Next Article in Journal
Hyperhomocysteinemia Induced by Methionine Excess is Effectively Suppressed by Betaine in Geese
Previous Article in Journal
The Effect of Transitioning between Feeding Methods on the Gut Microbiota Dynamics of Yaks on the Qinghai–Tibet Plateau
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 10
Issue 9
10.3390/ani10091640
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of Concentrate Supplementation on the Expression Profile of miRNA in the Ovaries of Yak during Non-Breeding Season

by
Jianpeng Xie
1,
Qudratullah Kalwar
2,
Ping Yan
1,* and
Xian Guo
2,*
1
College of Animal Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
2
Key Laboratory of Yak Breeding Engineering, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China
*
Authors to whom correspondence should be addressed.
Animals 2020, 10(9), 1640; https://doi.org/10.3390/ani10091640
Submission received: 20 August 2020 / Revised: 4 September 2020 / Accepted: 7 September 2020 / Published: 11 September 2020
(This article belongs to the Section Animal Reproduction)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

Yak (Bos grunniens) is an important and remarkable livestock species that survives in the challenging environment of the Qinghai–Tibetan Plateau. However, its growth rate is slower and reproductive ability is generally lower than cattle. This may be due to the yak living in high altitudes all year round where in the whole year, grasses are only available in July, August, and September (warm season), and from November to the next year of May (cold season), there is a scarcity of pastures. So, the reproductive efficiency of yak is very low. Meanwhile, it has been reported that enhanced nutrition improves the reproductive efficiency of animals. Therefore, this study aimed to explore the effects of supplemental nutrition on the growth traits and reproductive performance of yaks in the cold season. In addition, miRNAs related to yak reproductive traits were screened by miRNA sequencing technology. This research might be helpful for improving the reproductive potential of yak during the non-breeding season.

Abstract

Yak (Bos grunniens) is an important and remarkable livestock species that survives in the challenging environment of the Qinghai–Tibetan Plateau. However, its growth rate is slower and reproductive potential is generally lower than cattle. Meanwhile, it has been reported that enhanced nutrition improves the reproductive efficiency of animals. The purpose of this study was to investigate the effect of concentrate supplementation on the miRNA expression profile in the ovaries of yak during the non-breeding season. The study displayed that non-breeding season supplementation significantly improved growth performance, serum biochemical indicators, and reproductive hormone concentrations in yaks. In this study, we also examined the differential expression analysis of miRNA in the ovaries of yak during non-breeding seasons using Illumina Hiseq sequencing technology. As a result, 51 differentially expressed miRNAs were found in the experimental group (CS) and control group (CON). Gene Ontology (go) and Kyoto Genome Encyclopedia (KEGG) analysis of target genes showed that beta-alanine metabolism; tryptophan metabolism; sphingolipid metabolism; alanine, aspartate and glutamate metabolism; and the inositol phosphate metabolism pathway attracted our attention. Based on qRT-PCR, seven miRNAs were assessed to verify the accuracy of the library database. We predicted and identified potential miRNA target genes, including LEP, KLF7, VEGFA, GNAQ, GTAT6, and CCND2. miRNA and corresponding target genes may regulate yaks’ seasonal reproduction through their nutritional status. This study will provide an experimental basis for improving the reproductive efficiency of yaks by supplementation in the non-breeding season.

1. Introduction

Yaks (Bos grunniens) are the only bovine animals known in the world that can survive at high elevations (average altitude of 3000 m), and they are generally scattered in the Qinghai–Tibet Plateau and its adjacent mountains or sub-alpine area [1,2]. Yaks can grow and adapt well to the alpine grassland atmosphere and breed liberally under tough plateau conditions (such as thin air, low temperature, and shortage of forage) [3]. Moreover, yaks are valuable livestock to the economy of the plateau region by providing meat, milk, and transport to the local herdsmen [4,5]. However, its growth rate is slower and reproductive ability is generally lower than cattle [6,7]. This may be due to the yak living in high altitudes all year round, where in the whole year, only grasses are available in July, August, and September, and from November to May, there is a scarcity of pastures. Therefore, the breeding rate and reproductive efficiency of yak are meager, with an average of 1 birth within 2 years, or 2 births in 3 years [8,9]. The ovary is the primary reproductive organ of female animals, and its main role is follicular development, maturation, ovulation, and synthesis and secretion of hormones to maintain normal physiological and reproductive functions [10]. Understanding the molecular regulation mechanism of the ovary, follicular development, and hormone secretion is of great significance for improving the economic benefits of livestock production.
MicroRNA is a type of endogenous noncoding small RNA of about 22 nt in length, and widely exist in eukaryotes and are highly conserved in evolution [11]. MicroRNA generally cause target mRNA degradation or translational disruption by acting on the 3 UTR of the target gene mRNA, thereby participating in the expression of gene regulation at the post-transcriptional level and playing an essential role in a series of physiological processes, such as differentiation, metabolism, cell proliferation, and apoptosis [12]. Previously, studies evaluated the miRNA expression profile of ovarian tissues of humans [13], mice [12], pigs [14], cattle [15], and sheep [16]. Several studies have shown that miRNAs are widely involved in follicular development, atresia, degradation, corpus luteum formation, and suggested that miRNAs are essential for ovarian function [17,18].
Additionally, earlier findings illustrated that supplementation during the cold season stimulates the hypothalamic-pituitary gonadal axis and enhances follicular development in prepubertal Tibetan sheep ewes on the Qinghai–Tibetan plateau [19]. Meanwhile, it has been reported that enhanced nutrition improves the reproductive efficiency of animals. Therefore, in this study, Illu-mina HiSeq sequencing technology was used to explore the influence of concentrate supplementation on the expression profiling of miRNA in the ovaries of yak during the non-breeding season. This research might help to improve the reproductive potential of yak during the non-breeding season.

2. Materials and Methods

2.1. Ethical Declaration

Before the start of the trial, all experimental animals were inspected and agreed by the institutional animal care and use committee of Lanzhou Institute of Husbandry and Pharmaceutical Sciences, China. All animals were cared for according to the guide for the care and use of laboratory animals, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, China. Additionally, the present exploration was approved by the animal management and ethics committee of the Lanzhou Institute of Animal Science and Veterinary Medicine, Chinese Academy of Agricultural Sciences (Permit No. SYXK-2019-005).

2.2. Animals and Management

In this study, 20 non-pregnant mature yaks that had a similar age were randomly chosen and allocated into two groups (n = 10 each group). The duration of the study was approximately 120 days (February to May, cold season), plus an additional adaptation period (14 days) for the yaks to familiarize themselves with the staff and the diet. In addition, all animals were labeled and weighed prior to the start of the trial. Moreover, control group (CON) yaks did not receive any supplementary food, and they were kept on natural grazing conditions while, in the experimental group (CS), each yak was kept on natural grazing. In addition, they were also supplemented with 0.5 kg of concentrated supplement in the morning (08:00–09:00) and afternoon (18:00–19:00), for a total of 1 kg per day. The constituents and nutritional composition of the concentrated supplement and hay are shown in (Table 1).

2.3. Sample Collection

At the beginning and end of the cold season supplementary feed experiment, blood was collected from the jugular vein of 20 yaks, and serum was isolated for blood biochemical and reproductive hormone detection. Before the supplementary feed experiment, the selected animals were all about 6 years old and about the same weight. In addition, 3 yaks were selected from the CS and 3 yaks were selected from the CON and executed painlessly. Ovarian tissue samples were collected from 6 yaks for sequencing of miRNA, with 3 yaks were selected from the CS and 3 yaks from the CON. After ovarian sample collection of each animal, they were kept in liquid nitrogen for transportation and finally stored at −80 ℃.

