Abstract
MicroRNAs (miRNAs) are a class of small, non-coding RNA molecules that act as a negative regulator of most mRNAs. miRNAs influence the gene expression as transcriptional regulators and play an important role in many fundamental biological processes. It is generally acknowledged that miRNAs have a very important affection on mammalian pituitary. However, the answers of which role miRNAs play in the development of sexual function or how much they contribute to the pituitary function are not exactly. In our study, we used three female 21-day-old rats and three female 12-month-old rats to analysis the function of miRNAs. By the analyses of microarray data, we finished the stem-loop real-time RT-PCR for the differentially expressed miRNAs. We detected a total of 93 differentially expressed miRNAs between 21-day-old rats’ pituitary and 12-month-old rats’. Stem-loop real-time RT-PCR suggests that the obtained data is of high credibility. Among these miRNAs, 7 miRNAs’ expression (rno-miR-880, rno-miR-503, rno-miR-125a-3p, rno-miR-3596b, rno-miR-30e, rno-miR-214 and rno-miR-22) are significant different (P≤0.05). In a word, this study identified a number of specific changes in the expression of miRNAs, in rats by detecting the expression profile of miRNAs in rat’s pituitary, and all of that lay the foundation for elucidating the regulatory mechanisms of miRNAs in rat’s reproduction process. These differentially expressed miRNAs may play a very important role in rat’s reproduction process.
Keywords: MiRNAs, rat pituitary, reproduction
Introduction
MiRNAs are non-protein-coding small RNAs, 19-23 nucleotides in length, which are implicated in the posttranscriptional fine tuning of gene regulation [1]. By base pairing with the 3’ untranslated region (3’ UTR) of their target mRNA, miRNAs results in repression of the target genes expression or degradation of target genes [2-4]. Since the original discovery in nematodes [5], studies have revealed that miRNAs have key roles in diverse processes such as developmental control, hematopoietic cell differentiation, neural development, apoptosis, cell proliferation and organ development [6]. Recent studies indicated that miRNAs play a direct role in apoptosis of bovine corpus luteum [7], which means miRNAs are involved in the reproductive process.
Since the publication of the Rat Genome Sequence [8], rat is more and more important in scientific research. In the year of 2007, some researchers found rats had possessed metacognition and psychological ability previously only documented in humans and some primates [9,10]. From then on, there are many studies about rat’s brain and intelligence, but only a little about its pituitary.
The pituitary, as the most complex internal secretion gland of the mammal, releases seven kinds of hormones to play a part in the whole life course. Thus profiling pituitary miRNAs may enable us to elucidate not only how miRNAs are involved in regulating the development and function of the organ but also how miRNAs are involved in regulating the development of the individual or species characteristics of an animal [11]. However, there only are a few experiments stepped into the regular pattern of pituitary gland.
Drawing the support from microarray, target Combo and DAVID gene annotation tool, we studied the discipline of miRNAs in rat’s pituitary.
Methods
Tissue collection and RNA extraction
Euthanasia was performed by decapitation following anesthetic injection (chloraldurate, 10%), and pituitary glands from three female 21-day-old rats and three female 12-month-old rats (Wistar) were rapidly dissected, and store in liquid nitrogen. Total RNA was isolated by TRIzol according to the explanatory memorandum of manufacturer. This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Labo-ratory Animals of the National Institutes of Health. The animal use protocol has been reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Jilin University.
Ethics statement
We strictly abided the provisions of laboratory animal center of Jilin University. All animal procedures were conducted following the protocol (2011-036) approved by the Animal Care & Welfare Committee of Jilin University.
