International Journal of Genomics

International Journal of Genomics / 2016 / Article

Research Article | Open Access

Volume 2016 |Article ID 3472748 |

Pengfei Li, Jinzhu Meng, Wenzhong Liu, George W. Smith, Jianbo Yao, Lihua Lyu, "Transcriptome Analysis of Bovine Ovarian Follicles at Predeviation and Onset of Deviation Stages of a Follicular Wave", International Journal of Genomics, vol. 2016, Article ID 3472748, 9 pages, 2016.

Transcriptome Analysis of Bovine Ovarian Follicles at Predeviation and Onset of Deviation Stages of a Follicular Wave

Academic Editor: Ferenc Olasz
Received16 Dec 2015
Revised28 Feb 2016
Accepted29 Feb 2016
Published21 Mar 2016


For two libraries (PDF1 and ODF1) using Illumina sequencing 44,082,301 and 43,708,132 clean reads were obtained, respectively. After being mapped to the bovine RefSeq database, 15,533 genes were identified to be expressed in both types of follicles (cut-off RPKM > 0.5), of which 719 were highly expressed in bovine follicles (cut-off RPKM > 100). Furthermore, 83 genes were identified as being differentially expressed in ODF1 versus PDF1, where 42 genes were upregulated and 41 genes were downregulated. KEGG pathway analysis revealed two upregulated genes in ODF1 versus PDF1, CYP11A1, and CYP19A1, which are important genes in the steroid hormone biosynthesis pathway. This study represents the first investigation of transcriptome of bovine follicles at predeviation and onset of deviation stages and provides a foundation for future investigation of the regulatory mechanisms involved in follicular development in cattle.

1. Introduction

The ovarian follicle is an essential component of the reproductive process. It plays an important role in controlling the estrous cycle, determining estrous behaviour, ensuring oocyte competency and subsequent embryo survival rate, and determining both postovulation corpus luteum function and progesterone synthesis [1]. In a number of species, follicular growth is characterized by a wave-like pattern, with two or three waves occurring during the normal course of estrous cycles in cattle [2]. During each wave of follicular development, a cohort of antral follicles are induced to begin accelerated growth [3]. After a period of concurrent growth, a species specific number of follicles will then be selected to become dominant, while the remaining follicles will be lost through a process known as atresia. Diameter deviation is defined as the divergence in growth rates between the two largest follicles in a follicular wave [3]. The onset of diameter deviation occurs when the largest follicle reaches 8.5 mm in dairy cattle and marks initiation of divergence in growth rate and estradiol producing capacity between the F1 or largest (future dominant follicle) and F2 or second largest (future subordinate) growing follicles culminating the process of dominant follicle selection. While the exact mechanisms of dominant follicle selection are not completely understood, there have been many studies on the hormones and factors involved in follicular development. Antral follicles are dependent upon FSH for growth and each follicular wave is preceded by a transient rise in FSH concentrations [4]. Many growth factors linked to regulation of follicular development, such as inhibins, activins, and insulin-like growth factors and their binding proteins, have been identified in follicular fluid of individual bovine follicles [5]. These molecules can regulate follicular cell survival, proliferation, or death. Recent studies have attempted to understand the molecular regulation of follicular development in cattle [6, 7]. However, the molecular mechanisms governing the wave-like pattern of follicular development are incompletely described, particularly at the onset of diameter deviation which is the first morphological indication of follicular dominance.

Traditionally, gene expression studies in the field of follicular development focus on the study of expression of candidate genes of interest. With the development of next-generation sequencing technologies, transcriptome profiling has become a powerful approach for identification of genes globally expressed in various tissues including ovarian follicles [8]. In the present study, we performed RNA-Seq of granulosa cell RNA from bovine ovarian follicles at predeviation (PD) and onset of deviation (OD) stages of a follicular wave in cattle to catalog the transcriptome and identify potential differentially expressed genes associated with these key stages of a follicular wave. This study provides a comprehensive sequence resource for future studies on follicular development in cattle.

2. Materials and Methods

2.1. Materials

All materials were obtained from Sigma-Aldrich (St. Louis, MO) unless otherwise stated.

