Endocrine Journal
Online ISSN : 1348-4540
Print ISSN : 0918-8959
ISSN-L : 0918-8959
ORIGINAL
Expression profile of circular RNAs in placentas of women with gestational diabetes mellitus
Huiyan WangGuangtong SheWenbai ZhouKezhuo LiuJun MiaoBin Yu
Author information
JOURNAL FREE ACCESS FULL-TEXT HTML

2019 Volume 66 Issue 5 Pages 431-441

Details
Abstract

Forty-five pregnant women who underwent cesarean section, including 30 cases of gestational diabetes mellitus (GDM) and 15 normal pregnant women, were enrolled in this study to examine the differential expression of circular RNAs (circRNAs) in the placentas of women with GDM by RNA sequencing (RNA-seq) analysis. The differentially expressed circRNAs were analyzed bioinformatically using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and circRNA-microRNA (miRNA) interaction prediction. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the results. A total of 8,321 circRNAs were identified in the human placenta, among which 46 were differentially expressed (fold change ≥2 and p < 0.05), including three that were upregulated and 43 that were downregulated. According to the GO and KEGG enrichment results, these circRNAs may be associated with vital biological processes, cellular components, molecular functions, and signaling pathways. In particular, KEGG analysis shown they may be involved in advanced glycation end products-receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications, indicating that these circRNAs might participate in the occurrence and pathogenesis of GDM. qRT-PCR verified that the expression of circ_5824, circ_3636, and circ_0395 was consistent with RNA-seq analysis; their expression levels were significantly lower in the GDM group than in the control group. The circRNA-miRNA interaction was analyzed according to the molecular sponge mechanism, and its potential function is discussed. These results shed light on future functional studies of circRNAs related to GDM.

THE PRESENT STUDY suggests that circRNAs are associated with the occurrence and development of GDM. Our results may open a new chapter in the study of GDM.

Introduction

Gestational diabetes mellitus (GDM), also known as glucose intolerance with onset or first recognition during pregnancy [1], can cause severe maternal and neonatal complications, such as increased risk of preeclampsia, macrosomia, depression, and stillbirth. It is particularly noteworthy that uncontrolled GDM has long-term adverse effects on mothers and children, such as susceptibility to obesity and metabolic syndrome [2]. Because of its high incidence (affecting 3–9% of pregnancies [3]), GDM has attracted the attention of prenatal medical experts.

GDM is a heterogeneous disorder in which pregnancy can make recessive diabetes dominant (make pregnant women with no previous diabetes have GDM) or aggravate the condition of a woman with preexisting diabetes. The hallmark of GDM is increased insulin resistance [4]. Pancreatic beta cells are no longer able to compensate for the increased insulin resistance during pregnancy. However, the precise mechanisms underlying GDM remain unknown. On the other hand, Ilekis et al. [5] suggested that the occurrence of adverse pregnancy outcomes associated with GDM could be related to placental issues. As the main channel of energy transfer between mother and fetus, it should play an important role in the abnormal metabolism of GDM. Placental lactogen, prolactin, and estradiol also seem to contribute to the development of insulin resistance during pregnancy, among which cortisol and progesterone are the main culprits. Other studies have focused on the placental microenvironment, including inflammatory factors, genes, and proteins [6-8]. The recent discovery of small molecules with potential regulatory effects, such as microRNAs (miRNAs) [9, 10] and long non-coding RNAs (lncRNAs) [11, 12], may reveal the essence of GDM more accurately.

Circular RNAs (circRNAs) are a special type of non-coding RNA with characteristics of evolutionary conservation, structural stability, and tissue specificity [13, 14]. Because of these biological features, circRNAs have attracted wide attention in recent years. They play important roles in tumor development and nervous system diseases [15-17]. circRNAs act as miRNA sponges and affect the expression of downstream genes [18, 19]. Some reports have shown that circRNAs are also present in the human placenta and may be related to the occurrence of pregnancy complications [20]. circRNAs may also play a role in the pathogenesis of diabetes and thus serve as novel molecular targets for clinical therapy [21, 22]. For example, Zhao et al. reported that hsa_circ_0054633 presented a certain diagnostic capability for pre-diabetes and type 2 diabetes mellitus, and high glucose exposure profoundly altered circRNA expression in endothelial cells [23]. However, studies on the relationship between circRNAs and GDM are lacking.