2.4. Blood Biochemistry and Hormonal Concentration

The concentrations of CP (crude protein), EE (crude fat), CF (crude fiber), ash (crude ash), P (phosphorus), and Ca (calcium) in the diet were quantified by feed analysis and feed quality testing techniques. On the 0th and 120th day of the trial period, each yak was weighed in the morning prior to feeding, and the the ADG (average daily weight gain) calculated as follows:
Average Daily Gain (ADG) = (Final Weight − Initial Weight)/Days.
Albumin (ALB), total protein (TP), serum blood glucose (GLU), triglycerides (TRIG), and cholesterol (CHOL) were measured using a fully automatic bioanalysis machine. Serum follicle-stimulating hormone (FSH), gonadotropin-releasing hormone (GnRH), estrogen (E2), progesterone (PROG), and luteinizing hormone (LH) concentrations were established using ELISA kits and calibrated against standard curves using a micro plate reader.

2.5. Extraction of RNA and Sequencing of Small RNA

Total RNA was isolated from the ovaries of concentrate supplement group (CS) yaks and from control group (CON) yaks, by using Trizol reagent (Takara Biotechnology, Dalian, China). Afterward, low-molecular-weight RNA was isolated by using 1% agarose gel electrophoresis (PAGE) and enriched for RNA molecules in the range of 18–30 nt. The product was ligated to the 3′ and 5′ ends of the optimal temperature with a proprietary adaptor and amplified by RT qPCR. Later, sufficient product was obtained by PCR and tested by using the Agilent 2100 bioanalyzer and the ABI Step One Plus Real-time PCR System and sequencing using Illumina HiSeq (Biomarker Technologies, Beijing, China), which was tested in tissues collected from both (CS and CON) groups.

2.6. Analysis through Nioinformatics for Sequencing of Small RNA

The filtered sRNA sequences were mapped to the yak genome map by SOAP or bowtie software, and the expression of these sRNAs and their dissemination on the genome were analyzed and then passed through the GenBank database (http://www.ncbi.nlm.nih.gov/genbank/) and Rfam Database (http://rfam.xfam.org/), which screens and removes related sequences, such as repeating ribosomal RNA (rRNA), small interfer RNA (siRNA), small nucleolar RNA (snoRNA), nuclear small RNA (snRNA), and transport RNA (tRNA). The filtered sequence was then followed by miRBase (version 22.0) (http://www.mirbase.org/) to compare and identify identified miRNAs, and predict new miRNAs with mirdeep software (Versionv2.0.5).

2.7. Quantification and Differential Analysis of MicroRNA

The miRNA expression levels in each sample were counted and normalized by the TPM algorithm [20]. The TPM normalized treatment formula is as follows: TPM = Readcount * 1,000,000/over over Mappedreads. DESeq R software package was used for differential expression analysis. In the detection of differentially expressed miRNA, |log2 (FC)| ≥ 1.00 and p-value ≤ 0.05 were used as screening criteria.

2.8. Target Gene Prediction, Gene Ontology, and Pathway Analysis

Analysis of the pathways could contribute to understanding the biological functions of genes. The KEGG pathway examination was directed at the prediction results of target genes (herein after referred to as “candidate target genes”). In vivo, different genes are mutually coordinated to make their biological functions. The pathway analysis provides further insight into the biologicalfunctions of the gene. In addition, KEGG is the central public database of pathways, and the paths are categorized by significant enrichment analysis. The paths in KEGG are units, and hypergeometric tests are applied to find pathways that are significantly enriched in candidate target genes compared to the entire reference gene. We used target gene prediction for differentially expressed miRNAs, prediction with target scan and RNAhybrid software, and intersection or union as prediction. The gene ontology (GO) analysis of the potential target genes was based on the terms of the Gene Ontology database (http://www.geneontology.org/) by detecting the gene function-related biology process and putting the genes with similar functions together.

2.9. Validation of miRNA Expression by Quantitative Real-Time PCR

From the microRNA library, we randomly selected 7 miRNAs, and U6 was used as the internal reference gene. Afterwards, qRT-PCR was performed for expression analyses of miRNA from the ovaries of yak. The reverse primer was universal primer provided with the Mir-X™ miRNA First Strand Synthesis (Takara, Dalian, China) while the forward primers were designed based on the mature miRNA sequences (Table 2), which were obtained from miRBase (http://www.mirbase.org/), and all reactions were performed in 3 replications and the quantitative expression level of each target gene was calculated by using the threshold cycle 2−ΔΔCt method [21].

2.10. Statistical Analysis

SPSS version 21.0 software (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. The results of body weight and blood biochemical indexes were expressed as mean ± SEM. qRT-PCR verification results were expressed as mean ± SD. An independent t-test was used for the comparison of groups. Differences were considered significant when p < 0.05.

3. Results

3.1. Effect of Mixed Diet Supplementation during the Cold Season on the Growth Performance of Yaks

The effects of a mixed formula diet during the cold season on the growth of yaks are presented in Table 3. At the start and end of the experiment, each yak was weighed. Weight gain in the CS was significantly higher (p < 0.05) than in the CON after mixed diet supplementation during the cold season.

3.2. Effect of Mix Diet Supplementation during the Non-Breeding Season on Serum Biochemical Parameters of the Yaks

The serum biochemical parameters of the yak were also evaluated at the start and end of the study. The concentrations of serum biochemical parameters, such as, GLU, TP, ALB, TRIG, and CHOL, were significantly (p < 0.01) higher in the CS than the CON (Table 4).

3.3. Effect of Cold Season Mix Diet Supplementation on Serum Reproductive Hormone of Yaks

The concentrations of reproductive hormones in the serum of yaks were measured at the start and after completion of the study. The results revealed that the concentrations of GnRH, FSH, LH, E2, and P in the serum of the CS were significantly higher than those in the CON (Table 5).

3.4. Solexa Sequencing of Ovary Small RNAs

This study was designed to understand the expression patterns of small RNA in the yak ovary during the non-breeding season following different nutrient levels, through small RNA construction library analysis. Therefore, two miRNA libraries were constructed from six yaks’ ovary samples, including concentrate supplementation (CS) yaks with an enhanced forage nutrient status and CON yaks with a lower forage nutrient status. The two libraries obtained contained 52,727,701 and 41,208,552 clean reads, respectively (Table S1). Sequence length analysis indicated that the majority of the length was 21–23 nt (Figure 1). The Bowtie software was used to sequence clean reads with the Repbase database(http://www.girinst.org/repbase/), Silva database (http://www.arb-silva.de/), Rfam database(http://rfam.xfam.org/), and GtRNAdb database(http://lowelab.ucsc.edu/GtRNAdb/), respectively, filtering snRNA, snoRNA, rRNA, and tRNA, and a repeat sequence obtained unannotated reads containing miRNA. The sRNA annotation classification statistics are shown in Table 6 and Table S2.