2.2. Animal Model and Sample Collection

All animal procedures were approved by the Institutional Animal Care and Use Committee at Michigan State University. Estrus was synchronized in nonlactating Holstein dairy cows with two injections of prostaglandin F2α (PGF2α; Prostamate; IVX Animal Health, St. Joseph, MO) administered 14 days apart, and follicular growth was observed and recorded by daily ultrasonography.

Ovaries were removed from cows at the following stages of the first follicular wave: predeviation (PD; approximately Day 3 after estrus; 1.5 days after emergence [emergence is the first scan where a new follicle at least 4 mm is detected by ovarian ultrasonography]) and onset of deviation (OD; first scan where growth of the F1 [largest; future dominant] follicle to >8.5 mm was detected by ovarian ultrasonography). The F1 follicles were isolated from the PD and OD groups. Granulosa cells were isolated from the two types of follicles (PDF1 and ODF1), lysed, and stored at −80°C immediately.

2.3. RNA Isolation

Total RNA was isolated from the lysed granulosa cells using the RNeasy mini kit (Qiagen) and DNase treated on column according to the manufacturer’s protocol. The RNA integrity was evaluated by Agilient Bioanalyser and the RNA concentration was measured using a Nanodrop-1000 spectrophotometer. RNA samples with a RNA integrity number greater than 8 were selected for deep sequencing.

2.4. Library Preparation and Illumina Sequencing

RNA sequencing was performed by the WM Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign. RNA samples from four follicles were pooled within each group (ODF1 or PDF1). RNA-Seq libraries were prepared with a TruSeq RNA Sample Preparation kit (Illumina) according to the manufacturer’s instructions. The cDNA libraries were sequenced on one lane for 100 cycles using Illumina HiSeq 2000 by a TruSeq SBS kit v5 (Illumina) and analyzed with pipeline version 1.8.

2.5. Identification of Differentially Expressed Genes and Pathway Analysis

The CLC Genomics Workbench (CLC bio, Aarhus, Denmark) was used to map the sequence reads to the bovine RefSeq database. The reads per kilobase per million reads (RPKM) values were calculated as the normalized transcript expression values [9]. A -test [10] was used to identify differentially expressed genes between ODF1 and PDF1 (FDR corrected value , RPKM cut-off > 0.5, and RPKM fold change >1.5) using the CLC genomics workbench. DAVID software ( was used to perform GO annotations and KEGG pathway analysis for highly expressed (RPKM > 100) and differentially expressed genes.

3. Results

3.1. Illumina Sequencing

To identify differentially expressed genes involved in bovine follicular development, Illumina sequencing was used on two libraries constructed from RNA isolated from ODF1 and PDF1 follicles. After filtering, a total of 44,082,301 and 43,708,132 clean reads were obtained from PDF1 and ODF1 libraries, respectively. The clean reads were mapped to the bovine RefSeq database (containing 35,325 annotated transcripts). Using a cut-off value of RPKM > 0.5, a total of 15,533 genes were identified in both types of follicles (Additional file 1: Table S1 in Supplementary Material available online at, among which 719 are considered to be highly expressed (RPKM cut-off > 100) in bovine follicles (Additional file 2: Table S2).

3.2. GO Functional Classification and KEGG Pathway Analysis of Highly Expressed Genes

The top 30 highly expressed genes in granulosa cells of bovine follicles at the predeviation and onset of deviation stages are shown in Table 1. Many of them are known to be important for follicular growth and development, such as Serpin peptidase inhibitor clade E member 2 (SERPINE2), Inhibin alpha (INHA), Inhibin beta A (INHBA), and Follistatin (FST). GO functional classification of these highly expressed genes was performed using DAVID software. All 719 highly expressed genes can be assigned into 22 groups under three categories (biological process, 39%; cellular component, 44%; and molecular function, 17%) based on their putative functions (Figure 1). Many of the highly expressed genes are involved in metabolic process, multicellular organismal process, and binding. KEGG pathway analysis showed that the highly expressed genes are involved in 12 major pathways (Figure 2), of which the most significantly enriched genes are involved in ribosome pathway.