In the present study, we examined the differential expression of circRNAs in placentas from women with GDM by RNA sequencing (RNA-seq) and preliminarily investigated their biological functions via bioinformatics analysis. We hope to clarify the relationship between circRNAs and the occurrence and pathogenesis of GDM.

Materials and Methods

Patients

A total of 45 pregnant women who underwent cesarean section from August 2016 to June 2017, including 30 cases of GDM and 15 normal pregnant women, were enrolled in this study. Their baseline characteristics are shown in Table 1. The diagnosis of GDM was made by an oral glucose tolerance test (75 g) during the second trimester (24–28 weeks of gestation). All pregnant women with multiple gestations, infection, other pregnancy complications, congenital or chromosomal abnormalities of the fetus, or a family history of diabetes were excluded. The study was approved and reviewed by the ethics committee of Changzhou Women and Children Health Hospital (Changzhou, China, Approval No: CZFY20160103). Informed consent was obtained prior to cesarean section.

Table 1 The information of patients with GDM and normal control groups
Characteristics GDM (N = 30) Control (N = 15) p value
Age (year) 32.57 31.47 0.39
Fasting glucose (mmol/L) 5.09 4.35 0.03
2 h postgrandial glucose (mmol/L) 6.86 5.28 0.001
OGTT 1 h (mmol/L) 10.27 7.32 <0.01
OGTT 2 h (mmol/L) 8.98 6.06 <0.01
HbA1C (%) 5.18 4.85 0.13
BMI at delivery (kg/m2) 28.97 27.27 0.21
Pre-pregnancy BMI (kg/m2) 22.12 21.34 0.22
Birth weight (g) 3,445.67 3,362.85 0.61
Neonatal sex (male/female) 0.87 0.88 0.55

Sample collection

Placentas were obtained within 15 min after cesarean section. Placental fragments were collected in the middle of the initial placental depth. The decidual layer, chorionic surface, and membranes were removed. All placental samples were washed with saline and stored at −80°C following the addition of 1 mL Trizol (Invitrogen, Carlsbad, CA, USA). Three cases and three paired controls were chosen for RNA-seq analysis.

RNA extraction and sequencing

Total RNA was extracted from the placenta tissues using the mirVana miRNA Isolation Kit (Ambion, Inc., Foster City, CA, USA) following the manufacturer’s protocol. RNA integrity was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples with an RNA integrity number ≥7 were subjected to subsequent analyses. Libraries were constructed using TruSeq Stranded Total RNA with Ribo-Zero Gold according to the manufacturer’s instructions. Libraries were then sequenced on the Illumina sequencing platform (HiSeq 2500) and 150 bp/125 bp paired-end reads were generated.

Identification and quantification of human circRNAs

circRNAs were predicted by CIRCexplore2 [24] and compared with those in circBase (http://www.circbase.org/). DESeq [25] software was used to standardize the number of junction reads of each sample. A fold change (FC) >2 and p < 0.05 were considered to indicate significant differences.

Functional enrichment analysis and circRNA-miRNA associations

The differentially expressed circRNA genes were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. GO enrichment analysis was based on three aspects: biological process (BP), cellular component (CC), and molecular function (MF). circRNA-targeted miRNAs were identified and predicted by miRanda [26, 27]. The circRNA-miRNA interaction network was constructed based on the functional annotation of the miRNA target genes.

Quantitative real-time polymerase chain reaction (qRT-PCR) analysis

We randomly selected 10 circRNAs from the RNA-seq results to verify by qRT-PCR. RNase R-treated RNAs were diluted with water and used as a PCR template. cDNAs were obtained using a Reverse Transcription Kit (M-MLV; Promega, Madison, WI, USA). SYBR Master Mix (TaKaRa Bio Inc., Kusatsu, Japan) was used to examine the expression of circRNAs according to the manual. All primers were designed and synthesized by Ribo Bio (Guangzhou, China). Amplification was performed on an ABI 7500 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) under the following conditions: denaturation at 95°C for 10 min, followed by 38 cycles of amplification at 95°C for 10 s and 60°C for 1 min. The relative expression levels of the circRNA genes were calculated using the 2−ΔΔCt method. GAPDH was employed as the internal reference to normalize the expression levels of the target genes.

