Skip to main content
Download PDF

Proteomic alteration of endometrial tissues during secretion in polycystic ovary syndrome may affect endometrial receptivity

Clinical Proteomics volume 19, Article number: 19 (2022) Cite this article

Abstract

Embryo implantation is a complex developmental process that requires coordinated interactions among the embryo, endometrium, and the microenvironment of endometrium factors. Even though the impaired endometrial receptivity of patients with polycystic ovary syndrome (PCOS) is known, understanding of endometrial receptivity is limited. A proteomics study in three patients with PCOS and 3 fertile women was performed to understand the impaired endometrial receptivity in patients with PCOS during luteal phases. Through isobaric tags for relative and absolute quantitation (iTRAQ) analyses, we identified 232 unique proteins involved in the metabolism, inflammation, and cell adhesion molecules. Finally, our results suggested that energy metabolism can affect embryo implantation, whereas inflammation and cell adhesion molecules can affect both endometrial conversion and receptivity. Our results showed that endometrial receptive damage in patients with PCOS is not a single factor. It is caused by many proteins, pathways, systems, and abnormalities, which interact with each other and make endometrial receptive research more difficult.

Introduction

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder among women of reproductive age and perplexes researchers and doctors globally [1]. Even though many researchers focus on the pathophysiology of PCOS, the etiology underlying PCOS is still unknown. Many present studies mainly focused on improving clinical symptoms, such as insulin resistance, obesity, metabolic derangements, and increase in androgen, to achieve successful conceiving, reduce pregnancy-related complications, and enhance pregnancy outcomes [2, 3]. Ovulation disorders were previously considered the main cause of infertility in patients with PCOS. The pregnancy rates are still low in patients with PCOS and the high risk of biochemical abortion after ovulation disorders have been reduced. Many factors may lead to this situation, and impaired endometrial receptivity could be a responsible reason for adverse pregnancy outcomes in patients with PCOS. Unfortunately, only a few studies have elucidated the molecular mechanisms underlying impaired endometrial receptivity. Some important proteins involved in embryo implantation, such as forkhead box protein O1 (FOXO1), homeobox A10 (HOXA10), insulin-like growth factor-binding protein 1 (IGFBP-1), and inhibiting insulin growth factor 1 (IGF-1) are known to be abnormal in patients with PCOS compared with healthy individuals [4]. Single protein changes do not reflect the function of the endometrial microenvironment due to protein–protein interactions; therefore, the ongoing studies have increasingly focused on proteomic analyses. Proteomics-based analyses are not limited by previous information on the problem and can help discover the potential advantage of revealing novel associations with unexpected molecules that can lead to new mechanistic explanations for impaired endometrial implantation.

In the present years, proteomics analyses have been used to elucidate the potential mechanisms underlying adverse pregnancy outcomes in patients with PCOS. To the best of our knowledge, no research has been performed on the secretory endometrial proteome in patients with PCOS to date. To elucidate the molecular basis underlying infertility related to endometrium implantation in patients with PCOS, we compared the secretory endometrial proteomic profile of patients with PCOS with that of healthy fertile women using isobaric tags for relative and absolute quantitation (iTRAQ).

Materials and methods

Clinical sample preparation methods

The endometrial tissues were obtained from 3 patients with PCOS and 3 healthy volunteers who already had children. The patients with PCOS took letrozole on the 3rd day of menstruation; their ovulation was continuously monitored, starting from the 10th day of menstruation; and the endometrium was obtained on the 5th day of ovulation.

These patients were also screened for their glucose metabolism and endocrine normality through serum determinations of the levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol, glucose, and insulin on day 3 of the menstrual cycle. No participants demonstrated any evidence of chromosomal abnormality, pathological uterine disorder, or endometrial hyperplasia. None of the patients had used oral contraception or had undergone hormonal therapy during the past 3 months. The diagnosis of PCOS was made in accordance with the 2003 Rotterdam criteria, which included any two or all three of the following features: (1) oligo-/anovulation; (2) clinical or biochemical signs of hyperandrogenism; and (3) polycystic ovary morphology on ultrasound examination [5]. The main demographic characteristics of the patient and the control groups are summarized in Table 1. The results for the PCOS and control groups did not differ in terms of age, body mass index (BMI), FSH, LH, and testosterone, albeit it differed for the levels of insulin and glucose. Each biopsy was dry frozen at − 80 °C for protein extraction. The patients were recruited at the Reproductive Medicine Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, approved by the Institutional Ethics Committee (No: 20170609). All patients provided their informed consent prior to their participation in the study. Figure 1 displays the basic principle of iTRAQ quantitative proteomics and the main steps involved in the quantitative techniques.

Table 1 Demographic characteristics of PCOS and control subjects
Full size table
Fig. 1
figure 1

Experimental procedures. This figure shows the main procedures of the experiment of iTRAQ quantitative proteomics

Full size image

Protein extraction

We used the lysis buffer 3 (8 M urea, TEAB or 40 mM Tris–HCl with 1 mM PMSF, 2 mM EDTA and 10 mM DTT; pH 8.5) and two magnetic beads to extract the proteins. Then, we removed the mixtures into a tissue lyser for 2 min at 50 Hz to release the proteins. Next, the supernatant was transferred into a new tube after centrifugation at 25,000×g at 4 °C for 20 min, reduced with 10-mM dithiothreitol (DTT) at 56 °C for 1 h, and alkylated with 55-mM iodoacetamide (IAM) in the dark at room temperature for 45 min. Following centrifugation, the supernatant containing the proteins was quantified by Bradford assay.

QC of protein extraction

Protein quantitation by Bradford assay

First, we added 0, 2, 4, 6, 8, 10, 12, 14, 16, and 18 μL of the BSA solution, separately, into a 96-well plate, and to the corresponding wells, we added 20, 18, 16, 14, 12, 10, 8, 6, 4, and 2 μL of pure water, separately. Meanwhile, we prepared serial dilutions (20 μL/well) of the unknown sample for enumeration. Next, we added 180 μL of Coomassie blue to each well and mixed the contents of each well. The absorbance of each standard and sample well were read at 595 nm. Each sample had at least two duplicates. Then, the absorbance of the standards vs. their concentration was plotted. Finally, we calculated the extinction coefficient and the concentrations of the unknown samples.

Protein digestion

The protein solution (100 μg) containing 8 M urea was diluted 4 times with 100 mM TEAB. We then applied trypsin gold (Promega, Madison, WI, USA) to digest the proteins (protein: trypsin = 40:1) at 37 °C overnight. Next, we used the Strata X C18 column (Phenomenex) and vacuum-dried the specimens to desalt the peptides in accordance with the manufacturer's protocol.

Peptide labeling

We dissolved the peptides in 30 μL of 0.5 M TEAB by vortexing. Then, the iTRAQ labeling reagents were recovered to the ambient temperature and transferred and combined with the appropriate samples. Immediately before labeling the peptides, IBT precursors were treated with an equal molar ratio of TSTU (1,1,3,3-tetramethyl-O-(N-succinimidyl) uronium tetrafluoroborate) sourced from TCI (Shanghai, China) in isopropanol to a final concentration of 25 μg/μL and incubated at room temperature for 10 min. The activated IBT was mixed with a certain amount of peptides dissolved in 0.2 M triethylammonium bicarbonate (TEAB). In the labeling reaction, the isopropanol concentration was maintained at > 75%, and the labeling process was stopped by adding trifluoroacetic acid (TFA) at the end of the incubation period at the ambient temperature for 2 h. Then, we combined and desalted the labeled peptides on the Strata X C18 column and vacuum-dried them as per the manufacturer’s protocol.

Peptide fractionation

We separated the peptides through the Shimadzu LC-20AB HPLC Pump System coupled with a high-pH RP column. Next, we reconstituted the peptides with buffer A (5% ACN, 95% H2O, adjusted the pH to 9.8 with ammonia) to 2 mL and loaded them onto a column (5 μm, 20 cm × 180 μm; Gemini C18) containing 5-μm particles (Phenomenex). Then, we separated the peptides at the flow rate of 1 mL/min with a gradient of 5% buffer B (5% H2O, 95% ACN, adjusted pH to 9.8 with ammonia) for 10 min, 5–35% buffer B for 40 min, and 35–95% buffer B for 1 min. Then, the system was maintained in 95% buffer B for another 3 min and decreased to 5% within 1 min before equilibration with 5% buffer B for 10 min. Next, we monitored the elution by measuring the absorbance at 214 nm and collected the fractions every minute. Finally, we divided the eluted peptides into 20 fractions and vacuum-dried them for further analyses.

HPLC

First, each fraction was resuspended in buffer A (2% ACN, 0.1% FA) and centrifuged at 20,000×g for 10 min. Then, the supernatant was loaded on the Thermo Scientific™ UltiMate™ 3000 UHPLC system equipped with a trap and an analytical column. We loaded the samples on the trap column (PEPMAP 100 C18 5UM 0.3X5MM 5PK) at 5 μL/min for 8 min and eluted it into the homemade nanocapillary C18 column (ID 75 μm × 25 cm, 3-μm particles) with a 300 nL/min flow rate. The gradient of buffer B (98% ACN, 0.1% FA) was raised from 5 to 25% in 40 min, raised to 35% in 5 min, followed by a 2-min linear gradient to 80%, maintained at 80% B for another 2 min, returned to 5% in 1 min, and then equilibrated for 6 min.

Mass spectrometer detection

We subjected the peptides separated from nanoHPLC to tandem mass spectrometry Q EXACTIVE HF X (Thermo Fisher Scientific, San Jose, CA) for data-dependent acquisition (DDA) detection by nanoelectrospray ionization. The relevant parameters of the MS analysis were as follows: precursor scan range: 350–1500 m/z at the resolution of 60,000 in Orbitrap; electrospray voltage: 2.0 kV; MS/MS fragment scan range: in HCD mode with a 100 m/z scan, resolution at 15,000; normalized collision energy setting: 30%; dynamic exclusion time: 30 s; automatic gain control (AGC) for full MS target and MS2 target: 3e6 and 1e5, respectively; the number of MS/MS scans following one MS scan: 20 most abundant precursor ions above a threshold ion count of 10,000.

Protein quantification

We used an automated software called IQuant to quantitatively analyze the labeled peptides with isobaric tags. This software integrates the Mascot Percolator [6] to provide reliable significance measurements. To assess the confidence of peptides, the PSMs were prefiltered at 1% PSM-level FDR. Then, based on the “simple principle” (the parsimony principle), the identified peptide sequences were assembled into a set of confident proteins. To control the rate of false positives at the protein level, a protein FDR of 1%, which is based on the selected protein FDR strategy [7], was estimated after protein inference (protein-level FDR ≤ 0.01). The process of protein quantification comprised the following steps: protein identification, tag impurity correction, data normalization, missing value imputation, protein ratio calculation, statistical analysis, and result presentation [7]. Data normalization: We selected variance stabilization normalization (VSN) [8, 9] as our preferred normalization strategy. Protein ratio calculation: nonunique peptides and outlier peptide ratios were removed prior to their quantification [10]. The weight approach proposed elsewhere [11] was employed to evaluate the ratios of protein quantity based on the reporter ion intensities. Statistical analysis: Permutation tests were widely applied in the fields of microarray and RNA-Seq data analysis [12, 13]. To estimate the statistical significance of the protein quantitative ratios, IQuant adopted the permutation test, a nonparametric approach, as reported by Nguyen et al. [14]. For each protein, IQuant provided a significance evaluation that was corrected for multiple hypothesis testing by the Benjamini–Hochberg method [15].

Results

Altered levels of proteins in the endometrium of women with PCOS

We quantitatively identified 6524 proteins in samples from the PCOS group and the control group. We used CV to evaluate the reproducibility. CV is defined as the ratio of the standard deviation (SD) and the mean. Lower CV indicates better reproducibility. The mean CV (0.12) showed that the proteins identified in this study have good reproducibility. (Additional file 1: Fig. S1). Proteins with a 1.2-fold change and Q value less than 0.05 were determined as differentially expressed proteins (DEPs) in a single replicate. Compared with the control group, 232 proteins showed significant changes in their levels in the PCOS group. Of these, 108 proteins were increased and 124 proteins were decreased. The list of significantly regulated proteins along with their log 2 changes, corresponding p-values, and relevant biological processes are shown in Fig. 2 and Table 2.

Fig. 2
figure 2

Volcano of differentially expressed proteins. This plot depicts volcano plot of log2 fold-change (x-axis) versus -log10 Q value (y-axis, representing the probability that the protein is differentially expressed). Q value < 0.05 and Fold change > 1.2 are set as the significant threshold for differentially expression. The red and green dots indicate points-of-interest that display both large-magnitude fold-changes as well as high statistical significance. Dots in red mean significant up-regulated proteins which passed screening threshold. Dots in green mean significant down-regulated proteins which passed screening threshold. And gray dots are non-significant differentially expressed protein

Full size image
Table 2 List of significantly regulated proteins in PCOS and control groups
Full size table

Functional classification of differentially expressed proteins (DEPs) in the endometrium

To determine the functional differences in the increased and decreased proteins, the quantified proteins were analyzed for the following three types of enrichment-based clustering analyses: gene ontology (GO) enrichment analysis of DEPs, pathway enrichment analysis of DEPs, and eukaryotic orthologous groups (KOGs) annotation of DEPs.

GO enrichment analysis showed the GO terms in which the DEPs were enriched in all identified proteins. It represented the important or typical biological functions in the study. We performed pathway enrichment analysis of DEPs based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. KOGs were delineated by comparing protein sequences encoded in complete genomes, which represented major phylogenetic lineages.

Through the GO enrichment analysis of biological processes, we found that these different proteins were closely associated with cellular processes, metabolic processes, and biological regulation. Based on their molecular functions, these proteins with altered levels were strongly associated with binding, catalytic activity, and molecular function regulators (Fig. 3, Additional file 1: Fig. S2).

Fig. 3
figure 3

Gene Ontology Analysis of Differentially Expressed Proteins x-axis displays protein count, y-axis displays GO term

Full size image

The results from KEGG pathway enrichment showed that the DEPs were mainly involved in allograft rejection, cell adhesion molecules (CAMs), type I diabetes mellitus, allograft rejection, phagosomes, and the necrotic factor (NF)—kappa B signaling pathway (Fig. 4, Additional file 1: Fig. S3). Moreover, we constructed a scatter plot for the top 20 KEGG enrichment results as shown in Fig. 5.

Fig. 4
figure 4

Pathway analysis of differentially expressed proteins

Full size image
Fig. 5
figure 5

Statistics of pathway enrichment of differentially expressed proteins in each pairwise. RichFactor is the ratio of differentially expressed protein number annotated in this pathway term to all protein number annotated in this pathway term. Greater richFator means greater intensiveness. P value ranges from 0–1, and less P value means greater intensiveness. We just display the top 20 of enriched pathway terms.

Full size image

For DEPs, their KOG terms were also extracted and showed that the DEPs were mainly associated with inorganic ion transport and metabolism, lipid transport and metabolism, and energy production and conversion. We plotted bar plots accordingly (Fig. 6). Thus, we could easily obtain their functional categories.

Fig. 6
figure 6

KOG Annotation of differentially expressed proteins

Full size image

Predicted protein–protein interactions (PPI) of DEPs and subcellular localization prediction of DEPs

Proteins usually interact with each other to participate in certain biological functions. STRING is a database of known PPI. Based on Fig. 7, we determined the interaction between proteins (Fig. 7). Proteins can be targeted in the inner space of an organelle, different intracellular membranes, the plasma membrane, or to the exterior of the cell through secretion. This delivery process is performed on the basis of the information present in the protein. Correct sorting is important for the cell; errors can lead to the development of diseases. We predicted protein subcellular localization using bioinformatics tools (WoLF PSORT). The bar plot of subcellular localization prediction showed that different proteins are mainly present in the nucleus, extracellular space, cytosol, plasma membrane, and mitochondria (Fig. 8).

Fig. 7
figure 7

PPI Network of differentially expressed proteins. Red and blue circle represent up-regulated and down-regulated proteins separately. Edges with different colors represent classes of KEGG pathway (Red: Cellular Processes; Blue: Environmental Information Processing; Green: Genetic Information Processing; Purple: Human Diseasea; Orange: Metabolism; Yellow: Organismal Systems; Brown: Drug Development)

Full size image
Fig. 8
figure 8

Subcellular localization prediction of differentially expressed proteins x-axis displays subcellular structure; y-axis displays protein count

Full size image

Taken together, these results showed that these DEPs mainly play a role in metabolic processes, cell adhesion molecules, and immunity.

Discussion

Embryo implantation is a key process in pregnancy. For successful embryo implantation, the process must be sequential, which means that the three phases, namely apposition, adhesion, and invasion, should occur sequentially [16]. For pregnancy, endometrium transition to the pregnancy state is the key to embryo implantation, and a change in several proteins in the endometrium during this process is a prerequisite [17, 18]. The DEPs discovered in the present study were mainly involved in energy metabolism, inflammation, and cell–cell adhesion functions, as well as the cell and cell parts in cellular components and catalytic activity. Energy metabolism may affect embryo implantation, whereas inflammation and CAMs may affect both endometrial conversion and receptivity.

Impairment of embryo implantation because of energy metabolism deficit

The exact mechanism of embryo implantation is not clear, and probably energy metabolism is a crucial factor in implantation [19]. PCOS is an endocrine disorder characterized by hyperinsulinemia and obesity [20]. These characteristics can cause an insulin-resistant state and metabolic disorder in organs such as the endometrium [21, 22]. As insulin resistance in the endometrium leads to no response or sensitivity to the metabolic effects of insulin, the endometrium needs more insulin for normal metabolism [23]. The gene for insulin-like growth factor-binding protein 5 (IGFBP5) is downregulated in patients with PCOS than in healthy people, and IGFBP5 is an important member of the IGFBP family. IGFBP5 may affect cell metabolism. A decrease in IGFBP5 level may be associated with the pathogenesis of type 2 diabetes [24, 25], and decreased GLUT4 expression may be one of the mechanisms by which IGFBP causes insulin resistance [26]. Moreover, the results of our subcellular localization analysis show that many different proteins are located in mitochondria. Importantly, mitochondria play a key role in energy production by converting nutrients into energy, and altered proteins may negatively affect energy metabolism. For example, mitochondrial pyruvate carrier 1 (MPC1) and transcription elongation factor mitochondrial (TEFM) levels were significantly decreased in patients with PCOS. Pyruvate, carried by MPC1 into the mitochondrion, is essential to mitochondrial energy metabolism. The lack of MPC1 can impair pyruvate transport and then can damage mitochondrial energy metabolism [27]. The final site of glucose metabolism is in mitochondria, in which TEFM regulates the formation of mitochondrial RNA primers. As RNA primers are necessary for the initiation of mitochondrial DNA replication, the lack of TEFM reduces mitochondrial DNA replication [28]. Therefore, abnormalities in MPC1 and TEFM must affect mitochondrial oxidation, thus leading to a bioenergetic crisis. Therefore, we hypothesized that energy metabolism deficits may cause embryo implant failure, and treatment including energy supplements may improve the endometrial microenvironment.

CAM deficiency causes miscarriage

Apart from energy metabolism deficits, embryo implantation also requires adhesion molecules. Increasing or decreasing adhesion molecules can lead to embryo implantation failure. In our proteomics analysis results, we observed the differential expression of adhesion molecules in the PCOS group including CAMs, receptor–ligand activity, and cell adhesion. Among these, epithelial CAM (EpCAM) level was increased in endometrial samples of women with PCOS. EpCAM regulates many important cellular functions such as cell migration, metastasis, proliferation, and cell differentiation [29, 30]; however, the main role of EpCAM is intercellular adhesion [31]. A specific EpCAM is necessary for embryo implantation, and the amount of EpCAM during the implantation window should be reduced [32]. EpCAMs are maintained mainly at the basal cell surface to maintain a polarized epithelial surface, and then uterine epithelial cells connect with the underlying stroma to prevent premature detachment before implantation [33]. However, higher concentrations of EpCAM can impair adhesion or promote deadhesion by competitively binding to extracellular matrix proteins and blocking cell attachment. Proteomics analysis results show that T-lymphoma invasion and metastasis-inducing protein 1 (TIAM1) were decreased in the PCOS group, which regulates cell migration, motility, and cell adhesion in some cells [34, 35]. TIAM1 is decreased by estradiol and increased by progesterone in a dose-dependent manner [36]. Patients with PCOS lack a complete menstrual cycle as a result of oligo- or anovulation; thus, the endometrium is exposed to estradiol for an extended period and lacks progesterone [37]. The reduction in TIMA1 level is consistent with the characteristics of patients with PCOS. TIAM1 is essential in embryo implantation in mice by increasing the implantation site of the endometrium [38]. Studies have shown that increased levels of TIAM1 during the implantation window facilitates embryo implantation, and decreased TIAM1 levels might be associated with the failure of embryo implantation in patients with repeated implantation failure [35]. More studies need to be established to explore the details of adhesion mechanisms underlying the endometrium of PCOS.

Immune disorders lead to miscarriage

The embryo is a natural semi-allograft, and tolerance mechanisms for successful embryo implantation involve the acceptance of allografts [39]. A recent study highlighted that immune imbalance plays a key role in recurrent miscarriage [40]. Our pathway analysis reports that allograft rejection, natural killer (NK)-cell-mediated cytotoxicity, and primary immunodeficiency in patients with PCOS were significantly abnormal compared with those in healthy women. For instance, human leukocyte antigen C (HLA-C), a marker of recurrent miscarriage, was significantly increased in the PCOS group [41]. In the fetal–maternal interface, NK cells recognize and eliminate exogenous cells mainly resulting from HLA expressed on the foreign cell surface [42]. Thus, the increased HLA-C levels may negatively affect the process by which NK cells recognize embryo antigens, resulting in immune tolerance disorders. Hemeoxygenase 1 (HMOX1) was significantly decreased in patients with PCOS. HMOX1 is a central player in anti-inflammatory, antioxidant, and cytoprotective activities, and HMOX1 can inhibit the cytotoxicity of other immune cells, cytokine release, and proliferation [43, 44]. HMOX1 is necessary for protecting fetuses from rejection [45, 46]. Therefore, HMOX1 deficiency may affect fetal and allograft rejection, thereby leading to embryo implantation failure. Thus, curing immune disorders in the endometrium will improve the probability of embryo implantation success.

Strengths and limitations of the study

Our results show that endometrial receptive damage in patients with PCOS is not only associated with a single factor but also multiple proteins, pathways, systems, and other abnormalities; these factors also interact with each other. Due to difficulty in obtaining the desired endometrial tissues repeatedly at the same time, we only compared endometrial proteomics in the luteal phase between the experimental group and the control group, rather than comparing the endometrial proteomics in different phases in one group. Moreover, animal validation model tests are in preparation.

Conclusion

Our results show that endometrial receptive damage in patients with PCOS is not a single factor event but occurs because of multiple proteins, pathways, systems, and other abnormalities, and they also interact with each other, thereby greatly increasing the difficulty of endometrial receptive research. More studies are needed to support the hypothesis of this study and to establish a better understanding of the molecular mechanistic details underlying impaired endometrial implantation in patients with PCOS.

Availability of data and materials

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [47] partner repository with the dataset identifier PXD024735.

References

  1. Al WB, Teede H, Garad R, Franks S, Balen A, Bhide P, et al. Harmonising research outcomes for polycystic ovary syndrome: an international multi-stakeholder core outcome set. Hum Reprod. 2020;35:404–12.

    Article  Google Scholar 

  2. Gonzalez F, Considine RV, Abdelhadi OA, Acton AJ. Inflammation triggered by saturated fat ingestion is linked to insulin resistance and hyperandrogenism in PCOS. J Clin Endocrinol Metab. 2020. https://doi.org/10.1210/clinem/dgaa108.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Zhu X, Fu Y. Randomized, controlled pilot study of low-dose human chorionic gonadotropin administration beginning from the early follicular phase for women with polycystic ovarian syndrome undergoing ovarian stimulation using the progesterone protocol. Front Endocrinol. 2019;10:875.

    Article  Google Scholar 

  4. Piltonen TT. Polycystic ovary syndrome: endometrial markers. Best Pract Res Clin Obstet Gynaecol. 2016;37:66–79.

    Article  PubMed  Google Scholar 

  5. Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod. 2003;2004(19):41–7.

    Google Scholar 

  6. Wen B, Zhou R, Feng Q, Wang Q, Wang J, Liu S. IQuant: an automated pipeline for quantitative proteomics based upon isobaric tags. Proteomics. 2014;14:2280–5.

    Article  CAS  PubMed  Google Scholar 

  7. Brosch M, Yu L, Hubbard T, Choudhary J. Accurate and sensitive peptide identification with Mascot Percolator. J Proteome Res. 2009;8:3176–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Karp NA, Huber W, Sadowski PG, Charles PD, et al. Addressing accuracy and precision issues in iTRAQ quantitation. Mol Cell Proteomic. 2010;9:1885–97.

    Article  CAS  Google Scholar 

  9. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002;18(Suppl 1):S96–104.

    Article  PubMed  Google Scholar 

  10. Tukey JW. Exploratory data analysis, vol. 231. Reading: Addison-Wesley; 1977.

    Google Scholar 

  11. Breitwieser FP, Muller A, Dayon L, Kocher T, et al. General statistical modeling of data from protein relative expression isobaric tags. J Proteome Res. 2011;10:2758–66.

    Article  CAS  PubMed  Google Scholar 

  12. Langmead B, Hansen KD, Leek JT. Cloud-scale RNA sequencing differential expression analysis with Myrna. Genome Biol. 2010;11:R83.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Volinia S, Galasso M, Sana ME, Wise TF, et al. Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microRNA. Proc Natl Acad Sci USA. 2012;109:3024–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nguyen H, Wood I, Hill M. A robust permutation test for quantitative SILAC proteomics experiments. J Integr OMICS. 2012;2:80–93.

    Google Scholar 

  15. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B Methodol. 1995;57:289–300.

    Google Scholar 

  16. Lopes IM, Baracat MC, Simoes MJ, Simoes RS, Baracat EC, Soares JJ. Endometrium in women with polycystic ovary syndrome during the window of implantation. Rev Assoc Med Bras. 1992;2011(57):702–9.

    Google Scholar 

  17. Kasvandik S, Saarma M, Kaart T, Rooda I, Velthut-Meikas A, Ehrenberg A, et al. Uterine fluid proteins for minimally invasive assessment of endometrial receptivity. J Clin Endocrinol Metab. 2020. https://doi.org/10.1210/jcem/dgz019.

    Article  PubMed  Google Scholar 

  18. Sadigh AR, Mihanfar A, Fattahi A, Latifi Z, Akbarzadeh M, Hajipour H, et al. S100 protein family and embryo implantation. J Cell Biochem. 2019;120:19229–44.

    Article  CAS  PubMed  Google Scholar 

  19. Perakakis N, Upadhyay J, Ghaly W, Chen J, Chrysafi P, Anastasilakis AD, et al. Regulation of the activins-follistatins-inhibins axis by energy status: Impact on reproductive function. Metabolism. 2018;85:240–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Teede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, et al. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Fertil Steril. 2018;110:364–79.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Cabrera-Cruz H, Orostica L, Plaza-Parrochia F, Torres-Pinto I, Romero C, Vega M. The insulin-sensitizing mechanism of myo-inositol is associated with AMPK activation and GLUT-4 expression in human endometrial cells exposed to a PCOS environment. Am J Physiol Endocrinol Metab. 2020;318:E237–48.

    Article  CAS  PubMed  Google Scholar 

  22. Shafiee MN, Seedhouse C, Mongan N, Chapman C, Deen S, Abu J, et al. Up-regulation of genes involved in the insulin signalling pathway (IGF1, PTEN and IGFBP1) in the endometrium may link polycystic ovarian syndrome and endometrial cancer. Mol Cell Endocrinol. 2016;424:94–101.

    Article  CAS  PubMed  Google Scholar 

  23. Diamanti-Kandarakis E, Dunaif A. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr Rev. 2012;33:981–1030.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gleason CE, Ning Y, Cominski TP, Gupta R, Kaestner KH, Pintar JE, et al. Role of insulin-like growth factor-binding protein 5 (IGFBP5) in organismal and pancreatic beta-cell growth. Mol Endocrinol. 2010;24:178–92.

    Article  CAS  PubMed  Google Scholar 

  25. Rojas-Rodriguez R, Lifshitz LM, Bellve KD, Min SY, Pires J, Leung K, et al. Human adipose tissue expansion in pregnancy is impaired in gestational diabetes mellitus. Diabetologia. 2015;58:2106–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xiang A, Chu G, Zhu Y, Ma G, Yang G, Sun S. IGFBP5 suppresses oleate-induced intramyocellular lipids deposition and enhances insulin signaling. J Cell Physiol. 2019;234:15288–98.

    Article  CAS  Google Scholar 

  27. Li X, Zheng H, Shi L, Liu Z, He L, Gao J. Stress-seventy subfamily A 4, a member of HSP70, confers yeast cadmium tolerance in the loss of mitochondria pyruvate carrier 1. Plant Signal Behav. 2020;15:1719312.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Jiang S, Koolmeister C, Misic J, Siira S, Kühl I, Silva Ramos E, et al. TEFM regulates both transcription elongation and RNA processing in mitochondria. Embo Rep. 2019. https://doi.org/10.15252/embr.201948101.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Munz M, Zeidler R, Gires O. The tumour-associated antigen EpCAM upregulates the fatty acid binding protein E-FABP. Cancer Lett. 2005;225:151–7.

    Article  PubMed  CAS  Google Scholar 

  30. Osta WA, Chen Y, Mikhitarian K, Mitas M, Salem M, Hannun YA, et al. EpCAM is overexpressed in breast cancer and is a potential target for breast cancer gene therapy. Cancer Res. 2004;64:5818–24.

    Article  CAS  PubMed  Google Scholar 

  31. Schmidt DS, Klingbeil P, Schnolzer M, Zoller M. CD44 variant isoforms associate with tetraspanins and EpCAM. Exp Cell Res. 2004;297:329–47.

    Article  CAS  PubMed  Google Scholar 

  32. Lei Z, Maeda T, Tamura A, Nakamura T, Yamazaki Y, Shiratori H, et al. EpCAM contributes to formation of functional tight junction in the intestinal epithelium by recruiting claudin proteins. Dev Biol. 2012;371:136–45.

    Article  CAS  PubMed  Google Scholar 

  33. Poon CE, Madawala RJ, Day ML, Murphy CR. EpCAM is decreased but is still present in uterine epithelial cells during early pregnancy in the rat: potential mechanism for maintenance of mucosal integrity during implantation. Cell Tissue Res. 2015;359:655–64.

    Article  CAS  PubMed  Google Scholar 

  34. Murphy CR. Uterine receptivity and the plasma membrane transformation. Cell Res. 2004;14:259–67.

    Article  PubMed  Google Scholar 

  35. Diaz J, Mendoza P, Silva P, Quest AF, Torres VA. A novel caveolin-1/p85alpha/Rab5/Tiam1/Rac1 signaling axis in tumor cell migration and invasion. Commun Integr Biol. 2014. https://doi.org/10.4161/19420889.2014.972850.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Miyamoto Y, Yamauchi J, Tanoue A, Wu C, Mobley WC. TrkB binds and tyrosine-phosphorylates Tiam1, leading to activation of Rac1 and induction of changes in cellular morphology. Proc Natl Acad Sci USA. 2006;103:10444–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ma HL, Gong F, Tang Y, Li X, Li X, Yang X, et al. Inhibition of endometrial Tiam1/Rac1 signals induced by miR-22 up-regulation leads to the failure of embryo implantation during the implantation window in pregnant mice. Biol Reprod. 2015;92:152.

