Expression profiles of microRNAs from lactating and non-lactating bovine mammary glands and identification of miRNA related to lactation
© Li et al.; licensee BioMed Central Ltd. 2012
Received: 15 June 2012
Accepted: 20 December 2012
Published: 27 December 2012
MicroRNAs (miRNAs) have been implicated in the regulation of milk protein synthesis and development of the mammary gland (MG). However, the specific functions of miRNAs in these regulations are not clear. Therefore, the elucidation of miRNA expression profiles in the MG is an important step towards understanding the mechanisms of lactogenesis.
Two miRNA libraries were constructed from MG tissues taken from a lactating and a non-lactating Holstein dairy cow, respectively, and the short RNA sequences (18–30 nt) in these libraries were sequenced by Solexa sequencing method. The libraries included 885 pre-miRNAs encoding for 921 miRNAs, of which 884 miRNAs were unique sequences and 544 (61.5%) were expressed in both periods. A custom-designed microarray assay was then performed to compare miRNA expression patterns in the MG of lactating and non-lactating dairy cows. A total of 56 miRNAs in the lactating MG showed significant differences in expression compared to non-lactating MG (P<0.05). Integrative miRNA target prediction and network analysis approaches were employed to construct an interaction network of lactation-related miRNAs and their putative targets. Using a cell-based model, six miRNAs (miR-125b, miR-141, miR-181a, miR-199b, miR-484 and miR-500) were studied to reveal their possible biological significance.
Our study provides a broad view of the bovine MG miRNA expression profile characteristics. Eight hundred and eighty-four miRNAs were identified in bovine MG. Differences in types and expression levels of miRNAs were observed between lactating and non-lactating bovine MG. Systematic predictions aided in the identification of lactation-related miRNAs, providing insight into the types of miRNAs and their possible mechanisms in regulating lactation.
MicroRNAs (miRNAs) are small non-coding RNA molecules that are approximately 22 nucleotides (nt) in length, which negatively regulate specific target genes by mRNA degradation or translational repression. The role of miRNA was first reported in Caenorhabditis elegans, where aberrant expression of lin-4 caused abnormal cell division and proliferation, affecting the timing of cell division and development in larvae . Several developmental and physiological processes including stem cell differentiation, hematopoiesis, cardiac and skeletal muscle development, neurogenesis, insulin secretion, cholesterol metabolism and immune response have been shown to be regulated by miRNAs . The expression of most miRNAs has a spatio-temporal pattern, suggesting that they play specific functions in a variety of processes. Recently, several studies have explored miRNAs as molecular biomarkers for use in identifying biological pathways, aiding cancer diagnoses, and identifying disease activity and treatment effects [3, 4].
The bovine mammary gland (MG) is a complex organ that grows and develops after calf birth . The complex initiation of MG lactation has been extensively studied over the years at the genetic, physiological and morphological levels because of its important functions . It has been reported that many genes are expressed differently to maintain lactation [7–9]. However, only a few studies have assessed the potential implication of miRNAs in MG lactogenesis. Numerous miRNAs have been found to be involved in the regulation of milk protein expression and MG differentiation, and computational and experimental methods have been exploited to identify miRNAs in cattle . However, studies on the regulation of miRNA expression profiles in bovine MG during lactation are still in their infancy. There are currently 730 bovine miRNAs deposited in miRbase 17.0 , with only a few found in the MG [12, 13]. Therefore, identifying MG miRNA expression profiles is an important approach to explore the mechanism of lactation initiation and to identify biomarkers for lactogenesis.
To obtain miRNA expression profiles and to compare the difference in miRNA expression between periods of lactation and non-lactation, we used next-generation sequencing technology to sequence two miRNA libraries constructed from tissue samples taken during these two periods. Using computational prediction, potential targets for these miRNAs were identified, leading to the construction of an interaction network related to lactation. Our integrative analysis highlights the complexity of gene expression networks regulated by miRNAs in MG during lactation.
Determination of bovine MG period
Furthermore, mRNA expression of αs1-casein, a major milk protein, was measured using real-time PCR. As expected, αs1-casein mRNA was highly expressed during the lactation period and had barely detectable expression during the non-lactation period (Figure 1E).
Analysis of sequencing data
Statistics based on the reads of the sequencing data
All clean reads were then aligned against the bta genome and mammalian miRbase using the ACGT-miR program. This process detected 885 pre-miRNAs encoding 921 miRNAs, of which 884 were unique sequences, while 544 (61.5%) were expressed in both periods. The unique miRNAs were categorized into three groups based on their hits: 283 miRNAs matched with known bta miRNAs registered in the miRbase database; 96 miRNAs were conserved among other mammals but have yet to be identified in bovines; and 505 miRNAs were mapped to the bta genome, with the extended genome sequences having the potential to form hairpins.
