COMPOSITIONS AND METHODS FOR DETERMINING RECEPTIVITY OF AN ENDOMETRIUM FOR EMBRYONIC IMPLANTATION

Abstract

Provided herein are methods and kits for determining receptivity status of an endometrium for embryonic implantation.

Claims

1-24 (canceled)

25. A kit comprising: reagents suitable for determining an endometrial gene expression profile of a panel of genes, wherein the panel of genes consists of: ANXA4, CATSPERB, PTGFR, PTGS1, IL8, SCGB2A2, ANGPTL1, HPRT1, MMP10, PGR, ITGA8, IFNG, PROK1, FOXO1, CXCL1, STC1, MMP9, MUC1, RPL13A, CALCA, ITGA9, RACGAP1, GPX3, PPP2R2C, ARG2, SCGB3A1, ALDH1A3, APOD, C2CD4B, TFF3, AQP3, GJA4, ARHGDIA, SELL, APOL2, MT1H, MT1X, MT1L, MAOA and MT1F in a biological sample obtained from a subject.

26. The kit of claim 25, wherein the biological sample is an endometrial biopsy obtained from the uterine fundus.

27. The kit of claim 25, wherein the reagents are suitable for reverse transcription polymerase chain reaction.

28. The kit of claim 25, further comprising a chip, an array, a multi-well plate or a tube.

29. The kit of claim 25, further comprising instructions for use of the kit according to claim 1.

Description

DESCRIPTION OF DRAWINGS

[0046] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0047] FIG. 1 is a representative STRING database generated protein interaction network of the proteins codified by the 184 WO1 selected genes.

[0048] FIG. 2A is a representative volcano plot of gene expression differences for the 184 WOI genes on days LH+2 and LH+7 of fertile subjects menstrual cycles. The log 2 fold change is plotted on the x-axis and the negative log 10 p-value is plotted on the y-axis. Green dots represent gene probes with P value<0.05 by paired t-test and downregulated fold change (log 2FC<−0.5). Orange dots represent gene probes with p-value<0.05 by paired t-test and up-regulated fold change (log 2FC>0.5).

[0049] FIG. 2B is a representative bar graph showing log 2 fold changes of the 85 differentially expressed mRNAs (Paired t-test, p<0.05) in LH+7 vs LH+2. 71 mRNAs were upregulated and 14 mRNAs were downregulated in LH+7 compared to LH+2.

[0050] FIG. 3A is a representative chart for the variance (eigenvalue) provided by each principal component (PC) from the PCA and the cumulative percentage along the 40 PCs. The green bars illustrate the variance of each PC, and the orange line illustrates the cumulative variance explained by the retaining PCs. The genes with the highest coefficient value from each component are detailed below each PC number.

[0051] FIG. 3B is a representative canonical plot using discriminant functional analysis with the 40 genes selected to classify 312 endometrial samples. X, Y and Z axis represent the discriminant function scores for the first three dimensions. Non-receptive samples are represented as blue circle, pre-receptive as green circle, receptive as orange circle and post-receptive as purple circle.

DETAILED DESCRIPTION

[0052] The present disclosure is based, in part, on the unexpected discovery that it is possible to determine the receptivity status of an endometrium for embryonic implantation by combined qRT-PCR expression analysis of genes involved in endometrial proliferation and immune response.

[0053] One of the key processes for the establishment of a successful pregnancy is embryonic implantation into the endometrium. Implantation is a complex process that involves an intricate dialogue between the embryo and the endometrial cells (Singh et al., J. Endocrinol 2011; 210:5-14). This interaction is required for the apposition, adhesion and invasion of the blastocyst (Giudice and Irwin, Semin Reprod Endocrinol 1999; 17:13-21).

[0054] The human endometrium is a highly dynamic structure, which undergoes periodical changes during menstrual cycle in order to reach a receptive status adequate for embryonic implantation. This period of receptivity is known as the window of implantation (WOI) and occurs between Day 19 and Day 21 of the menstrual cycle (Navot et al., Fertil Steril 1991; 55:114-118; Harper, Baillieres Clin Obstet Gynaecol 1992; 6:351-371; Giudice, Hum Reprod 1999; 14 Suppl 2:3-16). In any other phase of the menstrual cycle, the endometrium is reluctant to pregnancy (Garrido-Gómez et al., Fertil Steril 2013; 99:1078-1085). Successful implantation requires therefore a viable embryo and synchrony between it and the receptive endometrium (Teh et al., J Assist Reprod Genet 2016; 33:1419-1430). The correct identification and prediction of the period of uterine receptivity is essential to maximize the effectiveness of assisted reproduction treatments (ART).

[0055] The study of endometrial receptivity is not new as histological analysis has been traditionally used for endometrial dating (Noyes et al., Fertil Steril 1950; 1:3-25); however, the accuracy of this method to predict endometrial receptivity has been shown to be limited (Coutifaris et al., Fertil Steril 2004; 82:1264-1272; Murray et al., Fertil Steril 2004; 81:1333-1343). Some alternative methods to evaluate endometrial receptivity have been developed in the last decade, these methods include: biochemical markers such as molecules involved in calcium sensing and signal transduction (Zhang et al., Reprod Biol Endocrinol 2012; 10:106), soluble ligands (Thouas et al., Endocr Rev 2015; 36:92-130), hormone receptors (Aghajanova et al., Fertil Steril 2009; 91:2602-2610), cytokines (Jones et al., J Clin Endocrinol Metab 2004; 89:6155-6167; Lédée-Bataille et al., Fertil Steril 2005; 83:598-605; Paiva et al., Hum Reprod 2011; 26:1153-1162), microRNAs (Sha et al., Fertil Steril 2011; 96; Kresowik et al., Biol Reprod 2014; 91:20-24) or HOX-class homeobox genes (Kwon and Taylor, Ann N Y Acad Sci 2004; 1034: p.1-18; Xu et al., Hum Reprod 2014; 29:781-790).

[0056] Other studies, focused on the understanding of the molecular mechanisms underlying the histological changes observed in the endometrium during the menstrual cycle, have identified specific genes responsible for the alterations observed (Talbi et al., Endocrinology 2006; 147:1097-1121; Zhang et al., Mol Reprod Dev 2013; 80:8-21). Some other reports have addressed this molecular analysis from a wider perspective, performing a global screening of the transcriptome at different moments of the menstrual cycle (Carson, Mol Hum Reprod 2002; 8:871-879; Ponnampalam et al., Mol Hum Reprod 2004; 10:879-893; Mirkin et al., Hum Reprod 2005; 20:2104-2117; Talbi et al., Endocrinology 2006; 147:1097-1121; Haouzi et al., Hum Reprod 2009; 24:198-205), under different infertility conditions (Koler et al., Hum Reprod 2009; 24:2541-2548; Altmäe et al., Mol Hum Reprod 2010; 16:178-187; Roy et al., Hum Reprod 2014; 29:2431-2438; Tapia-Pizarro et al., Reprod Biol Endocrinol 2014; 12:92; Koot et al., Sci Rep 2016; 6:19411), pathologies (Kao et al., 2003; Sun et al., Fertil Steril 2014; 101; Garcia-Velasco et al., Reprod Biomed Online 2015; 31:647-654) or ovarian stimulation protocols (Mirkin et al., J Clin Endocrinol Metab 2004; 89:5742-5752; Horcajadas et al., Mol Hum Reprod 2005; 11:195-205; Liu et al., Fertil Steril 2008; 90:2152-2164; Haouzi et al., Hum Reprod 2009; 24:1436-1445). Valuable information about the process of endometrial proliferation can be extracted from these studies. However, even though the list of studies published in this topic is long, the number of molecular diagnostic tools to identify the moment of uterine receptivity is reduced (Lessey et al., Fertil Steril 1995; 63:535-542; Lessey et al., Fertil Steril 2000; 73:779-787; Dubowy et al., Fertil Steril 2003; 80:146-156; Diaz-Gimeno et al., Fertil Steril 2011; 95:50-60, 60-15). Some studies looking at the utility of single molecule markers for endometrial receptivity have concluded that a single molecule may not suffice to describe a complex phenomenon like receptivity (Brinsden et al., Fertil Steril 2009; 91:1445-1447) and, in this sense, transcriptomic profiles may be a more reliable tool.