Statistical analysis

Statistical analyses were performed using SPSS 19 software. The t-test was used to analyze data between two groups. P < 0.05 was considered statistically significant.

Results

Identification and quantification of human circRNAs

A total of 8,321 circRNAs were indentified in human placentas from GDM and normal control pregnant women. A total of 7,804 circRNAs had already been reported in circBase, whereas 517 were newly discovered, among which 46 were differentially expressed in the placenta tissues of GDM women (FC > 2 and p < 0.05), three that were upregulated and 43 that were downregulated. Their related information is shown in Table 2. A heatmap (Fig. 1A) and volcano plot (Fig. 1B) reveal the differential expression profiles of circRNAs between GDM women and the control group.

Table 2 Statistic of differently expressed transcrips
ID CircBase name region gene symbol chr log2FC p regulation
circ_0395 NA exonic PAPPA2 1 –4.69 0.02 down
circ_1698 hsa_circ_0058092 exonic FN1 2 –6.46 0.05 down
circ_1840 hsa_circ_0081006 exonic KRIT1 7 –6.68 0.04 down
circ_1926 hsa_circ_0000155 exonic DCAF6 1 –6.74 0.04 down
circ_2308 hsa_circ_0120939 exonic EXOC6B 2 –6.61 0.05 down
circ_2372 hsa_circ_0006260 exonic SLC41A2 12 –7.90 0.01 down
circ_2415 hsa_circ_0007430 exonic NRDC 1 7.17 0.03 up
circ_2523 hsa_circ_0000857 exonic ZNF236 18 –7.19 0.03 down
circ_3003 hsa_circ_0005362 exonic PHC3 3 –6.65 0.04 down
circ_3223 hsa_circ_0002466 exonic TTBK2 15 –7.69 0.02 down
circ_3636 NA exonic ADAM12 10 –5.90 0.03 down
circ_3798 hsa_circ_0005243 exonic TMEM184B 22 –8.17 0.04 down
circ_3869 hsa_circ_0001578 exonic RANBP9 6 –7.85 0.01 down
circ_3993 hsa_circ_0008192 exonic PTBP3 9 –6.44 0.05 down
circ_4046 hsa_circ_0088249 exonic PAPPA 9 –6.55 0.05 down
circ_4390 hsa_circ_0006670 exonic SIPA1L3 19 –6.87 0.03 down
circ_4524 hsa_circ_0006380 exonic TCF12 15 –6.46 0.05 down
circ_4718 hsa_circ_0005029 exonic EPT1 2 –6.65 0.04 down
circ_4792 hsa_circ_0000417 exonic CPSF6 12 –6.87 0.03 down
circ_4802 hsa_circ_0002702 exonic RUSC2 9 –7.40 0.04 down
circ_5036 hsa_circ_0009049 exonic PLPP3 1 6.90 0.03 up
circ_5124 hsa_circ_0028319 exonic TMEM116 12 –6.49 0.05 down
circ_520 hsa_circ_0004919 exonic CARF 2 –6.69 0.04 down
circ_5754 NA exonic LOC100507487 4 –6.45 0.05 down
circ_5824 hsa_circ_0005243 exonic TMEM184B 22 –2.37 0.04 down
circ_6525 hsa_circ_0134318 exonic GLI3 7 –6.63 0.04 down
circ_6998 hsa_circ_0013218 exonic DNTTIP2 1 –7.01 0.03 down
circ_7167 hsa_circ_0002226 exonic ETFA 15 –6.77 0.04 down
circ_7224 hsa_circ_0002795 exonic SPAST 2 –6.99 0.03 down
circ_730 hsa_circ_0042170 exonic NCOR1 17 –6.52 0.04 down
circ_7360 hsa_circ_0002634 exonic ATXN7 3 –6.44 0.05 down
circ_7367 hsa_circ_0003218 exonic BMPR2 2 –7.88 0.01 down
circ_7402 hsa_circ_0126389 exonic SLC30A9 4 –6.69 0.04 down
circ_7466 hsa_circ_0125310 exonic LARP1B 4 6.76 0.04 up
circ_7540 hsa_circ_0091581 exonic GPC3 X –6.37 0.05 down
circ_7687 hsa_circ_0008667 exonic ADAMTS6 5 –7.19 0.03 down
circ_780 hsa_circ_0002814 exonic HERC2 15 –6.54 0.04 down
circ_7965 hsa_circ_0025641 exonic RASSF8 12 –6.95 0.03 down
circ_8068 hsa_circ_0017310 exonic CNST 1 –6.73 0.04 down
circ_8086 hsa_circ_0008234 exonic FOXP1 3 –7.11 0.03 down
circ_8122 NA splicing LIMS2 2 –6.64 0.04 down
circ_8133 hsa_circ_0000139 exonic GON4L 1 –8.05 0.01 down
circ_8210 hsa_circ_0002968 exonic MAPK8 10 –7.34 0.02 down
circ_8271 hsa_circ_0091206 exonic PCDH11X X –7.10 0.03 down
circ_967 hsa_circ_0035472 exonic RNF111 15 –7.25 0.05 down
circ_986 NA exonic PAPPA2 1 –6.70 0.04 down

