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Journal of Animal Science logoLink to Journal of Animal Science
. 2024 May 8;102:skae128. doi: 10.1093/jas/skae128

Identification and characterization of long non-coding RNAs in mammary gland tissues of Chinese Holstein cows

Yanru Wang 1,2, Yan Liang 3,4, Yuxin Xia 5,6, Mengqi Wang 7, Huimin Zhang 8,9, Mingxun Li 10,11, Zhangping Yang 12,13, Niel A Karrow 14, Yongjiang Mao 15,16,✉
PMCID: PMC11197003  PMID: 38715467

Abstract

LncRNAs (Long non-coding RNA) is an RNA molecule with a length of more than 200 bp. LncRNAs can directly act on mRNA, thus affecting the expression of downstream target genes and proteins, and widely participate in many important physiological and pathological regulation processes of the body. In this study, RNA-Seq was performed to detect lncRNAs from mammary gland tissues of three Chinese Holstein cows, including three cows at 7 d before calving and the same three cows at 30 d postpartum (early lactation stage). A total of 1,905 novel lncRNAs were detected, 57.3% of the predicted lncRNAs are ≥ 500 bp and 612 lncRNAs are intronic lncRNAs. The exon number of lncRNAs ranged from 2 to 10. A total of 96 lncRNAs were significantly differentially expressed between two stages, of which 47 were upregulated and 49 were downregulated. Pathway analysis found that target genes were mainly concentrated on the ECM-receptor interaction, Jak-STAT signaling pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway. This study revealed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows at non-lactation and early lactation periods, and provided a basis for studying the functions of lncRNAs in Holstein cows during different lactation periods.

Keywords: LncRNAs, mammary gland, Holstein cows, RNA-Seq

This study revealed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows at non-lactation and early lactation stages, and provided a basis for studying the functions of lncRNAs in Holstein cows during different lactation periods.

Introduction

Long non-coding RNAs (LncRNAs) are a class of RNAs that are more than 200 bp in length, have no protein-coding function, low expression, and poor conservation among species (Qureshi et al., 2010; Shen & Sun, 2021). LncRNAs have a similar structure to mRNA and undergo splicing, usually with a 5 ‘ 7 mC cap and a 3 ‘ polyA or no polyA tail (Carninci et al., 2005; Derrien et al., 2012; Li et al., 2019). They can be classified into five types based on their genomic location relative to protein-coding genes, including sense lncRNA, intronic lncRNA, bidirectional lncRNA, antisense lncRNA, and intergenic lncRNA (Iyer MK et al., 2015; Jin et al., 2022). LncRNAs can regulate gene expression at multiple levels, by interacting with DNA, RNA, and proteins. The lncRNAs can regulate chromatin structure and function and the transcription of adjacent and distant genes, and play big roles in RNA splicing, stability, and translation (Statello et al., 2021). Several studies have shown that lncRNAs play important roles in numerous life activities such as transcriptional regulation (Huarte et al., 2010), epigenetic regulation (Bhan et al., 2017), cell cycle regulation (Hung et al., 2011), and cell differentiation regulation (Xiao et al., 2009). In mammals, lncRNAs play regulatory roles in growth, reproduction, and health (Hansji et al., 2014; Dhanoa et al., 2018).

The mammary gland is an important organ for milk synthesis and secretion in mammals (Strucken et al., 2015). In dairy cows, the mammary gland is an essential organ for calf survival, passive immunity, early nutrition, and the production of dairy products we need (Liang et al., 2022). The lactation process, pregnancy, and involution of dairy cows are related to genetic, epigenetic, and environmental factors (Do & Ibeagha-Awemu, 2017). Lactation is a dynamic and complex process that involves mammary gland development and milk synthesis and secretion (McManaman & Neville, 2003). Now lncRNAs are proven as essential regulatory elements and play critical roles in mammary gland development. Although lncRNAs from cows have been identified, the number is very small. Huang et al. provided the first genome-wide catalog of bovine intergenic lncRNAs and 449 lncRNAs located in 405 intergenic regions were identified (Huang et al., 2012). Three thousand seven hundred and forty-six differentially expressed lncRNAs were identified from the dry and lactation mammary glands of Holstein cows and long noncoding RNAs have been found to have growth inhibitory effects in mammary epithelial cells (Ginger et al., 2006). Wang et al. found that 112 differentially expressed lncRNAs in lipopolysaccharides (LPS)—treated bovine mammary epithelial cells might regulate Notch, NF-κB, mTOR, MAPK, PI3K-Akt, and other inflammation-related signal pathways (Wang et al., 2021). Most of the studies on lncRNAs are related to their expression in the mammary gland or milk exocrine, as well as dairy product production (Cai et al., 2018; Ibeagha-Awemu et al., 2018; Yang et al., 2018; Zeng et al., 2019). However, the role of lncRNAs in the bovine mammary gland lactation processes is less clear.

