Markers for predicting oocyte copy number variation and applications thereof

Abstract

The application discloses a kind of marker for predicting oocyte copy number variation and application thereof, the marker includes the combination of CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP and APOO protein.By the marker of the application, the expression level of granulosa cell secretory protein can be determined by granulosa cell transcriptome sequencing, and the genomic CNV condition of corresponding oocyte can be inferred.The detection object of the technical solution is discarded granulosa cell in assisted reproductive technology, so it will not have any impact on oocyte, and it is non-invasive;The present study is a transcriptome and methylation group detection from single cell level, with high-throughput characteristics, and all granulosa cells and oocytes are one-to-one matched, which can reflect the difference between single cell level COCs.

Description

Technical Field

[0001] This invention belongs to the field of reproductive technology, specifically relating to biomarkers for predicting oocyte copy number variations and their applications. Background Technology

[0002] Oocyte quality is a fundamental indicator for evaluating female fertility. Unlike other mammals, humans have a higher proportion of oocyte copy number abnormalities, a phenomenon particularly pronounced in women of advanced reproductive age (generally defined as women over 35 years of age). Oocyte chromosome copy number abnormalities directly lead to abnormal embryonic development after fertilization, causing pregnancy failure. Clinical assisted reproductive technologies first select healthy oocytes for in vitro fertilization to obtain transferable fertilized eggs. Therefore, effectively predicting oocyte chromosome copy number and excluding abnormal oocytes is crucial for the success of assisted reproductive technologies. Current research suggests that high-throughput sequencing of polar bodies and PGT can obtain information on chromosomal abnormalities in oocytes or early embryos, but this procedure is complex and expensive. Furthermore, studies have reported that the follicular microenvironment during oocyte development has a significant impact on oocyte development. Granulosa cells are in direct spatial contact with the oocyte, and the signaling factors and nutrients secreted by granulosa cells directly affect the oocyte, playing a key role in normal oocyte development and maturation. Abnormalities in granulosa cells will lead to an imbalance in the signaling factors they secrete, thereby affecting oocyte quality.

[0003] In Anderson, RA, et al., "Cumulus gene expression as a predictor of human oocyte fertilisation, embryo development and competence to establish aplasticity," *Reproduction* 138.4 (2009): 629-637, researchers first demonstrated the feasibility of using granulosa cells to assess oocyte developmental potential. In an experimental study, 674 cocci (COC) samples were obtained from 75 women participating in ICSI. Granulosa cells were further isolated and their gene expression analyzed using quantitative RT-PCR. The study found that oocytes from granulosa cells with high GREM1 expression were more suitable for subsequent ICSI and embryo transfer (49% vs 33%, P < 0.02). It also found that oocytes from granulosa cells with low BDNF expression had a higher normal fertilization rate. However, the traditional qPCR assay used by the authors only supports six genes (HAS2, BDNF, GREM1, PTGS2, TNFAIP6, and PTX3), resulting in low throughput and a limited number of potential targets for screening. Furthermore, the authors themselves pointed out that the expression level of GREM1 in granulosa cells is not significantly related to the pregnancy rate. They only clarified that the expression level of GREM1 affects the embryo transfer rate and cryopreservation selection. Moreover, the method in this article does not involve predicting oocyte copy number variation (CNV).

[0004] In Assidi, M., et al., "Biomarkers of human oocyte developmental competitiveness expressed in cumulus cells before ICSI: a preliminary study," *Journal of Assisted Reproduction & Genetics* 28.2 (2011): 173-188, the authors first clarified that the morphological characteristics of cumulus cells can be used to reflect oocyte quality. These morphologically favorable co-occurrences (COCs) are associated with pregnancy outcomes. Furthermore, they used gene expression microarrays to identify differentially expressed genes in cumulus cells corresponding to oocytes from successful and failed pregnancies, and screened for potential cumulus cell biomarkers that could predict oocyte quality and pregnancy outcomes. They further validated, using qPCR, that the expression levels of DPP8, HIST1H4C, UBQLN1, CALM1, NRP1, and PSMD6 in cumulus cells could serve as biomarkers for assessing oocyte developmental potential. This article also uses granulosa cells to predict the developmental potential of oocytes and employs a high-throughput gene expression microarray detection method, which solves the problem of the limited number of potential genes available for screening. However, this study still has few indicators for detecting oocyte developmental potential, and assessing oocyte quality solely based on pregnancy status is relatively one-sided. It also lacks clarity on the physiological state of oocytes. The article does not propose a method for detecting oocyte CNV through granulosa cells, nor does it elucidate the specific molecular mechanisms by which granulosa cells affect oocyte function.