The detection of microarray assay
The miRCURY™ Hy3™/Hy5™ Power labeling kit (Exiqon, Vedbaek, Denmark) was used according to the manufacturer’s guideline for miRNA labelling. One microgram of each sample was 3’-end-labeled with Hy3TM fluorescent label by using T4 RNA ligase provided in the kit as described following. The RNA mixture (3 μL) with 1.0 μL of CIP Buffer (Exiqon) was incubated for 30min at 37°C, terminated by 95°C for 5 min. Then 3.0 μL labeling buffer, 1.5 μL fluorescent label (Hy3TM), 2.0 μL DMSO and 2.0 μL labeling enzyme were added into the mixture. The labeling reaction was incubated for 1 h at 16°C, and terminated by 65°C for 15 min.
The Hy3TM-labeled samples were hybridized on the miRCURYTM LNA Array (v.16.0) (Exiqon) according to array manual after the termination of the labeling step. All the mixture from Hy3TM-labeled samples mixed with 25 μL hybridization buffer were denatured at 95°C for 2 min, incubated on ice for 2 min and then hybridized to the microarray for 16-20 h at 56°C in a 12-Bay Hybridization Systems (Hybridization System-Nimblegen Systems, Inc., Madison, WI, USA), which provides an active mixing action and constant incubation temperature to improve hybridization uniformity and enhance signal. Following hybridization, the slides were achieved, washed several times with Wash buffer kit (Exiqon), and finally dried by centrifugation for 5 min at 400 rpm. Then the slides were scanned by using the Axon GenePix 4000 B microarray scanner (Axon Instruments, Foster City, CA).
Scanned images were then imported into GenePix Pro 6.0 software (Axon) for grid alignment and data extraction. Replicates were averaged and miRNAs that intensities ≥50 in all samples were chosen for calculating normalization factor. Expressed data were normalized using the median normalization. After normalization, differentially expressed miRNAs were identified through Volcano Plot filtering. Hierarchical clustering was performed using MEV software (v4.6, TIGR).
Stem-loop real-time RT-PCR
Total RNA was extracted as the description above. The primers used for Real-time PCR were provided by Jiusheng Corp (Shanghai, China). Our Stem-loop real-time RT-PCR steps followed the previously described [12].
Complementary DNA was reverse transcribed from 1 µg of total RNA mixing with with 1 μg miRNA-specific RT-primers (Jiusheng Corp, Shanghai, China) by incubating 50 min at 42°C and deactivating 15 min at 75°C. Power SYBR green PCR mix was used for the amplification mixture with each primer 1.5 µl and 1 µl cDNA for a total reaction volume [13] of 20 µl. PCR reactions were performed on STRATAGENE Mx3005P sequence detection system as previously described. Briefly, samples underwent amplification by denaturation at 95 C for 1 min, and then cycled 40 times using 95 C for 15 sec, 56 C for 15 sec and 72 C for 40 sec. All reactions were run in triplicate [14]. Last, we recorded the cycle threshold (Ct) for analysis with U6 RNA.
Results
The regular expressions of miRNAs in rat’s pituitary detected by microarray assay
We entrusted the microarray analysis with Kangchen Bio-tech. The 6th generation of miRNA array contains more than 1891 capture probes, covering all human, mouse and rat microRNAs annotated in miRBase 16.0, as well as all viral microRNAs related to these species. In addition, this array contains capture probes for 66 new miRPlus™ human microRNAs. These are proprietary microRNAs not found in miRBase. By the analysis of microarray data, we detected a total of 93 differentially expressed miRNAs between 21-day-old rats and 12-month-old rats (Tables 1 and 2). Among these miRNAs, 7 miRNAs’ with a fold change >1.5 and a p-test value <0.01 are significant different (Tables 3 and 4). These 7 miRNAs should be the main regulators between the 21-day-old rats and the 12-month-old rats. Then we used Volcano Plot and Heat Map and Hierarchical Clustering for further testing. The results are as follows (Figures 1, 2).
Table 1.