Given that lncRNAs regulate mammary gland tissue gene expression, high-throughput RNA sequencing (RNA-Seq) was used to identify the profiles of differentially expressed lncRNAs from Holstein cow mammary gland tissues during early lactation and before calving period in this study. This study will provide a new perspective for lncRNAs in lactation biology in dairy cows.

Materials and Methods

Animal ethics

All experiments were performed in accordance with the guide for the care and use of laboratory animals established by the Ministry of Science and Technology of the People’s Republic of China (approval number 2006-398). The mammary gland tissue sample collection process was in line with the welfare ethics of experimental animals, and a production license for experimental animals was obtained (SYDW-2019005). The experiment was also approved by Yangzhou University, Yangzhou, China.

Sample collection

In this study, mammary gland tissues were sampled from three Holstein cows in two different stages: the non-lactation period (n = 3, 7 d before calving) and the same three Holstein cows in early lactation period (n = 3, 30 d postpartum) on a large dairy farm in Jiangsu province. All cows were second-parity and had the same age. The three cows were fed the same TMR diet during the experiment. From the three cows without mastitis sampled in this study, we collected early lactation and non-lactation mammary gland tissue samples. Using the Lanzhou mastitis test of somatic cell count method, it was detected that the number of somatic cells in the milk of three experimental cows was less than 200,000/mL, which were healthy cows without mastitis. Milk was completely extruded from the mammary glands of lactating cows before collecting mammary gland tissue samples, and the mammary gland tissues of the control group were directly collected by biopsy. Mammary gland tissue biopsy was performed within 1 to 3 h after milking. Briefly, the skin at the selected biopsy sites was first shaved and disinfected with ethanol (75%), followed by anesthesia. A 1.5 cm incision was made in the midpoint skin of the posterior quarter of the mammary gland. Mammary gland tissue biopsies were then performed and immediately frozen in liquid nitrogen and stored at −80 °C until RNA isolation (Schmitz et al., 2004; Liang et al., 2022).

RNA extraction and sequencing

For RNA extraction, the mammary tissue was treated with TRIzol reagent (Invitrogen, Carlsbad, CA, USA), and the total RNA was then extracted using the RNAprep Pure Tissue Kit (Tiangen, RNAprep Pure Tissue Kit, Beijing, China). Frozen mammary gland tissue (10 to 20 mg) was combined with 300 μL lytic solution RL and was thoroughly grind with a grinding pestle and then 590 μL RNase-free ddH2O and 10 μL Proteinase K were added to the homogenate and mixed at 56 °C for 10 to 20 min. Finally, NanoDrop® (Thermo Scientific, DE, Waltham, MA, USA) was used to measure the RNA quantity. The concentration of total RNA was greater than 400 ng/μL, and the 260/280 requirement was 1.9 to 2.0 (Supplementary Table 1). Next, a sequencing library was constructed, and genomic sequence mapping and analysis were performed. Ribosomal RNA was removed from the mammary tissue RNA samples using transcriptome isolation kit (Ribominus Bacteria 2.0, Thermo Fisher). The remaining RNA were paired-end sequenced using an Illumina HiSeq Xten (Illumina Inc., San Diego, CA, USA) from Shanghai OE Biotechnology Company, Ltd. (Shanghai, China).