[0057] Most global transcriptomic analyses of the endometrium have been performed using an unselected source of genes involved in many biological processes, but not specifically expressed in the endometrial tissue or related to the process of endometrial receptivity acquisition. The selection of genes, specifically described to be expressed in the endometrium during the WOI and involved in the process of embryonic implantation, was chosen as a better strategy to accurately define the transcriptomic signature of the receptive endometrium and also to develop a reliable diagnostic tool for endometrial receptivity. Processes such as endometrial proliferation and immune response have been described as essential for endometrial preparation and embryonic implantation, so a selection of genes involved in those processes could provide interesting biological and clinical information about the process of endometrial receptivity (Sign et al., 2011; and Haller-Kikkatalo et al., Semin Reprod Med 2014; 32: 376-384).

[0058] For global endometrial transcriptomic analyses, the preferred technique has been gene expression microarrays (Sherwin et al., Reproduction 2006; 132:1-10; Horcajadas et al., Hum Reprod Updat 2007; 13:77-86; Haouzi et al., Reprod Biomed Online 2012; 24:23-34).

[0059] RT-qPCR has been shown to have the widest dynamic range, the lowest quantification limits and the least biased results and hence it is considered the gold standard method for gene expression analysis. In this context, we believe the use of RT-qPCR may be a more robust and reliable technique for the analysis of the expression of genes relevant for the process of endometrial receptivity and, also, for the development of diagnostic tools based on the identification of specific signatures associated to different endometrial status.

[0060] Without wishing to be bound by theory, the present inventors defined a new system for human endometrial receptivity evaluation, based on the analysis of the expression of genes related to endometrial proliferation and the immunological response associated to embryonic implantation using a high throughput RT-qPCR platform. A comprehensive solution to analyze the endometrial transcriptomic signature at the WOI was explored. Validation was achieved on 306 endometrial samples including fertile women and patients undergoing fertility treatment between July 2014 and March 2016. Expression analyses of 184 genes involved in endometrial receptivity and immune response were performed. Samples were additionally tested with an independent endometrial receptivity test. Gene ontology analyses revealed that cellular proliferation, response to wounding, defence and immune response are the most over-represented biological terms in the group of genes selected. Significantly different gene expression levels (fold change) were found in 85 out of 184 selected genes when comparing LH+2 and LH+7 samples (Paired t-test, p<0.05). Principal component analysis (PCA) and discriminant functional analysis revealed that 40 of the differentially expressed genes allowed accurate classification of samples into 4 endometrial status: proliferative, pre-receptive, receptive and post-receptive in both groups, fertile women and infertile patients.

[0061] The identification of the optimal time for embryo transfer is essential to maximize the effectiveness of assisted reproductive technologies. For successful embryo implantation, a healthy embryo at blastocyst state and a functional endometrium ready to receive it, are required. There is growing evidence that shows the importance of embryonic-endometrial synchrony for the achievement of a successful pregnancy (Navot et al., Fertil Steril 1991; 55:114-118; Prapas et al., Hum Reprod 1998; 13:720-723; Wilcox et al., N Engl J Med 1999; 340:1796-1799; Shapiro et al., Fertil Steril 2008; 89:20-26; Shapiro et al., Reprod Biomed Online 2014; 29:286-290; Reprod Biomed Online 2016; 33:50-55; Franasiak et al., Fertil Steril 2013; 100:597; Healy et al., Hum Reprod 2017; 32:362-367). This concept, however, has yet to be taken into the IVF clinical practice. Much effort is put in the production and selection of the most competent embryo to be transferred (Chen et al., Fragouli and Wells, Semin Reprod Med 2012; 30:289-301; Cruz et al., J Assist Reprod Genet 2011; 28:569-573; and Forman et al., Fertil Steril 2013; 100:100-107), but little attention is paid to the other essential part of the pregnancy; no detailed analysis of the functionality of the endometrium or the period of uterine receptivity is routinely performed in IVF centers. The identification of the optimal time for embryo transfer is essential to maximize the effectiveness of ART.

[0062] The present disclosure relates to methods useful for the characterization of (e.g., clinical evaluation, diagnosis, classification, prediction, or profiling) of endometrial receptivity based on the gene expression profile of a panel of genes (e.g., panel A). The panel of genes described herein are particularly useful for characterizing (e.g., assessing or predicting) a subject for having a receptive status for embryonic implantation. Thus, in some aspects, the disclosure provides methods that include determining the gene expression profile of a selected panel of genes in a biological sample obtained from a subject, wherein a panel comprises a plurality of genes associated with endometrial receptivity. The number of genes in the plurality of genes (e.g., at least two) of panel A may be two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, thirty or more, thirty-one or more, thirty-two or more, thirty-three or more, thirty-four or more, thirty-five or more, thirty-six or more, thirty-seven or more, thirty-eight or more, or thirty-nine or more.

[0063] Moreover, the methods described herein are useful for diagnosing whether a subject has a receptive endometrial status, a non-receptive endometrial status, a pre-receptive endometrial status, or a post-receptive endometrial status. As used herein, diagnosing includes both diagnosing and aiding in diagnosing. Thus, other diagnostic criteria may be evaluated in conjunction with the results of the methods described herein in order to make a diagnosis.

[0064] The disclosure further provides for the communication of the results of the methods described herein to, e.g., technicians, physicians, nurse practitioner or patients. In some embodiments of any of the methods described herein, the method further includes communicating the endometrial status (i.e. as having a receptive endometrial status, as having a non-receptive endometrial status, as having a pre-receptive endometrial status, as having a post-receptive endometrial status) as a report. Any of the methods described herein can include a step of generating or outputting a report providing the results of any of the methods described herein. This report can be provided in the form of a tangible medium (e.g., a report printed on a paper or other tangible medium), in the form of an electronic medium (e.g., an electronic display on a computer monitor), or communicated by phone. In some embodiments, computers are used to communicate results of the methods described herein or predictions, or both, to interested parties, e.g., physicians and their patients.

[0065] The methods described herein can be used alone or in combination with other clinical methods for endometrial receptivity stratification known in the art to provide a diagnosis, a prognosis, or a prediction of endometrial receptivity. For example, clinical parameters that are known in the art for predicting endometrial receptivity may be incorporated into the analysis of one of ordinary skill in the art to arrive at an endometrial receptivity assessment with any of the methods described herein.

Methods of Predicting

[0066] Also provided herein are methods of predicting endometrial receptivity for embryonic implantation in a human subject that include: (a) providing a first biological sample obtained from a human subject at a first time point within a menstrual cycle; (b) determining the gene expression profile of a panel of genes in the first biological sample, wherein the panel of genes consists of: ANXA4, CATSPERB, PTGFR, PTGS1, IL8, SCGB2A2, ANGPTL1, HPRT1, MMP10, PGR, ITGA8, IFNG, PROK1, FOXO1, CXCL1, STC1, MMP9, MUC1, RPL13A, CALCA, ITGA9, RACGAP1, GPX3, PPP2R2C, ARG2, SCGB3A1, ALDH1A3, APOD, C2CD4B, TFF3, AQP3, GJA4, ARHGDIA, SELL, APOL2, MT1H, MT1X, MT1L, MAOA and MT1F using reverse transcription polymerase chain reaction analysis; and (c) identifying the human subject as having: (i) a receptive endometrial status, wherein the determined gene expression profile corresponds to a gene expression profile of the panel of genes of a receptive endometrial receptivity reference group (ii) a non-receptive endometrial status, wherein the determined gene expression profile corresponds to a gene expression profile of the panel of genes of a non-receptive endometrial reference group, (iii) a pre-receptive endometrial status, wherein the determined gene expression profile corresponds to a gene expression profile of the panel of genes of a pre-receptive endometrial reference group, or (iv) a post-receptive endometrial status, wherein the determined gene expression profile corresponds to a gene expression profile of the panel of genes of a post-receptive endometrial receptivity reference group.

[0067] In some aspects, the methods can include transferring pre-implantation embryo into the identified human subject. In other aspects, the methods can include obtaining a second biological sample from the human subject at a second time point and repeating steps (b) and (c) on the second biological sample.