Group 1 vs Group 2 1.5 fold up regulated miRNAs
| Name | Fold change | P-value | ForeGround-BackGround | |
|---|---|---|---|---|
|
| ||||
| Group 1 vs Group 2 | Group 1 vs Group 2 | Mean of Group 2 | Mean of Group 1 | |
| Rno-miR-349 | 2.4485 | 0.2028 | 323.1667 | 809.5000 |
| Rno-miR-218b | 1.9746 | 0.2333 | 336.8333 | 1053.5000 |
| Rno-miR-409-3p | 1.7240 | 0.4844 | 41.8333 | 125.5000 |
| Rno-miR-672* | 4.5245 | 0.3571 | -3.1667 | 126.1667 |
| Rno-miR-743b | 1.6896 | 0.1930 | 32.1667 | 53.3333 |
| Rno-miR-183 | 1.6504 | 0.1842 | 484.0000 | 493.1667 |
| Rno-miR-431 | 1.6594 | 0.4630 | 2517.1667 | 4631.3333 |
| Rno-miR-3597-5p | 2.1797 | 0.1497 | 121.3333 | 267.0000 |
| Rno-miR-2964 | 5.7149 | 0.2536 | -52.6667 | 72.1667 |
| Rno-miR-494* | 21.6344 | 0.3779 | -27.5000 | 121.6667 |
| Rno-miR-3592 | 2.0292 | 0.1096 | 544.5000 | 1086.8333 |
| Rno-miR-3591 | 3.2684 | 0.4549 | 14.3333 | 254.6667 |
| Rno-miR-880 | 2.6260 | 0.0067 | 215.6667 | 572.8333 |
| Rno-miR-351 | 1.8834 | 0.1813 | 198.3333 | 366.5000 |
| Rno-miR-449c-3p | 2.4601 | 0.3121 | 409.6667 | 1109.0000 |
| Rno-miR-874 | 1.9655 | 0.3151 | 53.1667 | 110.3333 |
| Rno-miR-211 | 1.5107 | 0.3192 | 146.3333 | 231.5000 |
| Rno-miR-466b-2* | 1.7161 | 0.5069 | 117.0000 | 199.1667 |
| Rno-miR-340-3p | 1.9902 | 0.3923 | 1914.8333 | 4210.6667 |
| Rno-miR-199a-3p | 1.5757 | 0.4259 | 123.3333 | 188.8333 |
| Rno-miR-500 | 1.9307 | 0.4332 | -6.8333 | 30.3333 |
| Rno-miR-221 | 1.6870 | 0.3220 | 175.1667 | 286.0000 |
| Rno-miR-3563-5p | 1.7707 | 0.5996 | -7.8333 | 35.1667 |
| Rno-miR-433 | 1.6346 | 0.0770 | 659.6667 | 1069.1667 |
| Rno-miR-107 | 1.5089 | 0.2541 | 304.1667 | 455.0000 |
| Rno-miR-138 | 1.5057 | 0.2273 | 234.0000 | 345.0000 |
| Rno-miR-377* | 1.6294 | 0.5047 | 84.6667 | 154.5000 |
| Rno-miR-192 | 1.5644 | 0.4018 | 88.3333 | 221.0000 |
| Rno-miR-511* | 2.7856 | 0.2952 | -11.1667 | 148.5000 |
| Rno-miR-3597-3p | 1.6560 | 0.3463 | 734.6667 | 1180.0000 |
| Rno-miR-204 | 1.7792 | 0.3125 | 133.3333 | 245.0000 |
| Rno-miR-186 | 1.7421 | 0.1911 | 130.6667 | 227.0000 |
| Rno-miR-219-2-3p | 1.8049 | 0.2362 | 250.0000 | 435.1667 |
| Rno-miR-503 | 2.6714 | 0.0160 | 70.8333 | 385.5000 |
| Rno-miR-770* | 1.8442 | 0.1563 | 211.3333 | 381.0000 |
| Rno-miR-3581 | 1.5845 | 0.3631 | -23.0000 | 36.5000 |
| Rno-miR-136* | 2.4591 | 0.1559 | 1075.5000 | 2686.3333 |