Features of the three miRNA categories in bovine mammary glands
The 517 miRNAs representing 505 unique miRNA sequences comprising the third group were termed as novel bovine miRNAs and prefixed with “CN” because they mapped to the bta genome without homology to any known mammalian miRNAs, and the corresponding extended genome sequences are capable of forming hairpins (Additional file 3). This miRNAs in this group accounted for a large proportion of the total expressed unique miRNAs (more than 50%, 505/884) and were novel miRNAs in cattle. In this unique “CN” series of miRNAs, 239 (239 out of 505, 43.9%) novel bovine miRNAs were detected in both libraries, while the remaining miRNAs were expressed in a lactation period-specific manner. Unsurprisingly, the reads of novel bovine miRNAs were less abundant than those of known miRNAs. Only 6 and 7 novel bovine miRNAs produced more than 1,000 reads in the lactation and non-lactation libraries, respectively.
A custom-designed microarray assay was performed to validate these miRNA expressions in the MG of lactation and non-lactation dairy cows. After removing miRNAs that had less than 10 reads (as a cutoff value) from the original 884 unique miRNAs, the remaining 523 miRNAs (283 known, 51 conserved and 189 novel) were fabricated on the microarray. Only 304 of the 523 miRNAs were identified by the microarray, including 187 of the known miRNAs, 43 of the conserved miRNAs and 74 of the novel miRNAs (Additional file 4).
Identification of differential expression patterns of miRNA in bovine MG
Characteristics of chromosomal locations of pre-miRNAs in bovine mammary glands
The chromosomal positions (bta genome assembly) of all 885 pre-miRNAs detected in this experiment, including novel miRNAs, were searched by BLAST. It was determined that 800 of the pre-miRNAs (90.4%) matched with the bta genome and that 26 mature miRNAs of pre-miRNA hairpins were located at more than two genomic loci on different chromosomes (Additional file 6).
In gene studies, genes are clustered to identify co-expressed genes from the same primary transcript or to identify gene clusters that share similar functions. We followed the criteria proposed by miRbase and defined 10 Kbp as the maximum inter-distance for two pre-miRNAs to be considered as clustered. There were 230 pre-miRNAs grouped into 55 clusters, accounting for only 28.75% (230 out of 800) of the total pre-miRNAs (Figure 6B).
We predicted all of our mature miRNAs of pre-miRNA hairpins using UNAfold software and found that 104 pairs of known miRNAs and novel candidates share the same pre-miRNA structure and that their pre-miRNAs’ chromosomal locations were identical (Additional file 7). The two mature sequences were located in different arms of one pre-miRNA, including 39 known miRNAs located at the 3’ end and 65 known miRNAs at the 5’ end. We also found that reads of sequences were heavily biased towards the arm containing known miRNAs. For example, mir-26b-5p and PC-468-3p share the same hairpin structure and are located on different sides of chromosome 2 (genomic location of pre-miRNA: 110812687–110812771) (Figure 6C); the reads of each sequence for mir-26b-5p and PC-468-3p were 108,006 and 7 in the lactation period and 118,498 and 8 in the non-lactation period, respectively. It is possible that these lower-read novel candidates may be new miRNAs revealed by the highly sensitive deep sequencing approach.
Prediction of miRNA targets and lactation networks in bovine MG
Based on the results of target prediction, miRNAs and target gene interactions were integrated using EGAN to construct possible regulatory networks to investigate the relationship between miRNAs and lactation (Figure 7B). We chose lactation as the Gene Ontology central process to show all Entrez gene neighbors and then imported 283 known miRNAs to find possible relationships. The resulting network includes 37 miRNAs detected by this study and 15 expressed target genes relating to lactation. For example, miR-138, reported to have a role in lactation, has five predicted targets related to lactation, including SLC35b2, CABP4, GPR, MAPKAP and PRLR. Of these predicted targets, miR-138 is known to inhibit PRLR protein translation by regulating STAT5 and MAPK, thereby suppressing the proliferation and viability of mouse mammary epithelial cells . In this study, miR-138 was expressed more highly during lactation than during non-lactation, as detected by deep sequencing. Prolactin, which acts through its receptor, is a key factor in lactogenesis and lactation maintenance . PRLR causes ductal outgrowth and side branching when grafted into PRLR−/− epithelium .