3.5. Identification of Differentially Expressed miRNAs in the Ovary

Sequence alignment and subsequent exploration were achieved using the specified Bos_mutus.v2.0 (http://www.ncbi.nlm.nih.gov/genome/?term=yak) as the reference genome. The Bowtie software (Version v1.0.0, Johns Hopkins University, Baltimore, MD, USA) was used to sequence the unannotated reads with the reference genome to obtain the positional information on the reference genome, which is mapped reads. For the biological characteristics of miRNA, we used the miRDeep2 software package to compare the reads on the reference genome with the known miRNA precursor sequences in the miRBase database (http://www.mirbase.org/) to identify the expression of known miRNAs. After successive filtering of these data sets, we identified a total of 1074 miRNAs (670 known miRNA and 404 novels miRNA) in the libraries (Table S3). Amongst these miRNAs, 51 differentially expressed miRNAs were found, in which 25 miRNAs were downregulated and 26 miRNAs upregulated in the experimental group vs. the control group (Figure 2). The MA diagram provides an intuitive view of the overall distribution of the expression levels and differential multiples of the two groups (Figure 3). Systematic cluster analysis was performed to analyze the similarity of miRNAs with different expressions in the two groups of ovaries. The heat map shows the aggregation of CS and CON due to their similar expression profiles (Figure 4).

3.6. Validation of miRNA Expression with qRT-PCR

We performed qRT-PCR to detect the expression levels of the differentially expressed miRNAs and to confirm the reliability of the sequencing data (Figure 5). For this, we selected seven known miRNAs confirmed in the CS and CON group ovarian samples of yak. The relative expression levels of the seven selected miRNAs indicated that miR-449a, miR-205, and miR-200a were significantly upregulated in the CS than the CON group (p < 0.05), while miR-483, miR-200b, miR-223, and miR-2431-5p were downregulated in the CS group (p < 0.05). The relative expression levels of the seven differentially expressed miRNAs were consistent with the RNA sequencing results. It showed that the sequencing results were true and reliable.

3.7. miRNA Target Gene Prediction, GO Enrichment, and KEGG Pathway Analysis

Target gene prediction was implemented using miRNA and target scan based on newly predicted miRNAs, known miRNAs, and gene sequence evidence of corresponding species. After sequencing, 670 known miRNAs were found, but 418 known miRNAs were able to predict target genes. The remaining known miRNA did not predict target genes. These known miRNAs predicted a total of 12,485 target genes. In addition, GO enrichment analysis was used on the target genes of differentially expressed miRNAs. The differentially expressed miRNAs were enriched in 117 GO terms (p < 0.01), including 26 molecular functions (MFs), 20 cell component (CC), and 71 biological processes (BP) in the CS vs. the CON group (Table S4). Meanwhile, the KEGG pathway analysis of differentially expressed miRNA target genes help us to understand the biological functions of genes. The differentially expressed miRNAs target genes had no significant enriched signal pathway in CS and CON (p > 0.05). Although these signaling pathways were not significant, the top 20 signal pathways contain some metabolic pathways that may be indirectly related to yak reproduction (Table S5). These signaling pathways include beta-alanine metabolism, tryptophan metabolism; sphingolipid metabolism; alanine, aspartate, and glutamate metabolism; inositol phosphate metabolism, etc. (Figure 6). We also identified some key reproduction-related genes, such as LEP, NOTCH1, KLF7, VEGFA, GNAQ, GTAT6, and CCND2 (Table 7).

