular response to amino acid stimulus 7 0.0023 -1.967 positive regulation of cell-substrate adhesion 5 0.0024 -2.006 extracellular matrix organization 24 0.0025 -2.252 endodermal cell differentiation 10 0.0046 -1.995 collagen fibril organization 11 0.0047 -1.937 acyl-CoA metabolic process 5 0.0090 -1.814 positive regulation of glucose import 10 0.0092 -1.788 regulation of protein processing 4 0.0119 -1.784 cell-cell adhesion via plasma-membrane adhesion molecules 3 0.0156 -1.764 positive regulation of BMP signaling pathway 8 0.0165 -1.703 1 The Gene Ontology Biological Process (GO BP) terms were sorted by their enrichment P-values, which were calculated using the Expression Analysis Systematic Explorer (EASE) score. The terms were sorted in ascending order, with the top having the lowest P-value and the bottom having the highest P-value. 2 NES = Normalized Enrichment Score. Only the top 10 significantly upregulated and the top 10 significantly downregulated GO BP terms have been included in the list. 3.2.3. Interaction Network and Weighted Gene Co-Expression Network Analysis of Differentially Expressed Genes The PPI network analysis revealed that out of the 213 DEGs, a total of 47 DEGs were found to have interactions with other DEGs, resulting in 37 protein-protein interactions (Fig. 3 ). These 47 genes formed 11 subnetworks, among which the largest subnetwork consisted of 17 DEGs. The key participants in this subnetwork included PRKG1 and TSSK2 , which were found to have interactions with 11 and 4 DEGs, respectively, and they were directly connected. However, it is noteworthy that PRKG1 exhibited higher expression levels in the liver of HNUE group cows, whereas TSSK2 showed higher expression levels in the liver of LNUE group cows. Additionally, a subnetwork comprising 6 DEGs revolved around the HBB gene, which was found to have interactions with 5 other DEGs and displayed higher expression levels in the liver of HNUE group cows. Based on the WGCNA analysis, 2 gene modules were identified among the 213 DEGs (Fig. 4 a). The MEturquoise module, consisting of 53 DEGs, showed a significant positive correlation with NUE and MY ( P < 0.05) and a significant negative correlation with BW ( P = 0.01) (Fig. 4 b). The HBB gene is one of the members of the MEturquoise module. 3.3. Liver ATAC-Seq Data Analysis 3.3.1. Whole-Genome Accessible Chromatin Regions Detection The ACAT-Seq analysis of 8 samples was conducted, resulting in a total of 409.33 million raw reads. After quality filtering, 408.57 million clean reads were obtained (Table S4). Among the 8 samples, at least 97.25% of the clean reads were aligned to the reference genome. The majority of the insert fragment lengths fall between 150 to 300 bp (Fig. S1 c). The sequencing depth was saturated (Fig. S1 d). Uniquely mapped reads exhibit the strongest signal near the TSS (Fig. S1 e). According to the statistical analysis, the average number of ACRs in the LNUE group was 54719, with an average length of 305.5 bp (Fig. 5 a). The average number of ACRs in the HNUE group was 45973, with an average length of 279.3 bp (Fig. 5 b). The proportions of ACRs falling into different gene functional elements were similar between the two groups, showing no significant differences (Fig. 5 c). Despite the lack of statistical significance, it is worth noting that on each chromosome, the LNUE group of cows had a higher number of ACRs and longer average lengths compared to the HNUE group (Fig. 5 d). 3.3.2. Identification of differentially accessible regions A total of 3,716 DARs were identified between HNUE and LNUE groups of cows, with 2,374 DARs showing higher accessibility in the liver of HNUE group cows and 1,342 DARs showing higher accessibility in the liver of LNUE group cows (Fig. 6 a and 6 c). Through annotation, it was found that 5.27% of the DARs, which corresponds to 238 DARs, were annotated to the promoter regions (Fig. 6 b), out of which 143 DARs exhibited higher accessibility in the liver tissue of HNUE cows (Fig. 6 d). Subsequently, the 238 DARs annotated to the promoter regions will undergo gene annotation to identify associated genes, followed by GO BP enrichment analysis of these genes. The analysis revealed that these genes were significantly enriched in 173 GO BP terms (P-value < 0.05). Figure 6 e illustrates the top 20 BP terms that showed significant enrichment. These BP terms primarily related to the maintenance of liver structure and function, including cellular component organization, cellular component organization or biogenesis, centrosome localization, and maintenance of organelle location. 3.4. Comparison of differentially expressed genes in RNA-Seq and differentially accessible region in ATAC-Seq The genes related to DARs identified by ATAC-Seq analysis (genes represented by the TSS closest to the center of the DAR) and the DEGs identified by RNA-Seq analysis were compared analyzed. The gene types were classified into DAR_Gain-DEG_Up, DAR_Gain-DEG_Down, DAR_Loss-DEG_Up and DAR_Loss-DEG_Down. As a result, an overlapping upregulated gene, TGM5 , and an overlapping downregulated gene, ROR1 , were identified (Fig. 7 a). 4. Discussion There are notable individual differences in NUE in lactating dairy cows, even under the same production conditions [ 4 , 5 ]. Recently, Li et al. (2022) [ 12 ] investigated the individual variation in NUE in dairy cows based on differences in plasma 15 N and dietary 15 N (Δ 15 N), given that it is generally acknowledged that Δ 15 N is inversely proportional to NUE [ 41 ]. The Δ 15 N (‰) was quantified in lactating cows at the second parity and 48 ± 1 days in milk ( DIM ), with the minimum value being found to be no greater than 1.5‰, while the maximum value exceeded 3.0‰. Xue et al. (2022) [ 42 ] provided NUE data for a total of 18 mid-lactating cows, and reported a minimum NUE value of approximately 22% and a maximum value of approximately 33%. Individual differences in NUE among cows under the same feeding conditions provide a convenient basis for studying the role of the liver in regulating NUE in cows. Improving NUE is crucial for both environmental conservation and economic viability in dairy production systems [ 43 ]. Typically, N absorbed by lactating cows is used for maintenance, tissue growth, lactation, reproduction, and minor losses such as hair growth, scurf formation, and volatile N losses [ 3 ]. This study involved primiparous non-pregnant dairy cows, where dietary N was primarily allocated to maintenance, growth and lactation. Cows in the HNUE group showed lower BW but higher MY compared to cows in the LNUE group. Importantly, there were no significant differences in DMI and NI between the two groups. These results indicated that HNUE cows achieved better performance without consuming more feed or nitrogen. This finding was consistent with previous studies that thinner cows often produce more milk than fatter cows under similar conditions [ 44 , 45 ]. However, it suggested that HNUE cows may experience higher production pressure. In dairy production systems, it is widely accepted that dietary N losses primarily occur at three sites: the rumen, small intestine, and post-absorption. More than half of the N absorbed in the small intestine is not utilized for milk production [ 9 ]. Currently, our understanding of post-absorption N losses is limited, and effective strategies to reduce this aspect of N loss are lacking. The liver plays diverse roles in lactating cows [ 46 ], and its significance in nutrient regulation has prompted numerous studies on the liver transcriptome during lactation [ 47 , 48 ]. Regarding NUE in cows, the liver prominently participates in urea synthesis [ 13 , 14 ] and metabolizes amino acids substantially [ 19 ], particularly non-branch-chain amino acid catabolism, which impacts mammary gland amino acid supply [ 18 ]. Furthermore, liver passage and delivery to peripheral circulation are pivotal in converting dietary N to milk N [ 19 ]. Hence, the liver may rank only behind the mammary gland in regulating cow NUE [ 21 , 22 ]. This study explored, for the first time, differences in the liver transcriptome and chromatin accessibility of dairy cows with different NUE via RNA-seq and ATAC-seq. Notably, cow liver metabolism significantly fluctuates throughout the day. To maintain consistency and minimize time-related influences, samples were collected between 9:00 am and 11:00 am in this experiment. Using DESeq2, RNA-seq analysis identified 213 DEGs. However, these DEGs did not include genes associated with urea metabolism, indicating no significant differences in urea metabolism levels between the livers of cows in the HNUE and LNUE groups. This finding consistent with earlier research that found no significant differences in plasma and milk urea N concentrations between the two groups of cows [ 17 ], suggesting that liver urea N redistribution may not be a prioritized mechanism for liver involvement in regulating NUE in primiparous dairy cows. Indeed, increasing urea cycle N in cows is frequently an ineffectual way to improve NUE [ 8 , 49 ]. The only circumstances in which recycled urea N for reuse increases significantly and then improves NUE are those associated with low-protein diets. Despite our preliminary trials showed that the HNUE group of cows had a significantly negative N balance compared to the LNUE group, the feed composition and levels of crude protein were the same for both groups of cows [ 17 ]. The GSEA analysis revealed that BP terms with positive NES values were predominantly linked to immune processes. Specifically, 41 DEGs were notably enriched in the inflammatory response (NES value was 1.984). This suggests that cows in the HNUE group exhibit heightened liver immune capabilities compared to cows in the LNUE group. Fehlberg et al. (2023) [ 20 ] noted that supplementing postpartum cows with rumen-protected lysine reduces liver mRNA expression associated with immunity. In our previous research, we observed lower blood amino acid levels in HNUE group cows compared to LNUE group cows, particularly a 39.4% and 49.1% difference in arginine and lysine levels, respectively [ 17 ]. We hypothesized that this relates to the higher MY of HNUE group cows, leading to increased amino acid uptake from the blood by the mammary gland for milk protein synthesis [ 9 ]. The efficiency of branched-chain amino acid uptake by the liver relied on its supply, where higher supply leads to increased uptake and clearance rates [ 9 , 50 ]. Consequently, the amino acid flux in the liver of HNUE group cows was lower compared to that of LNUE group cows. This resulted in the downregulation of 2 BP terms: cellular response to amino acid stimulus and regulation of protein processing. It also likely contributed to the heightened immune capability observed in HNUE group cows compared to LNUE group cows. Furthermore, the immune status of the liver was influenced by the N balance [ 51 ], oxidative stress status [ 52 ], and liver function [ 53 ] of cows. The downregulation of liver function-related BP terms in HNUE group cows was associated with the upregulation of immune response and inflammatory response [ 54 ], as well as with more severe negative N balance and decreased blood amino acid content, especially of arginine and lysine [ 20 ]. In this experiment, the possible effect of negative N balance on liver function seems to be more significant, even if a negative N balance state could impair liver immune activity [ 51 ]. Moreover, previous study suggested that LNUE group cows might experience increased oxidative stress which impairs liver function and increase inflammation [ 54 ] compared to HNUE group cows [ 17 ]. These seemingly contradictory findings, however, may be attributed to the downregulation of acetyl coenzyme A metabolism, indicating an overall reduction in liver metabolism. This could reduce the differences in oxidative stress effects on liver function and immune capability between the two groups. Through PPI analysis and WGCNA of DEGs, genes PRKG1 and HBB identified as potential candidates involved in NUE regulation at the transcriptional level in the liver. Both genes exhibited centrality in the PPI network, with PRKG1 gene interacting with 11 DEGs. Gene PRKG1 played a role in regulating lipid breakdown metabolism in adipocytes, facilitating the hydrolysis of triglycerides to release fatty acids and glycerol [ 55 ]. Studies have suggested the involvement of the PRKG1 gene in inhibiting the proliferation of rat brown adipocytes via the cGMP-PKG signaling pathway [ 56 ]. Importantly, the relative expression of the PRKG1 gene in HNUE liver samples significantly exceeded that in LNUE liver samples, supporting the hypothesis that cows in the HNUE group required increased fat mobilization to meet production demands [ 17 ]. The HBB gene occupied a pivotal position in a PPI subnetwork, interacting with 5 DEGs. Moreover, it belonged to the MEturquoise gene module, which showed significant positive correlations with NUE and MY, and negative correlation with BW. The HBB gene encodes the hemoglobin subunit β protein, known for its distinct physiological and biochemical attributes [ 57 ]. Extensive research has identified HBB as a candidate gene associated with animal physiological traits [ 58 ], with genetic variations potentially impacting various phenotypes, including cattle growth traits [ 59 ]. Future investigations should focus on elucidating the relationship between HBB gene and NUE. Chromatin accessibility directly reflects the impact of chromatin structural modifications on gene transcription [ 27 ]. An intriguing finding emerged from this experiment: cows in the LNUE group exhibited a higher number and longer average lengths of ACRs on each chromosome compared to the HNUE group. However, these differences lacked statistical significance, possibly due to the small sample size limitation. This calls for further investigation in the future. ATAC-seq analysis identified 3716 DARs in liver samples from both groups of cows. The GO enrichment analysis of DARs annotated to promoter regions revealed significant enrichment in 173 BP terms, primarily related to maintaining liver structure and function, consistent with the results of RNA-seq analysis. Integrated analysis of RNA-seq and ATAC-seq identified 2 overlapping differentially expressed genes: an upregulated gene, TGM5 , and a downregulated gene, ROR1 . TGM5 is a member of the transglutaminase family, a calcium-dependent enzyme that catalyzes protein post-translational modifications by deamidating and crosslinking amines [ 60 ]. The ROR1 gene interacts with multiple signaling pathways, including NF-κB and PI3K/AKT. Li et al. (2023) [ 61 ] found a potential association between ROR1 gene and sheep MY traits. Additional, the ROR1 gene was considered to play a decisive role in determining milk SCC in cow [ 62 , 63 ]. These results suggested that the regulation of NUE in cows by liver transcription was affected by liver chromatin accessibility. This may be achieved by affecting liver function. Additionally, genes TGM5 and ROR1 might be key genes in this process. 