Note: NA, not available; chr, chromosome.

Fig. 1

Prediction and identification of circRNAs expressed in the placentas of women with GDM

A. Expression profiles of the circRNAs are displayed in a heatmap. Each column represents a sample and each row represents a circRNA. High expression is indicated in red and low expression is indicated in green. B. Differentially expressed circRNAs are displayed in volcano plots. Gray dots indicate circRNAs with no significant difference. Red dots indicate significantly upregulated circRNAs, whereas blue dots indicate significantly downregulated circRNAs. NS, not significant.

Functional analysis of differentially expressed circRNAs

The differentially expressed circRNA genes were analyzed by GO (Fig. 2A, B, C) and KEGG (Fig. 2D) enrichment. Based on the results, these differentially expressed circRNAs may be associated with GO functional annotation of biological processes (e.g., smoothened signaling pathway), cellular components (e.g., nuclear speck, transcriptional repressor complex), and molecular function (e.g., small ubiquitin-like modifier [SUMO] binding, ubiquitin−like protein binding). According to KEGG analysis, the host genes of these differentially expressed circRNAs are associated with focal adhesion, shigellosis, bacterial invasion of epithelial cells, endocrine resistance, and the advanced glycation end products-receptor for advanced glycation end products (AGE−RAGE) signaling pathway in diabetic complications. In particular, the AGE/RAGE pathway plays an important role in a variety of diabetic complications [28] and is associated with adverse outcomes in GDM [29].

Fig. 2

GO and KEGG enrichment terms of differentially expressed circRNA transcript genes

A. Top five classes of biological process (BP) enrichment terms. B. Top five classes of molecular function (MF) enrichment terms. C. Top five classes of cellular component (CC) enrichment terms. D. Top five classes of KEGG pathway terms.

qRT-PCR analysis

To validate the RNA-seq results, 10 circRNAs were randomly subjected to qRT-PCR analysis, among which the results of three circRNAs (circ_5824, circ_3636, and circ_0395) were consistent with RNA-seq data. Compared with the control group, their expression in the GDM group was significantly reduced (Fig. 3A). Their primer sequences are shown in Table 3. Images of the PCR products of these three circRNAs on 1.5% agarose gels did not show obvious primer dimers or non-specific PCR products (Fig. 3B). All bands of the GDM group were less bright than those in the control group, indicating their differential expression.

Fig. 3

qRT-PCR verification of differentially expressed circRNAs

A. The expression of circ_5824, circ_3636, and circ_0395 was confirmed by qRT-PCR, which was consistent with the sequencing results. * p < 0.05. B. Images of PCR products of the three circRNAs on a 1.5% agarose gel.