Bioinformatics analysis

Through the Illumina platform, a large amount of sample paired-end sequencing data was obtained. Given the impact of data error rate on the results, we employed Trimmomatic (Bolger et al., 2014) software to preprocess the raw data for quality and to statistically summarize the number of reads throughout the quality control process. The original sequencing data often contains numerous errors and low-quality reads, necessitating quality control measures to ensure the accuracy of subsequent analyses. The following criteria are employed for quality control: removal of reads with adapters, elimination of reads containing undetermined base information (indicated by N) exceeding 5% proportionally, and exclusion of low-quality reads (reads with a quality score Q < 10 constituting over 20% of the read length). The alignment of clean reads with the reference genome allows for the identification of position information on the reference genome or genes. We used hisat2 (Kim et al., 2015) to compare Clean Reads with the designated reference genome to obtain the position information on the reference genome or gene, as well as the sequence characteristics unique to the sequencing samples. The reference genome database version was Bos_taurus_UMD_3.1 in NCBI (http://www.ncbi.nlm.nih.gov/genome/guide/cow/index.html).

Identification and expression level of lncRNAs

Use StringTie (Pertea et al., 2015) software to reconstruct the transcripts in the samples based on the probability model for the comparison results of each sample. The transcripts obtained from each sample were merged by cuffemerge and compared with known transcripts by cuffecompare. Since lncRNAs do not encode proteins, it is possible to determine whether transcripts are lncRNAs by predicting their coding potential. Predict and analyze the coding ability of the transcripts screened, and screen out the transcripts with coding potential by using CPC (Kong et al., 2007), CNCI (Sun et al., 2013), Pfam (Sonnhammer et al., 1998) and PLEK (Li et al., 2014). Finally, quantitative analysis was performed.

The expression of transcripts was calculated using the FPKM (Fragments per kb Per Millennium Reads; Roberts et al., 2011). FPKM is the number of fragments from a transcript per kilobase length per million fragments. FPKM considers the effects of sequencing depth and transcript length on fragment count, which is the most commonly used method to estimate transcript expression levels.

Differentially expressed lncRNAs and differentially expressed mRNAs co-expression analysis

FPKM (Roberts et al., 2011) method can eliminate the influence of transcript length and sequencing quantity difference on the calculation of transcript expression, and the calculated transcript expression quantity can be directly used to compare transcript expression differences between different groups. DEseq (Anders and Huber, 2016) software was used to normalize the number of counts for each lncRNAs and mRNAs across samples (basemean value was used to estimate the expression), calculate the fold change, and use NB (the way of negative binomial distribution test) to test the significance of the difference in the number of reads, finally to screen the differential genes according to the fold change and the significance of the difference test results. Differences were filtered by default conditional on P < 0.05 and |fold change| > 2.7 d before calving (non-lactation period) was used as the control group.

Pearson’s correlation test was used to calculate the expression correlation between the two group. According to the differentially expressed lncRNAs and differentially expressed mRNAs expression data, the relationship pairs with a correlation coefficient not lower than 0.8 and the P < 0.05 were selected and considered to have a co-expression relationship.

Target gene prediction and functional analysis

All coding genes within 100 kb upstream and downstream of a differentially expressed lncRNA were searched, and the intersection of genes with significant co-expression (calculated by Pearson correlation) with that lncRNA was performed. These genes, which are genomically proximate and have co-expression patterns, are likely to be regulated by these lncRNAs, termed cis target genes. Based on the differential co-expression results, lncRNAs and mRNAs that were not located in the same chromosome were selected as candidate targets, and candidate sequences were extracted. Using the RNA interaction software RIsearch-2.0 to predict the binding of candidate lncRNAs and mRNAs at the nucleic acid level, according to the following conditions: the number of bases directly interacting between two nucleic acid molecules is no less than 10, and the free energy of base binding is no more than −50 for the screening conditions, the screened interacting lncRNAs and mRNAs may exist in direct regulation, which is called trans target genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to investigate the main function of the target genes of the differentially expressed lncRNAs using the DAVID tool (https://david.ncifcrf.gov) and KEGG (http://www.kegg.jp/) pathway analyses.

Results

Quality control and reference genome alignment results

Based on the transcriptome data of dairy cow mammary tissue obtained by RNA-seq, the expression level changes of lncRNAs before lactation stage and early lactation stage were obtained. Overall, 553.48 million clean reads were obtained, with an average of 92.25 million reads (ranging from 82.05 to 98.83 million). Average quality values for sequencing were 95.36% (Q30). The average GC content was 48.62%. The quality control analysis result is shown in Table 1. Approximately average 96.79% of the clean reads were mapped to the reference genome, 7.00% were multiple mapped and 89.79% were uniquely mapped (Table 2).

