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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 3  |  Issue : 3  |  Page : 141-147

Distinctive genes involved in steroidogenesis associated with follicular abnormal development in polycystic ovary syndrome model


1 Department of Nutrition and Food Hygiene, Public Health College of Zhengzhou University, Zhengzhou 450001, China
2 Department of Surgery, The First Affiliated Medical College of Zhengzhou University, Zhengzhou 450052, China
3 Department of Epidemiology, Public Health College of Southeast University, Nanjing 210009, China

Date of Submission07-May-2019
Date of Web Publication27-Sep-2019

Correspondence Address:
Zeng-Li Yu
Public Health College of Zhengzhou University, No. 100 Science Road, Zhengzhou 450001
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2096-2924.268157

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  Abstract 


Objective: Polycystic ovary syndrome (PCOS), a heterogeneous endocrine disorder, affects female reproductive function, but its etiology has not been elucidated. In this study, we analyzed the differential genes related to ovarian steroid biosynthesis in patients with PCOS, to explore the mechanism of PCOS.
Methods: The GSE59456 data were downloaded from the Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) in ovaries between the female Sprague–Dawley rats those implanted with 5α-dehydrotestestrone (DHT) and those in control (CTL, implanted with empty capsule). Gene ontology, pathway enrichment analysis, and protein–protein interaction (PPI) network construction were subsequently performed.
Results: In total, 530 upregulated DEGs and 522 downregulated DEGs were identified. The identified DEGs were mostly associated with steroid biosynthesis. In the PPI network, the module M1 was mainly related to steroid biosynthesis, and five genes (Hsd17b7, Tm7sf2, Idi1, Msmo1, and Sqle) of the module M1 were from the aforementioned group of upregulated genes. Furthermore, the 19 DEGs (Idi1, Cga, C1qb, Thy1, Gpx1, Ctss, Lpl, A2m, Cited2, Plppr4, Prkar2b, Slc44a1, Inha, Rbp4, Pla2g2a, Gata4, Fabp3, Cpa2, and Cpa1) between DHT and CTL groups were associated with the process of the transformation of primordial follicles to primary follicles.
Conclusions: These DEGs, such as Hsd17b7, Tm7sf2, Idi1, Msmo1, Sqle, Rbp4, Gata4, Inha, and Cited2, may be used to elucidate the etiology of PCOS, which may provide new insights into the exploration of pathological mechanism and biomarkers for polycystic ovary.

Keywords: Anovulation; Differentially Expressed Genes; Gene Ontology; Polycystic Ovary Syndrome


How to cite this article:
Yu KL, Zhang XL, Tan XM, Ji MM, Chen Y, Liu MM, Yu ZL. Distinctive genes involved in steroidogenesis associated with follicular abnormal development in polycystic ovary syndrome model. Reprod Dev Med 2019;3:141-7

How to cite this URL:
Yu KL, Zhang XL, Tan XM, Ji MM, Chen Y, Liu MM, Yu ZL. Distinctive genes involved in steroidogenesis associated with follicular abnormal development in polycystic ovary syndrome model. Reprod Dev Med [serial online] 2019 [cited 2019 Oct 17];3:141-7. Available from: http://www.repdevmed.org/text.asp?2019/3/3/141/268157




  Introduction Top


Polycystic ovary syndrome (PCOS), a heterogeneous endocrine disorder, affects 5%–10% of women of childbearing age worldwide.[1] It is characterized by ovulatory dysfunction, hyperandrogenism, and polycystic ovarian morphology.[2] Ovulatory dysfunction is a key feature of PCOS, which may result in infertility and amenorrhea.[3] Intrinsic ovarian abnormality can result in the pathogenesis of PCOS,[4] and abnormal folliculogenesis lies at the root of the etiology of anovulation.[5] Previous studies have suggested that abnormalities in folliculogenesis are characterized by two aspects: excessive preantral follicles and anovulation resulting from the lack of dominant follicular development.[6]