Regulation of lactation genes by miRNAs
MiRNA mammary gland expression
Fold Change (L/NL)
compared with non-lactation
The significant differences in the expressions of miRNAs and their targets between the two periods prompted us to explore the possible biological significance. We investigated the possible roles of these differentially expressed miRNAs in the regulation of gene expression using a cell-based model. MiR-141 mimic, miR-141 inhibitor, miR-181 mimic, miR-199a mimic, miR-484 mimic and miR-500 mimic were individually transfected into Mac-T cells.
Although the involvement of miRNAs in human MG has been extensively studied, no systematic work has been conducted on bovine MG. Some bovine miRNAs have been recently identified by computational and direct cloning approaches [12, 18–21], but most bovine MG miRNAs have not been identified or functionally studied. In this study, an extensive miRNA profile of lactation and non-lactation bovine MG was created. Two small RNA libraries generated a total of 33.2M sequencing reads, from which 27.9M reads of mappable sequences (15–26 nt) were derived. In total, 283 known miRNAs, 96 conserved miRNAs and 505 novel miRNAs were detected in bovine MGs. A custom-designed microarray assay was performed to confirm these sequenced miRNAs. Seventy-four novel miRNAs and 43 conserved miRNAs were detected using this microarray. We propose that these confirmed miRNAs are novel miRNA candidates in bovine MG.
Differentially expressed miRNAs in bovine MGs
The comparison of miRNA expression between lactation and non-lactation MG allowed us to identify 32 known miRNAs, 10 conserved bovine miRNAs and 14 novel miRNAs that were significantly differentially expressed between the two groups. Although there is no direct evidence showing the relationships between these miRNAs and MG development, it has been reported that the overexpression of miR-107 can lead to decreased rates of cell division with cell cycle arrest  and is related to the metabolism of cellular lipids. Milk lipid synthesis in MG is increased during the lactation period. The processes that take place between lactation and non-lactation resemble an epithelial-to-mesenchyme transition (EMT). The loss of inter-epithelial cell-cell contacts, including loss of the cell adhesion molecule E-cadherin, takes place between the two periods . Graziano Martello and colleagues showed that miR-107 induces EMT in breast cancer cells . Taking these observations together suggests that miR-107 could play important roles in lactation. It has been reported that miR-23b represses duct gene expression by down-regulating transforming growth factor-beta (TGF-β) signaling . Our data showed that the growth of the ductal system became active and miR-23b showed opposite expression patterns in the lactation and non-lactation periods. Therefore, we propose that miR-23b may be involved in MG duct system development.
MiRNA location and structure
There is evidence that miRNAs are often clustered and sometimes co-expressed from the same primary transcript, leading to the hypothesis that they may share functional relationships . MiRNAs clustered on genome loci are ubiquitous in animals. Characteristics of miRNA clusters have already been reported for many mammals [14, 27, 28]. Based on the consensus criteria, our results reveal a unique feature. The low percentage of clustering seen in our data may be due to the currently incompletely characterized bovine genome. Each cluster contained 2 to 15 pre-miRNAs. The chromosome with the most clusters is chromosome 21, with 8 clusters. Some miRNA clusters contain different types of pre-miRNA, such as cluster 31, housing miR-338, CN-287 and PC-3065 within chromosome 19 (genomic location of pre-miRNA: 53 057 553–53 057 638). These three pre-miRNAs may act cooperatively.
Our most interesting finding is that there were 104 pairs of known miRNAs and novel candidates with the same pre-miRNA structures but very different counts. One potential reason for this finding is that the highly sensitive deep sequence technology makes it possible to identify novel candidates with lower counts. Another possible explanation is that the pre-miRNA hairpin can be cleaved into two arms. One arm is the mature miRNA, which is loaded with Argonaute (Ago2) proteins into the RNA-induced silencing complex (RISC), where it guides the RISC to silence target mRNAs, whereas the other arm goes on to be degraded via the miRNA processing canonical pathway. Because mature miRNA is derived from different pre-miRNAs depending on the body’s needs, the amount of the degraded arm will vary under different physical conditions.