[0005] Therefore, how to obtain biomarkers for predicting oocyte copy number abnormalities as an indicator for assessing female fertility in clinical practice remains a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] For the reasons stated above, this invention proposes a biomarker for predicting oocyte copy number variation and its application. Specifically, to achieve the objectives of this invention, the following technical solution is proposed:

[0007] This invention relates to a biomarker for predicting oocyte copy number variation, the biomarker comprising a combination of CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP, and APOO proteins.

[0008] CLEC11A: This gene encodes a member of the C-type lectin superfamily (C-type lectin domain family 11, type A). The encoded protein is a secreted sulfated glycoprotein that acts as a growth factor for primitive hematopoietic progenitor cells.

[0009] P4HB: This gene encodes the β subunit of prolyl 4-hydroxylase, a highly diverse multifunctional enzyme belonging to the protein disulfide isomerase family.

[0010] EFEMP2: Fibrin-like extracellular matrix protein 2 containing EGF. The protein encoded by this gene contains four EGF2 domains and six calcium-binding EGF2 domains. This gene is essential for the formation of elastic fibers and the development of connective tissue. Defects in this gene are a cause of autosomal recessive cutis laxa syndrome.

[0011] IL32: This gene encodes interleukin-32, a member of the cytokine superfamily. The protein contains one tyrosine sulfation site, three potential N-myristylation sites, multiple putative phosphorylation sites, and an RGD cell attachment sequence. Its expression increases after mitogen-activated T cells or IL-2-activated NK cells. This protein can induce macrophages to produce TNFα.

[0012] FTL: This gene encodes the light chain subunit of ferritin. Ferritin is the main intracellular iron storage protein in prokaryotes and eukaryotes.

[0013] FLNA, or filamentin A gene, encodes an actin-binding protein that cross-links actin filaments and links them to membrane glycoproteins. The encoded protein participates in cytoskeleton remodeling, influencing changes in cell shape and migration.

[0014] COL6A3: This gene encodes the α3 chain of type VI collagen, which is one of the three α chains of type VI collagen, a bead-like filamentous collagen found in most connective tissues.

[0015] ACPP stands for Acid Phosphatase 3 gene, which encodes an enzyme that catalyzes the conversion of monophosphate into alcohol and orthophosphate. It is synthesized under androgen regulation and secreted by prostate epithelial cells.

[0016] APOO: This gene is a member of the apolipoprotein family. Members of this protein family are involved in lipid transport and metabolism. The encoded protein is associated with HDL, LDL, and VLDL lipoproteins and is characterized by chondroitin sulfate glycosylation. This protein may be involved in preventing lipid accumulation in the myocardium of obese and diabetic patients.

[0017] In a preferred embodiment of the present invention, the biomarker is a secretory protein in granulosa cells. In the assisted reproductive process, only the oocytes provided by the patient are required; the granulosa cells are discarded and not involved in the assisted reproductive technology procedure. Therefore, by evaluating the relevant secretory proteins in granulosa cells, they can be easily obtained and used for related auxiliary testing. Any evaluation or testing of granulosa cells will not affect the oocytes; therefore, assessing the developmental potential of oocytes through granulosa cells is non-invasive and will not adversely affect the outcome of assisted reproductive technology.

[0018] Another aspect of the present invention relates to the application of the above-mentioned biomarkers in the preparation of diagnostic reagents for predicting oocyte copy number abnormalities.

[0019] In a preferred embodiment of the present invention, the diagnostic reagent is used to assess the expression levels of secretory proteins CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP, and APOO in granulocytes.

[0020] In a preferred embodiment of the invention, the diagnostic reagent is used to assess female fertility.

[0021] In a preferred embodiment of the present invention, the woman is a woman aged 35 or older.

[0022] Beneficial effects

[0023] Using the biomarkers of this invention, the expression levels of granulosa cell secreted proteins can be determined through granulosa cell transcriptome sequencing, allowing for the inference of the corresponding genomic CNV status of oocytes. This technical solution detects discarded granulosa cells from assisted reproductive technologies, thus having no impact on oocytes and being non-invasive. This study performs transcriptome and methylome analysis at the single-cell level, offering high throughput, and all granulosa cells and oocytes are paired one-to-one, reflecting differences in COC at the single-cell level. Attached Figure Description

[0024] Figure 1 This shows the normal chromosome copy number in oocytes;

[0025] Figure 2 This refers to the copy number of chromosomes in oocytes with abnormal copy numbers.