Abnormal follicular development in polycystic ovaries is manifested not only in the later (antral) stages but also in the very early stages.[5] The number of primary follicles is increased in polycystic ovaries;[4],[7],[8],[9] however, it is still not clear why the number of primordial follicles increases. Several studies have demonstrated that the number of primordial follicles was similar in normal and polycystic ovaries. Webber et al. suggested that there was an increase in the proportion of primordial follicles in polycystic ovaries.[4] Therefore, there are two major hypotheses on the cause of the increased number of primary follicles in PCOS. One involved abnormally slow growth of primary follicles,[8] and the other was due to increased primordial follicle recruitment.[4]

A follicle comprises an oocyte surrounded by granulosa cells (GCs) and theca cells.[10] Primordial follicle recruitment, transition to primary follicle, and its subsequent development are primarily dependent on cytokines, growth factors, and gonadotropins through the interaction between oocytes, GCs, and theca cells.[11] These factors play a key role in determining the survival and growth of a proportion of follicles. For example, androgen may promote the preantral follicle growth [12] and play a critical role in follicle recruitment;[13] estrogen levels correlate with the rate of primordial follicle activation.[14] Increased GC proliferation is associated with the increased proportion of primary follicles.[9] However, little is known about the molecular mechanism of activation of primordial follicle and primary follicle growth in the polycystic ovary.

We hypothesized that the key genes involved in the folliculogenesis might be activated or repressed, placing abnormal folliculogenesis at the root of the etiology of anovulation.[5] In the current study, we downloaded the GSE59456 data, and the differentially expressed genes (DEGs) were identified between normal and polycystic ovaries of female Sprague–Dawley (SD) rats. Gene ontology (GO), pathway enrichment analysis, and protein–protein interaction (PPI) network construction were performed. Furthermore, DEGs involved in the process of primordial to primary follicles transition were identified by Kezele et al.[15] The shared DEGs between the two abovementioned groups of DEGs were identified using Venny. We aimed to explore the DEGs involved in molecular function, molecular pathways, and gene networks and then provide new insights into the molecular mechanism of PCOS.


  Methods Top


Microarray data

The raw data (GSE59456) were downloaded from Gene Expression Omnibus dataset based on the platform of the Affymetrix Rat Genome 230.2.0 Array (Affymetrix, Santa Clara, CA, USA). The dataset consisted of eight samples, including four samples of ovaries from SD rats implanted with 5α-dihydrotestosterone (DHT) to mimic the hyperandrogenic state in women with PCOS (a DHT-filled silicone capsule continuously releasing 83 μg DHT per day) and four samples of ovaries from CTL rats (implanted with empty capsule). The detailed methodology followed was previously described by Hossain et al.[16]

Differentially expressed genes screening and function exploration

The raw data were preprocessed in R environment version 3.1.1 (http://www.r-project.org/), and background adjustment, quantile normalization, and summarization were done using RMAExpress software. Then, the probes were matched to the annotation file downloaded from the Affymetrix official website (http://www.affymetrix.com), and the combined dataset was produced, which consisted of official gene symbols and microarray data. The DEGs between PCOS and control (CTL) groups were identified based on the Limma package in Bioconductor according to the predefined criteria (P ≤ 5% and LogFC ≥1 or ≤−1). To analyze the DEGs at the functional level, GO analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed with Database for Annotation, Visualization, and Integraore ≤0.1 and thresholds account ≥2. The DEGs involved in the process of primordial to primary follicle transition were identified from Kezele et al.[15] Then, Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to identify the intersection between the two group DEGs. Similarly, the shared DEGs were identified using GO analysis.

Protein–protein interaction network construction and module clustering

The Search Tool for the Retrieval of Interacting Genes (STRING) database (http://string-db.org/) was used to obtain a critical assessment and integration of PPIs. The new version 10.5 of STRING covers 9.6 million proteins from 2,031 organisms. In the current study, the DEGs were screened with a combined score >0.4, and the PPI network was constructed using the Molecular Complex Detection (MCODE) plug-in in Cytoscape software version 3.5.1 (http://www.cytoscape.org/download.php) under the preset condition (haircut mode, node score cutoff value: 0.2, K-core value: 2). Moreover, all the modules were screened with a cluster coefficient value ≥0.5, if not specified.