MiRNAs exert their effects by interacting with target mRNAs. Therefore, target-predicting software (Targetscan) was used to identify putative targets. On the basis of the predicted targets of the 283 known miRNAs, an interaction network composed of these miRNAs and their candidate targets expressed during lactation was constructed by EGAN. Using this network, it was demonstrated that 37 miRNAs interact with a total of 15 targets, which are involved in amino acid, fatty acid and lactose metabolism. It has been reported that the increased activity of the xanthine dehydrogenase (XDH) gene, a putative target of miR-29, miR-15b and miR-107, is an early event in mammogenesis in vivo and in vitro rather than a terminal component of differentiation . MiR-362, miR-25, miR-363 and miR-32 target BCAT2 encodes a branched chain aminotransferase found in mitochondria. It has recently been observed that branched chain amino acids can play a signaling role for protein synthesis in addition to serving as substrates . In a study designed to determine the effect of lactation, it was found that BCAT activity increased in mammary tissue during rat lactation and was 6-fold higher than in virgin rats . PRLR is the putative target of miR-142, miR-23, miR-374b, miR-30a and miR-27b and plays a function in MG development together with prolactin. Prolactin promotes alveolar survival, maintains tight junctions and regulates milk protein and lactose synthesis . STAT5 serves as a common point in the signal transduction pathways of several lactogenic and galactopoietic hormones in the MG [31–33]. Feuermann and colleagues found that one of the targets of STAT5 is the miR-17/92 cluster . Gene knockout experiments have demonstrated that the formation of lobular-alveolar structures depends upon the actions of progesterone and prolactin receptors functioning through the STAT5, cyclin D1 and Wnt pathways. Molecular analyses have established that Caveolin-1 (Cav-1) abrogates PRL-induced gene expression by sequestering JAK2. The loss of both Cav-1 alleles results in precocious MG development during pregnancy and the concomitant precocious activation of STAT5. All of the above indicate that although little is known about the exact functions of these miRNAs, the relationships with their respective target genes indicate potential roles in lactation.
Both HK2 and STAT5 are key genes in the constructed network. Five related miRNAs were used to validate the network. STAT5 expression patterns were opposite to those of miR-141 in the MG detected in this study. According to our target predictions, we found that the STAT5 3’UTR is paired to miR-141 by a 7-mer seed region. Our transfection assay using Mac-T cells revealed that increased levels of miR-141 could reduce STAT5 protein expression. These findings emphasize a potential role of miR-141 in lactation regulation.
The interaction network predicted that HK2 is a target of miR-125b, miR-181a, miR-199b, miR-484 and miR-500. Currently, there are no reports on any associations between HK2 and miRNAs. Although 5 miRNA binding sites were predicted in the HK2 3’UTR, only miR-484 and miR-500 at high concentrations resulted in significant reductions in HK2 protein level. It is possible that the network is regulated by other factors or that the three miRNA binding sites are located at the position of the HK2 3’UTR . The results of this analysis indicated miR-484 and miR-500 as putative miRNA regulators of HK2. Therefore, in this study, we have identified a relatively large number of miRNAs from bovine MG, validated 117 newly isolated miRNAs (43 conserved and 74 novel miRNAs), analyzed their expression and predicted their putative targets. Our results also demonstrated that miR-141, miR-484 and miR-500, characterized by the miRNA-gene regulatory networks, are probably essential for lactation via the targeting of STAT5 and HK2.
The aim of our work was to examine miRNA expression profiles in bovine MG and to evaluate miRNA functions through the identification of differentially expressed miRNAs in lactation and non-lactation MG. Our identification of novel miRNAs highlights the importance of miRNAs with low abundance and less conservation between species. An interaction network of known miRNAs and their target genes relating to lactation was constructed to postulate the functional roles of miRNAs in the MG. This integrated analysis provides important information that may inspire further experimental investigation into the field of miRNAs and their targets during lactation.
Experiments were performed according to the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China, revised in June 2004) and approved by the Institutional Animal Care and Use Committee at Zhejiang University, Zhejiang, China. Animals were allowed access to food and water ad libitum under controlled environmental conditions and were humanely sacrificed as necessary to ameliorate suffering.
Two multiparous dairy cows were used for miRNA library construction. The first was a 6-year-old cow that had been lactating for 2 months, which was used to make the lactation miRNA library, and the second was a 4-year-old non-lactating, non-pregnant cow, which was used to construct the non-lactation miRNA library.
In the microarray assay, two other multiparous cows were added to each period, and mixed RNA samples were made. The two additional lactation cows had been lactating for 3 and 4 months and were 4 and 5 years old, respectively. The two additional non-lactating, non-pregnant cows were 4 and 5 years old.
Bovine MG tissues were collected and immediately stored in liquid nitrogen until further use. Blocks of MG tissue were fixed in 4% formalin for 48 hours, processed and embedded into paraffin blocks according to routine procedures.
The paraffin-fixed blocks were serially sectioned into 8 μm coronal slices and stored at −20°C until further use. For routine histological studies, paraffin sections were stained with hematoxylin and eosin. Hematoxylin-eosin stained sections were analyzed by light microscopy using a Nikon fluorescence microscope (Nikon, Japan).