Glutathione S-transferase alpha 312445.3217611.44
Serpin peptidase inhibitor clade E member 26816.84711075.44
Inhibin alpha6341.6159658.832
Inhibin beta A5824.4629440.244
Cytochrome c oxidase subunit I-like5017.2073707.555
Cytochrome c oxidase subunit III-like3508.9432440.522
Cytochrome c oxidase subunit I-like3252.122372.119
Milk fat globule-EGF factor 8 protein2439.6392077.699
Lysosomal protein transmembrane 4 beta1539.4532025.655
Gap junction protein alpha 11408.2311975.614
Cytochrome P450 family 19 subfamily A polypeptide 1656.87691944.887
Eukaryotic translation elongation factor 1 alpha 11864.1281924.407
Low density lipoprotein receptor-related protein 8 apolipoprotein E receptor1061.5421897.644
Enolase 11005.4471707.612
Heat shock protein 81615.281700.711
Ribosomal protein L18a1559.3551680.399
Glyceraldehydes 3 phosphate dehydrogenase1342.9631670.066
Ribosomal protein S27a1576.1311589.545
Ribosomal protein1627.7511566.558
ST3 beta-galactoside alpha-2,3-sialyltransferase 41096.9541542.973
Ribosomal protein L41443.7071502.241
Ribosomal protein S81563.2221490.917
Tribbles homolog 21411.4941461.935
Ribosomal protein S3A1409.5271450.511
Cytochrome P450, family 11, subfamily A, polypeptide 1908.97371425.869

3.3. Differentially Expressed Genes in ODF1 versus PDF1

Using RPKM cut-off > 0.5 and fold change cut-off > 1.5 at FDR corrected value , a total of 83 differentially expressed genes were identified, with 41 downregulated genes (Table 2) and 42 upregulated genes (Table 3) in ODF1 versus PDF1. To understand the functions of these differentially expressed genes, GO analysis was performed. The upregulated genes were categorized into 14 functional groups under 3 major GO classifications: biological process (35%), cellular component (30%), and molecular function (35%) (Table 4). Many of the differentially expressed genes are known to play a role in ovarian follicular development (Table 5). For example, serine protease 23 (PRSS23) is expressed in granulosa cells and may play a crucial role in follicular atresia, whereas serine protease 35 (PRSS35) is also expressed in granulosa cells and may be involved in ovulation and CL formation and regression. KEGG pathway analysis of the upregulated genes demonstrated that two important genes (CYP11A1 and CYP19A1) in the steroid hormone biosynthesis pathway are upregulated in ODF1 versus PDF1.

Gene symbolGenBank numberPDF1 RPKMODF1 RPKMFold changeFDR corrected valueGene product functions

ACTR1ANM_001193248.117.310.43−39.452.63 × 10−2Vesicle motility
LOC787803XM_002700116.145.662.15−20.793.41 × 10−7Unknown
PPP1R14AXM_002694966.168.293.99−16.767.75 × 10−11Protein phosphatase inhibitor
OLA1NM_001046045.122.711.74−12.791.10 × 10−2Hydrolase activity and GTP binding
QRFPRNM_001192681.119.151.55−12.074.40 × 10−2Modulate adenylate cyclase
RMRPNR_036646.1149.7913.57−10.82Lncrna class
LOC100335749XR_083021.135.264.58−7.548.45 × 10−4Senescence-associated protein-like
C11H2orf40NM_001038113.141.025.84−6.882.22 × 10−4Esophageal cancer
BOLANM_001040532.174.9311.19−6.561.01 × 10−8Transcription
ANGPT2NM_001098855.139.047.91−4.833.56 × 10−3Angiogenic signal
VNN1NM_001024556.233.517.26−4.521.94 × 10−2Amidohydrolase
KRT2XM_001254015.140.649.41−4.236.20 × 10−3Keratinocyte activation
IHHNM_001076870.242.849.98−4.213.93 × 10−3Smoothened
BOLANM_001038518.1101.226.48−3.746.16 × 10−8Transcription
LOC100140002XR_084188.138.5310.81−3.493.63 × 10−2Envelope glycoprotein-like
4-SepNM_001034651.169.7722.91−2.989.40 × 10−4Cytokinesis, platelet secretion
ITPR1NM_174841.261.7922.18−2.739.78 × 10−3Intracellular channel
PRSS35NM_001035457.3134.0555.07−2.381.21 × 10−5Ovulation, CL formation and regression
LOC100140226XM_001787664.21183.02532.17−2.18Zinc finger protein 347-like
LOC511901XM_589328.5102.2949.83−2.011.42 × 10−2H1 histone
LOC100137883XM_002706880.198.4849.64−1.943.47 × 10−2Thymosin beta-4-like
CDH2NM_001166492.1103.6853.3−1.913.44 × 10−2Neuronal recognition
APOA1NM_174242.3126.5666.22−1.871.07 × 10−2Activates spermatozoa motility
PAPSS2NM_001076075.1119.7565.23−1.83.68 × 10−2Skeletogenesis
LOC100299201XR_084007.1327.62178.8−1.791.28 × 10−7Ribosomal protein
GSTA5NM_001099016.1138.1480.02−1.694.93 × 10−2Glutathione transferase
AKR1B1NM_001012519.1182.74108.97−1.641.33 × 10−2Electron carrier activity
SLCO1A2NM_174654.2192.47119.34−1.582.65 × 10−2Mediates transport
HERC1NM_001103282.1222.29138.46−1.571.01 × 10−2Membrane trafficking
CWC25NM_001105359.1433.81274.72−1.557.12 × 10−6Alternatively spliced transcripts
ACOT11NM_001103275.1693.81440.12−1.546.15 × 10−10Acyl-Coa thioesterase activity
LOC615589NM_001098467.1245.33157.07−1.531.10 × 10−2Keratin-like protein
C12H13orf18NM_001102041.1269.27173.55−1.526.34 × 10−3Unknown