Table 3 Primer sequences of the circRNAs
Gene 5'-3' 3'-5'
circ_5824 CACCGGACAGGCATCTAGTGA CAGTGTTGCAGGCTCTTTGA
circ_0395 AGACAGGAATTTGGGTACATC GAGTGCCATCCACATACAGG
circ_3636 GTGCTATGGTGCTCTGTCTA TGAGTGAGCCGAGTTGTTCT
GAPDH TGACTTCAACAGCGACACCCA CACCCTGTTGCTGTAGCCAAA

circRNA-miRNA interaction analysis

As target molecules of miRNAs, the interaction analysis of circRNA-miRNA can help explore the function and mechanism of circRNAs. Three circRNA (circ_5824, circ_3636, and circ_0395)-targeted miRNAs were identified and their potential functions were elucidated according to the miRNA target genes. The miRNAs that interact with these three circRNAs, as predicted using miRanda software, are shown in Table 4. circRNA_0395 was targeted by 88 miRNAs. The top three total scores of these miRNAs were hsa-miR-8485, hsa-miR-3135b and hsa-miR-1273g-3p, whereas circRNA_3636 and circRNA_5824 had three and eight miRNA binding sites, respectively. Furthermore, a network of circRNA-miRNA-mRNA interactions was established based on these three circRNAs and their target miRNAs (Fig. 4).

Table 4 Differentially expressed circRNAs and their targeted-miRNAs
Transcript miRNA Total score Total energy miRNA length Position
circRNA_0395 hsa-miR-197-3p 163 –30.67 22 16149
hsa-miR-143-5p 171 –30.17 22 6098
hsa-miR-363-5p 173 –32.57 22 21029
hsa-miR-328-5p 156 –30.21 23 29421
hsa-miR-328-3p 171 –33.9 22 15185
hsa-miR-504-3p 490 –91.63 21 17820 6102 37955
hsa-miR-619-5p 192 –42.14 22 5885
hsa-miR-33b-3p 163 –31.13 22 723
hsa-miR-762 152 –31.86 22 4010
hsa-miR-877-3p 173 –33.32 21 37405
hsa-miR-937-5p 164 –32.56 20 1045
hsa-miR-939-3p 161 –32.92 21 32279
hsa-miR-1226-3p 173 –39.96 22 2418
hsa-miR-1207-5p 325 –61.96 21 45023 52033
hsa-miR-1285-3p 183 –33.2 22 45215
hsa-miR-1303 184 –34.28 22 31482
hsa-miR-1304-3p 167 –30.42 22 17797
hsa-miR-1254 167 –30.39 24 29504
hsa-miR-1273a 195 –40.51 25 20806
hsa-miR-1911-5p 174 –31.86 23 27861
hsa-miR-1912 167 –30.6 22 3402
hsa-miR-1913 154 –32.71 22 57642
hsa-miR-1972 176 –34.62 22 21085
hsa-miR-1976 162 –30.12 20 30368
hsa-miR-2276-5p 164 –34.09 22 44308
hsa-miR-3127-3p 175 –34.76 22 3440
hsa-miR-3137 154 –30.13 24 29438
hsa-miR-3151-5p 154 –30.84 21 4010
hsa-miR-3184-3p 175 –31.45 23 35510
hsa-miR-3192-5p 168 –31.82 23 12012
hsa-miR-3200-5p 180 –31.15 22 58686
hsa-miR-4254 172 –33.9 23 6254
hsa-miR-4269 169 –30.86 21 22629
hsa-miR-3619-5p 161 –30.17 22 15775
hsa-miR-3135b 633 –134.88 22 6104 35487 37957 31448
hsa-miR-4518 159 –30.96 26 41381
hsa-miR-4640-3p 161 –30.85 22 22760
hsa-miR-4644 183 –31.99 23 29307
hsa-miR-4651 157 –30.53 20 45812
hsa-miR-4656 164 –30.76 23 29419
hsa-miR-4685-3p 174 –36.05 22 18357
hsa-miR-4687-5p 165 –33.59 22 44117
hsa-miR-4722-5p 163 –30.19 23 58192
hsa-miR-4728-5p 166 –30.49 23 45800
hsa-miR-4741 167 –32.11 23 254
hsa-miR-4758-5p 155 –34.04 23 870
hsa-miR-4763-5p 153 –30.28 21 22772
hsa-miR-4763-3p 174 –36.51 24 52031
hsa-miR-4436b-5p 162 –32.29 22 8141
hsa-miR-5090 162 –30.65 23 9284
hsa-miR-5095 187 –41.97 21 5879
hsa-miR-1273g-3p 548 –112.25 21 29358 20828 8006
hsa-miR-5096 179 –32.37 21 5957
hsa-miR-5187-5p 176 –30.51 22 31084
hsa-miR-5189-5p 168 –31.45 24 27993
hsa-miR-5196-3p 157 –36.54 21 302
hsa-miR-6089 340 –84.82 24 32436 21040
hsa-miR-6727-5p 157 –30.15 23 9285
hsa-miR-6734-5p 167 –32.06 23 15550
hsa-miR-6734-3p 173 –30.38 23 38422
hsa-miR-6751-5p 177 –32.76 23 34482
hsa-miR-6756-5p 533 –118.76 23 4014 45811 56027
hsa-miR-6764-5p 160 –31.16 22 32
hsa-miR-6771-5p 320 –65.79 22 12050 20896
hsa-miR-6776-3p 168 –31.44 23 22795
hsa-miR-6777-3p 166 –32.42 20 30373
hsa-miR-6782-5p 161 –30.39 25 4011
hsa-miR-6787-3p 166 –31.64 22 17799
hsa-miR-6793-5p 175 –34.09 22 41435
hsa-miR-6797-5p 165 –30.74 25 29034
hsa-miR-6799-3p 159 –31.14 23 905
hsa-miR-6803-5p 160 –33.98 22 6836
hsa-miR-6810-5p 165 –30.76 23 9210
hsa-miR-6810-3p 171 –39.89 23 57643
hsa-miR-6812-5p 328 –66.88 25 45808 56018
hsa-miR-6780b-5p 172 –31.94 23 3663
hsa-miR-6846-5p 162 –32.59 22 549
hsa-miR-6849-5p 171 –30.56 23 23141
hsa-miR-6856-5p 173 –31.35 24 31975
hsa-miR-6884-3p 179 –38.62 23 37374
hsa-miR-7108-3p 164 –31.3 20 8900
hsa-miR-7111-3p 347 –66.25 22 38374 38427
hsa-miR-7160-3p 157 –33.98 21 44310
hsa-miR-1273h-5p 334 –65.67 21 12016 29392
hsa-miR-7851-3p 176 –32 22 12018
hsa-miR-8085 171 –30.52 21 12693
hsa-miR-8089 160 –32.08 24 45819
hsa-miR-8485 928 –184.2 21 37704 44413 37720 44429 44383
circRNA_3636 hsa-miR-6734-5p 158 –32.46 23 111
hsa-miR-6852-5p 162 –31.45 21 73
hsa-miR-7155-5p 167 –30.87 19 78
circRNA_5824 hsa-miR-331-3p 154 –33.14 21 287
hsa-miR-486-3p 162 –30.4 21 340
hsa-miR-4486 170 –34.58 17 111
hsa-miR-4649-5p 153 –30.89 24 100
hsa-miR-4786-3p 168 –31.26 22 194
hsa-miR-6165 156 –34.53 19 26
hsa-miR-6729-5p 160 –32.59 22 105
hsa-miR-8073 176 –33.56 22 34

A total of 99 miRNAs could be combined with these 3 circRNAs. Total Score: the cumulative prediction score. The higher the value is, the more accurate. Total Energy: the accumulative complementary pair matches the free energy. The smaller the energy is, the more reliable.

Fig. 4

circRNA-miRNA-mRNA interaction network

The circRNA-miRNA-mRNA network consists of three circRNAs (blue), 21 miRNAs (red) and 120 disease-related genes (green).

Discussion

We successfully discovered 46 differentially expressed circRNAs in the placentas of women with GDM via RNA-seq analysis and confirmed the results using qRT-PCR. Their biological functions were predicted by bioinformatics analysis. These results suggest that the differentially expressed circRNAs are associated with the occurrence and development of GDM.