[0026] Figure 3 These are differentially expressed genes in the granulocyte transcriptome, among which those with gene names are potential markers of secreted proteins;

[0027] Figure 4 Differential expression of secreted proteins in granulocytes;

[0028] Figure 5 Differential expression of secreted proteins in abnormal granular cells (n=2);

[0029] Figure 6 Data on chromosome copy number in normal oocytes;

[0030] Figure 7 This is test data on chromosome copy number in oocytes with abnormal copy numbers. Detailed Implementation

[0031] To further understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0032] Unless otherwise specified, all reagents involved in the embodiments of this invention are commercially available products and can be purchased through commercial channels.

[0033] Example 1:

[0034] This study used oocytes donated by volunteers participating in assisted reproductive medical services (all volunteers had signed informed consent forms). Volunteers underwent strict screening based on their physical condition; they could not have the following conditions: polycystic ovary syndrome (PCOS), a history of ovarian surgery, ovarian cysts, ovarian teratomas, endometriosis, thyroid dysfunction, diabetes, or obesity. All patients had a body mass index (BMI) within the normal range (22.13±2.73). Ovulation induction protocols included: long protocol, short protocol, antagonist protocol, and natural cycle oocyte retrieval. When the follicle diameter reached 20 mm, COCs were aspirated via transvaginal ultrasound 36 hours after HCG injection. Genomic methylation sequencing was performed on the oocytes to determine their chromosome copy number characteristics, which would serve as an indicator of oocyte quality. Simultaneously, transcriptome sequencing was performed on periovarian granulosa cells to clarify the transcriptome status of granulosa cells corresponding to oocytes of different quality, and to screen for secretory proteins secreted by granulosa cells that could affect oocyte quality, serving as indicators for clinical female fertility assessment.

[0035] 1. Granulosa cell transcriptome amplification and sequencing

[0036] First, after removing the oocytes from the COC (co-occurring cells), the remaining translucent, cloudy clusters are granulosa cell clusters. These are washed with PBS buffer and then digested with 3‰ hyaluronidase to separate single-cell granulosa cells. 20-30 granulosa cells from each sample are randomly selected and transferred to pre-prepared mRNA extraction lysis buffer. RNA-seq sequencing libraries are prepared using Smart-seq2 single-cell library construction technology (commercial library construction kits are used to construct next-generation sequencing libraries). The libraries are sequenced using an Illumina-NovaSeq sequencer, with a paired-end sequencing mode of 150 bp. The sequencing depth for each RNA sample is 3 G.

[0037] 2. Preparation of single-cell transcriptomes and methylated libraries using oocyte Trio-seq method

[0038] After removing the zona pellucida by treating oocytes with granulosa cells in neutral PBS solution with 3‰ HCl for 2-3 seconds, the oocytes were transferred to Trio-Seq lysis buffer (simultaneously, 20-30 corresponding granulosa cell samples were transferred to Smart-Seq lysis buffer to ensure a one-to-one correspondence between granulosa cells and oocytes in the COC). Oocytes were picked up and placed in Trio-Seq lysis buffer, pre-cooled at 4°C on a magnetic rack, and vortexed for 2 minutes. After 5 minutes, the oocytes were placed on the magnetic rack, and CA beads were observed adsorbed onto one side of the rack. At this point, the DNA in the oocyte nucleus was adsorbed by the CA beads into the original tube. The oocyte nuclei retained in the 0.2 ml centrifuge tube were used for methylome library preparation (PBAT method, commercial library preparation kit). The libraries were sequenced using an Illumina-NovaSeq sequencer, with paired-end sequencing at 150 bp, and each PBAT library had a sequencing depth of 6 G.

[0039] 3. Sequencing data cleaning, alignment, and post-alignment processing

[0040] The obtained RNA sequencing data (reads) were cleaned using the `trim_galore` default parameter to remove next-generation sequencing adapter sequences and low-quality bases. Only sequences longer than 36 bp after processing were retained. The processed sequences were then aligned to the gencode hg38 human reference genome using STAR software with default parameters. After alignment, gene expression counts were determined using featureCounts software, and normalized TPM values ​​were calculated. For PBAT methylation sequencing data (reads), the `trim_galore` method was used for cleaning, followed by alignment to the gencode hg38 genome using bismark software with default parameters. Low-alignment-quality sequences and PCR repetitive sequences were removed using picard software. Only non-repetitive sequences aligned uniquely to the reference genome were obtained. Due to the close adhesion between oocytes and granulosa cells, cross-contamination may occur during library construction. We confirmed the reliability of the granulosa cell transcriptome library by assessing the mRNA expression levels of marker genes from granulosa cells and oocytes. Furthermore, by analyzing and comparing the methylation levels of the oocyte's signature DMR region with those verified in previously published literature using methylation sequencing results, the integrity and reliability of the oocyte methylation library in this study were verified.