Gene symbolGenBank numberPDF1 RPKMODF1 RPKMFold changeFDR corrected valueGene product functions

GAPDHXM_001252511.31.9159.6531.919.83 × 10−11Microtubule and NAD binding
PPP1R14ANM_001193070.16.0665.7511.088.98 × 10−10Smooth muscle contraction
LOC100337308XM_002684003.13.7223.276.394.40 × 10−2Unknown
MT1ANM_001040492.226.50150.755.81Bind heavy metals
LOC100125916NM_001105487.114.8083.885.781.51 × 10−9Unknown
TNFAIP6NM_001007813.111.1746.614.261.06 × 10−3Cell-cell and cell-matrix interactions
BEX2NM_001077034.193.97345.003.75Mitochondrial apoptosis
GPR85NM_001075150.210.0635.543.614.05 × 10−2G-protein coupled receptor
PPM1KNM_001046474.134.75108.323.182.52 × 10−7Cellular survival and development
CYP19A1NM_174305.1656.881944.893.02Estrogen biosynthesis
MT1ENM_001114857.154.11152.162.872.61 × 10−9Bind heavy metals
ETNK2XM_002693881.122.3561.962.834.65 × 10−3Ethanolamine phosphorylation
CHST11NM_001192668.157.39154.722.756.77 × 10−9Biosynthesis chondroitin sulfate
MT2ANM_001075140.149.37130.492.704.16 × 10−7Bind heavy metals
PRSS23NM_001080306.158.01151.792.672.74 × 10−8Follicular atresia
TXNIPNM_001101875.298.64231.472.406.64 × 10−11Oxidative stress mediator
NPR3NM_174127.247.55106.352.285.93 × 10−4Natriuretic peptide hormone receptor
GREB1NM_001205631.135.1778.202.271.30 × 10−2Estrogen-stimulated cell proliferation
EIF4EBP1NM_001077893.1138.11294.072.171.30 × 10−11Mediates protein translation regulation
PIK3R1NM_174575.1113.42210.951.901.21 × 10−5Insulin actions metabolic
LRP8NM_001097565.11061.541897.641.82Sperm maturation
SCD5NM_001076945.1124.08210.971.744.25 × 10−4Energy metabolism
ENO1NM_174049.21005.451707.611.73Tumor suppressor
LDHANM_174099.2221.85366.731.693.41 × 10−7Affiliated with lncrna
SERPINE2NM_174669.26816.8511075.441.66Serine protease inhibitor
INHBANM_174363.25824.469440.241.65Regulate gonadal stromal cell proliferation
TMEM176BNM_001099145.1106.45170.391.632.16 × 10−2Dendritic cells maturation
OATNM_001034240.1395.42630.731.631.71 × 10−11Ornithine aminotransferase
CYP11A1NM_176644.2908.971425.871.60Cholesterol to pregnenolone
OBSL1XM_002685586.1221.33338.401.561.16 × 10−4Regulate ubiquitin ligase complex
ARFGAP3NM_001075974.1273.92418.221.565.26 × 10−6Gtpase-activating protein
ITGB5NM_174679.2153.24233.561.567.68 × 10−3Fibronectin receptor
INHANM_174094.36341.619658.831.55Gonadal hormone secretion
OPTNNM_001034602.1160.13243.401.555.92 × 10−3Affect cell death
PTGR1NM_001035281.1200.07301.661.549.40 × 10−4Inactivation of the chemotactic factor
STBD1XM_002688357.1143.24215.661.542.15 × 10−2Bind to carbohydrates
LOC532189XR_083049.1340.18510.181.535.69 × 10−7Carboxypeptidase
TMEM20NM_001076470.1191.10286.481.532.10 × 10−3Solute carrier
ECE1NM_181009.2461.24691.331.538.98 × 10−10Converts big endothelin-1 to endothelin-1
GNG10NM_001114512.1617.73918.261.527.59 × 10−13Signal transducer