5. Conclusions The joint analysis of RNA-seq and ATAC-seq revealed the involvement of the liver in regulating cow NUE at the levels of chromatin accessibility and transcriptome. Liver chromatin accessibility was identified as potentially influencing gene transcription by regulating liver structure and function, thus contributing to NUE regulation. In this process, genes TGM5 and ROR1 emerged as potential candidate genes. Additionally, genes PRKG1 and HBB were suggested as key candidates involved in liver transcriptional regulation of NUE. Individual differences in cow NUE were associated with liver structure and function, immune capability, and metabolic levels. Abbreviations BP: Biological processes BW : Body weight DAR: Differentially accessible region DEG: Differentially expressed genes DIM : Days in milk DMI : Dry matter intake FDR: False detection rate FPKM : Fragments per kilobase of transcript per million fragments mapped GO : Gene Ontology GSEA: Gene-set enrichment analysis HNUE : High nitrogen utilization efficiency LNUE : Low nitrogen utilization efficiency MUN : Milk urea nitrogen MY : Milk yield N : Nitrogen NES: Normalized Enrichment Score NUE : Nitrogen utilization efficiency PPI: Protein-protein interactions SCC : Somatic cell count TMR : Total mixed ration WGCN: Weighted Gene Co-Expression Network Analysis Declarations Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the earmarked fund for China Agriculture Research System (CARS36); the Key Research and Development Special Project of Henan Province (221111111100), and the Key Scientific and Technological Project of Henan Province Department of China (232103810005). Author Contribution H.L. and S.L. performed the experiments, analyzed the data and drafted the manuscript. L.Z., G.L. and Z.T. contributed to the revision of the manuscript. S.L., Z.Y. and L.A. contributed data analysis and provided technical support. H.H. and T.G. were corresponding authors, conceived and supervised this study, wrote the manuscript. All authors reviewed and approved the manuscript. Acknowledgement The authors would like to extend their gratitude to Zhongli Dairy Farm for providing the experimental animals and facilities, and to the staff for their dedicated care of the animals. Data Availability The raw data related to transcriptomic and chromatin accessibility analysis have been deposited in the NCBI BioProject database, with accession numbers PRJNA1303741 and PRJNA1303735 respectively. References Prestegaard-Wilson JM, Daley VL, Drape TA, Hanigan MD. A survey of United States dairy cattle nutritionists' practices and perceptions of reducing crude protein in lactating dairy cow diets. Appl Anim Sci. 2021;37(6):697–709. Wu GY, Fanzo J, Miller DD, Pingali P, Post M, Steiner JL, et al. Production and supply of high-quality food protein for human consumption: sustainability, challenges, and innovations. Ann Ny Acad Sci. 2014;1321:1–19. Hristov AN, Bannink A, Crompton LA, Huhtanen P, Kreuzer M, McGee M, et al. Nitrogen in ruminant nutrition: A review of measurement techniques. J Dairy Sci. 2019;102(7):5811–52. Huhtanen P, Hristov AN. A meta-analysis of the effects of dietary protein concentration and degradability on milk protein yield and milk N efficiency in dairy cows. J Dairy Sci. 2009;92(7):3222–32. Zhang NN, Bai ZH, Luo JF, Ledgard S, Wu ZG, Ma L. Nutrient losses and greenhouse gas emissions from dairy production in China: Lessons learned from historical changes and regional differences. Sci Total Environ. 2017;598:1095–105. Lavery A, Ferris CP. Proxy measures and novel strategies for estimating nitrogen utilisation efficiency in dairy cattle. Animals. 2021;11(2):343. Reynolds CK, Kristensen NB. Nitrogen recycling through the gut and the nitrogen economy of ruminants: an asynchronous symbiosis. J Anim Sci. 2008;86(Suppl 14):E293–305. Schwab CG, Broderick GA. A 100-Year Review: Protein and amino acid nutrition in dairy cows. J Dairy Sci. 2017;100(12):10094–112. Pszczolkowski VL, Apelo SIA. The market for amino acids: understanding supply and demand of substrate for more efficient milk protein synthesis. J Anim Sci Biotechno. 2020;11(1):108. Newbold CJ, de la Fuente G, Belanche A, Ramos-Morales E, McEwan NR. The role of ciliate protozoa in the rumen. Front Microbiol. 2015;6:1313. Ayemele AG, Ma L, Park T, Xu JC, Yu ZT, Bu DP. Giant milkweed (Calotropis gigantea): A new plant resource to inhibit protozoa and decrease ammoniagenesis of rumen microbiota without impairing fermentation. Sci Total Environ. 2020;743:140665. Li M, Zhong HY, Li M, Zheng N, Wang JQ, Zhao SG. Contribution of ruminal bacteriome to the individual variation of nitrogen utilization efficiency of dairy cows. Front Microbiol. 2022;13:815525. Zhong CL, Long RJ, Stewart GS. The role of rumen epithelial urea transport proteins in urea nitrogen salvage: A review. Anim Nutr. 2022;9:304–13. Lapierre H, Berthiaume R, Raggio G, Thivierge MC, Doepel L, Pacheco D, et al. The route of absorbed nitrogen into milk protein. Anim Sci. 2007;80(01):11–22. Omphalius C, Lemosquet S, Ouellet DR, Bahloul L, Lapierre H. Postruminal infusions of amino acids or glucose affect metabolisms of splanchnic, mammary, and other peripheral tissues and drive amino acid use in dairy cows. J Dairy Sci. 2020;103(3):2233–54. Zou S, Ji S, Xu H, Wang M, Li B, Shen Y, et al. Rumen-protected lysine and methionine supplementation reduced protein requirement of holstein bulls by altering nitrogen metabolism in liver. Animals. 2023;13(5):843. Li H, La SK, Zhang LY, Li S, Yu ZB, Ao LM, et al. Metabolomics and amino acid profiling of plasma reveals the metabolite profiles associated with nitrogen utilization efficiency in primiparous dairy cows. Animal. 2024;18:101202. Cant JP, Reyes GC, Seymour DJ, Review. Influence of postabsorptive metabolism on essential amino acid partitioning in lactating dairy cows. Animal. 2022;16(Suppl 3):100573. Calsamiglia S, Ferret A, Reynolds CK, Kristensen NB, van Vuuren AM. Strategies for optimizing nitrogen use by ruminants. Animal. 2010;4(7):1184–96. Fehlberg LK, Guadagnin AR, Thomas BL, Ballou M, Loor JJ, Sugimoto Y, et al. Feeding rumen-protected lysine altered immune and metabolic biomarkers in dairy cows during the transition period. J Dairy Sci. 2023;106(4):2989–3007. Chen Y, Atashi H, Grelet C, Mota RR, Vanderick S, Hu H, et al. Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle. J Dairy Sci. 2023;106(5):3397–410. Liu SL, Gao YH, Canela-Xandri O, Wang S, Yu Y, Cai WT, et al. A multi-tissue atlas of regulatory variants in cattle. Nat Genet. 2022;54(9):1438–47. Tsompana M, Buck MJ. Chromatin accessibility: a window into the genome. Epigenet Chromatin. 2014;7:33. Buenostro JD, Wu BJ, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486–U264. Mezger A, Klemm S, Mann I, Brower K, Mir A, Bostick M, et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat Commun. 2018;9:3647. Chen X, Miragaia RJ, Natarajan KN, Teichmann SA. A rapid and robust method for single cell chromatin accessibility profiling. Nat Commun. 2018;9:5345. Yue JW, Hou XH, Liu X, Wang LG, Gao HM, Zhao FP, et al. The landscape of chromatin accessibility in skeletal muscle during embryonic development in pigs. J Anim Sci Biotechno. 2021;12(1):56. La S, Li H, Wang C, Liu Q, Guo G, Huo W, et al. Effects of rumen-protected folic acid and dietary protein level on growth performance, ruminal fermentation, nutrient digestibility and hepatic gene expression of dairy calves. J Anim Physiol N. 2019;103(4):1006–14. AOAC. Official methods of analysis. 2 18th ed. Association of Official Analytical Chemists; 2004. Müller CBM, Görs S, Derno M, Tuchscherer A, Wimmers K, Zeyner A, et al. Differences between Holstein dairy cows in renal clearance rate of urea affect milk urea concentration and the relationship between milk urea and urinary nitrogen excretion. Sci Total Environ. 2021;755:143198. Beltran IE, Calvache I, Cofre R, Salazar F, Keim JP, Morales A, et al. Nitrogen intake and its partition on urine, dung and products of dairy and beef cattle in chile. Agronomy-Basel. 2022;12(1):15. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(D1):D447–52. Shannon, Cytoscape P. A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. Zhao YX, Hou Y, Xu YY, Luan Y, Zhou HH, Qi XL, et al. A compendium and comparative epigenomics analysis of regulatory elements in the pig genome. Nat Commun. 2021;12(1):2217. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J. 2011;17(1):10–2. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137. Yu GC, Wang LG, He QY. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015;31(14):2382–3. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Yu GC, Wang LG, Han YY, He QY. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. Omics. 2012;16(5):284–7. Correa-Luna M, Johansen M, Noziere P, Chantelauze C, Nasrollahi SM, Lund P, et al. Nitrogen isotopic discrimination as a biomarker of between-cow variation in the efficiency of nitrogen utilization for milk production: A meta-analysis. J Dairy Sci. 2022;105(6):5004–23. Xue MY, Xie YY, Zhong YF, Ma XJ, Sun HZ, Liu JX. Integrated meta-omics reveals new ruminal microbial features associated with feed efficiency in dairy cattle. Microbiome. 2022;10(1):32. Castillo AR, Kebreab E, Beever DE, France JA. A review of efficiency of nitrogen utilisation in lactating dairy cows and its relationship with environmental pollution. J Anim Feed Sci. 2000;9(1):1–32. Garnsworthy PC, Topps JH. The effect of body condition of dairy cows at calving on their food intake and performance when given complete diets. Anim Sci. 2010;35(01):113–9. Roche JR, Friggens NC, Kay JK, Fisher MW, Stafford KJ, Berry DP. Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci. 2009;92(12):5769–801. DE B and WB C. Partitioning of Nutrients During Pregnancy and Lactation: A Review of Mechanisms Involving Homeostasis and Homeorhesis. J Dairy Sci. 1980; 63(9): 1514–29. Ghaffari MH, Sadri H, Trakooljul N, Koch C, Sauerwein H. Liver transcriptome profiles of dairy cows with different serum metabotypes. J Dairy Sci. 2024;107(3):1751–65. Grala TM, Kay JK, Phyn CVC, Bionaz M, Walker CG, Rius AG, et al. Reducing milking frequency during nutrient restriction has no effect on the hepatic transcriptome of lactating dairy cattle. Physiol Genomics. 2013;45(23):1157–67. Recktenwald EB, Ross DA, Fessenden SW, Wall CJ, Van Amburgh ME. Urea-N recycling in lactating dairy cows fed diets with 2 different levels of dietary crude protein and starch with or without monensin. J Dairy Sci. 2014;97(3):1611–22. Fleming AJ, Lapierre H, Martineau R, White RR, Hanigan MD. Modeling portal-drained viscera and liver fluxes of essential amino acids in dairy cows. J Dairy Sci. 2019;102(12):10964–82. Bell AW, Burhans WS, Overton TR. Protein nutrition in late pregnancy, maternal protein reserves and lactation performance in dairy cows. P Nutr Soc. 2000;59(1):119–26. Sordillo LM, Contreras GA, Aitken SL. Metabolic factors affecting the inflammatory response of periparturient dairy cows. Anim Health Res Rev. 2009;10(01):53–63. Bertoni G, Trevisi E, Han X, Bionaz M. Effects of inflammatory conditions on liver activity in puerperium period and consequences for performance in dairy cows. J Dairy Sci. 2008;91(9):3300–10. Zhou Z, Bulgari O, Vailati-Riboni M, Trevisi E, Ballou MA, Cardoso FC, et al. Rumen-protected methionine compared with rumen-protected choline improves immunometabolic status in dairy cows during the peripartal period. J Dairy Sci. 2016;99(11):8956–69. Shi LJ, Lv XQ, Liu L, Yang YZ, Ma Z, Han B, et al. A post-GWAS confirming effects of PRKG1 gene on milk fatty acids in a Chinese Holstein dairy population. Bmc Genet. 2019;20(1):53. Nisoli E, Clementi E, Tonello C, Sciorati C, Briscini L, Carruba MO. Effects of nitric oxide on proliferation and differentiation of rat brown adipocytes in primary cultures. Brit J Phamacol. 1998;125(4):888–94. Bettati S, Viappiani C, Mozzarelli A. Hemoglobin, an evergreen red protein. BBA-Proteins Proteom. 2009;1794(9):1317–24. Vazic BS, Rogic BS, Drinic MS, Przulj NM. Relationship between the Genetic Hemoglobin Polymorphism, Morphometry and Fertility of Pramenka Sheep Breed from Central Bosnia. Genetika-Belgrade. 2017;49(1):151–60. Boonprong S, Choothesa A, Sribhen C, Parvizi N, Vajrabukka C. Relationship between haemoglobin types and productivity of Thai indigenous and Simmental x Brahman crossbred cattle. Livest Sci. 2007;111(3):213–7. Garcia IS, Silva-Vignato B, Cesar ASM, Petrini J, Da Silva VH, Morosini NS, et al. Novel putative causal mutations associated with fat traits in Nellore cattle uncovered by eQTLs located in open chromatin regions. Sci Rep. 2024;14(1):10094. Li R, Zhao Y, Liang B, Pu Y, Jiang L, Ma Y. Genome-wide signal selection analysis revealing genes potentially related to sheep-milk-production traits. Animals. 