Alpha-casein was detected in frozen sections by immunofluorescence. Sections were fixed with 4% formaldehyde for 10 minutes. The slides were then rinsed 3 times in PBS for 5 minutes each and blocked for 60 minutes. The blocking solution was replaced by primary antibody solution (1:100, gene Tex, USA), and the samples were incubated overnight at 4°C. The next day, slides were rinsed 3 times in PBS for 5 minutes each. FITC-conjugated secondary antibody (1:200) with DAPI was added, and the slides were incubated for 1 hour at 37°C in the dark, followed by 3 rinses in PBS for 5 minutes each. The specimens were viewed under a fluorescence microscope (Nikon, Japan).
Total RNA isolation, small RNA library preparation and sequencing
Total RNA was extracted using a Qiagen miRNeasy Mini Kit (Qiagen, USA) according to the manufacturer’s protocol. Subsequently, the RNA samples were sent to LC Science (Houston, USA) to construct the small RNA libraries using an Illumina small RNA kit (Illumina, San Diego, USA) and to be sequenced using Genome Analyzer (Illumina, San Diego, USA).
Sequencing data analysis
Small RNA reads were processed using Illumina’s Genome Analyzer, and the ACGT101-miR program was then used to process the sequencing data. The mammalian miRbase (miRbase 17.0: http://www.mirbase.org/index.shtml) and the bovine mRNA Rfam, Repbase, genome and EST databases (http://www.ncbi.nlm.nih. gov/projects/genome/guide/cow/ and BTA 4.0: ftp://ftp.ensembl.org/pub/release-57/ fasta/bos_Tau-rus/dna/) were exploited. The sequencing data were first filtered into mRNA using Rfam and Repbase, and then mapped to miRbase. The mapped data were then aligned to genome and EST databases for annotation purposes. The remaining unmapped data were mapped to genome and EST data, secondary structures were predicted using UNAFold software , and IDEG 6 was used to identify significant differentially expressed miRNAs .
All sequencing data were categorized into three groups: (1) known miRNAs reported in miRbase; (2) conserved miRNAs sharing highly similar sequences corresponding to their precursors in other mammalian genome assemblies, and (3) bovine novel candidates where reads and the predicted secondary structures are not mapped to the miRNAs or pre-miRNAs in miRbase, but are mapped to the bta genome with extended sequences from the genome that form hairpins.
Total RNA was extracted using a Qiagen miRNeasy Mini Kit (Qiagen, USA). For each stage, equal quantities of total RNA isolated from three individual cows were pooled. A custom-designed microarray assay was performed to analyze miRNA expression patterns in lactating and non-lactating periods by LC Science (Houston, USA). The array included probes for 523 miRNA derived from the sequencing data and reported bovine miRNA (from miRbase) with 5S rRNA as a data normalization control. The probes were synthesized by in situ synthesis using PGR (photogenerated reagent) chemistry. Hybridization was performed overnight on a μParaflo microfluidic chip using a micro-circulation pump (Atactic Technologies) . Hybridization images were collected using a laser scanner (GenePix 4000B, Molecular Device) and digitized using Array-Pro image analysis software (Media Cybernetics). Data were analyzed by first subtracting the background and then normalizing the signals using a LOWESS (Locally weighted Regression) filter .
Target prediction and network construction
The starting point of the miRNA target prediction strategy was the utilization of known miRNAs listed in Additional file 1. TargetScan (Version 5.0) was used to predict putative targets with an established miRNA seed database and a bovine 3’EST database. EGAN software was used to depict the relationships between miRNAs, target genes and lactation. Due to limited data in cattle, data from human orthologs were also included for these targets in EGAN software .
Quantitative RT-PCR assay
The gene expression assay and differentially expressed miRNAs identified using deep sequencing were validated using real-time PCR. Total RNA were extracted from the MG tissues in both periods separately using Trizol reagent (Invitrogen, USA). The RNA was divided into two portions, one for genetic testing and the other for miRNA detection. Genetic testing started with 500 ng of total RNA, and this RNA was reverse transcribed to cDNA using a SYBR® PrimeScript® RT-PCR Kit (TAKARA, Japan). For miRNA detection, 2 μg of total RNA was reverse transcribed to cDNA with a specific stem-loop primer using M-MLV (Invitrogen, USA), with incubation for 60 minutes at 42°C, followed by heating for 10 minutes at 95°C and storage at 4°C. These cDNA were then used as templates in a SYBR® Premix Ex Taq™ kit (TAKARA, Japan) with specific primers (Additional file 8). Real-time PCR was performed on an ABI7500 system (Applied Biosystem, USA). The reaction mixtures were incubated in a 96-well plate at 95°C for 30 seconds, followed by 40 cycles of 95°C for 5 seconds and 60°C for 34 seconds. All reactions were run in triplicate. GAPDH was used as a gene assay control and bovine S18 rRNA as a miRNA control. Fold changes were determined by the threshold cycle (CT). Fold changes of miRNA expression were calculated using the 2−ΔCt method, where ΔCt = (Ct target − Ct control) Sample.