Biological processHemoglobin biosynthetic processINHBA, INHA
Biological processAntigen processing and presentationLOC505676, LOC100125916, BOLA-N
Biological processInsulin receptor signaling pathwayEIF4EBP1, PIK3R1
Biological processAntigen processing and presentation of peptide antigenLOC100125916, BOLA-N
Biological processResponse to insulin stimulusEIF4EBP1, PIK3R1
Biological processCellular hormone metabolic processECE1, CYP11A1
Biological processCellular response to hormone stimulusEIF4EBP1, PIK3R1
Biological processRegulation of myeloid cell differentiationINHBA, PIK3R1
Cellular componentInhibin complexINHBA, INHA
Cellular componentMHC protein complexLOC505676, LOC100125916, BOLA-N
Cellular componentPlasma membraneLOC505676, ARFGAP3, ECE1, GNG10, ITGB5, INHA, LOC100125916, BOLA-N, ENO1
Molecular functionMetal ion bindingARFGAP3, PTGR1, ECE1, MT1A, CYP11A1, PPM1K, LRP8, SCD5, CYP19A1, ENO1
Molecular functionPeptidase activityECE1, SERPINE2, PRSS23, ENO1
Molecular functionIron ion bindingCYP11A1, SCD5, CYP19A1

Gene symbolFold changeFDR corrected valueUp- or downregulation in ODF1/PDF1Gene product functions

PPM1K3.182.52 × 10−7UpregulationCellular survival and development
BEX23.75UpregulationMitochondrial apoptosis
CYP19A13.02UpregulationEstrogen biosynthesis
PRSS232.672.74 × 10−8UpregulationFollicular atresia
GREB12.271.30 × 10−2UpregulationEstrogen-stimulated cell proliferation
SERPINE21.66UpregulationSerine protease inhibitor
INHBA1.65UpregulationRegulate gonadal stromal cell proliferation
CYP11A11.60UpregulationCholesterol to pregnenolone
INHA1.55UpregulationGonadal hormone secretion
TNFAIP64.261.06 × 10−3UpregulationCell-cell and cell-matrix interactions
OPTN1.555.92 × 10−3UpregulationAffect cell death
PRSS35−2.381.21 × 10−5DownregulationOvulation and CL formation and regression
APOA1−1.871.07 × 10−2DownregulationActivates spermatozoa motility
GSTA5−1.694.93 × 10−2DownregulationGlutathione transferase

4. Discussion

Follicular growth occurs in a characteristic wave-like pattern in monotocous species such as cattle [3, 5, 11]. A transient increase in FSH triggers initiation of each follicular wave [5, 11, 12]. Emergence is defined as the first day a new follicle >4 mm in diameter is detected and is the first chronological event marking a new follicular wave that is detectable by ultrasonography. After emergence, follicles in the cohort initially grow at a similar rate (common growth phase) prior to deviation [3]. However, the molecular mechanisms involved regulating the onset of deviation are not well understood, in order to characterize the differences in gene expression that associated with follicular development in different follicles sized in diameter, which the previous studies examined using microarray technology [1317]. To further investigate the bovine granulosa cell transcriptome and molecular alterations associated with onset of deviation, we examined the transcriptome at specific stages of the estrous cycle.