4. Discussion

The blood biochemistry index can reflect the change of organism metabolism to a certain extent. The concentration of total protein and albumin in serum is closely related to protein metabolism of the body, which can reflect the protein level in the animal’s diet and the degree of digestion and absorption of protein. The results showed that the concentration of total protein and albumin in yak serum in the supplementary group was significantly higher than that in the control group. This indicated that the supplementation of concentrate increased the intake of yak protein, so the synthesis of protein in vivo increased, and the concentration of total serum protein and albumin increased. This is consistent with published research [22,23,24]. Serum triglyceride levels are closely related to fat deposition and energy metabolism in animals, which can be broken down for energy and converted to adipose tissue for storage. After the end of the experiment, the serum triglyceride content of the supplement group was significantly higher than that of the control group. The results showed that the supplementary feed increased the intake of nutrients and provided a substrate for lipid synthesis in yaks, which was beneficial to the fat deposition of the growing yaks. Blood sugar is an important component of the body and an important source of energy. The body needs a lot of sugar every day to provide energy for the normal operation of various tissues and organs to provide power. Cholesterol is an indispensable and important substance for animal tissue cells. It is not only involved in the formation of cell membranes but also the raw material for the synthesis of bile acids, vitamin D, and steroids. The results showed that the serum glucose and cholesterol levels of the supplement group were significantly higher than those of the control group.
For female reproduction, appropriate nutrition is an essential factor at all stages [25]. Under adequate nutrient conditions, follicular growth and the ovulation rate increased magnificently in livestock while these were reduced during malnutrition [26]. Meza-Herrera et al. [27] illustrated that additional supplementation in the diet improves the reproductive potential of animals. Similarly, some other findings displayed that nutritional supplementation influenced the serum biochemical parameters [28,29,30]. The current findings are also in agreement with the above findings and our results revealed that concentrate supplementation enhances the biochemical parameters and concentration of reproductive hormones.
Yak is an endemic species in high-altitude areas, and it grazes all year round. Therefore, yaks have certain wild habits. Compared with dairy cattle, sheep, and other domestic animals, yak sampling and experimental data collection is difficult. Therefore, three yaks were selected in the experimental group and the control group, respectively. Then, the miRNA sequencing analysis was carried out. Ma X et al. [31] studied the transcriptome analysis of the molecular mechanism of yak longissimus dorsi in different development stages. Three biological repeats were selected for follow-up analysis in each group. LAN D et al. [32] studied the comparative transcriptome analysis of yak and yellow cattle ovaries during estrus. Three biological replicates in each group were selected for follow-up analysis. Therefore, three biological repeats in each group were statistically significant.
Yak is an endemic species in high-altitude areas, and it grazes all year round. Therefore, yaks have certain wild habits. Compared with dairy cattle, sheep, and other domestic animals, yak sampling and experimental data collection is difficult. Therefore, three yaks were selected in the experimental group and the control group, respectively. Then, the miRNA sequencing analysis was carried out. Ma X et al. [31] studied the transcriptome analysis of the molecular mechanism of yak longissimus dorsi in different development stages. Three biological repeats were selected for follow-up analysis in each group. LAN D et al. [32] studied the comparative transcriptome analysis of yak and yellow cattle ovaries during estrus. Three biological replicates in each group were selected for follow-up analysis. Therefore, three biological repeats in each group were statistically significant.
Understanding the molecular regulation of miRNAs in the ovaries helps to explore the molecular mechanisms by which yak regulates reproduction during the non-breeding season. Many studies have shown that miRNA plays an essential role in almost all ovarian biological processes, including follicular development, follicular atresia, corpus luteum development, and degeneration [16]. This study used Solexa sequencing technology to compare the small RNA profiles of yak ovaries during the non-breeding season after concentrate supplementation. The objective was to understand the role of sRNA-mediated post-transcriptional regulation in seasonal reproduction. By obtaining miRNAs with significant differences, we found some miRNAs, such as bta-miR-483, bta-miR-449a, bta-miR-223, bta-miR-200b, bta-miR-200a, and bta-miR-2431-5p. Earlier findings illustrated that miR-483 plays a vital role in the development and regulation of livestock reproduction. Besides, another study presented that miR483 is a hormone-sensitive testis miRNA [33]. Gabriella Guelfi et al. [34] found that the reproductive efficiency of buffalo was affected by a long calving interval and late estrus. Therefore, the expression level of mir-200b in buffalo progesterone mature oocytes and pregnancy was evaluated. It was found that there were differences in the expression between pregnant and non-pregnant buffalo. Heng Yang et al. [35] explored the regulation of miRNA in sheep on seasonal reproduction and the nutritional status. It was found that mir-200b was related to the nutritional status and seasonal reproduction of sheep. Meanwhile, Sohel et al. [36] established 27 differentially expressed miRNAs, such as miR-223, between the mature oocytes and non-exosome part of follicular fluid.
In our study, RT-qPCR results showed that miR-483, miR-223, miR-200b, and miR-2431-5p were significantly downregulated in the experimental group as compared to the control group, but miR-449a, miR-205, and miR-200a were upregulated in the experimental group. These results indicate that miR-483, miR-200b, miR-2431-5p, miR-449a, miR-223, and miR-200a may be involved in the reproduction of yak through nutritional changes. In addition, the top 20 pathways, including beta-alanine metabolism; tryptophan metabolism; sphingolipid metabolism; alanine, aspartate, and glutamate metabolism; and inositol phosphate metabolism, have attracted our attention. Through KEGG pathway and software prediction, we screened the target genes of differentially expressed miRNA. Due to different nutritional levels, differentially expressed mir-483, mir-200b, mir-200a, mir-2431-5p, and mir-223 and their corresponding target genes, such as LEP, VEGFA, GNAQ, GTAT6, CCND2, and KLF7, may have potential regulatory effects on yak reproductive traits (Table 8).
Leptin may act as a metabolic signal, transmitting nutrients to the reproductive endocrine system. The hypothalamic-pituitary-gonadal axis (HPG) is activated when the body is suitable, which plays an important role in female animal estrus, fertilized egg implantation, early embryo development, pregnancy maintenance, fetal growth and development, and other reproductive activities [37,38]. Previous studies presented that leptin (LEP) controls the reproductive process at the periphery of the hypothalamic-pituitary and reproductive tissues (e.g., ovaries). Additionally, the leptin is expressed in the ovary, indicating that leptin and its receptors might have autocrine and paracrine effects [39]. Leptin may affect the process of steroidogenesis either indirectly or directly by modulating the actions of metabolic hormones, for example, IGF-I GH and insulin [40]. Meanwhile, another study displayed that leptin was associated with a shorter interval of estrus and a higher concentration of leptin was associated with it, demonstrating the relationship between leptin and estrus expression [41]. Additionally, Meikle et al. [42] displayed that in primiparous lean cows, the ovarian cycle started late, with longer intervals from birth to first conception, and the endocrine signal that most likely represents the negative energy balance of the reproductive axis is leptin. In addition, another study illustrated that leptin has different regulatory effects on the gene expression of oocytes and cumulus cells. Additionally, leptin promotes oocyte maturation and development through mechanisms independent of cumulus cells and dependent mechanisms [43]. In our study, mir-483 was upregulated in the CS group, and LEP was the key target gene predicted by mir-483. These results suggest that mir-483 targeting LEP may be involved in the reproductive process of yak.
FSH and LH are the most famous endocrine regulatory factors, which mainly promote the development and growth of follicles. However, the normal occurrence of this process also needs the participation of other factors, including vascular endothelial growth factor A (VEGFA) [44]. VEGFA is a member of the VEGF family of cysteine knot growth factors. Its main function is to stimulate the mitosis of endothelial cells. In the ovary, VEGFA was expressed in both granulosa cells and follicles as ovulation approached, and the expression of VEGFA in granulosa cells was significantly increased [45]. Meanwhile, some evidence shows that VEGFA plays an important role in follicular survival and inhibition of granulosa cell apoptosis [46]. The researchers injected VEGFA into the ovaries to promote the development of follicles [47]. The results of Gao et al. showed that the expression of VEGFA reached a peak in tertiary-generation follicles during the follicular development stage, mainly distributed in the capsule layer and the inner granular layer. During follicular development, VEGFA mRNA was mainly expressed in the inner layer of granules [48]. GNAQ is a member of G protein-coupled receptor superfamily. As an important G protein-coupled receptor, GNAQ promotes glucose-induced insulin release and participates in the process of nutritional metabolism [49,50]. In addition, GNAQ can also affect the ovary and increase the size of ovarian follicles during ovulation. GNAQ affects follicular growth and ovarian atresia by inhibiting PI3K-Akt signaling pathway [51,52]. Heng Yang et al. found that the effect of mir-200b differentially expressed in sheep and target gene GNAQ had a regulatory effect on the seasonal reproduction and nutritional status of sheep [35]. In our study, mir-200b expression was downregulated in the CS group, and VEGFA and GNAQ were key target genes predicted by mir-200b. These results suggest that mir-200b targeting VEGFA and GNAQ may be involved in yak reproductive process.
GATA6 is a member of the GATA family. It plays an important role in the proliferation, differentiation, and apoptosis of ovarian cells. In the mouse ovary, GATA6 mRNA was detected in granulosa cells and strongly expressed in the corpus luteum but not in membrane cells [53]. In the human ovary, GATA6 mRNA was located in the granulosa cells and membrane cells of pre sinus and pre sinus follicles [54]. GATA-6 protein was found in granulosa cells of the pig ovary [55]. Cyclins D (CCNDs) include three subtypes: CCND1/CCND 2, and CCND3. In the ovary, CCND 2 is mainly expressed in granulosa cells. As one of the downstream factors of follicle-stimulating hormone (FSH), CCND 2 promotes the proliferation of granulosa cells during follicular development [56]. In different stages of bovine follicular development, the expression level of CCND 2 in granulosa cells of the active estrogen stage and preovulatory follicle was high, which was closely related to the proliferation of granulosa cells [57]. Through positive regulation of CCND 2 expression, it can promote the cell cycle process and accelerate the proliferation of granulosa cells [58]. In our study, mir-200a was upregulated and mir-2431-5p was downregulated in the CS group. GTAT6 and CCND2 are key target genes predicted by mir-200a and mir-2431-5p. These results suggest that mir-200a and mir-2431-5p targeting GTAT6 and CCND 2 may participate in the reproductive process of yak at reproductive age.
Besides, previous studies stated that KLF7 plays an essential role in cell proliferation, differentiation, and other processes, and participates in physiological processes, such as embryo development and glucose metabolism [59,60]. Whereas, genetic correlation analysis indicated that KLF7 is a candidate gene for human obesity, which inhibits the differentiation of adipose precursor cells and downregulates many genes involved in adipose differentiation [61]. Overexpression of KLF7 in already differentiated adipocytes inhibits the expression of adipocytokines, such as adiponectin [62]. In our study, KLF7 is a key target gene predicted by mir-223. The expression of mir-223 was upregulated in the CS group. These results suggest that mir-223 targeting KLF7 may be involved in the yak reproductive process.

5. Conclusions

In summary, our findings illustrated that non-breeding season supplementation significantly improved growth performance, serum biochemical indicators, and reproductive hormone concentrations in yaks. In addition, we found six differentially expressed miRNAs (mir-483, mir-449a, mir-223, mir-200b, mir-200a, mir-2431-5p). Through target gene prediction, GO enrichment, and KEGG pathway analysis, we identified that target genes corresponding to the potential miRNA may regulate the seasonal reproduction of yaks and be related to nutritional status. This study provides a new reference for the identification of new genetic markers in yaks during the non-breeding period.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-2615/10/9/1640/s1, Table S1: Summery of small RNA sequence, Table S2: sRNA classification annotation statistics, Table S3: All miRNA expression profiles, Table S4: Enrichment of differentially expressed miRNAs by topGO, Table S5: Top 20 signal pathway results.