| Rno-miR-675* | 1.6565 | 0.5534 | 110.1667 | 376.5000 |
| Rno-miR-125a-3p | 2.2408 | 0.0131 | 153.0000 | 346.1667 |
| Rno-miR-505* | 2.5868 | 0.1550 | 141.0000 | 357.1667 |
| Rno-miR-206 | 2.0189 | 0.2072 | 103.5000 | 328.5000 |
| Rno-miR-331* | 1.6290 | 0.7458 | 16.5000 | 74.0000 |
| Rno-miR-18a* | 1.5098 | 0.5932 | -16.1667 | 40.0000 |
| Rno-miR-3596a | 1.9819 | 0.2679 | 491.3333 | 979.8333 |
| Rno-miR-3544 | 1.9393 | 0.3228 | 276.1667 | 712.3333 |
| Rno-miR-30c-1* | 2.3394 | 0.6563 | -14.8333 | 107.0000 |
| Rno-miR-199a-5p | 1.5305 | 0.5148 | 46.1667 | 115.6667 |
| Rno-miR-34b* | 1.9660 | 0.0945 | 5347.3333 | 11030.1667 |
| Rno-miR-330* | 1.7412 | 0.1497 | 206.3333 | 350.1667 |
| Rno-miR-133b | 1.5794 | 0.2379 | 207.3333 | 348.0000 |
| Rno-miR-122* | 2.4618 | 0.4716 | 35.3333 | 379.1667 |
| Rno-miR-466d | 5.3503 | 0.2543 | 3.5000 | 125.1667 |
| Rno-miR-100 | 1.7910 | 0.2371 | 131.3333 | 245.3333 |
| Rno-miR-382* | 1.6467 | 0.3734 | 196.0000 | 308.6667 |
| Rno-miR-154 | 1.6600 | 0.0859 | 254.8333 | 443.5000 |
| Rno-miR-3596b | 2.3654 | 0.0479 | 4769.8333 | 11446.3333 |
Table 2.
Group 1 vs Group 2 1.5 fold down regulated miRNAs
| Name | Fold change | P-value | ForeGround-BackGround | |
|---|---|---|---|---|
|
| ||||
| Group 1 vs Group 2 | Group 1 vs Group 2 | Mean of Group 2 | Mean of Group 1 | |
| Rno-miR-200b | 0.3483 | 0.1571 | 177.5000 | 69.1667 |
| Rno-miR-30e | 0.4015 | 0.0291 | 350.1667 | 142.6667 |
| Rno-miR-665 | 0.4601 | 0.6049 | 24.3333 | 8.1667 |
| Rno-miR-330 | 0.2593 | 0.4208 | 506.6667 | 134.6667 |
| Rno-miR-466c* | 0.5133 | 0.5372 | 1263.6667 | 634.0000 |
| Rno-miR-29a | 0.3096 | 0.1740 | 3679.0000 | 1167.8333 |
| Rno-miR-425 | 0.4685 | 0.4351 | 660.5000 | 291.8333 |
| Rno-miR-125b-5p | 0.5454 | 0.2492 | 2974.1667 | 1652.3333 |
| Rno-miR-540 | 0.5276 | 0.2840 | 25.3333 | 25.6667 |
| Rno-miR-434 | 0.4180 | 0.3902 | 2510.6667 | 1071.3333 |
| Rno-miR-352 | 0.3318 | 0.3136 | 335.8333 | 110.1667 |
| Rno-miR-181a | 0.3047 | 0.4014 | 1490.0000 | 457.3333 |
| Rno-miR-30a | 0.2803 | 0.3533 | 2771.3333 | 793.1667 |
| Rno-miR-214* | 0.1826 | 0.0257 | 7.6667 | 3.8333 |
| Rno-miR-101a | 0.2092 | 0.1388 | 1368.8333 | 285.8333 |
| Rno-miR-23a | 0.4345 | 0.1547 | 202.0000 | 84.5000 |
| Rno-miR-34c | 0.5321 | 0.3468 | 542.5000 | 287.1667 |
| Rno-miR-341 | 0.4803 | 0.3416 | 971.3333 | 492.0000 |