Table 1.

Sequencing data quality preprocessing and mapping results

Sample raw_reads raw_bases clean_reads clean_bases valid_bases Q30 GC Mapped_reads
2103_-7d 98.83M 14.82G 96.81M 14.09G 95.02% 96.41% 49.69% 93.20M (94.30%)
2042_-7d 98.42M 14.76G 96.26M 14.03G 95.04% 96.16% 47.51% 93.48M(94.98%)
2048_-7d 95.93M 14.39G 92.71M 13.31G 92.48% 94.14% 52.26% 87.94M(92.05%)
2103_30d 82.05M 12.31G 79.4M 11.47G 93.20% 94.39% 47.66% 77.48M(94.43%)
2042_30d 94.79M 14.22G 91.85M 13.36G 93.96% 94.39% 46.19% 89.45M(94.37%)
2048_30d 98.41M 14.76G 96.45M 14.04G 95.13% 96.68% 48.41% 94.05M(95.57%)
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Table 2.

Statistical results of mapping rate with the reference genome

Sample clean_reads Total mapped_reads Multiple mapped_reads Uniquely mapped_reads
2103_-7d 96.81M 93.20M(96.27%) 54.92M(5.67%) 87.70M(90.59%)
2042_-7d 96.26M 93.48M(97.11%) 41.01M(4.26%) 89.37M(92.85%)
2048_-7d 92.71M 87.94M(94.85%) 60.19M(6.49%) 81.92M(88.36%)
2103_30d 79.40M 77.48M(97.58%) 68.84M(8.67%) 70.60M(88.91%)
2042_30d 91.85M 89.45M(97.39%) 54.12M(5.89%) 84.04M(91.50%)
2048_30d 96.45M 94.05M(97.52%) 10.60M(10.99%) 83.45M(86.52%)
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Identification and characterization of lncRNAs

According to the characteristics of lncRNAs, we used CPC, CNCI, Pfam and PLEK software to predict and analyze the coding ability of the transcripts obtained, and screened out the transcripts with coding potential. A total of 1,905 lncRNAs were screened, as shown in Figure 1. 51% of lncRNAs’ length were ≥1,000 bp. The max and the minimum length were 18,449 and 201 bp, respectively. These lncRNAs’ average length was 1,065 bp. The identified lncRNAs were mainly distributed on chromosomes 3, 5, 10, 11, and X (Figure 2A), and chromosome 10 contained the most lncRNAs (n = 103). According to the characteristics of genome mapping, most 612 lncRNAs are located in intronic regions, while 196, 518, and 579 lncRNAs are antisense, sense-overlapping, and intergenic, respectively (Figure 2B). The transcript length of identified lncRNAs ranged from 201 to 184, 449 nucleotides, with 70.6% of lncRNAs shorter than 1,000 nucleotides (Figure 3A). Exon number of lncRNAs ranged from 2 to 10 (Figure 3B). The distribution of GC content for the newly predicted lncRNA sequences is shown in Figure 4.

Figure 1.

Figure 1.

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Venn diagram of coding ability of candidate lncRNAs predicted by PLEK, Pfam, CNCI, and CPC.

Figure 2.

Figure 2.

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Characterization of lncRNAs. (A) Distribution of lncRNAs on cows’ chromosomes. The vertical axis represents the number of lncRNAs and the horizontal axis represents the chromosome where lncRNAs are located. (B) Percentage of different types of lncRNAs.

Figure 3.

Figure 3.

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LncRNAs sequence information. (A) Distribution map of lncRNAs sequence length. The vertical axis represents the number of lncRNAs and the horizontal axis represents the length of lncRNAs. (B) Statistical of exon numbers of lncRNAs. The vertical axis represents lncRNAs numbers and the horizontal axis represents the exon of per lncRNA.

Figure 4.

Figure 4.

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GC content of lncRNAs. The vertical axis is the number of lncRNAs and the horizontal axis shows the CG content of lncRNAs.

Differentially expressed lncRNAs analysis

Compared with the 7 d before the calving group (non-lactation stage), 96 differentially expressed lncRNAs were found in the 30 d postpartum (early lactation stage), of which 47 were upregulated and 49 were downregulated in the 30 d postpartum (Figure 5B). The top 20 differentially expressed lncRNAs (10 upregulated and 10 downregulated) between two groups were listed in Table 3, in which 50% of lncRNAs’ class codes were intergenic. And the top 20 differentially expressed lncRNAs’ exon numbers ranged from 2 to 5. Based on the heatmap, it showed the expression profiling of lncRNAs, and the cluster analysis results are shown in Figure 5A.