    Article  PubMed  CAS  Google Scholar 

  38. Li X, Feng Y, Lin JF, Billig H, Shao R. Endometrial progesterone resistance and PCOS. J Biomed Sci. 2014;21:2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Ma HL, Zhang T, Meng J, Qin ZY, Du F, Wang QY, et al. The role of T-lymphoma invasion and metastasis inducing protein 1 in early pregnancy in mice. Mol Hum Reprod. 2008;14:589–94.

    Article  CAS  PubMed  Google Scholar 

  40. Sollwedel A, Bertoja AZ, Zenclussen ML, Gerlof K, Lisewski U, Wafula P, et al. Protection from abortion by heme oxygenase-1 up-regulation is associated with increased levels of Bag-1 and neuropilin-1 at the fetal-maternal interface. J Immunol. 2005;175:4875–85.

    Article  CAS  PubMed  Google Scholar 

  41. Franasiak JM, Scott RT. Contribution of immunology to implantation failure of euploid embryos. Fertil Steril. 2017;107:1279–83.

    Article  CAS  PubMed  Google Scholar 

  42. Meuleman T, van Beelen E, Kaaja RJ, van Lith JM, Claas FH, Bloemenkamp KW. HLA-C antibodies in women with recurrent miscarriage suggests that antibody mediated rejection is one of the mechanisms leading to recurrent miscarriage. J Reprod Immunol. 2016;116:28–34.

    Article  CAS  PubMed  Google Scholar 

  43. Kofod L, Lindhard A, Hviid T. Implications of uterine NK cells and regulatory T cells in the endometrium of infertile women. Hum Immunol. 2018;79:693–701.

    Article  CAS  PubMed  Google Scholar 

  44. Chauveau C, Remy S, Royer PJ, Hill M, Tanguy-Royer S, Hubert FX, et al. Heme oxygenase-1 expression inhibits dendritic cell maturation and proinflammatory function but conserves IL-10 expression. Blood. 2005;106:1694–702.

    Article  CAS  PubMed  Google Scholar 

  45. Sun L, Shi T, Qiao H, Jiang X, Jiang H, Krissansen GW, et al. Hepatic overexpression of heme oxygenase-1 improves liver allograft survival by expanding T regulatory cells. J Surg Res. 2011;166:e187–94.

    Article  CAS  PubMed  Google Scholar 

  46. Ozen M, Zhao H, Lewis DB, Wong RJ, Stevenson DK. Heme oxygenase and the immune system in normal and pathological pregnancies. Front Pharmacol. 2015;6:84.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, et al. The ProteomeXchange consortium in 2020: enabling “big data” approaches in proteomics. Nucleic Acids Res. 2020;48:D1145–52.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge support from Professor Haijian Cai in the Center for Scientific Research of Anhui Medical University.

Funding

This research was funded by the National Natural Science Foundation of China Youth Science Fund Development Project of the First Affiliated Hospital of Anhui Medical University [Grant Number 2017kj02] and University Natural Science Research Project of Anhui Province [KJ2020A0201].

Author information

Author notes

  1. Jun Li, Xiaohua Jiang and Caihua Li contributed equally to this work

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230022, China

    Jun Li & Lin Ling

  2. Department of Obstetrics and Gynecology, Reproductive Medicine Center, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China

    Xiaohua Jiang, Caihua Li, Huihui Che & Zhaolian Wei

  3. Anhui Province Key Laboratory of Reproductive Health and Genetics, Anhui Medical University, Hefei, 230022, China

    Xiaohua Jiang, Caihua Li & Zhaolian Wei

  4. Anhui Provincial Engineering Technology Research Center for Biopreservation and Artificial Organs, Hefei, 230022, China

    Xiaohua Jiang, Caihua Li & Zhaolian Wei

Authors
  1. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohua Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caihua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huihui Che
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhaolian Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JL and XJ designed research. JL and CL performed the research. JL, XJ and LL analyzed the data and drafted the final version of the manuscript. CL and ZW supervised the study, and provided financial support, editing and final approval of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhaolian Wei.

Ethics declarations

Competing interests

No conflicts of interest, financial or otherwise, are declared by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Fig. S1. CV distribution in replicate. Fig. S2. Gene Ontology Analysis of Differentially Expressed Proteins. Fig. S3. Pathway analysis of Differentially Expressed Proteins.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Jiang, X., Li, C. et al. Proteomic alteration of endometrial tissues during secretion in polycystic ovary syndrome may affect endometrial receptivity. Clin Proteom 19, 19 (2022). https://doi.org/10.1186/s12014-022-09353-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12014-022-09353-1

Keywords

Clinical Proteomics

ISSN: 1559-0275