Methods of Determining

[0068] As used herein, an endometrial gene expression profile using the selected 40 genes (i.e. panel A) can be determined using any quantitative real-time PCR machine (e.g., a Biomark HD™ System (Fluidigm®)). In some aspects, determining an endometrial gene expression profile of a biological sample (e.g., an endometrial biopsy sample) can include: extracting RNA from the biological sample, performing reverse transcription to generate cDNA, contacting the generated cDNA with pairs of primers targeting the genes of panel A and the control genes, collecting gene expression data using real-time PCR analysis software, performing principal component analysis (PCA) and/or discriminant functional analysis (DA) to determine the endometrial receptivity status of the biological sample as compared to the gene expression profile of panel A of a reference group (e.g., the receptive endometrial reference group, the non-receptive endometrial reference group, the pre-receptive endometrial reference group, the post-receptive endometrial reference group).

[0069] Each reverse transcription PCR reaction occurs in a reaction volume that includes all of the components required to carry out a reaction, e.g., primers, buffer, DNA polymerase, reverse transcriptase, sample. The determining step of each gene within the panel of genes is performed in a reaction volume of 0.005 μL to 100 μL (e.g., 0.005 μL to 100 μL, 0.005 μL to 90 μL, 0.005 μL to 80 μL, 0.005 μL to 70 μL, 0.005 μL to 60 μL, 0.005 μL to 50 μL, 0.005 μL to 40 μL, 0.005 μL to 30 82 L, 0.005 μL to 20 μL, 0.005 μL to 10 μL, 0.01 μL to 100 μL, 0.01 μL to 90 μL, 0.01 μL to 80 μL, 0.01 μL to 70 μL, 0.01 μL to 60 μL, 0.01 μL to 50 μL, 0.01 μL to 40 μL, 0.01 μL to 30 μL, 0.01 μL to 20 μL, 0.01 μL to 10 μL, 0.02 μL to 100 μL, 0.02 μL to 90 μL, 0.02 μL to 80 μL, 0.02 μL to 70 μL, 0.02 μL to 60 μL, 0.02 μL to 50 μL, 0.02 μL to 40 μL, 0.02 μL to 30 μL,0.02 μL to 20 μL,0.02 μL to 10 μL, 0.05 μL to 100 μL, 0.05 μL to 90 μL, 0.05 μL to 80 μL, 0.05 μL to 70 μL, 0.05 μL to 60 μL, 0.05 μL to 50 μL, 0.05 μL to 40 μL, 0.05 μL to 30 μL, 0.05 μL to 20 μL, 0.05 μL to 10 μL, 1 μL to 100 μL, 1 μL to 90 μL, 1 μto 80 μL, 1 μL to 70 μL, 1 μL to 60 μL, 1 μL to 50 μL, 1 μL to 40 μL, 1 μL to 30 μL, 1 μL to 20 μL, 1 μL to 10 μL, 5 μL to 100 μL, 5 μL to 90 μL, 5 μL to 80 μL, 5 μL to 70 μL, 5 μL to 60 μL, 5 μL to 50 μL, 5 μL to 40 μL, 5 μL to 30 μL, 5 μL to 20 μL, 5 μL to 10 μL, 10 μL to 100 μL, 10 μL to 90 μL, 10 μL to 80 μL, 10 μL to 70 μL, 10 μL to 60 μL, 10 μL to 50 μL, 10 μL to 40 μL, 10 μL to 30 μL, 10 μL to 20 μL, 15 μL to 100 μL, 15 μL to 90 μL, 15 μL to 80 μL, 15 μL to 70 μL, 15 μL to 60 μL, 15 μL to 50 μL, 15 to 40 μL, 15 μL to 30 μL, 15 μL to 20 μL, 20 μL to 100 μL, 20 μL to 90 μL, 20 μL to 80 μL, 20 μL to 70 μL, 20 μL to 60 μL, 20 μL to 50 μL, 20 μL to 40 μL, 20 μL to 30 μL, 50 μL to 100 μL, 50 μL to 90 μL, 50 μL to 80 μL, 50 μL to 70 μL, 50 μL to 60 μL, 25 μL to 100 μL, 30 μL to 100 μL, 40 μL to 100 μL, 50 μL to 100 μL, 60 μL to 100 μL, 70 μL to 100 μL, 80 μL to 100 μL, 90 μL to 100 μL).

[0070] Methods of digesting a tissue sample (e.g., an endometrial biopsy sample) and extracting RNA from a tissue sample are well-known in the art and are described herein.

[0071] As used herein, the term “principal component analysis” or “principal component algorithm” refers to a statistical method that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It finds the principal components of the dataset and transforms the data into a new, lower-dimensional subspace. The principle component, which can be represented by an eigenvector, mathematically corresponds to a direction in the original n-dimensional space, so that the first principal component accounts for as much of the variance in the data as possible, and each succeeding component accounts for as much of the remaining variance as possible.

[0072] Principal component analysis (PCA) is a statistical method that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It finds the principal components of the dataset and transforms the data into a new, lower-dimensional subspace. The transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.

[0073] Mathematically, the principal components are the eigenvectors of the covariance or correlation matrix of the original dataset. As the covariance matrix or correlation matrix is symmetric, the eigenvectors are orthogonal. The principal components (eigenvectors) correspond to the direction (in the original n-dimensional space) with the greatest variance in the data. Each eigenvector has a corresponding eigenvalue. An eigenvalue is a scalar. The corresponding eigenvalue is a number that indicates how much variance there is in the data along that eigenvector (or principal component). A large eigenvalue means that that principal component explains a large amount of the variance in the data. Similarly, a principal component with a very small eigenvalue explains a small amount variance in the data.

[0074] Detailed descriptions regarding how to perform PCA are described in numerous references, e.g., Smith, Lindsay I. “A tutorial on principal components analysis.” Cornell University, USA 51 (2002): 52; Shlens, Jonathon. “A tutorial on principal component analysis.” arXiv preprint arXiv:1404.1100 (2014), each of which is incorporated by reference in its entirety.

[0075] To apply principle component analysis for the disclosed methods, a set of data comprising expression profile of a panel of genes is created for each sample. The set of data for a sample can be represented by a vector. The dataset can include the expression profile for all subjects in reference group of interest (e.g., a receptive endometrial reference group, a non-receptive endometrial reference group, a pre-receptive endometrial reference group, a post-receptive endometrial reference group) and/or the expression profile of the panel of the genes for tested subjects. The principal component analysis (PCA) converts the dataset into a dataset with lower dimensions. The positions of the each subject (including subjects in the reference group and the tested subject) are determined in this lower dimensional space. In this lower dimension space, if the tested subject is closer to, or is clustered with a particular reference group, then it can be determined that this tested subject corresponds to this particular reference group.

[0076] The methods to determine whether a test subject is closer to, or is clustered with, a particular reference group are known in the art, and can be determined by algorithms known in the art, e.g., hierarchical clustering algorithm, k-means clustering algorithm, a statistical distribution model, etc. Various computer algorithms for data analysis and classification are known in the art to compare gene expression profiles. See, e.g., Diaz-Gimeno et al., Fertil Steril 2011 95(1): 50-60; Diaz-Gimeno et al., Fertil Steril 2013; 99: 508-517.

Kits

[0077] Also provided herein are kits that include any of the reagents suitable for predicting endometrial receptivity for transplantation of a pre-implantation embryo. The kits include reagents suitable for determining an endometrial gene expression profile of a panel of genes (e.g., panel A). In some embodiments, the kits can include instructions for performing use of the kit in the methods described herein. In some embodiments, the reagents suitable for determining the endometrial gene expression profile of the biological sample are disposed in an array, a chip, a multi-well plate (e.g., a 96-well plate or a 384-well plate), or a tube (e.g., a 0.2 mL microcentrifuge tube). In some embodiments of any of the kits described herein, the kit includes an array, a chip, a multi-well plate (e.g., a 96-well plate or a 384-well plate), or a tube (e.g., a 0.2 mL microcentrifuge tube). In some embodiments of any of the kits described herein, the kit includes one or more reference groups (e.g., the receptive endometrial reference group, the non-receptive endometrial reference group, the pre-receptive endometrial reference group, the post-receptive endometrial reference group) for determining endometrial gene expression profile of a sample based on computer-assisted algorithms (e.g., principle component analysis, or any other classification algorithms known in the art). In some cases, the kits include software useful for comparing the endometrial gene expression profile of a sample with a reference group (e.g., a prediction model). The software may be provided in a computer readable format (e.g., a compact disc, DVD, flash drive, zip drive etc.), or the software may be available for downloading via the intemet. The kits described herein are not so limited; other variations will be apparent to one of ordinary skill in the art.