2023;13(10):1654. Gutiérrez-Reinoso MA, Aponte PM, García-Herreros M. Genomic and phenotypic udder evaluation for dairy cattle selection: A review. Animals. 2023;13(10):1588. Irina L, Sermyagin AA, Ignatieva LP, Elena G, Alexander E, Zinovieva NA. PSXII-7 milk somatic cells monitoring in russian holstein cattle population as a base for determining genetic and genomic variability. J Anim Sci. 2021;99(Suppl 3):252. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesandFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7262943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512040500,"identity":"51c50d66-9368-485a-94f7-ba4ee4550220","order_by":0,"name":"Hao Li","email":"","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Li","suffix":""},{"id":512040501,"identity":"bd47c0ae-a2a0-4438-8cd2-9a8e8c812362","order_by":1,"name":"Shaokai La","email":"","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shaokai","middleName":"","lastName":"La","suffix":""},{"id":512040502,"identity":"7b552148-37ab-453d-a8d1-f488a93b8abf","order_by":2,"name":"Liyang Zhang","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Liyang","middleName":"","lastName":"Zhang","suffix":""},{"id":512040503,"identity":"d2ad86a5-9cc6-48c5-8e4c-0557888c9461","order_by":3,"name":"Gaiying Li","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Gaiying","middleName":"","lastName":"Li","suffix":""},{"id":512040504,"identity":"58c7895c-7546-4b5f-b076-d648383350c5","order_by":4,"name":"Zhanwei Teng","email":"","orcid":"","institution":"Henan Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhanwei","middleName":"","lastName":"Teng","suffix":""},{"id":512040505,"identity":"807b7f01-d472-49d3-82c4-6bf80d9a9c4b","order_by":5,"name":"Sheng Li","email":"","orcid":"","institution":"ZhongLi (Hinggan League) Animal Husbandry Co. LTD","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Li","suffix":""},{"id":512040506,"identity":"50b20ca3-2fca-4eab-a5fe-a33508eb4d1b","order_by":6,"name":"Zhibin Yu","email":"","orcid":"","institution":"ZhongLi (Hinggan League) Animal Husbandry Co. LTD","correspondingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Yu","suffix":""},{"id":512040507,"identity":"3b3ef26f-1fca-4d1f-b02d-07892f8cf660","order_by":7,"name":"Lima Ao","email":"","orcid":"","institution":"ZhongLi (Hinggan League) Animal Husbandry Co. LTD","correspondingAuthor":false,"prefix":"","firstName":"Lima","middleName":"","lastName":"Ao","suffix":""},{"id":512040508,"identity":"bac4a416-3f7d-4a29-8149-43e4022b2e0d","order_by":8,"name":"Hetian Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACAxDB2MAgZwAVALKJ02JgTLqWxA1EazGXSH728OuOP+nb2XsPfuZhsJHdcID52QN8WixnpJkby54xyN3Zcy5ZmochzXjDATZzA3xaDG4kmElLthnkbriRYwDUcjhxwwEeNgn8WtK/gbSkG9x/Y/ybh+E/MVpyzCQ/thkkGNzgMQPacoAILWfelEkzthkbbjiTY2Y5xyDZeOZhNjP8Wo6nb5P82SYnb3D8jPGNNxV2sn3Hm5/h1QICzDwIE0BcQuqBgPEHEYpGwSgYBaNgBAMAuPxJRsOwQ3kAAAAASUVORK5CYII=","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Hetian","middleName":"","lastName":"Huang","suffix":""},{"id":512040509,"identity":"5ed0b551-c223-4b66-976c-18fa4331741a","order_by":9,"name":"Tengyun Gao","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tengyun","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-07-31 13:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7262943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7262943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91212098,"identity":"faf077c1-7f0e-4f4a-91bf-8ed74a0bf915","added_by":"auto","created_at":"2025-09-12 18:22:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":199333,"visible":true,"origin":"","legend":"\u003cp\u003eThe levels of milk nitrogen utilization efficiency of experiment animals.\u003c/p\u003e\n\u003cp\u003eThe X-axis indicates the experimental animal number, while the Y-axis represents the milk nitrogen utilization efficiency (NUE) level. The low NUE group (LNUE) consisted of 8 cows with an average NUE of 22.6 ± 6.2%, while the high NUE group (HNUE) comprised 8 cows with an average NUE of 33.1 ± 2.2%.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/66a9b78dede696008b1da71c.png"},{"id":91212099,"identity":"24696405-6540-4ffc-bfb7-4eccb1705f90","added_by":"auto","created_at":"2025-09-12 18:22:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":821489,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the liver RNA-Seq experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea:\u003c/strong\u003e The number of differentially expressed genes (DEGs) between the high milk nitrogen utilization efficiency (HNUE, NUE = 33.1 ± 2.2%%, n=4) group and low milk nitrogen utilization efficiency (LNUE, NUE = 22.6 ± 6.2%, n=4) group..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb:\u003c/strong\u003e Volcano plot on differential expression. In volcano plot, each dot represents a gene. The X-axis indicates the log\u003csub\u003e2\u003c/sub\u003e (Fold change); the Y-axis indicates the -log\u003csub\u003e10\u003c/sub\u003e (P-value). Dots farther to y=0 represent genes with large difference in expression between two samples. Dots farther to x=0 represents genes of which the difference is more reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec:\u003c/strong\u003e qPCR results of some DEGs.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/e1083530865b8c25c2ad62f2.png"},{"id":91212641,"identity":"e91e8d2c-6f8b-4c25-8ea8-9358d0245bc3","added_by":"auto","created_at":"2025-09-12 18:30:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":836552,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of differentially expressed genes.\u003c/p\u003e\n\u003cp\u003eIn the figure, the triangle represents up-regulated DEGs, while the circle represents down-regulated DEGs. The color of the nodes represents the p-value, with a redder color indicating a smaller p-value. The size of the nodes represents the |log\u003csub\u003e2\u003c/sub\u003e (Fold change)|, with a larger value resulting in a larger node. The thickness of the lines represents the strength of the protein-protein interaction between the connected proteins, with thicker lines indicating stronger interactions.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/5fbdcbfa2a7bdf118e983ae4.png"},{"id":91213208,"identity":"ce83d68c-7e2d-4409-b047-4752b1f515b6","added_by":"auto","created_at":"2025-09-12 18:38:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":634750,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis of differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Hierarchical clustering dendrogram of genes and heatmaps of trait correlation. This figure consists of three parts: The first part is the hierarchical clustering dendrogram of genes. The second part shows the color representation of modules to which corresponding genes belong. The third part displays the correlation between genes in each trait-related sample and their associated modules. The intensity of red indicates positive correlation, while blue represents negative correlation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Modules with milk N-utilization efficiency (NUE), milk yield (MY), and body weight (BW) correlation heatmap.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/645224c388ddfebe7ccfe956.png"},{"id":91212642,"identity":"8cdff90b-2de1-40d6-9764-0f4cdd05a2fb","added_by":"auto","created_at":"2025-09-12 18:30:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1648230,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the ATAC-Seq experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Number of the accessible chromatin regions (ACRs) in the high milk nitrogen utilization efficiency group (HNUE, NUE=33.1 ± 2.2%, n=4) and low milk nitrogen utilization efficiency group (LNUE, NUE=22.6 ± 6.2%, n=4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Mean length of ACRs in the HNUE group and LNUE group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Percentages of ACRs in different genomic regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Distributions of ACRs numbers and length across different chromosomes.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/081405d0354701a9a7ce1c70.png"},{"id":91213587,"identity":"86f3b08c-6637-4c35-b864-0538179dae00","added_by":"auto","created_at":"2025-09-12 18:46:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1685086,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the differentially accessible regions in ATAC-Seq experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Number of DARs in HNUE-cows \u003cem\u003evs.\u003c/em\u003e LNUE-cows. Where HNUE represents the high milk nitrogen utilization efficiency group (NUE=33.1 ± 2.2%, n=4) and LNUE represents the low milk nitrogen utilization efficiency group (NUE=22.6 ± 6.2%, n=4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Percentages of DARs in different genomic regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Genome-wide distribution of DARs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e MA diagram of DARs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e Number of DARs enriched in the promoter region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef\u003c/strong\u003e Go enrichment analysis of genes associated with DARs in promoter region.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/d1037ef13e97abc97b2cb687.png"},{"id":91212104,"identity":"df2bdc7a-59c5-4c94-be9f-bbac4a72dba9","added_by":"auto","created_at":"2025-09-12 18:22:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":536955,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of overlap genes between differentially accessible regions (DARs) related genes and differentially expressed genes (DEGs).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/aa7b5ad119fa4cb8ebf3baa8.png"},{"id":107524586,"identity":"f3c75e33-e198-415c-9d5b-81b040217c27","added_by":"auto","created_at":"2026-04-22 09:29:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6447150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/23f5a48e-d7b5-4d0c-910e-e77fc2d1a75e.pdf"},{"id":91212645,"identity":"ae1f5951-0b87-4ba0-8f90-3e71f80f0040","added_by":"auto","created_at":"2025-09-12 18:30:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":523895,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesandFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7262943/v1/c2d2355759b0a4ef23a3c048.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A compendium and comparative analysis of hepatic transcriptome and chromatin accessibility in primiparous lactating cows with different nitrogen utilization efficiency","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNitrogen (\u003cb\u003eN\u003c/b\u003e) is a vital nutrient for dairy cows and represents the largest expense in their diet [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As natural resources for feed production diminish [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], optimizing protein feed utilization has become increasingly important. Furthermore, excessive N discharge from dairy systems poses significant environmental concerns [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Holstein dairy cows exhibit a N utilization efficiency (\u003cb\u003eNUE\u003c/b\u003e, milk N yield [g/d] /N intake [g/d]) ranging from 14\u0026ndash;45%, with an average of 24.7% in North America [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and only 17.0% in China [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Low NUE leads to wastage of protein resources and contributes to excessive N emissions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accordingly, improving NUE in dairy cow production is crucial.\u003c/p\u003e\u003cp\u003eNumerous studies have investigated ways to improve NUE in dairy cows [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], often focusing on the rumen, which is the first significant site of dietary N losses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, reducing rumen protozoa abundance has been shown to lower rumen ammonia N levels and enhance microbial protein synthesis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Protozoa play a role in microbial N recycling within the rumen, which can decrease NUE [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Li et al. (2022) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported that cows with high NUE (\u003cb\u003eHNUE\u003c/b\u003e) exhibit a lower acetate to propionate ratio and less diverse rumen bacterial communities compared to cows with low NUE (\u003cb\u003eLNUE\u003c/b\u003e). However, the post-absorption stage, where the most significant nitrogen losses occur\u0026mdash;over 50% of absorbed amino acids are not used for milk production\u0026mdash;remains less understood and lacks effective mitigation strategies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As is well known, the liver is where ammonia is used to synthesis urea [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. About 57% of absorbed intestinal N is in the form of ammonia, and most of the ammonia is converted to urea in the liver. On average, only 47% of liver-synthesized urea is recycled to the intestines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Alterations in dietary amino acids or energy supply could affect urea N flux in the liver [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Zou et al. (2023) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported that supplementation of rumen-protected lysine and arginine in a low-protein diet increased the relative expression of liver urea metabolism-related genes (e.g., \u003cem\u003earginase\u003c/em\u003e and \u003cem\u003eornithine carbamoyltransferase\u003c/em\u003e) in Holstein bulls. In our previous study, the plasma concentrations of L-arginine and L-lysine in the HNUE-cows decreased by 39.4% and 49.1%, respectively, compared with the LNUE-cows [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Liver non-branch-chain amino acid (except for lysine) catabolism is one of the metabolic pathways influencing mammary gland amino acid supply [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, a recent literature suggested that supplementing rumen-protected lysine to transition dairy cows effected liver function and inflammatory status [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Taken together, the liver play a crucial role in regulating cow NUE [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Chen et al. (2023) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported that the liver was the highly expressed tissue for key candidate genes, such as \u003cem\u003eDGAT1\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, and \u003cem\u003eAHSG\u003c/em\u003e, which are associated with the genetic mechanisms of NUE-related traits. Similarly, Liu et al. (2022) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that the liver was the most enriched tissue among \u003cem\u003ecis\u003c/em\u003e-eQTLs for milk yield (\u003cb\u003eMY\u003c/b\u003e)-related SNPs, following mammary gland tissue and mammary epithelial cells. Previous research has already demonstrated that MY was a key indicator directly impacting NUE [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Understanding the role of the liver in regulating NUE in dairy cows is fundamental for designing new strategies to further improve NUE.