[0041] 4. Inferring oocyte chromosome copy number from PBAT methylation data

[0042] First, the aligned sequence counts, GC content, and alignment rate within each window of the reference genome were calculated. The reference genome was divided into 1Mb windows, and readCounter was used to count the aligned sequences in each window. gcCounter was used to calculate the GC content of each window. mapCounter was used to calculate the alignment rate of each window. The chromosome copy number of the oocyte was calculated using the R software 'hmmcopy'. The results are as follows: Figure 1 and Figure 2 As shown.

[0043] 5. Analyze the transcriptome differences of granulosa cells corresponding to oocytes with copy number differences to identify marker secreted proteins.

[0044] After inferring the chromosomal CNV in oocytes using PBAT methylation data, granulosa cells corresponding to oocytes with normal CNV were used as the control group (n=9), while granulosa cells corresponding to oocytes with abnormal CNV were used as the experimental group (n=5). Figure 1Differential gene analysis was performed using the R software Seurat. The specific steps were as follows: First, the standardized TPM values ​​were transformed using log2. Then, a Seurat object was created, and the gene expression data distribution was adjusted using the `scaledata` command. Next, the `findmarker` command (with parameters: `min.pct=0.3`, `logfc.threshold=1`, `test.use='wilcox'`) was used to obtain genes with significant differences. Genes with p < 0.05 were then screened. Finally, the significantly different genes were compared with gene sets of human follicular fluid proteins and human secretory proteins obtained from published literature to identify differentially secreted proteins between granulosa cells in oocytes with CNV abnormalities and those in normal oocytes. The results are as follows: Figure 3 and Figure 4 As shown.

[0045] Example 2: Application of marker genes for predicting oocyte quality in granulosa cells

[0046] We obtained four COC samples from the IVF laboratory and further used the transcriptome sequencing library construction and analysis methods described in Example 1 above. We found significant differences in the expression trends of the 10 marker genes screened in Example 1 in the granulocyte samples. Two samples showed both low expression of CLEC11A, P4HB, EFEMP2, IL32, FTL, and FLNA, and high expression of COL6A3, ACPP, and APOO. Figure 5 Based on this, we hypothesized that the oocytes corresponding to these two COC samples had CNV abnormalities. We further analyzed the oocytes from these four COC samples using methylation libraries to infer genomic CNV patterns. We found that the oocytes corresponding to the two COC samples with abnormal expression of CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP, and APOO had CNV abnormalities. In one COC sample, the oocyte showed copy number amplification on chromosome 6, while in the other COC sample, there was copy number loss on chromosome 4 and copy number amplification on chromosome 15. Figure 6 and Figure 7 This result is consistent with our hypothesis. In this case, by assessing the expression levels of secretory proteins CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP, and APOO in granulosa cells, we obtained accurate genomic CNV status information of oocytes.

[0047] The preferred embodiments of the present invention have been described above, but are not intended to limit the invention. Those skilled in the art can make modifications and variations to the embodiments disclosed herein without departing from the scope and spirit of the invention.

Claims

1. Application of a combination of protein markers in granulosa cells in the preparation of diagnostic reagents for predicting oocyte copy number abnormalities, wherein the combination of protein markers is a combination of CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP and APOO proteins, wherein CLEC11A, P4HB, EFEMP2, IL32, FTL and FLNA are expressed at low levels, and COL6A3, ACPP and APOO are expressed at high levels, which indicates that the oocytes corresponding to granulosa cells have copy number abnormalities.

2. The application according to claim 1, wherein the diagnostic reagent is used to evaluate the expression levels of the protein marker combination CLEC11A, P4HB, EFEMP2, IL32, FTL, FLNA, COL6A3, ACPP and APOO in granulocytes.

3. The application according to claim 1, wherein the diagnostic reagent is used to assess female fertility.

4. In the application according to claim 3, the woman is a woman aged 35 or older.

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