Author Contributions

J.X. and X.G. conceptualization and designed the experiments; J.X. analyzed the data; X.G. Methodology and data curation; J.X. and Q.K. wrote the manuscript; P.Y. supervision and approved the final draft of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study: including experiments design, samples collection, data analysis and manuscript writing were funded by the China Agriculture Research System (CARS–37).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Qiang, Q.; Guojie, Z.; Tao, M.; Wubin, Q.; Junyi, W.; Zhiqiang, Y.; Changchang, C.; Quanjun, H.; Jaebum, K.; Larkin, D.M. The yak genome and adaptation to life at high altitude. Nat. Genet. 2012, 44, 946–949. [Google Scholar] [CrossRef] [Green Version]
  2. Xiong, X.; Fu, M.; Lan, D.; Li, J.; Zi, X.; Zhong, J. Yak response to high-altitude hypoxic stress by altering mRNA expression and DNA methylation of hypoxia-inducible factors. Anim. Biotechnol. 2015, 26, 222–229. [Google Scholar] [CrossRef] [PubMed]
  3. Miao, F.; Guo, Z.; Xue, R.; Wang, X.; Shen, Y. Effects of grazing and precipitation on herbage biomass, herbage nutritive value, and yak performance in an alpine meadow on the qinghai-tibetan plateau. PLoS ONE 2015, 10, e0127275. [Google Scholar] [CrossRef] [PubMed]
  4. Guo, X.; Long, R.; Kreuzer, M.; Ding, L.; Shang, Z.; Zhang, Y.; Yang, Y.; Cui, G. Importance of functional ingredients in yak milk-derived food on health of Tibetan nomads living under high-altitude stress: A review. Crit. Rev. Food Sci. Nutr. 2014, 54, 292–302. [Google Scholar] [CrossRef] [PubMed]
  5. Mei, F.; Yabing, C.; Xianrong, X.; Daoliang, L.; Jian, L. Establishment of mammary gland model in vitro: Culture and evaluation of a yak mammary epithelial cell line. PLoS ONE 2014, 9, e113669. [Google Scholar] [CrossRef] [Green Version]
  6. Long, R.J.; Zhang, D.G.; Wang, X.; Hu, Z.Z.; Dong, S.K. Effect of strategic feed supplementation on productive and reproductive performance in yak cows. Prev. Vet. Med. 1999, 38, 195–206. [Google Scholar] [CrossRef]
  7. Prakash, B.S.; Sarkar, M.; Mondal, M. An update on reproduction in yak and mithun. Reprod. Domest. Anim. 2010, 43, 217–223. [Google Scholar] [CrossRef]
  8. Fu, M.; Xiong, X.R.; Lan, D.L.; Li, J. Molecular characterization and tissue distribution of estrogen receptor genes in domestic yak. Asian-Australas. J. Anim. Sci. 2014, 27, 1684–1690. [Google Scholar] [CrossRef] [Green Version]
  9. Xiao, X.; Zi, X.D.; Niu, H.R.; Xiong, X.R.; Zhong, J.C.; Li, J.; Wang, L.; Wang, Y. Effect of addition of FSH, LH and proteasome inhibitor MG132 to in vitro maturation medium on the developmental competence of yak (Bos grunniens) oocytes. Reprod. Biol. Endocrinol. 2014, 12, 30. [Google Scholar] [CrossRef] [Green Version]
  10. Mcgee, E.; Hsueh, A. Initial and cyclic recruitment of ovarian follicles. Endocr. Rev. 2000, 21, 200–214. [Google Scholar] [CrossRef]
  11. Lagos-Quintana, M.; Rauhut, R.; Lendeckel, W.; Tuschl, T. Identification of novel genes coding for small expressed RNAs. Science 2001, 294, 853–858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Kim, V.N.; Han, J.; Siomi, M.C. Biogenesis of small RNAs in animals. Nat. Rev. Mol. Cell Biol. 2009, 10, 126–139. [Google Scholar] [CrossRef] [PubMed]
  13. Landgraf, P.; Rusu, M.; Sheridan, R.; Sewer, A.; Iovino, N.; Aravin, A.; Pfeffer, S.; Rice, A.; Kamphorst, A.O.; Landthaler, M.; et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 2007, 129, 1401–1414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Li, M.; Liu, Y.; Wang, T.; Guan, J.; Luo, Z.; Chen, H.; Wang, X.; Chen, L.; Ma, J.; Mu, Z.; et al. Repertoire of porcine microRNAs in adult ovary and testis by deep sequencing. Int. J. Biol. Sci. 2011, 7, 1045–1055. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Hossain, M.M.; Ghanem, N.; Hoelker, M.; Rings, F.; Phatsara, C.; Tholen, E.; Schellander, K.; Tesfaye, D. Identification and characterization of miRNAs expressed in the bovine ovary. BMC Genom. 2009, 10, 443. [Google Scholar] [CrossRef] [Green Version]
  16. McBride, D.; Carre, W.; Sontakke, S.D.; Hogg, C.O.; Law, A.; Donadeu, F.X.; Clinton, M. Identification of miRNAs associated with the follicular-luteal transition in the ruminant ovary. Reproduction 2012, 144, 221–233. [Google Scholar] [CrossRef] [Green Version]
  17. Otsuka, M.; Jing, Q.; Georgel, P.; New, L.; Chen, J.; Mols, J.; Kang, Y.J.; Jiang, Z.; Du, X.; Cook, R.; et al. Hypersusceptibility to vesicular stomatitis virus infection in Dicer1-deficient mice is due to impaired miR24 and miR93 expression. Immunity 2007, 27, 123–134. [Google Scholar] [CrossRef]
  18. Otsuka, M.; Zheng, M.; Hayashi, M.; Lee, J.D.; Yoshino, O.; Lin, S.; Han, J. Impaired microRNA processing causes corpus luteum insufficiency and infertility in mice. J. Clin. Investig. 2008, 118, 1944–1954. [Google Scholar] [CrossRef] [Green Version]
  19. Jing, X.; Peng, Q.; Hu, R.; Wang, H.; Yu, X.; Degen, A.; Zou, H.; Bao, S.; Zhao, S.; Wang, Z. Effect of supplements during the cold season on the reproductive system in prepubertal Tibetan sheep ewes. Anim. Sci. J. 2017, 88, 1269–1278. [Google Scholar] [CrossRef]
  20. Li, B.; Ruotti, V.; Stewart, R.M.; Thomson, J.A.; Dewey, C.N. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 2010, 26, 493–500. [Google Scholar] [CrossRef] [Green Version]
  21. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
  22. Roseler, D.K.; Ferguson, J.D.; Sniffen, C.J.; Herrema, J. Dietary Protein Degradability Effects on Plasma and Milk Urea Nitrogen and Milk Nonprotein Nitrogen in Holstein Cows. J. Dairy Sci. 1993, 76, 525–534. [Google Scholar] [CrossRef]
  23. Campanile, G.; Filippo, C.D.; Palo, R.D.; Taccone, W.; Zicarelli, L. Influence of dietary protein on urea levels in blood and milk of buffalo cows. Livest. Prod. Sci. 1998, 55, 135–143. [Google Scholar] [CrossRef]