\u003c/p\u003e\u003cp\u003eChromatin accessibility, a fundamental element of epigenomics, serves as a direct indicator of the influence exerted by chromatin structural modifications on gene transcription [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Among the various techniques employed in chromatin accessibility analysis, ATAC-Seq has emerged as a preferred method. Its superiority lies in its requirement for a relatively small number of starting cells and a significantly shorter sample-processing time when compared to other established techniques [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The integration of ATAC-Seq and RNA-Seq enables a comprehensive and intuitive visualization of the intricate relationship between chromatin openness and gene expression. This integrative analysis further allows for in-depth monitoring of the underlying regulatory mechanisms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Given the advantages of these techniques, this experiment was designed to analyze and compare the hepatic transcriptome and chromatin accessibility of dairy cows with different NUEs. Specifically, we sought to identify the key genes in the liver that are involved in regulating NUE in dairy cows. The ultimate goal was to offer novel insights into the epigenetic regulatory mechanisms of different NUE in Holstein cattle.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Animals and management\u003c/h2\u003e\u003cp\u003e The utilization and management of cows, as well as the experimental protocols, were evaluated, approved, and monitored by the Institutional Animal Care and Use Committee (IACUC) of Henan Agricultural University (Zhengzhou, China) (HNND2021072798). The study was conducted at the ZhongLi dairy farm in Ulanhot City, Inner Mongolia Autonomous Region, spanning from October to December in the year 2021. For the experiment, 16 primiparous and non-pregnant Holstein cows with DIM ranging between 95 and 115 d, body weight (\u003cb\u003eBW)\u003c/b\u003e ranging between 512.9 and 643.0 kg, and an average MY over the past 7 days ranging between 20.11 and 26.87 kg/d, were selected from a group of 1,221 primiparous cows located in the same barn. All animals were adapted for 14 days after being transferred from a large herd to an individual pen measuring 2.4 m \u0026times; 6 m. The diet and environmental conditions were not changed from large herd to individual pen. The diets in this experiment were formulated according to the NRC based on the actual production levels. Staff members uniformly prepared the total mixed ration (\u003cb\u003eTMR\u003c/b\u003e). Each cow was individually fed and allowed ad libitum access to feed to facilitate accurate calculation of dry matter intake (\u003cb\u003eDMI\u003c/b\u003e). The nutritional composition and levels of the experimental diets are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Throughout the experiment, cows were milked 3 times daily at 07:30 h, 15:00 h, and 22.30 h in the rotary milking parlor and provided with 60% TMR at 07: 00 h and 40% TMR at 19: 00 h. The cows were fed to \u003cem\u003ead-libitum\u003c/em\u003e intake and had unrestricted access to water. Barn cleaning and disinfection occurred when the cows were taken to the milk parlor for milking at 15: 00 h.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data recording and sample collection\u003c/h2\u003e\u003cp\u003eDuring the final 2 days of adaptation in individual pens, the BW were measured and recorded for each cow, with the average values used for statistical analysis. The adaptation period was followed by a 7-day digestive and metabolic analysis period during which the daily feeding and leftover amounts and MY were recorded for each cow, and samples of TMR, leftover feed, and milk were collected. At the end of the experiment, the TMR and leftover samples collected over the 7 d were separately mixed for chemical analysis. Each day, a 50-mL milk sample (collected in a 4:3:3 ratio for morning, midday, and evening) per cow was mixed with potassium chromate preservative and stored at 4\u0026deg;C until analysis. After the digestive and metabolic analysis period, for the following two days, liver samples from 8 cows were collected each morning from 9:00 h to 11:00 h. Due to the undetermined NUE of each individual cow at the time of sample collection, the order of sample collection was randomized. Fortunately, on the first day of sampling, we obtained 4 LNUE samples (with experimental animal numbers 1, 3, 7 and 8) and 4 HNUE samples (with experimental animal numbers 10, 13, 14 and 15), while the remaining 8 samples were obtained on the second day of sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The liver was biopsied under local anaesthesia by blind percutaneous needle biopsy (8 mm \u0026times; 300 mm, Wuhan Kelibo Animal Technology Co., LTD.). For the local anaesthesia, 15 mL of 0.25% procaine hydrochloride solution was used: 10 mL was injected subcutaneously and 5 mL was administered deeper into the muscle layer. The procedure was initiated after waiting for about 15 minutes to ensure the anaesthetic took effect [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Each cow contributed approximately 150 mg of liver sample, which was divided equally into two portions for different analyses. One portion of the liver sample was directly immersed in RNAlater (Ambion, Applied Biosystems, Austin, TX) and stored at -80℃ for RNA-seq analysis. The other portion of the liver sample was rapidly frozen in liquid nitrogen immediately after collection and transferred to -80℃ after 5 minutes of freezing for ATAC-seq analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Chemical analysis and calculation\u003c/h2\u003e\u003cp\u003eThe content of DM in TMR and leftover samples were analyzed using the methods described in AOAC [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The content of N in TMR and leftover samples were determined using an automatic Kjeldahl N analyzer (SKD-2000, Shanghai Haineng Experimental Instrument Technology Co., LTD). Milk samples were sent to the DHI Determination Center in Henan Province for analysis of fat, protein, lactose, total solids, milk urea nitrogen (\u003cb\u003eMUN\u003c/b\u003e), and somatic cell count (\u003cb\u003eSCC\u003c/b\u003e) in milk using a MilkoScan FT\u0026thinsp;+\u0026thinsp;analyzer (Foss Electric A/S). The average values of these indicators over the 7-day digestive and metabolic analysis period were used for statistical analysis. The NUE of each cow was calculated using the following formula: NUE (%)\u0026thinsp;=\u0026thinsp;milk N yield (g/d)/NI (g/d) \u0026times; 100% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], where milk N yield (g/d)\u0026thinsp;=\u0026thinsp;MY (kg/d) \u0026times; milk protein content (%)/6.38 \u0026times; 1000 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The 16 cows were retrospectively divided into 2 groups (LNUE and HNUE) based on their NUE levels. The LNUE group consisted of 8 cows with an average NUE of 22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2%, while the HNUE group comprised 8 cows with an average NUE of 33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2%.\u003c/p\u003e\u003cp\u003eDue to the limited amount of liver samples contributed by each individual cow, in order to ensure the quality of RNA-Seq and ATAC-Seq, the liver samples were systematically pooled into composite samples in pairs based on the number of experimental animals. Fortunately, on the first day of sampling, we obtained 4 LNUE samples (with experimental animal numbers 1, 3, 7 and 8) and 4 HNUE samples (with experimental animal numbers 10, 13, 14 and 15), while the remaining 8 samples were obtained on the second day of sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Grouping information was blinded to the experimental personnel for RNA-Seq and ATAC-Seq.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Transcriptome sequencing of liver and data analysis\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. RNA extraction, Library preparation, and Sequencing\u003c/h2\u003e\u003cp\u003eHepatic total RNA was extracted with TRlzol Reagent (Life Technologies, California, USA). RNA concentration and purity were determined using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE). RNA integrity was evaluated using the Agilent Bioanalyzer 2100 system and RNA Nano 6000 Assay Kit (Agilent Technologies, CA, USA). All RNA samples meet the high-quality criteria (OD 260/280 ranging between 1.8 and 2.2, OD 260/230\u0026thinsp;\u0026gt;\u0026thinsp;2.0, RIN\u0026thinsp;\u0026ge;\u0026thinsp;8, 28S : 18S\u0026thinsp;\u0026ge;\u0026thinsp;1.0, and \u0026gt;\u0026thinsp;1\u0026micro;g). According to the instructions provided by Hieff NGS Ultima Dual-mode mRNA Library Prep Kit for Illumina (Yeasen Biotechnology Co., Ltd, Shanghai, China), 1 \u0026micro;g of total RNA was used to generate sequencing libraries. Index codes were included to attribute sequences to individual samples. The main procedure is as follows: (1) Eukaryotic mRNA was enriched using magnetic beads with Oligo(dT) to capture the poly(A) tail; (2) Fragmentation Buffer was added to randomly break the mRNA into shorter fragments; (3) The first-strand cDNA and second-strand cDNA were synthesized using the mRNA as a template, followed by cDNA purification; (4) The purified double-stranded cDNA underwent end repair, A-tailing, and sequencing adapter ligation. Fragment size selection was performed using AMPure XP beads; (5) Finally, the cDNA library was enriched through PCR amplification. After library construction, an initial quantification was performed using the Qubit 3.0 Fluorometer. The concentration of all cDNA librarys was higher than 1 ng/\u0026micro;L. Subsequently, the insert fragments in the library were analyzed using the Qsep400 high-throughput analysis system. Once the insert fragments met the expected criteria (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea), the effective concentration of the library (library effective concentration\u0026thinsp;\u0026gt;\u0026thinsp;2 nM) was accurately quantified using qPCR to ensure library quality. After passing the library quality control, the PE150 mode sequencing was performed on the Illumina NovaSeq6000 sequencing platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Data analysis\u003c/h2\u003e\u003cp\u003eAfter the sequencing data was generated from the sequencing platform, the raw reads were further processed with a bioinformatic pipeline tool, BMKCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biocloud.net/\u003c/span\u003e\u003cspan address=\"https://www.biocloud.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) online platform. Initially, reads containing adapters, ploy-N, and those of low quality were removed to obtain clean data (clean reads). Simultaneously, the clean data underwent calculations for Q20, Q30, GC content, and sequence duplication level. All subsequent analyses relied on the high-quality clean data. These clean reads were subsequently mapped to the reference genome sequence (ARS_UCD1.3) using the Hisat2 tools software. Only reads with a perfect match or a single mismatch were subject to further analysis and annotation, based on the reference genome. For the quantification of gene expression levels, fragments per kilobase of transcript per million fragments mapped (\u003cb\u003eFPKM\u003c/b\u003e) were utilized. The formula for estimating gene expression levels is as follows:\u003c/p\u003e\u003cp\u003eFPKM\u0026thinsp;=\u0026thinsp;cDNA Fragments / over [Mapped Fragments (Millions) \u0026times; TranscriptLength (kb)].