Figure 5.

Figure 5.

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Differential expression of lncRNAs between two groups. (A) Heatmap showing expression abundance of lncRNAs showing significant differences in expression. (B) Differential expression lncRNAs volcano map. The x-axis is the log2FoldChange and the y-axis is the -log10P-value.

Table 3.

The top 20 differentially expressed lncRNAs

LncRNA-id FoldChange log2FoldChange P-value Regulation Class_code Locus Chromosome Exon
TCONS_00059180 86.8246457 6.440032714 1.40E-16 Up intergenic AC_000186.1:44278771:44312607:+ Chr29 2
TCONS_00059195 8.68832768 3.119078515 4.91E-07 Up sense-overlapping AC_000186.1:44337488:44414405:+ Chr29 2
TCONS_00024512 204.219619 7.67397766 5.91E-06 Up intergenic AC_000167.1:7542404:7543329:+ Chr10 2
TCONS_00045655 4.76517806 2.252530125 0.00029899 Up sense-overlapping AC_000176.1:43833111:43901313:− Chr19 3
TCONS_00017554 6.9817197 2.803582437 0.00052037 Up sense-overlapping AC_000164.1:45517353:45522083:+ Chr7 2
TCONS_00024513 137.944672 7.107945921 0.00068778 Up intergenic AC_000167.1:7542407:7543329:+ Chr10 2
TCONS_00017362 9.49282672 3.246837749 0.00091571 Up sense-overlapping AC_000164.1:27353143:27714458:+ Chr7 5
TCONS_00026788 11.165217 3.480939385 0.00116449 Up antisense AC_000167.1:76581299:76595027:− Chr10 3
TCONS_00012452 4.59606438 2.200399006 0.00118579 Up intergenic AC_000162.1:110282142:110304045:+ Chr5 3
TCONS_00041097 5.01203751 2.325397211 0.0012822 Up antisense AC_000174.1:73408350:73416770:− Chr17 4
TCONS_00051550 6.124E-39 −126.9407315 1.19E-05 Down intergenic AC_000180.1:48531905:48534468:+ Chr23 2
TCONS_00034310 0.00324771 −8.266361517 6.50E-05 Down intronic AC_000171.1:38057300:38079605:+ Chr14 2
TCONS_00011428 0.00645822 −7.274648003 0.00014708 Down intergenic AC_000162.1:26225400:26228928:+ Chr5 3
TCONS_00024978 0.02057746 −5.602791146 0.00032873 Down intergenic AC_000167.1:42671310:42679134:+ Chr10 2
TCONS_00034279 0.02441398 −5.356148826 0.00043758 Down intergenic AC_000171.1:35371474:35476424:+ Chr14 4
TCONS_00028606 0.02052139 −5.606727986 0.00304505 Down sense-overlapping AC_000168.1:10735099:10802611:− Chr11 3
TCONS_00007864 0.23527453 −2.087582925 0.00402691 Down sense-overlapping AC_000160.1:78437209:78470388:− Chr3 3
TCONS_00024982 0.04364291 −4.518108967 0.00418279 Down intergenic AC_000167.1:42671311:42676963:+ Chr10 2
TCONS_00047678 0.04228916 −4.563568448 0.00570516 Down intergenic AC_000178.1:28286050:28310751:+ Chr21 3
TCONS_00038789 0.06222642 −4.006328928 0.00638186 Down antisense AC_000173.1:49575647:49582299:− Chr16 2
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Differentially expressed lncRNAs and mRNAs co-expression analysis

We identified 4,084 differentially expressed mRNAs between the two groups, and the top 20 differentially expressed mRNAs were listed in Supplementary Table 2. Circos software was used to compare the differentially expressed lncRNAs and mRNAs of the comparison group as shown in Figure 6. Total of 152,791 pairs were identified, and the top 500 differentially expressed lncRNAs and mRNAs co-expression network diagram as shown in Supplementary Figure 1 was drawn by Cytoscape (Version:3.7.2). Red arrow nodes represent differentially expressed lncRNAs and green circle nodes represent differentially expressed mRNAs. These genes and lncRNAs co-expression pair include genes related to lactation, such as TCONS_00059195-CSN3 and TCONS_00051550-DGAT2; also include genes related to inflammation, such as TCONS_00010180-CCL2, TCONS_00051550-CXCR1, TCONS_00059180-CFB, TCONS_00011428-IL6, TCONS_00024978-CXCL3 and TCONS_00010180-CD36. These genes have always been the focus of our research on dairy cows’ mammary gland health. Therefore, we will continue to explore these genes and lncRNAs in the discussion.