  24. Law, R.A.; Young, F.J.; Patterson, D.C.; Kilpatrick, D.J.; Wylie, A.R.G.; Mayne, C.S. Effect of dietary protein content on animal production and blood metabolites of dairy cows during lactation. J. Dairy Sci. 2009, 92, 1001–1012. [Google Scholar] [CrossRef] [Green Version]
  25. Hazeleger, W.; Soede, N.M.; Kemp, B. The effect of feeding strategy during the pre-follicular phase on subsequent follicular development in the pig. Domest. Anim. Endocrinol. 2005, 29, 362–370. [Google Scholar] [CrossRef]
  26. Armstrong, D.G.; McEvoy, T.G.; Baxter, G.; Robinson, J.J.; Hogg, C.O.; Woad, K.J.; Webb, R.; Sinclair, K.D. Effect of dietary energy and protein on bovine follicular dynamics and embryo production in vitro: Associations with the ovarian insulin-like growth factor system. Biol. Reprod. 2001, 64, 1624–1632. [Google Scholar] [CrossRef] [PubMed]
  27. Meza-Herrera, C.A.; Hallford, D.M.; Ortiz, J.A.; Cuevas, R.A.; Sanchez, J.M.; Salinas, H.; Mellado, M.; Gonzalez-Bulnes, A. Body condition and protein supplementation positively affect periovulatory ovarian activity by non LH-mediated pathways in goats. Anim. Reprod. Sci. 2008, 106, 412–420. [Google Scholar] [CrossRef]
  28. O’Callaghan, D.; Yaakub, H.; Hyttel, P.; Spicer, L.J.; Boland, M.P. Effect of nutrition and superovulation on oocyte morphology, follicular fluid composition and systemic hormone concentrations in ewes. J. Reprod. Fertil. 2000, 118, 303–313. [Google Scholar] [CrossRef] [Green Version]
  29. Beam, S.W.; Butler, W.R. Effects of energy balance on follicular development and first ovulation in postpartum dairy cows. J. Reprod. Fertil. 1999, 54, 411–424. [Google Scholar] [CrossRef] [Green Version]
  30. Fitz-Rodríguez, G.; Santiago-Miramontes, M.A.D.; Scaramuzzi, R.J.; Malpaux, B.; Delgadillo, J.A. Nutritional supplementation improves ovulation and pregnancy rates in female goats managed under natural grazing conditions and exposed to the male effect. Anim. Reprod. Sci. 2009, 116, 85–94. [Google Scholar] [CrossRef]
  31. Ma, X.; Jia, C.; Chu, M.; Fu, D.; Lei, Q.; Ding, X.; Wu, X.; Guo, X.; Pei, J.; Bao, P.; et al. Transcriptome and DNA Methylation Analyses of the Molecular Mechanisms Underlying with Longissimus dorsi Muscles at Different Stages of Development in the Polled Yak. Genes 2019, 10, 970. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Dao-Liang, L.; Xian-Rong, X.; Zhi-Xin, C.; Yi, A.I.; Cai, H.; Jian, L.I. Comparative Transcriptome Analysis between Yak and Cattle Estrus Ovary. Chin. J. Anim. Vet. Sci. 2016, 47, 1830–1839. [Google Scholar] [CrossRef]
  33. Nicholls, P.K.; Harrison, C.A.; O’Donnell, L.; Stanton, P.G. 148. Hormonal regulation of miRNA in the tistis. Reprod. Fertil. Dev. 2009, 21, 66. [Google Scholar] [CrossRef]
  34. Guelfi, G.; Stefanetti, V.; De Luca, S.; Giontella, A.; Barile, V.L.; Barbato, O. Serum microRNAs in buffalo cows: Potential biomarkers of pregnancy. Res. Vet. Sci. 2017, 115, 294–300. [Google Scholar] [CrossRef] [PubMed]
  35. Yang, H.; Liu, X.; Hu, G.; Xie, Y.; Lin, S.; Zhao, Z.; Chen, J. Identification and analysis of microRNAs-mRNAs pairs associated with nutritional status in seasonal sheep. Biochem. Biophys. Res. Commun. 2018, 499, 321–327. [Google Scholar] [CrossRef]
  36. Sohel, M.M.H.; Salilew-Wondim, D.; Rings, F.; Schellander, K.; Tesfaye, D. 207 circulatory microRNA signatures in follicular fluid in relation to the growth status of bovine oocytes. Reprod. Fertil. Dev. 2012, 25, 251–252. [Google Scholar] [CrossRef]
  37. Paczoska-Eliasiewicz, H.E.; Proszkowiec-Weglarz, M.; Proudman, J.; Jacek, T.; Mika, M.; Sechman, A.; Rzasa, J.; Gertler, A. Exogenous leptin advances puberty in domestic hen. Domest. Anim. Endocrinol. 2006, 31, 211–226. [Google Scholar] [CrossRef]
  38. Seeber, R.M.; Smith, J.T.; Waddell, B.J. Plasma Leptin-Binding Activity and Hypothalamic Leptin Receptor Expression During Pregnancy and Lactation in the Rat. Biol. Reprod. 2002, 66, 1762–1767. [Google Scholar] [CrossRef] [Green Version]
  39. Sarkar, M.; Schilffarth, S.; Schams, D.; Meyer, H.H.; Berisha, B. The expression of leptin and its receptor during different physiological stages in the bovine ovary. Mol. Reprod. Dev. 2010, 77, 174–181. [Google Scholar] [CrossRef]
  40. Nicklin, L.T.; Robinson, R.S.; Marsters, P.; Campbell, B.K.; Mann, G.E.; Hunter, M.G. Leptin in the bovine corpus luteum: Receptor expression and effects on progesterone production. Mol. Reprod. Dev. 2007, 74, 724–729. [Google Scholar] [CrossRef]
  41. Wang, Y.H.; Xu, M.; Wang, F.N.; Yu, Z.P.; Yao, J.H.; Zan, L.S.; Yang, F.X. Effect of dietary starch on rumen and small intestine morphology and digesta pH in goats. Livest. Sci. 2009, 122, 48–52. [Google Scholar] [CrossRef]
  42. Meikle, A.; Kulcsar, M.; Chilliard, Y.; Febel, H.; Delavaud, C.; Cavestany, D.; Chilibroste, P. Effects of parity and body condition at parturition on endocrine and reproductive parameters of the cow. Reproduction 2004, 127, 727–737. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Paula-Lopes, F.F.; Boelhauve, M.; Habermann, F.A.; Sinowatz, F.; Wolf, E. Leptin promotes meiotic progression and developmental capacity of bovine oocytes via cumulus cell-independent and -dependent mechanisms. Biol. Reprod. 2007, 76, 532–541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Sirotkin, A.V. Growth factors controlling ovarian functions. J. Cell. Physiol. 2011, 226, 2222–2225. [Google Scholar] [CrossRef]
  45. Kaczmarek, M.M.; Schams, D.; Ziecik, A.J. Role of vascular endothelial growth factor in ovarian physiology—An overview. Reprod. Biol. 2005, 5, 111. [Google Scholar] [CrossRef]
  46. Kosaka, N.; Sudo, N.; Miyamoto, A.; Shimizu, T. Vascular endothelial growth factor (VEGF) suppresses ovarian granulosa cell apoptosis in vitro. Biochem. Biophys. Res. Commun. 2007, 363, 733–737. [Google Scholar] [CrossRef]
  47. Quintana, R.; Kopcow, L.; Sueldo, C.; Marconi, G.; Rueda, N.G.; Barañao, R.I. Direct injection of vascular endothelial growth factor into the ovary of mice promotes follicular development. Fertil. Steril. 2004, 82, 1101–1105. [Google Scholar] [CrossRef]