EXAMPLES

[0078] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.

Example 1

Endometrial Receptivity Testing on Biomark HD™ System (Fluidigm®)

Study Design

[0079] In order to define the method for endometrial receptivity evaluation, gene expression data from endometrial biopsies obtained at different moments of the menstrual cycle from healthy fertile donors (group A) and subfertile women (group B) were analyzed. Endometrial biopsies from group A were used to define endometrial receptivity transcriptomic signature. Endometrial samples from group B were tested and diagnosed for receptivity according to the methods described herein and the endometrial receptivity array ERA® (Igenomix, Spain). Receptivity status concordance between the present method and ERA classification was evaluated in this group of samples.

Patient Selection and Sample Collection

[0080] Group A consisted of 96 healthy fertile donors (aged between 18 and 34 years), with regular menstrual cycles and normal body mass indicator (BMI) (25-30). Endometrial biopsies from this group were obtained on two different days of the same natural menstrual cycle: LH+2, i.e. two days after the luteinizing hormone (LH) surge and LH+7, i.e. 7 days after the LH surge. Group B consisted of 120 subfertile patients (aged 30-42 years) seeking ART treatment and undergoing hormone replacement (HRT) cycles. Endometrial biopsies from this group of patients were obtained after 5 full days of progesterone impregnation (P.sub.4+5).

[0081] Endometrial biopsies were obtained from the uterine fundus using a Pipelle catheter (Gynetics, Namont-Achel, Belgium) under sterile conditions. A piece of endometrial tissue of approximately 30 mg was obtained per donor or subfertile patient. The day of the biopsy was calculated in natural cycles as the number of days after the LH surge. The day of the LH surge was considered LH+0. LH urine levels were measured daily using a commercially available detection kit (Clearblue, SPD Swiss Precision Diagnostics; Geneva, Switzerland). In HRT cycles, the day of the biopsy was calculated as the number of days after the first progesterone intake. The day of the first progesterone intake is considered P.sub.4+0. After endometrial biopsy collection, tissue was placed in a CryoTube® (Nunc, Roskilde, Denmark) containing 1 ml RNAlater® (Sigma-Aldrich, St Louis, Mo., USA) and stored at −20° C. until further processing. Ethical approval for the study was obtained from Centro Hospital Universitario Virgen del Rocío (Sevilla, Spain, CEI #2014PI/025). All fertile donors and subfertile patients signed an informed consent document.

Reference Genes Selection

[0082] Eight candidate reference genes were selected: actin (ACTN), beta-2 microglobulin (B2M), cytochrome C1 (CYC1), EMG1 N1-specific pseudouridine methyltransferase (EMG1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), TATA-box binding protein (TBP), topoisomerase (DNA) I (TOPI) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ). The expression stability of these reference genes was calculated using the two freeware Microsoft Excel-based applications GeNorm (Vandesompele et al., Genome Biol 2002; 3:34-1) and NormFinder (Andersen et al., Cancer Res 2004; 64:5245-5250) by following the software developer's manual.

RNA Extraction and cDNA Preparation

[0083] Total RNA was extracted using RNeasy mini kit (Qiagen, London, UK) following manufacturer's instructions. RNA purity and concentration was confirmed by NanoDrop 2000 Spectrophotometer (Thermo Scientific, Waltham, Mass., USA) and RNA integrity was assessed using Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif., USA) according to standard protocol provided by the manufacturer. Each total RNA sample was diluted into 250 ng/μl and reverse transcribed into cDNA using Fluidigm® Reverse Transcription Master Mix (Fluidigm®, San Francisco, Calif., USA) following the instructions of the supplier. The cDNA samples were immediately used or stored at −20° C. until further downstream processing for analysis on the BioMark HD™ platform.

Gene Expression Analysis

[0084] Pairs of primers targeting the selected and reference genes were designed using the software platform D3 Assay Design (Fluidigm®, San Francisco, Calif.) and obtained from DELTAGene™ Fluidigm®, San Francisco, Calif.). Specific target amplification (STA) was carried out on cDNA samples using Fluidigm® PreAmp Master Mix and DELTAgene assays (Fluidigm®, San Francisco, Calif.) following the manufacturer's instructions. RT-qPCR reactions were performed following the Fast Gene Expression Analysis Using Evagreen on the Biomark HD™ System, Advanced Development Protocol (PN 100-3488, Rev.C1) (Fluidigm®, San Francisco, Calif.) and 96.96 Dynamic Array™ IFC. The BioMark™ HD System uses microfluidic distribution of samples and requires approximately 7 nL per reaction. Data was collected with Fluidigm® Real-Time PCR analysis software using linear baseline correction method and global auto Cq threshold method. Data were then exported to Excel as .csv files and Cq values normalized using the 3 reference genes included in the analysis.

Principal Component Analysis (PCA) and Discriminant Functional Analysis

[0085] Differential expression of genes in the proliferative and secretory phases was assessed by comparing ΔCq values from LH+2 and LH+7 groups. In order to define the genes that had altered mRNA abundance among the groups, a paired t-test (p<0.05) was performed. Fold change (−ΔΔCq) was calculated to determine up-regulated and down regulated genes in the WOI. In order to assess if receptivity status could be established with a reduced number of genes, a principal component analysis (PCA) of the genes showing significant fold change between LH+2 and LH+7 was performed. Discriminant functional analysis (DA) was then used to evaluate the ability of the genes with the highest absolute coefficient value from each of the leading principal components to accurate discriminate samples into the following states: proliferative, receptive, pre-receptive and post-receptive. A Split-Sample validation of the DA was performed to assess the reliability and robustness of discriminant findings. Both fertile and infertile patient samples were split into two subsets. One data set (70% of the samples) was used as a training set and the other one as testing set (remaining 30% of the samples). The percentage of correct classifications was calculated to determine the reliability of the DA model. Data analyses were performed by using IBM SPSS Statistics software version 19.0.

Gene Function Analysis

[0086] To study the biological functions and pathways of the genes selected, DAVID v.6.7 bioinformatics resources were used (Huang et al., Nucleic Acids Res 2009; 37:1-13). Assessment and integration of protein-protein interactions was performed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING v.10.0 database, (http://string-db.org) (Szklarczyk et al., Nucleic Acids Res 2015; 43:D447-D452).

Results: Gene Expression, Principal Component Analysis (PCA) and Discriminant Functional Analysis

[0087] A total of 184 genes related to endometrial receptivity and embryonic implantation were carefully chosen after extensive literature review (Table 1).