\u003c/p\u003e\u003cp\u003eGenes with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |(fold change)| \u0026ge; 1.5 found by DESeq2_edgeR were assigned as differentially expressed. To analyze the enrichment of differentially expressed genes (\u003cb\u003eDEG\u003c/b\u003e) in Gene Ontology (\u003cb\u003eGO\u003c/b\u003e) biological processes (\u003cb\u003eBP\u003c/b\u003e), we performed a gene-set enrichment analysis (\u003cb\u003eGSEA\u003c/b\u003e). The sequences of DEG were aligned (blastx) to the genomes of relevant species to obtain predicted protein-protein interactions (\u003cb\u003ePPI\u003c/b\u003e) for these genes. The STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] was used to explore the protein-protein interaction data. Subsequently, the PPI network of DEGs was visualized using Cytoscape software [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To investigate the co-expression relationship between NUE, MY, and BW with liver genes, we performed Weighted Gene Co-Expression Network Analysis (\u003cb\u003eWGCNA\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Validation of RNA-Seq results analyzed by quantitative PCR (qPCR)\u003c/h2\u003e\u003cp\u003eIn order to validate the gene expression data obtained from RNA-Seq, 6 DEG were randomly selected to perform qPCR. According to the instructions of HiScript III RT SuperMix for qPCR (+\u0026thinsp;gDNA wiper) (R323, Vazyme Biotech Co., Ltd, Nanjing, China), 1 \u0026micro;g total RNA was reverse transcribed to generate cDNA. After a 20 fold dilution, 2 \u0026micro;L of cDNA was used for qPCR, which was performed in an LightCycler\u0026reg;96 (Roche, CH). ChamQ Universal SYBR qPCR Master Mix (Q711, Vazyme Biotech Co., Ltd, Nanjing, China) was used for qPCR analysis. Gene \u003cem\u003eACTB\u003c/em\u003e was selected as a reference gene that was unaffected by experimental factors. All the primers, listed in Table S2, were synthesized by Shangya Biotechnology Co., Ltd. (Zhengzhou, China).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. ATAC sequencing of liver and data analysis\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1. DNA extraction, Library preparation and Sequencing\u003c/h2\u003e\u003cp\u003eApproximately 50 mg of liver sample was homogenized into a fine powder using liquid nitrogen. The pulverized liver tissue was then suspended in 1 mL of ice-cold PBS. Approximately 50,000 cells were centrifuged at 4℃, 500 g for 5 minutes, and the supernatant was carefully removed. The cells were then washed once with cold PBS using the same centrifugation conditions. After removing the supernatant, the cells were resuspended in a cold lysis buffer according to the protocol described by Zhao et al. (2021) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The suspension was centrifuged again at 4℃ for 10 minutes at 500 g, and the supernatant was carefully removed. For the transposing reaction system was prepared by combining 10 \u0026micro;L of ATAC-Seq\u0026rsquo;d cells, 10 \u0026micro;L of 5\u0026times; TTBL, 5 \u0026micro;L of TTE Mix V50, and 25 \u0026micro;L of nuclease-free water from the TruePrep\u0026reg; DNA Library Prep Kit V2 for Illumina (TD501-TD503). The Tn5 transposase was added to the cell nuclei suspended in the transposing reaction system, and the DNA was purified after being incubated at 37℃ for 30 minutes. The resulting purified DNA was subsequently used as a template for PCR amplification. The final DNA libraries were prepared and sequenced on an Illumina platform after purification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2. Data analysis\u003c/h2\u003e\u003cp\u003eThe raw reads were filtered using the Cutadapt software [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] to remove adapters and reads shorter than 35 bp in length. Additionally, low-quality reads were eliminated, including reads with an N ratio greater than 10% and reads where bases with a quality value Q\u0026thinsp;\u0026le;\u0026thinsp;10 accounting for more than 50% of the entire Read. Subsequently, high-quality clean reads in FASTQ format were obtained for further analysis. High-quality reads obtained from sequencing each sample were compared to the reference genome (ARS_UCD1.3) using the Bowtie2 software [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This comparison allowed for the determination of alignment efficiency of the sample reads as well as the position information of the reads on the genome. Subsequent analysis was performed using only the uniquely mapped reads aligned to the reference genome. The coverage of bases on the reference genome and the length of insert fragments were calculated and recorded. The density distribution of sequencing reads in the 3 kb interval upstream and downstream of the transcription start site (\u003cb\u003eTSS\u003c/b\u003e) of each gene was determined using DeepTools v2.07. The results were visualized using heat maps.\u003c/p\u003e\u003cp\u003eThe process of peak extraction was performed using MACS2 v2.1.1 software [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. MACS2 entailed four essential steps: (1) removal of redundant reads; (2) adjustment of read positions; (3) calculation of peak enrichment; and (4) estimation of the empirical false detection rate (\u003cb\u003eFDR\u003c/b\u003e). Peaks were identified based on an FDR threshold of \u0026lt;\u0026thinsp;0.05. The ChIPseeker software package [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] was utilized for annotating the distribution of the detected peaks across the entire genome. Based on the distance relationship between the peak regions and various genomic functional elements, the peak regions were annotated to determine the proportion of peaks falling into different genomic functional elements.\u003c/p\u003e\u003cp\u003eThe DiffBind package was utilized for conducting difference peak (ie, differentially accessible region [\u003cb\u003eDAR\u003c/b\u003e]) analysis. This analysis involved calculating the read count supported by each peak in each sample and deriving an affinity score based on the count (referred to as standardized read count). The generated affinity scores were then used as input for DESeq2 software [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] to perform differential screening among samples within each group. The criteria for differential screening were set as follows: |fold change| \u0026ge; 1.5, P-value\u0026thinsp;\u0026le;\u0026thinsp;0.01. The MA diagram was employed to visually assess the overall distribution of differential fold changes of DARs between the two groups. According to the distance relationship between the DARs and the functional elements of each gene on the genome, the DARs was annotated. The R package clusterProfiler [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was used to respectively perform enrichment analysis of BP of genes associated with DARs enriched in the promoter region.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Comparison of differentially expressed genes in RNA-Seq and differentially accessible region in ATAC-Seq\u003c/h2\u003e\u003cp\u003eWe performed a statistical analysis on genes that exhibited differential accessibility in ATAC-Seq (genes represented by the nearest TSS to the center of the DAR) and differential expression levels. These genes were categorized into 4 types: DAR gain and DEG up, DAR gain and DEG down, DAR loss and DEG up, and DAR loss and DEG down.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e\u003cp\u003eThroughout the entire experimental period, none of the test animals presented any abnormal conditions, and no outliers (\u0026plusmn;\u0026thinsp;3 standard deviations from the mean) were detected in the data from them, which were all used for statistical analysis. The data obtained from the qPCR experiment were analyzed using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method to calculate the relative gene expression levels. Animal characteristics, production performance, and relative gene expression levels in the liver of LNUE (n\u0026thinsp;=\u0026thinsp;8) and HNUE (n\u0026thinsp;=\u0026thinsp;8) cows were analyzed using the t-test in the SAS. A significant trend was considered if 0.05\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, and a significant difference was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Animal characteristics, Milk yield and composition\u003c/h2\u003e\u003cp\u003eThe NUE of 16 experimental cows ranged from 11.9\u0026ndash;36.9%, averaging 27.9% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was a significant difference in NUE between cows in the HNUE and LNUE groups, with a difference of 10.6% units (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average BW of the HNUE group cows was 43.9 kg lower than that of the LNUE group cows (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Although no significant differences in DMI and NI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the HNUE group cows produced an additional 38.8 g of milk N per day compared to the LNUE group cows (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). Additionally, the HNUE group cows exhibited significantly higher MY, as well as higher yield of milk protein, milk fat, and lactose (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the milk composition remained similar (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnimal characteristics and production performance of lower milk nitrogen utilization efficiency cows and high milk nitrogen utilization efficiency cows\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGroup\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSEM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLNUE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHNUE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays in milk (d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e592.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e548.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDry matter intake (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrogen intake (g/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNUE (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.6\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYield (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.631\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.879\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.768\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.012\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.980\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.344\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilk nitrogen yield (g/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilk composition (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal solids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilk urea nitrogen content (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomatic cell counts (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003e LNUE\u0026thinsp;=\u0026thinsp;lower milk nitrogen utilization efficiency (NUE\u0026thinsp;=\u0026thinsp;22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2%, n\u0026thinsp;=\u0026thinsp;8); HNUE\u0026thinsp;=\u0026thinsp;higher milk nitrogen utilization efficiency (NUE\u0026thinsp;=\u0026thinsp;33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2%, n\u0026thinsp;=\u0026thinsp;8).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003e SEM\u0026thinsp;=\u0026thinsp;Standard error of mean.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea, b\u003c/sup\u003e Means with different superscripts in each row differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Liver RNA-Seq Data Analysis\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Identification of Differentially Expressed Genes\u003c/h2\u003e\u003cp\u003eThe RNA-Seq analysis of 8 liver samples obtained a total of 4,105.93\u0026nbsp;million raw reads. After quality filtering, a total of 3,958.45\u0026nbsp;million clean reads were obtained (Table S3). Among the 8 samples, 95.88\u0026ndash;96.60% of the clean reads were successfully mapped to the reference genome. The insert fragments conformed to the expected standards (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea) and the sequencing depth was sufficient to achieve transcriptome coverage (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). The DEGs were identified based on the criteria of \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |(fold change)| \u0026ge; 1.5, resulting in 213 DEGs, with 104 downregulated and 109 upregulated genes in the liver of HNUE-group cows compared to LNUE-group cows (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The qPCR analysis of 6 differential DEGs was conducted to validate the RNA-Seq data. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec shows the qPCR results, which were consistent with the RNA-Seq results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Identification of Biological Processes Associated with Liver Regulation of NUE\u003c/h2\u003e\u003cp\u003eThe GSEA analysis revealed significant enrichment (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of 126 GO BP terms in the comparisons between the HNUE group and LNUE group. Among these, 97 terms had a positive Normalized Enrichment Score (\u003cb\u003eNES\u003c/b\u003e), while 29 terms had a negative NES. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the top 10 significantly enriched BP terms with positive NES and the top 10 with negative NES, along with the number of genes assigned to each term. The BP terms with positive NES were mainly associated with immune processes, with the most significant examples including immune response, inflammatory response, immune response-activating cell surface receptor signaling pathway, immune response-regulating cell surface receptor signaling pathway, and immune response-activating signal transduction. The BP terms with negative NES were mainly associated with metabolic processes, such as cellular response to amino acid stimulus, acyl-CoA metabolic process, positive regulation of glucose import, and regulation of protein processing, and the maintenance of liver structure and function, such as positive regulation of cell-substrate adhesion, extracellular matrix organization, endodermal cell differentiation, collagen fibril organization, cell-cell adhesion via plasma-membrane adhesion molecules, and positive regulation of BMP signaling pathway.