| Rno-miR-3583-5p | 0.6639 | 0.3414 | 843.8333 | 591.6667 |
| Rno-miR-335 | 0.0466 | 0.1690 | 1906.6667 | 46.5000 |
| Rno-miR-135a | 0.1962 | 0.2150 | 293.1667 | 25.3333 |
| Rno-miR-329* | 0.6218 | 0.5528 | 191.1667 | 117.3333 |
| Rno-miR-144 | 0.5820 | 0.4766 | 306.3333 | 173.3333 |
| Rno-miR-200c | 0.3558 | 0.1318 | 1732.3333 | 614.5000 |
| Rno-miR-551b* | 0.2037 | 0.1200 | 1108.1667 | 219.0000 |
| Rno-miR-296 | 0.5130 | 0.4816 | 80.1667 | 81.6667 |
| Rno-miR-22 | 0.4079 | 0.1896 | 4353.6667 | 1814.3333 |
| Rno-miR-127* | 0.4520 | 0.0511 | 161.6667 | 71.3333 |
| Rno-miR-375 | 0.3724 | 0.0913 | 938.1667 | 348.1667 |
| Rno-miR-7a | 0.5096 | 0.2571 | 14681.1667 | 7672.8333 |
| Rno-miR-16 | 0.3363 | 0.1206 | 697.0000 | 232.8333 |
| Rno-miR-141 | 0.2726 | 0.2616 | 2722.6667 | 455.6667 |
| Rno-miR-22* | 0.4228 | 0.0253 | 46.8333 | 43.6667 |
| Rno-miR-339-5p | 0.6450 | 0.5270 | 314.5000 | 212.5000 |
| Rno-miR-30b-5p | 0.5224 | 0.1993 | 777.8333 | 426.3333 |
| Rno-miR-374 | 0.3231 | 0.1900 | 355.5000 | 123.0000 |
| Rno-miR-324-5p | 0.5221 | 0.1374 | 313.1667 | 163.1667 |
| Rno-miR-551b | 0.0159 | 0.1570 | 227.1667 | -4.3333 |
Table 3.
Group 1 vs Group 2 1.5 fold up regulated miRNAs
| Name | Fold change | P-value | ForeGround-BackGround | |
|---|---|---|---|---|
|
| ||||
| Group 1 vs Group 2 | Group 1 vs Group 2 | Mean of Group 2 | Mean of Group 1 | |
| Rno-miR-880 | 2.6260 | 0.0067 | 215.6667 | 572.8333 |
| Rno-miR-503 | 2.6714 | 0.0160 | 70.8333 | 385.5000 |
| Rno-miR-125a-3p | 2.2408 | 0.0131 | 153.0000 | 346.1667 |
| Rno-miR-3596b | 2.3654 | 0.0479 | 4769.8333 | 11446.3333 |
Table 4.
Group 1 vs Group 2 1.5 fold down regulated miRNAs
| Name | Fold change | P-value | ForeGround-BackGround | |
|---|---|---|---|---|
|
| ||||
| Group 1 vs Group 2 | Group 1 vs Group 2 | Mean of Group 2 | Mean of Group 1 | |
| Rno-miR-30e | 0.4015 | 0.0291 | 350.1667 | 142.6667 |
| Rno-miR-214* | 0.1826 | 0.0257 | 7.6667 | 3.8333 |
| Rno-miR-22* | 0.4228 | 0.0253 | 46.8333 | 43.6667 |
Figure 1.
The vertical lines correspond to 1.5-fold up and down, respectively, and the horizontal line represents a P-value of 0.05. So the red point in the plot represents the differentially expressed genes with statistically significance. The red refer to the significant differently expressed miRNAs.