  48. Gao, X.; Zhang, J.; Pan, Z.; Li, Q.; Liu, H. The distribution and expression of vascular endothelial growth factor A (VEGFA) during follicular development and atresia in the pig. Reprod. Fertil. Dev. 2019, 32. [Google Scholar] [CrossRef]
  49. Mizuno, N.; Itoh, H. Functions and regulatory mechanisms of Gq-signaling pathways. Neuro-Signals 2009, 17, 42–54. [Google Scholar] [CrossRef] [PubMed]
  50. Mönkkönen, K.S.; Aflatoonian, R.; Lee, K.-F.; Yeung, W.S.B.; Tsao, S.-W.; Laitinen, J.T.; Fazeli, A. Hormonal regulation of Galphai2 and mPRalpha in immortalized human oviductal cell line OE-E6/E7. Mol. Hum. Reprod. 2007, 13, 845–851. [Google Scholar] [CrossRef] [Green Version]
  51. Sánchez-Fernández, G.; Cabezudo, S.; García-Hoz, C.; Benincá, C.; Aragay, A.M.; Mayor, F.; Ribas, C. Gαq signalling: The new and the old. Cell Signal. 2014, 26, 833–848. [Google Scholar] [CrossRef] [PubMed]
  52. Mishra, S.; Ling, H.; Grimm, M.; Zhang, T.; Bers, D.M.; Brown, J.H. Cardiac hypertrophy and heart failure development through Gq and CaM kinase II signaling. J. Cardiovasc. Pharmacol. 2010, 56, 598–603. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Markku, H.; Marina, E.; Malgorzata, B.; Rahman, N.A.; Naoko, N.; Huhtaniemi, I.T.; Tapanainen, J.S.; Wilson, D.B. Expression and hormonal regulation of transcription factors GATA-4 and GATA-6 in the mouse ovary. Endocrinology 1997, 138, 3505–3514. [Google Scholar] [CrossRef]
  54. Laitinen, M.P.; Anttonen, M.; Ketola, I.; Wilson, D.B.; Ritvos, O.; Butzow, R.; Heikinheimo, M. Transcription factors GATA-4 and GATA-6 and a GATA family cofactor, FOG-2, are expressed in human ovary and sex cord-derived ovarian tumors. J. Clin. Endocrinol. Metab. 2000, 85, 3476–3483. [Google Scholar] [CrossRef] [Green Version]
  55. Gillio-Meina, C. GATA-4 and GATA-6 transcription factors: Expression, immunohistochemical localization, and possible function in the porcine ovary. Biol. Reprod. 2003, 68, 412–422. [Google Scholar] [CrossRef] [Green Version]
  56. Sicinski, P.; Donaher, J.L.; Geng, Y.; Parker, S.B.; Gardner, H.; Park, M.Y.; Robker, R.L.; Richards, J.S.; McGinnis, L.K.; Biggers, J.D.; et al. Cyclin D2 is an FSH-responsive gene involved in gonadal cell proliferation and oncogenesis. Nature 1996, 384, 470–474. [Google Scholar] [CrossRef]
  57. Shimizu, T.; Hirai, Y.; Miyamoto, A. Expression of cyclins and cyclin-dependent kinase inhibitors in granulosa cells from bovine ovary. Reprod. Domest. Anim. 2013, 48, e65–e69. [Google Scholar] [CrossRef]
  58. Zhang, Q.; Sun, H.; Jiang, Y.; Ding, L.; Wu, S.; Fang, T.; Yan, G.; Hu, Y. MicroRNA-181a suppresses mouse granulosa cell proliferation by targeting activin receptor IIA. PLoS ONE 2013, 8, e59667. [Google Scholar] [CrossRef] [Green Version]
  59. Haldar, S.M.; Ibrahim, O.A.; Jain, M.K. Kruppel-like Factors (KLFs) in muscle biology. J. Mol. Cell. Cardiol. 2007, 43, 1–10. [Google Scholar] [CrossRef] [Green Version]
  60. Moore, D.L.; Apara, A.; Goldberg, J.L. Krüppel-like transcription factors in the nervous system: Novel players in neurite outgrowth and axon regeneration. Mol. Cell. Neurosci. 2011, 47, 233–243. [Google Scholar] [CrossRef] [Green Version]
  61. Kanazawa, A.; Kawamura, Y.; Sekine, A.; Iida, A.; Tsunoda, T.; Kashiwagi, A.; Tanaka, Y.; Babazono, T.; Matsuda, M.; Kawai, K. Single nucleotide polymorphisms in the gene encoding Krüppel-like factor 7 are associated with type 2 diabetes. Diabetologia 2005, 48, 1315. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Yoshihiro, K.; Yasushi, T.; Ryuzo, K.; Shiro, M. Overexpression of Kruppel-like factor 7 regulates adipocytokine gene expressions in human adipocytes and inhibits glucose-induced insulin secretion in pancreatic beta-cell line. Mol. Endocrinol. 2006, 20, 844–856. [Google Scholar] [CrossRef]
Animals 10 01640 g001 550
Figure 1. Length distribution of the sequences. (Different colors represent the distribution of miRNA length).
Figure 1. Length distribution of the sequences. (Different colors represent the distribution of miRNA length).
Animals 10 01640 g001
Animals 10 01640 g002 550
Figure 2. Volcano plot of the differentially expressed miRNAs in the CS and CON groups.
Figure 2. Volcano plot of the differentially expressed miRNAs in the CS and CON groups.
Animals 10 01640 g002
Animals 10 01640 g003 550
Figure 3. MA diagram of differentially expressed miRNA in the CS and CON groups.
Figure 3. MA diagram of differentially expressed miRNA in the CS and CON groups.
Animals 10 01640 g003
Animals 10 01640 g004 550
Figure 4. Heat map of the differentially expressed miRNAs in the CS and CON groups.
Figure 4. Heat map of the differentially expressed miRNAs in the CS and CON groups.
Animals 10 01640 g004
Animals 10 01640 g005a 550Animals 10 01640 g005b 550
Figure 5. Validation for the RNA sequencing data by qRT-PCR.RNA-Seq results (A): qRT-PCR results (B): The results are presented as the mean ± SD (p < 0.05).
Figure 5. Validation for the RNA sequencing data by qRT-PCR.RNA-Seq results (A): qRT-PCR results (B): The results are presented as the mean ± SD (p < 0.05).
Animals 10 01640 g005aAnimals 10 01640 g005b
Animals 10 01640 g006 550
Figure 6. KEGG pathways of differentially expressed miRNA target genes in the CS and CON groups.
Figure 6. KEGG pathways of differentially expressed miRNA target genes in the CS and CON groups.
Animals 10 01640 g006
Table
Table 1. Nutritional composition of concentrated feed mixture and hay (%).
Table 1. Nutritional composition of concentrated feed mixture and hay (%).
ConstituentHay (%)Concentrate Mixture (%)
Crude protein3.6516.74
Crude fat1.723.15
Crude fiber68.36.00
Crude ash7.535.27
Calcium0.540.84
Phosphorus0.470.50
Table
Table 2. MiRNA primers for quantitative real-time PCR.
Table 2. MiRNA primers for quantitative real-time PCR.
miRNAmiRNA Sequence (5′–3′)Forward Primer Sequence (5′–3′)