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnriched gene ontology (GO) biological process terms associated with differentially expressed genes in the liver\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGO biological process term\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNES\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eimmune response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einflammatory response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.984\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eregulation of cell shape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.710\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emulti-organism process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresponse to external biotic stimulus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eimmune response-activating cell surface receptor signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eimmune response-regulating cell surface receptor signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresponse to other organism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eresponse to external stimulus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eimmune response-activating signal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecellular response to amino acid stimulus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.967\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epositive regulation of cell-substrate adhesion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eextracellular matrix organization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eendodermal cell differentiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecollagen fibril organization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.937\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eacyl-CoA metabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epositive regulation of glucose import\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eregulation of protein processing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecell-cell adhesion via plasma-membrane adhesion molecules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epositive regulation of BMP signaling pathway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003e The Gene Ontology Biological Process (GO BP) terms were sorted by their enrichment P-values, which were calculated using the Expression Analysis Systematic Explorer (EASE) score. The terms were sorted in ascending order, with the top having the lowest P-value and the bottom having the highest P-value.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e2\u003c/sup\u003e NES\u0026thinsp;=\u0026thinsp;Normalized Enrichment Score.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eOnly the top 10 significantly upregulated and the top 10 significantly downregulated GO BP terms have been included in the list.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. Interaction Network and Weighted Gene Co-Expression Network Analysis of Differentially Expressed Genes\u003c/h2\u003e\u003cp\u003eThe PPI network analysis revealed that out of the 213 DEGs, a total of 47 DEGs were found to have interactions with other DEGs, resulting in 37 protein-protein interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These 47 genes formed 11 subnetworks, among which the largest subnetwork consisted of 17 DEGs. The key participants in this subnetwork included \u003cem\u003ePRKG1\u003c/em\u003e and \u003cem\u003eTSSK2\u003c/em\u003e, which were found to have interactions with 11 and 4 DEGs, respectively, and they were directly connected. However, it is noteworthy that \u003cem\u003ePRKG1\u003c/em\u003e exhibited higher expression levels in the liver of HNUE group cows, whereas \u003cem\u003eTSSK2\u003c/em\u003e showed higher expression levels in the liver of LNUE group cows. Additionally, a subnetwork comprising 6 DEGs revolved around the \u003cem\u003eHBB\u003c/em\u003e gene, which was found to have interactions with 5 other DEGs and displayed higher expression levels in the liver of HNUE group cows. Based on the WGCNA analysis, 2 gene modules were identified among the 213 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The MEturquoise module, consisting of 53 DEGs, showed a significant positive correlation with NUE and MY (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a significant negative correlation with BW (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The \u003cem\u003eHBB\u003c/em\u003e gene is one of the members of the MEturquoise module.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Liver ATAC-Seq Data Analysis\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1. Whole-Genome Accessible Chromatin Regions Detection\u003c/h2\u003e\u003cp\u003eThe ACAT-Seq analysis of 8 samples was conducted, resulting in a total of 409.33\u0026nbsp;million raw reads. After quality filtering, 408.57\u0026nbsp;million clean reads were obtained (Table S4). Among the 8 samples, at least 97.25% of the clean reads were aligned to the reference genome. The majority of the insert fragment lengths fall between 150 to 300 bp (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). The sequencing depth was saturated (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed). Uniquely mapped reads exhibit the strongest signal near the TSS (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ee). According to the statistical analysis, the average number of ACRs in the LNUE group was 54719, with an average length of 305.5 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The average number of ACRs in the HNUE group was 45973, with an average length of 279.3 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The proportions of ACRs falling into different gene functional elements were similar between the two groups, showing no significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Despite the lack of statistical significance, it is worth noting that on each chromosome, the LNUE group of cows had a higher number of ACRs and longer average lengths compared to the HNUE group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2. Identification of differentially accessible regions\u003c/h2\u003e\u003cp\u003eA total of 3,716 DARs were identified between HNUE and LNUE groups of cows, with 2,374 DARs showing higher accessibility in the liver of HNUE group cows and 1,342 DARs showing higher accessibility in the liver of LNUE group cows (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Through annotation, it was found that 5.27% of the DARs, which corresponds to 238 DARs, were annotated to the promoter regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), out of which 143 DARs exhibited higher accessibility in the liver tissue of HNUE cows (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Subsequently, the 238 DARs annotated to the promoter regions will undergo gene annotation to identify associated genes, followed by GO BP enrichment analysis of these genes. The analysis revealed that these genes were significantly enriched in 173 GO BP terms (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee illustrates the top 20 BP terms that showed significant enrichment. These BP terms primarily related to the maintenance of liver structure and function, including cellular component organization, cellular component organization or biogenesis, centrosome localization, and maintenance of organelle location.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Comparison of differentially expressed genes in RNA-Seq and differentially accessible region in ATAC-Seq\u003c/h2\u003e\u003cp\u003eThe genes related to DARs identified by ATAC-Seq analysis (genes represented by the TSS closest to the center of the DAR) and the DEGs identified by RNA-Seq analysis were compared analyzed. The gene types were classified into DAR_Gain-DEG_Up, DAR_Gain-DEG_Down, DAR_Loss-DEG_Up and DAR_Loss-DEG_Down. As a result, an overlapping upregulated gene, \u003cem\u003eTGM5\u003c/em\u003e, and an overlapping downregulated gene, \u003cem\u003eROR1\u003c/em\u003e, were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThere are notable individual differences in NUE in lactating dairy cows, even under the same production conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently, Li et al. (2022) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] investigated the individual variation in NUE in dairy cows based on differences in plasma \u003csup\u003e15\u003c/sup\u003eN and dietary \u003csup\u003e15\u003c/sup\u003eN (Δ\u003csup\u003e15\u003c/sup\u003eN), given that it is generally acknowledged that Δ\u003csup\u003e15\u003c/sup\u003eN is inversely proportional to NUE [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The Δ\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;) was quantified in lactating cows at the second parity and 48\u0026thinsp;\u0026plusmn;\u0026thinsp;1 days in milk (\u003cb\u003eDIM\u003c/b\u003e), with the minimum value being found to be no greater than 1.5\u0026permil;, while the maximum value exceeded 3.0\u0026permil;. Xue et al. (2022) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] provided NUE data for a total of 18 mid-lactating cows, and reported a minimum NUE value of approximately 22% and a maximum value of approximately 33%. Individual differences in NUE among cows under the same feeding conditions provide a convenient basis for studying the role of the liver in regulating NUE in cows.\u003c/p\u003e\u003cp\u003eImproving NUE is crucial for both environmental conservation and economic viability in dairy production systems [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Typically, N absorbed by lactating cows is used for maintenance, tissue growth, lactation, reproduction, and minor losses such as hair growth, scurf formation, and volatile N losses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This study involved primiparous non-pregnant dairy cows, where dietary N was primarily allocated to maintenance, growth and lactation. Cows in the HNUE group showed lower BW but higher MY compared to cows in the LNUE group. Importantly, there were no significant differences in DMI and NI between the two groups. These results indicated that HNUE cows achieved better performance without consuming more feed or nitrogen. This finding was consistent with previous studies that thinner cows often produce more milk than fatter cows under similar conditions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, it suggested that HNUE cows may experience higher production pressure.\u003c/p\u003e\u003cp\u003eIn dairy production systems, it is widely accepted that dietary N losses primarily occur at three sites: the rumen, small intestine, and post-absorption. More than half of the N absorbed in the small intestine is not utilized for milk production [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Currently, our understanding of post-absorption N losses is limited, and effective strategies to reduce this aspect of N loss are lacking. The liver plays diverse roles in lactating cows [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and its significance in nutrient regulation has prompted numerous studies on the liver transcriptome during lactation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Regarding NUE in cows, the liver prominently participates in urea synthesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and metabolizes amino acids substantially [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], particularly non-branch-chain amino acid catabolism, which impacts mammary gland amino acid supply [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, liver passage and delivery to peripheral circulation are pivotal in converting dietary N to milk N [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hence, the liver may rank only behind the mammary gland in regulating cow NUE [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study explored, for the first time, differences in the liver transcriptome and chromatin accessibility of dairy cows with different NUE via RNA-seq and ATAC-seq.\u0026nbsp;Notably, cow liver metabolism significantly fluctuates throughout the day. To maintain consistency and minimize time-related influences, samples were collected between 9:00 am and 11:00 am in this experiment.