Illumina sequencing technology was used to determine gene expression levels in ODF1 and PDF1 follicles. A total of 15,533 genes were identified in both types of follicles and 83 of them were identified as differentially expressed between ODF1 and PDF1. Our study provided novel information on the bovine granulosa cell transcriptome and identified specific transcripts highly expressed in granulosa cells of bovine follicles prior to and at onset of deviation, including transcripts encoding for several housekeeping genes (e.g., ribosomal proteins L18a, S27a, and L4) and genes with well-established roles in regulation of ovarian function (e.g., INHBA, INHBB, and FST). Of particular interest was SERPINE2, which is abundantly expressed in granulosa cells of follicles collected at both the predeviation and onset of deviation stages of a follicular wave, illustrating its potential importance in bovine ovarian follicular development. Estradiol and SERPINE2 secretion are positively correlated, but estradiol treatment cannot alter the expression of SERPINE2. FSH and growth factors can directly regulate the expression and secretion of SERPINE2 in granulosa cells, and SERPINE2 is an antiapoptotic factor, which may regulate atresia in bovine follicles [18]. Eleven SERPINE genes are expressed in bovine follicles, but only SERPINE2, SERPINE5, and SERPINE6 are expressed in the granulosa cells [19].

KEGG analysis revealed upregulated genes associated with onset of deviation (CYP11A1 and CYP19A1) involved in the steroid hormone biosynthesis pathways that play an essential role during follicular development. Proteins encoded by CYP11A1 and CYP19A1 genes are members of the cytochrome P450 superfamily, which are monooxygenases that catalyze many reactions involved in steroidogenesis. Previous studies suggested that CYP19A1 was regulated by multiple pathways, including estrogen receptors and cAMP/protein kinase A which are activated by FSHR in granulosa cells, and these regulatory mechanisms are likely critical for acquisition of follicular dominance in cattle [20]. Our transcriptome sequencing data is consistent with these results. The increase in transcript abundance for CYP19A1 in ODF1 versus PDF1 follicles is consistent with the increase in estradiol producing capacity associated with diameter deviation [3].

It is acknowledged that study design was not optimal due to limited biological replication because single pooled samples ( per group) were used in Illumina sequencing analysis. Despite such limitations, results have significantly enhanced understanding of bovine follicle transcriptome composition and potential differences in gene expression associated with follicular development that are foundational to further study in the future; several interesting candidates were revealed for future investigation, particularly genes linked to regulation of cell proliferation and survival. For example, results of present studies suggest that PPM1K, a Mn2+/Mg2+-dependent protein phosphatase of PPM family, is potentially upregulated with the onset of deviation in the granulosa cell layer of bovine follicles. This protein is critical for cell survival and embryonic development and can regulate the mitochondrial membrane permeability transition pore opening [21]. Potential upregulation of granulosa cell BEX2 and GREB1 transcript abundance was also noted in association with onset of dominance and may be associated with enhanced granulosa cell survival. BEX2 can downregulate apoptosis and activate the JNK (Jun NH2-terminal kinase) pathway, and these effects can be abolished by administration of a JNK specific inhibitor [22]. GREB1 is an estrogen receptor and coactivator linked to cell proliferation and GREB1 expression is estrogen dependent. It is possible that increased expression of PPM1K, BEX2, and GREB1 may be associated with granulosa cell proliferation and survival during the onset of deviation [23]. TNFAIP6/TSG6 is tumor necrosis factor and alpha-induced protein 6; it is suggested that TSG-6 plays a role in cell-cell or cell-cell matrix interactions during inflammation and tumorigenesis. High LH/hCGR gene expression intensity was associated with TNFAIP6/TSG6 gene expression which has a pivotal importance in the mucification of the COC during the preovulatory period [24]. It is suggested that the expression levels of TNFAIP6/TSG6 were nearly 280-fold in granulosa cells of large follicles than that of small follicles [14]. In our study, TNFAIP6/TSG6 was also differentially expressed in ODF1 and PDF1 with a 4.26-fold change. Altogether, these characteristics suggest that TNFAIP6/TSG6 plays a crucial role in accelerating follicle growth during follicular waves in cattle.

5. Conclusions

The present study characterized the granulosa cell transcriptome of bovine follicles at specific stages of follicular development and identified 83 differentially expressed genes potentially associated with onset of deviation, many of which are linked to regulation of follicular development. The study provides a foundation for future studies to investigate regulation of granulosa cell expressed genes and the regulatory mechanisms controlling antral follicle development during follicular waves in cattle.