Figure 6.

Figure 6.

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Circos plot of differentially expressed lncRNAs and mRNAs’ chromosome distribution and regulation. The outermost is a schematic representation of the autosomal distribution of the species; The second circle is the distribution of differentially expressed lncRNAs across the chromosome, with red lines indicating upregulation and green lines indicating downregulation; The third circle is a bar chart of differentially expressed lncRNAs at different locations, with higher columns indicating greater number of differential genes; The fourth circle is the distribution of differentially expressed mRNAs on the chromosome, and the color distribution is the same with lncRNAs; The innermost circle is the bar chart of differentially expressed mRNAs in different positions, and the color distribution is the same with lncRNAs.

Differentially expressed lncRNAs target genes and functional analysis

To explore the functional role of differentially expressed lncRNAs, we identified their cis and trans target genes. All differentially expressed lncRNAs target genes were subjected to GO and KEGG analysis. As shown in Figure 7A, in terms of biological processes, target genes were mainly involved in developmental process, biological regulation, regulation of biological process, single-organism process, metabolic process, and immune system process. Cellular components were mainly related to extracellular region, extracellular matrix component, and cell junction. In terms of molecular function, they were significantly correlated with nucleic acid binding transcription factor activity, binding, and structural molecule activity. The results of KEGG pathway analysis showed that target genes mainly enriched in axon guidance, cell adhesin molecules (CAMS), PPAR signaling pathway, and ECM-receptor interaction (Figure 7B). Meanwhile, the target genes are also enriched in Jak-STAT signaling pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway.

Figure 7.

Figure 7.

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Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differential lncRNAs’ target genes. (A) Major enrichment and meaningful GO terms of target genes. (B) Top 20 KEGG pathways of differentially target genes.

Discussion

With the development of the whole transcriptome sequencing technology, long non-coding RNAs have been shown to be important regulators in life, most of which remain focused on mice and humans(Jin et al., 2022). It has been proved that lncRNAs play a critical role in mammary gland development and breast cancer biology(Shen et al., 2020). The mammary glands of cows can produce milk, which has irreplaceable effects on neonatal life and is also great for human health. Therefore, the mammary gland health of cows is important. In recent years, there have been more and more studies on lncRNAs of cow mammary glands.

In cows, about 23,515 lncRNA transcripts have been reported (http://v5.noncode.org/index.php). Zheng et al. were the first to systematically identify the lncRNAs by RNA-Seq of the bovine mammary gland between peak and late lactation period, and 1,657 lncRNAs transcripts from 1,181 candidate lncRNAs loci were finally obtained(Zheng et al., 2018). Chen et al found 19 differentially expressed lncRNAs from Staphylococcus aureus infected and non-infected cow mammary epithelial cell lines, and these differentially expressed lncRNAs were involved in inflammation-related signaling pathways (Notch signaling pathway, TNF signaling pathway, and NF-κB signaling pathway; Chen et al., 2021). The above studies demonstrated that lncRNAs have regulatory effects on different lactation stages and mammary gland health in dairy cows.