Figure 2.
Each row represents a miRNA and each column represents a sample. The miRNA clustering tree is shown on the left, and the sample clustering tree appears at the top. The color scale shown at the top illustrates the relative expression level of a miRNA in the certain slide: red color represents a high relative expression level; green color represents a low relative expression levels.
Real-time RT- PCR for miRNA microarray validation
In order to confirm the correctness and the reliability of the microarray data, we finished the Stem-loop real-time RT-PCR for the 7 miRNAs. The result of real-time RT-PCR suggested that our microarray data are reliable (Figure 3).
Figure 3.
To verify and evaluate the reliability of the results from the microarray data, we selected 7 differentially expressed miRNAs for stem-loop real-time PCR assay. All the miRNAs were successfully detected by the real-time PCR, suggesting that the miRNAs identified by our microarray analysis were reliable for their existence. The expression levels determined by real-time PCR assay were quite consistent with those determined by microarray analysis.
Discussion
MiRNAs are small, approximately 22 nucleotide (nt), single-stranded non-coding RNA molecules. MiRNAs were first discovered in the nematode Caenorhabditis elegans in 1993 by Lee et al. [5] MiRNAs have been identified in a wide range of species, including bacteria, plants, animals, and even viruses [15,16]. MiRNAs have been shown to play an important role in regulating many fundamental biological processes, including cell proliferation, differentiation, apoptosis, cell adhesion, metabolism, cell migration, neurogenesis, stress resistance, and hemopoiesis [17].
Under normal circumstances, the mature miRNAs are involved in maintaining normal cell homeostasis by regulating the translation or stability of target gene mRNAs. Therefore, the abnormal expression of miRNAs may lead to the abnormal expression at the level of the corresponding target gene transcript [18]. Currently, the main mechanism of miRNAs is that miRNAs complementarily combine with the 3’UTR of the target gene by RNA-induced Gene Silencing Complexes (RISCs) [19]. MiRNAs perform the function that estimate regulate 10-30% of all protein coding genes in two ways [20]. The first mode is operative in plants, where in miRNAs bind to perfectly complementary base pairs on the target mRNA, thus inducing its cleavage [21,22]. The alternative and more common method involves the imperfect binding of miRNAs to partially complementary sites on the 3’ UTR of target mRNA leading to some degree of mRNA degradation and inhibition of protein translation [23].
In recent years, some scholars using target scan method predicted 400 miRNAs target genes, which are related to the mammalian development of 13 percent. And a plurality of experiments has shown that miRNAs play an important role in mammalian development. However, the studies of which role miRNAs play in the development of sexual function or how much they contribute to the pituitary function are not sufficient. This study detected the expressions of relevant miRNAs in rat’s pituitary by gene-chip. By the analysis of microarray data, it detected a total of 93 differentially expressed miRNAs between 21-day-old rats and 12-month-old rats, including 56 up regulated miRNAs and 38 down regulated miRNAs. These 7 miRNAs should be the main regulators between the 21-day-old rats and the 12-month-old rats. These miRNAs may play an important regulating role in the function of rat’s pituitary. And then this study utilized Target Combo to predicting the 7 miRNAs’ target genes. It is possible to work through these genes in the development of rat sexual function, and the mechanism will be elucidated by further experiments. In a word, this study identified a number of specific changes in the expression of miRNAs in rats by detecting the expression profile of miRNAs in Rat’s Pituitary. It was also preliminarily validated by PT-PCR, and all of that lay the foundation for elucidating the regulatory mechanisms of miRNAs in rat’s reproduction process.
Acknowledgements
National Key Technology R&D Program (2011BAD19B04), The National High-tech Research and Development Program (2013AA102505). National Natural Science Foundation of China (31030058, 31501954).
Disclosure of conflict of interest
None.
References
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