\u003c/p\u003e\u003cp\u003eUsing DESeq2, RNA-seq analysis identified 213 DEGs. However, these DEGs did not include genes associated with urea metabolism, indicating no significant differences in urea metabolism levels between the livers of cows in the HNUE and LNUE groups. This finding consistent with earlier research that found no significant differences in plasma and milk urea N concentrations between the two groups of cows [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], suggesting that liver urea N redistribution may not be a prioritized mechanism for liver involvement in regulating NUE in primiparous dairy cows. Indeed, increasing urea cycle N in cows is frequently an ineffectual way to improve NUE [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The only circumstances in which recycled urea N for reuse increases significantly and then improves NUE are those associated with low-protein diets. Despite our preliminary trials showed that the HNUE group of cows had a significantly negative N balance compared to the LNUE group, the feed composition and levels of crude protein were the same for both groups of cows [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe GSEA analysis revealed that BP terms with positive NES values were predominantly linked to immune processes. Specifically, 41 DEGs were notably enriched in the inflammatory response (NES value was 1.984). This suggests that cows in the HNUE group exhibit heightened liver immune capabilities compared to cows in the LNUE group. Fehlberg et al. (2023) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] noted that supplementing postpartum cows with rumen-protected lysine reduces liver mRNA expression associated with immunity. In our previous research, we observed lower blood amino acid levels in HNUE group cows compared to LNUE group cows, particularly a 39.4% and 49.1% difference in arginine and lysine levels, respectively [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We hypothesized that this relates to the higher MY of HNUE group cows, leading to increased amino acid uptake from the blood by the mammary gland for milk protein synthesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The efficiency of branched-chain amino acid uptake by the liver relied on its supply, where higher supply leads to increased uptake and clearance rates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Consequently, the amino acid flux in the liver of HNUE group cows was lower compared to that of LNUE group cows. This resulted in the downregulation of 2 BP terms: cellular response to amino acid stimulus and regulation of protein processing. It also likely contributed to the heightened immune capability observed in HNUE group cows compared to LNUE group cows. Furthermore, the immune status of the liver was influenced by the N balance [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], oxidative stress status [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and liver function [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] of cows. The downregulation of liver function-related BP terms in HNUE group cows was associated with the upregulation of immune response and inflammatory response [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], as well as with more severe negative N balance and decreased blood amino acid content, especially of arginine and lysine [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this experiment, the possible effect of negative N balance on liver function seems to be more significant, even if a negative N balance state could impair liver immune activity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Moreover, previous study suggested that LNUE group cows might experience increased oxidative stress which impairs liver function and increase inflammation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] compared to HNUE group cows [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These seemingly contradictory findings, however, may be attributed to the downregulation of acetyl coenzyme A metabolism, indicating an overall reduction in liver metabolism. This could reduce the differences in oxidative stress effects on liver function and immune capability between the two groups.\u003c/p\u003e\u003cp\u003eThrough PPI analysis and WGCNA of DEGs, genes \u003cem\u003ePRKG1\u003c/em\u003e and \u003cem\u003eHBB\u003c/em\u003e identified as potential candidates involved in NUE regulation at the transcriptional level in the liver. Both genes exhibited centrality in the PPI network, with \u003cem\u003ePRKG1\u003c/em\u003e gene interacting with 11 DEGs. Gene \u003cem\u003ePRKG1\u003c/em\u003e played a role in regulating lipid breakdown metabolism in adipocytes, facilitating the hydrolysis of triglycerides to release fatty acids and glycerol [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Studies have suggested the involvement of the \u003cem\u003ePRKG1\u003c/em\u003e gene in inhibiting the proliferation of rat brown adipocytes via the cGMP-PKG signaling pathway [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Importantly, the relative expression of the \u003cem\u003ePRKG1\u003c/em\u003e gene in HNUE liver samples significantly exceeded that in LNUE liver samples, supporting the hypothesis that cows in the HNUE group required increased fat mobilization to meet production demands [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The \u003cem\u003eHBB\u003c/em\u003e gene occupied a pivotal position in a PPI subnetwork, interacting with 5 DEGs. Moreover, it belonged to the MEturquoise gene module, which showed significant positive correlations with NUE and MY, and negative correlation with BW. The \u003cem\u003eHBB\u003c/em\u003e gene encodes the hemoglobin subunit β protein, known for its distinct physiological and biochemical attributes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Extensive research has identified \u003cem\u003eHBB\u003c/em\u003e as a candidate gene associated with animal physiological traits [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], with genetic variations potentially impacting various phenotypes, including cattle growth traits [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Future investigations should focus on elucidating the relationship between \u003cem\u003eHBB\u003c/em\u003e gene and NUE.\u003c/p\u003e\u003cp\u003eChromatin accessibility directly reflects the impact of chromatin structural modifications on gene transcription [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. An intriguing finding emerged from this experiment: cows in the LNUE group exhibited a higher number and longer average lengths of ACRs on each chromosome compared to the HNUE group. However, these differences lacked statistical significance, possibly due to the small sample size limitation. This calls for further investigation in the future. ATAC-seq analysis identified 3716 DARs in liver samples from both groups of cows. The GO enrichment analysis of DARs annotated to promoter regions revealed significant enrichment in 173 BP terms, primarily related to maintaining liver structure and function, consistent with the results of RNA-seq analysis. Integrated analysis of RNA-seq and ATAC-seq identified 2 overlapping differentially expressed genes: an upregulated gene, \u003cem\u003eTGM5\u003c/em\u003e, and a downregulated gene, \u003cem\u003eROR1\u003c/em\u003e. TGM5 is a member of the transglutaminase family, a calcium-dependent enzyme that catalyzes protein post-translational modifications by deamidating and crosslinking amines [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The \u003cem\u003eROR1\u003c/em\u003e gene interacts with multiple signaling pathways, including NF-κB and PI3K/AKT. Li et al. (2023) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] found a potential association between \u003cem\u003eROR1\u003c/em\u003e gene and sheep MY traits. Additional, the \u003cem\u003eROR1\u003c/em\u003e gene was considered to play a decisive role in determining milk SCC in cow [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. These results suggested that the regulation of NUE in cows by liver transcription was affected by liver chromatin accessibility. This may be achieved by affecting liver function. Additionally, genes \u003cem\u003eTGM5\u003c/em\u003e and \u003cem\u003eROR1\u003c/em\u003e might be key genes in this process.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe joint analysis of RNA-seq and ATAC-seq revealed the involvement of the liver in regulating cow NUE at the levels of chromatin accessibility and transcriptome. Liver chromatin accessibility was identified as potentially influencing gene transcription by regulating liver structure and function, thus contributing to NUE regulation. In this process, genes \u003cem\u003eTGM5\u003c/em\u003e and \u003cem\u003eROR1\u003c/em\u003e emerged as potential candidate genes. Additionally, genes \u003cem\u003ePRKG1\u003c/em\u003e and \u003cem\u003eHBB\u003c/em\u003e were suggested as key candidates involved in liver transcriptional regulation of NUE. Individual differences in cow NUE were associated with liver structure and function, immune capability, and metabolic levels.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBP:\u0026nbsp;\u003c/strong\u003eBiological processes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBW\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Body weight\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDAR:\u003c/strong\u003e Differentially accessible region\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEG:\u003c/strong\u003e Differentially expressed genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDIM\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Days in milk\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDMI\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Dry matter intake\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDR:\u003c/strong\u003e False detection rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPKM\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Fragments per kilobase of transcript per million fragments mapped\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Gene Ontology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSEA:\u003c/strong\u003e Gene-set enrichment analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHNUE\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e High\u0026nbsp;nitrogen utilization efficiency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLNUE\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Low nitrogen utilization efficiency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMUN\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Milk urea nitrogen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMY\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Milk yield\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Nitrogen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNES:\u003c/strong\u003e Normalized Enrichment Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNUE\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Nitrogen utilization efficiency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI:\u003c/strong\u003e Protein-protein interactions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCC\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Somatic cell count\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMR\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Total mixed ration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCN:\u003c/strong\u003e Weighted Gene Co-Expression Network Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConsent for publication\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the earmarked fund for China Agriculture Research System (CARS36); the Key Research and Development Special Project of Henan Province (221111111100), and the Key Scientific and Technological Project of Henan Province Department of China (232103810005).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.L. and S.L. performed the experiments, analyzed the data and drafted the manuscript. L.Z., G.L. and Z.T. contributed to the revision of the manuscript. S.L., Z.Y. and L.A. contributed data analysis and provided technical support. H.H. and T.G. were corresponding authors, conceived and supervised this study, wrote the manuscript. All authors reviewed and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to extend their gratitude to Zhongli Dairy Farm for providing the experimental animals and facilities, and to the staff for their dedicated care of the animals.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data related to transcriptomic and chromatin accessibility analysis have been deposited in the NCBI BioProject database, with accession numbers PRJNA1303741 and PRJNA1303735 respectively.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrestegaard-Wilson JM, Daley VL, Drape TA, Hanigan MD. A survey of United States dairy cattle nutritionists' practices and perceptions of reducing crude protein in lactating dairy cow diets. Appl Anim Sci. 2021;37(6):697\u0026ndash;709.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu GY, Fanzo J, Miller DD, Pingali P, Post M, Steiner JL, et al. Production and supply of high-quality food protein for human consumption: sustainability, challenges, and innovations. Ann Ny Acad Sci. 2014;1321:1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHristov AN, Bannink A, Crompton LA, Huhtanen P, Kreuzer M, McGee M, et al. Nitrogen in ruminant nutrition: A review of measurement techniques. J Dairy Sci. 2019;102(7):5811\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuhtanen P, Hristov AN. A meta-analysis of the effects of dietary protein concentration and degradability on milk protein yield and milk N efficiency in dairy cows. J Dairy Sci. 2009;92(7):3222\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang NN, Bai ZH, Luo JF, Ledgard S, Wu ZG, Ma L. Nutrient losses and greenhouse gas emissions from dairy production in China: Lessons learned from historical changes and regional differences. Sci Total Environ. 2017;598:1095\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLavery A, Ferris CP. Proxy measures and novel strategies for estimating nitrogen utilisation efficiency in dairy cattle. Animals. 2021;11(2):343.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReynolds CK, Kristensen NB. Nitrogen recycling through the gut and the nitrogen economy of ruminants: an asynchronous symbiosis. J Anim Sci. 2008;86(Suppl 14):E293\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwab CG, Broderick GA. A 100-Year Review: Protein and amino acid nutrition in dairy cows. J Dairy Sci. 2017;100(12):10094\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePszczolkowski VL, Apelo SIA. The market for amino acids: understanding supply and demand of substrate for more efficient milk protein synthesis. J Anim Sci Biotechno. 2020;11(1):108.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewbold CJ, de la Fuente G, Belanche A, Ramos-Morales E, McEwan NR. The role of ciliate protozoa in the rumen. Front Microbiol. 2015;6:1313.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyemele AG, Ma L, Park T, Xu JC, Yu ZT, Bu DP. Giant milkweed (Calotropis gigantea): A new plant resource to inhibit protozoa and decrease ammoniagenesis of rumen microbiota without impairing fermentation. Sci Total Environ. 2020;743:140665.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Zhong HY, Li M, Zheng N, Wang JQ, Zhao SG. Contribution of ruminal bacteriome to the individual variation of nitrogen utilization efficiency of dairy cows. Front Microbiol. 2022;13:815525.