  Results Top


Identification and function exploration of the differentially expressed genes

From GSE59456, based on the predefined criteria (P ≤ 5% and LogFC ≥1 or ≤−1), a total of 1,052 DEGs were identified between DHT and CTL groups, of which 530 were upregulated and 522 were downregulated [Figure 1]. DAVID provides investigators to understand the biological rationale behind the large list of genes. In the current study, GO analysis was used to identify the functions of DEGs from three aspects, including biological process, cellular components, and molecular function. GO analysis showed that DEGs between PCOS and CTL groups were mainly localized in the endoplasmic reticulum, cytoplasm, mitochondrion, intracellular membrane-bounded organelle, and so on, and mainly related to the molecular functions, such as steroid delta-isomerase activity, 3-beta-hydroxy-delta5-steroid dehydrogenase activity, and triphosphopyridine nucleotide (NADPH) binding. The DEGs were especially involved in the oxidation–reduction process, steroid biosynthetic process, and response to drugs, nutrients, and gonadotropin levels [Supplementary Table 1]. The results of our KEGG analysis suggested that the DEGs between the polycystic and normal ovaries were mainly associated with steroid biosynthesis, including ovarian steroidogenesis and terpenoid backbone biosynthesis and related metabolic pathways [Table 1].
Figure 1: Heatmap plot of differentially expressed genes in the dihydrotestosterone-treated and control rat groups, the upregulated genes are shown in red, and the downregulated genes are shown in blue. Sample clustering result showed a good fit for the actual grouping method.

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Table 1: KEGG pathway enrichment analysis of DEGs between normal and polycystic ovaries

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Protein–protein interaction network construction

The PPI database was filtered using a combined score >0.4, and then, the records that satisfied the filter criteria were imported into Cytoscape for remodeling. Module clustering of DEGs was identified using a score ≥4 as the criteria. The constructed PPI network is shown in [Table 2]. The PPI network comprised 11 module clusters of DEGs between polycystic ovaries and normal ovaries. The follow-up KEGG analysis indicated that the module M1 was mainly associated with steroid biosynthesis [Table 3]. Furthermore, in the module M1, the five genes (Hsd17b7, Tm7sf2, Idi1, Msmo1, and Sqle) were among the top 20 upregulated genes between PCOS and CTL groups [Figure 2].
Table 2: PPI module clustering of DEGs

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Table 3: Pathways and DEGs in module M1

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Figure 2: The module M1 protein–protein interaction network. The protein-protein interaction database was filtered using combined score >0.4, then records, which satisfied the filter criteria, were imported into Cytoscape for remodeling. According to the preset screening criteria (haircut mode, node score cutoff value 0.2, and K-core value 2), 11 modules were returned, of which, M1 contains five genes (yellow) which were on the top 20 upregulated genes between polycystic ovary syndrome and control group.

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Differentially expressed genes related to primordial to primary follicles transition

From a previous study,[15] a total of 152 DEGs related to the process of primordial to primary follicle transition were found. The Venn diagram demonstrated 19 DEGs (Idi1, Cga, C1qb, Thy1, Gpx1, Ctss, Lpl, A2m, Cited2, Plppr4, Prkar2b, Slc44a1, Inha, Rbp4, Pla2g2a, Gata4, Fabp3, Cpa2, and Cpa1) between PCOS and CTL groups related to the process of primordial to primary follicle transition [Figure 3]. Four genes (Rbp4, Gata4, Inha, and Cited2) were involved in the biological process of male gonad development as revealed by GO analysis [Table 4].
Figure 3: Venn diagram of differentially expressed genes between dihydrotestosterone-treated and control rat groups and differentially expressed genes related to primordial to primary follicles transition. 1052 differentially expressed genes between dihydrotestosterone-treated and control rat groups was identified from GSE59456 and 152 differentially expressed genes related to primordial to primary follicles transition was identified from GSE3600 (Kezele et al., 2005). The 19 genes were shared by the two groups of differentially expressed genes.