In this study, we used a high-throughput sequencing method to detect lncRNAs in the early lactation stage and 7 d before the calving stage of Holstein cows, and screen the important lncRNAs in the mammary gland of these two stages. Using the methods of transcripts splicing, transcripts comparison as well as coding potential screening, a total of 1,905 lncRNAs were identified, of which 96 lncRNAs were differentially expressed, including 47 upregulated and 49 downregulated. Upregulated lncRNAs had a high expression level in early lactation stage (30 d postpartum), and downregulated lncRNAs had a high expression level in non-lactation stage (7 d before calving). 57.3% of these predicted lncRNAs are ≥ 500 bp, and 12.9% are ≥ 2,000 bp. 612 lncRNAs are intronic lncRNAs, which are the largest of the four types of lncRNAs. In addition, exon number of lncRNAs ranged from 2 to 10, but only 1 lncRNA has 10 exons. During a lactation cycle, cow mammary epithelial cells undergo four processes of proliferation, invasion, differentiation, and degradation. Mammary epithelial cells begin to proliferate during pregnancy and stop proliferating in the later stages of pregnancy (Billa et al., 2019). At this period, epithelial cells undergo functional differentiation, and the mammary gland is able to express, synthesize, and secrete some milk components. During this process, a large number of gene expressions are increased or suppressed. Compared with the 7 d before calving group, the 30 d postpartum (early lactation stage) group that lactation enters peak period, upregulated lncRNAs target genes mainly were mainly associated with immune response, such as AMPK signaling pathway, MAPK signaling pathway, CAMs, and PI3K-Akt signaling pathway. This may be an intrinsic manifestation of the body’s defense against mammary gland infection, and proper immune function of the body is essential for the defense against mammary gland (Gao et al., 2013), and this is consistent with our research results.

The function of lncRNAs has not been fully clarified. They can interact with coding genes in cis and trans ways to realize their functions (Kopp & Mendell, 2018). It was reported that lncRNAs can act as both cis and trans regulators of gene expression (Balandeh et al., 2021; Singh et al., 2022). GO and KEGG pathway analysis were performed to predict the related functions of lncRNAs’ cis and trans target genes. GO analysis showed that target genes were mainly involved in developmental process, extracellular region, and nucleic acid binding transcription factor activity. KEGG pathway analysis showed that target genes mainly enriched in CAMs, ECM-receptor interaction, Jak-STAT signaling pathway, PI3K-Akt signaling pathway and TGF-beta signaling pathway, etc. Jak-STAT signaling pathway enables many cytokines and growth factors to transmit signals within cells and mediate many important biological processes, such as cell proliferation, apoptosis, and immune regulation, especially in the development of inflammation (Jiang et al., 2021). The PI3K-Akt signaling pathway has been implicated as a critical signaling pathway for growth, proliferation, and survival of mammalian cells, including mammary epithelial cells, and have also recently been shown to be involved in the regulation of inflammatory responses (Kang et al., 2018; Kim et al., 2020).

The lncRNA TCONS_00010180 cis target genes CD36 (cluster of differentiation 36) that is a membrane glycoprotein found on some epithelial cells and can contribute to inflammatory responses (Silverstein et al., 2009). Both lncRNA TCONS_00010180 and CD36 gene expression levels were higher in 30 d postpartum (early lactation stage) than in 7 d before the calving stage (non-lactation stage). CD36, which is a highly glycosylated single-chain transmembrane protein and belongs to type B scavenger receptor, can recognize many pathogenic microorganisms and their structures, and mediate cells to regulate homeostasis and immune defense through phagocytosis (Neculai et al., 2013; Płóciennikowska et al., 2015). In this study, CD36 is involved in several pathways related to the inflammatory response, such as ECM-receptor interaction and AMPK signaling pathway. CD36 can mediate AMPK phosphorylation, thereby participating in the AMPK signaling pathway (Zhao et al., 2018). In this study, our cows were in transition period (from 3 wk prepartum to 3 wk postpartum). Cows were accompanied by marked physiologic inflammatory regulatory mastitis during the transition period. It has been established that the inflammatory response and immune activation of the mammary gland are normal components of the biology of transitional cows (Miles et al., 2020; HorstKvidera et al., 2021). The present study found that CD36 can assist TLR4 to synergistically mediate LPS stimulatory signals, which in turn induce IL-1β production in cells β, IL-6, IL-8, TNF-α, and other inflammatory factors (Yang et al., 2017). In addition, TLR4 can also activate PI3K to form the TLR4-PI3K signaling pathway, upregulate CD36 gene expression, and in turn activate the CD36-TLR4 complex. Ultimately, cells will initiate a series of immune-inflammatory reactions, clearing invading pathogenic microorganisms (Wang et al., 2018). The lncRNA TCONS_00059195 trans target gene CSN3 (κ -casein) expression levels also were higher in the early lactation stage than in non-lactation stage, which played an important regulatory role in lactation process (Shekar et al., 2006), and encodes about 80% bovine milk protein content (Farrell et al., 2004).