TABLE-US-00001 TABLE 1 Panel of Selected Genes Gene NCBI Accession Symbol Gene Name Reference No. ABCC3 ATP-binding cassette, Díaz-Gimeno NM_003786.3 sub-family C et al. 2011 (CFTR/MRP), member 3 ACTA1 Actin, alpha skeletal Altmäe et al. NM_001100.3 muscle 2010 ALDH1A3 Aldehyde dehydrogenase Dominguez et NM_000693.3 family 1 member A3 al. 2009; Haouzi et al 2009, 2013 AMIGO2 Adhesion molecule with Díaz-Gimeno NM_001143668.1 Ig-like domain 2 et al. 2011 ANGPTL1 Angiopoietin-like 1 Haouzi et al NM_004673.3 2009, 2012 ANXA2 Annexin A2 Dominguez et NM_001002857.1 al. 2009; Haouzi et al. 2012; Tracey et al. 2013 ANXA4 Annexin A4 Li et al. 2006; NM_001153.4 Chen et al 2009; Díaz- Gimeno et al. 2011; Ruíz- Alonso et al. 2012; Haouzi et al. 2012; Tracey et al. 2013 APOD Apolipoprotein D Ruíz-Alonso NM_001647.3 et al. 2012 APOE Apolipoprotein E Ruíz-Alonso NM_001302688.1 et al. 2012 APOL2 Apolipoprotein L, 2 Dominguez et NM_030882.3 al. 2009; Haouzi et al 2009, 2013 AQP3 Aquaporin-3 Díaz-Gimeno NM_004925.4 et al. 2011; Ruíz-Alonso et al. 2012 AREG Amphiregulin Aghajanova NM_001657.3 et al. 2008; Bamea et al. 2012 ARG2 Arginase 2 Díaz-Gimeno NM_001172.3 et al. 2011 ARHGDIA Rho GDP-dissociation Chen et al. NM_001185077.2 inhibitor (GDI) alpha 2009; Tracey et al. 2013 ATP5B ATP synthase, H+ Sadek et al. NM_001686.3 transporting, 2012 mitochondrial F1 complex, beta polypeptide BTC Probetacellulin Barnea et al. NM_001729.3 2012 C2CD4B C2 calcium-dependent Haouzi et al. NM_001007595.2 domain-containing 2009, 2012 protein 4B C4BPA Complement component Díaz-Gimeno NM_000715.3 4 binding protein, alpha et al. 2011 CALCA Calcitonin-related Otsuka et al., NM_001033953.2 polypeptide alpha 2007 CALR Calreticulin Parmar et al. NM_004343.3 2009; Tracey et al. 2013 CAPN6 Calpain-6 Altmäe et al. NM_014289.3 2010; Díaz- Gimeno et al. 2011 CATSPERB Cation channel sperm Díaz-Gimeno NM_024764.3 auxiliary subunit beta et al. 2011 CCL2 Chemokine (C-C motif) Barnea et al. NM_002982.3 ligand 2 2012 CCR7 Chemokine (C-C motif) Altmäe et al. NM_001838.3 receptor 7 2010 CD55 CD55 molecule, decay Ruíz-Alonso NM_000574.4 accelerating factor for et al. 2012 complement (Cromer blood group) CDA Cytidine deaminase Díaz-Gimeno NM_001785.2 et al. 2011 CDH1 Cadherin-1, type Banerjee et al. NM_004360.4 1, Epitelial- Cadherin 2013 CIR1 Corepressor interacting Ruíz-Alonso NM_004882.3 with RBPJ 1 et al. 2012 CLDN4 Claudin-4 Ruíz-Alonso NM_001305.4 et al. 2012 CLIC1 Chloride intracellular Chen et al. NM_001287593.1 channel protein 1 2009; Tracey et al. 2013 CLU Clusterin Díaz-Gimeno NM_001831.3 et al. 2011 CMTM5 CKLF-like MARVEL Altmäe et al. NM_138460.2 transmembrane domain- 2010 containing protein 5 COL16A1 Collagen, type XVI, Altmäe et al. NM_001856.3 alpha 1 2010; Díaz- Gimeno et al. 2011 CRHR2 Corticotropin-releasing Makrigianakis NM_001883.4 factor receptor 2 et al. 2004 CRISP3 Cysteine-rich secretory Díaz-Gimeno NM_006061.3 protein 3 et al. 2011; Ruíz-Alonso et al. 2012 CSF1 Colony stimulating factor Gargiulo et al. NM_000757.5 1 (macrophage) 2004; Aghajanova et al. 2008; Tawfeek et al. 2012 CSF3 Colony stimulating factor Lédée et al. NM_000759.3 3 (granulocyte) 2011 CSRP2 Cysteine and glycine-rich Díaz-Gimeno NM_001321.2 protein 2 et al. 2011 CTNNA2 Catenin alpha-2 Altmäe et al. NM_ 001282597.2 2010; Díaz- Gimeno et al. 2011 CXCL1 Growth-regulated alpha Barnea et al. NM_001511.3 protein 2012 CXCL14 C-X-C motif chemokine Díaz-Gimeno NM_004887.4 14 et al. 2011; Ruíz-Alonso et al. 2012 CXCL6 C-X-C motif chemokine Altmäe et al. NM_002993.3 6 (Chemokine alpha 3) 2010 DEFB1 Beta-defensin 1 Díaz-Gimeno NM_005218.3 et al. 2011 DKK1 Dickkopf WNT signaling Díaz-Gimeno NM_012242.3 pathway inhinitor 1 1 et al. 2011; Ruíz-Alonso et al. 2012 EGF Epidermal Growth Factor Gargiulo et al. NM_001963.5 2004; Aghajanova et al. 2008; Sing et al. 2011; Barnea et al. 2012 EPHB3 EPH receptor B3 Díaz-Gimeno NM_004443.3 et al. 2011 EREG Proepiregulin Barnea et al. NM_001432.2 2012 ESR1 Estrogen receptor 1 Gao et al. NM_000125.3 2012 ESR2 Estrogen Receptor 2 (ER Altmäe et al. NM_001437.2 Beta) 2010 EZR Ezrin Chen et al. NM_003379.4 2009; Tracey et al. 2013 FAM3B Family with sequence Altmäe et al. NM_058186.3 similarity 3, member B 2010 FAM3D Family with sequence Altmäe et al. NM_138805.2 similarity 3, member D 2010 FASLG Fas ligand (TNF Makrigianakis NM_000639.2 superfamily, member 6) et al. 2004 FGF7 Fibroblast growth factor 7 Cavagna et al. NM_002009.3 2003 FOXO1 Forkhead box protein O1 Ruíz-Alonso NM_002015.3 et al. 2012 FOXP3 Forkhead box protein P3 Chen et al. NM_014009.3 2012 FUT4 Fucosyltransferase 4 Liu et al. NM_002033.3 (alpha (1, 3) 2012 fucosyltransferase, myeloid-specific FZD5 Frizzled-5 Liu et al. NM_003468.3 2010 GABARAPL1 Gamma-aminobutyric Díaz-Gimeno NM_031412.2 acid (GABA(A) receptor- et al. 2011 associated protein-like 1 GADD45A Growth arrest and DNA Díaz-Gimeno NM_001924.3 damage-inducible protein et al. 2011; GADD45 alpha Ruíz-Alonso et al. 2012 GAST Gastrin Díaz-Gimeno NM_000805.4 et al. 2011 GDF15 Growth differentiation Díaz-Gimeno NM_004864.3 factor 15 et al. 2011 GJA4 Gap junction protein, Ruíz-Alonso NM_002060.2 alpha 4, 37 kDa et al. 2012 GNLY Granulysin Díaz-Gimeno NM_001302758.1 et al. 2011; Ruíz-Alonso et al. 2012 GPX3 Glutathione peroxidase 3 Díaz-Gimeno NM_002084.4 et al. 2011; Ruíz-Alonso et al. 2012 HBA1 Hemoglobin, alpha 1 Altmäe et al. NM_000558.4 2010 HBEGF Heparin Binding-EGF- Stavreus- NM_001945.2 like growth factor Evers et al. 2002; Aghajanova et al. 2008; Altmäe et al. 2010; Sing et al. 2011; Barnea et al. 2012 HBG1 Hemoglobin, gamma A Altmäe et al. NM_000559.2 2010 HMBS Hydroxymethylbilane Vestergaard NM_000190.3 synthase et al. 2011 HOXA10 Homeobox A10 Aghajanova NM_018951.3 et al. 2008; Wei et al. 2009; Kakmak et al. 2011; Ruíz- Alonso et al. 2012; Garrido- Gomez et al. 2013; Jana et al. 2013 HOXA11 Homeobox A11 Lynch et al., NM_005523.5 2009 HOXB7 Homeobox B7 Ruíz-Alonso NM_004502.3 et al. 2012 HPRT1 Hypoxanthine Vestergaard NM_000194.2 phosphoribosyltransferase et al. 2011 1 HPSE Heparanase Díaz-Gimeno NM_006665.5 et al. 2011 ICAM1 Intercellular adhesion Zhao et al., NM_000201.2 molecule 1 2010 ID4 DNA-binding protein Díaz-Gimeno NM_001546.3 inhibitor ID-4 et al. 2011; Ruíz-Alonso et al. 2012 IDH1 Isocitrate dehydrogenase Díaz-Gimeno NM_005896.3 1 (NADP+), soluble et al. 2011 IER3 Immediate early response Díaz-Gimeno NM_003897.3 3 et al. 2011 IFNG Interferon gamma Banerjee et al. NM_000619.2 2013 IGFBP1 Insulin-like growth Altmäe et al. NM_000596.3 factor-binding protein 1 2010; Díaz- Gimeno et al. 2011 IGFBP3 Insulin-like growth Ruíz-Alonso NM_001013398.1 factor-binding protein 3 et al. 2012 IL10 Interleukin 10 Banerjee et al. NM_000572.2 2013 IL11 Interleukin 11 Altmäe et al. NM_000641.3 2010; Sing et al. 2011; Tawfeek et al. 2012 IL15 Interleukin-15 Lédée et al. NM_000585.4 2011; Díaz- Gimeno et al. 2011; Ruíz- Alonso et al. 2012 IL18 Interleukin-18 Lédée et al. NM_001562.3 2011 IL1B Interleukin 1 Beta Gargiulo et al. NM_000576.2 2004; Aghajanoya et al. 2008; Altmäe et al. 2010; Cheong et al. 2012; Koot et al. 2012; Banerjee et al. 2013 IL1R1 Interleukin-1 Receptor Garrido- NM_001288706.1 type Gómez et al. 2013 IL2 Interleukin 2 Banerjee et al. NM_000586.3 2013 IL21 Interleukin-21 Altmäe et al. NM_021803.3 2010 IL4 Interleukin 4 Banerjee et al. NM_000589.3 2013 IL5 Interleukin 5 (colony- Teklenburg et NM 000879.2 stimulating factor, al., 2010 eosinophil) IL6 Interleukin 6 Sing et al. NM_000600.4 2011; Cheong et al. 2012; Koot et al. 2012; Barnea et al. 2012; Tawfeek et al. 2012 IL8 Interleukin 8 Banerjee et al. NM_000584.3 2013 ITGAV Integrin, alpha V Lessey et al., NM_002210.4 2000; Nardo et al. 2003; Aghajanova et al. 2008; Barnea et al. 2012; Ruíz- Alonso et al. 2012; Koot et al. 2012; Jana et al. 2013; Tracey et al. 2013 ITGA2 Integrin, alpha 2 (CD49B, Barnea et al. NM_002203.3 alpha 2 subunit of VLA-2 2012 receptor) ITGA8 Integrin, alpha 8 Altmäe et al. NM_003638.2 2010 ITGA9 Integrin, alpha 9 Barnea et al. NM_002207.2 2012 ITGB1 Integrin, beta 1 Barnea et al. NM_002211.3 2012 ITGB3 Integrin, beta 3 Barnea et al. NM_000212.2 