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Table 4: GO analysis of DEGs related to 19 shared DEGs (between normal and polycystic ovaries and related to primordial to primary follicles transition)

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  Discussion Top


The sex hormones, estradiol (E2) and testosterone, belonging to the steroid class, play a key role in ovarian follicular development. In the current study, 530 upregulated DEGs and 522 downregulated DEGs were identified. The DEGs were mostly associated with steroid biosynthesis. In the PPI network, the module M1 was mainly related to steroid biosynthesis, and five genes (Hsd17b7, Tm7sf2, Idi1, Msmo1, and Sqle) of M1 were among the 20 upregulated genes between the PCOS and CTL groups. Furthermore, 19 DEGs (Idi1, Cga, C1qb, Thy1, Gpx1, Ctss, Lpl, A2m, Cited2, Plppr4, Prkar2b, Slc44a1, Inha, Rbp4, Pla2g2a, Gata4, Fabp3, Cpa2, and Cpa1) between normal and polycystic ovaries were associated with the process of primordial to primary follicle transition.

Physiological ovarian follicular development was coordinated by multiple steroid hormones, including estrogen, androgen, follicle-stimulating hormone (FSH), and luteinizing hormone. Previous studies have shown that estrogen might increase the rate of primordial follicle activation.[14] FSH mainly recruited follicles at the antral follicle stage (2–5 mm).[17] Androgen might promote the growth of early follicles and play a central role in follicle recruitment.[12],[13] When the steroid hormone biosynthesis is compromised, the steroid hormones reach abnormal levels in ovaries which probably result in abnormalities of follicular development in polycystic ovaries. Previous studies also proved that the circulating androgen level was increased and the level of E2 was decreased in polycystic ovaries.[3],[16] Thus, the genes associated with abnormal steroid hormone biosynthesis could contribute to the abnormal folliculogenesis in PCOS. The DEGs between polycystic and normal ovaries were mostly associated with steroid biosynthesis in this study. Therefore, we speculated that the identification of DEGs between polycystic and normal ovaries was important to understand the underlying mechanisms of PCOS.

The module M1 was mainly related to steroid biosynthesis, and the five genes (Hsd17b7, Tm7sf2, Idi1, Msmo1, and Sqle) of the M1 were found to be activated in the DHT-treated ovaries in the present study. Hydroxysteroid (17β)-dehydrogenases (HSD17Bs) are enzymes catalyzing the conversion of less active 17β-ketosteroids to highly active 17β-hydroxysteroid, which is an essential step in the formation of the conventional, highly active sex steroids, E2 and testosterone (T). It includes HSD17B1, HSD17B8, HSD17B12, and HSD17B7. Previous studies showed that rodent HSD17B7 resembled human HSD17B1 but differed from the rodent HSD17B1.[17],[18] HSD17B7 is involved in the regulation of corpus luteum function to maintain pregnancy and also plays a role in the development of endocrine-related cancers.[19],[20] However, few studies have reported on the roles of HSD17B7 in follicle development and mechanism of PCOS. In the current study, the gene Hsd17b1 was activated in polycystic ovaries, and while hsd17b8 and hsd17b12 mRNA expression did not differ between polycystic and normal ovaries. These findings suggested that HSD17B7 was associated with polycystic ovaries.

The gene Tm7sf2 encodes 3β-hydroxysterol-Δ14-reductase (C14-SR), an endoplasmic reticulum enzyme involved in cholesterol biosynthesis during the conversion of lanosterol to cholesterol.[21] The Tm7sf2 mRNA is predominantly expressed in the ovary, while its significance in the ovary and follicle development is not yet clear.[22] Methylsterol monooxygenase 1 (Msmo1) is involved in step three of the subpathway of synthesis of zymosterol from lanosterol. This subpathway is part of the zymosterol biosynthesis pathway, which is, in turn, part of steroid biosynthesis. In addition, Hsd17b7 is involved in step five of this subpathway. Squalene monooxygenase (Sqle) catalyzes the first oxygenation step in sterol biosynthesis and is suggested to be one of the rate-limiting enzymes of this pathway. The genes Tm7sf2, Msmo1, and Sqle were activated in the polycystic ovaries in this study, which led to abnormalities in steroid hormone secretion. This was probably associated with the pathogenesis of polycystic ovary.