The lncRNA TCONS_00051550 and TCONS_00011428 in the early lactation stage and their target genes CXCR1 and IL-6 both had lower expression levels in the early lactation stage. TCONS_00051550 trans target gene CXCR1 (chemokine (C-X-C motif) receptor 1), identified as a hub gene that was a protein-coding gene for a major proinflammatory cytokine receptor (Zemanova et al., 2022), was introduced as a potential genetic marker for resistance to mastitis in dairy cows (Mao et al., 2015). Proinflammatory cytokines can induce antimicrobial responses to epithelial cells and ensure migration of neutrophils and dendritic cells to the site of infection (Huang, 2021). However, the lncRNA TCONS_00011428 trans-target gene IL-6 acts as an anti-inflammatory factor that regulates cells to control and terminate inflammatory responses during late stage of mastitis infection (Lewandowska-Sabat et al., 2012; Huang, 2021). Overall, most of the pathways enriched by the target genes of the differentially expressed lncRNAs in this study were associated with the development of mastitis(Fang et al., 2016; Luoreng et al., 2018). In our results, above immune-related genes (CD36, CXCR1, and IL-6) not only participate in immune response reactions, but also play an important role in milk production traits of cows. So, this is an important connection between the body’s immune response and lactation. We speculated that lncRNA TCONS_00010180, TCONS_00059195, TCONS_00051550, and TCONS_00011428 may affect mammary gland development and lactation by regulating the expression of CD36, CSN3, CXCR1, and IL-6 in the bovine mammary gland. Therefore, subsequently we will focus on upregulated lncRNA TCONS_00010180 and TCONS_00059195 in the early lactation stage, downregulated lncRNA TCONS_00051550 and TCONS_00011428 in early lactation stage, and their target genes CD36, CSN3, CXCR1, and IL-6 in-depth study.

Conclusions

In this study, RNA-Seq was used to identify lncRNAs in Holstein cow mammary tissues at the 7 d before calving and the 30 d postpartum (early lactation stage), 1,095 lncRNAs were detected. Compared with the 7 d before calving group, 96 lncRNAs were differentially expressed in the 30 d postpartum group (early lactation stage) . These lncRNAs are mainly distributed on chromosomes 1 to 21 and the average length is 1,065 bp. The main type of these lncRNAs is intronic lncRNA. Pathway analysis found that they were mainly concentrated on the ECM-receptor interaction, Jak-STAT signaling pathway, PI3K-Akt signaling pathway, and TGF-beta signaling pathway. This study revealed the expression profile and characteristics of lncRNAs in the mammary gland tissue of Holstein cows and provided the basic for studying the function of lncRNAs in different lactation stages of Holstein cows.

Supplementary Material

skae128_suppl_Supplementary_Materials
skae128_suppl_supplementary_materials.pdf (149.7KB, pdf)

Acknowledgments

This study was supported by the National Natural Science Foundation of China (31972555), Jiangsu Province Graduate Research and Practice Innovation Program (KYCX24-3804) and Modern Agricultural Development Project of Jiangsu Province (JATS[2022]486).

Glossary

Abbreviations

° C

degree Celsius

CD36

cluster of differentiation 36

CXCR1

chemokine (C-X-C motif) receptor 1

CSN3

κ -casein

GO

Gene Ontology

IL-6

interleukin 6

IL-8

interleukin 8

NB

the way of negative binomial distribution test

RNA-Seq

RNA sequencing

TNF-α

tumor necrosis factor α

TLR4

toll-like receptor 4

Contributor Information

Yanru Wang, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Yan Liang, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Yuxin Xia, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Mengqi Wang, Department of Animal Science, Laval University, Quebec, Quebec, G1V0A6, Canada.

Huimin Zhang, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Mingxun Li, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Zhangping Yang, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Niel A Karrow, Center for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada.

Yongjiang Mao, Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, P R China; Joint International Research Laboratory of Agriculture and Agri-Product Safety of Ministry of Education of China, Yangzhou University, Yangzhou 225009, China.

Conflicts of interest statement

The authors declare no conflict of interest.

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Supplementary Materials

skae128_suppl_Supplementary_Materials
skae128_suppl_supplementary_materials.pdf (149.7KB, pdf)

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