Competing Interests

The authors declare that they have no competing interests.


This study was supported by the Chinese Natural Science Foundation (Grant no. 31172211), Introduction of Advanced International Agricultural Science and Technology Plan Project Fund (Grant no. 2010-Z43) to Lihua Lyu, Shanxi Province Science and Technology Research Project (Grant no. 20130311027-2) and Shanxi Agriculture University Introduction of Doctor Scientific Research Startup Fund (Grant no. 2014ZZ04) to Pengfei Li. Agriculture, and Food Research Initiative Competitive Grant no. 2009-65203-05700 from the USDA National Institute of Food and Agriculture to George W. Smith and by Michigan AgBioResearch (George W. Smith). The authors thank Liying Qiao and Wenge Hu, technicians of the College of Animal Science and Technology, Shanxi Agricultural University, for their technical assistance.

Supplementary Materials

Table S1: Shows all the genes (15,533) expressed in both types of follicles (ODF1 and ODF2) with a cut-off RPKM of 0.5.

Table S2: Shows the highly expressed genes (719) in PDF1 and ODF1 follicles with a cut-off RPKM > 100.

  1. Supplementary Material


  1. M. G. Diskin, D. R. Mackey, J. F. Roche, and J. M. Sreenan, “Effects of nutrition and metabolic status on circulating hormones and ovarian follicle development in cattle,” Animal Reproduction Science, vol. 78, no. 3-4, pp. 345–370, 2003. View at: Publisher Site | Google Scholar
  2. A. C. O. Evans, “Characteristics of ovarian follicle development in domestic animals,” Reproduction in Domestic Animals, vol. 38, no. 4, pp. 240–246, 2003. View at: Publisher Site | Google Scholar
  3. M. A. Beg and O. J. Ginther, “Follicle selection in cattle and horses: role of intrafollicular factors,” Reproduction, vol. 132, no. 3, pp. 365–377, 2006. View at: Publisher Site | Google Scholar
  4. A. C. O. Evans and M. J. Canty, “Physiology of follicle development in cattle,” in Proceedings of the WBC Congress, vol. 23, pp. 11–16, 2004. View at: Google Scholar
  5. J. J. Ireland, M. Mihm, E. Austin, M. G. Diskin, and J. F. Roche, “Historical perspective of turnover of dominant follicles during the bovine estrous cycle: key concepts, studies, advancements, and terms,” Journal of Dairy Science, vol. 83, no. 7, pp. 1648–1658, 2000. View at: Publisher Site | Google Scholar
  6. A. C. O. Evans, J. L. H. Ireland, M. E. Winn et al., “Identification of genes involved in apoptosis and dominant follicle development during follicular waves in cattle,” Biology of Reproduction, vol. 70, no. 5, pp. 1475–1484, 2004. View at: Publisher Site | Google Scholar
  7. T. Fayad, V. Lévesque, J. Sirois, D. W. Silversides, and J. G. Lussier, “Gene expression profiling of differentially expressed genes in granulosa cells of bovine dominant follicles using suppression subtractive hybridization,” Biology of Reproduction, vol. 70, no. 2, pp. 523–533, 2004. View at: Publisher Site | Google Scholar
  8. L. Lv, F. Jimenez-Krassel, A. Sen et al., “Evidence supporting a role for cocaine- and amphetamine-regulated transcript (CARTPT) in control of granulosa cell estradiol production associated with dominant follicle selection in cattle,” Biology of Reproduction, vol. 81, no. 3, pp. 580–586, 2009. View at: Publisher Site | Google Scholar
  9. A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold, “Mapping and quantifying mammalian transcriptomes by RNA-Seq,” Nature Methods, vol. 5, no. 7, pp. 621–628, 2008. View at: Publisher Site | Google Scholar
  10. A. J. Kal, A. J. van Zonneveld, V. Benes et al., “Dynamics of gene expression revealed by comparison of serial analysis of gene expression transcript profiles from yeast grown on two different carbon sources,” Molecular Biology of the Cell, vol. 10, no. 6, pp. 1859–1872, 1999. View at: Publisher Site | Google Scholar