The gene Idi1 was also associated with the process of primordial to primary follicle transition as shown in a previous study.[15] Isopentenyldiphosph delta-isomerase (IDI) is involved in a central reaction in the biosynthesis of isoprenoids by catalyzing isopentenyl diphosphate to its highly nucleophilic isomer dimethylallyl diphosphate.[23],[24] This process is necessary for the synthesis of steroid hormones.[25] However, there have been few studies on the role of IDI1 in ovarian follicular development and the pathogenesis of polycystic ovary.

The follicular development is an orderly process, such as transition from primordial to primary follicle, primary to secondary follicle, and secondary follicle to ovulation. In the current study, the 19 DEGs (Idi1, Cga, C1qb, Thy1, Gpx1, Ctss, Lpl, A2m, Cited2, Plppr4, Prkar2b, Slc44a1, Inha, Rbp4, Pla2g2a, Gata4, Fabp3, Cpa2, and Cpa1) between polycystic and normal ovary were also related to the process of primordial to primary follicle transition. This suggested that the 19 genes may be associated with the abnormalities in follicular development in polycystic ovaries at the stage of primordial to primary follicle transition. GO analysis for the 19 genes showed that four genes (Rbp4, Gata4, Inha, and Cited2) were associated with male gonad development. Retinol-binding protein 4 (RBP4) transports retinol from the liver to the peripheral tissues. Retinol (Vitamin A) and its derivatives are collectively referred to as retinoids. Retinoid signaling plays pivotal roles in follicular development.[10],[26],[27],[28] Thus, abnormal retinoid signaling, such as abnormal expression of Rbp4, might be associated with PCOS.[29] Previous studies have shown that RBP4 levels were higher in PCOS group than in non-PCOS group.[30],[31] It might be because RBP4 contributes to endocrine changes and higher RBP4 levels were associated with higher androgen levels.[32] However, the gene Rbp4 was found to be suppressed in the polycystic ovary in this study. Further studies are needed to elucidate the potential contribution of RBP4 to the pathophysiology of PCOS.

Transcription factor GATA-4 is an important regulator of steroidogenesis, and its target genes mainly include enzymes involved in the steroidogenic cascade, such as 17 beta-hydroxysteroid dehydrogenase type 1 (17β-HSD, which synthesizing E2) and CYP19 (which encodes for estrogen-synthesizing enzyme P450 aromatase; P450arom).[33] Although a previous study showed that GATA-4 was expressed in preovulatory follicles, there was no significant difference in the mRNA expression levels of GATA-4 between the PCOS and CTL groups.[33] However, the gene Rbp4 was found to be suppressed in polycystic ovary in the present study. CITED2 is a transcriptional coactivator, but it does not binding DNA directly.[34] The gene Cited2 is involved in oocyte development and plays an important role in small follicle development.[35] Inhibins are glycoprotein hormones consisting of α and β subunits and may selectively inhibit FSH secretions.[36],[37],[38] Previous studies also showed that active immunization against INH-α might increase FSH secretion and ovulation rate in females.[37],[39],[40],[41],[42] The mRNA expression of Inhα was downregulated in the polycystic ovaries which inhibited FSH secretions and decreased the ovulation rate in females. This phenomenon was likely associated with the etiology of anovulation in polycystic ovary.

In conclusion, the DEG screening was used to identify the marker genes related to abnormal follicular development in polycystic ovaries. These DEGs, including Hsd17b7, Tm7sf2, Idi1, Msmo1, Sqle, Rbp4, Gata4, Inha, and Cited2, may be associated with PCOS. The findings of the current study may provide new insights into the exploration of the pathological mechanism and biomarkers for polycystic ovary; however, subsequent studies are still needed to test and verify the hypothesis. Further studies are needed to explore whether these genes are involved in the pathogenesis of polycystic ovary and their underlying mechanisms.

Acknowledgment

We are grateful to Lin Zhang at School of Public Health, Zhengzhou University, for his technical assistance with data analysis.

Financial support and sponsorship

This work was funded by National Natural Science Foundation of China (No. 21577119).

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

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