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong CL, Long RJ, Stewart GS. The role of rumen epithelial urea transport proteins in urea nitrogen salvage: A review. Anim Nutr. 2022;9:304\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLapierre H, Berthiaume R, Raggio G, Thivierge MC, Doepel L, Pacheco D, et al. The route of absorbed nitrogen into milk protein. Anim Sci. 2007;80(01):11\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmphalius C, Lemosquet S, Ouellet DR, Bahloul L, Lapierre H. Postruminal infusions of amino acids or glucose affect metabolisms of splanchnic, mammary, and other peripheral tissues and drive amino acid use in dairy cows. J Dairy Sci. 2020;103(3):2233\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZou S, Ji S, Xu H, Wang M, Li B, Shen Y, et al. Rumen-protected lysine and methionine supplementation reduced protein requirement of holstein bulls by altering nitrogen metabolism in liver. Animals. 2023;13(5):843.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, La SK, Zhang LY, Li S, Yu ZB, Ao LM, et al. Metabolomics and amino acid profiling of plasma reveals the metabolite profiles associated with nitrogen utilization efficiency in primiparous dairy cows. Animal. 2024;18:101202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCant JP, Reyes GC, Seymour DJ, Review. Influence of postabsorptive metabolism on essential amino acid partitioning in lactating dairy cows. Animal. 2022;16(Suppl 3):100573.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCalsamiglia S, Ferret A, Reynolds CK, Kristensen NB, van Vuuren AM. Strategies for optimizing nitrogen use by ruminants. Animal. 2010;4(7):1184\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFehlberg LK, Guadagnin AR, Thomas BL, Ballou M, Loor JJ, Sugimoto Y, et al. Feeding rumen-protected lysine altered immune and metabolic biomarkers in dairy cows during the transition period. J Dairy Sci. 2023;106(4):2989\u0026ndash;3007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Atashi H, Grelet C, Mota RR, Vanderick S, Hu H, et al. Genome-wide association study and functional annotation analyses for nitrogen efficiency index and its composition traits in dairy cattle. J Dairy Sci. 2023;106(5):3397\u0026ndash;410.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu SL, Gao YH, Canela-Xandri O, Wang S, Yu Y, Cai WT, et al. A multi-tissue atlas of regulatory variants in cattle. Nat Genet. 2022;54(9):1438\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsompana M, Buck MJ. Chromatin accessibility: a window into the genome. Epigenet Chromatin. 2014;7:33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuenostro JD, Wu BJ, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486\u0026ndash;U264.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMezger A, Klemm S, Mann I, Brower K, Mir A, Bostick M, et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat Commun. 2018;9:3647.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Miragaia RJ, Natarajan KN, Teichmann SA. A rapid and robust method for single cell chromatin accessibility profiling. Nat Commun. 2018;9:5345.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYue JW, Hou XH, Liu X, Wang LG, Gao HM, Zhao FP, et al. The landscape of chromatin accessibility in skeletal muscle during embryonic development in pigs. J Anim Sci Biotechno. 2021;12(1):56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLa S, Li H, Wang C, Liu Q, Guo G, Huo W, et al. Effects of rumen-protected folic acid and dietary protein level on growth performance, ruminal fermentation, nutrient digestibility and hepatic gene expression of dairy calves. J Anim Physiol N. 2019;103(4):1006\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAOAC. Official methods of analysis. 2 18th ed. Association of Official Analytical Chemists; 2004.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM\u0026uuml;ller CBM, G\u0026ouml;rs S, Derno M, Tuchscherer A, Wimmers K, Zeyner A, et al. Differences between Holstein dairy cows in renal clearance rate of urea affect milk urea concentration and the relationship between milk urea and urinary nitrogen excretion. Sci Total Environ. 2021;755:143198.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeltran IE, Calvache I, Cofre R, Salazar F, Keim JP, Morales A, et al. Nitrogen intake and its partition on urine, dung and products of dairy and beef cattle in chile. Agronomy-Basel. 2022;12(1):15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(D1):D447\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShannon, Cytoscape P. A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao YX, Hou Y, Xu YY, Luan Y, Zhou HH, Qi XL, et al. A compendium and comparative epigenomics analysis of regulatory elements in the pig genome. Nat Commun. 2021;12(1):2217.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J. 2011;17(1):10\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu GC, Wang LG, He QY. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics. 2015;31(14):2382\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu GC, Wang LG, Han YY, He QY. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. Omics. 2012;16(5):284\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorrea-Luna M, Johansen M, Noziere P, Chantelauze C, Nasrollahi SM, Lund P, et al. Nitrogen isotopic discrimination as a biomarker of between-cow variation in the efficiency of nitrogen utilization for milk production: A meta-analysis. J Dairy Sci. 2022;105(6):5004\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue MY, Xie YY, Zhong YF, Ma XJ, Sun HZ, Liu JX. Integrated meta-omics reveals new ruminal microbial features associated with feed efficiency in dairy cattle. Microbiome. 2022;10(1):32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastillo AR, Kebreab E, Beever DE, France JA. A review of efficiency of nitrogen utilisation in lactating dairy cows and its relationship with environmental pollution. J Anim Feed Sci. 2000;9(1):1\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarnsworthy PC, Topps JH. The effect of body condition of dairy cows at calving on their food intake and performance when given complete diets. Anim Sci. 2010;35(01):113\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoche JR, Friggens NC, Kay JK, Fisher MW, Stafford KJ, Berry DP. Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci. 2009;92(12):5769\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDE B and WB C. Partitioning of Nutrients During Pregnancy and Lactation: A Review of Mechanisms Involving Homeostasis and Homeorhesis. J Dairy Sci. 1980; 63(9): 1514\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhaffari MH, Sadri H, Trakooljul N, Koch C, Sauerwein H. Liver transcriptome profiles of dairy cows with different serum metabotypes. J Dairy Sci. 2024;107(3):1751\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrala TM, Kay JK, Phyn CVC, Bionaz M, Walker CG, Rius AG, et al. Reducing milking frequency during nutrient restriction has no effect on the hepatic transcriptome of lactating dairy cattle. Physiol Genomics. 2013;45(23):1157\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRecktenwald EB, Ross DA, Fessenden SW, Wall CJ, Van Amburgh ME. Urea-N recycling in lactating dairy cows fed diets with 2 different levels of dietary crude protein and starch with or without monensin. J Dairy Sci. 2014;97(3):1611\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFleming AJ, Lapierre H, Martineau R, White RR, Hanigan MD. Modeling portal-drained viscera and liver fluxes of essential amino acids in dairy cows. J Dairy Sci. 2019;102(12):10964\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBell AW, Burhans WS, Overton TR. Protein nutrition in late pregnancy, maternal protein reserves and lactation performance in dairy cows. P Nutr Soc. 2000;59(1):119\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSordillo LM, Contreras GA, Aitken SL. Metabolic factors affecting the inflammatory response of periparturient dairy cows. Anim Health Res Rev. 2009;10(01):53\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBertoni G, Trevisi E, Han X, Bionaz M. Effects of inflammatory conditions on liver activity in puerperium period and consequences for performance in dairy cows. J Dairy Sci. 2008;91(9):3300\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Z, Bulgari O, Vailati-Riboni M, Trevisi E, Ballou MA, Cardoso FC, et al. Rumen-protected methionine compared with rumen-protected choline improves immunometabolic status in dairy cows during the peripartal period. J Dairy Sci. 2016;99(11):8956\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi LJ, Lv XQ, Liu L, Yang YZ, Ma Z, Han B, et al. A post-GWAS confirming effects of PRKG1 gene on milk fatty acids in a Chinese Holstein dairy population. Bmc Genet. 2019;20(1):53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNisoli E, Clementi E, Tonello C, Sciorati C, Briscini L, Carruba MO. Effects of nitric oxide on proliferation and differentiation of rat brown adipocytes in primary cultures. Brit J Phamacol. 1998;125(4):888\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBettati S, Viappiani C, Mozzarelli A. Hemoglobin, an evergreen red protein. BBA-Proteins Proteom. 2009;1794(9):1317\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVazic BS, Rogic BS, Drinic MS, Przulj NM. Relationship between the Genetic Hemoglobin Polymorphism, Morphometry and Fertility of Pramenka Sheep Breed from Central Bosnia. Genetika-Belgrade. 2017;49(1):151\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoonprong S, Choothesa A, Sribhen C, Parvizi N, Vajrabukka C. Relationship between haemoglobin types and productivity of Thai indigenous and Simmental x Brahman crossbred cattle. Livest Sci. 2007;111(3):213\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia IS, Silva-Vignato B, Cesar ASM, Petrini J, Da Silva VH, Morosini NS, et al. Novel putative causal mutations associated with fat traits in Nellore cattle uncovered by eQTLs located in open chromatin regions. Sci Rep. 2024;14(1):10094.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi R, Zhao Y, Liang B, Pu Y, Jiang L, Ma Y. Genome-wide signal selection analysis revealing genes potentially related to sheep-milk-production traits. Animals. 2023;13(10):1654.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuti\u0026eacute;rrez-Reinoso MA, Aponte PM, Garc\u0026iacute;a-Herreros M. Genomic and phenotypic udder evaluation for dairy cattle selection: A review. Animals. 2023;13(10):1588.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIrina L, Sermyagin AA, Ignatieva LP, Elena G, Alexander E, Zinovieva NA. PSXII-7 milk somatic cells monitoring in russian holstein cattle population as a base for determining genetic and genomic variability. J Anim Sci. 2021;99(Suppl 3):252.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nitrogen utilization, Liver, Epigenetics, RNA-Seq, ATAC-Seq, Holstein cattle","lastPublishedDoi":"10.21203/rs.3.rs-7262943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7262943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe liver is central to regulating nitrogen utilization efficiency (NUE), defined as the ratio of milk nitrogen yield (g/d) to nitrogen intake (g/d) in dairy cows. Identifying the regulatory elements in the liver that affect nitrogen utilization is essential for understanding the factors influencing NUE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin sequencing (ATAC-seq) to profile the liver transcriptome and chromatin accessibility in primiparous lactating cows with divergent NUE. We monitored 16 primiparous lactating cows with days in milk ranging from 95 to 115. Over a period of 7 consecutive days, we measured their nitrogen intake and milk nitrogen yield to calculate individual NUE. Based on the NUE values obtained, the cows were categorized into two groups: low NUE (LNUE) with an average NUE of 22.6 ±6.2% (n = 8) and high NUE (HNUE) with an average NUE of 33.1 ±2.2% (n = 8). Liver samples were used for RNA-Seq and ATAC-Seq analysis, identifying 213 differentially expressed genes (DEGs, |fold change| ≥ 1.5, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and 3716 differential accessible regions (DARs, |fold change| ≥ 1.5, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01), respectively. Among these, 109 DEGs and 1342 DARs were upregulated, while 104 DEGs and 2374 DARs were downregulated in HNUE samples compared to LNUE samples. The DEGs were significantly enriched in 126 biological processes (gene ontology), with 97 normalized enrichment scores (NES) being positive, primarily related to immune processes, while 29 NES were negative, mainly related to metabolic processes and the maintenance of liver structure and function. Promoter-annotated DAR-associated genes were significantly enriched in 173 biological processes, primarily related to the maintenance of liver structure and function. Protein-protein interaction network analysis showed that 47 DEGs generated 37 protein-protein interactions, with genes \u003cem\u003ePRKG1\u003c/em\u003e and \u003cem\u003eHBB\u003c/em\u003ebeing central in the network. Integrated analysis of RNA-seq and ATAC-seq identified one overlapping upregulated gene, \u003cem\u003eTGM5\u003c/em\u003e, and one overlapping downregulated gene, \u003cem\u003eROR1\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings demonstrate that hepatic transcriptome and chromatin accessibility epigenetically regulate NUE in primiparous lactating cows.\u003c/p\u003e","manuscriptTitle":"A compendium and comparative analysis of hepatic transcriptome and chromatin accessibility in primiparous lactating cows with different nitrogen utilization efficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 18:22:50","doi":"10.21203/rs.3.rs-7262943/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cffadb68-af15-4641-9c77-c4c7f7248ebe","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T09:27:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 18:22:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7262943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7262943","identity":"rs-7262943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}