2012 KCNG1 Potassium voltage-gated Díaz-Gimeno NM_002237.3 channel subfamily G et al. 2011 member 1 LCP1 Lymphocyte cytosolic Dominguez et NM_002298.4 protein (L-plastin) al. 2009; Haouzi et al. 2009, 2013 LEP Leptin Labarta et al., NM_000230.2 2011 LIF Leukaemia Inhibitor Aghajanova NM_002309.4 Factor et al. 2003; Gargiulo et al. 2004; Aghajanova et al. 2008; Altmäe et al. 2010; Sing et al. 2011; Díaz-Gimeno et al. 2011; Tawfeek et al. 2012; Ruíz- Alonso et al. 2012; Tawfeek et al. 2012; Jana et al. 2013; Garrido- Gómez et al. 2013 LIFR Leukemia inhibitory Aghajanova NM_001127671.1 factor Receptor alpha et al. 2003; Aghajanova et al. 2008; Tawfeek et al. 2012 LPAR3 Lysophosphatidic acid Wei et al. NM_012152.2 receptor 3 2009 LRPPRC Leucine-rich PPR motif- Tawfeek et NM_133259.3 containing protein al., 2012; Tracey et al. 2013 LRRC17 Leucine-rich repeat- Díaz-Gimeno NM_001031692.2 containing protein 17 et al. 2011 LYPD3 Ly6/PLAUR domain- Díaz-Gimeno NM_014400.2 containing protein 3 et al. 2011 MAOA Monoamine oxidase A Dominguez et NM_000240.3 al. 2009; Díaz-Gimeno et al. 2011; Ruíz-Alonso et al. 2012; Haouzi et al. 2012 MAP2K1 Mitogen-activated protein Barnea et al. NM_002755.3 kinase 1 2012 MAP3K5 Mitogen-activated protein Ruíz-Alonso NM_005923.3 kinase 5 et al. 2012 MAPK1 Mitogen-activated protein Barnea et al. NM_002745.4 kinase 1 2012 MAPK3 Mitogen-activated protein Barnea et al. NM_002746.2 kinase 3 2012 MAPK8 Mitogen-activated protein Barnea et al. NM_001278547.1 kinase 8 2012 MFAP5 Microfibrillar-associated Haouzi et al. NM_003480.3 protein 5 2009, 2012; Díaz-Gimeno et al. 2011 MMP10 Matrix metallopeptidase Altmäe et al. NM_002425.2 10 (Stromelysin-2) 2010 MMP2 Matrix Metalloproteinase Banerjee et al. NM_004530.5 2 (gelatinase A, 72 kDA 2013 gelatinase, 72 kDA type IV collagenase) MMP26 Matrix metallopeptidase Altmäe et al. NM_021801.4 26 2010; Ruíz- Alonso et al. 2012 MMP8 Matrix metallopeptidase 8 Altmäe et al. NM_002424.2 (Neutrophil collagenase) 2010 MMP9 Matrix Metallopeptidase9 Banerjee et al. NM_004994.2 (gelatinase B, 92 kDa 2013 gelatinase, 92 kDa type IV collagenase) MT1E Metallothionein 1E Ruíz-Alonso NM_175617.3 et al. 2012 MT1F Metallothionein 1F Ruíz-Alonso NM_005949.3 et al. 2012 MT1G Metallothionein 1G Díaz-Gimeno NM_001301267.1 et al. 2011; Ruíz-Alonso et al. 2012 MT1H Metallothionein 1H Ruíz-Alonso NM_005951.2 et al. 2012 MT1L Metallothionein 1L Ruíz-Alonso NR_001447.2 et al. 2012 MT1X Metallothionein 1X Ruíz-Alonso NM_005952.3 et al. 2012 MT2A Metallothionein 2 Díaz-Gimeno NM_005953.4 et al. 2011; Ruíz-Alonso et al. 2012 MUC1 Mucin 1, cell surface Altmäe et al. NM_002456.5 associated 2010; Koot et al. 2012; Garrido- Gómez et al. 2013 MUC16 Mucin-16, cell surface Altmäe et al. NM_024690.2 associated 2010; Díaz- Gimeno et al. 2011 MUC4 Mucin-4, cell surface Aghajanova NM_018406.6 associated et al. 2008; Altmäe et al. 2010 MUC5B Mucin-5B, oligomeric Aghajanova NM_002458.2 mucus/gel-forming et al. 2008; Altmäe et al. 2010 NFKB1 Nuclear factor of kappa Barnea et al. NM_003998.3 light polypeptide 2012 enhancer in B cells 1 NFKBIA Nuclear factor of kappa Barnea et al. NM_020529.2 light polypeptide 2012 enhancer in B cells inhibitor, alpha NFKBIE Nuclear factor of kappa Barnea et al. NM_004556.2 light polypeptide 2012 enhancer in B cells inhibitor, epsilon NNMT Nicotinamide N- Díaz-Gimeno NM_006169.2 methyltransferase et al. 2011 OPRK1 Opiod receptor, kappa 1 Díaz-Gimeno NM_000912.4 et al. 2011 PAEP Progestagen-associated Stavreus- NM_001018049.2 endometrial protein Evers et al. 2006; Aghajanova et al. 2008; Wei et al. 2009; Díaz- Gimeno et al. 2011, Ruíz- Alonso et al. 2012; Ming- Qing et al. 2013 PGR Progesterone Receptor Stavreus- NM_000926.4 Evers et al. 2001; Aghajanova et al. 2008; Gao et al. 2012 PGRMC1 Progesterone receptor Chen et al. NM_006667.4 membrane component 1 2009; Tracey et al. 2013 PLA2G16 Phospholipase A2, group Díaz-Gimeno NM_007069.3 XVI et al. 2011 PLA2G4A Phospholipase A2, group Berlanga et NM_024420.2 IVA (cytosolic, calcium- al. 2011 dependent) PPP2R2C Protein phosphatase 2, Barnea et al. NM_020416.3 regulatory subunit B, 2012 gamma PRDX1 Peroxiredoxin 1 Stavreus- NM_002574.3 Evers et al. 2002; Aghajanova et al. 2008 PRDX2 (Peroxiredoxin 2 Stavreus- NM_005809.5 Evers et al. 2002; Aghajanova et al. 2008 PRKCG Protein kinase C, gamma Altmäe et al. NM_001316329.1 2010 PROK1 Prokineticin-1 Haouzi et al NM_032414.2 2009, 2012 PTGER3 Prostaglandin E receptor Banerjee et al. NM_001126044.1 3 (subtype EP3) 2013; Vilella et al. 2013 PTGFR Prostaglandin F receptor Berlanga et NM_000959.3 (FP) al. 2011 PTGS1 Prostaglandin- Aghajanova NM_000962.3 endoperoxide synthase 1 et al. 2008; (prostaglandin G/H Sing et al. synthase and 2011; Koot et cyclooxygenase) al. 2012 PTGS2 Prostaglandin- Aghajanova NM_000963.3 endoperoxide synthase 2 et al. 2008; (prostaglandin G/H Sing et al. synthase and 2011; Koot et cyclooxygenase) al. 2012; Banerjee et al. 2013 PTPRZ1 Protein-tyrosine Barnea et al. NM_002851.2 phosphatase, receptor 2012 type, Z polypeptide 1 RAC1 Ras-related C3 botulinum Grewal et al., NM_018890.3 toxin substrate 1 (rho 2008 family, small GTP binding protein Rac1 RACGAP1 Rac GTPase-activating Grewal el al. NM_013277.4 protein 1 2008 RHOA Ras homolog family Heneweer NM_001664.3 member A et al., 2008 RPL13A Ribosomal protein L13a Vestergaard NM_012423.3 et al. 2011 S100A1 S100 calcium binding Díaz-Gimeno NM_006271.1 protein A1 et al. 2011 S100A10 S100 calcium binding Dominguez et NM_002966.2 protein A10 al. 2009; Haouzi et al 2009, 2013; Ruíz-Alonso et al. 2013 S100A2 S100 calcium binding Altmäe et al. NM_005978.3 protein A2 2010 S100P S100 calcium binding Díaz-Gimeno NM_005980.2 protein P et al. 2011; Zhang et al. 2012 SCGB2A2 Secretoglobin, family 2A, Díaz-Gimeno NM_002411.3 member 2 et al. 2011 SCGB3A1 Secretoglobin, family 3A, Altmäe et al. NM_052863.2 member 1 2010 SDHA Succinate dehydrogenase Vestergaard NM_004168.3 complex, subunit A, et al. 2011; flavoprotein (Fp) Sadek et al. 2012 SELL Selectin L Genbaced et NM_000655.4 al. 2003; Aghajanova et al. 2008; Ruíz-Alonso et al. 2012; Banerjee et al. 2013 SERPINA1 Serpin peptidase Parmar et al. NM_000295.4 inhibitor, clade A (alpha- 2009; Tracey 1 antiproteinase, et al. 2013 antitrypsin), member 1 SERPING1 Serpin peptidase Díaz-Gimeno NM_000062.2 inhibitor, clade G (C1 et al. 2011; inhibitor), member 1 Ruíz-Alonso et al. 2012 SGK1 Serine/glucocorticoid Altmäe et al. NM_005627.3 regulated kinase 1 2010 SLPI Secretory leukocyte Díaz-Gimeno NM_003064.3 peptidase inhibitor et al. 2011 SOD2 Superoxide dismutase 2, Díaz-Gimeno NM_000636.3 mitochondrial et al. 2011 SPDEF SAM pointed domain Díaz-Gimeno NM_012391.2 containing ETS et al. 2011 transcription factor SPP1 Secreted phosphoprotein Lessey et al. NM_001251830.1 1 (Osteopontin) 2003; Aghajanova et al. 2008; Wei et al. 2009; Díaz- Gimeno et al. 2011; Barnea et al. 2012; Ruíz-Alonso et al. 2012, Garrido- Gómez et al. 2013 STAT3 Signal transducer and Catalano et al. NM_139276.2 activator of transcription 2005 3 (Acute-phase response factor) STC1 Stanniocalcin-1 Ruíz-Alonso NM_003155.2 et al. 2012 STMN1 Stathmin Chen et al. NM_001145454.2 2009; Dominguez et al. 2009; Haouzi et al. 2012; Tracey et al. 2013 TAGLN2 Transgelin 2 Dominguez et NM_001277224.1 al. 2009; Haouzi et al 2009, 2013; Díaz-Gimeno et al. 2011 TFF3 Trefoil factor 3 Altmäe et al. NM_003226.3 (intestinal) 2010; Ruíz- Alonso et al. 2012 TGFB1 Transforming growth Gargiulo et al. NM_000660.6 factor, beta 1 2004; Aghajanova et al. 2008; Sing et al. 2011; Barnea et al. 2012; Banerjee et al. 2013 TNC Tenascin Barnea et al. NM_002160.3 2012 TNF Tumor Necrosis Factor Banerjee et al. NM_000594.3 alpha 2013 TNFRSF11B Tumor necrosis factor Barnea et al. NM_002546.3 receptor superfamily, 2012 member 11B TSPAN8 Tetraspanin 8 Díaz-Gimeno NM_004616.2 et al. 2011 VCAM1 Vascular cell adhesion Díaz-Gimeno NM_001078.3 protein 1 et al. 2011; Barnea et al. 2012 VEGFA Vascular Endothelial Banerjee et al. NM_001025366.2 Growth Factor A 2013 WISP2 WNT1-inducible- Altmäe et al. NM_001323370.1 signaling pathway protein 2010 2