Cell culture and transfection
The cells used in this study were from the Mac-T cell line, which was donated by Dr. Zhao (University of Vermont, Burlington, USA). Cells were maintained in Dulbecco's Modified Eagle's Medium (DMEM, Gibco, USA) supplemented with 10% (V/V) fetal bovine serum (FBS, Gibco, USA), 100 U/mL penicillin and 100 mg/mL streptomycin. The cells were maintained at 37°C with 5% CO2 and subcultured every other day.
Transfection of Mac-T cells with mimics and inhibitors
Cells were seeded in a 24-well plate at concentration of 1×105 cells/ml/well on the day before the transfection. Mimics of miR-125b, miR-141, miR-181, miR-199a, miR-484 and miR-500 and the antisense inhibitor miR-141 were transfected by Lipofectamine 2000 (Invitrogen, USA) according to the manufacturer’s protocol. The transfection efficiency was examined using FAM-conjugated siRNA. The mimics were RNA duplexes, the inhibitors were single-stranded, and the negative controls (NC) and inhibitor negative controls (INC) for all miRNA mimics and inhibitors were designed by Invitrogen and had no homology to any bovine genome sequences (Additional file 9). The culture medium was changed 6 hours after the transfection of 20 pmol/L of mimics or inhibitors. All transfection data are representative of three independently repeated transfections and each 3-well group of cells were treated as one experimental unit.
Western blotting analysis
Proteins were extracted using a protein extraction kit according to the manufacturer’s instructions (Kegen, China). Equal amounts of protein lysate were separated by SDS- polyacrylamide gel electrophoresis (PAGE) and then electrophoretically transferred to polyclonal difluoride membranes. Each protein was incubated with a specific antibody and detected with an electrogenerated chemiluminescence (ECL) kit. Beta-actin was used as a loading control. Antibodies for STAT5 and β-actin were manufactured by Boster (China), and the HK2 antibody was purchased from Santa Cruz (USA). The intensity of the protein fragments was quantified using Imagpro-Plus software. All data are from three independently repeated experiments.
All data were analyzed using SPSS software (V16.0, SPSS Inc., USA). Values in the texts and figures represent the results of at least three separate experiments. Group comparisons were performed using ANOVA with the Student’s t-test. Differences were considered statistically significant at P<0.05.
Coefficients of variation
Fetal bovine serum
Inhibitor negative control
RNA-induced silencing complex
SDS-polyacrylamide gel electrophoresis
Small interference RNA
Signal transducers and activators of transcription
Transforming growth factor-beta
We thank Dr. Feng-Qi Zhao, University of Vermont, USA, for the donation of Mac-T cells. We also thank Dr. JinRong Peng, Laboratory of Functional Genomics, Zhejiang University, for experimental design assistance and for critically reading the manuscript. This study was supported partly by a grant from the earmarked fund for China Agriculture Research System (CARS-37) and the National Natural Science Foundation of China (No. 30901034).
- Lee RC, Feinbaum RL, Ambros V: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993, 75 (5): 843-854. 10.1016/0092-8674(93)90529-Y.View ArticlePubMedGoogle Scholar
- Song L, Tuan RS: MicroRNAs and cell differentiation in mammalian development. Birth Defects Res C Embryo Today. 2006, 78 (2): 140-149. 10.1002/bdrc.20070.View ArticlePubMedGoogle Scholar
- Jay C, Nemunaitis J, Chen P, Fulgham P, Tong AW: miRNA profiling for diagnosis and prognosis of human cancer. DNA Cell Biol. 2007, 26 (5): 293-300. 10.1089/dna.2006.0554.View ArticlePubMedGoogle Scholar
- Hu X, Macdonald DM, Huettner PC, Feng Z, El NI, Schwarz JK, Mutch DG, Grigsby PW, Powell SN, Wang X: A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer. Gynecol Oncol. 2009, 114 (3): 457-464. 10.1016/j.ygyno.2009.05.022.View ArticlePubMedGoogle Scholar