bta-miR-483UCACUCCUCUCCUCCCGUCUUTCACTCCTCTCCTCCCGTCTT
bta-miR-449aUGGCAGUGUAUUGUUAGCUGGUTGGCAGTGTATTGTTAGCTGGT
bta-miR-200bUAAUACUGCCUGGUAAUGAUGTAATACTGCCTGGTAATGATG
bta-miR-200aUAACACUGUCUGGUAACGAUGUUTAACACTGTCTGGTAACGATGTT
bta-miR-205UCCUUCAUUCCACCGGAGUCUGTCCTTCATTCCACCGGAGTCT
bta-miR-223UGUCAGUUUGUCAAAUACCCCATGTCAGTTTGTCAAATACCCCA
bta-miR-2431-5pCAGGUCAUAUAAGUGUGGAGUUCAGGTCATATAAGTGTGGAGTT
Table
Table 3. Effect of mixed diet supplementation during the cold season on the growth performance of the yaks.
Table 3. Effect of mixed diet supplementation during the cold season on the growth performance of the yaks.
ParameterGroupsSEMp-Value
CON (kg)CS (kg)
Initial Weight171.7 ± 24.83 a172.8 ± 31.32 a7.200.901
Final Weight170.4 ± 23.88 a202.9 ± 31.75 b9.570.037
Average Daily Gain−0.008 ± 0.01 a0.283 ± 0.11 b0.034≤0.01
a, b values within the row showing dissimilar superscript specify a significant difference at p < 0.01.
Table
Table 4. Effect of cold season mix diet supplementation on serum biochemical parameters of the yaks.
Table 4. Effect of cold season mix diet supplementation on serum biochemical parameters of the yaks.
ParameterDaysGroupsSEMp-Value
CONCS
GLU (mmol/L)02.84 ± 0.25 a2.87 ± 0.21 a0.060.761
1062.69 ± 0.28 a3.63 ± 0.31 b0.10≤0.01
TP (g/L)069.60 ± 4.37 a70.40 ± 3.27 a1.340.649
10666.50 ± 3.86 a84.00 ± 5.85 b1.85≤0.01
ALB (g/L)022.20 ± 1.03 a22.60 ± 1.65 b0.320.523
10621.00 ± 1.30 a32.80 ± 2.57 b0.55≤0.01
TRIG (mmol/L)00.15 ± 0.06 a0.13 ± 0.03 a0.020.483
1060.11 ± 0.03 a0.22 ± 0.06 b0.01≤0.01
CHOL (mmol/L)02.27 ± 0.37 a2.24 ± 0.31 a0.120.855
1062.11 ± 0.16 a3.59 ± 0.24 b0.08≤0.01
a, b values within the row showing dissimilar superscript specify significant difference, p < 0.01 glucose (GLU), total protein (TP), albumin (ALB), triglyceride (TRIG), cholesterol (CHOL).
Table
Table 5. Effect of cold season supplementation on serum reproductive hormone of the yaks.
Table 5. Effect of cold season supplementation on serum reproductive hormone of the yaks.
ParameterDaysGroupsSEMp-Value
CONCS
GnRH (mIU/mL)08.1875 ± 1.3287 a8.5225 ± 1.2386 a0.420.567
1067.8782 ± 1.8952 a15.9439 ± 2.8643 b0.59≤0.01
FSH (mIU/mL)01.1149 ± 0.2541 a1.1788 ± 0.1697 a0.080.517
1060.9870 ± 0.2471 a2.6079 ± 0.1806 b0.07≤0.01
LH (mIU/mL)01.0563 ± 0.2505 a1.1102 ± 0.2677 a0.070.648
1060.9431 ± 0.2780 a2.6328 ± 0.2877 b0.09≤0.01
E2 (pg/mL)010.2806 ± 4.1495 a10.9653 ± 4.2415 a0.680.719
1066.2496 ± 1.4354 a16.1687 ± 2.0088 b0.63≤0.01
P (pmol/L)0450.4571 ± 50.2930 a458.0429 ± 29.6429 a9.370.686
106471.1857 ± 44.5153 a715.0143 ± 780,343 b14.0≤0.01
a,b values within the row showing a dissimilar superscript specify a significant difference, p < 0.01. Gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), estrogen (E2), progesterone (PROG).
Table
Table 6. Distribution of small RNA among differently expressed categories in the library
Table 6. Distribution of small RNA among differently expressed categories in the library
TypesCSCON
NumberPercentageNumberPercentage
rRNA14250293.46%18000913.41%
snRNA1300.00%1420.00%
scRNA00.00%00.00%
snoRNA1451520.35%1125440.21%
tRNA893380.22%722600.14%
Repbase1316960.32%1741940.33%
Unannotated5902095.65%5056847095.91%
Total16362355100%52727701100%
CS: The experimental group; CON: Control group.
Table
Table 7. The matching results of the differential miRNA and the target gene were obtained by using Target Scan software.
Table 7. The matching results of the differential miRNA and the target gene were obtained by using Target Scan software.
MiRNA-mRNAPredicted Consequential Pairing of Target Region (Top) and miRNA (Bottom)Site TypeContext ScoreContext Score PercentileWeighted Context ScoreConserved Branch LengthPCT
Position 1903-1909 of LEP 3′ UTR
bta-miR-483		5′ ....AGUAGGGCAGAGGGCAGGAGUGU...
        | | | | | | | | | | |
3′   UUCUGCCCUCCUCUCCUCACU		7mer-m8−0.1686−0.160.748N/A
Position 190-196 of NOTCH1 3′ UTR
bta-miR-449a		5′ ...UAUUUUACAUGGAAACACUGCCU...
            | | | | | | |
3′  UGGUCGAUUGUUAUGUGACGGU		7mer-m8−0.4998−0.496.9340.79
Position 2688-2694 of KLF7 3′ UTR
bta-miR-223		5′ ...UUAAUUUAAUUUUUG-AACUGACC...
       | | | |    | | | | | | |
3′  ACCCCAUAAACUGUUUGACUGU		7mer-m8−0.2187−0.213.0820.33
Position 1328-1334 of VEGFA 3′ UTR
bta-miR-200b		5′ ...GAGUAGGGUUUUUUUCAGUAUUC...
             | | | | | | |
3′   GUAGUAAUGGUCCGUCAUAAU		7mer-m8−0.2395−0.194.1840.62
Position 4877-4883 of GNAQ 3′ UTR
bta-miR-200b		5′ ...AACAUUUUUAACUUGCAGUAUUU...
       | | |   | | | | | | |
3′  GUAGUAAUGGUCCGUCAUAAU		7mer-m8−0.1891−0.153.7660.53
Position 736-743 of GATA6 3′ UTR
bta-miR-200a		5′ ....UUGCGUUGCAGCAAUCAGUGUUA...
   | | | |       | | | | | | |
3′ .UUGUAGCAAUGGUCU--GUCACAAU		8mer−0.4399−0.4210.6340.86
Position5142-5148 of CCND2 3′ UTR
bta-miR-2431-5p		5′ ....ACAAUAAACUCACCUUGACCUAA...
            | | | | | | 
3′ UUGAGGUGUGAAUAUACUGGAC		7mer-A1−0.19910.000N/A
Table
Table 8. Reproduction-related target genes for differentially expressed miRNAs.
Table 8. Reproduction-related target genes for differentially expressed miRNAs.
miRNAsGenes
bta-miR-483LEP
bta-miR-449aNOTCH1
bta-miR-223KLF7
bta-miR-200bVEGFA
GNAQ
bta-miR-200aGTAT6
bta-miR-2431-5pCCND2

Share and Cite

MDPI and ACS Style

Xie, J.; Kalwar, Q.; Yan, P.; Guo, X. Effect of Concentrate Supplementation on the Expression Profile of miRNA in the Ovaries of Yak during Non-Breeding Season. Animals 2020, 10, 1640. https://doi.org/10.3390/ani10091640

AMA Style

Xie J, Kalwar Q, Yan P, Guo X. Effect of Concentrate Supplementation on the Expression Profile of miRNA in the Ovaries of Yak during Non-Breeding Season. Animals. 2020; 10(9):1640. https://doi.org/10.3390/ani10091640

Chicago/Turabian Style

Xie, Jianpeng, Qudratullah Kalwar, Ping Yan, and Xian Guo. 2020. "Effect of Concentrate Supplementation on the Expression Profile of miRNA in the Ovaries of Yak during Non-Breeding Season" Animals 10, no. 9: 1640. https://doi.org/10.3390/ani10091640

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop