Gene expression profiles in mouse cumulus cells derived from in vitro matured oocytes with and without blastocyst formation

Accepted Manuscript Gene expression profiles in mouse cumulus cells derived from in vitro matured oocytes with and without blastocyst formation Yixin Xu, Tuanping Zhou, Li Shao, Zhang Bei, Kailu Liu, Chao Gao, Li Gao, Jiayin Liu, Yugui Cui, Ri-Cheng Chian PII:

S1567-133X(17)30078-9

DOI:

10.1016/j.gep.2017.05.002

Reference:

MODGEP 1042

To appear in:

Gene Expression Patterns

Received Date: 20 April 2017 Revised Date:

26 May 2017

Accepted Date: 30 May 2017

Please cite this article as: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Gene expression profiles in mouse cumulus cells derived from in vitro matured

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oocytes with and without blastocyst formation

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Yixin Xu1,2, Tuanping Zhou1, Li Shao1, Zhang Bei1, Kailu Liu1, Chao Gao1, Li Gao1, Jiayin Liu1,

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Yugui Cui1*, Ri-Cheng Chian1,2*

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The First Affiliated Hospital of Nanjing Medical University, Nanjing, P. R. of China

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State Key Laboratory of Reproductive Medicine, Center for Clinical Reproductive Medicine,

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Department of Obstetrics and Gynecology, McGill University, Montreal, Canada

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Corresponding authors: Prof. Ri-Cheng Chian & Prof. Yugui Cui. State Key Laboratory of Reproductive Medicine, Center for Clinical Reproductive Medicine, The First Affiliated

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Hospital of Nanjing Medical University, Nanjing 210029, 300# Guangzhou Road, Jiangsu

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Province, P. R. of China. Tel & Fax: 86-25-68302608; E-mail: [email protected];

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[email protected]

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Abstract

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Cumulus cells (CCs) are considered as an important source to predict oocyte quality. Despite

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numerous candidate genes in CCs have been identified for embryonic developmental competence,

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the results are inconsistent. The next generation RNA-sequencing was used to investigate the

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transcriptomic differences in CCs from in vitro matured oocytes did or did not develop to

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blastocyst stage following in vitro fertilization (IVF). In our study, the corresponding mouse

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oocytes were traced using a single-cell tracking system, and CCs were pooled into groups based

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on the embryonic developmental outcomes. In vivo matured oocytes with blastocyst development

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were set as a reference group. The transcriptomic differences in mouse CCs from in vitro

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maturated oocytes with or without blastocyst formation were tested by RNA-sequencing.

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Real-time PCR was used to verify the expression levels of those candidate genes. A total of 103

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transcripts were significantly up-regulated, and 97 down-regulated, in the CCs with the oocytes

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developed to blastocyst stage. The bioinformatics study showed that those genes were involved in

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tube morphogenesis, cell-cell signaling and cell projection formation. Nine genes were selected

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from the most significantly changed transcripts after comparison with the reference group: Arrb1,

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Atp2c1, Cdh5, Cntnap1, Mkln1, Lgr4, Rhobtb1, Smc2 and Six2, as the candidate target genes.

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They were associated with the regulation of G-protein coupled receptors, Wnt and MAPK

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signaling, actin filaments and cell adhesion. Real-time PCR verified the up-regulation of all 9

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genes, and significantly increased of Rhobtb1, Mkln1, Smc2, Arrb1, Atp2c1, Cdh5 and Lgr4.

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Based on RNA-sequencing, we found the changes in gene transcription of mouse CCs that were

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critical for the communication between CCs and oocytes. The results could provide novel

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insights on non-invasively predicting the oocyte quality and improving developmental

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competence.

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Key Words: oocyte; cumulus cell; gene transcript; oocyte quality; embryonic developmental

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competence

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1. Introduction

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In vitro fertilization (IVF) attempts to get the increased rate of live birth by transfering multiple

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embryos per treatment cycle. However, this also leads to an increase in multiple births, which

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increases the risk of major complications of IVF, such as pregnancy loss and low body-weight

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newborn. In fact, studies have shown that implanting multiple embryos does not improve the

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pregnancy rate in women under 35 years (Min et al., 2006). Elective single embryo transfer is a

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recommended approach to reduce the chance of multiple births. The most critical factor for the

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success of IVF (especially intracytoplasmic sperm injection (ICSI)) is embryo quality, which

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primarily depends on the competence of the oocytes selected for fertilization (Santos et al., 2010).

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Currently, morphological characteristics, such as oocyte diameter, cumulus compactness and

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thickness, cytoplasm brightness and perivitelline space, are used as the primary criteria for

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selecting oocytes (Lasiene et al., 2009). However, these criteria are highly subjective parameters

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and cannot truly predict the oocyte competence. Therefore, the development of non-invasive and

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more accurate methods to predict oocyte quality is a considerable challenge and would represent

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a great advance in the field.

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High dose of gonadotropins are generally administered in order to obtain sufficient matured

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oocytes for assisted reproduction technologies (ARTs). However, the protocols are not

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recommended in those patients with the high risks of ovarian hyper-stimulation syndrome

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(OHSS), such as polycystic ovarian syndrome (PCOS). The in vitro matured (IVM) oocytes are

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used in ARTs to reduce the ovarian stimulation by gonadotropins (Reavey et al., 2016; Walls et al.,

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2015). It is also benefit for many cancer patients who commence the chemo/radiation therapy

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(Creux et al., 2017). However, it is true that the development competence of IVM oocytes is

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lower than that of in vivo matured oocytes (Huang et al., 2010). The improvement of IVM and

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the non-invasively selected high-quality IVM oocytes will significantly improve the clinical

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outcomes of IVM, suggesting the importance of those non-oocyte's markers (such as some

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markers in cumulus cells) in the evaluation of oocyte quality and developmental competence.

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A single oocyte is surrounded by a group of the unique somatic cells called cumulus cells 3

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(CCs), which occupy the immediate interface of the oocytes, forming the cumulus. The

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bi-directional communication between the oocyte and the surrounding CCs is an essential

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requirement for oocyte maturation. It is widely accepted that CCs provide energy substrates for

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oocyte metabolism and support oocyte developmental competence following fertilization.

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Therefore, CCs were considered a good tool to assess oocyte quality for assisted reproduction

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(Hammond et al., 2015; Labrecque and Sirard, 2014). Moreover, some clinical studies have used

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the expression levels of specific genes in CCs to select oocytes for ICSI patients (Ekart et al.,

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2013; Wathlet et al., 2011).

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In a previous microarray study, we reported the significant changes in gene expression and

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some potential target genes in CCs between the germinal vesicle (GV) and metaphase-II stages

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(Shao et al., 2015). Although real-time PCR confirmed the validity and reproducibility of the

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differential expression of the selected genes, it was unclear whether these genes are the

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biomarkers for oocyte developmental competence following fertilization. In addition, some

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previous studies investigated the gene-expression differences between CCs surrounding oocytes

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with high or low developmental competence by the quantitative reverse transcriptase PCR

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(qRT-PCR) and microarray analysis. But their results were inconsistent, very few of genes were

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reported by different research groups, and those studies failed to exclude other confounding

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factors (Fragouli et al., 2014; Uyar et al., 2013). The IVM procedure allowed tight control of the

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extracellular environment, in order to exclude extraneous influences. Compared to earlier

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methods of measuring gene expression, the new generation RNA sequencing (RNA-seq) provides

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more precise detection with higher sensitivity (Wang et al., 2009). Since it does not rely on

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probes, RNA-seq enables more complete profiling of the transcriptome, including of

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low-abundance transcripts and biologically specific isoforms. Original RNA-seq methods

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required a relatively large amount of starting material; thus, analysis of follicular cells by an

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RNA-seq approach was rare (Antoniou and Taft, 2012; Uyar et al., 2013). The pioneering

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application of RNA-seq to screen the whole transcriptome of CCs and explore the potential

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targets for improving in vitro embryo production and the prediction of embryonic development in

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clinical reproductive medicine is a brand-new field. In the present study, we tracked each IVM cumulus-oocyte complex (COC) following IVF to

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identify which oocytes developed to the blastocyst stage and then grouped the mouse CCs based

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on the developmental competence of their corresponding oocyte. We compared the gene

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expression profiles in mouse CCs by the RNA-seq, so as to identify those differentially expressed

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genes related to oocyte quality during maturation as potential molecular biomarkers.

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2. Results

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2.1 Transcriptional profiles of CCs with different oocytes

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Mouse COCs were in vitro cultured, the maturated oocytes were those with the first polar body.

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CCs dissociated from each single oocyte were collected, and grouped according to the maturation

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state of the oocyte. Approximately 45-55% of the oocytes were characterized as reaching the

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blastocyst stage. The rate of blastocyst development in vivo matured COCs was nearly 90%, CCs

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from in vivo matured blastocyst-positive COCs served as a reference group. The experimental

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design is shown in Figure 1.

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We detected 62,173 total expressed transcripts in the three groups of samples and 57,801

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expressed transcripts in the IVM groups. Firstly, we compared the IVM non-blastocyst and

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blastocyst groups; approximately 83.9% of the sequenced transcripts overlapped in these two

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groups, with 10.8% of the transcripts expressed in only the IVM blastocyst group and 5.3%

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expressed in only the IVM non-blastocyst group (Figure 2A). Among these transcripts, 209 were

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significantly differentially expressed (fold change ≥ 2.0, higher RPKM ≥ 1 and P ≤ 0.05). We

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excluded 9 transcripts that were differentially expressed between the in vivo matured blastocyst

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and IVM blastocyst groups, as those transcripts were not supposed to affect the formation of

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blastocyst. Thus, we obtained 97 up-regulated and 103 down-regulated transcripts in the

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comparison of the IVM non-blastocyst and blastocyst groups.

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2.2 GO analysis of differential gene expression

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We performed the GO analysis to highlight the biological processes, molecular functions and

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cellular components related to the differentially expressed transcripts. The top GO terms are

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listed in Table 2. We assessed the predominant GO terms for the set of 200 significantly

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differentially expressed transcripts using DAVID bioinformatics tools, according to the

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enrichment P-values (Figures 2B, 2C and 2D). Many differentially expressed genes were

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involved in biological processes relevant to skeletal system morphogenesis, tube morphogenesis,

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or cell-cell signaling; cellular components relevant to neuron and cell projection, vesicles, or the

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endomembrane system; and Swiss-Prot and

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(SPPIRKEYWORD) relevant to the nucleus.

protein information resource keywords

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2.3 Gene network of differential gene expression

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We performed a KEGG pathway analysis of the differentially expressed genes, but the genes

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were dispersed in many pathways; only a few pathways were enriched, including the MAPK

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(mitogen-activated protein kinase) signaling pathway, cell adhesion molecules and the cell cycle

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(Figure 3, 4, 5). To further investigate the connections among these genes, we processed a set of

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genes using the GeneMANIA bioinformatics tools. To reduce the analysis complexity and narrow

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the list of target genes for further verification, we set another threshold at fold change ≤ 2.0 for

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the IVM blastocyst group compared to the in vivo matured reference group, and a higher read

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count (≥ 20) for the comparison of the IVM blastocyst and non-blastocyst groups. Then, we

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obtained 41 transcripts from 29 known genes and 3 unknown genes (Table 3). The generated

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molecular networks are shown in Figure 6. In spite of no directly interacting genes were

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identified, the knowledge database suggested that some genes shared protein domains, such as

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Mkln1 (muskelin 1) and Cntnap1 (contactin associated protein 1), Six2 (sine oculis homeobox

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homolog 2) and Hoxc9 (hox c gene 9), Atp2c1 (calcium transporting ATPase type 2C member 1)

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and Atp11b (calcium transporting ATPase type 11b), and that some genes were within

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co-expression networks, including Arrb1 (β-arrestin 1), Arhgef5 (Rho guanine exchange factor 5),

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Cdh5 (cadherin 5), Cntnap1, Golim4 (Golgi integral membrane protein 4), Lgr4 (leucine-rich

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repeat-containing G-protein coupled receptor 4), Mkln1, Six2, Sirt2 (silent information regulator

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2), Smc2 (structure maintenance of chromosome 2) and Steap2 (six transmembrane epithelial

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antigen of prostate 2).

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2.4 Target gene selection and real-time PCR validation

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To verify our RNA-seq results, we performed real-time PCR on selected target transcripts or

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genes. We applied the following criteria for target selection: (1) transcripts that were significantly 7

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differentially expressed in blastocyst and non-blastocyst CCs, as shown in Table 3; (2) genes that

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were reportedly involved in cell-cell communication or biological processes such as

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tube/cytoskeleton morphogenesis, cell-cell signaling, and cell division, especially those that

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might have connections in the gene network analysis; and (Pierce et al., 2014) transcripts that had

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distinct and detectable sequences if their isoforms were abundantly expressed in two groups

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without significant changes. Those genes with many isoforms that were not differently expressed,

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such as Steap2, were excluded. Therefore, we focused on the transcripts/genes that are presented

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in bold font in Table 3: Cntnap1, Cdh5, Six2, Mkln1, Smc2, Rhobtb1, Lgr4, Arrb1, and Atp2c1.

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These 9 genes are involved in G protein-coupled receptor (GPCR) signaling, ATPase, Wnt

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(Wingless type MMTV integration site family member) signaling, MAPK (mitogen-activated

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kinase) signaling, actin filaments, cell adhesion and cell migration; the detailed functions are

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discussed in the discussion section.

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The expression patterns of these 9 genes in the IVM blastocyst and non-blastocyst groups

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were similar in the real-time PCR data and the RNA-seq data. Arrb1, Atp2c1, Cdh5, Mkln1,

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Smc2, Rhobtb1 and Lgr4 were significantly up-regulated in CCs from the IVM blastocyst group

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compared to the non-blastocyst group. The expression of Cntnap1 and Six2 showed an increasing

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trend in the IVM blastocyst group; the differences were not significant, but the same trends were

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observed in replicate trials (Figure 7).

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3. Discussion

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To date, many genes expressed in CCs have been considered potential biomarkers for predicting

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oocyte developmental competence, but the results have been inconsistent due to the use of

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different screening approaches and sample cells collection methods (Chronowska, 2014; Fragouli

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et al., 2014; Uyar et al., 2013). HAS2 (hyaluronic acid synthetase 2), GREM1 (gremlin 1),

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PTGS2 (prostaglandin-endoperoxide synthase 2), TNFAIP6 (tumor necrosis factor alpha induced

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protein 6), PTX3 (pentraxin 3), CDC42 (cell division cycle protein 42) and dozens of genes were

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identified as associated with oocyte or embryo quality by qRT-PCR (Anderson et al., 2009; Cillo

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et al., 2007; Feuerstein et al., 2007; Feuerstein et al., 2012; Gebhardt et al., 2011; McKenzie et al.,

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2004; Wathlet et al., 2012). Microarrays have also been utilized to identify many other genes

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(Anderson et al., 2009; Assidi et al., 2008; Hamel et al., 2008; van Montfoort et al., 2008; Zhang

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et al., 2005). These genes are involved in various cellular events, including cell cycle, growth

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factor signaling, extracellular matrix production, metabolism and apoptosis. A few of the

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significantly differentially expressed genes that we identified in our study have been reported to

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be associated with oocyte quality, such as fibroblast growth factor 10 (FGF10) (Zhang et al.,

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2010). Our RNA-seq data revealed a new transcriptomic profile of CCs in the context of

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blastocyst forming ability by using the next generation RNA-seq. Although some RNA-seq or

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miRNA sequencing studies have been performed on intra-follicular somatic cells (Assou et al.,

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2013; Liu et al., 2015; Tong et al., 2014; Yerushalmi et al., 2014), to the best of our knowledge,

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there has been not a study that uses RNA-seq to study the whole transcriptome of CCs associated

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with oocyte competence. Recently, Yerushalmi et al. (2014) investigated the transcriptome of

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human CCs during IVM via RNA-seq (Yerushalmi et al., 2014). However, they focused on the

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changes between immature and mature cells in a limited sample size. A just released RNA-seq

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study on the links between transcripts in corona cells and oocyte competence revealed

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enrichments in Wnt signaling, MAPK signaling, ATP generation and cell-matrix adhesion, which

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is partly consistent with our research (Parks et al., 2016).

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In this study, two in vitro maturated groups were cultured under strictly the same conditions. 9

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Therefore, we were not surprised that only approximately 200 genes were significantly changed

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among the groups. Our single COC tracking system enabled the purity within each group. To

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obtain sufficient RNA for whole transcriptome sequencing, we had to pool CCs with the same in

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vitro developed outcomes, which minimized individual differences. As approximately 8

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IVM-IVF blastocyst+ were obtained from one mouse (if 20 COCs per mouse were retrieved), and

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about a hundred COCs were required for one sample. Blastocyst formation rates in the in vivo

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maturated oocytes were really high, which increased the difficulty to obtain sufficient in vivo

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matured blastocyst-negative samples. Thus, the difference comparison between in vivo groups

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would be one of our future studies. In the present study, we utilized an in vivo matured

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blastocyst-positive group to reduce the randomness of the results, but not an experimental group,

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as the samples from in vivo group was collected by a different stimulation method.

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The enriched Go terms in our results highlighted two important aspects of cell-cell

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communication in the CCs and oocyte microenvironment: (1) molecular signaling from the cell

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membrane into CCs through receptor activation and subsequent signaling (such as MAPK- and

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Wnt- signaling pathways), it enable the molecular transport from culture environment to CCs; (2)

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physical connections (such as cell-cell junctions) between CCs and oocytes that provide a

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communication channel. The abnormal expression of certain genes disrupt these communication

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may lead to oocyte development problems.

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In this study, we verified 9 previously unidentified genes that were more highly expressed in

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the CCs associated with higher oocyte competence. Among these genes, Arrb1, Lgr4, Smc2 and

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Six2 are relevant to Wnt signaling pathway. The Smc2 protein is a subunit of chromosome

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condensation complexes (Hudson et al., 2009; Wu and Yu, 2012). Davalos et al. (2012) revealed a

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novel transcriptional regulation of SMC2 by the Wnt signaling in which β-catenin coupled on the

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SMC2 promoter. The Six2 gene encodes a transcriptional regulator shares regulatory gene

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networks with the Wnt signal transducer β-catenin (Pierce et al., 2014; Park et al., 2012). Wnt

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signaling is critical for oocyte maturation (Boyer et al., 2010; Prunskaite-Hyyrylainen et al.,

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2014). The up-regulation of these genes might reflect the inducing of effective Wnt signaling

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events during COC maturation. Arrb1 is a member of the arrestin protein family, which is thought to regulate

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agonist-mediated desensitization and internalization of GPCRs. It is involved in the cellular

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responses to certain hormones and growth factors (Hupfeld et al., 2003), and Arrb1 can decrease

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cAMP levels (Hupfeld et al., 2003; Tripathi et al., 2010). cAMP is well known for its key role on

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oocyte maturation through the regulation of oocyte meiosis, and it exhibits crosstalk with Wnt

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signaling (Tripathi et al., 2010). Recently, Feuerstein et al. (2012) verified RGS2 (regulator of G

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protein signaling 2) in human CCs was associated with oocyte developmental competence. Thus

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we suggested Arrb1 might affect oocyte quality through regulating GPCR signaling and cAMP

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accumulation. Furthermore, Arrb1 was reported to be required for MAPK activation (Figure 3)

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(Bourquard et al., 2015), and to participate in metabolic reprogramming (Semenza, 2013;

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Zecchini et al., 2014). These indicate that Arrb1in CCs may play a critical role in oocyte

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development.

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Lgr4 encodes a GPCR for the family of secreted R-spondins. Lgr4 is a functional activator of

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the canonical Wnt/β-catenin signaling pathway (Carmon et al., 2014; Mazerbourg et al., 2004; de

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Lau et al., 2011). It has high homology with the FSH and LH receptors, and is abundantly

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expressed in reproductive organs (Mohri et al., 2010; Van Schoore et al., 2005). Animal models

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showed an important role of Lgr4 on fertilization in females (Hsu et al., 2014; Kida et al., 2014).

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R-spondin2 is expressed by oocytes. R-spondin2/Lgr4 signaling not only promotes the

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development of follicles (Biason-Lauber and Chaboissier, 2015; Cheng et al., 2013), but also

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induce the expression of follistatin, thereby may influence the developmental competence of

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oocytes (Han et al., 2014; Patel et al., 2007). In addition, Lgr4 was recently shown to have a

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surprising role in triggering the formation of long actin-rich, cytoneme-like membrane

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protrusions (Snyder et al., 2015). Cytonemes are a type of filopodium that provide a platform for

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the exchange of signaling proteins, such as Wnt8, between cells (Stanganello et al., 2015).

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On the other hand, a type of membranous extension called a transzonal projection (tzp) can

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stretch from the cumulus through the zona pellucid to the oocyte cell membrane. Gap junctions or 11

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adherens junctions are formed with the oocyte cell membrane at the end of tzps (Kidder and

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Mhawi, 2002). Tzps might be critical for oocyte quality, as they can transport many essential

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molecules for oocyte growth. Studies found these extensions mainly consist of actin filaments

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(Coticchio et al., 2012). In this study, the verified genes Rhobtb1, Mkln1, and Cntnap1 are

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relevant to the formation of actin filaments or cell synapses.

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Rhobtb1 belongs to the Rho GTPase family. Many Rho proteins have specific roles in cell

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adhesion and cell migration (Aspenstrom et al., 2004). For instance, RhoA was reported to

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regulate cytoskeleton dynamics in oocyte maturation (Zhang et al., 2014). Rhobtb1 had a

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moderate influence on the organization of actin filaments, it probably affects transcriptional

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regulation and endocytosis during COC development (Aspenstrom et al., 2004). Our pathway

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analysis also highlighted some cell adhesion genes, including Cdh5, a calcium-dependent

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cell-cell adhesion glycoprotein also known as VE (vascular endothelial)-cadherin. Cdh5

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potentially plays a critical role in organizing intercellular junctions and associated with OHSS

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and gonadotropin-dependent folliculogenesis (Dejana and Orsenigo, 2013; Harris and Nelson,

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2010). Thus, it may participate in adherens junctions in COCs.

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Mkln1 is a regulator of actin-cytoskeleton reorganization in response to THBS1

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(thrombospondin 1), which is a cell-cell or cell-matrix interaction factor (Heisler et al., 2011). A

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recent study found that Mkln1 was crucial during Drosophila oocyte maturation (Kronja et al.,

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2014). The cell adhesion molecule Cntnap1, also known as Caspr (contactin-associated protein),

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shares protein domains with Mkln1. Cntnap1 was reported to be an essential constituent in

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paranodal junctions between glial cells and axons (Bhat et al., 2001; Rios et al., 2000). Therefore,

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we speculated these genes might be involved in the interactions between oocytes and CCs.

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Interestingly, the differentially expressed Mkln1 transcript is an alternatively spliced transcript

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which does not code protein directly. However, the intron retention is believed to relative to the

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regulation of functional proteins (Ge and Porse, 2014; Sammeth et al., 2008). This provided a

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new insight for post-transcriptional gene regulation by long noncoding RNA in COC

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development.

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The Atp2c1 gene encodes SPCA1 (secretary pathway Ca2+-ATPase isoform 1), a P-type

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ATPase that transports calcium ions in the Golgi complex. It regulates cytoplasmic calcium levels,

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and transports calcium into the secretory pathway. Ca2+release is part of the reactivation of oocyte

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meiosis and associates with cell migration, thus Atp2c1might play an essential role in embryonic

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development (Okunade et al., 2007).

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4. Conclusion

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We used a new technique of global transcriptome RNA-seq and an in vitro single-cell tracking

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culture system to compare the gene expression profiles in CCs based on the early embryo

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development of their corresponding oocytes. The results provide new directions for identifying

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the genetic determinants in CCs for oocyte development. We propose 9 novel candidate genes

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(Arrb1, Atp2c1, Cdh5, Cntnap1, Mkln1, Lgr4, Rhobtb1, Smc2 and Six2) as potential biomarkers

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for predicting oocyte developmental competence, which has the potential to improve the quality

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of utilized oocytes. And it provided a foundation for our further clinical studies.

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5. Experimental Procedures

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5.1 Animals

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CD-1 mice (6-8 weeks old) were obtained from the Medical Experimental Animal Center of

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Nanjing Medical University. All the mice were housed in barrier facilities for 1 week and

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subjected to routine procedures. All the mouse studies were performed in accordance with the

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Guide for Animal and Human Ethics Board of Nanjing Medical University. All the mice received

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a standard mouse diet and were maintained at 20-22°C on a 12:12h light: dark cycle.

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5.2 COC retrieval and IVM

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Mice were primed with 5 IU of equine chorionic gonadotropin (eCG) (Folligon, Australia) by

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intraperitoneal injection (i.p.); 48 h later, the mice were euthanized by cervical dislocation. The

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ovaries were removed and placed in M2 medium (M7167, Sigma, USA). COCs were obtained

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from antral follicles by needle puncture under a dissecting microscope. COCs with several layers

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of compact CCs surrounding the oocyte were selected using a micropipette. After two washes,

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groups of 10 COCs were placed in 30-µl droplets of IVM medium [(Alpha-MEM with 5% FBS

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(fetal bovine serum), 0.25mmol/l Sodium pyruvate, 3ng/ml EGF (epidermal growth factor) and

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50mIU/ml rhFSH (recombinant human follicle stimulating hormone)] that were covered with

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mineral oil. COCs in the same droplet were kept apart from each other to avoid the merging of

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CCs after expansion. COCs were cultured at 37°C with high humidity in a 5% CO2 incubator for

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16-18h. The presence of the first polar body is considered to be the marker for maturated oocytes.

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The success rate of the IVM is approximately 90.6% (Shao et al., 2015b).

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The In vivo matured COCs served as a reference. Briefly, 48 h after the injection of 5.0 IU of

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ECG, 5.0 IU of human chorionic gonadotropin (hCG, Sigma, USA) was administered by i.p.

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injection. At 12-14 h after the HCG injection, the mice were sacrificed and the matured COCs

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were released from the oviducts by tearing the ampullae under a dissecting microscope.

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5.3 Collection of cumulus cells 15

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After maturation, CCs were isolated from individual morphologically normal MII oocytes by a

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digestion and mechanical technique: repeated pipetting in 50-µl drops of hyaluronidase solution

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(100 units/ml) (H-3506, Sigma, USA). CCs dissociated from each single oocyte were carefully

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collected using glass micropipettes, re-suspended in buffer RLT (Qiagen, USA), and stored at

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-80°C for future studies. After the embryo development results were obtained, the CCs were

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grouped according to the maturation state of the oocyte, which was evaluated under a

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stereomicroscope (Figure 1).

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5.4 IVF and early embryo culture

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Male CD-1 mice (8-12 weeks of age) were euthanized, and two epididymides with vasa

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deferentia were removed. The sperm were released and transferred using scissors and forceps to

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HTF (human tubal fluid) medium (Invitrogen, USA) [with 1mg/ml BSA (bovine serum albumin)],

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and the dish was placed in an incubator for capacitation at 37°C under 5% CO2 and 95%

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humidity for 1-2 h. Then, the capacitated sperm suspension was added to the 10-µl drops of

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insemination HTF medium (with 10mg/ml BSA) containing one freshly transferred maturated

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oocyte, for a final sperm concentration of 1-4×106/ml. Oocytes and sperm were co- incubated for

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4-6 h, and the oocytes/zygotes were then washed several times to remove sperm. Each single

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oocyte/zygote was transferred to a 10-µl drop of KSOM medium (Invitrogen, USA) (including

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1mg/ml BSA) and cultured in an incubator at 37°C under 5% CO2 and 95% humidity. After 24-h,

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unfertilized oocytes were excluded from the study, two pronuclei observed zygotes were

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sequentially cultured. Each oocyte development was assessed at 24-h intervals. Formation of

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blastocysts was evaluated on day 5, based on Gardner’s blastocysts grading system.

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5.5 Total RNA extraction

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For RNA-seq and real-time PCR verification, CCs were pooled from three groups of COCs: 371

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IVM COCs that developed to blastocysts after IVF, 458 IVM COCs that did not develop to

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blastocysts after IVF, and 368 in vivo matured COCs that developed to blastocysts after IVF. 16

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Total RNA from the pooled samples was extracted as described previously (Dai et al., 2014; Li et

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al., 2015), using the RNeasy Mini Kit (74104, Qiagen, USA) according to the manufacturer’s

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instructions. RNA quantity was determined using a NanoDrop ND-1000 spectrophotometer.

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5.6 RNA sequencing

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RNA samples were purified using the Dynabeads mRNA Direct Kit (Life Technologies, USA)

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according to the manufacturer’s instructions. The Ion Total RNA-Seq Kit v2 (Life Technologies,

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USA) was used to construct an Ion Torrent library according to the manufacturer’s instructions

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(#4476282, chapter 2). In brief, we fragmented RNA corresponding to the whole transcriptome

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using RNase III and then purified the fragmented RNA. Next, we subjected the ligated RNA to

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reverse transcription (RT), purified and amplified the cDNA, pooled the barcoded whole

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transcriptome libraries and determined the dilution for template preparation. Then, we prepared

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the template Ion Sphere Particles (ISPs) for sequencing. Template-positive ISPs were prepared as

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described in the Ion PGM Template OneTouch 2 System 200 Kit User Guide (#4480974). The

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Ion PGM Sequencing 200 Kit was used for sequencing reactions according to the recommended

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protocol (#4474276, chapter 4). In brief, we added control ISPs, annealed the sequencing primer

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to the enriched ISPs described above, and performed the chip check test. Next, we loaded the Ion

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318 Chip v2 and selected the Planned Run to perform the run (Supplementary material). The Ion

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Torrent platform-specific pipeline software Torrent Suite v3.6 was used to initially process data

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from the PGM runs, generate sequence reads, trim adapter sequences, filter reads, and remove

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reads with a poor signal profile.

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5.7 RNA-seq data analysis

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Raw RNA-seq data were mapped to the musculus GRCm38.75, and transcripts were identified

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using the Ensemb l gene browser (ensembl.org). ENSEMBLGENEID was used to upload the

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target transcript list for the analysis tools. The cutoffs for identifying differentially expressed

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transcripts (both up-regulated and down-regulated) between the IVM blastocyst and IVM non17

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blastocyst groups were as follows: fold change ≥ 2.0, P ≤ 0.05 and RPKM (reads per kilobase of

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transcript per million mapped sequence reads) ≥ 10. To assess the function of the differentially

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expressed transcripts, gene ontology (GO) analysis was performed using TopGo (Bioconductor

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package)

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(https://david.ncifcrf.gov/tools.jsp) were used to identify the enrichment of those significantly

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changed transcripts within the enriched GO annotation categories. Based on the Kyoto

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Encyclopedia of Genes and Genomes (KEGG) database and DAVID functional annotation,

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pathway analysis of differentially expressed genes was performed. Target genes involved in the

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most enriched pathway terms are highlighted in a red box. Network analysis was performed using

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the Gene MANIA bioinformatics database (http://www.genemania.org) after filtering the gene list

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for differentially expressed transcripts with a read count < 20.

Fisher’s

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5.8 Real-time qPCR

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RNA samples were reverse transcribed at 42°C for 1 h according to the Reverse Transcription Kit

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instructions (M510F, Promega, USA). Then, cDNA was used as a template for real-time PCR,

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which was performed using Quanti Tect SYBR Green PCR kits (204145, Takara, Japan) and a

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Step One Plus Real-time PCR System (ABI, USA). The expression of selected genes/transcripts

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was normalized to GAPDH expression and calculated using the 2^(−∆∆Ct) method. Each sample

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was assayed at least three times. The transcript sequences were obtained from the UCSC Genome

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Browser (http://genome.ucsc.edu), and the specific primer sequences were shown in Table 3, and

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the annealing temperature was 60°C for all sequences.

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5.9 Statistical analysis

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Raw real-time PCR data were analyzed using SPSS version 20.0 (IBM, USA), and these data are

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presented as the mean ± SEM (GraphPad Prism 5.0, GraphPad, USA). The significance of

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differences in target gene RNA expression in independent samples was evaluated by a two-tailed

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Student’s t-test, and P < 0.05 was considered significant. 18

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List of Abbreviations

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COC: cumulus-oocyte complex; CCs : Cumulus cells; NGS: next generation RNA-sequencing;

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RNA-seq: RNA sequencing; GO: gene ontology; RT: reverse transcription; qRT-PCR:

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quantitative reverse transcriptase PCR; ART: assisted reproduction technology; IVF: in vitro

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fertilization; ICSI: intracytoplasmic sperm injection; PGD: pre-implantation genetic diagnosis;

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IVM: in vitro maturation; PCOS: polycystic ovarian syndrome; OHSS: ovarian hyper-stimulation

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syndrome; GV: germinal vesicle; MII: metaphase-II; tzp: transzonal projection; eCG: equine

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chorionic gonadotropin; hCG: human chorionic gonadotropin; rhFSH: recombinant human

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follicle stimulating hormone; EGF: Epidermal Growth Factor; HTF: human tubal fluid; FBS:

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fetal bovine serum; BSA: bovine serum albumin; GPCR: G protein-coupled receptor; Wnt：

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wingless type MMTV integration site family member; THBS1: thrombospondin 1; RGS2 :

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regulator of G protein signaling 2; Sirt2: silent information regulator 2; VE: vascular endothelial;

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FGF10: fibroblast growth factor 10; Caspr: contactin-associated protein; SPCA1: secretary

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pathway Ca2+-ATPase isoform 1; Steap2：six transmembrane epithelial antigen of prostate 2;

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Hoxc9: hox c gene 9; CDC42: cell division cycle protein 42; Golim4: Golgi integral membrane

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protein 4; MAPK: mitogen-activated kinase; HAS2: hyaluronic acid synthetase 2; PTGS2:

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prostaglandin-endoperoxide synthase 2; TNFAIP6: tumor necrosis factor alpha induced protein 6;

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Rhobtb1: Rho-related BTB domain protein 1; Smc2: structure maintenance of chromosome 2;

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Lgr4: leucine-rich repeat-containing G-protein coupled receptor 4; Six2：sine oculis homeobox

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homolog 2; Arrb1: β-arrestin 1; Mkln1: muskelin 1; Cntnap1: contactin associated protein 1;

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Cdh5: cadherin 5; Atp2c1: calcium transporting ATPase type 2C member 1; PTX3: pentraxin 3;

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GREM1：gremlin 1; FGF8: fibroblast growth factor 8.

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Declarations

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This study was approved by the Animal Ethics Committee of Nanjing Medical University, China.

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All mice were obtained from the Medical Experimental Animal Center of Nanjing Medical

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University. All the mouse studies were performed in accordance with the Guide for Animal and

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Human Ethics Board of Nanjing Medical University.

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Consent for Publication

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Not applicable.

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Availability of data and materials

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Please contact author for data requests.

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Competing Interest

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The authors declare that they have no competing interests.

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Funding

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This study was supported by the 973 Program of China [grant numbers 2012CB944703], the

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NNSFC [grant numbers 81270746, 81370754] and the Research Fund of NHFPC [grant numbers

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201302013, 201402004).

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Author Contributions

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Xu Y analyzed data and wrote the first draft of the manuscript. Zhou T, Shao L, Zhang B and Liu

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K are Masters or PhD students in the Laboratory. Zhou T and Shao L collected samples and

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performed the main experiments. Zhang B and Liu K helped perform the verification experiments.

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Gao C and Gao L, two researchers in our group, provided assistance with laboratory techniques

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and animal work. Liu J participated in the project design and data analysis. Cui Y and Chian RC,

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two principal investigators, designed the study, analyzed the data and revised the manuscript. All

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authors reviewed and approved the final version of manuscript.

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Legend of Figures

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Figure 1. Experimental design, groups and sample collection protocol.

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Figure 2. Transcriptional profiles of CCs with different oocytes. (A) Overlapping genes and GO

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enrichment analysis in the IVM blastocyst and non-blastocyst groups. RNA-seq detected 54,725

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transcripts in IVM blastocyst samples and 51,582 transcripts in non-blastocyst samples. Among

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them, approximately 83.9% were expressed in both groups of IVM cumulus cells, and

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approximately 10.8% were expressed in only blastocyst-positive IVM cumulus cells. (B, C and D)

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GO enrichment analysis of 200 highly differentially expressed RNA transcripts. Top GO terms

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significantly enriched by P-value in biological processes (B), cellular components (C) and

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SP-PIR keywords (D).

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Figure 3. Differentially expressed genes (shown by red frames) mapped to proteins in the

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significantly enriched molecular pathways. KEGG mapping of Cdh5, Igsf4b (also called Cadm3)

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and Cntnap1 involvement in cell adhesion.

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Figure 4. KEGG mapping of FGF and ARRB family members involved in the MAPK signaling

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pathway. Differentially expressed genes (shown by red frames) mapped to proteins in the

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significantly enriched molecular pathways.

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Figure 5. KEGG mapping of SCF and Mad1 involvement in cell cycle. Differentially expressed

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genes (shown by red frames) mapped to proteins in the significantly enriched molecular

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pathways.

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Figure 6. Interaction networks of differentially expressed genes using GeneMANIA online tools.

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(A) The presumed network of the 29 most significantly changed known genes (shown in black

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background). (B) The sub-network of co-expressed genes: Arrb1, Arhgef5, Cdh5, Cntnap1, 36

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Golim4, Lgr4, Mkln1, Six2, Sirt2, Smc2 and Steap2.

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Figure 7. Real-time PCR validation of select genes identified by RNA-seq in samples from IVM

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blastocyst positive (white) and negative (diagonal) groups. Fold changes were presented as the

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mean ± SD. The differences were significant at P<0.1 for all genes. * P<0.05.

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Table 1 Top 10 GO terms enriched by all differentially expressed genes.

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GO ID GO:0008150 GO:0008283 GO:0045944 GO:0006355 GO:0008544 GO:0031668 GO:0001656 GO:0007049 GO:0007165 GO:0006281

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GO ID GO:0003674 GO:0005515 GO:0005524 GO:0046872 GO:0003676 GO:0008270 GO:0005388 GO:0000166 GO:0042803 GO:0005525

nucleolus integral component of Golgi membrane cytoplasm cellular component intracellular plasma membrane membrane mitochondrion endoplasmic reticulum condensed chromosome Molecular Function GO Term molecular_function protein binding ATP binding metal ion binding nucleic acid binding zinc ion binding calcium-transporting ATPase activity nucleotide binding protein homodimerization activity GTP binding Biological Process GO Term biological_process cell proliferation positive regulation of transcription from RNA polymerase II promoter regulation of transcription, DNA-templated epidermis development cellular response to extracellular stimulus metanephros development cell cycle signal transduction DNA repair

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GO ID GO:0005730 GO:0030173 GO:0005737 GO:0005575 GO:0005622 GO:0005886 GO:0016020 GO:0005739 GO:0005783 GO:0000793

Cellular Component GO Term

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Table 2 The table of most significantly differentially expressed genes.

Transcript

positive

Length

control

673 198 4252 3571 5440 Cntnap1 Rdh10 3587 1812 Six2 Nsun4 1984 Hoxc9 2374 3986 Cdh5 Steap2 1574 Tmem50b 2347 Fgf10 4114 Cabp5 1777 3110053B16Rik 2004 2128 Six2 Celf6 2973 Gm16245 895 Gm9006 841 Atp11b 3028 Celf6 2924 Celf6 3087 Golim4 5230 Smyd5 964 3617 Mkln1 Kcnb2 4065 5328 Smc2 5404 Smc2 4352 Rhobtb1 4422 Rhobtb1 Sirt2 1843 Sirt2 2117 Asgr1 600 750 Lgr4 Arhgef5 2319 Cstf3 2817 3715 Arrb1 2409 Atp2c1

25.39 262.35 1.61 9.86 4.21 1.81 9.81 3.62 8.21 4.46 6.3 3.79 5.4 7.89 8.87 11.56 4.6 21 13 2.6 5.49 3.76 1.63 8.86 10.02 6.22 1.35 1.33 4.79 4.71 7.05 6.13 23.35 7.75 10.46 5.34 5.89 3.69

blastocyst

(RPKM)

(RPKM)

34.05 159.15 2.81 7.35 2.19 4.39 6.32 5.78 5.63 3.83 9.4 6.31 3.6 5.91 9.53 8.97 4.18 14.94 14.19 3.94 3.92 3.71 2.19 10.4 5.54 2.7 1.79 1.77 4.28 4.21 9.33 8.12 15.91 14.01 4.53 5.76 4.37 7.93

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Zfp451 Gm11027 Sv2c Serpina9

IVM Non-

3.33 22.61 0.18 1.46 0.27 0.83 0.82 0.75 0.94 0.75 1.9 1.27 0.73 0.84 2.23 2.1 0.75 3.33 2.66 0.74 0.77 0.73 0.43 1.55 1.44 0.55 0.28 0.28 1.2 1.18 2.43 2.11 49.74 2.98 0.97 1.59 1.21 2.48

Ratio

8.09 6.78 3.23 3.39 2.51 2.95 4.02 3.87 3.42 2.76 3.59 3.22 2.66 3.76 3.26 3.22 2.96 3.68 4.15 2.84 2.78 2.72 2.23 4.47 2.68 2.39 2.18 2.16 2.4 2.39 3.01 2.93 0.33 3.77 2.81 2.61 2.43 2.57

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(RPKM)

IVM Blastocyst

P Value

0.00085 0.00154 0.00455 0.00728 0.01143 0.01354 0.01441 0.01441 0.01551 0.01627 0.01961 0.01961 0.01961 0.02324 0.02329 0.02329 0.02363 0.02874 0.02932 0.02932 0.02932 0.02932 0.02932 0.02972 0.03029 0.03652 0.0382 0.0382 0.03995 0.03995 0.04215 0.04215 0.04525 0.04565 0.04565 0.04908 0.04908 0.04953

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Gene name

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Blastocyst

Table 3

Primer sequences for qPCR analysis.

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Primer sequences (5'->3')

GenBank accession No.

Position

Product size (bp)

Arrb1

Forward: CCG AGG ACA AGA AGC CAC TGA C Reverse: GGA TGA CTA GCC GCA CAG AGT TC

NM 177231.2

565-798

234

Atp2c1

Forward: GCA CCG AAC ACA GCA GGA CAG A Reverse: GAC CAG ACG CCA AGG CAA GAA CT

NM 001253831.1

1607-1800

194

Cdh5

Forward: TGG TCA CCA TCA ACG TCC TA Reverse: GCA CAA TGG ACT CTT TCC CTA C

NM 009868.4

1249-1566

318

Cntnap1

Forward: CAC TCC AAC CAG ACA GCA TTC CA Reverse: CGG TAA GTT CGC AGT AGC AGA TGA

NM 016782.2

1633-1852

220

Lgr4

Forward: CCC GAC TTC GCA TTC ACC AA Reverse: GCC TGA GGA AAT TCA TCC AAG TT

NM 172671.2

1051-1202

152

Mkln1

Forward: GGG AGC CTG GTT TAG AGA AAG TGA Reverse: CAT CAG TGA GGT CAG CAG TGA ACT

*

2175-2321

147

Rhobtb1

Forward: GTG GCG AAG GAA CTT GGC ATC Reverse: GTT GTC CAG TAA ACA GGC AGC AT

NM 001252636.1

651-908

258

Six2

Forward: GCC AAG GAA AGG GAG AAC AGC Reverse: GAG CAA CA GAG CGG GAC TGG

NM 011380.2

852-1013

162

Smc2

Forward: GTC GCT CAG AAT CTT GTT GGT CCT Reverse: GCA CCT CCA CTC AAT GTT CCA TGA

NM 001301412.1

2022-2245

224

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* Transcript Ensembl ID: ENSMUST00000137621

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