- Hennighausen L, Robinson GW: Information networks in the mammary gland. Nat Rev Mol Cell Biol. 2005, 6 (9): 715-725.View ArticlePubMedGoogle Scholar
- Silveri L, Tilly G, Vilotte JL, Le Provost F: MicroRNA involvement in mammary gland development and breast cancer. Reprod Nutr Dev. 2006, 46 (5): 549-556. 10.1051/rnd:2006026.View ArticlePubMedGoogle Scholar
- Rhoads RE, Grudzien-Nogalska E: Translational regulation of milk protein synthesis at secretory activation. J Mammary Gland Biol Neoplasia. 2007, 12 (4): 283-292. 10.1007/s10911-007-9058-0.View ArticlePubMedGoogle Scholar
- Watson CJ, Oliver CH, Khaled WT: Cytokine signalling in mammary gland development. J Reprod Immunol. 2011, 88 (2): 124-129. 10.1016/j.jri.2010.11.006.View ArticlePubMedGoogle Scholar
- Bernard L, Leroux C, Chilliard Y: Expression and nutritional regulation of lipogenic genes in the ruminant lactating mammary gland. Adv Exp Med Biol. 2008, 606: 67-108. 10.1007/978-0-387-74087-4_2.View ArticlePubMedGoogle Scholar
- Nagaoka K, Tanaka T, Imakawa K, Sakai S: Involvement of RNA binding proteins AUF1 in mammary gland differentiation. Exp Cell Res. 2007, 313 (13): 2937-2945. 10.1016/j.yexcr.2007.04.017.View ArticlePubMedGoogle Scholar
- Griffiths-Jones S, Grocock RJ, Van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 2006, 34 (Database issue): D140-D144.PubMed CentralView ArticlePubMedGoogle Scholar
- Gu Z, Eleswarapu S, Jiang H: Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Lett. 2007, 581 (5): 981-988. 10.1016/j.febslet.2007.01.081.View ArticlePubMedGoogle Scholar
- Coutinho LL, Matukumalli LK, Sonstegard TS, Van Tassell CP, Gasbarre LC, Capuco AV, Smith TP: Discovery and profiling of bovine microRNAs from immune-related and embryonic tissues. Physiol Genomics. 2007, 29 (1): 35-43.View ArticlePubMedGoogle Scholar
- Li M, Xia Y, Gu Y, Zhang K, Lang Q, Chen L, Guan J, Luo Z, Chen H, Li Y, Li Q, Li X, Jiang AA, Shuai S, Wang J, Zhu Q, Zhou X, Gao X, Li X: MicroRNAome of porcine pre- and postnatal development. PLoS One. 2010, 5 (7): e11541-10.1371/journal.pone.0011541.PubMed CentralView ArticlePubMedGoogle Scholar
- Chun-Mei W, Qing-Zhang L, Ye L: miR-138 function and its targets on mouse mammary epithelial cells. Prog Biochem Biophys. 2008, 07: 834-838.Google Scholar
- Kelly PA, Bachelot A, Kedzia C, Hennighausen L, Ormandy CJ, Kopchick JJ, Binart N: The role of prolactin and growth hormone in mammary gland development. Mol Cell Endocrinol. 2002, 197 (1–2): 127-131.View ArticlePubMedGoogle Scholar
- Brisken C, Kaur S, Chavarria TE, Binart N, Sutherland RL, Weinberg RA, Kelly PA, Ormandy CJ: Prolactin controls mammary gland development via direct and indirect mechanisms. Dev Biol. 1999, 210 (1): 96-106. 10.1006/dbio.1999.9271.View ArticlePubMedGoogle Scholar
- Tripurani SK, Xiao C, Salem M, Yao J: Cloning and analysis of fetal ovary microRNAs in cattle. Anim Reprod Sci. 2010, 120 (1–4): 16-22.View ArticlePubMedGoogle Scholar
- Long JE, Chen HX: Identification and characteristics of cattle microRNAs by homology searching and small RNA cloning. Biochem Genet. 2009, 47 (5–6): 329-343.View ArticlePubMedGoogle Scholar
- Jin W, Grant JR, Stothard P, Moore SS, Guan LL: Characterization of bovine miRNAs by sequencing and bioinformatics analysis. BMC Mol Biol. 2009, 10: 90-10.1186/1471-2199-10-90.PubMed CentralView ArticlePubMedGoogle Scholar
- Jin W, Dodson MV, Moore SS, Basarab JA, Guan LL: Characterization of microRNA expression in bovine adipose tissues: a potential regulatory mechanism of subcutaneous adipose tissue development. BMC Mol Biol. 2010, 11: 29-10.1186/1471-2199-11-29.PubMed CentralView ArticlePubMedGoogle Scholar