The roles of several small molecules in the occurrence of GDM have recently attracted our attention, namely, miRNAs and lncRNAs. Thus far, more than 600 miRNAs expressed in the human placenta have been reported [30]. Human placenta tissue exhibits specific miRNA expression in a time-dependent manner during pregnancy and is reflected in the maternal plasma. Some placental miRNAs are dysregulated in plasma and are involved in GDM [9]. As a newly discovered non-coding RNA, circRNAs have gained increasing attention from researchers. Several functional circRNAs that act as competitive endogenous RNAs by effectively adsorbing miRNAs and regulating their target genes were recently identified [31, 32]. To date, there have been few reports on the relationship between circRNAs and pregnancy. Maass et al. detected 63 circRNAs in the human placenta. By functional prediction, they reported that some circRNAs may be related to pregnancy complications, such as early onset preeclampsia, fetal growth restriction, and infection during pregnancy [20]. In this study, we identified 46 differentially expressed circRNAs in the placentas of women with GDM and preliminarily discussed the possible mechanisms of their participation in GDM. Yan et al. [33] recently reported differentially expressed circRNAs in placenta tissues from patients with GDM. Their circRNA expression profile differed from that described herein, which could be due to the different prediction tools used (different algorithms have different sensitivities and accuracy rates; dramatic differences between the algorithms were observed specifically regarding the highly expressed circRNAs and the circRNAs derived from proximal splice sites) [34]. Different regions and populations, as well as database sequencing systems, will also cause differences.

Among the differentially expressed circRNAs, the qRT-PCR results of three circRNAs (circ_5824, circ_3636 and circ_0395) were consistent with RNA-seq analysis. circRNA_0395 attracted our attention; it was significantly decreased in the placentas of women with GDM and overlaps with the PAPPA2 (pregnancy-associated plasma protein A 2) gene, which encodes a pregnancy-related protein. PAPPA2 can be used to predict macrosomia at birth in GDM pregnancies [35] and is related to metabolic diseases beyond total adiposity [36]. Thus, it was important to examine the interactions between circRNAs and miRNAs that could play a key role in the occurrence and development of GDM. Accumulating evidence has indicated that circRNAs have a series of important biological functions, acting as miRNA sponges [16, 37]. Bioinformatics analysis revealed that circRNA_0395 is targeted by 88 types of miRNAs. We then performed a systematic review of the literature to examine these miRNAs more closely. miRNA-1273g-3p piqued our interest. miR-1273g-3p, a member of the miR-1273 family, was first identified as an miRNA in 2011 and can bind to 1,074 genes [30]. Guo et al. [38] reported that miR-1273g-3p participates in acute glucose fluctuation and is an important factor that leads to endothelial dysfunction and autophagy. It is also involved in the progression of several complications caused by diabetes by modulating the autophagy-lysosome pathway and could serve as a new target for disease therapy [39]. However, there has been no report on the role of miRNA-1273g-3p in pregnancy. Thus, without direct evidence that circRNA_0395 is targeted by miRNA-1273g-3p, further research was needed to confirm their relationship.

In conclusion, our study provides a preliminary landscape of the differential expression of circRNAs that may be involved in the occurrence and pathogenesis of GDM. The current study also provides new insight into the molecular mechanism of GDM.

Authors Contribution Statement

Huiyan Wang, Guangtong She, Wenbai Zhou and Bin Yu carried out the assays and participated in designing the study. Huiyan Wang, Guangtong She, Kezhuo Liu, Jun Miao carried out clinical consultation. Guangtong She, Wenbai Zhou and Bin Yu carried out sample collection, laboratory tests and performed the statistical analysis. Guangtong She and Bin Yu conceived the study, participated in its design and coordination and draft the manuscript.

Acknowledgements

We thank all of the project participants for their contributions.

Disclosure of Interests

The authors declare that they have no competing interests.

Details of Ethics Approval

The study design and protocol were reviewed and approved by the ethics committee of Changzhou Maternity and Child Health Care Hospital affiliated to Nanjing Medical University (Approval No: CZFY20160103).

Funding

This work was supported by grants from Jiangsu province health and family planning commission (F201439, QNRC305), Changzhou health and family planning commission (ZD201412), Changzhou municipal bureau of science and technology (CJ20159055).

References
 
© The Japan Endocrine Society
feedback
Top