[0088] Several biological processes mainly related to cellular proliferation, response to wounding, defense and immune response were found to be statistically over-represented as analyzed by DAVID bioinformatics tool (Table 2).

TABLE-US-00002 TABLE 2 GO functional enrichment of the 192 WO1 genes Category Term Genes % p-value BP Regulation of cell proliferation 47 24.6 9.0 E−19 BP Positive regulation of cell 33 17.3 1.6 E−16 proliferation BP Response to wounding 35 18.3 5.1 E−15 BP Defense response 36 18.8 6.8 E−14 BP Positive regulation of immune 23 12.0 3.9 E−13 system process BP Negative regulation of transport 18 9.4 1.4 E−12 MF Cytokine activity 30 15.7 3.7 E−23 MF Growth factor activity 23 12.0 6.0 E−17 MF Cadmium ion binding 5 2.6 4.9 E−6 MF Antioxidant activity 7 3.7 2.6 E−5 CC Extracellular region part 65 34 6.3 E−30 CC Extracellular space 55 28.8 2.0 E−28 BP = biological process, MF = molecular function; CC = cellular component

[0089] Exploration of the interactions of proteins codified by the selected genes rendered the following results: a total of 1,334 protein-protein interactions when the expected was 425 in the network analysis (clustering coefficient=0.616) (FIG. 1). The set of proteins codified by the selected genes have more interactions among themselves than what would be expected for a random set of proteins of similar size, drawn from the genome. Such enrichment indicated that these proteins were biologically connected as a group.

[0090] Expression stability analysis of the eight selected reference genes showed that Cytochrome C1 (CYC1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), TATA-box binding protein (TBP) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ) were the most stable genes. These genes, previously found to be useful for normalizing endometrial gene expression data (Vestergaard et al., Mol Hum Reprod 2011; 17:243-254; Sadek et al., Hum Reprod 2012; 27:251-256), and were selected and used for normalization of gene expression values.