  11. J. E. Fortune, G. M. Rivera, A. C. O. Evans, and A. M. Turzillo, “Differentiation of dominant versus subordinate follicles in cattle,” Biology of Reproduction, vol. 65, no. 3, pp. 648–654, 2001. View at: Publisher Site | Google Scholar
  12. J. E. Fortune, “Ovarian follicular growth and development in mammals,” Biology of Reproduction, vol. 50, no. 2, pp. 225–232, 1994. View at: Publisher Site | Google Scholar
  13. L. Nemcova, D. Jansova, K. Vodickova-Kepkova et al., “Detection of genes associated with developmental competence of bovine oocytes,” Animal Reproduction Science, vol. 166, pp. 58–71, 2016. View at: Publisher Site | Google Scholar
  14. N. Hatzirodos, H. F. Irving-Rodgers, K. Hummitzsch, M. L. Harland, S. E. Morris, and R. J. Rodgers, “Transcriptome profiling of granulosa cells of bovine ovarian follicles during growth from small to large antral sizes,” BMC Genomics, vol. 15, article 24, 2014. View at: Publisher Site | Google Scholar
  15. R. Romar, T. De Santis, P. Papillier et al., “Expression of maternal transcripts during bovine oocyte in vitro maturation is affected by donor age,” Reproduction in Domestic Animals, vol. 46, no. 1, pp. e23–e30, 2011. View at: Publisher Site | Google Scholar
  16. P. L. Pfeffer, B. Sisco, M. Donnison, J. Somers, and C. Smith, “Isolation of genes associated with developmental competency of bovine oocytes,” Theriogenology, vol. 68, no. 1, pp. S84–S90, 2007. View at: Publisher Site | Google Scholar
  17. N. Ghanem, M. Hölker, F. Rings et al., “Alterations in transcript abundance of bovine oocytes recovered at growth and dominance phases of the first follicular wave,” BMC Developmental Biology, vol. 7, article 90, 2007. View at: Publisher Site | Google Scholar
  18. M. Cao, E. Nicola, V. M. Portela, and C. A. Price, “Regulation of serine protease inhibitor-E2 and plasminogen activator expression and secretion by follicle stimulating hormone and growth factors in non-luteinizing bovine granulosa cells in vitro,” Matrix Biology, vol. 25, no. 6, pp. 342–354, 2006. View at: Publisher Site | Google Scholar
  19. K.-G. Hayashi, K. Ushizawa, M. Hosoe, and T. Takahashi, “Differential gene expression of serine protease inhibitors in bovine ovarian follicle: possible involvement in follicular growth and atresia,” Reproductive Biology and Endocrinology, vol. 9, article 72, 2011. View at: Publisher Site | Google Scholar
  20. W. Luo and M. C. Wiltbank, “Distinct regulation by steroids of messenger RNAs for FSHR and CYP19A1 in bovine granulosa cells,” Biology of Reproduction, vol. 75, pp. 217–225, 2006. View at: Google Scholar
  21. G. Lu, S. Ren, P. Korge et al., “A novel mitochondrial matrix serine/threonine protein phosphatase regulates the mitochondria permeability transition pore and is essential for cellular survival and development,” Genes and Development, vol. 21, no. 7, pp. 784–796, 2007. View at: Publisher Site | Google Scholar
  22. X. Zhou, Q. Meng, X. Xu et al., “Bex2 regulates cell proliferation and apoptosis in malignant glioma cells via the c-Jun NH2-terminal kinase pathway,” Biochemical and Biophysical Research Communications, vol. 427, no. 3, pp. 574–580, 2012. View at: Publisher Site | Google Scholar
  23. L. A. Laviolette, K. M. Hodgkinson, N. Minhas, C. Perez-Iratxeta, and B. C. Vanderhyden, “17β-estradiol upregulates GREB1 and accelerates ovarian tumor progression in vivo,” International Journal of Cancer, vol. 135, no. 5, pp. 1072–1084, 2014. View at: Publisher Site | Google Scholar
  24. D. Haouzi, S. Assou, K. Mahmoud et al., “LH/hCGR gene expression in human cumulus cells is linked to the expression of the extracellular matrix modifying gene TNFAIP6 and to serum estradiol levels on day of hCG administration,” Human Reproduction, vol. 24, no. 11, pp. 2868–2878, 2009. View at: Publisher Site | Google Scholar

Copyright © 2016 Pengfei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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