- Finnerty JR, Wang WX, Hebert SS, Wilfred BR, Mao G, Nelson PT: The miR-15/107 group of microRNA genes: evolutionary biology, cellular functions, and roles in human diseases. J Mol Biol. 2010, 402 (3): 491-509. 10.1016/j.jmb.2010.07.051.PubMed CentralView ArticlePubMedGoogle Scholar
- Wright JA, Richer JK, Goodall GJ: microRNAs and EMT in mammary cells and breast cancer. J Mammary Gland Biol Neoplasia. 2010, 15 (2): 213-223. 10.1007/s10911-010-9183-z.View ArticlePubMedGoogle Scholar
- Martello G, Rosato A, Ferrari F, Manfrin A, Cordenonsi M, Dupont S, Enzo E, Guzzardo V, Rondina M, Spruce T, Parenti AR, Daidone MG, Bicciato S, Piccolo S: A MicroRNA targeting dicer for metastasis control. Cell. 2010, 141 (7): 1195-1207. 10.1016/j.cell.2010.05.017.View ArticlePubMedGoogle Scholar
- Rogler CE, Levoci L, Ader T, Massimi A, Tchaikovskaya T, Norel R, Rogler LE: MicroRNA-23b cluster microRNAs regulate transforming growth factor-beta/bone morphogenetic protein signaling and liver stem cell differentiation by targeting Smads. Hepatology. 2009, 50 (2): 575-584. 10.1002/hep.22982.View ArticlePubMedGoogle Scholar
- Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004, 116 (2): 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Bentwich I, Avniel A, Karov Y, Aharonov R, Gilad S, Barad O, Barzilai A, Einat P, Einav U, Meiri E, Sharon E, Spector Y, Bentwich Z: Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet. 2005, 37 (7): 766-770. 10.1038/ng1590.View ArticlePubMedGoogle Scholar
- Weber MJ: New human and mouse microRNA genes found by homology search. FEBS J. 2005, 272 (1): 59-73.View ArticlePubMedGoogle Scholar
- Hayden TJ, Brennan D, Quirke K, Murphy P: Xanthine oxidase/dehydrogenase in mammary gland of mouse: relationship to mammogenesis and lactogenesis in vivo and in vitro. J Dairy Res. 1991, 58 (4): 401-409. 10.1017/S0022029900030004.View ArticlePubMedGoogle Scholar
- DeSantiago S, Torres N, Hutson S, Tovar AR: Induction of expression of branched-chain aminotransferase and alpha-keto acid dehydrogenase in rat tissues during lactation. Adv Exp Med Biol. 2001, 501: 93-99. 10.1007/978-1-4615-1371-1_11.View ArticlePubMedGoogle Scholar
- McManaman JL, Neville MC: Mammary physiology and milk secretion. Adv Drug Deliv Rev. 2003, 55 (5): 629-641. 10.1016/S0169-409X(03)00033-4.View ArticlePubMedGoogle Scholar
- Liu X, Robinson GW, Wagner KU, Garrett L, Wynshaw-Boris A, Hennighausen L: Stat5a is mandatory for adult mammary gland development and lactogenesis. Genes Dev. 1997, 11 (2): 179-186. 10.1101/gad.11.2.179.View ArticlePubMedGoogle Scholar
- Yang J, Kennelly JJ, Baracos VE: The activity of transcription factor Stat5 responds to prolactin, growth hormone, and IGF-I in rat and bovine mammary explant culture. J Anim Sci. 2000, 78 (12): 3114-3125.PubMedGoogle Scholar
- Feuermann Y, Robinson GW, Zhu BM, Kang K, Raviv N, Yamaji D, Hennighausen L: The miR-17/92 cluster is targeted by STAT5 but dispensable for mammary development. Genesis. 2012Google Scholar
- Mignone F, Gissi C, Liuni S, Pesole G: Untranslated regions of mRNAs. Genome Biol. 2002, 3 (3): 1-10. reviews0004View ArticleGoogle Scholar
- Markham NR, Zuker M: UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol. 2008, 453: 3-31. 10.1007/978-1-60327-429-6_1.View ArticlePubMedGoogle Scholar
- Romualdi C, Bortoluzzi S, D'Alessi F, Danieli GA: IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments. Physiol Genomics. 2003, 12 (2): 159-162.View ArticlePubMedGoogle Scholar
- Gao X, Gulari E, Zhou X: In situ synthesis of oligonucleotide microarrays. Biopolymers. 2004, 73 (5): 579-596. 10.1002/bip.20005.View ArticlePubMedGoogle Scholar
- Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193. 10.1093/bioinformatics/19.2.185.View ArticlePubMedGoogle Scholar
- Paquette J, Tokuyasu T: EGAN: exploratory gene association networks. Bioinformatics. 2010, 26 (2): 285-286. 10.1093/bioinformatics/btp656.PubMed CentralView ArticlePubMedGoogle Scholar