[0091] Comparison of gene expression data of the selected WOI genes in fertile subjects on days LH+2 and LH+7 of their cycles showed a total of 85 genes presenting significant differences in the fold change (p<0.05; paired t-test) between the proliferative (LH+2) and the secretory phase (LH+7). Most genes were up regulated (n=71) rather than downregulated (n=14) (FIGS. 2A and 2B). Gene ontology (GO) analysis revealed that these 85 genes were related to cell division and proliferation, cell signaling and response, extracellular organization and communication, immunological activity, vascular proliferation, blood pressure regulation and embryo implantation. Additionally, comprehensive analysis of protein-protein interactions showed a total of 23 interactions when the expected number was 10 (clustering coefficient=0.218, P=0.000344).

[0092] Principal component analysis (PCA) of the 85 genes showing significant fold change between LH+2 and LH+7 revealed that 40 components explained more than 99.5% of total sample variance. The variance provided by each component and the cumulative percentage along the 40 components together with the genes with the highest absolute coefficient value from each of the leading principal components are represented in FIG. 3A. These genes were selected for further discriminant function analysis (DA) (Jolliffe Appl Stat 1972; 21:160-173; Jolliffe Applied Stat 1973; 22:21-31). DA assessed the effectiveness of the selected genes to accurately classify the receptivity status of endometrial biopsies from both fertile donors and subfertile patients (FIG. 3B).

[0093] Within the group of donors, the selected 40 genes disclosed in FIG. 3A (endometrial receptivity panel A genes) allowed accurate classification of samples into two endometrial receptivity statuses: proliferative (non-receptive) and receptive. Using a DA model based on the 40 genes selected, 100% of LH+2 samples were categorised as non-receptive, and all LH+7 samples were classified as receptive in both the training and test sets (Table 3).

TABLE-US-00003 TABLE 3 Discriminant Functional Analysis Classification Results ORIGINAL PREDICTED GROUP MEMBERSHIP (%).sup.a,b,c,d GROUP Non- Pre- Post- MEMBERSHIP N receptive receptive Receptive receptive DONORS Training Set LH + 2 67 100.0 — 0.0 — LH + 7 67 0.0 — 100.0 — Test Set LH + 2 29 100.0 — 0.0 — LH + 7 29 0.0 — 100.0 — PATIENTS Training Set Pre-receptive 29 — 100.0 0.0 0.0 Receptive 41 — 2.4 95.1 2.4 Post-receptive 13 — 0.0 0.0 100.0 Test Set Pre-receptive 13 — 92.3 7.7 0.0 Receptive 18 — 5.6 94.4 0.0 Post-receptive 6 — 0.0 16.7 83.3 .sup.aDonors training set: 100% of original grouped cases correctly classified .sup.bDonors testing set: 100% of original grouped cases correctly classified .sup.cPatients training set: 97.59% of original grouped cases correctly classified .sup.dPatients testing set: 91.67% of original grouped cases correctly classified

[0094] Within the patient group, the endometrial receptivity panel A genes classification matched the endometrial biopsy status prediction provided by an independent endometrial receptivity test (ERA) in 97.59% samples in the training set and 91.67% in the testing set. In the training set, two samples were classified differently by the two tests and, in the testing set, there were three.

[0095] The accurate identification of the period of endometrial receptivity could be key for the achievement of a successful pregnancy in many couples. The importance of embryonic-endometrial synchrony for successful implantation have been reported in several studies. Shapiro et al. (2008) showed that the lower implantation rates observed in Day 6 embryos transferred fresh compared to Day 5 embryos were not due to an embryonic factor but rather to the endometrial moment where embryos were transferred. No differences in implantation rates were detected in cryotransfers of either day 5 or day 6 blastocysts. Similar results were reported by Franasiak et al. (2013) that showed that the diminished ART outcomes from embryos with delayed blastulation, traditionally attributed to reduced embryo quality, result from an embryonic-endometrial dissynchrony. These studies highlight the importance of embryo-endometrial synchrony to increase implantation rates.

[0096] Reports exploring the concept of the WOI, show that the timing of implantation can also influence pregnancy loss. Wilcox et al. (1999) showed a strong increase in the risk of early pregnancy loss with late implantation. Further studies looking at the impact of endometrial-embryo asynchrony on ART outcomes have found that the combination of elevated progesterone on the day of trigger (advanced endometrium) and slow growing embryos results in low live birth rates (Healy et al., Hum Reprod 2017; 32:362-367). This problem seems to be influenced by maternal age. Shapiro et al. in a recent study (2016) reported elevated incidence of factors associated with embryo-endometrium asynchrony in women over 35 years, high pre-ovulatory serum progesterone levels and increased numbers of delayed-growth embryos. This, together with the already well known decrease in gamete quality of women of advanced reproductive age (Fragouli et al., Hum Genet 2013; 132:1001-1013), underlines the importance of women's age for reproductive success and the need for the development of diagnostic and therapeutic tools to increase the chances of these women becoming a mother.

[0097] In contrast to previous studies aimed at developing tools for endometrial receptivity evaluation (Horcajadas et al., Fertil Steril 2008; 88:S43-S44; Diaz-Gimeno et al., Fertil Steril 2011; 95: 50-60,60-15), a selection of genes was chosen which are involved in biological processes taking place on the endometrium during the WOI and which are related to endometrial preparation for embryonic implantation. Upon the selection performed based on the literature, an over-representation of processes very relevant to the phenomenon of endometrial receptivity acquisition such as cellular proliferation, response to wounding, defense and immune response, were found. Within this group of genes, a subset of 85 especially were found to be interesting as they showed significant differences in expression between the proliferative and secretory phases. These genes GO analyses revealed cellular components, biological processes and molecular functions related to cell signaling and response, extracellular organization, cell division and proliferation, immunological activity, vascular proliferation and embryo implantation. Interestingly an over-representation of processes involving vesicles and exosomes was also found. These terms match with previously described processes known to occur at the time of implantation. Cellular matrix remodeling and an increase in vascular proliferation permeability and angiogenesis at the implantation site are one of the earliest prerequisites for embryo implantation (Zhang et al., Mol Reprod Dev 2013; 80:8-21). Also intense communication through cell signaling between the embryo and the endometrial cells has been described as part of the embryo-endometrial crosstalk essential for adequate embryonic implantation involving, in some cases, extracellular vesicles/exosomes (Ng et al., PLoS One 2013; 8:58502). Also, immune responses have been proven to play important roles in early pregnancy (Altmae et al., 2010; and Haller-Kikkatalo et al., Semin Reprod Med 2014; 32: 376-384).

[0098] PCA analysis, a dimension-reduction tool that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set, revealed that a subset of 40 of the 85 genes differentially expressed genes, called endometrial receptivity panel A genes could accurately differentiate between LH+2 and LH+7. These genes, listed in FIG. 3A, allow 100% correct classification of endometrial samples from donors into these two status groups. This panel of genes is also able to assess the receptivity status of samples from infertile patients obtained at the secretory phase, classifying samples into: “receptive’, this means the WHO matches the day on which the biopsy was taken; “pre-receptive”, meaning that the endometrium has not reached its WOI yet or “post-receptive”, i.e., this endometrium has already passed its WOI.

[0099] Focusing on the technical aspects of the development, high-throughput RT-qPCR was chosen for the analysis of such a panel of endometrial biopsies. RT-qPCR is the most robust and reliable technique currently available for gene expression analysis. Alternative methodologies output such as microarray results and RNA-seq expression data need to be validated using RT-qPCR methods (Mortazavi et al., Nat Methods 2008; 5:621-628; and Costa et al., Transl lung cancer Res 2013; 2:87-91).

[0100] The implementation of endometrial receptivity tests such as the one developed in the present study into the clinical practice routine may help guide embryo transfers to be performed in the best endometrial moment, guaranteeing embryo-endometrial synchrony and thus, allowing for the achievement of better ART results. Couples with repeated implantation failure, previously failed IVF cycles and also couples with recurrent miscarriage would benefit from the detailed analysis of endometrial receptivity and embryo-endometrial synchronization. This study is a new step in the field of personalized medicine in human reproduction in the management of the endometrium in preparation for embryo transfer, with the final goal of achieving better ART results increasing embryo implantation rate and the likelihood of successful pregnancies.

OTHER EMBODIMENTS

[0101] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.